Improving Zoonotic Disease Outbreak Detection Practice in the United States

by Heather Anne Allen

B.A. in Government, May 2006, Cornell University M.P.A. in Science and Technology Policy, May 2007, Cornell University

A Dissertation submitted to

The Faculty of the Columbian College of Arts and Sciences of The George Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

August 31, 2011

Dissertation co-directed by

Kathryn E. Newcomer Professor of Public Policy and Public Administration

Rebecca L. Katz Assistant Professor of Health Policy and Emergency Medicine

The Columbian College of Arts and Sciences of The George Washington University certifies that Heather Anne Allen has passed the Final Examination for the degree of

Doctor of Philosophy as of May 10, 2011. This is the final and approved form of the dissertation.

Improving Zoonotic Disease Outbreak Detection Practice in the United States

Heather Anne Allen

Dissertation Research Committee:

Kathryn E. Newcomer, Professor of Public Policy and Public Administration,

Dissertation Co-Director

Rebecca L. Katz, Assistant Professor of Health Policy and Emergency Medicine,

Dissertation Co-Director

Steven J. Balla, Associate Professor of Political Science, Public Policy and Public

Administration, and International Affairs, Committee Member

Larissa May, Assistant Professor of Emergency Medicine, Microbiology and

Epidemiology, Committee Member

iii

Acknowledgements

I would like to thank my committee—Professors Kathy Newcomer, Rebecca

Katz, Steve Balla, and Larissa May—for their encouragement, insight, and expertise throughout my dissertation research. I couldn’t have asked for a better committee.

During my time at GWU, Kathy Newcomer has been a mentor, advisor, and provided me with wonderful opportunities for research and scholarship. Rebecca Katz, in addition to helping me immerse myself in the world of biosecurity and preparedness, also encouraged me, from our first meeting to pursue my interest in zoonotic diseases. I sincerely thank both of them for their support and guidance throughout the entire doctoral program.

I would also like to thank my colleagues in the doctoral program: in particular,

Jennica Larrison, Mariglynn Collins, Eric Boyer, and Jill Egan have become not only trusted ears but good friends and a great support network. I look forward to attending their defenses in the near future! Thanks to Dr. Jonathan Zack, and Kiana Moore for their continuous encouragement to finish my dissertation while I was at work—and to

LMI and the Health Systems Management Group for supporting the end of my doctoral studies.

Professor Lori Brainard and Lieutenant Colonel Chessley Atchison also provided wonderful comments and insight during the final dissertation defense as outside examiners. There are others to thank here as well, those who provided assistance to me over the course of my research for which I am grateful (in no particular order): Beth

Gaston, Daniel Bachmann, Stephanie David, and Taylor Burke. In addition, a number of veterinarians and professionals provided insight as I was preparing my proposal and conducting my case study research. I am grateful to them for graciously lending their time.

In addition, I would like to acknowledge the Trachtenberg School for 3 full years of fellowship funding.

As this list grows longer, I cannot skip my parents. I would like to thank my father for his continued support of my education. My mother—who I know doesn’t believe this degree is the last—has always stood behind my decisions and in many cases made my varied endeavors possible.

Finally—Dr. Jeff Ballyns—thank you for cooking me dinner, listening to me whine, and dealing with my tantrums when my computer was uncooperative. You also spent many hours formatting my very long dissertation and helping with coding. Thank you for everything.

Oh, and Toasty—nothing like an all in one model of a canine personal-trainer, alarm-clock, foot-warmer, biting-teddy bear, in a fuzzy, wild, terrier with big brown eyes that can make me do anything.

Abstract of Dissertation Improving Zoonotic Disease Outbreak Detection Practice in the United States

Zoonotic diseases are a serious threat to public health; 75% of all emerging infections are zoonotic. There have been few efforts to describe or understand the practice of zoonotic disease outbreak detection and reporting practice in the United States, which is complicated by the federal and bureaucratic system of governance in the United

States. Using a mixed-methods approach, this dissertation completes three phases of empirical research: (1) a legal analysis of state reportable animal disease statutes and regulations, (2) an outbreak database to determine who detects zoonotic disease outbreaks and how fast they are detected, and (3) embedded case studies using a survey and interview instrument to understand how federalism and bureaucracy impact zoonotic disease detection and reporting. This dissertation finds that most states have some type of reportable animal disease list, but that there is a wide variation in these lists, with 88 diseases listed on average. The median time to detection of zoonotic disease outbreaks in the United States was 13 days, and laboratories detect zoonotic disease outbreaks both most frequently and most quickly.

Finally, respondents reported diagnostics as the most critical factor for rapid zoonotic disease detection and reporting, and highlighted the positive impact that institutional design and bureaucratic behavior can have on outbreak detection and reporting. The results of this dissertation offer not only a description of practice, they provide evidence to support the improvement of zoonotic disease detection and reporting practice. This dissertation suggests a future focus on the development of and increased resources for diagnostic capabilities, particularly at the local level.

Table of Contents

Acknowledgments…………….………………………………………………….……….iv

Abstract of Dissertation….………………………………………………………………..vi

List of Figures………………………………………………………………...…………..ix

List of Tables..………………………………………………………………………...….xii

List of Abbreviations……………………………………………………………………xvi

Chapter 1: Introduction.……………………………………………………….………….1

Chapter 2: Literature Review …………………………………………...……………….21

Chapter 3: Methods …...………………………………………………...……………….87

Chapter 4: Legal Analysis…..………………………………………..…………………151

Chapter 5: Development and Analysis of Outbreak Database...... ……………………242

Chapter 6: Case Studies ………………………………………..………………………310

Chapter 7: Policy Recommendations and Conclusions….……..…………………….…354

References…………………………………………………………..…………………..390

Appendix A………………………………………………………..……………………418

Appendix B…………………………………………………..…………………………423

Appendix C………………………………………………..……………………………449

vii

Appendix D………………………………………………………..……………………452

viii

List of Figures

Figure 2.1: Zoonotic Disease Transmission Pathways………………………………..…28

Figure 2.2: Disease Detection and Reporting Practice…………………………………..37

Figure 2.3: Power for Disease Detection & Reporting in a Federal System…………….41

Figure 2.4: Zoonotic Disease Detection in the United States……………………………51

Figure 2.5: Diagram of Literature Chapter Integration…………………………………..84

Figure 3.1: Screenshot of Database………………………………………………….…122

Figure 4.1: Number of Diseases by State……………………………………………....181

Figure 4.1a: Number of Diseases by State, Continued…………………………………182

Figure 4.2: Map of Number of Diseases by State Regulation/Statute………………….183

Figure 4.3: Number of Reportable Animal Diseases by Region……………………….184

Figure 4.4: Does the State Explicitly Mention Reporting by Laboratories...... 196

Figure 4.5: Fastest Reporting Category by State (In Hours)……………….…….…….202

Figure 4.6: Number of Categorizations for Timeliness of Reporting………………….203

Figure 4.7: Who are Animal Diseases to be Reported To?...... 209

Figure 4.8: Does the State Reportable Animal Disease List Segregate by Species?...... 213

Figure 4.9: Does the State List Provisions for Unknown Diseases?...... 217

Figure 4.10: Do Reportable Animal Diseases Lists Mention Wildlife?...... 221

Figure 4.11: Do Reportable Animal Diseases Lists Mention Public Health?...... 225

Figure 5.1: Number of Outbreaks Per Year ……….………………………….………..245

Figure 5.2: Distribution of Outbreaks by Disease ……….………………….………....247

Figure 5.3: Disease Outbreaks by State………………………………………….……..249

Figure 5.4: Disease Outbreaks by Year……………………………...…………………251

Figure 5.5: Disease outbreaks by Disease………………………………………………252

Figure 5.6: Disease Outbreaks by Species Group Affected……………………………254

Figure 5.7: Disease Outbreaks by Type of Transmission………………………………255

Figure 5.8: Number of Detections by Entity……………………………………………257

Figure 5.9: Number of Detections by Entity……………………………………………258

Figure 5.10: Who Detects-Entity Type…………………………………………………264

Figure 5.11: Comparing Outbreak Data……………………………….……………...... 266

Figure 5.12: Frequency Distribution of “How Fast” Outbreaks are Detected…….…....272

Figure 5.13: Histogram of “How Fast” Outbreaks are Detected……………………….273

Figure 5.14: Histograms of “How Fast” Outbreaks are Detected (without Outliers)…..274

Figure 5.15: Box Plot of Median Values (Days to Outbreak Detection)…………….…276

Figure 5.16: Boxplot of How Fast Outbreaks are Detected By Species Affected……..278

Figure 5.17: Boxplot of How Fast Outbreaks are Detected by Mode of Transmission...280

Figure 5.18: Boxplot of How Fast Outbreaks are Detected by Primary Symptom…….282

Figure 5.19: Number of Outbreaks that Originated in Other Countries by World Region versus United States………………………………………………………….…………284

Figure 5.20: Boxplot of How Fast Outbreaks are Detected by Region…………..…….285

Figure 5.21: Boxplot of Who Detects versus How Fast Outbreaks are Detected……...289

Figure 5.22: Boxplot of How Fast by Who Detects…………………………………...291

Figure 5.23: How Fast are Outbreaks Reported to the State…...………………………294

Figure 7.1: A Model Relating Empirical Results to Expectations from Literature…….366

Figure D.1: Scatter Plot of Number of Diseases vs Animal Total……….……………..453

Figure D.2: Animal Totals, Including Products, Sales Measured in 2007 US$...... 454

List of Tables

Table 2.1: Key Literature Indicating Problems Suggested by Federalism and Institutional

Design/Bureaucratic Behavior…………………………………………………………...56

Table 2.2: Empirical Research on Disease Detection and Reporting……………………71

Table 3.1: Overview of Methodology…………………………………………………...90

Table 3.2: Key Definitions………………………………………………………………95

Table 3.3: Coding Scheme for Legal Analysis……………………………………..…..101

Table 3.4 Key Coding Rules for Disease Lists…………………………………………105

Table 3.5: Key Coding Rules for “Who Reports”……………………………………...106

Table 3.6: List of Zoonotic Diseases for Inclusion in Database………………………..114

Table 3.7: Database Fields…………………………………………………………..….121

Table 3.8: Inclusion of Cases in Outbreak Database…………………………………...125

Table 3.9: Explanatory Notes in Outbreak Database……………………………….….129

Table 3.10: Outbreaks in Case Studies…………………………………………………141

Table 3.11: Questions used in the Interviews and Surveys…………………………….144

Table 4.1: Coding Scheme for Legal Analysis………………………………….……...157

Table 4.2: Legal Variations in Reporting Requirements……………………………….159

xii

Table 4.3: Summary of State Reportable Animal Disease Lists………………….…….164

Table 4.4: Summary of Reportable Disease Lists………………………………………178

Table 4.5: Descriptive Statistics of Disease Lists for States……………………………180

Table 4.6: Top 10 Reportable Animals Diseases in the United States…………………186

Table 4.7: Distribution of Reportable Diseases………………………………………...187

Table 4.8: Disease Breakdown by Category of Animal………………………………..188

Table 4.9: Frequency Zoonotic Diseases Appear in State Lists…………………….….190

Table 4.10: Summary of “Who” Has to Report Diseases by State……………………..193

Table 4.11: Top 10 Diseases, Who Has to Report……………………………………...194

Table 4.12: Timeliness Requirements by State…………………………………………198

Table 4.13: Where Diseases are to be Reported To…………………………………….205

Table 4.14: Does the List Segregate by Species………………………………………..210

Table 4.15: Does the State List Provisions for Unknown Diseases…………………….214

Table 4.16: Wildlife Mention in Reportable Animal Disease Lists……………………219

Table 4.17: Reference to Public Health in State Lists………………………………….223

Table 4.18: Summary of Legal Landscape……………………………………………..227

Table 4.19: Location of Disease Lists on Internet and Date……………………………234

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Table 4.20: List of Diseases/Disease Categories in Lists………………………………238

Table 5.1: Variables in Outbreak Database…………………………………………….243

Table 5.2: Subset of Outbreaks with Data for Analysis………………………………...248

Table 5.3: Disease Outbreaks that are an A, B, C Agent…………………………...….253

Table 5.4: Who Detects—Other (n=34)………………………………………………..257

Table 5.5: Who Detects—Other (n=14) ...……………………………………………..258

Table 5.6: Frequency Table—Who Detects based on Species Affected……………....260

Table 5.7: Contingency Table for Species of Origin and Who Detects………………..262

Table 5.8: Contingency Table for Who Detects and A, B, and C Agent………………265

Table 5.9: How Fast Outbreaks are Detected…………………………………………..270

Table 5.10: How Fast Outbreaks are Detected (n=101)………………………………..271

Table 5.11: Summary Statistics on How Fast Diseases are Reported to the State….….293

Table 5.12: Summary of Key Outbreak Database Results……………………………..297

Table 5.13: Additional Observational Data………………………………………….....307

Table 6.1: Outbreaks Selected for Case Studies………………………………………..311

Table 6.2: Interview and Survey Questions…………………………………………….312

Table 6.3: Response Information for Interviews and Surveys……………………….....323

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Table 6.4: Response Information for Laboratories……………………………………..324

Table 6.5: Response Information for Fastest and Slowest Cases………………………324

Table 6.6: Top Three Impediments for Outbreak Detection and Reporting……………326

Table 6.7: Top Three Facilitators for Outbreak Detection and Reporting……………...327

Table 6.8: Top Recommendations for Improving Practice……………………………..337

Table 6.9: Key Findings from Literature for Outbreaks………………………………..345

Table 6.10: Key Interview/Survey Findings Compared by Outbreak Type……………347

Table 7.1: Key Results from Empirical Chapters………………………….………….. 363

Table 7.2: Comparing the Policy Options based on a Set of Criteria…………………..378

Table 7.3: Results Linked to Research Questions………………………………….…..387

Table B.1: List of Diseases under Consideration………………………………………424

Table C.1: Zoonotic Disease with Primary Symptoms and Mode of Transmission……449

List of Abbreviations

ADC Administrative Code

APHIS Animal and Plant Health Inspection Service

AVMA American Veterinary Medical Association

AVIC Area Veterinarian in Charge

BOAH Board of Animal Health

CDC Centers for Disease Control

CFR Code of Federal Regulations

CSTE Council of State and Territorial Epidemiologists

DHS Department of Homeland Security

DOD Department of Defense

DOH Department of Health

DOI Department of Interior

DOT Department of Transportation

EEE Eastern Equine Encephalomyelitis

END Exotic Newcastle Disease

EIS Epidemic Intelligence Service

FDA Food and Drug Administration

FOIA Freedom of Information Act

FSMA Food Safety Modernization Act

GW/GWU The George Washington University

HHS Department of Health and Human Services

HSPD Homeland Security Presidential Directive

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HPAI Highly Pathogenic Avian Influenza

IOM Institute of Medicine

IRB Institutional Review Board

MMWR Morbidity and Mortality Weekly Report

NAHRS National Animal Health Reporting System

NNDSS Nationally Notifiable Disease Surveillance System

OIE World Organization for Animal Health

PFGE Pulse Field Gel Electrophoresis

SARS Severe Acute Respiratory Syndrome

STD Sexually Transmitted Diseases

USDA United States Department of Agriculture

WER Weekly Epidemiological Reports

WHO World Health Organization

VPH Veterinary Public Health

ZVED Zoonotic, Vector-Borne and Enteric Diseases

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Chapter 1: Introduction

Preventing and mitigating zoonotic disease outbreaks are critical to the protection of the public‟s health (Enserink, 2004; Fasina et al., 2007; Havelaar et al., 2007). Yet there have been few efforts to comprehensively assess the functioning of zoonotic disease outbreak detection and reporting practice in the United States. The purpose of the dissertation is to provide empirical evidence on the state of zoonotic disease outbreak detection and reporting practice in the context of the U.S. federal system of governance.

For example, how fast are zoonotic disease outbreaks are detected? Who recognizes or detects an outbreak? In addition, this dissertation identifies the elements that are needed for improved zoonotic disease outbreak detection and reporting in the United States. This introductory chapter of the dissertation: 1) presents the context behind the issues to be studied; 2) characterizes the problems arising from the lack of unified, comprehensive national zoonotic disease outbreak detection in the United States; 3) states the research questions to guide the dissertation; and 4) briefly discusses the scope and methods of this research.

Context of Zoonotic Pathogens

Animals, throughout history, have transmitted infectious microbes to human beings.

Zoonoses are those diseases that have their reservoirs in the animal population. Of the

1,700 known pathogens that infect humans, approximately half are zoonotic (Taylor,

Latham, & Woolhouse, 2001; World Health Organization, 2008). Of emerging infectious diseases, it is estimated that 75% are zoonotic (Chomel, Belotto, & Meslin, 2007; Los

Alamos National Laboratory, 2005; Taylor et al., 2001). Some zoonotic diseases have

1 developed into major public health problems, such as HIV/AIDS: “Here was an example of a new zoonosis rapidly expanding outward from its animal reservoir in Africa to infect millions around the world by the twentieth century” (Price-Smith, 2002, p. 4). Today, zoonoses remain a critical and salient public health concern (U.N. Food and Agriculture

Organization, World Health Organization, & World Organization for Animal Health, 2004;

Watanabe, 2008). Despite the disproportionate representation of zoonoses as emerging infections and as diseases that threaten human health, surveillance, detection, and reporting research has not focused exclusively on zoonoses, and we have little empirical knowledge about the functioning of outbreak detection and reporting practice with regard to these diseases.

Tight coupling between human and animal health is not a new notion: in the 1960‟s

Calvin Schwabe, the father of veterinary epidemiology, championed the “One Medicine” approach, explicitly recognizing that human and animal health are intimately interconnected and highlighting the vast overlap between animal and human diseases

(Cardiff, 2007; Kahn, Kaplan, Monath, & Steele, 2008; Schwabe, 1984). Today, One

Medicine promotes the idea that veterinarians as well as physicians are responsible for human health. This approach, under the name “One World, One Health” was the comprehensive strategy for the 2007 New Delhi International Ministerial Conference on

Avian and Pandemic Influenza (U.N. Food and Agriculture Organization et al., 2008).

Despite calls for better linkages, a comprehensive, integrated human-animal disease outbreak detection and reporting system does not exist in the United States, and there is a notable lack of communication between those concerned with animal health and those concerned with human health (Graczyk, Tamang, & Doocy, 2005; McNamara, 2002;

Zinsstag, Schelling, Wyss, & Bechir Mahamat, 2005). Even outbreak detecting and reporting in humans is fragmented, conducted by a “de facto U.S. outbreak detection system” made up of over 60 different technical detection systems (Dato, Wagner, &

Fapohunda, 2004, p. 465; Zinsstag et al., 2005). This number is constantly changing, and likely larger than 60 in 2010, particularly with the addition of syndromic surveillance systems, where many states use at least one system (Uscher-Pines et al., 2009). Disease surveillance is under the purview of a myriad of state, federal, and private entities.

The lack of comprehensive, “One World, One Health” zoonotic disease outbreak detection in the United States does not reflect the lack of a zoonotic disease threat: the likelihood that an emerging infection is a zoonotic pathogen has increased greatly in recent decades, due to changes in the environment and in social behavior (Chomel & Osburn,

2006; Gibbs, 2005; Havelaar et al., 2007; Heymann, 2005; King, Marano, & Hughes, 2004;

Lederberg, Shope, & Oaks, 1992; Pappaioanou, 2004; Reaser, Clark, & Meyers, 2008;

Smolinkski, Hamburg, & Lederberg, 2003). Humans now live in closer proximity and are more likely to interact with both domestic and wild animals, particularly in zoos in the

United States (Bender & Shulman, 2004). This increases the frequency of contact between species, and increases the likelihood that microbes will infect humans as well as animals

(Beatty, Scott, & Tsai, 2008; Daszak, Cunningham, & Hyatt, 2000; Daszak et al., 2007;

Graczyk et al., 2005; Kock, Kebkiba, Heinonen, & Bedane, 2002; Lederberg et al., 1992;

McNamara, 2002; Pappaioanou, 2004; Reaser et al., 2008; Wang et al., 2008; Wolfe,

Panosian Dunavan, & Diamond, 2007).

Environmental conditions like deforestation make vector borne diseases (frequently zoonotic) more likely (Heymann, 2005; John, 1999; Murphy, 1999; U.N. Food and

Agriculture Organization et al., 2004). Increased animal production (intensive agriculture) and consumption, more rapid international trade flows, and a booming illegal market in wildlife mean that an outbreak is more likely to spread quickly and internationally (Brower

& Chalk, 2003; Daszak et al., 2000; Delgado, Rosegrant, Steinfeld, Ehui, & Courbois,

1999; Dudley, 2004; Fèvre, de C. Bronsvoort, Hamilton, & Cleaveland, 2006; Journal of the American Veterinary Medical Association, 2008; Pappaioanou, 2004; Smolinkski et al.,

2003; Wang et al., 2008). It is estimated that 37,000 animals cross into the United States legally on a daily basis (King, 2008). In the United States, the majority of households also have pets, and non-traditional pets (such as lizards and monkeys) are increasing in popularity (Bernard & Anderson, 2006; Chomel et al., 2007; B. Goodman, 2009; Reaser et al., 2008; Ryan, 2006). There is evidence that even pathogens that are now strictly in human populations, such as HIV-1 M, initially were exclusively animal diseases (Wolfe et al., 2007). To prevent and mitigate disease outbreaks in the human population, attention must be paid to the interaction (both physical and microbial) between human and animal species: surveillance and detection of zoonotic disease outbreaks both in animals and in humans is critical to public health (Gibbs, 2005; Wolfe et al., 2007).

Furthermore, the lack of a comprehensive U.S. outbreak surveillance and detection system for zoonoses does not reflect the absence of zoonotic disease outbreaks in the

United States in either human or animal populations. In the United States: since 1999, there have been outbreaks in humans and animals of West Nile encephalitis, monkeypox, and many other zoonoses, such as rabies, brucellosis, and anthrax (Beatty et al., 2008;

Current Challenges in Combating the West Nile Virus, 2004; Gibbs, 2005; Keusch,

Pappaioanou, Gonzalez, Scott, & Tsai, 2009; McNamara, 2002; United States Department

4 of Agriculture: Animal and Plant Health Inspection Service, 2008). Moreover, zoonotic outbreaks in animals—such as brucellosis, tularemia, bovine tuberculosis—have all been present in the United States in 2008 alone (United States Department of Agriculture:

Animal and Plant Health Inspection Service, 2009).

Zoonotic diseases are also a salient concern due to their potential to be used as a biological weapon in bioterrorism or biowarfare (Elzer, 2004; Gibbs, 2005; Ryan, 2008;

Swearengen, 2006). Historically, zoonoses have been (or suspected to have been) used during war time as biological weapons or for biological weapons testing, including in the

Middle Ages as well as during World War II (Martin, 2006; Ryan, 2008). The concern about bioterrorism in the United States increased after the 2001 attack using Bacillus anthracis (Ashford et al., 2003; Lombardo & Ross, 2007; Martin, 2006; Noah, Noah, &

Crowder, 2002). One study suggested that 80% of the most common, potential biological pathogens are zoonotic (Franz et al., 1997).

Pathogens are broken down into three categories, based on their risk to national security. Indeed, many of the agents listed as Category A, B, or C agents are zoonotic; five of the six Centers for Disease Control (CDC) Category A pathogens are zoonoses (Ablah et al., 2008; Davis, 2004a, 2004b; Kahn, 2006; King, 2006). Category A pathogens are “high- priority agents”, because they are easily disseminated, have high mortality rates, have the potential to cause public disruption, and “require special action for public health preparedness” (Centers for Disease Control and Prevention, 2009). Category A pathogens include anthrax, botulism, plague, smallpox, tularemia, and viral hemorrhagic fevers

(Centers for Disease Control and Prevention, 2009). Category B agents are of slightly lesser priority, but include agents that are disseminated moderately easily and result in

5 moderate morbidity and low mortality, including zoonotic diseases such as brucellosis.

Category C agents are the third priority, and are emerging pathogens that pose a threat due to their availability, potential for high morbidity and mortality, and possible ease of dissemination (Centers for Disease Control and Prevention, 2009; Noah et al., 2002).

Research Landscape

Despite this threat of zoonotic diseases, and the increasing role of these pathogens in our public health, empirical research has not specifically focused on the practice of zoonotic disease outbreak detection in the United States. Certainly, past literature broadly indicates the importance of understanding and assessing outbreak detection in the United

States in order to improve practice in zoonotic disease outbreak detection and reporting, to plan against bioterrorism, and to protect public health (Ashford et al., 2003; Dato et al.,

2004; Jajosky & Groseclose, 2004). The most recent National Strategy on Countering

Biological Threats clearly echoes the need for improving “identification, notification, and assessment capabilities” (National Security Council, 2009, p. 4). Other literature acknowledges—usually with anecdotal evidence—that various difficulties have occurred in the detection of zoonotic disease outbreaks in the United States, but there are conflicting opinions on the extent and cause of these problems (American Veterinary Medical

Assocation, 2007; Enserink, 2004; Gibbs, 2005; Government Accountability Office, 2000;

Lederberg, 2002; Los Alamos National Laboratory, 2005; Pappaioanou, 2004).

Various stakeholders, including professional associations and the federal government, acknowledge that current practice in regard to zoonotic disease outbreak detection in the United States is insufficient: the American Veterinary Medical Association

6 has continuously lobbied for a national, unified zoonotic disease surveillance system, multiple Homeland Security Presidential Directives (HSPD) and experts at the Centers for

Disease Control (CDC) have articulated the need for improved and integrated (animal- human) disease surveillance and reporting systems and the 2009 National Strategy for

Countering Biological Threats calls for enhanced capacities and information sharing within the U.S. government (American Veterinary Medical Association, 2007; Homeland Security

Presidential Directive/ HSPD-9, 2004; Homeland Security Presidential Directive/ HSPD-

10, 2004; Homeland Security Presidential Directive/ HSPD-21, 2007; King et al., 2004; T.

Lynn, 2005; National Research Council: Committee on Effectiveness of National

Biosurveillance Systems, 2009; National Security Council, 2009; Wenzel & Wright, 2007).

Yet very limited empirical research has been conducted on zoonotic disease outbreak detection in the United States. There is clear indication of „a problem‟, but there is a lack of descriptive—much less analytical research—that indicates what practice actually is, and how zoonotic disease outbreak detection works in the United States.

There are many important elements in zoonotic disease outbreak detection in the

United States. Infectious disease surveillance and detection is often described as having three legs: 1) the systematic collection of data, 2) the assessment of these data, and 3) the timely dissemination of this assessment (M'ikanatha, Lynfield, Julian, Van Beneden, & de

Valk, 2007; World Health Assembly, 2005). Much research has been conducted on the first leg of this tripod, and recommendations for improvement have been developed by mathematicians and statisticians involved in the technical and algorithmic aspects of infectious disease surveillance (for example, Audigé, Doherr, & Wagner, 2003; Clements

& Pfeiffer, 2008; Hurd & Kaneene, 1983). This research has filtered into the second leg,

7 but computers have not yet fully replaced humans in assessing data (Levi & Inglesby,

2006; Nguyen, Thorpe, Makki, & Mostashari, 2007; Wagner, Gresham, & Dato, 2006).

But both the second and the third elements of infectious disease surveillance and detection—elements of particular importance to actual outbreak detection—have drawn far less empirical or theoretical research. In describing these two legs, three primary characteristics seem to broadly underlie existing research that seeks to describe the practice of disease outbreak detection: state and federal regulation, who detects outbreaks, and how fast outbreaks are detected. Certainly there are other interesting and relevant elements of practice. However these three components mentioned are some of the most fundamental, most tangible, and most important for both describing practice in the United States and laying the foundation for future research. These elements are discussed in turn.

First, while the “One World, One Health” model, or a comprehensive national system may be a worthy objective, the practice of zoonotic disease outbreak detection currently operates within the U.S. federal system of governance. Typically, the USDA offers reporting guidelines and recommendations for animal disease surveillance and detection for veterinarians (Stone & Hautala, 2008). For all human diseases, CDC broadly provides guidelines (for clinical care, testing, and reporting) to physicians and public health practitioners based on professional guidance. However, the regulation regarding zoonotic diseases in both animals and humans exists at the state, not the federal level: each state has different reporting requirements for zoonotic diseases (Kahn, 2006). While work has been completed on the variation in state law in regard to human diseases and reporting (Council for State and Territorial Epidemiologists, 2009a, 2009b; Doyle, Glynn, & Groseclose,

2002; Gostin, Burris, Lazzarini, & Maguire, 1998; Roush, Birkhead, Koo, Cobb, &

Fleming, 1999), such work has not been completed in regard to mapping reporting requirements for zoonotic or animal disease reporting (Lynn, 2005). This variation in law—as a result of the federal system of government in the United States—frames zoonotic disease outbreak detection in the United States, but we lack a thorough understanding of the consistency between federal guidelines and state regulation for veterinarians and other animal-health officials and laboratories. Moreover, there is not empirical evidence if variation in law impacts disease detection.

Second, regardless of how well complex, algorithmic disease surveillance works, we remain primarily reliant on clinicians, health departments, and veterinarians to identify and react to outbreaks (Babcock, Marsh, Lin, & Scott, 2008; Levi & Inglesby, 2006;

M'ikanatha et al., 2007; Nguyen et al., 2007; Wagner et al., 2006). Even for high profile diseases like influenza, surveillance, detection, and reporting is often done manually by physicians (Butler, 2006). Practitioners are key—in many fields of medical practice—to detecting disease outbreaks. Yet enforcement mechanisms for reporting are weak and reporting is often incomplete (Doyle et al., 2002). Moreover there is a lack of empirical knowledge about how well veterinarians are prepared to play a public health role by recognizing zoonoses (Ashford, Gomez, Noah, Scott, & Franz, 2000; Chomel & Osburn,

2006; Murnane, 2000; Noah et al., 2002; Steele, 2000). In addition, due to the institutional design of our executive branch bureaucracies and federal system of government, knowledge about specific outbreaks is often compartmentalized rather than shared, and executive agencies do not coordinate on detection and reporting efforts (Kettl, 2004; King et al., 2004; Lynn, 2005; Reaser et al., 2008; Stone & Hautala, 2008). Who detects is critical to assessing the relative importance of different types (and levels) of public and

9 private stakeholders in outbreak detection, and to recognize patterns that may impede or facilitate rapid detection (Ashford et al., 2003; Dato et al., 2004; Roush et al., 1999).

Third, the „how fast‟ component is a fundamental tenet of public health practice, as based on germ theory (Cantor & Kludt, 2005; Nelson & Masters Williams, 2007;

Thurmond, 2003). It is crucial that an outbreak in either animals or people is quickly recognized so appropriate interventions can occur, mitigating enormous life and economic consequences that can build up rapidly during a disease outbreak (Ashford et al., 2003;

Brown & King, 2008; Cantor & Kludt, 2005; Daszak et al., 2000; Fasina et al., 2007; Grein et al., 2000; Hopkins, 2005; Jajosky & Groseclose, 2004; Keusch et al., 2009; Kulldorff et al., 2004; Nelson & Sifakis, 2007; Nguyen et al., 2007; Wagner, 2006; Wang et al., 2008).

“Control of infectious diseases is a cornerstone to public health” (Lombardo & Ross, 2007, p. 1). Rapid detection and subsequent reaction to an infectious disease threat is a foundation of epidemiological practice and generally considered a primary purpose of infectious disease and surveillance practice (R. A. Goodman, Remington, & Howard,

2000). Indeed, “early detection is particularly important for emerging zoonoses” to mitigate the threat (King et al., 2004, p. 721). If outbreaks are not quickly identified, previous disease control efforts can regress (M'ikanatha et al., 2007). Emerging pathogens can spread quickly and widely if appropriate measures are not taken. “Reducing the time delay” in the recognition of an outbreak is an ultimate objective of empirical research on the subject and also one impetus for syndromic surveillance (Lazarus, Kleinman,

Dashevsky, DeMaria, & Platt, 2001; May, Chretien, & Pavlin, 2009; Moore, Edgar, &

McGuinness, 2008; Scotch, Odofin, & Rabinowitz, 2009; Wagner, 2006, p. 7).

Three aspects of the relevant context are fundamental to understanding zoonotic outbreak detection practice: 1) the regulatory incongruities resulting from a federal system of government, 2) the institutional design of agencies that results in a disconnect between animal health agencies (veterinarians) and human health agencies (physicians and clinicians), and 3) the potential for a zoonotic disease in an animal population to quickly move into a human population (Ablah et al., 2008; Los Alamos National Laboratory, 2005;

Pappaioanou, 2004; Reaser et al., 2008; Ryan, 2006). Zoonoses are a salient and relevant public health threat in the United States, but without greater knowledge about detection and reporting practice, evidence-based changes cannot be made. Though many contend that partnerships between animal and human health practitioners need to be built, understanding current practice is fundamental to forging new strategic cooperation (King et al., 2004).

This dissertation provides the foundation for further study of the zoonotic disease outbreak detection and surveillance practice in the United States.

Research Questions

The dissertation addresses two primary research questions, with the first research question having three subsidiary questions.

1. What is the current state of practice in zoonotic disease outbreak detection in the United

States? a. What is the current variation in state law with respect to animal disease reporting? b. Who detects zoonotic disease outbreaks in the United States in animals and people? c. How fast are zoonotic disease outbreaks recognized in animals and people?

2. What elements are needed for improved zoonotic disease outbreak detection in the

United States?

Contribution to the Field

There are three primary ways in which this dissertation contributes to the practice of biosurveillance, which exists with the field of public health (given the U.S. system of governance) (Thomas & Brubaker, 2008). To confirm the need for this type of study, a quote from the most recent IOM publication on zoonotic disease surveillance is appropriate: “Social science expertise and capacity is needed to further study the responses of health systems to outbreaks of zoonotic disease at all levels, inclusive of factors positively and negatively that affect and influence vertical (within sector) and horizontal

(intersectoral) communication and cooperation is extremely important” (Keusch et al.,

2009, p. 121).

First, this dissertation provides a thorough understanding of practice of zoonotic disease outbreak detection, as it operates in the complex system of governance in the

United States. We currently lack a thorough understanding of the consistency (or lack thereof) between federal and state regulatory reporting requirements for veterinarians and other animal-health officials and agencies. In order to improve zoonotic disease outbreak surveillance and to move towards a comprehensive system, a thorough understanding of this information is necessary. This is particularly important as calls for U.S. leadership in zoonotic disease surveillance grow (Keusch et al., 2009).

Second, despite the close connection between zoonoses and human health, as well as many reasons to be concerned about zoonoses as a particular class of infectious

12 microbes, there is a lack of very basic empirical evidence about the detection and reporting of zoonotic disease outbreaks in the United States. In addition, literature on emerging infectious disease detection does not address zoonotic diseases outside the nationally notifiable human disease list (Government Accountability Office, 2004). This dissertation helps to fill this gap, highlighting outbreaks of these critical microbes and giving future research a foundation so that more complex problems can be analyzed and more complicated impediments (to detection and reporting) can be identified. This empirical research provides a foundation for developing more complex decision models, complex systems analysis, technical approaches to policy analysis, and comprehensive evaluations of zoonotic disease detection practice (for example, Bankes, 2002; Stark, 2003).

Descriptive studies that show how things work “are valuable sources for higher forms of analysis” (Hoover & Donovan, 2008, p. 45; M'ikanatha et al., 2007)

Finally, given the importance of zoonoses, the lack of research, and the purported problems that have occurred in zoonotic disease outbreak surveillance, this dissertation supplies empirical findings that can be used to inform and improve policy about zoonotic disease detection and reporting—an existing gap in biosurveillance, epidemiology, and public health research (Beaglehole, Bonita, Horton, Adams, & McKee, 2004). This dissertation provides guidance for an improved zoonotic outbreak detection system in the

United States.

Brief Overview of Methodology

This dissertation uses a mixed-method approach (Creswell, 2009). The research is an exploratory and descriptive study using archival data to analyze existing practice with

13 regard to zoonotic disease outbreak detection and reporting in the United States (Hoover &

Donovan, 2008; Trochim & Donnelly, 2008; Yin, 2003). Empirical research focusing on description is a fundamental piece of evaluating the quality of a disease surveillance and detection system (M'ikanatha et al., 2007; Stark, 2003). This dissertation explores the functioning of the current practice in regard to three specific aspects: legal environment, who detects outbreaks, and timeliness. Because this research is exploratory in nature, this dissertation is more appropriately framed by research questions rather than hypotheses

(Creswell, 2009). Due to the lack of empirical evidence on U.S. zoonotic disease outbreak surveillance and detection, as well as the unavailability of existing qualitative or quantitative data in this specific subject area, the mixed-method approach is most appropriate.

This mixed-method study is conducted in three phases; this research methodology is further elaborated in Chapter 3 of this dissertation. The first phase of this dissertation analyzes state statutes with regard to reporting zoonotic diseases, by specifically reviewing reporting requirements for animal diseases. Statutes are compared across states and also to federal and international reporting requirements. In particular, key elements of interest in the statues include which diseases must be reported at the state level and how quickly they must be reported. This step frames the legal environment for zoonotic disease detection and reporting, which is a critical element of practice (Fairchild, 2003; Gostin et al., 1998;

Roush et al., 1999).

The second phase of this disseration creates a database of zoonotic disease outbreaks that have been reported in open source, national literature, in both animal and human populations. Excel in addition to Epi Info, a free software from the CDC, is used

14 for data management (Centers for Disease Control and Prevention, 2008). Sampling procedures for outbreaks are detailed in Chapter 3, but are purposive, and include specific microbes that cause zoonotic disease outbreaks (Dato et al., 2004; Shadish, Cook, &

Campbell, 2002). Data has been gathered from a number of archival sources from 1998-

2008, including Morbidity and Mortality Weekly Reports (Dato et al., 2004) and Pro-Med

Mail (Cowen et al., 2006). There is some attrition, as expected, for incomplete records.

Who detected the outbreak and how fast the outbreak was detected are the key variables of interest that are extracted from these data: this requires substantial textual analysis.

After the data are gathered and coded into the database, they are analyzed using univariate and bivariate analyses, assessing differences across the outbreak categories.

Minitab is used for statistical analysis. In addition, Epi Map—a component of Epi Info—is employed to visually map these data by state. These results indicate not only who is detecting zoonotic outbreaks and how fast they are recognized, but patterns between speed and who is detecting, between speed and geographical area, between who is detecting and species of origin, as well between characteristics of detection based on the biologic agent that caused the outbreak.

Finally, the third phase of this research conducts limited, primarily qualitative, embedded case studies on specific zoonotic disease oubreaks (for information on embedded case studies, see Yin, 2003). These case studies are selected based on the results of the previous step, as two of the „best‟ cases and two of the „worst‟ cases in terms of time to initial outbreak recognition (Yin, 2003). Investigating specific outbreaks provides information that could not be gathered from aggregated data. Specific attention is focused on the interaction between human health stakeholders and animal health stakeholders, as

15 well as on the interplay of state law, actors involved, and the influence of these factors on detection. These case studies use any available archival records, as well as four to six expert telephone interviews and/or surveys per case, with veterinarians, physicians, and public health officials per case, and outbreak specific legal reviews.

Overall, this research methodology is appropriate for the research questions provided in this dissertation. Taken together, the legal analysis, database creation and analysis, and the case studies creates a rigorous description of zoonotic disease outbreak detection and reporting practice in the United States that can be used to answer the second research question. It is expected, from previous research (Ashford et al., 2003; Dato et al.,

2004), that the sample size for the outbreak database (given attrition for incomplete records) would be between 100 and 200 outbreaks; the final sample size was 101, corroborating this expectation. Based on past research, this suggests that these data are relatively generalizable to the population of heavily reported disease outbreaks in the

United States. In addition, the use of clear and consistent definitions for coding archival records, appropriate data management, and frequent expert consultations helps to ensure reliable findings (Creswell, 2009). Further discussion of data limitations and strategies are thoroughly discussed in Chapter 3.

Scope, Assumptions, & Key Definitions

There are two limitations to scope and key definitions which should be presented before continuing to elaborate the remainder of the research. First this dissertation focuses on zoonotic disease outbreaks only in the United States. International zoonotic disease surveillance and reporting is certainly relevant (Reintjes, Thelen, Reiche, & Csohán, 2007)

16 to the United States—as outbreaks which originate outside of the United States can easily be transmitted to the United States. However, this is a needed limitation due to time and resource constraints as well as the accessibility to archival data.

Second, all disease reporting which occurs from the state to the federal level is voluntary, both for animal and human diseases (Gostin et al., 1998; Kahn, 2006; Roush et al., 1999; Stone & Hautala, 2008). In practice, the system generally works appropriately.

However, there is evidence that the completeness of notifiable infectious disease reporting varies by state as well as by disease (Doyle et al., 2002). Admittedly, states are offered uniform guidelines by the Council of State and Territorial Epidemiologists, but many states include different diseases that they require to be reported (there is no formal consequence for changing the list) (Centers for Disease Control and Prevention, 2001; Council for State and Territorial Epidemiologists, 2009a, 2009b). Because this dissertation is primarily reviewing only data at the federal level, it must be noted that these outbreaks are representative only of those which are reported to the federal level by the states or in open- source literature, not of all cases of zoonotic outbreaks which occur. Moreover, not all zoonotic disease outbreaks are even detected. Strategies to confront and estimate the magnitude of this problem are discussed in Chapter 3.

In addition, in terms of key definitions, when the surveillance, detection, and reporting practice is used in this dissertation, it refers to the practice and elements in the

United States which are intended to “together function to identify outbreaks” (in this case, zoonoses) (Dato et al., 2004, p. 464; Wagner et al., 2006). While further specification is provided in Chapter 2 and Chapter 3, it is important to note that this research is not intended to assess or evaluate syndromic surveillance, or syndromic surveillance systems.

These systems monitor warning signs such as pets (sentinels), medication purchases, various symptoms and other indicators of an outbreak (Buehler, Sosin, & Platt, 2007;

Lazarus, Kleinman, Dashevsky, DeMaria, & Platt, 2001; Moore, Edgar, & McGuinness,

2008; Scotch, Odofin, & Rabinowitz, 2009). Such an endeavor would include a formal evaluation of the types of outbreaks which should be detected, whether the system does so in a timely fashion, and if the system is an effective use of resources (Buckeridge,

Thompson, Babin, & Sikes, 2007; Buehler et al., 2007). An evaluatory approach would address questions such as sensitivity of the system to identify cases, predictive value of the system to be accurate, and flexibility of the system to adapt when confronted with emerging infections or adverse situations (National Research Council: Committee on

Effectiveness of National Biosurveillance Systems, 2009; Nelson & Sifakis, 2007; Stark,

2003; Thurmond, 2003). It should be noted that syndromic surveillance is intended to augment capabilities, and is not a replacement for traditional surveillance. This dissertation does not directly answer these questions or specifically evaluate syndromic (or any other type of) surveillance, and rather seeks to describe the actual practice of outbreak detection in the United States given a disease-based (rather than syndrome-based) approach.

However, the approach of this dissertation may include the discussion of diseases discovered or outbreaks detected by syndromic surveillance.

Finally, an „outbreak‟ is broadly identified when an infectious microbe is at higher than normal levels in a population; the CDC often draws no distinction between epidemic and outbreak (Dato, Shephard, & Wagner, 2006). The World Health Organization (WHO) defines an outbreak as

..the occurrence of cases of disease in excess of what would normally be

expected in a defined community, or geographical area, or season. An outbreak may

occur in a restricted geographical area, or may extend over several countries. It may

last for a few days or weeks, or for several years.

A single case of a communicable disease long absent from a population, or

caused by an agent (e.g. bacterium or virus) not previously recognized in that

community or area, or the emergence of a previously unknown disease, may also

constitute an outbreak and should be reported and investigated (World Health

Organization, 2010).

Given this definition, this dissertation operationalizes a zoonotic outbreak using a series of criteria detailed in Chapter 3, to ensure the inclusion of outbreaks that consist of just one case, and also to capture outbreaks that ought to be reported from a health and security standpoint (also see Buckeridge et al., 2007). In all instances, the inclusion of an outbreak is clearly justified and reviewed to ensure „outbreak‟ is operationalized appropriately as cases above a baseline.

Conclusion

Chapter 1 introduces the context, research questions, and methodology of this dissertation. This introductory chapter provides a clear rationale for the dissertation: zoonotic diseases are a salient threat to public health, and a growing problem because of various environmental and social changes. While we understand the U.S. federal system and constitutional design leading to specific bureaucratic behavior has resulted additional complexities in zoonotic disease detection and reporting, we do not have a clear grasp on how zoonotic disease detection and reporting practice works in the United States. Next,

Chapter 2 offers a literature review. This literature review tells the story of the dissertation, beginning with the history and the foundations of public health practice, moving into a

19 thorough discussion of this dissertation‟s theoretical underpinnings. Finally, using this information, Chapter 2 reviews past empirical research on the topic and leads to Chapter 3, the research methodology which was only briefly covered in this first Chapter. Chapters 4,

5, and 6 present the three phases of empirical research. Chapter 7 provides a policy analysis and conclusions.

Chapter 2: Literature Review

The objectives of Chapter 2 are to 1) provide a thorough landscape of what underpins zoonotic disease detection and reporting practice in the United States, 2) illuminate the theoretical frameworks which provide a foundation for practice (and how subsequent implications have been portrayed in literature), and 3) detail what we currently know about this practice through empirical research. To achieve these objectives, this chapter first provides a brief history of infectious disease detection and reporting practice in the United States, focusing on the underlying role of germ theory, and detailing additional information about the threat of zoonotic diseases. Second, the theoretical, policy sciences framework for disease detection and reporting in the United States—federalism and institutional design—is presented. The third section draws upon the previous two sections to provide perspective as we review the empirical research that has assessed disease detection and reporting. Finally, the fourth and final section offers a brief summary and explanatory framework that leads into Chapter 3 on methodology. Because of the potential breadth of the literature reviewed, this analysis attempts to focus most on literature that directly frames the importance of the subject or otherwise implicates zoonotic disease detection and reporting practice in the United States.

Background: Surveillance, Germ Theory, & Zoonoses

This section provides a brief history of infectious disease detection practice in the

United States, and clarifies the role of germ theory in biosurveillance activities. Then, zoonoses are examined, detailing the ways in which their emergence and resurgence has become more likely. Given that biosurveillance is a critical component of public health

21 practice, and since zoonotic diseases are a growing public health threat, this section then addresses current detection and reporting practice, and highlights relevant public health infrastructure in the United States. The final section discusses a framework for analyzing governance in relation to current practice.

History

For many centuries, humans have been fascinated with the prevalence and spread of disease. The idea of surveillance emerged from the field of statistics, particularly through the contributions of John Gaunt (1620-1674), later considered one of the first epidemiologists in Britain, who used statistics to understand infectious disease (Nelson &

Masters Williams, 2007; Nelson & Sifakis, 2007; Susser & Susser, 1996). Surveillance and detection information were used as a data source to evaluate and document pandemics.

William Farr (1807-1883), another British vital statistician, carefully followed the infamous

London cholera epidemic, and these data were used by British physician John Snow (1813-

1858) to support his at that time radical conclusion that cholera was being transmitted through the water (Lawson, 2001; Nelson & Masters Williams, 2007; Wagner, 2006).

Today, U.S. disease detection rests upon this foundation built in the mid to late 1800‟s in

Great Britain (Aschengrau & Seage III, 2008; Nelson & Sifakis, 2007; Ritz, Tager, &

Balmes, 2005; Thacker, 2000).

Public health surveillance—broadly defined—has many purposes, including identifying changes in health care practices, monitoring the distribution of health events, and detailing the patterns of infectious microbes from a historical perspective (Thacker,

2000). The United States first started collecting national mortality data in 1850 (Gostin,

2008; Nelson & Sifakis, 2007; Thacker, 2000) and disease specific reporting in states

22 began in the mid-late 1800‟s (Gostin, 2008; Thacker, 2000; Velikina, Dato, & Wagner,

2006). Disease surveillance, detection, and reporting in the United States evolved rapidly after World War II. At this time, the CDC was established and disease surveillance activities were led by Alexander Langmuir. A strong proponent of biosurveillance,

Langmuir later founded the Epidemic Intelligence Service (EIS). The biosurveillance activities of detection and reporting of infectious diseases—the topic of this dissertation— emerged early in the CDC‟s tenure as key actions that were needed to protect the public‟s health (Nelson & Sifakis, 2007; Velikina et al., 2006). While Langmuir was a significant proponent of infectious disease detection and reporting, the focus on infectious diseases in public health practice emerged from an underlying framework: germ theory. The next section provides an overview of germ theory, and explains how public health practice, as shaped by germ theory, is of great relevance to this dissertation.

Germ Theory

Public health practice has been shaped by theories which rest in the sciences of biology and epidemiology. Practice and research in biosurveillance has been based on an underlying conception of germ theory, or the microbial model, developed by Louis Pasteur and Edward Koch in 1880 (Garrett, 2000; Zgodzinkski & Fallon Jr., 2005). Germ theory focuses on “disease causation by external noxious agents; notably the living contagion, the germs and viruses” (Freeman, 1960; Galdston, 1954, p. 13; Gostin, 2008; Steele, 2000;

Zgodzinkski & Fallon Jr., 2005). Germ theory suggests that the key objective of public health practice (and practitioners) is to prevent, control, and mitigate these infectious microbes (Gostin, 2008). This objective has also long been a fundamental goal of veterinarians and veterinary practice, as well as physicians (Schwabe, 1984).

There are alternatives or supplements to germ theory, including the behavioral model and the ecological model (see Galdston, 1954). The behavioral model suggests that human behavior is a key determinant of disease, while the ecological model stresses the environment as a key determinant: subsequently, public health practice should focus on interventions in behaviors or into the environment, respectively (Freeman, 1960; Gostin,

2008; Terris, 1992). These two models are not usually interpreted as “negat[ing] or deny[ing] germ theory” but serve as a supplement: in some ways, they are applicable to this dissertation as the risk of emerging zoonoses is influenced by not only microbes, but by human behavior and the environment (Galdston, 1954, p. vii). These various models guide epidemiological practice.

Germ theory dictates that public health should focus on the need to prevent and control infectious diseases, and the public health system must undertake biosurveillance activities (including surveillance, detection, and reporting) to protect the population from infections microbes (M'ikanatha, Lynfield, Julian, Van Beneden, & de Valk, 2007). In order to prevent and control infectious diseases following the principles of germ theory, biosurveillance must 1) detect cases or outbreaks so an appropriate intervention can occur

(Singer & Ryff, 2007; Susser & Susser, 1996) and 2) recognize these cases as quickly as possible to limit the consequences of the disease (Cantor & Kludt, 2005; Nelson & Masters

Williams, 2007; Thurmond, 2003). Timeliness “reflects the delay between any two (or more) steps in a surveillance system” (Romaguera, German, & Klaucke, 2000). Timely detection is a fundamental tenet of public health practice, as the longer an infectious disease is allowed to spread, the more humans and animals will be infected, resulting in important social and economic consequences (Jajosky & Groseclose, 2004; Keusch, Pappaioanou,

Gonzalez, Scott, & Tsai, 2009; Romaguera et al., 2000). As is discussed later in this chapter, timeliness has frequently been studied because of its importance in biosurveillance activities. Indeed, the principle that timely detection is key is a fundamental contribution of germ theory to public health practice.

Despite the strong foundation that germ theory provides for this dissertation, it should be noted that many contend that the paradigm, or theory, guiding public health practice has shifted since the 1950‟s from infectious disease epidemiology and germ theory to chronic disease epidemiology and the behavior and environment models (Freeman,

1960; Galdston, 1954; Gostin, 2008; Susser & Susser, 1996; Terris, 1992). However, the focus on biosurveillance activities has seen a rapid resurgence in public health practice, and many argue that infectious disease control deserves more attention, and much focus has returned to infectious disease surveillance and detection (Chavers, Fawal, & Vermund,

2002; Garrett, 1994; Singer & Ryff, 2007). One impetus for the renewed focus on infectious diseases is the threat of emerging and novel infections, that is, microbes that reoccur in increasing incidence or in new locations, or have not been identified previously.

In the early 1990‟s, the Institute of Medicine and other stakeholders brought the threat of novel infections to light (Chomel & Osburn, 2006; Garrett, 1994; Lederberg, Shope, &

Oaks, 1992; Morse, 2004; Nelson & Sifakis, 2007; Thacker, 2000; Wagner, Moore, &

Aryel, 2006).

Why is this important? Most of the recently emerging microbes—almost 75%—are zoonotic (Chomel, Belotto, & Meslin, 2007; Taylor, Latham, & Woolhouse, 2001).

Biosurveillance—based on germ theory—still is a fundamental component of public health practice. Because zoonotic diseases are a specific concern in biosurveillance, the next

25 section reviews these pathogens and the factors that contribute to their emergence and re- emergence. Notably, because this research focuses on describing current practice, this chapter does not further discuss the normative role of public health practice in the control of factors which contribute to the likelihood of these threats (Beatty, Scott, & Tsai, 2008).

Zoonoses

It is important to discuss zoonotic infections, particularly as these microbes are a key reason that infectious disease biosurveillance is resurging as a critical component of public health. In spite of the tremendous amount of recent attention on zoonoses (for example B. Goodman, 2009), the idea that animal and human health are linked and the knowledge that vertebrate species problematically share many diseases is far from new.

Famously, in an early edition of his book Veterinary Medicine and Human Health, Calvin

Schwabe quotes Rudolf Virchow (1821-1902) in stating, “between animal and human medicine there is no dividing law—nor should there be” (Herbold, 2000, p. 2; Schwabe,

1984). In fact, since 1907, private veterinarians have been involved in animal disease detection (Wenzel & Wright, 2007). Professional veterinary associations recognized the importance of animal diseases in human health, and indeed the concept of Veterinary

Public Health (VPH) developed more broadly soon after WWII (Murnane, 2000). Yet despite the professional attention to the critical links between animal and human diseases, veterinarians and physicians historically, and currently, have worked quite independently of each other (American Veterinary Medical Association, 2007; Beaglehole, Bonita, Horton,

Adams, & McKee, 2004; Dudley, 2004; L. H. Kahn, 2006; Shephard, Aryel, & Shaffer,

2006). In addition, few physicians or veterinarians are formally educated in foreign or exotic animal diseases (Wenzel & Wright, 2007).

The growing threat which zoonotic diseases pose to public health was discussed earlier. It is useful to briefly return to this issue, and in particular, identify some of the mechanisms through which human populations, wildlife populations, and domestic animal populations (primarily livestock, but also companion animals) transmit pathogens amongst themselves in an increasingly rapid fashion. Figure 2.1, (adapted from Daszak,

Cunningham, & Hyatt, 2000) provides a conceptual vision of the substantial role of zoonotic pathogens in these populations.

Notably, the understanding of the causes of zoonotic disease emergence and re- emergence is still developing (Daszak et al., 2007; Dixon, 2001; Keusch et al., 2009;

Wolfe, Panosian Dunavan, & Diamond, 2007). Indeed, some zoonoses pose more of a threat than others—in particular, those that are transmitted not only between a vertebrate species and a human, but pathogens in which human to human transmission becomes sustained (like HIV) (R. E. Kahn, Clouser, & Richt, 2009; Watanabe, 2008; Wolfe et al.,

2007). “In general, there is no way to predict when or where the next important new pathogen will emerge; neither is there any way to reliably predict its ultimate importance”

(Murphy, 1999, p. 20).

Figure 2.1: Zoonotic Disease Transmission Pathways

Wildlife

Domestic Animals Humans

Note: Venn diagram not to scale of relative number of pathogens. Source: Adapted from Daszak et al. 2007.

First, human populations and wildlife populations interact in a variety of ways. As

Figure 2.1 pictures, the pathways are bi-directional, where wildlife can introduce pathogens into the human population, and humans can also increase the likelihood of diseases entering the wildlife population. These various interactions also increase the potential for a zoonotic disease to emerge. In particular, humans now have increased contact with wildlife populations as human settlements expand (urban sprawl), the wildlife trade is growing, exotic pets are becoming popularized in developed countries, and more people have interaction with wild animals through leisure and travel activities (Blancou, Chomel,

Belotto, & Meslin, 2005; Chavers et al., 2002; Chomel & Osburn, 2006; Daszak et al.,

2007; Fèvre, de C. Bronsvoort, Hamilton, & Cleaveland, 2006). In addition, wildlife now come into contact with humans more frequently, as changes in their natural environment, for example from deforestation, logging, increased farming, or changes in water patterns,

28 bring wild animals into new areas inhabited by humans (Blancou et al., 2005; Kruse,

Kirkemo, & Handeland, 2004).

Second, domestic animal populations and wildlife populations are increasingly more likely to transmit pathogens. As domestic animal populations grow to meet human consumption demands, domestic and wild animals are more likely to come into contact

(Blancou et al., 2005; Chavers et al., 2002). In many cases, wildlife are a natural reservoir for a disease, and increasing contact with domestic animals reintroduces a previously controlled or eradicated pathogen into a domestic animal population (Chomel & Osburn,

2006; Kruse et al., 2004). The trade in wildlife populations is also growing substantially, including trade for game farms as well as personal collections, which increases the likelihood that wildlife will come into contact with domestic animals.

Third, humans and domestic animals are more likely to transmit diseases between their populations due to the increased industrialization and rapidly growing animal production and consumption (Keusch et al., 2009). This has led to the emergence and re- emergence of pathogens that can be transmitted to humans, both directly and through foodborne infections, such as salmonella and influenza strains (Blancou et al., 2005; Kelly &

Marshak, 2007; Kock, Kebkiba, Heinonen, & Bedane, 2002). The livestock industry and trade in livestock is enormous, and growing yearly (Fèvre et al., 2006). For example, from

2000-2007 (latest data available) it is estimated that the total production of cattle for meat has increased from 6,578,000 to 7,900,000 metric tons in Brazil and from 4,968,000 to

7,250,000 metric tons in China (U.N. Food and Agriculture Organization, 2009). This has resulted in changing industrial practices, often which make it easier for microbes to thrive in high density populations of livestock (Murphy, 1999). Humans also can transmit

29 diseases to their pets, as seen in the recent transmission of H1N1 to a domestic cat (Mullen,

2009). Pets also transmit diseases to their humans, including cases of salmonellosis from small rodents and turtles as well as monkeypox from exotic rodents.

Indeed, there are some situations in which humans, wild animals, and domestic animals all interact. The most well-known of these are „wet-markets‟ in Asia, where severe acute respiratory syndrome (SARS) and other diseases like H5N1 have emerged (C.

Brown, 2004; Fèvre et al., 2006). These intersections are magnified through the ever increasing speed of global trade and travel (Daszak et al., 2007; King, Marano, & Hughes,

2004). From 2000 to 2004, the United States imported over 1 billion wild animals from over 150 countries—and this is just the legal trade (Keusch et al., 2009; Marano, Arguin, &

Pappaioanou, 2007) A simple plane trip can introduce pathogens from one continent to another. Other anthropogenic factors, such as land development and dam building, are also occurring more rapidly, altering human and animal environments (Daszak et al., 2007;

Keusch et al., 2009; Murphy, 1999). Moreover, changes in global climate have changed the weather patterns, thereby increasing the appearance of certain zoonoses, particularly those that are vector-borne (Blancou et al., 2005; Keusch et al., 2009). In addition, individuals that are immuno-suppressed are also more likely to be infected by a zoonoses, and the number of immuno-compromised individuals is extremely high due to the

HIV/AIDS epidemic (Blancou et al., 2005; Graczyk, Tamang, & Doocy, 2005).

This section demonstrates that zoonotic diseases are a serious public health threat and a growing public health issue. Given that public health practice is intended to mitigate and control infectious diseases, where does the responsibility for zoonotic disease detection and reporting lie? This is discussed next.

Responsibility & Infrastructure

Current practice in zoonotic disease detection in the United States is the responsibility of the government, grounded in the theory that governments have a duty and obligation to protect the public‟s health (Gostin, 2004). Indeed the United States and governments worldwide have generally maintained responsibility for disease surveillance, detection, and control in both human and animal populations, albeit in different intensities due to varying capabilities (Gostin, 2004; Nelson & Sifakis, 2007; Velikina et al., 2006).

More recently, the private sector has also become more actively involved in disease surveillance and detection, primarily in a supportive rather than duplicative manner (Beatty et al., 2008; M'ikanatha, Lynfield, Van Beneden, & de Valk, 2007; Shephard et al., 2006;

Zacher, 1999). In the United States, responsibility for disease detection and reporting lies primarily with the states, for reasons that are discussed in the next section. However, it is helpful to provide a brief outline of the federal government stakeholders involved in disease detection and reporting with reference to zoonoses.

As mentioned, the CDC (within the U.S. Department of Health and Human

Services) has led many efforts to combat zoonotic infections in the United States (Morse,

2004), including obtaining fiscal year 2007 funding to form a new center focusing particularly on zoonotic, vector-borne, and enteric diseases (King, 2006). This new center, the National Center for Zoonotic, Vector-Borne, and Enteric Diseases-ZVED, was expected to create a collaborative and comprehensive approach to zoonotic, vector-borne, foodborne, and waterborne diseases. Along with the U.S. Department of Health and

Human Services, the U.S. Department of Agriculture (USDA), U.S. Department of

Homeland Security (DHS), U.S. Department of Defense (DOD), and U.S. Department of

31 the Interior (DOI) all play roles in zoonotic disease surveillance, detection, and reporting

(Dudley, 2004; Reaser, Clark, & Meyers, 2008). More specific activities of each of these departments are not directly relevant here to detection and reporting, and are discussed later in this chapter (in the case of the CDC and USDA) or in the appendices (all others). There are also international organizations involved in zoonotic disease detection and reporting, such as the World Health Organization (WHO) and World Organization for Animal Health

(OIE). Key international organizations involved in zoonotic disease detection and reporting with brief descriptions are also noted in the appendices, as this dissertation has a

U.S. focus.

While there are some federal activities, it is state governments that have regulatory jurisdiction over zoonotic disease detection and reporting in the United States. The state

„police powers‟ to protect the public‟s health through disease detection and reporting emerges from the U.S. Constitution. The next section discusses disease detection and reporting from the lens of the U.S. system of governance. Complications in disease detection are likely to arise from the federal and bureaucratic structure of governance in the

United States. Many consequences of the complex federal, bureaucratic system have been described in the biosurveillance literature.

The Impact of the Federal Bureaucracy on Disease Detection and Reporting in the

United States

While the previous section has addressed public health practice as framed by germ theory and zoonotic disease threats, this section discusses the impact of the U.S.

Constitution and the U.S. federal system of government on disease detection in the United

States. The three primary purposes of the Constitution of the United States impact disease detection (Gostin, 2008). First, powers are separated between executive, legislative, and judicial branches of government. Second, the Constitution protects individual liberties.

Third—and of most interest here—all powers not explicitly delegated to the federal government are reserved to the states. In particular, federalism and institutional design, as related to zoonotic disease detection and reporting: 1) provides a framework shaping zoonotic disease detection and reporting practice, and 2) suggests potential problems that may hinder zoonotic disease detection and reporting. In particular, a lack of collaboration and variation in state disease detection and reporting are fundamental problems. A lack of resources is also an important concern, particularly in animal disease surveillance. In sum, the U.S. constitution “provide[s] a broad explanation for behavior” in disease detection and reporting (Creswell, 2009, p. 61). But prior to examining the impact of the federal system upon zoonotic disease detection and reporting, there are two important issues which merit attention.

First, while protecting individual liberties is a key purpose of the U.S. Constitution, and highly salient in the field of surveillance, this chapter (and the dissertation) does not delve independently into issues such as privacy. Substantial work has been completed on these legal issues, and they are simply beyond the scope of this research (for example, see

Fairchild, 2003; Fairchild, Bayer, & Colgrove, 2007; Gostin, 2008). Certainly privacy (as an example of a relevant individual liberty) is a key legal issue affecting response efforts, and outbreak notification from the state to the federal level. Indeed, this empirical research might indicate that privacy concerns delay detection of disease outbreaks. However, such issues are not elaborately discussed here.

Second, before examining the impact of federalism and institutional or bureaucratic design, an important delimitation needs to be made on the distinction between „detection‟ and „reporting‟. The importance of detection is frequently mentioned in this chapter, but here we also touch on the importance of reporting. While more extensive definitions are offered in Chapter 3, „detection‟ refers to the initial recognition that an outbreak of an infectious disease is occurring (Ashford et al., 2003; Jajosky & Groseclose, 2004).

„Reporting‟ refers to the act of notification to a government entity, for example, in which states report disease outbreaks to a federal entity (Doyle, Glynn, & Groseclose, 2002;

Jajosky & Groseclose, 2004). Theoretically, if a disease outbreak is „detected‟ it should be

„reported‟, but this is not the case in practice (Doyle et al., 2002). But public health practice often relies on reporting for response. Figure 2.2 (adapted from Jajosky &

Groseclose, 2004) provides the basic outline of current practice. Later in this section, the implications and potential problems that result from federalism and bureaucratic complexity are discussed using Figure 2.2 as a frame of reference.

Figure 2.2 displays the pathway from the case, or an outbreak of the disease if multiples cases, to the initial recognition of the outbreak (detection), to reporting from whomever detected the outbreak to the appropriate state official, to reporting from the state to the federal level. This process is typical for diseases that are on the human nationally notifiable disease list issued by the CDC (in Chapter 3) (Jajosky & Groseclose, 2004).

While it may sound simple, reporting is a complex practice in and of itself, even removing the „detection‟ question from the equation. Reporting invokes questions of privacy, confidentiality, as well as federalism and institutional design as this chapter discusses. Moreover, the United States has international reporting obligations, as well as

34 obligations at local, state, and federal levels. There are obligations for both animal diseases

(to the OIE), and public health emergencies of international concern (WHO). The United

States, as a Member State of the OIE, is obligated to report specific animal diseases under

Article 5 of the OIE Organic Statutes (World Organization for Animal Health, 2010).

Under the new International Health Regulations, State Parties are required to public health emergencies of international concern to the WHO (World Health Assembly, 2005).

In addition to state obligations to report, federal veterinarians are also required to report certain animal diseases, particularly foreign animal disease, per the United States

Code of Federal Regulations (9 CFR 161) (Code of Federal Regulations, 2009). Moreover, some jurisdictions (for example, LA County and New York City) also have disease reporting requirements (see http://publichealth.lacounty.gov/acd/reports/diseaselist2010.pdf). Tribal nations also have independent reporting requirements. Taken together, physicians, veterinarians, as well as other entities which have to report (like laboratories) work under a myriad of legal obligations at the local, state, and in the case of veterinarians, federal regulations. The

United States has the obligation to report both animal and human infectious diseases internationally, which makes state reporting imperative to fulfill international obligations.

Many individuals also report diseases based on practitioner oaths and ethical standards for practice, to protect public and animal health. This patchwork of standards and law for infectious disease reporting reminds us of the patchwork of zoonotic disease detection in the United States.

In addition to legal and ethical obligations at many levels, reporting is also critical for an effective and timely response to a disease outbreak. In many cases this may not even

35 mean a full mobilization of medical countermeasures, personnel, or resources, but an assessment of the risk the disease outbreak poses to human or animal populations to inform calculated actions. However, in some cases, reporting to a government official or entity is necessary for authorization of regulatory interventions, like quarantine and isolation

(Gostin, 2008). While the capacity of either state or federal response to infectious disease outbreaks is outside the scope of this dissertation, the importance of reporting in response should be noted.

Admittedly, there are different barriers to detection and to reporting, but detection can be viewed as an intermediary step to reporting—indeed, a disease has to be detected in order to be reported, and it is unlikely that individuals widely know a disease was detected unless it was reported (Step 3). In many cases, where information about detection is not available, reporting (at either the state or the federal level) can be used as a proxy for indicating that a zoonotic disease was detected. Literature focuses more on reporting than on detection, mostly because detection can include more algorithmic or pathological endeavors. This is not to say that detection is not impacted by the U.S. system of governance. As is explained in this chapter, much of the literature on federalism and institutional/bureaucratic design suggests there are potential effects on both reporting and detection, and they are often discussed as one. Detection and reporting are both parts of practice in the United States. Literature on either is limited, and therefore, relevant literature specifically on either detection or reporting are incorporated into this review.

Figure 2.2: Disease Detection and Reporting Practice

Step 1 Health Event Occurs

CASE or OUTBREAK

Step 2 Initial Outbreak Recognition

DETECTION

Step 3 Reported to State System STATE REPORTING

Step 4 Verification of Event & Investigation of Health Event

Report Intermediary steps are likely to occur

between Step 4 and 5 but are not required for national reporting, such Step 5 as analysis of disease data and Voluntary Reporting to the dissemination of findings to National System appropriate stakeholders NATIONAL REPORTING/ NOTIFICATION

Source: Adapted from Jajosky and Groseclose, 2004.

Federalism, & Public Health

The Federalist Papers, a set of highly influential writings about the creation of the

Constitution of the United States, as well as Woodrow Wilson‟s prominent essay on administration, highlight the undesirability of too much central authority (Hamilton, 1786;

Madison, 1961; W. Wilson, 1887). As a reaction to central power in England, the framers of the United States chose to clearly separate powers held by the federal government and by the states. Federalism, or the framework in which states and the federal government

37 interact, has highly significant consequences on infectious disease detection and reporting practice.

In theory, federalism is conceived as an “ideal political order”; but U.S. federalism diverges from the ideal type and has changed over time (Buchanan, 1995; Diamond, 1993;

Howard, 1993; Rivlin, 1992). There are three mainstream theories of federalism that have applied to different eras of U.S. governance: dual federalism (1789-1933), cooperative federalism (1933-1970), and competitive federalism (1980-now) (Conlan, 2006; Gais &

Fossett, 2005; Peterson, Rabe, & Wong, 1986; Rivlin, 1992). Competitive federalism—the type of federalism some suggest is now being experienced in the United States—as its name indicates, suggests that states and the federal government are “competing with each other for leadership in domestic policy” (Rivlin, 1992). Many suggest that the United

States is increasingly a more centralized government, where distinct realms of state and federal power do not exist (Buchanan, 1995). While a discussion of these competing visions is tangentially relevant, it is more useful to specifically focus on the impact of federalism on public health practice to illustrate how public health operates in the U.S. federal system. Notably, while other types of health policy such as financing and care are very distinct as related to the U.S. Constitution and federalism; the topics should not be conflated (for more on health financing and care, see Gostin, 2008; Holahan, Weil, &

Wiener, 2003). Substantially more theoretical conversation has occurred about health financing and care in a federal system than on disease detection and reporting (Holahan et al., 2003; Peterson et al., 1986).

With regard to public health, the U.S. Constitution grants the federal government specific, enumerated powers (R. A. Goodman, Kocher, O'Brien, & Alexander, 2007;

Tolles, 2005). There are two primary functions the U.S. Congress can perform that affect public health efforts: it can tax (and spend) and regulate interstate commerce (Gostin,

2008). In addition to the enumerated powers, the federal government can also, through the

“Necessary and Proper Clause,” employ additional means, as necessary, to achieve the powers that are actually enumerated in the constitution (Gostin, 2008; U.S. Constitution,"

1791). Then, as stated in the 10th Amendment, all powers not delegated to the federal government are reserved to the States (Gostin, 2008; U.S. Constitution," 1791; Velikina et al., 2006). Moreover, when federal and state powers conflict, the Supremacy Clause (in

Article VI) states that federal law precedes state law under the doctrine of preemption

(Gostin, 2008; U.S. Constitution," 1791). It should be noted that the federal government can and does often exert influence on states using grant in-aid programs and/or revenue sharing to avoid direct federal (and unconstitutional) regulation—though this has not occurred frequently in issues of disease detection and surveillance (Holahan et al., 2003;

Nathan, 1976; O'Toole, 1993; Porter, 1976; Thomas, 1976). Figure 2.3 (adapted from

Gostin, 2008, p. 79) shows how federalism, federal powers, and state powers integrate in respect to public health. Ideally, a working federal system should have strong players at both the federal and state level for a suitable balance (Conlan, 2006).

In Figure 2.3, “federal public health powers flow from the U.S. Constitution”

(Gostin, 2008, p. 79). However, these powers are also limited by the constitutional doctrine of federalism. The dotted line around federalism indicates the Constitutional doctrine that legally separates state powers and federal powers. This dotted line also signifies that while in theory federal powers and state powers should not “impede on one another”, in practice this has sometimes occurred and the judicial system has interpreted the powers granted to

39 the federal government quite broadly (Gostin, 2008, p. 79; Grodzins, 1966). For example, the U.S. Public Health Service and CDC are “federal activities that may be generally supported by the authority” of the U.S. Constitution (Neslund, Goodman, & Hadler, 2007, p. 223). The Public Health Services Act (42 U.S.C. § 201) broadly defines the authority of the federal government in the realm of public health ("Public Health Services Act," 1944).

The Public Health Services Act grants the Secretary of Health and Human Services authority over preventing the introduction, transmission, and spread of infectious diseases in the United States (Gostin, 2008). While this power seems quite broad, the extent to which federal authority can extend to public health activities, including disease reporting and detection has not yet been fully tested in the court of law—disease detection and reporting remain primarily state activities.

Distinct from the federal powers are the powers reserved for the state. In particular, there are two powers that are reserved to the states that are of particular relevance to public health: most importantly, police powers and less relevant to disease detection, the parens patriae power (the power to protect minors) (Gostin, 2008). These police powers enable state governments to legally conduct disease surveillance within a population (Neslund et al., 2007). However, various debates about privacy and individual liberties have occurred: for example, is notification a violation of privacy? These are important legal issues but fall outside the scope of this dissertation (see Fairchild, 2003; Fairchild et al., 2007; Gostin,

2008).

Figure 2.3: Power for Disease Detection & Reporting in a Federal System

Federal Powers (Tax & Spend, Regulate Interstate Commerce)

U.S. Constitution Federalism

Disease Powers Reserved for Surveillance, the States Detection & (Police Powers) Reporting

Under the auspices of their police powers, states can and do constitutionally mandate disease reporting within the state from laboratories, physicians, and other entities; this is usually referred to as “notifiable disease reporting” (Dato, Wagner, & Fapohunda,

2004; Gostin, 2008, p. 67; Neslund et al., 2007; Velikina et al., 2006). Therefore, if a disease is detected or recognized, the individual, lab, or other entity is legally bound to report the case to a state government official (usually the state health or public health agency or department of agriculture in the case of animal diseases). Though the enumerated powers of the federal government have been very broadly interpreted by the

Supreme Court (Gostin, 2008; Velikina et al., 2006), and though many perceive the U.S. to be highly centralized, the fact remains that disease reporting regulation is legislated at the state—not federal—level. In this arena, states are currently the key players and the federal government offers merely a suggestion of „performance standards‟ (Anderson, 2006). “To encourage uniformity across states” the CDC collaborates with the Council for State and

Territorial Epidemiologists (CSTE) to publish the Nationally Notifiable Disease List

(Velikina et al., 2006). But the heavier role of states is clearly indicated by the fact that some states do not mandate reporting even for diseases on this strongly suggested list

(published by the CDC) (see Council for State and Territorial Epidemiologists, 2009a,

2009b).

The sometimes unclear balance of powers between the states and the federal government has resulted in a contentious debate about disease detection and reporting.

Because states individually pursue regulation regarding disease detection and reporting under their police powers, there is little uniformity amongst state statutes (Buchanan, 1995;

Gostin, 2008). With concerns about emerging infections (predominately zoonotic diseases), this debate has “only intensified” (Gostin, 2008, p. 110). The next section reviews federalism as it affects public health practice, and examines the potential problems—in both theory and in practice—that the federal system presents for the detection and reporting of disease outbreaks.

Implications of Federalism for Disease Detection & Reporting

There are two specific interconnected problems that federalism presents for the practice of disease detection and particularly, disease reporting. First, because detection and reporting powers are reserved to states, the federal government does not have power (at least directly) over how they are exercised. Second, when powers are reserved to states, states are likely to exercise the powers in different ways. Simply, “the exercise of public health powers squarely raise problems of federalism, because states are sovereign governments” (Gostin, 2008, p. 311). This section discusses the nature of these problems flowing from the federal structure literature that clarifies the practical implications on disease surveillance. The relevant literature from the field of detection and reporting is

42 summarized in Table 2.1. Table 2.1 only includes „conceptual‟ or general literature that provides general discussion or anecdotal evidence; empirical literature is reserved for later in the chapter.

First, every state in the United States has discretion over disease detection and reporting within their state. The federal government does not have the power to dictate such regulation, nor does it have the power to mandate that diseases be reported to the federal government. Indeed, states voluntarily report disease outbreaks which occur at the state level to the federal government, as Figure 2.2 indicates (Beatty et al., 2008; Centers for

Disease Control and Prevention, 2009; Doyle et al., 2002; Gostin, 2008; L. H. Kahn, 2006;

Stone & Hautala, 2008). This is true for both disease in humans (reported to the CDC) and for diseases in animals (reported to the USDA). When discussing disease detection and reporting in the United States, the voluntary nature of state reporting is often neglected or ignored, despite its clear significance (see Blancou et al., 2005; B. V. Brown, 2001).

Indeed, states—as sovereign governments—often make the decision to not report diseases, as indicated by varying completeness in reporting (Doyle et al., 2002).

Second, a key purpose of federalism is to allow states to innovatively pursue state- specific needs and objectives (Baumgartner & Jones, 1993; Conlan, 2006). “Federal systems of government offer many advantages, including for…region specific policy approaches to be developed” (K. Wilson, McDougall, & Upshur, 2006, p. 0030). Because states vary widely, and because the federal government (due to the federal structure) has not dictated disease surveillance or reporting regulation across the nation, disease reporting regulation and disease detection practice both vary tremendously across the 50 states.

Indeed, “the breadth and specificity of public health laws vary significantly across states

43 given differences in political and legal environments” (R. A. Goodman et al., 2007; Hodge,

Gostin, Gebbie, & Erickson, 2006, p. 77). Particularly at the state level, public health law is frequently outdated, and new laws are simply piled on top of old (Babcock, Marsh, Lin,

& Scott, 2008; Neslund et al., 2007). In the absence of a federal regulation as discussed, the result is “inconsistent, redundant, and ambiguous law” (Gostin, 2008). States‟ rights result in not only differences in regulation, but differences in detection and reporting capacities because of different priorities. In practice, the variety in state law results in large variations in how, how well, and how quickly states report diseases (Doyle et al., 2002;

Gostin, 2008; Roush, Birkhead, Koo, Cobb, & Fleming, 1999).

The implications of the federal structure on issues of disease detection and reporting practice are important. On the one hand, it is possible that states can craft policy in a way that is most appropriate for their state and is most satisfactorily for constituents (Peterson et al., 1986), considering that disease threats vary significantly over the nation. On the other hand, federalism results in state laws that vary and are unlikely to be „ideal‟, and the federal government has little direct control over state level regulation (Riker, 1993). In practice, this results in a myriad of state laws on an issue that is not easily constrained to state boundaries: diseases can rapidly cross state lines through transport, trade, as well as animal movements. Regulation to “protect against the spread of zoonotic disease has not kept pace with these transformations” (Babcock et al., 2008, p. 276) Problematically, these regulatory “variations can inhibit efficient responses in cases where coordinated, collaborative efforts are critical” (Hodge et al., 2006). Federalism, and the disconnect between state and federal regulations presents a challenge in protecting the security of the population (Kettl, 2004).

Indeed, characteristics of disease detection and reporting practice that are shaped by the federal structure are frequently cited as primary impediments to disease detection and reporting in the United States. Because states have different issues and objectives and great discretion over their police powers, state capacities and the level of attention given to disease detection and reporting varies amongst the 50 states, particularly with regard to animal diseases (Ashford, Gomez, Noah, Scott, & Franz, 2000; Gubernot, Boyer, & Moses,

2008; Noah, Noah, & Crowder, 2002; Reaser et al., 2008; Stone & Hautala, 2008). States, with regard to livestock, have widely varied surveillance, diagnostic, and communication capabilities. States with minimal livestock populations have little incentive to devote resources and attention to such infrastructure (Murphy, 1999). Granted, while this is the purpose of federalism, the current lack of interconnectedness in the United States suggests this can be problematic in timely disease control efforts. Positively, as encouraged by federalism, some states have emerged as innovators (Baumgartner & Jones, 1993; Holahan et al., 2003) in both disease detection and reporting, such as Minnesota and Georgia (Lynn,

2009; McNamara, 2009). Other states have urged individual practitioner responsibility in the absence of state regulation (Babcock et al., 2008). However, even when state practice is innovative, cooperation and integration between the state and federal level is tenuous.

Perhaps “we tolerate constant battles” about federal and state jurisdiction “because we cherish local self-government, even if it is inefficient” (Kettl, 2004, p. 90). However, given the federal system which produces variation in state disease detection law, some stakeholders have called for public health law reform in disease surveillance and reporting

(Hodge et al., 2006; Institute of Medicine, 1988). There is concern that in some states the detection and reporting of outbreaks is delayed, or even non-existent. But there has been

45 no empirical review of how much these various state regulations actually vary in the case of animal disease reporting. In addition, there has been no systematic empirical examination to assess how state law correlates to more timely or complete detection or reporting of disease outbreaks for either animals or humans. There is a clear indication that state law matters in disease reporting. For example, a higher percentage of isolates for enteric diseases were sent to laboratories where state regulation required such submission—indicating the legal environment is an important aspect of rapid disease detection (Hedberg et al., 2008). State regulation is examined in this dissertation, as is the connection between these concepts and outbreak detection and reporting.

The federal system of government provides an underlying explanation of differences in disease detection and reporting practice, and the literature reinforces the reality of these consequences. Federalism is not the only theoretical framework which broadly explains disease detection and reporting: the next section discusses institutional design and bureaucratic cooperation, which is similarly important in explaining disease detection and reporting practice.

Institutional Design, Bureaucratic Behavior, & Public Health

As a consequence of federalism, the institutional design, or the design of the agencies of state governments, is largely left to the states, who act as sovereign governments. At both the state and federal level, specific institutional design is also the result of many other factors, including the U.S. Constitution and separation of powers doctrine, and influence of various stakeholders (and other theories which are not be discussed in depth here) (Gostin, 2008; Pelikan, 2005; J. Q. Wilson, 1989). There has been substantial literature on the consequences of institutional design, such as the role of

46 executive agencies in policy-making and on intergovernmental relations (Anderson, 2006;

Conlan, 2006; Crum, 2005; Fallon Jr. & Zgodzinkski, 2005; Goodnow, 1900; Jones &

Thomas, 1976; Wright, 2004). But of more interest here is an understanding of what institutional design theories explain in public health and disease detection practice, at both the federal and state level. Specifically—because disease detection and reporting is largely handled by the executive branch of government (state agencies)—given the institutional design of state agencies, how do we expect these executive agencies to behave?

At both the state and the federal level, policy responsibilities are dispersed amongst executive agencies. But policy responsibilities also overlap, leading to some type of implicit or explicit interaction between agencies (O'Toole, 1993). In the field of public health, this is particularly salient, as many executive agencies at either the state or the federal level have a distal or proximate role in public health (Dudley, 2004; J. Q. Wilson,

1989). Agencies handling broad subject areas such as the environment, trade, agriculture, health, and education all are involved in public health. For example, in the 2003 outbreak of monkeypox, five different cabinet-level agencies were involved federally, including the

U.S. Fish and Wildlife Service (DOI), the Animal & Plant Health Inspection Service

(USDA), the CDC (HHS), Customs and Border Protection (DHS), and the Plant Protection

Quarantine Service (DOT) (Matthews, Abbott, Hoffman, & Cetron, 2007).

This is not to say that all agencies are „equal‟: some receive more “deference” or support than others, often based on their field of knowledge and expertise (Riley &

Brophy-Baermann, 2006, p. 69; Rourke, 1968). Indeed, the importance of human medicine

(at least in some specific areas) has long garnered substantial public respect, including the support of the professional workforce (Riley & Brophy-Baermann, 2006). Health agencies

47 usually have more resources and more power than other state agencies. The Department of

Health and Human Services has a quite substantial agency budget, compared to that of the

Departments of Agriculture or Interior (Campbell, 2005). A lack of resources is frequently cited as a problem in Table 2.1. Albeit, this could be an absolute lack of resources or a relative lack of resources in particular program areas. Nonetheless it is clear that primary stakeholders may have a heavy influence in executive agency behavior.

Given that executive agencies frequently have overlapping interests in a policy issue area and varying levels of initial „power‟ (Riley & Brophy-Baermann, 2006; Rourke,

1965), what does the literature suggest about the effects of institutional design in bureaucratic behavior? One popular hypothesis about executive agency interaction is that agencies constantly seek greater autonomy vis-à-vis other agencies (Rourke, 1968; J. Q.

Wilson, 1989). While many scholars have commented on this competition among executive agencies (Kettl, 2004; Kingdon, 2003; Riley & Brophy-Baermann, 2006;

Rourke, 1968, 1965), Wilson (1989) specifies the ways in which an executive agency is likely to act in order to “minimize the number of rivals and constraints” (J. Q. Wilson,

1989, p. 188). Generally, the view that executive agencies are autonomy seekers predicts that agencies may not cooperate or coordinate, and may show little enthusiasm for any joint-agency endeavors among the set of agencies that perform similar tasks (J. Q. Wilson,

1989). Admittedly, there are also other theories about bureaucratic behavior; some suggest this autonomy-seeking view of the U.S. bureaucracy is simply incorrect, and that executive agencies behavior is not always explained by the theory of autonomy (Carpenter, 2005; J.

Q. Wilson, 1989). Many others factors influence agency behavior and often (though not always) impede coordination, including powerful leaders, reputation, varying expertise,

48 organizational culture, and importantly—resources (Carpenter, 2005; Rourke, 1968; J. Q.

Wilson, 1989).

Regardless of what theory of bureaucratic behavior one subscribes to, the consequences and implications of institutional design remain markedly similar: institutional design and associated theories suggest that coordination problems are quite likely to exist amongst executive agencies in the U.S. government (Barnard, 1938; Horgan &

Zgodzinkski, 2005; Kettl, 2004; Seidman, 1998; Weingast, 2005). Moreover, the design of the executive branch means information and knowledge can frequently become compartmentalized within a specific agency, making cooperation between executive agencies—even on issues of mutual or shared interest—quite difficult (Horgan &

Zgodzinkski, 2005; Kettl, 2004; Weingast, 2005). As Harold Seidman stated, coordination in a bureaucracy is the “twentieth-century equivalent of the medieval search for the philosopher‟s stone” (quoted in J. Q. Wilson, 1989, p. 268). In essence, as explained by this theoretical lens, the behavior of executive agencies is broadly characterized by independent action and a lack of cooperation, including “turf disputes” which can impede executive action (Kingdon, 2003, p. 156; Riley & Brophy-Baermann, 2006; J. Q. Wilson,

1989).

Difficulties in achieving coordination across executive agencies are the well- acknowledged results of institutional design, and interagency committees have been one solution to promote communication between various agencies, though the result of these committees has been mixed (J. Q. Wilson, 1989). In some cases, the design of the executive branch has changed through agency reorganization, as in the creation of the

Department of Homeland Security after September 11, 2001 when the Homeland

Security Act of 2002 was enacted: such reorganizations are often the result of coordination problems (Campbell, 2005; Carpenter, 2005; Homeland Security Act of

2002," 2002; Kettl, 2004; Kingdon, 2003; J. Q. Wilson, 1989). In other instances, the failures of interagency coordination have encouraged more power or jurisdiction within the executive office of the President or even the office of the state Governor (Campbell,

2005; J. Q. Wilson, 1989). Due to the multidisciplinary nature of public health and disease detection and reporting, the lack of interagency coordination is an acute concern.

The next section reviews the predictions of institutional design and cooperation in theory and their practical significance in zoonotic disease detection and reporting practice.

Implications of Institutional Design & Cooperation on Disease Detection and Reporting

There are two related implications which are predicted to emerge from the theories of bureaucratic institutional design. One, many agencies can be involved in a single policy issue area, and these agencies usually have different levels of interest, influence, and knowledge (due to federalism, this also varies by state). Two, given theories on the behavior of executive agencies, we can expect them to be poor cooperators and coordinators (at both the state and federal level). These two effects of institutional design have a number of powerful consequences for disease detection and reporting that are now discussed.

For reference, key literature which identifies problems with disease detection and reporting is summarized in Table 2.1. To better explain the implications of institutional design and bureaucratic behavior, Figure 2.4 offers a „sample‟ state system of zoonotic disease detection for reference purposes.

Figure 2.4: Zoonotic Disease Detection in the United States

Vet Labs Veterinarians Zoos Physicians Labs

State Wildlife State Animal Health State Health (Usually Department of (Usually Department of (Department of Health

Agriculture) Environment or Wildlife) or Public Health)

Voluntary Reporting USDA CDC (HHS)

Source: Adapted from Cantor and Kludt, 2005.

First, one agency is not wholly responsible for zoonotic disease detection and reporting at either the state or the federal level, as seen in Figure 2.4. The current situation has often been described as the lack of a command and control agency (Dudley, 2004), that has resulted in the inefficient “policy piecemeal” reality predicted by our institutional design (Weingast, 2005, p. 335). Human health issues as well as domestic pets (cats and dogs) usually fall under the purview of state and local health departments. Livestock, on the other hand, usually fall under the agricultural department. Finally, wildlife issues usually are covered by an environmental or wildlife agency (Government Accountability

Office, 2000). And, zoo animals often do not fall within the purview of a state agency at

51 all (outlined in red in Figure 2.4) (Government Accountability Office, 2000; McNamara,

2002). Subsequently, zoo veterinarians often do not have an entity to which they can report.

As also identified in Figure 2.4, state wildlife agencies (as outlined in red) do not receive substantial guidance and/or assistance from either the CDC or the USDA, unless disease outbreaks otherwise impact humans or domestic animals; in fact, wildlife disease surveillance, detection, and reporting is generally left to the states. (Beatty et al., 2008;

Gaston, 2009; Gibbs, 2005; Lynn, 2009; McNamara, 2002; Murphy, 1999). While all fifty states do have some sort of wildlife agency, capabilities vary drastically and states often take little action in terms of disease surveillance and detection (Association of Fish &

Wildlife Agencies, 2009). It should be noted that in National Park Service land, the federal government has jurisdiction over wildlife disease surveillance (while in National Forests or other federal land, the jurisdiction is delegated to the states) (Mendoza, 2009).

Second, as a result of the first point, institutional design and theories of bureaucratic behavior suggest a lack of coordination occurs (Horgan & Zgodzinkski, 2005), and that the result is inefficient or ineffective action or policy. This has certainly been the case with zoonotic disease detection and reporting. At both the state and the federal level, executive agencies (like the State Department of Agriculture and the State Department of Health) rarely coordinate to create a unified policy approach to disease detection and reporting; instead information is compartmentalized and not shared (Kettl, 2004; Keusch et al., 2009;

King et al., 2004; Lynn, 2005; Reaser et al., 2008; Stone & Hautala, 2008). In many cases, literature has shown that bureaucracies with disease detection and reporting responsibilities simply don‟t communicate (at least effectively) to come to effective collaborative agreements (American Veterinary Medical Association, 2007; Blancou et al., 2005; Cantor

& Kludt, 2005; Enserink, 2004; Government Accountability Office, 2000; Gubernot et al.,

2008; Horgan & Zgodzinkski, 2005; Pappaioanou, 2004; Stone & Hautala, 2008). Figure

2.4 indicates a lack of coordination as arrows do not exist between either the state agencies or the federal agencies since such formal or informal cooperation rarely exists. Granted, there are some exceptional and innovative states—as would be expected in a federal system—that have launched effective cross-agency and cross-sectoral initiatives, including collaborative exercises between various state agencies, which link these boxes together

(Lynn, 2009; McNamara, 2009).

Third, a lack of inter-agency coordination and collaboration and expertise compartmentalization has resulted in some level of jurisdictional overlap between executive agencies and some serious implications, as predicted by the theory of bureaucratic behavior (Kingdon, 2003). For example, there have been disagreements about who is responsible for informing people about the risk of zoonotic diseases amongst agencies responsible for both animal and human diseases (Babcock et al., 2008).

Moreover, despite the fact that policy issue areas overlap and that most agencies recognize that their particular information is only a piece of the larger puzzle, agencies generally still do not collaborate. In fact, this particular problem of bureaucratic cooperation was cited as one reason that the September 11 attacks were not „prevented‟ by the intelligence agencies: these agencies were operating independently, and did not share information with each other

(Kettl, 2004). Similarly, in New York City in 1999, agencies and sectors did not communicate to piece together what was going on in the city, substantially delaying the detection, reporting, and reaction to the rapidly growing West Nile viral outbreak (Current

Challenges in Combating the West Nile Virus, 2004; Government Accountability Office,

2000; McNamara, 2009). Institutional design helps to explain why these events occurred.

Granted, in some cases jurisdictional overlap can facilitate action by encouraging competition (Kingdon, 2003). But from the literature, this does not appear to have occurred in the realm of disease detection and reporting (Beaglehole et al., 2004; Cantor & Kludt,

2005; Graczyk et al., 2005; Murphy, 1999).

Finally, it is important to note that much of the lack of interagency coordination that exists in zoonotic disease detection and reporting can be traced back to the quite separate professions of veterinary medicine and human health. Even within the sectors themselves, there are long-standing implicit or explicit disputes about the role that various practitioners play in various public health issues (the first row in Figure 2.4), how practitioners should be educated, and how collaboration between the sectors might be improved (Chomel &

Osburn, 2006; Hendrix, McClelland, Kahn, Thompson, & Pence, 2002; L. H. Kahn,

Kaplan, Monath, & Steele, 2008; Kelly & Marshak, 2007; Keusch et al., 2009; Ryan, 2008;

Schwabe, 1984; World Health Organization, 2008; Zinsstag, Schelling, Wyss, & Bechir

Mahamat, 2005). These professional tensions manifest themselves in executive agencies at both the state and federal level, as the professional workforce is a highly influential stakeholder to executive agencies (Riley & Brophy-Baermann, 2006). The practical implications of these disputes are predicted by the institutional design and bureaucratic behavior theories of the executive branch (Dixon, 2001; Kruse et al., 2004; McNamara,

2002).

The institutional design of the U.S. bureaucracy and theories of bureaucratic behavior explain potential problems with zoonotic disease detection and reporting: in particular, overlapping jurisdiction and lack of coordination. The literature reinforces the

54 existence of these consequences in practice. Taking the previous contextual and theoretical sections, we now turn to review the empirical research that has been conducted. Notably, the empirical research has only implicitly connected the theoretical predictions which were just discussed and findings. This explicit link between theory and practice developed in this dissertation is a key contribution of this research.

Table 2.1: Key Literature Indicating Problems Suggested by Federalism & Institutional Design/ Bureaucratic Behavior

Author (Year) Subject Focus on U.S. Primary Concerns Theory Predicting Zoonoses? Focus? Noted this Problem American To discuss a National Yes Yes Fragmented practice; Institutional Veterinary Zoonotic Infectious lack of communication design/Bureaucratic Medical Disease Surveillance between human and behavior Association System. animal health agencies. (2007)

Ashford et al. Biological terrorism & No Yes Lack of surveillance in Federalism (2000) veterinary medicine. animal populations; varied state capacities.

Babcock et al. Legal implications of Yes Yes Veterinarians need to Federalism (2008) zoonoses for aim for a higher standard veterinarians of practice than mandated by law, and be leaders in the prevention of zoonotic diseases.

Beaglehole et al. Improving public health No Yes Neglect of public health Institutional (2004) practice. infrastructure & funding; design/Bureaucratic lack of government behavior leadership.

Blancou et al. Description of the Yes No Need more financial Institutional (2005) growing problem of resources; administrative design/Bureaucratic bacterial zoonoses & difficulties in behavior main obstacles to collaboration and control. agreement.

Author (Year) Subject Focus on U.S. Primary Concerns Theory Predicting Zoonoses? Focus? Noted this Problem Cantor & Kludt Developing a Zoonotic Yes Yes Lack of collaboration & Institutional (2004) Disease Surveillance communication between design/Bureaucratic System in Massachusetts. public health & behavior veterinary partners. Chomel & Zoological Medicine and Yes No Veterinarians need more Other Osburn (2006) the Intersection with training in public health. Public Health. Dudley (2004) To discuss the need for Yes No Lack of command & Institutional zoonotic disease control responsibility in design/Bureaucratic surveillance with the government. behavior globalization. Enserink (2004) World Health No No Lack of international Institutional Organization‟s role in cooperation and design/Bureaucratic combating novel resources. behavior pathogens. Gibbs (2005) Emerging zoonoses in a Yes No Lack of surveillance in Institutional globalized world. wildlife, lack of design/Bureaucratic communication between behavior sectors.

Graczyk et al. Public Health & Yes No Lack of collaboration Institutional (2005) Veterinary Perspectives between animal & design/Bureaucratic on Parasitic Zoonoses human health sectors. behavior

Gubernot et al. Animal-Human Yes Yes Current practice is Institutional (2008) Integrated Zoonotic compartmentalized design/Bureaucratic Disease Surveillance. among sectors and behavior; agencies (federal & state Federalism divide as well).

Author (Year) Subject Focus on U.S. Primary Concerns Theory Predicting Zoonoses? Focus? Noted this Problem Kahn et al. Teaching “One Yes No Lack of collaboration in Other; Institutional (2008) Medicine, One Health”. education between design/Bureaucratic sectors. behavior

King et al. Collaboration between Yes No Early detection of Institutional (2004) animal health and public outbreaks in animals; design/Bureaucratic health agencies. improved coordination; behavior; Other more workforce training. Lynn (2005) Coordination of Public Yes Yes Lack of collaboration Institutional Health and Agriculture. between sectors; lack of design/Bureaucratic funding; lack of behavior; surveillance coordination Federalism McNamara Zoos in Biosurveillance. Yes No Little funding for Institutional (2002) wildlife disease design/Bureaucratic surveillance; lack of behavior communication. Murphy (1999) Livestock Zoonoses Yes No Lack of unified or Institutional comprehensive design/Bureaucratic surveillance, diagnostics, behavior; or communications. Federalism Noah et al. Biological Terrorism Yes Yes Lack of practitioner Federalism (2002) (veterinary) knowledge about disease incidence & symptoms; variation in state capacities & personnel; lack of laboratory capabilities.

Author (Year) Subject Focus on U.S. Primary Concerns Theory Predicting Zoonoses? Focus? Noted this Problem Pappaioanou Veterinary Medicine in Yes No Lack of collaboration Institutional (2004) Public Health. across sectors, both design/Bureaucratic strategically and behavior politically. Reaser et al. Companion Animal Yes No There is not a Institutional (2008) Zoonoses. comprehensive, design/Bureaucratic collaborative approach behavior; to zoonotic disease Federalism prevention, detection, and surveillance. Ryan (2008) Zoonoses and Biological Yes No Need for an early (non- Institutional Terrorism human) warning system, design/Bureaucratic need for better human behavior and animal health collaboration. Stone et al. National Companion Yes Yes Lack of cross sector Institutional (2008) Animal Disease collaboration, design/Bureaucratic Surveillance System— comprehensive behavior; Strategies for detection, inter-agency Federalism Design/Implementation. coordination.

Zinsstag et al. Cooperation between Yes No Need to continue Institutional (2005) human and animal moving towards a „one design/Bureaucratic health. medicine‟ approach. behavior

Empirical Literature

This section presents empirical literature that is directly relevant to the characteristics that have been identified as key characteristics and potential problems from the previous two sections: the theoretical review of federalism and institutional design, as framed by the imperative of zoonotic diseases and germ theory. The set of studies reviewed in summarized in Table 2.2. In particular, there have been analyses on the legal concerns

(predicted by federalism), on who detects (predicted by institutional design and bureaucratic behavior), and on timeliness (predicted by federalism, institutional design, and highlighted by germ theory). Table 2.2 also indicates whether the research had a zoonotic disease focus, and if so, whether that focus was exclusively on zoonoses or whether zoonoses were simply included more broadly in the scope of the inquiry. Finally, this section mentions the key threats to validity and limitations of each study, also highlighted in Table 2.2. While this empirical literature may seem scant, this reflects the overall lack of empirical research in the field of detection and outbreak surveillance rather than any type of systematic exclusion of empirical research. This section clearly frames the importance of continued work, as in this dissertation.

Studies were selected for inclusion in Table 2.2 through key word searches of online databases. In particular, the phrases “disease detection”, “disease reporting”,

“disease” “reporting” and “timeliness”, “zoonotic disease detection”, “zoonotic disease reporting” were used in searches of databases, including ProQuest, PubMed, Academic

Search Premiere, and Google Scholar. Additional searches were necessary as specific journals not included in these databases. In particular, the Journal of the American

Veterinary Medical Association and Journal of Veterinary Medical Education were

60 searched separately using these same keywords. In some cases, the review of citations in already identified journal articles yielded additional articles or books for inclusion.

Searches of the George Washington University and Cornell University library catalogs were also completed using these key words. Searches were conducted only in English. The time span was not purposively limited. For an idea about the amount of literature available, the phrase “zoonotic disease surveillance” produced two hits on the ProQuest database and one hit in Business Source Premiere. For the key words “zoonoses” and “disease reporting” ProQuest returned zero hits and Business Source Premiere returned one hit.

Subsequently, literature not including zoonoses has been incorporated into this review because it is relevant in other ways; there is a scarcity of empirical research specifically on zoonotic disease detection and reporting. All of the studies listed in Table 2.2 contained an empirical research component on an aspect of disease detection or disease reporting.

The Legal Environment for Disease Detection and Reporting

Roush et al. (1999) offers the most comprehensive study addressing the variation in state law with respect to nationally notifiable human diseases in the United States.

Notably, the current variation in state reporting is descriptively summarized online

(Council for State and Territorial Epidemiologists, 2009a, 2009b). Roush et al. (1999) conducted a questionnaire of all state and some territorial epidemiologists. They found that of the 58 diseases that were nationally notifiable at that time, 25 were reportable by state law in 90% of states and territories, while 8 diseases were reportable by state law in less than 75% of states and territories (Roush et al., 1999). Subsequently, they not only assessed the variation in state law, they also implicitly captured the knowledge (or lack thereof) of state and territorial epidemiologists. Certainly this is a primary limitation to Roush et al.

(1999), as the demarcation between actual law and law as perceived or recalled by the state epidemiologists was not thoroughly discussed or identified. Granted, the knowledge of the epidemiologists about state law may be as relevant in practice to what state law actually is.

Regardless, Roush et al. (1999) do suggest that there is significant variation among state law in regard to reporting requirements for nationally notifiable human diseases.

Interestingly, different countries in the European Union have similarly varied numbers of diseases on their national notifiable lists (Reintjes, Thelen, Reiche, & Csohán, 2007).

The current variation in state law is descriptively listed online in a number of tables

(Council for State and Territorial Epidemiologists, 2009a, 2009b). While systematic assessment of the variation in state laws is identified in this dissertation on state animal notifiable disease reporting, a cursory assessment of state law for human notifiable diseases shows that substantial variation remains, as it did in 1999. For example, 17 states do not mandate reporting of the hantavirus pulmonary syndrome (Council for State and Territorial

Epidemiologists, 2009b).

Moving away from nationally notifiable human diseases, there are a number of other studies which provide empirical evidence about various aspects of notifiable animal diseases, reporting, and veterinarian knowledge in the United States, though these studies—even pieced together—offer a far from comprehensive assessment of the legal variation which exists between the states. These studies generally focus on a small geographic area of a state or a county within a state, rather than national variation. In the empirical research reviewed here, it is therefore concerning that the samples are not representative of the larger population. Surveys have been the most widely used mode of data collection. While useful, there are a number of reliability and validity concerns that

62 are related to using surveys as a mode of data collection, including that individuals may not reliably recall past behavior or particular legal statutes.

Most broadly, are veterinarians aware that certain animal diseases are reportable, and that state law exists to that effect? In 2005, a survey was sent to Michigan veterinarians to collect information about disease reporting within the state (Mauer & Kaneene, 2005).

While only animal disease reporting was targeted as the subject of interest (so zoonoses in humans were excluded), this survey demonstrated the knowledge of veterinarians about reportable disease laws for animal diseases in their state. While 93% of veterinarians

(n=84) reported they knew that such a list existed, the study did not query whether they knew what diseases were actually on the list. Moreover, the survey results suggested that veterinarians had substantial concerns about privacy, as related to animal disease reporting.

Unfortunately, there was no comparison group outside of Michigan for comparison in this research, and the convenience sample of veterinarians was quite small. Interestingly, the results of a similar query were drastically different in King County, Washington, when surveyed about human diseases. In a survey to licensed veterinarians, only 38% were

„very‟ or „mostly‟ familiar with Washington notifiable disease law (for human disease), of which diseases were to be reported to the Washington State Department of Health (Lipton,

Hopkins, Koehler, & DiGiacomo, 2008). It is unclear whether these different results are a function of geographic location, survey population, type of diseases, or if they really depict differences in knowledge. Nonetheless, the surveys demonstrate that there is varied knowledge among veterinarians about the legal environment for zoonotic disease detection and reporting. A separate survey conducted by Bender & Shulman (2004) further found that in many situations involving zoonotic diseases—such as animal exhibitions—nearly all

63 states simply do not have standardized guidelines regarding zoonoses, indicating more than simply a lack of knowledge.

Given that reportable lists exist in states, what diseases appear on these lists? A study published in 2000 surveyed the National Assembly of Chief Livestock Health

Officers in the United States to identify which of the zoonotic diseases listed (all with potential as biological weapons) were reportable by state (Fitzpatrick & Bender, 2000).

With a response rate of 92%, only 23.9% of states reporting swine influenza, while the surveyed officers reported that 100% of states reported brucellosis and foreign animal diseases (Fitzpatrick & Bender, 2000). Again, there is some concern that officials may not correctly recall or take the time to review what state regulation actually is, something that was addressed in the article. Moreover, the generalizability of this research is restricted by the limited number of zoonoses which were included. However, this is one of the few studies which specifically reviews reportable disease laws for animal diseases. While the contribution of this article is significant for this reason, it has been a decade since a similar review of state law has been completed.

Finally, given that a reportable animal disease list exists in the states, Kahn (2006) conducted research about where veterinarians are required to report such diseases. A random sample of veterinarians was taken from 4 U.S. states, and a convenience sample of veterinarians was taken. In addition, a survey was sent to the State Veterinarian in all 50 states. Only 8 of 43 State Veterinarians who responded to the survey reported that they were required to report a notifiable zoonotic disease to a public health agency. It remains possible that State Veterinarians are replying from memory, rather than actually referring to the state statute regarding the issue (possibly limiting the validity of the responses).

The burden of knowing the disease reporting requirements is on the veterinarian

(Babcock et al., 2008). In addition, over 10% of the veterinarians surveyed from the 4 U.S. states noted that they would likely notify a federal rather than a state agency in the case of an unusual infectious disease (L. H. Kahn, 2006). This finding suggests only what veterinarians would hypothetically do in cases where they are legally required to report. It is unclear if these four states are representative of the entire country.

There have been very few studies that have been conducted on the legal environment in which zoonotic disease detection exists. The comprehensive legal landscape amongst all 50 states has not been identified or assessed, particularly in reference to animal disease reporting. Surveying those at the front lines is important and useful, but in order to better understand the system in which they operate, a more comprehensive statutory analysis is necessary, particularly given the import of zoonoses. Again, the literature reviewed here does not elaborately or sufficiently tie the variation in state regulation to federalism, a connection which is made in this dissertation.

Who Detects Disease Outbreaks

The next key characteristic is „who detects‟ disease outbreaks in practice. There are two key studies which address this question: Ashford et al. (2003) and Dato et al. (2004).

Ashford et al. reviews Epidemic Intelligence Service (EIS) investigations from 1988-1999 that involved potential agents of bioterrorism to discover that healthcare workers reported

24.6% of outbreaks. Health departments, in total, were responsible for 30.5% of the reports

(Ashford et al., 2003). Notably, the accuracy of who reported (which in this research is analogous to the detection of the outbreak) is dependent on the data which are contained in the EIS trip report. Other sources of initial recognition included surveillance systems,

65 foreign ministries of health, nongovernmental organizations, and others (Ashford et al.,

2003). Veterinarians or veterinary laboratories were not mentioned in the article. The other study used Morbidity and Mortality Weekly Reports (MMWR) from 1999 and 2000 to identify who detected (initially recognized the outbreak) (Dato et al., 2004). Like

Ashford et al. 2003, this study found that health departments were predominately responsible, detecting 53% of the outbreaks through aggregated test results or clinical reports (Dato et al., 2004). Notably, this is significantly higher than the results of the

Ashford et al. (2003) study; variation could be attributed to the analysis of different diseases, varying sample sizes, or nature of the outbreak. For example, more complex outbreaks—those in which EIS officers are called—may be less easily detected by health departments. Dato et al. (2004) also note that clinicians and school health services recognized outbreaks. Together, these two studies demonstrate that health departments are key in outbreak detection, but detection can also come from other stakeholders.

Other studies have investigated more broadly who may be involved in outbreak detection, but did not focus on actual outbreaks. A study published in 2002 which reported results from a survey of state veterinarians and state public health officers indicated these individuals felt there was „some integration during an outbreak‟ to „routine integration‟ on average (Tharatt, Case, & Hird, 2002). Another study published in 2008 found that 57% of veterinarians in one county in Washington had discussed zoonoses with a public health agency in the last year (Lipton et al., 2008). Similarly, a study published in 2006 confirmed that increasing numbers of states (since 1999) had access to a veterinarian who could perform in a public health role (Lemmings, Robinson, Hoffman, Mangione, &

Humes, 2006).

From this empirical literature it remains unclear how frequently zoonotic disease outbreaks are detected by various human health stakeholders and practitioners or animal health stakeholders and practitioners. While both Dato et al. (2004) and Ashford et al.

(2003) provide useful information, neither looked specifically at zoonotic diseases or analyzed recent data. Given that reporting is inherently tied to detection, this dissertation also links the problems indicated by theories of institutional design and cooperation to the

„who detects‟ question, given the apparent lack of sectoral and agency cooperation.

Timeliness in Disease Detection and Reporting

Based on the foundation of germ theory and the perceived importance of rapid disease detection, this section discusses „timeliness‟ of surveillance activities, and is inclusive of research which has been conducted on how fast a „case‟ has been detected as well as how fast an outbreak has been detected. Moreover, because of the importance of reporting, it also includes empirical literature which discusses how fast disease reporting occurs as well. First, in terms of detection of a disease outbreak, Ashford et al. (2003) and

Dato et al. (2004) both note that detection was delayed. While Ashford et al. (2003) does not disaggregate results, Dato et al. (2004) suggests that timeliness varies based on the disease in question as well as by the level of geographical dispersion of the individual cases. Based on a convenience sample, the time to detection was found to be between less than two days to over two months (Dato et al., 2004). This finding was similar to that reported in Ashford et al. (2004), where the number of days to problem identification ranged from 1 to 26. Hedberg et al. (2008) in a review of enteric diseases also found that time from symptom onset to outbreak detection varies (a median range to be 1-16 days),

67 and suggested that timeliness is affected by how quickly physicians and laboratories decide to notify the public health department (Hedberg et al., 2008).

Notably, these studies while similar are not perfectly comparable as they use slightly different definitions of timeliness. Moreover, they each deal with different diseases. Broadly, this literature indicates that detection of outbreaks can be quite delayed, and timeliness is related to a number of factors, including type of disease. These studies are generally limited by their narrow geographical or disease scope. Unfortunately, no empirical literature has focused on detection timeliness specifically with zoonotic diseases in the United States, particularly in animal populations.

Other empirical literature has reviewed slightly different aspects of timeliness, such as how fast certain nationally notifiable disease cases are reported to a community health system or to the CDC (Jajosky & Groseclose, 2004). Jajosky & Groseclose (2004), like studies previously mentioned, found that timeliness varies by type of disease (for research on reporting Meningococcal disease, see Rea & Pelletier, 2009; and for a discussion in

Korea, see Yoo et al., 2009). The median time to community health system reporting ranged from 9 to 26 days and median time to national reporting ranged from 12 to 40 days

(Jajosky & Groseclose, 2004). In addition, they also indicate that some states are timelier in their reporting than others. Jajosky & Groseclose (2004) only included seven various diseases and it is unclear if these diseases are representative and findings are more generalizable. Lemmings et al. (2006) narrowed their focus to West Nile, asking how fast a case was reported to the surveillance program and then to the CDC. They found that for both human and bird specimens, the median reporting time to program notification was one week and to the CDC was 16.5 days (Lemmings et al., 2006). Interestingly, they do not

68 specify what “program notification” implies, but it is likely comparable to reporting to a community health system (again, refer to Figure 2.2). There is no comparison between

West Nile and other diseases. Granted, the results of Jajosky & Groseclose (2004) and

Lemmings et al. (2006) are reasonably similar. Again, literature searches did not produce research on how long it takes zoonoses to be reported to a local or national reporting system.

Other studies have reviewed whether electronic reporting identifies cases of infectious disease faster than paper based reporting. Electronic reporting was found— generally—to improve speed of identifying cases and reporting (Nguyen, Thorpe, Makki,

& Mostashari, 2007; Overhage, Grannis, & McDonald, 2008). However, for three conditions (some foodborne zoonoses), electronic reporting was actually slower: for

Escherichia coli O157, salmonellosis, and histoplasmosis (Overhage et al., 2008). This is a particularly interesting finding given the zoonotic nature of the diseases in question; unfortunately, little attention was given to explaining why electronic reporting was less timely in these cases. And again, these studies focused on a specific and small geographic area.

While there are occasionally anecdotal remarks on the subject, the existing empirical research does not describe how variation in state laws and a lack of bureaucratic coordination affect timeliness. This dissertation provides a clear description of current practice and assesses how fast zoonotic diseases are detected in the United States. It also describes how who detects may be plausibly related to timeliness.

Other

Importantly, some empirical research has reviewed the completeness of notifiable disease reporting in the United States. As this dissertation is primarily reviewing federal and open source data, this empirical research is critical. Doyle et al. (2002) reviewed 33 separate studies in a meta-analysis of completeness of disease reporting. This meta- analysis indicated that reporting completeness—like timeliness—relates to the disease being reported and varies from 9 to 99% (also see Doyle et al., 2002; Rea & Pelletier,

2009). For the two diseases reviewed in this meta-analysis that are also incorporated for study in this dissertation, reporting ranged between 42-67% for salmonellosis and was estimated at 69% for malaria (Doyle et al., 2002). Certainly, this meta-analysis is limited by the various statistical estimation methods employed by each individual study, as well as in the meta-analysis itself. Nevertheless, the vast variation in completeness indicates that disease cases reported to the federal government are not always indicative of the number of disease cases (or likely outbreaks) that are occurring at the state level. Other research has indicated that electronic reporting improves the completeness of overall disease reporting in particular geographic areas (Nguyen et al., 2007; Overhage et al., 2008). While this dissertation does not focus specifically on completeness, it is considered in Chapter 5, particularly as a limitation to the generalizability of empirical findings.

Table 2.2 Empirical Research on Disease Detection and Reporting (Zoonotic and non-Zoonotic)

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Ablah et Other Survey of Does training Yes. In both Training - Small sample al. (2008) (Education) Veterinary help humans and improves size. Professionals in a veterinarians animals. veterinary - No comparison Training improve their knowledge. group. Program knowledge of - Limited zoonoses? geographical area. Ashford et Who; How Review of Who detects Yes. Health - Only diseases al. (2003) Fast Epidemic outbreaks and Potential departments with biological Intelligence how fast are biological reported 30.5% weapons potential. Service they detected agents of outbreaks; - No further Investigations and how fast are include detection was elaboration on from 1988-1999 they reported? zoonoses. delayed what caused detection and reporting delays. Bender & Legal (& Survey of State Are there Yes. Very few states - Recall bias. Shulman Public Veterinarians or guidelines for Zoonoses in have guidelines - Focused on (2004) Policy) State zoonotic humans from governing zoonotic diseases Epidemiologists diseases from animals. animal exhibits from animal animal exhibits and petting zoo exhibits. (zoos, etc). type situations (41 states did not have standard guidelines).

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Dato et al. Who; How Morbidity and Who detects the Yes. Health - Convenience (2004) Fast Mortality outbreak and Convenience department sample of Weekly Reports how fast are sample staff detected outbreaks. from the CDC they detected? included 53% of the - Unstandardized 1999-2000. zoonoses. outbreaks; time calculation of to detection times to detection varied by due to inadequate disease and data. level of geographical dispersion. Doyle et Other Meta-Analysis of Assess current Yes. Reporting - Potential threats al. (2002) (Completene 33 Studies knowledge on Notifiable completeness to validity of ss of the diseases varies from 9% secondary data, as Reporting) completeness of include to 99%; varies 1/3 of studies used notifiable zoonoses in particularly on a type of infectious humans. the disease estimation method disease being reported. to assess reporting. completeness. - Limited discussion of statistical bias in combining studies, given variety in study method.

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Fitzpatrick Legal Survey to To identify Yes. For the - Only potential & Bender National which potential Diseases of potential biological agents (2000) Assembly of biological interest were biological were included. Chief Livestock weapons were livestock agents, - Reporting by Health Officials reportable by zoonoses in reporting officials could be state. animals. ranged from incorrect. 23.9% of states - Decade old data. requiring reporting to 100% of states requiring reporting (swine influenza was lowest, brucellosis and foreign animal diseases were highest).

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Hedberg How Fast Records, Public What is the Yes. Only Detection (in - Data from 6 U.S. et al. Health median number foodborne median number states only. (2008) Departments & of days from zoonoses in of days) was - Did not factor in Laboratories onset to specific humans. delayed the speed. certain surveillance & because of the steps could detection multiple steps realistically occur benchmarks for required. Not given location and four enteric significant setting restrictions. diseases? differences -Did not provide between the distribution of diseases. the cases to identify outliers. Jajosky & How Fast National How fast are Yes. Some Substantial - Used secondary Groseclos Notifiable nationally notifiable variation in sources that could e (2004) Diseases notifiable diseases are timeliness by have data Surveillance diseases zoonoses. state and by reliability System reported? disease. concerns, particularly in regard to reported dates. - Many cases excluded because of inadequate information. - Only 7 diseases analyzed.

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Kahn Legal Survey of State To collect Yes. Animal Only 8 of 43 - Did not include (2006) Veterinarians; information disease respondents questions about Survey of about animal reporting stated that a wildlife. Random Sample disease includes public health - Only 4 U.S. in 4 U.S. States reporting zoonoses in agency should states (same requirements. animals. be notified in geographical area). the case of a - Did not examine notifiable what the law disease; over actually was, only 10% of what veterinarians veterinarians might do indicated they (hypothetical). would notify a federal rather than state agency.

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Lemmings Who; How Survey of State What is the state Yes. Median time - West Nile Virus et al. Fast; Other West Nile Virus of West Nile Exclusively from suspected only. (2006) (Surveillance Programs, Virus West Nile human case to - Lack of Practice) Including 6 surveillance and only. report of discussion about Cities reporting? confirmed case why such wide to the CDC variation exists was 16.5 days; between states. more states had access to veterinarians than in 1999; medical entomologists still were scarce; 94% states conduct surveillance on mosquitoes or collect information from other agencies.

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Lipton et Legal; Other Survey to To understand Yes. Veterinarians - King County al. (2008) (Veterinarian Licensed the extent that were far more only. Practice) Veterinarians in practicing likely to - Potential non- King County, veterinarians discuss response bias with Washington. engage in zoonoses if the those who did not zoonotic disease owner was at respond to the prevention high risk; 57% survey (17% not practices. of veterinarians returned). had discussed - Did not zoonoses with disaggregate the a public health responses about agency in the notifiable disease last year; 38% knowledge of veterinarians amongst different were very or types of mostly familiar veterinarians. with Washington notifiable disease law.

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Mauer & Legal Survey to To collect Yes. Found that - Small sample Kaneene Michigan information Discussed 93% of size (n=84). (2005) veterinarians about the only animal veterinarians - Michigan only & current disease disease knew there was convenience reporting reporting. a reportable sample. system. animal disease - Lack of follow list. up on whether Veterinarians they knew what stated that disease were concerns about actually reportable privacy existed (not that such a list in animal simply exists). disease reporting. Nguyen et How Fast; Data from the To assess the Yes. Electronic - Limited sample al. (2007) Other Electronic completeness Discussed reporting is size. (Completene Clinical and timeliness notifiable sometimes - One year only, ss of Laboratory of electronic diseases more complete close to initiation Reporting) Reporting reporting in which than paper of electronic System, New comparison to include some reporting, but reporting. York City paper reporting. zoonoses in more complete - Limited scope in Department of humans. especially for understanding the Health and STDs. strengths and Mental Hygiene Electronic weaknesses of and Staff reports were electronic Interviews faster. reporting.

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Overhage How Fast; Data from local Is electronic Yes. Disease Electronic - One Indiana et al. Other public health reporting of list included reporting county only. (2008) (Completene department, notifiable zoonoses in identified more - Connection not ss of hospitals, and diseases more humans. cases, and explicitly made Reporting) community complete and identified cases between detecting information faster than faster. cases and system in Marion paper-based detecting an County, Indiana reporting? outbreak. - Electronic reporting not available throughout the United States. -Does not include data from national or regional laboratories. Rea & How Fast; Data from Maine How fast and No. 79% of cases - Meningococcal Pelletier Other Department of complete is were reported disease has higher (2009) (Completene Health and reporting for in three days; reporting ss of Human Services meningococcal completeness completeness Reporting) and hospital disease? of reporting is documented than discharge data 97.6% other diseases. 2001-2006. - Not a zoonoses. - One state only. - Small sample size (52 cases).

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Reintjes et Legal; Other Analysis of Benchmarking Assumed An ideal - Limited data al. (2007) (Completene surveillance of national yes. system is available on the ss of systems in surveillance Surveillance difficult to surveillance Surveillance) England, systems to is likely to realize due to systems. Finland, France, understand their include different needs - Finding Germany, strengths and zoonoses in of EU benchmarks Hungary, and the weaknesses. humans. members. suitable for all Netherlands Number of systems was notifiable difficult. diseases ranges - Distinct variation from 26 to 82, in EU surveillance and timeliness systems is not of reporting expressed. varies from days to weeks. Roush et Legal Questionnaire Review of state Yes. Of 58 diseases - Old data. al. (1999) completed by all and territorial Notifiable on the national - Potential recall State and some reporting disease list reporting list, bias from Territorial requirements for includes 35 were epidemiologists of Epidemiologists notifiable zoonoses in reportable in actual reporting diseases. humans. more than 90% regulation. of states/territorie s; 8 diseases were reportable in less than 75% of states/ territories.

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Tharatt et Other Survey to State To assess Yes. Surveillance - Median scores al. (2002) (Perceptions Public Health perceptions of List included activities for were not provided of Officers and bioterrorism animal and bioterrorism for integration Practitioners) State (risk) in the human were perceptions, could ; Who Veterinarians United States. zoonoses. considered less be regression important than toward the mean. other activities; - Possible non- similar response bias, as responses 14% of surveys between state were not returned. veterinarians - Lack of and state public assessment of how health officers; well the systems scores for are actually integration integrated (rather between than just human and perceptions) animal health - Event bias systems were (Bacillus anthracis between some attacks)—may integration have changed during perceptions. outbreak investigation and routine integration.

Author Focus Data Source Primary Zoonotic Relevant Limitations to (Year) (legal, who, Research Diseases Findings Studies how fast, Question/ Included & other) Objective Which? Yoo et al. How Fast Data from the Review of six Yes. Some of Delay occurred - Korea only. (2009) Korean National notifiable the diseases when there was - Limited number Notifiable infectious included a long time of diseases. Disease diseases—time were between - Completeness of Surveillance lag identified zoonoses. disease onset to data is a problem System 2001- between diagnosis; this noted by the 2006 symptom onset delay is disease authors. and date of specific. - Laboratory notification to confirmations the Korea CDC. were not incorporated into this research.

Conclusion

Certainly there are many issues tangentially related to those discussed that are relevant to this dissertation. Detection and reporting are complex, founded in local, state, federal, and international law and obligations, as well as in deeper frameworks of federalism, institutional design, and bureaucratic complexity. Some are examined, where appropriate, in the methods chapter (for example, the CDC list of nationally notifiable diseases is used to create a list of zoonoses). Other more technical information is located in this dissertation‟s appendices. These appendices include information such as definitions of transmission pathways of zoonoses and key symptoms.

As intended, Chapter 2 of this dissertation 1) provides salient context for this research and demonstrated the topic‟s relevance, 2) discusses the theories which frame and explain zoonotic disease detection and practice as well as inquiries into the topic and 3) carefully examines the landscape of empirical literature which has been written on the topic of disease detection and reporting. Figure 2.5 offers a diagram that pictures how this chapter comes together, blending the contextual background, the implications of the theoretical frameworks, and the empirical literature.

Figure 2.5: Diagram of Literature Chapter Integration

Zoonoses Key Public Health Threat

Disease Reporting & Detection Key Aspects: Legal Elements, Who Detects, and Timeliness

Federalism Institutional Design & Bureaucratic Behavior Theories

Frames Disease Reporting and Detection

Germ Theory- Conception of Public Health Practice Fundamental Importance of Disease Detection and Reporting

As Figure 2.5 indicates, the idea that zoonotic disease detection and reporting is fundamental to the health of a population is strongly indicated by the conception of public health practice that emerges from germ theory. Therefore, this is the foundation of the pyramid. Given that we have this theory of public health practice, we now move up a level to public health policy, where two theoretical fields provide guidance. First, the federalism predicts that disease detection and reporting is likely to be inconsistent, given the U.S. federal system of governance where states have widely varying regulations, leading to large variations in disease detection and reporting. In addition, the federal government does not

84 currently have the power to mandate states to report diseases to the federal level. In part, theories of institutional design and bureaucratic behavior relate to federalism, as the system of U.S. governance, which allows states to design their own executive branches. In addition, the institutional design and resulting bureaucracy of the federal and state executive branches results in executive agencies that do not coordinate or collaborate. This results in a patchwork of disease detection practice. In addition, jurisdictional overlap and poor collaboration between sectors has resulted in critical problems in timely disease detection. These theoretical frameworks provide broad explanatory power.

The third level depicts the actual practice of disease detection and reporting. As implicated by the previous two tiers, three aspects are critical in disease detection and reporting. First, what is the legal framework for disease detection and reporting (predicted as a potential issue from the federalism), second, who detects (and/or reports) disease outbreaks (expected from institutional design and cooperation), and finally, how fast outbreaks are detected (and/or reported) (a key aspect explained in part by federalism, institutional design, and emphasized by germ theory). At the very top of the pyramid are zoonoses, the category of diseases focused on in this dissertation. This dissertation focuses on the zoonotic disease detection and reporting practice as shaped by the U.S. governmental system.

Through the empirical research, more specific propositions reflecting the federal system, institutional design, and bureaucratic behavior frame recommendations on how to improve zoonotic disease surveillance. Many stakeholders have called for reform in disease detection and reporting, in part to ensure that a coordinated response can be

85 mounted in the case of an outbreak (Government Accountability Office, 2000; Hodge et al.,

2006; Institute of Medicine, 2003; Journal of the American Veterinary Medical

Association, 2008; U.N. Food and Agriculture Organization et al., 2008; K. Wilson et al.,

2006). Much talk has been devoted towards a movement to a „One Health‟ or „One

Medicine‟ approach, where animal and human health agencies and sectors are interconnected (Cantor & Kludt, 2005; Pappaioanou, 2004; Schwabe, 1984). However, as this review has highlighted, there are many unanswered questions about zoonotic disease detection and reporting practice in the United States. We do not know the legal environment for reporting of animal diseases. We are not aware of who is detecting zoonotic disease outbreaks, or how fast they are being detected. Subsequently, we do not have empirical evidence on relationships between the legal environment and timely detection, or how sectors vary on their speed of detection. Moreover, policy recommendations have rarely been founded on both a theoretical understanding of practice and empirical knowledge of disease detection and reporting practice in the United States.

These are fundamental issues that must be resolved for effective and useful reform to occur, and present the contributions this dissertation offers to the field of biosurveillance.

Next, Chapter 3 explains the methods that are used answer the research questions which guide the dissertation.

Chapter 3: Research Methods

Overview

Given the problem of zoonotic diseases, the growing threat they pose to public health, and the U.S. system of governance, this dissertation collects and analyzes evidence about the state of zoonotic disease detection and reporting practice in the United

States. The research methods are driven by the theoretical, conceptual, and empirical literature discussed in Chapter 2. This dissertation uses a qualitative mixed-methods approach, organized into three different sequential phases.

The first phase of this research provides a descriptive analysis of the legal environment for animal disease reporting. The second phase creates a database and conducts analyses of this database to identify ‗who‘ detects zoonotic disease outbreaks and ‗how fast‘ these outbreaks are detected. Finally, the third phase follows the results of the second phase and uses a case study approach to further analyze the complexities of zoonotic disease detection practice. While the research approach is primarily qualitative, quantitative techniques are used to analyze the empirical evidence and relationships between variables of interest.

This chapter first reviews the research questions and outlines the overall design of the dissertation. It also provides a list of key definitions for this research. Then, it thoroughly discusses each of the three phases of the research to be undertaken. The data source, limitations, methods, objectives, and analysis techniques of each phase are summarized for easy reference in Table 3.1. Next, this chapter briefly discusses the methods behind the policy recommendations section. Finally, the potential limitations of

87 these methodological approaches are identified, followed by the strengths of this approach.

Research Questions

Due to the exploratory nature of this research, research questions are more appropriate than hypotheses (Creswell, 2009). As presented in Chapter 1, the research questions of this dissertation are:

1. What is the current state of practice in zoonotic disease outbreak detection in the

United States?

a. What is the current variation in state law with respect to animal disease

reporting?

b. Who detects zoonotic disease outbreaks in the United States in animals

and people?

c. How fast are zoonotic disease outbreaks recognized in animals and

people?

2. What elements are needed for improved zoonotic disease outbreak detection in

the United States?

In general, the first set of research questions is largely descriptive. The second question has a more prescriptive lens, where policy alternatives are offered to improve the zoonotic disease outbreak detection system (as in Bonham & Heradstveit, 2008; Tsang,

2007).

The second phase of the dissertation (the outbreak database in Chapter 5) was deemed not human study research by the GW Institutional Review Board (IRB), and no

IRB approval was required. At the completion of the second phase of this dissertation

(the outbreak database in Chapter 5), an IRB application for the third phase (the case studies) was submitted to the GW IRB for approval. The case study component required exempt IRB review. IRB #0011107 was approved on 1/26/2011. This record is on file with the IRB office and also available upon request from the author.

Table 3.1 summarizes the research design and methodological approach of this dissertation. It lists the three phases of this research, the purpose of each phase, the methods of each phase, the data analysis techniques that are used, and finally the citations and past research which provide grounds for each phase.

Table 3.1: Overview of Methodology

Research Purpose Data Source Methods Data Potential Key Related Phase Analysis Limitations Research Legal To provide the legal Legal statutes Content Descriptive Describes law, Roush et al. Analysis landscape and an retrieved online. analysis. statistics and not necessarily (1999); Kahn assessment for comparative practice. (2006) animal disease analysis. reporting. Outbreak To assess who 1) MMWRs Content Descriptive Limited Ashford et al. Database detects zoonotic 2) ProMed-mail analysis. statistics; generalizability. (2003), Dato et al. diseases in both 3) OIE Annual bivariate (2004), Jajosky & animals and people, Reports & Related methods. Groseclose (2004), and how fast they Literature Chan et al. (2010) are detected. 4) NNDSS Reports & Related Literature

Embedded To provide a more 1) Physicians and Interviews; Theme Limited Similar to (but is Case in-depth analysis on veterinarians literature analysis; generalizability; less detailed than) Studies zoonotic disease 2) Diagnostic review. frequency potential (Government outbreak detection laboratory counts. interviewee Accountability and reporting personnel bias due to Office, 2000) practice, using 3) State officials faulty recall. specific cases 4) Existing (outbreaks) records/literature

Outline of Research Design & Justification

This dissertation uses a primarily qualitative mixed-method approach, that is predominately descriptive and exploratory in nature: the dissertation is designed to

―describe what is going on‖ (Creswell, 2009; Hoover & Donovan, 2008; Trochim &

Donnelly, 2008, p. 5). Because of the lack of existing empirical research on zoonotic disease detection and reporting practice beyond anecdotal or observational evidence, this type of approach is most appropriate and fundamentally necessary to facilitate for future research on the topic. Two other research approaches that have been used to evaluate specific aspects of surveillance systems or have been considered as potential methods for this study and also deserve brief mention.

First, modeling techniques using statistical and mathematical methods have been used to improve computer algorithms and sensitivity of existing surveillance systems

(Eubank et al., 2004; Stark, 2003). Modeling simulations have also been used to model disease outbreaks predictively to develop and/or evaluation potential mitigation strategies

(Audigé, Doherr, & Wagner, 2003; Hurd & Kaneene, 1983; Martin, Meek, & Willeberg,

1987). Spatial modeling and spatial epidemiology have been used to review

―relationships between ecology and disease to study and predict spatial distributions of diseases, their vectors or hosts‖ (Blackburn et al., 2008; Clements & Pfeiffer, 2008, p. e2).

Other types of models more frequently undertaken in public policy have also been used with regard to disease surveillance and response. For example, decision analysis and decision models have been employed to attribute disease outbreaks to a particular source as well as for rapid detection of disease threats (Hald, Vose, Wegener, & Koupeev, 2004;

M.M. Wagner et al., 2001). Signal theory has also been used to evaluate and assess how detection systems are triggered, and how to improve this triggering(M.M. Wagner et al.,

2001). Similarly, complex systems modeling has moved to try to highlight how ‗complex systems‘ (including the human component) work, taking into account non-linearity and path dependence (Costanza, Wainger, Folke, & Maler, 1993). But despite these technological advances and the prospects of these tools, they have not been implemented or pursued to their full potential due to a lack of empirical data and complexities in the modeling process (Audigé et al., 2003; Clements & Pfeiffer, 2008). Specific aspects of surveillance and detection have been modeled, but these have been highly technical endeavors that cannot be replicated here due to a lack of empirical data and the overall complexity of detection and reporting practice.

Second, other studies have taken a risk assessment approach, using epidemiology and economics to evaluate various types of surveillance or response systems, networks, or practices based on the idea that humans will choose or decide to pursue the best possible options when the costs and benefits (monetary or otherwise) are revealed

(Havelaar et al., 2007). Because of the complex and intertwined components of both designing an appropriate surveillance system and securing support, including various behavioral, technical, organizational, and policy elements (Keusch et al., 2009), and the lack of empirical data to evaluate any of these elements, this approach has not produced conclusive results (Havelaar et al., 2007). There are too many aspects for comprehensive risk assessment of surveillance and detection as undertaken here, though more limited analyses may be appropriate. However, it is difficult, if not impossible, to decide what

92 the best approach to detection and reporting practice should be through such techniques, when empirical research has not adequately described what practice actually is.

These two alternative research approaches are not feasible or suitable for this dissertation for a number of reasons. Both modeling and risk-assessment have produced limited and focused on very specific aspects of surveillance and disease detection, not drawing up to the bigger picture. Mathematical complexities in the modeling process are significant. And fundamentally, due to the lack of existing empirical data on zoonotic disease detection and reporting, modeling is not an appropriate method. While technical

(computerized) surveillance systems are improving in their disease detection capabilities, many if not most disease outbreaks, zoonotic or otherwise, are still detected by humans rather than by an independent system and/or related spatial or decision models (Babcock,

Marsh, Lin, & Scott, 2008; Levi & Inglesby, 2006; M'ikanatha, Lynfield, Julian, Van

Beneden, & de Valk, 2007; Nguyen, Thorpe, Makki, & Mostashari, 2007; Michael M.

Wagner, Gresham, & Dato, 2006). Therefore, the human component must be taken into account: modeling and risk assessment rarely does this, or incorporates human action under significant assumptions. In sum, without research to describe zoonotic disease detection and reporting practice, more complicated research such as decision models, evaluations of specific surveillance systems, complex systems analyses, fault trees, and other methods simply cannot be completed (Hoover & Donovan, 2008, p. 45; M'ikanatha et al., 2007). Subsequently, this dissertation lays the foundation for more complex modeling approaches through exploratory analysis and including important theoretical foundations.

There are three interconnected empirical phases in this research: 1) a legal analysis, 2) an outbreak database, and 3) embedded case studies. The first phase addresses part a) of the first research question. The second phase addresses parts b) and c) of the first research question. Independently, the third phase helps to address second research question, drawing upon the results and conclusions of both the legal analysis and outbreak database. Taken together, these three phases inform the second research question.

Because each of these three research phases have different characteristics, each is now discussed in turn, with attention towards data sources, data management techniques, data analysis, objectives, and potential limitations relevant to each phase. This chapter also discusses the integration of these three phases: this dissertation culminates in a policy analysis of various options, based on specified criteria that are suggested from the previous phases of research (for more on policy analysis, see Dryzek, 1993; Lynn, 1999;

Stokey & Zeckhauser, 1978; Stone, 2002; Teitelbaum & Wilensky, 2007; Weimer &

Vining, 2005).

Key Working Definitions

While many definitions have been previously reviewed, it is useful to review key definitions that are relevant particularly to this methods section before describing the first phase. These definitions are effectively the operationalizations of the variables of interest in this dissertation. Construct validity is strengthened by carefully and clearly defining these terms and substantiating them with previous research (Yin, 2003). These are listed in Table 3.2 in alphabetical order.

Table 3.2: Key Definitions

Detection (or Initial Recognition): ―Detection‖ or ―initial recognition‖ of an outbreak are synonyms, defined as ―the person, persons, or institution that originally brought the outbreak or health emergency to the attention of health authorities‖ (Ashford et al., 2003, p. 515). This definition is analogous to that used by Dato et al., 2004. They also specify that ―when individual cases were reported to the health department‖ the outbreak was classified as ―having been detected by the health department through an aggregation of individual reports‖ (clinical or laboratory) (Dato, Wagner, & Fapohunda, 2004, p. 467).

Emerging Zoonosis: ―An emerging zoonosis is a zoonosis that is newly recognized or newly evolved, or that has occurred previously but shows an increase in incidence or expansion in geographical, host, or vector range‖ (U.N. Food and Agriculture Organization, World Health . Organization, & World Organization for Animal Health, 2004, p. 6).

Notifiable Diseases: Notifiable diseases are diseases that are designated by the government as reportable to government public health agencies (Velikina, Dato, & Wagner, 2006). The CDC has a frequently updated list of notifiable diseases which are (voluntarily) reportable by states to the federal level, listed in Appendix A (Gostin, 2008; Roush, Birkhead, Koo, Cobb, & Fleming, 1999; Velikina et al., 2006).

Outbreak: An outbreak is defined, based on the World Organization for Animal Health (OIE) and World Health Organization (WHO) definitions (World Health Organization, 2010), and operationalized through a series of criteria discussed later in this chapter. Simply, an outbreak can be defined as ―occurrence of one or more cases in an epidemiological unit‖ (World Organization for Animal Health, 2010). This is slightly different than the definition operationalized in Dato et al. (2004) of two or more cases of a disease clustered in time or place.‖ Notably, the CDC often draws no distinction between epidemic and outbreak (Dato, Shephard, & Wagner, 2006). The definition used in this research ensures that a single case of a disease is captured when the incidence level is higher than usual in a given population, and two cases of a disease are not captured as an outbreak if this is the normal incidence of the disease.

Reporting: For this dissertation, reporting is the act of an individual notifying a state authority or a state notifying a federal entity (likely the CDC) about an infectious disease (Doyle, Glynn, & Groseclose, 2002; Jajosky & Groseclose, 2004). It is ―a form of biosurveillance in which clinicians and laboratories report designated diseases…to governmental public health…at the time of diagnosis‖ (Velikina et al., 2006, p. 67).

Surveillance: While surveillance and biosurveillance are used interchangeably, biosurveillance is preferred because it is more inclusive. Biosurveillance includes both disease surveillance and public health surveillance as practiced by governmental public health, but also ―allows us to broaden the scope…to include many other organizations that monitor for disease, such as hospitals, agribusinesses, and zoos‖ (Michael M. Wagner, 2006, p. 4). Surveillance is defined by the WHO as ―the systematic ongoing collection, collation, and analysis of data for public health purposes and the timely dissemination of public health information for assessment and public health response as necessary‖ (Beatty, Scott, & Tsai, 2008; Keusch, Pappaioanou, Gonzalez, Scott, & Tsai, 2009). This is also parallel to the CDC definition.

Timeliness: ―Timeliness‖ or ―how fast‖ is the time from when a case of a disease is recognized by the presentation of clinical symptoms (onset of illness) to the time the outbreak is detected (Ashford et al., 2003; Dato et al., 2004; Jajosky & Groseclose, 2004).

Legal Analysis

The first phase of the research identifies the legal environment surrounding zoonotic disease detection and reporting in the United States. The approach taken is similar to the empirical research conducted by Roush et al. (1999): this section identifies the legal landscape surrounding zoonotic disease detection and reporting in the United

States by reviewing animal disease reporting law and regulation. However, rather than using a survey of state epidemiologists such as used by Roush et al. (1999), this legal analysis reviews the actual statutes and/or regulations for animal disease reporting in each state. The research for this phase is a textual analysis of laws and written regulations (for more on textual analysis, see Mason, 2002). Indeed, the National Biosecurity Resource

Center for Animal Health Emergencies has lists of reportable diseases for each state on their website ("National Biosecurity Resource Center for Animal Health Emergencies,"

2010). However, it is unclear whether these lists are entirely current, and unless listed in the disease list itself, the associated codes and statutes are not linked. Additionally, no analysis has been undertaken on these statutes. This is what this empirical phase adds to the literature.

Data Sources

The laws and regulations for each of the 50 states and the District of Columbia concerning animal disease reporting were identified through two data sweeps (using statutes as data, see Mason, 2002). The first sweep of data collection collected the disease lists and associated statutes and regulations that were obtainable through state websites.

This was primarily an exercise to see what was there. The second sweep of data collection used WestLaw, an online legal database used frequently by lawyers for legal

96 research. WestLaw was used to ensure that 1) all relevant statutes and regulations related to animal disease reporting were captured by the online searches, 2) that the most up-to- date statutes and regulations were collected, and also to verify 3) that there were not repealed or revised statutes and regulations that had not yet been updated or removed from state websites. Predominately, state statutes and regulations were up-to-date and available online. However, approximately 20% of the states did not provide current legal material through the internet.

An ‗animal disease reporting statute/regulation‘ is identified as a statute, regulation, or online disease list that includes information about 1) what animal diseases are reportable, 2) who has to report such diseases, and/or 3) how fast such diseases have to be reported. This list of three criteria is not an exhaustive or exclusive list, but has been used to guide the identification process of relevant statutes. The data analysis and results section in Chapter 4 reflects the results of both data collection sweeps, using the most current statutes or regulations available.

For the first sweep, internet searches were conducted using the terms ―animal disease reporting‖ and ―the name of the state.‖ In most cases, these key phrases resulted in the relevant statute, regulation or a reportable animal disease list. For example, using these key words, the relevant regulation for the state of Washington was located here: http://apps.leg.wa.gov/WAC/default.aspx?cite=16-70&full=true# and the statutes are linked to the administrative code. In all cases, the terms ―livestock disease reporting‖ and

―the name of the state‖ were also used to ensure that regulations were not excluded accidentally that were concerning animal disease reporting. Statutes that are explicitly

97 focused on humans and human disease reporting have been excluded from this analysis, because this analysis focuses on animal disease reporting.

However, some states do not list their administrative code online (for example,

Connecticut). In addition, the currentness of the statutes, regulations, and disease lists needed to be ensured. For this reason, a second sweep of data collection was undertaken using the WestLaw database, which contains current laws and regulations, both state and federal. In WestLaw the statutes and regulations for animal disease reporting were reviewed for each state by searching the actual statutes and administrative regulations for that particular state. In a few cases, such as Kentucky, both the internet and WestLaw searches did not yield an animal disease reporting list. In these cases, a request was made to the appropriate state agency for a copy of the statute, regulation or disease list by calling the public telephone number listed on the Department of Agriculture site and asking the operator to be provided with a notifiable animal disease list. Ultimately, a phone call was necessary to three states: Connecticut, Kentucky, and Colorado. All

Departments of Agriculture promptly provided animal disease reporting lists or information regarding their list. In two cases, the lists were provided directly by the State

Veterinarian.

These data collection methods identify statutes, regulations, and online disease lists only for the 50 states and District of Columbia. U.S. territories are excluded from this analysis due to the difficulties in finding appropriate regulations, the fact that systematic internet searches did not yield statutes for the U.S. territories, prior low response rates from agencies in U.S. territories in research, and the precedent of previous research (Fitzpatrick & Bender, 2000; Kahn, 2006; Katz & Allen, 2009).

Coding

Coding was conducted as a literal content analysis to assess ―what is there‖

(Krippendorff, 2004; Mason, 2002, p. 149). To undertake rigorous content analysis of legal texts, a researcher must have: ―systematic selection; standardize[d] coding; reliability checks; [and] statistical or rigorous qualitative analysis‖ (Hall, Jacobson,

Stoltzfus Jost, & Rosenbaum, 2009). These four criteria are used as the framework throughout this phase of the research (for an example of legal content analysis following these criteria, see Hammer & Sage, 2002).

There are nine elements that were coded from each state statute. Table 3.3 provides a list of the elements, the categorical manner in which they are coded in the database, and also cites relevant research that uses, discusses, or cites the importance of these specific criteria. The general questions to be addressed are:

1. Is there a list of reportable animal diseases, and is this list codified in a law or

regulation?

2. What diseases are listed?

3. Who has to report diseases and is laboratory reporting explicitly mentioned in

the disease list?

4. How fast are the disease listed to be reported (to state official or entity)?

5. Who are the diseases to be reported to?

6. Are the diseases segregated by the species in which they occur?

7. Are there provisions for unknown or emerging diseases in the list, statute, or

regulation?

8. Is wildlife mentioned in the disease list, statute, or regulation?

9. Are public health and/or zoonotic diseases mentioned in the list, statute, or

regulation?

While the issue of enforcement and such mechanisms is an important issue, this is not discussed in Chapter 4 (Gostin, 2008; Horton et al., 2002). Enforcement has frequently been discussed in reference to public health law, as agencies‘ authority to sanction offenders for violations and enforce public health regulation is a complicated legal subject (Center for Law and the Public's Health at Georgetown and Johns Hopkins

Universities, 2001; Gostin, 2008; Horton, Misrahi, Matthews, & Kocher, 2002).

100

Table 3.3: Coding Scheme for Legal Analysis of State Laws and Regulations

Element Categories Literature that Supports Use of Element 1.Law or Law, Regulation, Lipton et al. (2008); Mauer & Kaneene (2005); Regulation/Are Disease List or Burke (2010) Disease Lists Combination Codified 2. Diseases Listed Number of Roush et al. (1999); Reintjes et al. (2007) (a. State by State Diseases, What Comparison and Diseases b. Disease Comparison) 3. Who has to Who & Yes/No Roush et al. (1999) report diseases & laboratory reporting in law? 4. How fast are If Yes, Time Jajosky & Groseclose (2004); Hedberg et al. diseases to be (Hours); Time (2008); Gostin (2008) reported? Categories 5. Who to report Entity Fitzpatrick & Bender (2000); Kahn (2006); to? Gostin (2008) 6. Segregated by Species See literature on zoonotic diseases in Chapter Species? 2. 7. Provisions for Yes/No Council for State and Territorial Unknown Epidemiologists (2009a, 2009b) Diseases 8. Mention of Yes/No Beatty et al. (2008); T. Lynn (2009); wildlife? McNamara (2002) 9. Mention of Yes/No See Chapter 1 for importance of zoonotic human diseases in public health. disease/public health/zoonoses?

These criteria were identified from the literature review as important elements in zoonotic disease detection and reporting. Table 3.3 lists the literature that has either undertaken a similar analysis or recommends the need for examining that specific aspect of detection and reporting law, or justifies that criterions existence in law. In particular, criterion two has been reviewed by the Council of State and Territorial Epidemiologists for human diseases (Council for State and Territorial Epidemiologists, 2009a, 2009b). In

101 addition to the literature, the development of this list included an iterative process, in which the laws and regulations were reviewed multiple times in order for the common elements in the laws and regulations to be identified and subsequently coded. This process also included the consultation of others on the inclusion or exclusion of certain criteria. These nine criteria provide more than sufficient information for analytical purposes, and far exceed any data or coding procedure in prior research.

Many content analyses use a criterion of the number of words in a statute

(Krippendorff, 2004; Rourke, Anderson, Garrison, & Archer, 2001; Sittig, 2003).

However, this legal analysis does not use this measure because of the large variation in the format of laws and regulations amongst the states. For example, as is carefully discussed in Chapter 4, some states list the diseases in the law, while others do not. Some states have a single regulation, while some states have an amalgam of laws and regulations. For this reason, comparing the number of words across multiple states is not an appropriate or useful measure.

Disease Coding Issues

Diseases frequently have multiple names, and in many cases, diseases are referred to either by the name of the biological organism that causes the disease or by a common name. For example, Teschen disease is—for the purposes of disease reporting— synonymous with porcine enteroviral encephalomyelitis. However, one state lists

―Teschen disease‖ on their reportable disease list, and another lists ―porcine enteroviral encephalomyelitis‖. In order to compare disease list across states, differences such as this are identified, and the biological agent/various disease names that refer, for all intents and purpose to the same disease, are condensed into one category. The Merck Veterinary

102 manual, located at www.merckveterinarymanual.com, was employed, along with the

United States Animal Health Association Foreign Animal Diseases, 7th Edition (Merck,

2010; United States Animal Health Association, 2008). This manual lists the diseases with common names so that they may be condensed. In addition, information from

Veterinary Services, a unit of the Animal and Plant Health Inspection Service (APHIS), at the United States Department of Agriculture (USDA), was consulted to ensure that the categorizations were appropriate.

These condensed codings are identified in the results section of Chapter 4. Where the diseases are condensed, all diseases (and biologic agents) are listed, and separated by slashes. Typically, the more common name is listed first (for alphabetizing purposes). For example, Teschen disease and porcine enteroviral encephalomyelitis are entered in the table as Teschen disease/porcine enteroviral encephalomyelitis/porcine polioencephalomyelitis. Furthermore, diseases are entered separately in the master spreadsheet so if any mistakes emerge in the process of collapsing diseases, they can be easily recognized and rectified.

In addition to these collapsed disease categories, there are cases in which the diseases are consistently listed one way by all disease lists and a different way by one or two disease lists. After ensuring that the disease and/or biological agent definitely referred to the same thing, these disease names are collapsed without any further note in the tables below. For example, the state of New Hampshire refers to bovine spongiform encephalopathy as Mad Cow Disease. In the state-by-state disease analysis, bovine spongiform encephalopathy has been checked and there is not an entry for Mad Cow

Disease. Another instance of this occurring is with paratuberculosis and Johne‘s disease.

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In nearly all cases, the disease list of the state lists ―paratuberculosis‖; only a few list

―Johne‘s disease‖. This entry has been collapsed, and is now ―paratuberculosis‖, and

Johne‘s disease is not mentioned.

In cases where a ―common name‖ causes multiple diseases or conditions (for example, the Southern Cattle Tick causes both bovine anaplasmosis and piroplasmosis), the disease/biologic agent is listed exactly as it appears in the state disease lists. This method is for consistency on a state-by-state basis.

The coding rules employed to determine coverage of the reportable animal disease lists are listed in Table 3.4 for reference, and include rules governing a number of other specific coding issues. In many cases, these rules were developed iteratively so that meticulous and consistent coding could be conducted to allow for reliable and valid comparisons across states. For example, in cases where a disease ―category‖ was listed by a state, the disease category was listed as a disease. Subsequently, in the disease listing below, there are categories such as ―any contagious or infectious disease.‖ In the instances where the OIE lists are referenced, these diseases from these lists are included in full, because it is a typical reference list. All other references to specific items, (e.g. a veterinary diagnostic handbook), are simply listed as such. In many cases, the categories are so broad or the substantive reference was not defined sufficiently to enable disease- by-disease inclusion. This method ‗rewards‘ states that have taken the time to highlight specific diseases that are relevant or important to the state for reporting.

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Table 3.4 Key Coding Rules for Disease Lists & Animal Diseases

1. Animal Bites: Have not been included in the analysis, even when listed in a list of reportable diseases. 2. Avian Influenza: Where avian influenza is listed and the pathogenicity is not specified, both ―highly pathogenic avian influenza‖ and ―low pathogenic avian influenza‖ are checked. 3. Babesiosis: Caused by Babesia species, but Babesia causes a variety of diseases, so diseases and agent is listed separately. 4. Bonamia spp: Infection with any bonamia species (molluscs) is listed together. 5. Brucellosis: In the case of brucellosis, if the state disease list says ―multiple species diseases‖ and then lists brucellosis, or lists ―bovine brucellosis‖ or ―porcine brucellosis‖ then ―brucellosis, all other or all species, including bovine, ovine, porcine‖ is checked in the analysis. In the case of caprine and canine brucellosis, this type had to be specifically mentioned by the state in order to be checked in the analysis. In addition, brucellosis melitensis also had to be specifically mentioned to be checked in the analysis. This does not inherently imply that ―brucellosis, all species‖ does not include brucellosis melitensis, it only implies that it was not explicitly in the state‘s disease list. 6. Chorioptes: All species of chorioptes are marked (bovis, capre, equi). 7. Contagious Pleuropneumonia: If contagious pleuropneumonia is listed, both ―bovine contagious plueropneumonia‖ and ―caprine contagious pleuropneumonia‖. 8. Cysticercosis: If cysticercosis is listed without further specification, both bovine and porcine are listed. 9. Dourine: Though dourine is caused by trypanosomiasis, trypanosomiasis does not always cause dourine so the disease and agent are listed separately. 10. Equine Encephalomyelitis (EE): If Eastern EE, Western EE, and Venezuelan EE are listed together in the state disease list, they are checked independently in the analysis. In the case where the state list says ―equine encephalomyelitis‖, then ―All equine encephalomyelitis conditions‖ is checked in the analysis. 11. Equine herpesvirus: Equine rhinopneumonitis is listed separately from equine herpesvirus. 12. Erysipelas: If erysipelas is listed without further specification, both swine and avian erysipelas is coded in the analysis. 13. Glanders: This disease is caused by Burkholdia species, but Burkholdia does not always cause glanders, so disease and agent are listed separately. 14. Infectious Bovine Rhinotracheitis/Infectious Pustular Vulvovaginitis: Though this disease is caused by Bovine herpesvirus, the disease and biologic agent are listed separately as bovine herpesvirus does not always cause this disease. 15. Malignant Catarrhal Fever: Different types are not delineated in the analysis. 16. Martelia spp.: Infection with any martelia species (molluscs) is listed together. 17. Melioidosis: This disease is caused by Burkholdia species, but Burkholdia species do not always cause Melioidosis, so disease and agent are listed separately. 18. Menangle Virus: Caused by a specific paramyxovirus, so not listed with paramyxovirus. 19. Newcastle disease: Includes all types, exotic, lentogenic, low pathogenic. 20. Perkinsus spp.: Infection with any perkinsus spp (molluscs) is listed together. 21. Piroplasmosis: Caused by Babesia species, but Babesia does not always cause piroplasmosis, so disease and agent is listed separately. 22. Salmonellosis: If species of salmonella is provided, coded by species. If listed by animal species infected, it is listed this way. Otherwise under ―salmonellosis‖. 23. Vesicular Diseases: Or ―all vesicular conditions‖ are both coded as ―all vesicular or ulcerative conditions‖. 24. If a disease is listed and then says ―including‖, then all diseases in that list are included in the analysis (for example, ―Transmissible Spongiform Encephalopathies, including Chronic Wasting and Scrapie‖). 25. If a disease is listed as reportable only in a particular set of animals—for example ratites or dairy herds only—this is not denoted in the disease105 list. 26. If a statute/regulation/disease list says ―any foreign animal disease‖ then ―any foreign or exotic or emerging disease‖ is coded in the analysis; no other diseases are coded. Other Coding Issues

In addition to the disease coding issues noted in Table 3.4, additional coding rules were needed to code into the database ―who reports‖ animal diseases—in other words, who is explicitly mentioned as responsible for reporting diseases (see Table 3.5).

Table 3.5: Key Coding Rules for “Who Reports”

1. Veterinarian: If the statute/regulation/or disease list is coded as the veterinarian or practitioner needs to report, a. Veterinarian or practitioner is explicitly mentioned, b. Implies that positive test results may not be necessary, and/or c. Clinical signs may be sufficient. 2. Laboratory: If the statute/regulation is coded as the laboratory need to report, the statute or regulation states: a. ―laboratories must report‖ or b. must report cases ―diagnosed by laboratory procedures‖ or c. report from ―laboratory confirmation‖ d. some similar variation, ―laboratory‖ –means other persons & practitioners do not need to report. 3. Both: If statute, regulation, or disease list states: a. ―any licensed veterinarian, any person operating a diagnostic laboratory, or any person that has been informed…‖, or b. ―any person‖ or c. ―clinical diagnosis, laboratory confirmation, or suspicion‖, or d. ―veterinarian, owner, or custodian‖ or e. some variation of these phrases, ―both‖ – meaning laboratory and provider – need

to report. 4. Unspecified: If statute, regulation, or disease list states: a. ―if you suspect‖ b. ―must report of suspicion or diagnosis‖ c. or in general, does not include ―who‖ is to report, is thereby coded as ―unspecified‖. 5. Diseases are coded individually ―laboratory‖ or ―both‖, and will vary according to the specifications of the state regulation/code/disease list. 6. If a disease list has multiple specifications, or the specifications conflict, the disease will be coded with the more inclusive code. For example ―both‖ rather than laboratory, if in one place the list requires laboratory reporting and in another place, reporting for clinical signs. 7. If the negative lab tests also need to be reported this is typically not indicated in the analysis unless the disease is not otherwise listed.

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Additional data coding issues related to codification are identified in Chapter 4, as they are part of the results of this phase of research because they describe the variation in animal disease reporting requirements between the states.

Coding Reliability

To ensure coder reliability, Taylor Burke (J.D., L.L.M) a lawyer with expertise in health policy (from the George Washington Department of Health Policy) assisted in the coding and interpretation of a subset of the statutes (Creswell, 2009; Mason, 2002). He was also consulted in the data collection procedures.

In addition, Daniel Bachmann, a summer intern at George Washington

University, Department of Health Policy, coded a small subset of states (5 states or 10%).

These five states were selected at random (starting with the second state and then choosing every tenth state). The coder received a list of coding instructions, including the coding rules presented in this chapter and the statutes, regulations, and disease lists for the state. They were then asked to code the statutes, regulations, and disease lists based on the nine criteria presented here, and record these results in an excel spreadsheet.

Re-coding these statutes, particularly the list of reportable diseases, is a significant task, so only one secondary coder was used.

Though the disease coding has been completed multiple times and cross-checked as the results sections were completed, mistakes may still exist in the disease listing due to sheer number of diseases and biologic agents listed by the states. While confirmatory coding was undertaken by another individual in a handful of states, this may not prevent all coding errors.

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Data Analysis

In the data analysis and results section of Chapter 4, univariate statistics are provided for the criteria listed above in Table 3.3. For example, the percentage of state laws or regulations requiring the reporting of a certain disease is provided. The states are also compared to each other visually, through maps of the United States illustrating the differences in their reportable disease lists. For example, the speed in which diseases are to be reported or the number of diseases which each state requires to be reported. In addition, data analysis in Chapter 4 also compares the diseases listed in these reportable animal disease lists with the zoonotic diseases of interest list (provided in the next chapter). The number of reportable disease lists is also reviewed in comparison to the total sales of animals by state, to evaluate whether there is a relationship between these variables.

While public policy and public health research often use some type of quantitative indices, for example, to gauge the level of risk for a particular condition (for example,

Appel, Harell, & Deng, 2002; Mangen & Peterson, 1982; Slivinske & Fitch, 1987), further quantification of these results is not conducted in this analysis for multiple reasons. First, problems with indices, including issues with measurement, are documented in the literature (Krippendorff, 2004; Stone, 2002). Second, it was not clear what additional value-added would result from constructing such an index in this case, not to mention the difficulties in appropriately and validly weighting the different criteria into a single index.

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Objectives

This legal analysis achieves two key objectives. First, there are tables and figures that summarize the nine identified criteria of the state laws, regulations, and reportable disease lists for animal diseases for the 50 states and DC (similar to Roush et al., 1999).

Second, in addition to summarizing these lists, this chapter also assesses the variation in the reportable disease lists of the 50 states and DC, including through the use of illustrations to visualize the qualitative data (for more on diagramming qualitative data, see Mason, 2002). This descriptive and visual presentation clearly describes the legal landscape of animal disease reporting in the United States, analyses which have not previously been published.

Potential Limitations

In addition to potential coding errors previously discussed (and mitigated by a secondary coder and re-coding), there is a key potential limitation to this phase of the research. Statutory content analysis may not be analogous to what reporting practice actually is in reality. In other words, reporting practice on the ground may not reflect state law. For example, as presented in Chapter 2, veterinarian knowledge about state reporting law is quite varied (Lipton, Hopkins, Koehler, & DiGiacomo, 2008; Mauer &

Kaneene, 2005). Indeed, this is why many empirical inquiries in the past have used a survey instrument rather than content analysis. However, content analysis of the actual regulations and/or legislation remains a useful way to gauge the legal landscape of animal disease reporting by state, without the limitations related to survey research methods

(such as response rates). Nonetheless, this is an important limitation to acknowledge.

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Outbreak Database

The previous section summarizes the first phase of the research, the legal analysis.

The second phase involves the creation and analyses of an outbreak database. Similar to research completed by Dato et al. (2004) and Ashford et al. (2003), this phase of the research describes ‗who‘ initially recognizes outbreaks and ‗how fast‘ zoonotic disease outbreaks are initially detected. The definitions employed in this phase of the dissertation research are summarized in Table 3.2 (for example, outbreak and timeliness).

This outbreak database collects data on zoonotic disease outbreaks. Primarily

―who detects‖ and ―how fast‖ data are collected, but other variables such as how fast outbreaks are reported, the geographical location of the outbreak, and number of cases in the outbreak are also collected if available. This section outlines the research methodology of this section, thoroughly discussing each step of the creation and analysis of the outbreak database.

Disease Selection

Zoonotic diseases are the population of diseases at interest. In short, zoonoses are a key threat to public health, and a growing problem (Beatty et al., 2008; Chavers, Fawal,

& Vermund, 2002; Chomel, Belotto, & Meslin, 2007; Daszak, Cunningham, & Hyatt,

2000; Kahn, 2006; Ryan, 2008; Watanabe, 2008; Wolfe, Panosian Dunavan, & Diamond,

2007). Though the majority of infectious diseases are zoonoses, the selection of zoonotic diseases is not intended to indicate that other classifications of infectious diseases are less important. Instead, it is to suggest that zoonoses are a category of infectious diseases that are of particular concern, and deserve additional study.

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Because of the large list of zoonoses this dissertation could pursue, the list of zoonoses of interest is narrowed down through a systematic process. The full list of considered zoonoses is provided the Appendices. The first step in narrowing down the list of diseases is to index the zoonoses by a number of lists, noting where each disease appeared. The first list consulted is the Centers for Disease Control Nationally Notifiable

List (diseases in humans) (Centers for Disease Control and Prevention, 2007). The CDC list includes non-zoonotic diseases; these are removed through the selection process. The second list consulted is the OIE (World Organization for Animal Health) notifiable disease list (which is the list used by the USDA as well) and both the old OIE A list and

OIE B list from which the new list recently emerged, to ensure that no disease was mistakenly absent (see World Organization for Animal Health, 2004, 2005, 2006). These lists also introduced non-zoonotic diseases to the list (animal-only), but again, these are removed through the selection process. Third, the list of diseases of interest in wildlife is reviewed (United States Department of Agriculture: Animal and Plant Health Inspection

Service, 2009). There is not an official list of wildlife diseases that are under U.S. national surveillance, so no list was available for inclusion here (Gaston, 2009). Finally, the list of Category A, B, and C potential bioterrorism agents is identified and used

(Centers for Disease Control and Prevention, 2009; Gubernot, Boyer, & Moses, 2008).

For the purposes here, it is not necessary to differentiate between the different categories

(A, B, and C—based on level of threat) as identified by the CDC. All cestode

(tapeworms), nematode (roundworms), and annelid (leeches) zoonoses are excluded from the list because they are not typically considered to be significant infectious zoonotic

111 public health threats in the United States, and are not included on any of the lists which are used to construct the disease list.

After identifying the pool of possible diseases, a narrowing process selects the most relevant zoonotic diseases from a biological and policy standpoint. Where there are multiple species of the biologic agent under the same genus (for example Brucella abortus and Brucella suis), this is considered as one disease (Brucellosis). Once a disease is added to the list of included diseases, it is not considered in any subsequent step. This narrowing process entails the following steps:

1. If the disease is listed by the CDC and OIE notifiable lists, it is highlighted

yellow for potential inclusion. This yields 10 diseases.

2. Next, if the disease is listed by the CDC or OIE notifiable list, and is also

contained in the CDC list of Category A, B, and C agents, it is highlighted blue

for potential inclusion. This yields 9 additional diseases.

3. Next, if the disease is listed by the CDC or OIE notifiable list, and is also

contained in the wildlife disease of interest list, it is highlighted green for

potential inclusion. This yields 5 additional diseases.

4. Next, if the disease is specifically mentioned in key conceptual literature as a

problematic or relevant zoonoses (particularly because of its high transmissibility

between humans and animals), it is highlighted orange for potential inclusion.

This yields 9 additional diseases.

5. Next, if a disease is listed as a Category A, B, or C agent and considered

zoonotic, it was highlighted red for potential inclusion. This yields no additional

diseases.

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6. Next, because some of the previous steps enabled the inclusion of non-zoonotic

diseases, these diseases are removed, as indicated by the strike-through of text and

an accompanying citation indicating that the disease is not zoonotic. This

eliminates 5 diseases. For example, while some consider Foot-and-Mouth disease

to be zoonotic, there have been very few cases ever documented, and most do not

categorize the disease as zoonotic for public health purposes, so this pathogen is

excluded (Merck & Co, 2008).

7. Finally, this list was presented to a physician with specialized knowledge in

zoonotic diseases and surveillance issues. Dr. Larissa May (of The George

Washington University Medical Center) recommended the inclusion of 13

additional Diseases (highlighted in grey), particularly those that are highly

transmissible between animals and people and which emerge outside the United

States but may be imported into the country. Of these 13 diseases, 8 are listed on

one of the previously identified lists. This step effectively captures the remaining

important zoonotic diseases listed solely on the CDC nationally notifiable disease

listings.

The process results in a list of 41 zoonotic diseases for inclusion into the outbreak database. These diseases are listed in Table 3.6. It should be acknowledged that there are diseases on this list for which outbreaks of the disease are highly unlikely. Based on data from 1998-2008, some of these diseases rarely appear in the United States (the agents would have to be imported through travel and trade). However, because most emerging infectious zoonoses are going to be imported to the United States rather than originate in

113 the country, excluding these pathogens would limit the analysis and lead to gaps in U.S. surveillance and detection systems. It is important to include all relevant diseases rather than exclude them prematurely.

Table 3.6: List of Zoonotic Diseases for Inclusion in Database

Anthrax Blastomycosis Bovine Spongiform Encephalopathy (variant Creutzfeldt Jacob Disease) Bovine Tuberculosis B rucellosis (multiple spp.) Campylobacter Coccidioidomycosis Crimean -Congo Hemorrhagic Fever Cryptococcosis Ebola Hemorrhagic Fever Ehrlichiosis/Anaplasmosis Eastern Equine Encephalomyelitis Glanders Hantavirus Histo plasmosis Highly Pathogenic Avian Influenza Japanese Encephalitis Lassa Fever Leishmaniasis Leptospirosis Lyme Disease Malaria Marburg Monkeypox Newcastle Disease Virus Nipah Virus Novel Influenza A Viruses Plague Psittacosis Q Fever Rabies Rocky Mountain Spotted Fever Rift Valley Fever Salmonellosis Shiga Toxin (producing Escherichia coli) St. Louis Encephalitis Virus Trichinellosis Tularemia Venezuelan Equine Encephalomyelitis West Nile Virus Western Equine Encephalomyelitis

Note: The primary symptom and mode of transmission will be listed in the Appendices.

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Data Sources

After identifying the diseases of interest within zoonotic diseases in general, this section discusses the data sources for the outbreak database. All data sources are reviewed from 1998-2008, and outbreaks within this publication range are be included in this analysis. This means that outbreaks that occur prior to this date range, but have been published within this date range, are included. This range is selected due to the need for timely, recent, empirical analysis, as well as the need for a sufficient number of data points to ensure the data are generalizable. Federal level data—in other words, data for the outbreaks that have been reported to the federal level—are primarily used. This is because, as discussed in Chapter 2, there is a large amount of variation in state detection and reporting practice. Federal level data ensure that there is consistency across the data source; in addition, otherwise data would need to be mined from every state individually.

In addition, outbreaks may cross state jurisdictional boundaries, making federal data more appropriate for a measure.

However, it is important that multiple data sources are used. Granted, some of them produce overlapping results, i.e., discuss the same outbreak. In fact, this is a benefit because there could be additional information, or at least confirmatory data. These data sources are discussed in turn. Previous research suggests that this outbreak database is likely to have between 100-200 outbreaks as a sample size; construction and analysis of the database in Chapter 5 confirms this sample size (n=101). The data sources are as follows.

First, as in Dato et al. (2004), the archived Morbidity and Mortality Weekly

Reports (MMWR) from the CDC are a data source. These reports are available

115 electronically through the CDC website. These MMWR reports include information from the Nationally Notifiable Disease Surveillance System in the cases where publication regarding an outbreak was deemed appropriate (Centers for Disease Control and Prevention, 2001). Not every zoonotic disease outbreak reported to the federal level appears in the MMWRs, particularly as the MMWR focuses more on human than animal diseases (zoonotic or not). In addition, other animal populations like sea animals and animals in zoos are not the target of the MMWRs. However, the MMWR does capture important outbreaks in both zoo animals and ocean animals. The MMWRs, as indicated by Dato et al. (2004), provide an appropriate and useful data source for the outbreak database with the information needed for this dissertation.

Second, ProMed-mail is an important source of data for the outbreak database.

ProMed-mail is an open-source reporting system that operates as a moderated list serve

(ProMed-mail, 2009). While not a formal source of federal data, the information is reported to ProMed-mail by independent parties or states to a global information system

(for more on similar systems, see Keller et al., 2009).Therefore, while not formally submitted to the federal government of the United States, ProMed-mail data are not restricted to the state level and are shared broadly: sufficiently comparable to federal level data. Notably, while data on ProMed-mail may come from many sources, posts are actively moderated, and literature has demonstrated that ProMed-mail reports are very valid and reliable (Cowen et al., 2006). Moreover, informal data sources have been cited as key in effective zoonotic disease surveillance (Keusch et al., 2009). The ProMed-mail archives are also an important source as they include outbreaks in both animals and people, and specifically outbreaks where the zoonotic disease does not spread to humans

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(Cowen et al., 2006). ProMed-mail also captures disease outbreaks in sea and zoo animals.

ProMed-mail archives are searched using the date range noted above, with the key word of the ―disease‖ and ―USA‖. In cases where diseases/agents commonly go by different names (such as the disease and also the biologic agent), all names are included in the searches (for example, salmonellosis and salmonella).

Third, as specified earlier, the United States each year provides the OIE with a report on the absence or presence of these diseases in the United States in animals, which are publicly available online. These reports list the number of outbreaks and number of cases, as well as the species of the animal, and the month and state in which the outbreak occurred. These reports provide evidence that an outbreak did occur. Unfortunately, they do not provide additional information. Open source literature and ProMed-mail reports are used to provide any additional information on these outbreaks.

Fourth, the CDC each year summarizes the notifiable human diseases that have appeared and been reported in the United States (see Centers for Disease Control and

Prevention, 2008). These diseases have been voluntarily reported to the CDC from U.S. states or territories through the National Notifiable Disease Surveillance System

(NNDSS) (Centers for Disease Control and Prevention, 2001). Like the previous OIE reports, these archived records are all aggregated and provide an indication of how many cases occurred, and list the state in which the cases occurred. Again, all types of literature are searched to identify additional information on the outbreaks identified in these aggregate reports. Many of these cases are likely overlap with the MMWR reports.

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Fifth, Weekly Epidemiological Reports (WER), produced by the World Health

Organization (WHO) are reviewed for any additional cases. There are not a significant number of outbreak reports from the United States in this literature, but these reports do provide significant detail on the outbreak detection and investigation.

Finally, corresponding to outbreaks identified in the OIE reports and NNDSS data, open source literature is reviewed. This includes key word searches by state and by disease or biologic agent on Google Scholar, as well as in Lexis-Nexis to capture any news articles related to these outbreaks. This literature is also reviewed for outbreaks identified in ProMed-mail and MMWRs that required additional data. For example, in

2005, these reports state there were three outbreaks of bovine brucellosis, two in Texas and one in Idaho. Literature searches using the terms ―outbreak‖, ―brucellosis‖, ―Idaho‖,

―2005‖, are conducted to yield additional information regarding these outbreaks.

Past research also used Epidemic Intelligence Service reports for data on outbreaks. These reports, as used by Ashford et al. (2003), are constructed when the CDC is requested to send a team to investigate a specific outbreak both internationally and domestically (typically approximately 100 per year) (Goodman et al., 1990; Pappaioanou,

Garbe, Glynn, & Thacker, 2003). These reports are not available for use in this dissertation, but future research should explore analyzing these records specifically for zoonotic dieases.

It is likely that these various data sources contain some of the same outbreaks. In fact, this is an important way to substantiate and validate these data: validation is a very important part of any content analysis (Krippendorff, 2004). However, it is possible that

118 there could be variations in the sources. This variation is simply noted in the database, no averaging occurred.

To summarize, the list of data sources and the websites at which these sources are available is listed here:

Morbidity and Mortality Weekly Reports (MMWR)

o Publicly available from the CDC, http://www.cdc.gov/mmwr/.

ProMed-mail open source reports

o Publicly available from ProMed, http://www. promedmail.com.

Reports from the United States to the OIE (both for animal diseases and

zoonoses)

o Some provided through personal communication with an APHIS

employee, most available here: http://www.oie.int/hs2/report.asp.

Nationally Notifiable Disease Surveillance System (NNDSS) data

o Published by the CDC annually,

http://www.cdc.gov/mmwr/mmwr_nd/index.html

Weekly Epidemiological Reports (WER)

o Available from the WHO: http://www.who.int/wer/en/.

Open source literature.

o Key word searches on Google Scholar for academic and peer reviewed

material.

o Key word searches on Lexis-Nexis (GW Access) for all news sources.

In particular, the MMWR reports and ProMed mail reports are significant in the creation of this database, as they included more details about the outbreak, and the

119 timeline of those events. Unlike the MMWRs and ProMed-mail reports that are written reports on the circumstances of the outbreaks, the NNDSS and OIE Reports generally provided only raw data—listing only the number of cases or number of outbreaks in a given time period or state. For example, 32 cases of disease x in state y in 2007. Where possible, these are matched with the associated record from the MMWRs or ProMed- mail. However, in many cases, this is not possible. Therefore, these cases are noted and then a literature search is performed on the cases in both Lexis Nexis and Google

Scholar. For example, if NNDSS noted a single case of eastern equine encephalitis

(EEE) in 2003 in Louisiana, the search terms are ―2003‖ ―Louisiana‖ and ―Equine

Encephalitis‖. In the event that there were thousands of cases in the NNDSS (for example, salmonellosis often had 40,000-50,000 cases), the words ―salmonellosis‖ and

―outbreak‖ were searched. Interestingly, this additional literature search did not yield a substantial amount of new data for the database. In part, this is because ProMed-mail is open source and includes many media reports about outbreaks already. National Animal

Health Reporting System Data (NAHRS) were not used as it is not publicly available; however, reports from NAHRS are predominately captured in the OIE reports submitted by the United States.

Database Creation

Given the data sources discussed above, it is important to elaborately delineate what is included in the outbreak database and how the database is constructed. An excel database was created to capture this information about the outbreaks. Dropdown menus were created in the columns to prevent additional coding errors. Free software from the

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CDC, EpiInfo, is used to capture any data which required geographical analysis, and is also used for making maps of the United States.

There are 22 fields in the database. These are listed in Table 3.7, with the associated options for data entry.

Table 3.7: Database Fields

Field Options for Field Identification Number YEAR, DATA SOURCE, # (sequential) Outbreak Certain Yes, No, Recheck Data Source Not pre-specified. Outbreak Date Month, Year Country Origin Not pre-specified. Country First Detected Not pre-specified. State 50 states Cases Not pre-specified. Incidence Rate Not pre-specified. Case Fatality Rate Not pre-specified. Disease 41 identified zoonoses CDC A, B, C Agent (all Yes/No agents combined) Species of Origin Not pre-specified. Species Group Affected Humans, Domestic Animals, Wildlife, Humans & Wildlife, Humans & Domestic Animals, Wildlife & Domestic Animals, All All Species Affected Not pre-specified. Who Detects Physician or Clinical (human), Veterinarian or Clinical (animal), State Health Department, State Animal Health Department, Syndromic Surveillance, Lab-Human, Lab- Animal, Zoo, Wildlife Personnel, Other (specified in next entry) Date of First Case MM/DD/YYYY Date of Initial Recognition MM/DD/YYYY Date of State Reporting MM/DD/YYYY Date of Federal Reporting MM/DD/YYYY Date of Response MM/DD/YYYY Comments Open.

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Figure 3.1 offers a screenshot of the actual database.

Figure 3.1: Screenshot of Database

As discussed in the previous section on the legal analysis, this section also proposes the use of literal content analysis, but this time of archival reports and literature rather than legal statutes (Mason, 2002). This is the only available way to aggregate information about zoonotic disease outbreaks, as more formal, standardized, or aggregated records do not exist. Indeed, research questions accompanied by such content analysis is an appropriate tool when the questions ―concern currently inaccessible phenomena‖ (Krippendorff, 2004, p. 33) Content analysis is a particularly important tool for these data as the data are unstructured (Krippendorff, 2004).

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Coding an Outbreak

In order to include data as an ―outbreak‖ in this database (the outbreak is the unit of analysis), a series of criteria are developed and aimed at including any outbreaks that ideally should be reported to minimize public health and animal health consequences.

Initially, I began by operationalizing outbreak using the definition in Dato et al. (2003) and others in this research, which is best paraphrased as ‗2 or more cases (of a disease) clustered in place or time‘. From this basis in previous literature, I revise this definition for this research to ensure it (1) captured the true meaning of ‗outbreak‘—that it is above normal incidence, even if an outbreak is just one case, and (2) includes cases that ought to be reported from a public health and security standpoint. The definition that is used in this chapter emerges from the World Health Organization (WHO) definition of ―disease outbreaks‖ and the World Organization of Animal Health (OIE) definition of ―outbreak.‖

The WHO defines a disease outbreak as (World Health Organization, 2010):

…the occurrence of cases of disease in excess of what would normally be expected in a defined community, geographical area, or season. An outbreak may occur in a restricted geographical area, or may extend over sever countries. It may last for a few days or weeks, or for several years.

A single case of a communicable disease long absent from a population, or caused by an agent (e.g. bacterium or virus) not previously recognized in that community or area, or the emergence of a previously unknown disease, may also constitute an outbreak and should be reported and investigated.

The definition in this outbreak matches this definition and most simply is defined via the OIE definition. The OIE defines an outbreak as the ―occurrence of one or more cases in an epidemiological unit‖ (World Organization for Animal Health, 2010).

Importantly, the criteria that are used for the inclusion/exclusion of cases in the outbreak database revolves around the central notion of what is important to report—for health of

123 humans and animals, given economic and security implications. Subsequently, the criteria may be slightly broader than a typical definition of an outbreak. Not only did this allow for the inclusion of singular cases as the WHO and OIE definitions suggest, it also enabled the inclusion of cases of select agents that would have significant public health implications if they were intentionally released and not reported.

Table 3.8 lists the criteria which were systematically applied to the inclusion/exclusion of cases in this outbreak database. Outbreak inclusion/exclusion into the final data set occurs after all data is entered into the database. These criteria were reviewed by both Dr. Larissa May (GWU), and Dr. Rebecca Katz (GWU), and revisions were made incorporating their comments. A PhD post-doc doing work at the National

Institutes of Health also coded a small subset of the outbreaks (as to whether they were outbreaks or not) as a secondary coder. There was a high level of correspondence in secondary coding. The coding was the same for 9 of the 10 outbreaks coded (yes it was an outbreak, or no it was not). The one different outbreak was resolved through a minor language change in the coding rules to specify criteria.

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Table 3.8: Inclusion of Cases in Outbreak Database

Outbreak Inclusion Criteria An ―outbreak‖ fulfills one of the following criteria: 1) Stated as an outbreak in the MMWR or any CDC report linked in the MMWR. 2) Above average disease incidence (almost always stated in report). a. Temporally. b. Spatially. c. Can include a single case of a communicable disease long absent from a population.

i. Long absent—usually stated in report when last case was observed.

Above average incidence is identified as: as above average incidence in the data source. If not identified here: 1) review of CDC info and state info to try to identify # of cases/year, # of cases/100,000, and # of cases in the United States. If incidence is generally constant or rising, barring the absence of other factors on these criteria lists, it is not considered an outbreak. If there is a significant jump for a month or year, the change in incidence is relatively assessed. A 2% jump in a defined population is included as an outbreak. If the incidence is low (<4%) an increased incidence of >.05% is considered an outbreak.

3) Lab-acquired infection of foreign (diseases not typically seen in the United States), emerging, or on the A, B, or C CDC agent list. 4) Intentionally dispersed agents. 5) Disease cases that originate outside of the United States, but are detected in the United States. 6) Novel or unusual transmission pathway. 7) Drug resistant strain. 8) Emerging disease, previously unrecognized, any number of cases.

Or, an outbreak fulfills two of the following criteria: 1) Diseases which have jumped from one species to a different species (i.e. animal to human) with a clear epidemiological link (isolated from both species or a clear trace back). 2) Case clusters in space or time. 3) Cases involving an agent on the CDC A, B, or C list. 4) Disease cases in an atypical population. 5) Agent not previously recognized in that community, species, or area.

If, an outbreak fulfills 2 of these requirements, BUT it is clearly stated in the data source that it is NOT above average incidence, OR it is NOT an outbreak, it is not coded as an outbreak.

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Coding “Who” and “How Fast”

―Who‖ detects and ―how fast‖ the outbreak is detected are two of the most critical variables in the outbreak database. This section discusses how these variables are identified and coded into the outbreak database.

―Who detects‖ is defined as the individual or entity that first detects the outbreak.

This does not inherently mean they identify the biologic agent. For example, a clinician could detect a high incidence of a particular set of clinical symptoms (a symptomatic outbreak), and this would be considered detection, even if a laboratory then had to confirm the biologic agent causing the disease. This separates cases where the clinician suspects a specific disease or an infectious agent and recognizes the ―symptomatic outbreak‖ from cases where the clinician sends routine or investigative (with no clear idea of agent) samples to the laboratory. This is clearly identified in nearly all the records which contained information about ―who detects‖. Prior research has not necessarily disaggregated these two groups, but this is an important step forward to consider the implications of laboratories in zoonotic disease detection and reporting, as well as the importance of rapid and accurate diagnostic tests.

Detection is also referred to ―initial detection‖ or ―initial recognition.‖ This was defined by Ashford et al. (2003, p.515) as ―the person, persons, or institution that originally brought the outbreak or health emergency to the attention of health authorities‖, similar to Dato et al. (2004). This can also incorporate single cases that are reported to a health department, so when the health department recognizes ―the aggregation of individual reports‖, they are the ―who detects‖ entity (Dato et al., 2004, p.467). Importantly, I code instances where the laboratory suggests a preliminary

126 diagnosis as a ―laboratory detection‖, but if the laboratory performs a confirmatory diagnosis as a ―clinician detection‖.

How fast a zoonotic disease outbreak is identified, or ―timeliness‖ (these terms are also used interchangeably), is the time from when a case of a disease is recognized

(by any person) to the time that the outbreak is actually detected. This is similar to the definition from Ashford et al. (2003), Dato et al. (2004), and Jajosky and Groseclose

(2004). Because of the data sources I was using, I adapted the ―how fast‖ to be the time from the recognition of the first case as the presentation of clinical symptoms—to the time the outbreak was detected. This is the same as in Ashford et al. (2003) who defined the ―beginning of the outbreak as onset of illness in the first case of outbreak cluster.‖ I chose to use the first presentation of clinical symptoms, as reported in the data by either a clinician or the individual themselves, despite the possibility of reporting error for two important reasons. First, and most importantly, this allows for the most reliable coding across outbreaks. This ‗first case‘ date is consistently available, while other dates, such as the date the veterinarian is first called or the date the individual calls their physician, is frequently not. Second, this avoided any issue with the incubation period, as some diseases have longer incubation periods than others. By the time clinical symptoms present, the incubation period has passed, placing all zoonoses on an even playing field for time to detection. Granted, reporting error is a significant concern, however, the consistency with which this date was reported in the data makes it the most reliable choice for a valid statistical analysis. In addition, it is possible that the date of the first recognition—the first case—may favor diseases with more apparent symptoms.

Subsequently, to counter this potential limitation an analysis is also performed to assess

127 whether the primary symptom of the disease impacted the median number of days to detection.

In rare cases, the data source said that the disease outbreak was detected ―in the middle of the month‖ at the ―end of the month‖ or ―at the beginning of the month‖.

These are coded as the 15th day for the middle, the 25th day for the end, and the 5th day for the beginning. While this is somewhat arbitrary, this allowed the inclusion of a few additional outbreaks. Furthermore, because this was consistently coded throughout the database, I suggest that this does not pose a significant issue in the reliability or validity of these data. Moreover, there was almost always, in addition to a broad date such as

―end of the month‖, a framing date that provided supportive evidence that this date was close. For example, reports often cited that at the ―end of the month‖ the physician recognized the outbreak, and sent samples for laboratory confirmation on the 26th. This framing date provided additional evidence that the arbitrary dates I chose were not far from the actual date of the detection (or first case). Subsequently, I argue that for the purposes of this analysis, this is an important assumption to incorporate for a higher sample size.

Additional Coding Explanation

In addition to the coding rules already discussed, there are a number of other coding issues which deserve discussion. First, there are additional variables coded into the database, as pictured in Table 3.7. Most of these are self-explanatory. Second, Table

3.9 presents other explanatory notes, the first set relating to disease coding and inclusion in the database, and the second relating to other variables.

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Table 3.9: Explanatory Notes used in creating the Outbreak Database

Explanatory Notes on Outbreak Database

Disease-Related Coding 1. Rabies: Rabies exposures are not considered outbreaks. For example, if prophylactics were provided to an individual after a suspicious animal contact, this is not considered an outbreak. 2. E-Coli/Shiga Toxin Producing E-Coli: Only shiga-toxin producing e-coli (STEC) cases of e-coli are included in the database. 3. Avian Influenza: Low-pathogenic outbreaks of avian influenza are not included in the database, as this is not typically considered an important zoonosis and is distinct from its highly pathogenic counterpart. 4. Influenza: Similarly, known LPAI viruses in poultry are not considered ―novel influenza A‖, as they are not novel. Same with seasonal influenza, that are not novel strains. 5. Vector-Borne Agents: If the agent is isolated from only a vector (for example, routine mosquito surveillance for West Nile), these cases are not coded as an outbreak.

6. Salmonellosis: These outbreaks are not separated by type, as there are not a sufficient number of each type to make any meaningful statistical analysis possible. Other Variable Coding 1. (a) Species of Origin: The species of origin does not imply that the biologic agent caused disease in that species, as many animals can carry biologic agents without presenting clinical symptoms. 2. (b) Species of Origin: If bodily fluid, like poultry manure, is the source of an

outbreak, this is coded as ―domestic animals‖ as the species of origin. However, food items are coded separately.

3. Number of Cases: While there is a field in the database for the number of cases, this is not included in any analysis because of the lack of consistent information. Often, only the first key cases were reported, or in others, the number of confirmed cases and suspected cases were not separated, making it difficult to compare one outbreak to another. 4. Case Fatality Rate: Again, a field is included in the database, however, there is not sufficient information in many cases for more analysis using this variable. In addition, this number is not reliable in animal outbreaks, because animals often are not tested if additional animals in the herd have been confirmed. The case fatality rate does not include euthanasia in animals. 5. Country of Origin: Listed as USA, unless there is another country explicitly noted in the data source. 6. Case Incidence Rate: This is a field in the database but was not consistently report in a way that allowed reliable statistical analysis, beyond that the incidence rate was ―higher‖ or ―average‖. This is often because the distinct population could not be identified.

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Data Analyses

There are four steps to these data analyses. First is to summarize the data that are collected on all of the outbreaks. Second is to analyze the ―who detects‖ data. Third, the

―how fast‖ data are analyzed. Finally, the ―how fast‖ data are analyzed in relation to the

―who detects‖ data. These four steps are performed through the use of three general methods used in this analysis. First, univariate statistics are calculated wherever possible.

This includes statistics on the median and mean times to detection (in days), the percent of outbreaks detected by the health sector versus the animal sector, and the number of outbreaks which occur in both animals and people, versus just animals and just people.

Geographic illustrations are also used to display the number of outbreaks per state that have been captured by this outbreak database, as visualizations have been considered an excellent way to produce biosurveillance information (Hauenstein et al., 2007). Second, bivariate analyses are undertaken through contingency tables undertaken (for more on this method, see Trochim & Donnelly, 2008). For example, is there a relationship between the species of origin and who detects the disease outbreak? Is the primary mode of transmission related to time to detection? The chi-square test for significance is used for most of these analyses (Trochim & Donnelly, 2008). Third, with the ―how fast‖ data,

Kruskal-Wallis tests are employed to analyze the data by assessing whether there are differences in the medians of the different populations (Y. Chan & Walmsley, 1997;

Doyle et al., 2002; Kruskal & Wallis, 1953). For example, is ―how fast‖ an outbreak is detected related to the species affected by an outbreak? Dunn‘s method is then used to isolate the group or groups that differ significantly from the others, via a multiple comparison procedure ("Dunn Method for Unplanned Comparisons; Dunn, 1964). While

130 the H-statistic of this test is often not as powerful as the similar parametric F-statistic,

Kruskal-Wallis tests are appropriate as the data are not in a normal distribution and sample sizes between the populations vary. These data are also visualized through boxplots (E. H. Chan et al., 2010).

Causality and the directionality of these relationships are discussed in Chapter 5, and are not further reviewed here as it is related to the data and results of the outbreak database. However, in general, these analyses only determine whether there is a statistical relationship, not whether there is causality—though in some cases, because there is a clear temporal relationship between the variables, some explanatory power is gleaned. Additionally, the practical significance of statistical significance is also reviewed in Chapter 5.

Objectives

The objectives of the analyses of outbreak data are to provide reliable and valid results about how fast zoonotic disease outbreaks are detected and about who detects these zoonotic disease outbreaks. In addition, this phase of the research identifies interesting relationships between how fast disease outbreaks are detected and who detects them. Neither of these objectives have been thoroughly addressed in past empirical research. These objectives are instrumental to informing the embedded case study phase of the research, and also are critical for the policy analysis of this dissertation which provides recommendations to improve practice.

Potential Limitations

Importantly, judging the response or capacity to respond is simply outside the scope of this dissertation. There are typical limitations involved with statistical tests and

131 data collection, such as bias towards cases that are reported in the literature (may have characteristics different from cases not reported), and low statistical power in some cases.

Internal validity may also be a concern in outbreak detection, for example, changes in the policy or environment over time or due to catastrophic events like 9/11, may impact the dependent variable). In addition, construct validity may be a concern. However, I contend that these limitations have been mitigated throughout this dissertation as well as possible, by transparent coding procedures, clear and consistently applied operationalizations, valid and reliable coding, and careful discussion of statistical tests and techniques, including strengths and weaknesses. However, there remain four limitations which I believe deserve to be addressed specifically.

First, despite the many data sources, there are issues with the generalizability of the outbreak database and the results generated. This database reflects a small number of cases in the total world of cases, and it is unclear how well these outbreaks generalize to the rest of zoonotic disease outbreaks. While importantly these results do not appear to conflict with results from previous studies, including those with larger sample sizes, this does not necessarily imply that the results of this analyses can be generalized to a broader population. While this research is valid and reliable, with clear statistical conclusion validity, care should be taken to not emphatically overstate how these results can be generalized to zoonotic disease outbreaks more generally. Because it is the only research that has been conducted on these outbreaks, these dissertation results can be used to inform future research and policy recommendations, but broad ―certain‖ conclusions should not be formed. More research needs to be conducted to determine the generalizability of these results. However, the research presented here is an important

132 pathway forward: research specifically on zoonotic disease outbreaks has not been conducted, for any sample size, across the range of variables collected by this research. I contend this important contribution significantly compensates for the generalizability issue. Moreover, it is important to note that additional data are likely not publicly available, and will require the buy-in of additional stakeholders for additional research to be completed.

Second, in many cases, the various data sources did not report the same number of cases. For example, the reports that the United States submit to the World Organization for Animal Health (OIE) often do not match with ProMed or MMWR records. In other words, there are outbreaks in ProMed and MMWR that are not reported to the OIE; the zoonoses cases reported to the OIE in humans do not match the NNDSS records. For example, in 2008, 91 cases of human brucellosis were reported to the OIE from the

United States, though there are only 80 cases in the NNDSS, possibly due to a difference in reporting streams. Some differences are not major—17 cases of hantavirus were reported to the OIE by the United States in 2008, but 18 cases were reported to the

NNDSS. Certainly there are questions over case definitions, and this can explain some differences in case reporting. It would be interesting in future research to pursue to what extent these differences exist in domestic and international reporting. However, further analysis on conflicting reporting between different national and international reports is outside the scope of this analysis and research and is noted as a potential limitation to the reliability of the data.

Third, an additional limitation is the possibility that disease outbreaks in zoo and sea populations (also aquaculture), may be missed as these animal populations are not

133 under the surveillance of a single agency and surveillance and disease detection is fragmented and not systematically reported. However, key outbreaks in these populations are captured by MMWRs and ProMed-mail. So while these outbreaks may be under- represented, they are represented as well as possible given time and resource constraints, and while more research should be conducted in the future, this is not considered a significant limitation to the validity of the conclusions drawn by this dissertation.

Finally, and perhaps most importantly, the outbreaks to be analyzed are the

‗numerator‘ of an unknown ‗denominator‘. The denominator is the number of outbreaks which actually have occurred, while the numerator is the number of outbreaks which have been reported to a data source. States often have an incentive not to report (and rarely penalize for non-reporting), so this numerator could be far from representative of the denominator (Neslund, Goodman, & Hadler, 2007).This limitation is compounded by the fact that not all outbreaks are even detected; it is be nearly impossible to estimate how many zoonotic disease outbreaks are actually occurring if nobody detects themA full section in Chapter 5 is devoted to estimating the denominator of this numerator (the sample size of this outbreak database). I briefly describe the methods used to estimate the denominator here.

In essence, there are two specific issues. First, it is difficult to know how many outbreaks are actually occurring, but are simply not detected by anyone. Second, states may not report outbreaks to the federal level or to an open-source system. This dissertation does two things to confront these problems. First, an additional literature review is undertaken of the technical surveillance literature to see if estimates about the number of outbreaks of specific diseases (that go unreported) exist (see literature in

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Michael M. Wagner, Moore, & Aryel, 2006). Second, an additional analysis of specific states is conducted. Experts consulted in the writing of this dissertation have highlighted

Georgia and Minnesota as jurisdictions with ‗best practices‘ in zoonotic disease detection. These jurisdictions may be more likely to detect zoonotic disease outbreaks because of exemplary practices. However, these jurisdictions could be less likely to report these outbreaks to the federal level, as they have the infrastructure to capably deal with the zoonotic disease outbreaks without federal notification. Unfortunately, no empirical data are identified to reinforce either claim. A more specific discussion of this secondary analysis is discussed in the next section.

Because of this significant limitation, in addition to the outbreak database above, limited data are collected from these two ‗best practice‘ jurisdictions (likely the states rather than the locality). There are limited data available, though one state did respond to a request for data on specific diseases for comparison. In this analysis, the number of zoonotic disease outbreaks reported internally in that state is compared to the number of zoonotic disease outbreaks in that state that have appeared in the data sources that were previously discussed. This provides both a numerator and a denominator, estimating the proportion of the population of zoonotic disease outbreaks which the outbreak database cases likely represent. In addition, this measure of detection ‗completeness‘ is compared to other measures of reporting completeness identified in previous studies which examined the completeness of reporting from the state to the federal level (Doyle et al.,

2002; Overhage, Grannis, & McDonald, 2008; Rea & Pelletier, 2009). This triangulation provides a general idea about the population of zoonotic disease outbreaks (of which the database contains just a sample). Albeit, this remains an imperfect measure. This

135 limitation, in coordination with the idea that some states are more likely to detect/report is inherently part of any type of research on disease detection and reporting. This limitations suggests we must be careful in over-generalizing the results of this research.

So while drawing clear and careful conclusions, I maintain that more research can be conducted with improved data.

Finally, we turn to the case study phase of the dissertation, which is the final phase of empirical research.

Case Studies

The final research phase of this dissertation is a series of embedded and comparative case studies. This approach is most similar (but is less detailed) to the

Government Accountability Office report on the West Nile Virus which highlighted problems in detection and reporting of the virus (Government Accountability Office,

2000). These case studies are considered to be embedded because not only is this dissertation interested in how quickly outbreaks are reported, it is also interested in identifying the entities important in the detection and reporting process, the legal environment, and other institutional factors that may have impeded or facilitated detection of zoonotic disease outbreaks (for more on embedded case studies, see Yin,

2003). This research was approved by the GWU Institutional Review Board, Exempt

Review #0011107.

This phase uses multiple methods for data collection. A review of empirical literature has not yielded any other comparable case studies on zoonotic diseases, though a study of disaster management in the United Kingdom used a relatively similar approach

(Smith & Dowell, 2000). This phase connects the important factors from the legal

136 analysis and outbreak database to more descriptive research in a detailed and in-depth manner, reviewing the difficulties in zoonotic disease detection and reporting through a case study approach. While a case study approach is not appropriate to examine ―the prevalence of phenomena‖, it is a useful approach to answer the question of how and why zoonotic disease detection and reporting practice works in the way it does in the United

States (Yin, 2003, p. 48).

While this phase of research transparently and consistently conducts qualitative research to improve the validity of the findings, understanding the phenomena is also equally important to ‗validity‘ in the statistical or quantitative sense (Maxwell, 1992).

This type of qualitative description is often referred to as ―thick‖ description, first used by Ryle in 1949 (Ryle, 1949). Thick description takes a detailed account of experiences in practice which are then interpreted and put into a political and social context, with an eye towards better understanding the external validity of the results—in other words, if they are generalizable (Lincoln & Guba, 1985). The case studies employ interviews and surveys to detail practice in the detection and reporting of zoonotic diseases, and subsequently embeds results in a theoretical and legal context. The case studies address questions about relationships and ‗the reasons why‘. By doing so, this phase of research creates a careful picture of the detection and reporting of specific disease outbreaks, but also draws from the findings to develop lessons on how to improve zoonotic disease outbreak detection.

A quantitative analysis could not capture this thick description as effectively. In addition, a large-n multivariate analysis is simply not possible at this time given the enormous range of variables that impact detection and reporting processes (Van Evera,

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1997). Moreover, this qualitative approach can also examine how federalism, institutional design, and bureaucratic behavior zoonotic disease detection and reporting practice.

Case Selection

The case, or unit of analysis, is a zoonotic disease outbreak (Yin, 2003). Each of the outbreaks was identified through the outbreak database, the prior research phase. Four cases were initially selected for the case studies. Because the purpose of these case studies is to illuminate current practice in thorough description, the criteria for selecting these cases were carefully reviewed. For example, are representative cases the best to select—in other words, those that had the median time to detection? Or the median ‗fast‘ case and the median ‗slow‘ case? As stated previously, generalizability does not always need to be the primary objective of case studies: good explanation can be a form of validity in and of itself.

This phase of the research uses the outlying cases from the outbreak database for the case studies. These cases are the fastest and slowest cases on the variable of time to detection. This allowed me to not only explore these cases, but to assess whether there are differences in the practices of the ‗fast‘ and the ‗slow‘ cases. Because it is not clear whether these faster detection cases actually are ‗better‘ than the slower cases, I hereon refer to these cases as ‗fast‘ and ‗slow‘ rather than ‗best‘ and ‗worst‘. More than one type of each case is preferred, as it is a stronger methodological design than a single-case approach (Yin, 2003). The objective of choosing the outlying cases is to have demonstrative results that—ideally—can inform policy recommendations, by ―selecting

138 cases that resemble current situations of policy concern‖ (Van Evera, 1997, p. 83). This approach highlights lessons learned as well as promising practices.

Using the Outliers

In the outbreak database, there are two clear outliers for the outbreaks with the slowest time to detection. The first is a salmonella outbreak in 2006-2007, which was detected in 452 days. The second is a salmonella outbreak in 2003-2004, which took 251 days to detect. These outbreaks are subsequently referred to as Salmonella (2006) and

Salmonella (2003), or the ‗slow‘ outliers. In addition to being outliers, I needed to ensure that these cases had sufficient literature and a high probability of identifying subjects for the interviewing and/or survey. Both of these outbreaks were significant, multi-state outbreaks.

For the ‗fast‘ outliers, there are three outbreaks, as defined in chapter 3, that were detected in less than 1 day. The ―0 day‖ detection outbreak is excluded in the analysis because it is a lab-exposure to anthrax, a select agent. After a brief literature review, I decided to move to the next ‗fast‘ case because I did not feel this case would be demonstrative for policy lessons, considering that there was no investigation or animal- human cross-sectoral interaction during the outbreak. While I did not foresee that this would be an issue, I feel this modification makes the ‗fast‘ outliers case study section more meaningful. The next fastest case (1 day to detection), is a case of rabies, subsequently referred to as Rabies (2006). The second outbreak is of e-coli, subsequently referred to as E-Coli (2003). Conveniently, it worked out that the two ‗fast‘ cases and the two ‗slow‘ cases are from the same years (2003 and 2006).

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Initially, I was concerned with the ‗fast‘ outliers, in that there were not going to be sufficient subjects for interviewing or surveying because these outbreaks were small, and included only 3 cases in total. Indeed, no subjects responded to my initial requests regarding the 2003 e-coli cases. I decided to have a ‗back up‘ case for the ‗fast‘ outliers, which is an outbreak of anthrax in livestock, in addition to a human case, in 2000, which was detected in 2 days (known as Anthrax 2000). I decided to include an additional ‗fast‘ outliers case deliberately, rather than having to decide to choose an additional case post- hoc, mid analysis, as this removes any bias of prior findings that would influence my choice of the outbreak to add. I chose the next fastest case, which was also detected in both humans and animals, which allows an assessment of cross-sectoral collaboration.

Moreover, it is another CDC A, B, or C agent, and a disease which has significant animal health and human health implications. A backup outbreak is not necessary for the ―slow outliers‖, due to the outbreak sizes. This additional ―fast outliers‖ case helps to ensure the sample size is comparable between the ―slow outliers‖ and the ―fast outliers‖, so that the findings are more comparable between the two distinct categories.

The case studies play a vital role in this dissertation, providing thicker, more descriptive information about zoonotic disease detection practice in the United States.

They need to be as illustrative and informative as possible. Table 3.10 provides the list of outbreaks included in this analysis.

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Table 3.10: Outbreaks for Case Studies

Year Disease Fastest or Slowest

2000 (added case) Anthrax Fastest 2003 Salmonellosis Slowest 2003 E-Coli (Shiga producing) Fastest *(No interviews/survey rc‘d) 2006 Salmonellosis Slowest 2006 Rabies Fastest

Certainly there many criteria to employ in selecting cases from the outbreak database for further research and review. For example, outbreaks involving a CDC A, B, or C agent could be selected. Or, outbreaks that involve a clear epidemiological link between species could be selected. However, the selection of the outbreaks for this chapter is based on time to detection for the reasons cited throughout this dissertation: rapid zoonotic disease outbreak detection is key to protecting public health and animal health. It is a fundamental principle of public health and animal health surveillance, and subsequently the research design focuses on how to decrease the time to detection.

Methods & Data Sources

There are three key data sources for this phase of research that are discussed in turn: (1) the literature of the cases, (2) the survey instrument and interviews, (3) the legal follow-up. First, a review of the literature is conducted for each of the cases. This involved searching for the disease agent, state, and primary species or source, so for example ―salmonella‖ and ―pet rodents‖ and ―2003‖. The first five pages of the Google search are reviewed for information, as well as Google Scholar. In addition, the search terms are used in ProQuest, EBSCOhost, and PubMed, all literature databases. In

141 addition, the records from ProMed-mail used in the outbreak database are re-reviewed for additional description.

This literature review is primarily conducted for background information on the case, and to identify any key factors which may have impacted how fast the zoonotic disease was detected and reported, or to see if the authors and principal investigator‘s in the outbreak published any recommendations for future practice. The published documentation will also be used to characterize, qualitatively, the severity of the outbreak for reference.

Second, structured interviews and surveys are used to gain additional information.

The interview questions and survey questions are exactly the same, and designed to give the respondents more flexibility in responding for a higher response rate (i.e., some individuals dislike phone appointments, and others prefer to speak on the phone). The objective was to conduct 4-6 interviews or surveys per case, for a minimum of 16 interviews. However, it was initially acknowledged that the number of interviews per case may vary, particularly because these outbreaks occurred between five and eight years ago. In this phase of research, eighteen interviews/surveys were conducted and these data are analyzed. The subjects are identified from published articles, and include practitioners, state officials, diagnostic laboratory personnel, and federal officials both on the side of animal health as well as on the side of human health. In addition, individuals who were interviewed or surveyed were asked to provide additional contacts, with the permission of that contact. This resulted in additional interviews, and expansion of the interviews beyond the ‗fast‘ and ‗slow‘ cases to include some individuals who are more

142 generally familiar with zoonotic disease research and practice. This is further discussed in the analysis and results of Chapter 6.

Because of the use two modes of data collection, the protocol is used across across all the cases, to identify the impediments and facilitators of rapid zoonotic disease outbreak detection and reporting. At the end of both the survey and the interview, respondents were asked if there were any additional factors that they would like to mention about the cases, in the event that there were particular mitigating or extenuating circumstances that were not captured in other questions. An invitation to interview or take the survey instrument was sent via email with the informed consent form, both approved by the GWU IRB. An email reminder was sent 7 business days after the initial contact. The interview questions are provided in Table 3.11.

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Table 3.11: Questions used in the Interviews and Surveys

1. What were the most important impediments to rapid detection [recognizing that there is an outbreak] and reporting [reporting this recognition of an outbreak to a state or federal authority] of the zoonotic disease [for example, practitioner education, training, laboratory diagnostics, aware individuals, communication, government programs, policies, law, or any other factor]? 2. What were the most important facilitators for rapid detection and reporting of the zoonotic disease? 3. If in the animal health sector, did you contact or consult human health agencies, sectors, entities, or experts during the outbreak? a. If yes, how would you describe this relationship? 4. If in the human health sector, did you contact or consult animal health agencies, sectors, entities, or experts during the outbreak? a. If yes, how would you describe this relationship? 5. Did federalism (the separation of state and federal government) impact the detection or reporting of this outbreak? a. If yes, how, generally speaking? 6. Did institutional design or bureaucracy (for example, procedures and processes of the entity, communication between entities, chain of authority) impact the detection and reporting of this outbreak? If so, how, generally speaking? 7. Why was this disease reported to a state or federal entity? 8. Did past experiences with zoonotic diseases or outbreaks impact the detection and reporting of this outbreak? If so, how, generally speaking? 9. Are there any factors that we haven‘t already discussed that you feel are important to improve zoonotic disease outbreak detection and reporting in the United States?

The survey instrument was hosted in Surveymonkey, and was anonymous and encrypted. Invitations were sent via email. However, survey collectors were separated so that I would be able to identify which individuals provided information on the fastest cases, and which were replying in reference to the slowest cases, or alternatively, were referred by another study participant. Interestingly, many of the responses contain identifying information, so care has been taken to de-identify all of the data which have been collected.

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The interviews were not recorded: past experience in transcription allowed me to effectively transcribe while interviewing, and because the interviews are structured, this enabled me to listen carefully without needing to worry about what to ask next. In addition, while the IRB did allow for recording with consent, because most of the respondents are in positions in state and federal government, I decided the answers may be more candid if not recorded.

Third, and finally, follow-up legal analyses are performed on the laws and regulations which are specifically identified in the interviews and surveys. Stephanie

David, a lawyer and Assistant Research Professor at the George Washington University assisted in identifying whether these laws and regulations may impact disease detection, reporting, and response, and whether the legal impediments or facilitators the respondents identified were real and perceived. We also discussed the legal issues which emerged in the interviews. Taylor Burke, an Assistant Professor from the same institution, also assisted in providing answers to legal questions. These laws and regulations were provided either by the respondents (volunteered), or are available online or through

WestLaw.

Analysis

The analysis for the literature and legal components consists of thick description of the outbreaks and legal environment. Therefore, this section focuses on the analysis of the interview/survey data.

Theme analysis and frequency counts are the primary tools used for this phase. In terms of frequency, the data are presented in the form, for example, ―5 of X people indicated that federalism mattered.‖ Common themes emerge through careful analysis of

145 the transcripts and survey remarks. For example, reportable disease lists are a common theme, which was reported by respondents in some manner in most outbreaks. In addition, specific examples that are not identifiable are used to elaborate on the frequency counts and key themes which emerge from this analysis.

Objectives

The overall goal of this chapter is to identify ways to improve zoonotic disease detection and reporting practice in the United States. In order to achieve this goal, there are two important objectives of the case study research: 1) to identify impeding and facilitating factors for rapid zoonotic disease detection and reporting, and 2) to identify whether federalism, institutional design, and bureaucratic complexity are important facilitating or impeding rapid zoonotic disease detection and reporting. All decisions made in the design of this phase of research focused on these two objectives. The survey and interview questions are specifically targeted at these objectives. These questions query respondents on impeding and facilitating factors as well as federalism, institutional design, and bureaucratic complexity. Because no past literature has discussed these factors in this manner, there is not past survey research that could be used as a closely related model for this survey.

This phase of research provides an important link between theory and practice

(and the practice operationalized in the first two phases), which is a key benefit of qualitative research: ―theory supplies the frameworks within which concepts and variables acquire substantive significance‖ (Hoover & Donovan, 2008, p. 33). In addition, the thick description provided through the case studies produces evidence that

146 can assist in the policy analysis in Chapter 7. The case study phase is designed to provide explanatory information supporting the legal analysis and outbreak database analyses.

Potential Limitations

There are potential limitations specific to the case study research that should be acknowledged. First, there are reliability and validity issues with using interviews.

Individuals may be vague or not candid due to the nature of the information at hand.

Second, the accuracy of recall is a concern due to the time that has passed since these outbreaks have occurred. Moreover, interviewee responses could reflect the perceived interpretations or persuasions of the interviewer. Great care is taken to ensure that respondent‘s confidentiality is protected to the greatest extent possible, and that the questions are posed in a neutral manner.

Second, there are potential limitations to the external validity or generalizability of the case study results (Yin, 2003). The relatively small sample size of the interviews/surveys may pose a limitation in the generalizability or external validity. The case studies are not intended to be generalizable to all zoonotic disease outbreaks: indeed, the cases were selected because they were outliers. However, the analysis of ‗fast‘ cases and the ‗slow‘ cases are intended to provide generalizable lessons about zoonotic disease detection and reporting practice in the United States from both easy and difficult scenarios. In addition, the selected subjects had the appropriate knowledge and expertise to respond to the questions, and may be viewed as representative of a much bigger population.

Third, and finally, there are reliability issues. Careful and conscientious theme analysis, helps to ensure the reliability of the interview results and case study analysis.

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The steps of this phase of research and analyses are explained transparently and systematically, allowing for more certainty that the results may be replicated and/or confirmed in the future (Creswell, 2009; Hoover & Donovan, 2008; Yin, 2003).

Policy Analysis

While not a separate research phase of this dissertation, it is important to identify and specify how the policy recommendations of this dissertation is undertaken and presented. The policy analysis, in Chapter 7, is based on the evidence constructed through the case study methodology, and reinforced by both the legal analysis and outbreak database. In particular, issues such as: the relevance of state law, the relationship between the human health and the animal health sector, and impediments identified by interviewees will be highlighted. The policy analysis is structured in a rational policy analysis style, and generally (but not strictly), following health-policy framework (Teitelbaum & Wilensky, 2007; Weimer & Vining, 2005). Policy alternatives are identified and evaluated on the basis of selected criteria, discussed in Chapter 7

(Teitelbaum & Wilensky, 2007). In this case, the policy analysis also takes a more persuasive and argumentative approach to suggest a specific alternative(s) as potential policy recommendations (Majone, 1989). A policy analysis in addition to the typical conclusions section provides a more comprehensive look at how policy and practice could be improved, through a careful analysis of selected options.

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Potential Limitations of this Methodological Approach

Any empirical research is subject to potential threats to its validity and reliability.

While the research has emphasized reliable coding, data triangulation, and expert assistance to counter many of the typical threats to validity and reliability, three important potential limitations remain to be addressed. First, this research focuses only on disease outbreaks that occur within the United States. In a globalized world, disease outbreaks outside of the United States may be just as relevant to public health. The outbreak database does capture some cases which did not originate in the United States, but were identified in the United States. But more generally, international questions are simply outside the scope of this research. This is a necessary limitation due to time and resource constraints. Clearly, this is an important direction for further research.

Second, and as mentioned previously, there are generalizability concerns. For example, data describing these outbreaks may be unavailable, and this unavailability may be systematically related to some characteristic specific to the outbreak. This is problematic for generalizability, as the outbreaks discussed here then are not representative of the larger population of outbreaks. Attempts are made to counter (or at the very least acknowledge) this potential limitation (such as the discussion of the denominator previously, and in Chapter 5), but this remains a legitimate concern.

Third, the policy relevance of this dissertation may be limited by the disease- based analysis. This research can not tell us a great deal about how we should improve our system to better detect and report novel agents—agents that are not on any list and for which not diagnostic test exists, because we don‘t know about them yet. Fortunately, research on syndromic surveillance does do more to provide recommendations about

149 novel agents (Babin, Magruder, Hakre, Coberly, & Lombardo, 2007; Buehler, Sosin, &

Platt, 2007; Lazarus, Kleinman, Dashevsky, DeMaria, & Platt, 2001; Moore, Edgar, &

McGuinness, 2008; Scotch, Odofin, & Rabinowitz, 2009). Moreover, the interviews and surveys often did touch on how improvements would not only benefit the detection and reporting of known diseases, but also of the unknown.

Summary of Methodological Approach & Contributions of this Approach

This research uses a mixed-methods approach. There are three phases to this research, a legal analysis to assess the legal environment of animal disease reporting, a outbreak database to examine how fast zoonotic disease outbreaks are detected and who detects them, and a series of case studies to learn more about the impediments and facilitators to zoonotic disease detection and reporting practice in the United States.

These methods have been carefully explained in Chapter 3.

This dissertation provides critical information about zoonotic disease outbreak detection and reporting practice in the United States. Many of the contributions of this dissertation have been elaborated in Chapter 1, and only a brief note about methodology is warranted. In particular, there has been no research specifically focusing on zoonotic disease outbreaks. In addition, the integration of research here will present a unique and substantiated description of practice in the United States. The qualitative research to be undertaken not only will provide information about how the system actually works, it will highlight impediments in current practice given our complex system of governance.

Such research is necessary to inform policy deliberations as well more technical research in the future (Hoover & Donovan, 2008, p. 45; M'ikanatha et al., 2007).

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Chapter 4: Legal Analysis (Allen, 2011)

This chapter presents the analysis and results of the legal analysis phase of this dissertation, as detailed in Chapter 3. In each of the 50 States and the District of

Columbia, there are both laws (statutes) passed by the state legislatures and administrative codes (regulations) which are promulgated by state agencies. This legal landscape assessment reviews these statutes and regulations of animal disease reporting requirements in the United States.

First, Chapter 4 reviews data collection issues not already discussed in Chapter 3, specifically, coding notes that were iteratively discovered during this Chapter‘s research and could not be identified prior to data collection. Second, this chapter reviews from

Chapter 3 how the data analysis is undertaken. Third, the results of this analysis are presented, complete with maps and figures. Finally, the conclusion section discusses the relevance and importance of these results, particularly given the frame of federalism and our complex bureaucratic system in the United States, and discusses the utility of this analysis for the other phases of this dissertation research and its contribution to the literature.

There are a few important legal concepts which should briefly be explained, that apply to statutes, regulations (regulations and administrative code are used interchangeably in this chapter), and other policy guidance issued by states (typically online disease lists in this research). First, under the non-delegation doctrine, state legislatures cannot delegate legislative functions (i.e. policy) to state agencies, or any executive branch department. In other words, if it is a matter of policy, the legislative

151 branch is to create a law, not delegate authority to a state agency to executive policy.

That said, federal courts have rarely employed the non-delegation doctrine to constrict powers of executive agencies—therefore, while this doctrine is important, it is not further discussed in this chapter (Gostin, 2008). Second, administrative codes have the full force and effect of law (Burke, 2010). Most simply stated, Chevron deference, from the case Chevron U.S.A. Inc v Natural Resources Defense Council (1984) (476 U.S. 837

[1984]) provides significant deference to administrative agencies when Congress has not directly addressed the specific question, and when agencies are neither arbitrary or capricious (Gostin, 2008). Third, policy guidance from a state agency, whether provided online or through another medium, also garners some deference, but to a significantly lesser standard, depending on whether the guidance is specifically referenced in an administrative code or not, and is typically interpreted in the courts under Skidmore deference, from Skidmore v Swift & Co, (323 U.S. 134 [1944]) based on the power of persuasion. These legal concepts help to put this phase of research into context.

Data Collection & Coding

States usually offer information about the reportable animal diseases on their official state websites. The purpose of this legal analysis is to review the legal landscape of animal disease reporting.

Online Disease Lists

The statutes, regulations, and/or reportable disease lists are reviewed for each of the 50 states and the District of Columbia (hereafter referred to synonymously as the 50 states and D.C. or the 51 states). Of these states, nearly all had some derivation of a

152 reportable animal disease list on their website. This was usually in the form of a webpage explaining reportable diseases and listing the diseases that needed to be reported: For example, Idaho‘s list of reportable diseases in animals is listed at http://www.agri.state.id.us/Categories/Animals/animalHealth/healthreportable.php,

Maine lists their list at http://www.maine.gov/agriculture/ahi/diseases/repdis.htm. In some cases, the state‘s reportable animal disease list is actually a link to their administrative code where reportable animal diseases are listed, for example, in Illinois

(http://www.agr.state.il.us/Laws/Regs/Diseased.pdf). The link to each state‘s animal disease reporting list or administrative code (with an embedded disease list) is found in

Table 4.19 the end of this chapter, and is current as of February 2010. As mentioned in

Chapter 3, I was unable to find any sort of animal disease list online on an official state website in two states: Connecticut and Kentucky. These states were contacted via telephone, and both provided animal disease lists for this analysis. The state of Colorado was also called, because I could not locate the disease list online. The State Veterinarian subsequently asked the web-developer to help me locate the disease list, which is indeed on their website.

In some cases a disease list with numerous reportable animal diseases is located in a single section of law or a regulation, and in other cases, one reportable animal disease has an entire section of devoted to it. In these cases, the relevant titles of the code of regulations are reviewed in their entirety to capture the diseases that required reporting.

Given the federal system of governance, there is tremendous variation in state reportable animal disease lists. Some states list a tremendous number of diseases

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(Georgia lists 122 different diseases), while some list relatively few (Kansas lists 24 different diseases). Some of the lists are up-to-date—for example, Alabama‘s disease list is from 2009—while some lists are nearly a decade old, such as Hawaii, which has a disease list dating from 2001 (this information is also listed in Table 4.19 at the end of the chapter).

Some online webpages may be updated infrequently, and perhaps may not offer the newest reportable animal disease list. For this reason, a small subset of the states were selected (5%) at random to inquire if the most recent disease list was indeed on the website, if its currency could not be verified in another way (for example, an updated date on the disease list itself, or the currency of state regulation if the disease list is codified). In each case, the state department or agency governing animal health was contacted via the public phone number provided on their website. In all cases where telephone calls were made, the state employee responded that the animal disease reporting lists on the website are the most up-to-date list the states had available. This suggests that there is little reason to be concerned that this analysis has used out-of-date material.

It is reasonable to expect that states with more recent/updated reportable animal disease lists (in regulation or online) may be reacting to disease outbreaks or a disease outbreak event that had occurred within the state. However, this is not assessed in this legal landscape assessment. We will return to this issue in Chapter 6 in reference to specific zoonotic disease outbreaks, and further investigate whether any regulations or statutes are implicated after a disease outbreak detection for an emerging or novel threat.

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Codification

In the process of data collection, it is observed that many states do not codify their disease lists in statute or regulation, and simply have and maintain a ―disease list‖ which lists the animal diseases that are reportable by state. The lawyer consulted in this research (Professor Taylor Burke, J.D., L.L.M. of The George Washington University) — selected as specified in Chapter 3—suggested that these disease lists may have the full force and effect of law—though admitted that if presented in court proceedings, it is possible that these online disease lists may not be considered equal to a reportable animal disease list that is codified in a statute or regulation. Importantly, if the state lists a reference to these lists in regulation, or references updating these lists in regulation, a court would grant more deference to the disease list. Indeed, the USDA is moving towards this type of ‗internet list system‘, which is specified in regulation and goes through a comment period, instead of codification of the actual list, to facilitate posting these types of lists and enable more rapid changes to the list to reflect changing circumstances (Animal and Plant Health Inspection Service, 2007). Because it is interesting and subsequently important to see which states codify their animal disease reporting requirements and which don‘t, Chapter 4 presents whether the criteria being analyzed (and in Table 4.1) is in code in a state disease list.

As such, online disease lists are included in this analysis for two reasons. First, because they will be considered in a court of law. Second, online lists, in the day of the internet, are likely to be referenced by veterinarians, citizens, and animal owners. These lists are essentially what a person would find if they went online to search and see what

155 diseases were reportable. The section on data analysis, including Table 4.2, elaborates on the implications of how a state manages its reportable animal disease list.

Two Stages of Data Collection

As mentioned, many states actually list their administrative code on their website to either show their reportable animal diseases (which are listed in the administrative code) or to show where the statutory authority for animal disease reporting exists. Some states comprehensively offer all of their administrative code and/or statutes online.

Therefore, simple web searching many of these statutes and regulations related to reportable animal diseases were obtainable by following the various links provided by the state. For example, Montana offers their reportable animal disease list online with reference to the relevant statutes and regulations. However, the statutes and regulations are not always available. For example, the state of Connecticut does not provide their administrative code on the state website.

The results and analysis in this chapter reflect the online and West Law searches. Predominately, state statutes and regulations are up-to-date and available online. However, approximately 20% of the states did not provide current legal material through the internet. The data analysis and results section reflects the results of both data collection sweeps, using the most current statutes or regulations available.

Data Analysis

Criteria for Analysis

As described in Chapter 3, nine criteria in Table 4.1 are coded from state statutes and regulations. All 50 states have some form of law or regulation which addresses

156 animal disease reporting within the state. For each of the following criteria, summarizing statistics are offered in a table. In most cases, a coordinating color-coded map is provided to display current practice on each criterion and to facilitate comparison across states.

This table is repeated from Chapter 3.

Table 4.1: Coding Scheme for Legal Analysis of State Statutes and Regulations

Element Categories Literature that Supports Use of Element 1.Law or Law, Regulation, Lipton et al. (2008); Mauer & Kaneene (2005); Regulation/Are Disease List or Burke (2010) Disease Lists Combination Codified 2. Diseases Listed Number of Roush et al. (1999); Reintjes et al. (2007) (a. State-by-state Diseases, What Comparison and Diseases b. Disease Comparison) 3. Who has to Who & Yes/No Roush et al. (1999) report diseases & laboratory reporting in law? 4. How fast are If Yes, Time Jajosky & Groseclose (2004); Hedberg et al. diseases to be (Hours); Time (2008); Gostin (2008) reported? Categories 5. Who to report Entity Fitzpatrick & Bender (2000); Kahn (2006); to? Gostin (2008) 6. Segregated by Yes/No See literature on zoonotic diseases in Chapter Species? 2. 7. Provisions for Yes/No Council for State and Territorial Unknown Epidemiologists (2009a, 2009b) Diseases 8. Mention of Yes/No Beatty et al. (2008); T. Lynn (2009); wildlife? McNamara (2002) 9. Mention of Yes/No See Chapter 1 for importance of zoonotic human diseases in public health. disease/public health/zoonoses?

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Various Ways of Handling Disease Reporting

Coding of the statutes and regulations reveals that states handle animal disease reporting in a wide variety of ways. In some states there are statutes, regulations, and a disease list on the website. In other states, there are only regulations. With assistance from attorney and Professor Taylor Burke, Table 4.2 was constructed to display the implications of the various ways in which states handle animal disease reporting. As the clarity of authority decreases, the question of whether the provisions are enforceable increases. Notably, if a disease list coexists with either a regulation (an administrative rule) or a statute, then there is sufficient authority for the disease list to be considered as generally having the force and effect of law, even if the disease list is not contained within in the statute or administrative rule, or explicitly mentioned in the statute or administrative rule. However, if there is only a disease list, then it is not clear that there is sufficient legal authority to require animal disease reporting. Furthermore, there is a problem if the statute or administrative code explicitly states that a regulation will be promulgated listing the diseases that must be reported to the state and then such a regulation does not exist. The various combinations of statute, administrative rule, and disease list are highlighted below, along with the implications of that particular combination. There are three distinct categories, ―best‖ meaning that there is clear legal authority for animal disease reporting, ―sufficient‖ meaning that authority is most likely broad enough for authority, and ―not sufficient‖ meaning that there is not sufficient legal authority for animal disease reporting in the state.

This chapter does not conduct and was not intended to further analyze the legal integrity of the animal disease reporting requirements of each state, but rather compares

158 the reporting requirements across the states. The legal integrity of a statute or regulation is essentially only truly important when the statute or regulation comes into play—i.e. there is a case of a reportable disease and a subsequent legal proceeding. Chapter 6 reviews relevant statues and regulations as indentified by survey respondents, to provide more in-depth analysis of the practical implications of the legal integrity of reportable disease lists.

Table 4.2: Legal Variations in Written Animal Disease Reporting Requirements

Category Does a Does an Is there a Legal implications statute administrative disease exist? rule exist? list online? Best Yes Yes Yes Statute provides for administrative rules that present a clear legal authority for disease list and animal disease reporting requirements Sufficient Yes No Yes Authority in statute is broad enough to provide authority for reportable animal diseases thru a disease list Sufficient Yes Yes No Administrative rule provides directives for animal disease reporting. Sufficient No Yes Yes Likely that authority granted to executive branch is sufficient to promulgate administrative rules that require reporting. Not No No Yes Unclear that there is legal Sufficient authority for animal disease reporting

Table 4.3, in the results section of this chapter, indicates on a state-by-state basis whether a statute exists in that state that is applicable to animal disease reporting, whether an administrative rule exists that is applicable to animal disease reporting, and whether there is an disease list and if that disease list is codified in statute or regulation. In

159 addition, Table 4.3 provides the appropriate references to these statutes and administrative regulations.

Please note that regulations that are promulgated under Departments of Health and Public Health and refer primarily to human disease are not included in the section of this legal analysis that reviews the variation in animal disease reporting lists by state.

Even though veterinarians may be required to report under these statutes or regulations, the focus of this legal analysis is on animal disease reporting, and therefore these statutes and regulations are excluded. Previous studies have reviewed the requirements promulgated under the auspices of public health (refer to Chapter 2). This does not significantly affect the results of this analysis as diseases listed in these statutes are usually noted in the reportable animal disease list as well. Furthermore, the anlaysis below shows whether animal disease statutes and regulations note or refer to public health or zoonotic diseases.

Specific State Issues in Data Analysis

In analyzing the statutes, the Commonwealth of Virginia was also contacted about their animal disease reporting requirements. In Virginia Administrative Code, Schedules

A and B of VDACS-03016 from 8/87 are referenced. Such schedules are not available on the state website, so contact was made with a staff veterinarian for the

Commonwealth. The veterinarian confirmed that the reportable animal disease list available on the website is current, and that the administrative code—citing the 1987 documents—had not been updated since August of 1987 to reflect the current reportable animal disease list.

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In the case of Arizona, the OIE 1999 list of notifiable diseases (in animals) is incorporated by reference into the Arizona Administrative Code (AZ ADC). This 1999

OIE list is not the current OIE notifiable animal disease list. I was not able to locate this

1999 OIE list on the internet, and attempts to retrieve the list through professional contacts at Federal agencies were not successful. Subsequently, the 2005 OIE list is used in lieu of the 1999 list. There are likely to be minor differences in the 1999 and 2005 lists, however, the 2005 OIE list still separates the diseases into ―List A‖ and ―List B‖, otherwise known as ―The Old Classification‖, similar to the 1999 OIE list. Iterations of the OIE list published after 2005 did not have separate A and B lists, and was instead consolidated into one list.

In the case of California, the disease list contains a reference to a separate list for molluscs and bees, which was not obtained for this analysis. In cases where disease lists include mollusc and bee diseases, these diseases are noted in the disease listing within the analysis. However, special effort was not given to locate bee and mollusc disease lists where they were not readily available. In the case of California, the availability of this list is clearly referenced in the analysis.

In the case of the District of Columbia, the DC Department of Health is granted authority for the control of animal disease through a DC Statute. This makes sense, given that DC is a exclusively urban area. The reportable disease list (both the list in regulation and the list online) includes diseases both exclusive to humans and diseases that are found in both animals and humans. Diseases that are found only in humans are extracted and excluded from this list for the analyses reported below. In the reportable

161 disease list online, the diseases included are those diseases that are to be reported to the

Animal Disease Prevention Unit in the Department of Health.

In the cases of Kentucky and Connecticut, as mentioned, a disease list was not located through the sweep analysis of statutes and regulations of the state or from the internet. In both cases, the States‘ Department of Agriculture was contacted through the public number available on the website. Connecticut emailed a disease list, while

Kentucky instructed me verbally that they are working on an appropriate regulation/disease list but in the meantime are using the current OIE list as their reportable animal disease list. This list is not included in this analysis as it is not official or documented by the state in a manner that would suggest sufficient legal obligation.

Coding Issues

Please refer to Chapter 3 for a thorough discussion of coding issues for the disease agents, as well as how disease ―categories‖ like ―foreign animal diseases‖ are coded in the results. This section in Chapter 3 also covers how the ―who reports‖ variable is coded to capture who is responsible for reporting these animal diseases.

Results

State Statutes and Regulations

Table 4.3 lists the relevant statutes and administrative codes (regulations) from each of the 50 states and the District of Columbia. Usually, the statute is used to grant authority to a specific executive department for animal disease reporting requirements.

Less frequently, the reportable disease list is actually listed in the statute. More frequently, the state lists diseases in the administrative code (regulations) of the state.

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Some states do not codify the disease list at all, but only have a disease list available online. If this is the case, this is noted in the fourth column, ―Is there a reportable disease list?‖ In cases where the reportable animal disease list available online does not list the same diseases as the statute or administrative code, the box is surrounded by a bold border. These bold boxes do not include instances where there is not a comprehensive animal disease reporting lists but the statute or administrative code does list only one or two diseases.

In this table, where a state does not have one of the elements listed in the columns, the box is shaded. Relevant dates are provided in the right hand column. These dates are not always available, and are not searched for more systematically because of the substantial legal historical analysis (by a qualified attorney) this would require, and the lack of relevance to this analysis (which focuses on current reporting requirements).

The effective date issue may arise again in Chapter 6, should it become relevant.

However, where available, the date of the disease list (if a list was available online), is also listed in Table 4.19 at the end of the chapter. It is clear that there is a wide variation in how frequently disease lists are updated among the states. These codes and regulations are the latest available as of February 2010.

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Table 4.3: Summary of State Reportable Animal Disease Statute, Code, and Online Disease Lists

State Is there an applicable Is there an applicable Is there a reportable disease Relevant Dates statute(s)? administrative code(s)? list that lists all reportable As available: Statute typically provides May also be called diseases in the state? Is the list statute/regulation authority. Relevance of Administrative Rules or in the statute or regulation or current thru ? the code to animal disease Regulations neither? If multiple lists exist, reporting is listed if not are they the same? obvious. Alabama Yes. Yes. AL ADC 80-3-6-.04, Yes, there is a disease list Ala. Code current (AL) Alabama Code 1975 §2- 80-3-6-.05, 80-3-6-.07 available online, but it is not in thru 2009; AL ADC 15-170 admin. code or statute. current thru 2008 Alaska (AK) No, but authority may be Yes. 18 AK ADC 36.100- Yes, there is a disease list AK ADC current granted elsewhere. 130 available online, but it is not in thru October 2009 Alaska Statute AS admin. code or statute. 03.05.020; 03.45.050. These statutes have been repealed. Arizona (AZ) Yes. Yes. AZ ADC § R3-2-402 Yes, disease list in admin. code AZ ADC Current Arizona Revised Statutes § and clear reference in admin. thru 2008; Arizona 3-1203 and § 3-1205 code to OIE list. No separate Revised Statutes (grant authority) disease list online. thru 2009 Arkansas Yes. Arkansas Code § 2- Yes. Arkansas Yes, disease list in admin. code. AAR Effective (AR) 33-101 (creates Livestock Administrative Rules § No separate disease list online. 2004; ACA & Poultry Commission), 125.00.03-009 (Arkansas Code and § 2-33-107 (which Annotated) is gives powers to the current thru 2009. commission)

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California Yes. Food and Agriculture Yes. California Code of Yes, there is a disease list 3 CA ADC 797 (CA) Code § 9101. Regulations 3 CA ADC § available online, but not in current thru 2010; 797 or 3 CCR § 797 (same); admin. code or statute. FAC current thru Administrative code explicitly Jan 2010 states that departments shall periodically publish and make available list of reportable animal diseases. Colorado Yes. Colorado Revised No. No, there is a disease list CRSA Current thru (CO) Statutes Annotated § 35- available online, but it is not in 2009. 50-108 and 35-50-103 regulation or statute. Only (definitions) tuberculosis appears in the statute. Because Co.R.S.A. explicitly states the state veterinarian is to define diseases reportable to the board in a rule, this has been coded as ―no‖. Connecticut Yes. Connecticut General Yes. CT ADC §22-278-A3 Yes, there is a disease list CGSA Current thru (CT) Statute Annotated §22-26f (reporting scrapie) available by request but not in 2010; CT ADC statute or code. Only scrapie Current thru 2010. appears in the regulation. Statute explicitly states State Veterinarian shall annually issue a list of reportable animal and avian diseases. Delaware Yes. Del Code 3 § 7101 Yes. Then in DE ADC, DE No, there is not a comprehensive Current thru 2010 (DE) (broad powers to ADC 3 300 303 (Scrapie) disease list. Equine Infectious (all Del C.); Current Department of and DE ADC 13 100 007 Anemia, Bee diseases, scrapie, thru Dec 2009 (DE Agriculture); Del Code 3 (Pseudorabies) and pseudorabies appear in the ADC). § 7402 (EIA); Del C. 3 § Delaware Code (statutes) or 7506 (Bees). Delaware Administrative Regulations (DE ADC).

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District of Yes. DC ST § 7-731 Yes. DC Municipal Yes, in DC ADC. Online disease Current thru Nov Columbia (grants authority to control Regulation (DC ADC): 22 list does not dictate the same 2009 (DC ADC); (DC) animal diseases to the DC DC ADC § 200, 201, 202 reporting as the DC ADC. DC Current thru Jan DOH) ADC includes all diseases (not 2010 (DC ST) only animal diseases). Diseases that occur in animals have been extracted for this analysis. Florida (FL) Yes. FL ST§ 585.145 and Yes. Florida Administrative Yes, in FL ADC. No separate FL ADC current 585.15 Code 5C-20.002 and 5C- disease list online. thru Feb 5 2010; 20.004 FSA current thru 2009. Georgia (GA) Yes. Off. Code of Georgia Yes. GA ADC, 40-13-4-.02 Yes, in OGCA and in GA ADC, OCGA and GA Ann OCGA § 4-4-6 GA ADC includes pet, worm, ADC current thru and fish diseases not in OGCA. 2009; Online disease list has slightly different reporting directives but lists the same diseases as OGCA and GA ADC combined. Hawaii (HI) Yes. Hawaii Revised Yes. Hawaii Administrative Yes, list in HI ADC. No HI ADC Current Statutes (HRS) § 142-2, Rules (HI ADC) § 4-22-1 to separate disease list online. thru Sept 2009, 142-3 § 4-22-5 HRS current thru 2009 and effective 1986 and 1961 respectively.

Idaho (ID) Yes. Idaho Code (IC) § Yes. ID ADC 02.04.03.201 Yes, disease list in statute and Statute current thru 25.211-25.212 & 02.04.03.300-309 & regulation. IC lists a number of 1/2010 and last 02.04.03.330-338 diseases, ID ADC lists more credited 1993; ID diseases than IC. Online disease ADC Current thru list matches the diseases in IC Dec 2 2009. and ID ADC.

166

Illinois (IL) Yes. Illinois Compiled Yes. 8 IL ADC §85.10, Yes, in IL ADC. No separate IL ADC current thru Statutes (ILCS) 510 ILCS 85.12 disease list online. Feb 5 2010; ILCS 50/1,2,3,9,22 current thru Jan 2010.

Indiana (IN) Yes. Indiana Code § 15- Yes. 345 IN ADC 1-6-1.5, Yes, in IN ADC. Minor IN ADC current 17-3-21, 15-17-3-13, 15- 1-6-2 and 1-6-3. differences (3 diseases) in thru Dec 23 2009, 17-10-1 laboratory reporting Indiana Code requirements of sheep/goats in current thru last IN ADC and online reportable 2009 session. disease list. Iowa (IA) Yes. Iowa Code 163.1 and Yes. IA ADC 189A.12, 13 Yes, in IA ADC. Disease list in IA Code from 2004, 163.2, 163.15 (regarding & 197.5; IA ADC 21- IA ADC is much more but current thru threat to public health). 64.1(163), IA ADC 571- comprehensive than online 1.21.10; IA ADC 115.9 covers CWD; IA disease list. current thru ADC 21-64.17(163) covers 1/26/2010 certain reporting from city to state. Kansas (KS) Yes. KSA 47-622 and 47- Yes. KS ADC 9-27-1 Yes, in KS ADC. No separate KSA current thru 610 disease list online. 2009 Reg Session; KS ADC current thru Jan 4 2010. Kentucky Yes. Kentucky Revised Yes. Kentucky No. Not in KY ADC or KRS. KRS current thru Statutes (KRS) § 257.080 Administrative Rules Title No reportable disease list. 2009. 302 Verbally instructed that KY uses OIE list. KRS explicitly states that the department shall promulgate administrative regulations listing all reportable diseases and conditions for reporting, but none currently exists.

167

Louisiana Yes. LRS § 3:2093-2095 Yes. Louisiana Yes. In LA ADC. A number of Statute current thru (LA) Administrative Code 7 LA diseases are listed in LA ADC 2009; Code current ACD Pt XXI § 121 but not in the reportable disease thru Aug 31 2009 list provided online.

Maine (ME) Yes. 7 M.R.S.A §1801; 7 Yes. ME ADC 01-001 Ch. Yes. Chronic wasting in MRSA, MRSA 2001, MRSA § 1752 and 1812 206 §5 all other diseases in ME ADC. current thru 2009; (Authority for disease Number of diseases in the online ME ADC current reporting) disease list that are not listed in thru Nov 2009 ME ADC. ―All other exotic or eradicated diseases‖ listed in code but not in online disease list. Maryland Yes. Maryland Code, Yes. MD ADC 15.11.08.04 Yes, but list available online is MD Code 104 from (MD) Agriculture § 3-104 and § (CSF), MD ADC not in statute or administrative 1973, MD Code 105 3-105 15.11.09.02 (TB in code. Few specific diseases are from 1986, current swine/poultry), MD ADC listed in MD ADC, but no ―list‖. thru 2009; MD 15..11.15.02 (psuedorabies), ADC all current MD ADC 15.11.18.06 thru Jan 15 2010 (scrapie) Massachusetts Yes. MGLA 129 § 1 and 2 Yes. 105 CMR 300.140 (To Yes, but list is not in statute or MGLA current thru (MA) and 28 (1 defines disease also report to the administrative code. Specific 2010; MA ADC and 2 gives powers to the Department of Public diseases are listed in MGLA, current thru Feb 5 director for control of Health in certain diseases) and then ―any contagious 2010. disease) disease‖ is listed in MGLA.

Michigan Yes. Michigan Compiled No. Not in MI ADC Yes, there is a disease list, but it Statute Effective (MI) Law (MCLA) § 287 (esp is not in MCLA or MI ADC. 2002 and current 287.703, 287.706, and Statute explicitly states a (all are) thru 2010. 287.709). reportable disease is a disease on a list maintained by the state veterinarian.

168

Minnesota Yes. Minnesota Statutes Yes. MN ADC 1705.0040, Yes, there is a disease list, but Statute Amended (MN) Annotated (MSA) § 35.06 1100, 1220, 1560, 1420, not in statute or administrative 1985, Statutes & 35.03 (last grants 2720, 0070, 1960, 1435 code. Specific program diseases current thru 2010 authority to the board to are listed for reporting in MN regular session. All protect health of domestic ADC. MN ADC current animals) thru Nov 2009.

Mississippi Yes. Mississippi Code Yes. MS ADC 02 010 002 Yes, MCA has a list of MS ADC current (MS) Annotated (MCA) § 69- reportable diseases, MS ADC thru Oct 2009, and 15-9 refers to OIE A and B lists. Miss Code Ann. Also a reportable disease list current thru 2009. online which varies slightly from list in MCA and MS ADC.

Missouri Yes. VAMS 267.400 Yes. 2 MO ADC 30-1.010 Yes, but disease list available 2 MO ADC 30- (MO) (grants the state which grants the Division of online is not in statute or 1.010 current thru veterinarian power to Animal Health administrative code. Only 12/31/2009 (same make regs over responsibility for animal diseases codified as reportable for 8.010); VAMS tuberculosis), VAMS diseases. are pullorum and fowl typhoid. current thru 1st 267.723 (grants session 2009 department to make rules and regs regarding scabies), 2 MO ADC 30- 8.010 (requires reporting of pullorum or typhoid) Montana Yes. Montana Code Yes. Administrative Rules Yes, in MT ADC. The MT ST § 81-2-107 (MT) Annotated § 81-2-107 of Montana (MT ADC) reportable disease list available from 1974. MT ST 32.3.104 online and the list in MT ADC current thru 2009, are different (disease list not in MT ADC current MT ADC lists more diseases). thru 6/9/09.

169

Nebraska Yes. Nebraska Revised Yes. Nebraska Yes, in NE ADC. Disease list Statute current (NE) Statutes §54-742 Administrative Code--23 online matches diseases listed in through first special (primarily), but whole NE ADC Ch. 1 § 001-008 NE ADC. session of 2009. NE admin code authorized by ADC current thru §§54-701 to 54-753.05 Nov 30 2009 Nevada (NV) Yes. Nevada Revised Yes. NV ADC 571.450- Y, but disease list not in statute Statute current thru Statute § 571.120 pseudorabies or administrative code. Only 25th Spec. Session (authority) pseudorabies is listed in NV 2008; NV ADC ADC. current thru July 31 2009 New Yes. NH Rev Stat § 436.8 No. Nothing in NH ADC. Yes, in NH Rev. Stat. The All NH Rev Stat Hampshire (grants state veterinarian disease list available online is current thru (NH) power over everything in much more comprehensive than 2/10/2010. the Diseases of Domestic the list in the NH Rev. Stat. Animals chapter); NH Rev Stat. § 436:33, 436:31.

New Jersey Yes. New Jersey Statutes Yes. NJ ADC § 2:2-1.1 and Yes, in NJ ADC. No other Code effective 2005 (NJ) Annotated § 4:5-4 and NJ ADC § 2.2-1.5. disease list available online. (1.5 effective 1989), 4:5-5 current thru 2009.

New Mexico Yes. New Mexico Statutes Yes. New Mexico Yes, in NM Administrative Statutes current thru (NM) Annotated (NMSA) § 77- Administrative Code (NM Code. No separate disease list 1st session 2009. 2-7, 77-3-2, 77-3-1, 77-8-5 ADC) § 21.30.4.8 and online. Code current thru 21.30.4.9. In addition, under Dec 1 2009. human, a few diseases for animal reporting in § 7.4.3.12.

170

New York Yes. NYS Law AGM Yes. 1 NY ADC 57.3 and 1 Yes, but full disease list Statute current thru (NY) (Agriculture and Markets) NY ADC 68.2 available online is not in statute 2009. ADC is § 73 or administrative code, though current thru March specific diseases (chronic 3 2010 wasting, fowl typhoid and pullorum) are listed in NY ADC. North Yes. North Carolina Yes. 02 North Carolina Yes, in NC Administrative NCGSA current Carolina (NC) General Statute Ann Administrative Code (NC Code. Disease list available thru end of reg sess (NCGSA) § 106-307.2 ADC) 52C .0603, online & NC ADC are the same. 2009, NC ADC current thru Jan 7 2010 North Dakota Yes. North Dakota No. No. There is a disease list ND Century Code (ND) Century Code (Statutory online, but not in administrative current thru 2009 Code) §36-01-12. code. Because Century code reg session. explicitly states the Board of Animal Health is to define diseases reportable to the board in administrative code, this has been coded as ―no‖. Ohio (OH) Yes. Ohio Revised Code Yes. Ohio Administrative Yes, in OH ADC. Also disease Statutory Authority (ORC), § 941.01-941.06 Code (OH ADC), 901:1-21, specific statutes providing from 1994, current Also disease specific additional guidance. No thru 2/28/10, OH statutes all in 901, 1-13-02 separate disease list online. ADC all current (scrapie), 1-11-01 thru 2/21/2010 (pseudorabies), 1-12-04 (paratb), 1-19-02 (EIA) Oklahoma Yes. 59 Okl. St. Ann. § Yes. OK ADC 35:15-3-1, Yes, in OK ADC. List provided statute current thru (OK) 698.15 OK ADC 35:15-3-2, OK online and OK ADC are the 1st sess 2009; OK ADC 35:15-3-4; same. ADC is current thru 12/15/2009

171

Oregon (OR) Yes. ORS (Oregon Yes. Oregon Administrative Yes, in OR ADC. List provided OR ADC from 2001 Revised Statute) 569.321 Rules (OR ADC) 603-011- online and OR ADC are the but current thru Dec 0212, same. 15 2009/ORS from 2001 current thru first reg sess 2009. Pennsylvania Yes. 3 Pennsylvania No. Yes, in statutes. The disease list Effective 1996 (PA) Consolidated Statutes available online is much more (Pa.C.S.A.2321), Annoted (Pa.C.S.A.) § comprehensive than the list in and current thru 2321 (lists dangerous the Pa.C.S.A. 2009. diseases); 3 Pa.C.S.A § Pa.C.S.A.2327 2327 (lists duty to report). effective 1996, There are specific current thru 2009. requirements for reporting John'es, EIA, pseudorabies, pullorum/fowl typhoid, equine encephalopathlies. Rhode Island Yes. Rhode Island General Yes. RI ADC 12 020 043 No. There is a disease list RI ST is current (RI) Law (RI ST) § 4-4-3 requires scrapie reporting online, but not in administrative thru Jan 2009, RI code or general law. Because RI ADC is current thru ST explicitly states the director Dec 2009 of environmental management is to promulgate by rule a list of reportable diseases, and has not done so, this has been coded as ―no‖. Scrapie is reportable in RI ADC.

172

South Yes. § 47-4-50 of the Code Yes. SC ADC 27-1011 lists Yes, in SC ADC. Online Statutory authority Carolina (SC) of Laws of SC (1976) the diseases. disease list and list in SC ADC from 1976, statutes (grants authority for the is the same. current thru reg sess regulation of diseases and 2009; SC ADC listing of diseases) current thru 2009 (effective 2009) South Dakota Yes. South Dakota Yes. SD ADC 12:68:03:09- Yes, but not in statute or Statutes current thru (SD) Codified Law (Powers to Board publishes list of administrative code. Code 2009 reg session. the Animal Industry reportable and explicitly states Board is to SD ADC current Board): § 40-5-7 and § 40- quarantinable diseases publish list yearly. thru Dec 31 2009 3-9. every year on July 1. Tennessee Yes. Tennessee Code No. Yes. A reportable disease list TN Code current (TN) (State Law): § 4-3-203 does exist, but not in state law or thru reg sess 2009. (Powers to the Dept of administrative code. Agriculture), § 44-2-101 Questionable authority from (Disease as anything statute for reporting. Disease list deemed relevant by State available online says that Veterinarian), § 44-2-102 reporting is required by state (Powers to State law, but no clear mention in law Veterinarian), § 4-7-06 or regulation about this (Enforcement for animal reporting. Coded ―yes‖ because diseases from TN highway enforcement is noted in TN patrol) Code. Texas (TX) Yes. TX AGRIC § Yes. 4 TX ADC § 45.2; Yes, in TX ADC. No separate TX ADC current 161.041 (grants disease list online. thru 2/28/10 and TX commission authority) AGRIC current thru reg sess 2009

173

Utah (UT) Yes. Utah Statute § 4-31- Yes. Utah Administrative Yes, disease list available Admin Code: 2004, 15, 4-31-17, and 4-2-2 Code § R58-2-2 online. Not in UT statute or current thru Oct 1 (grants authority to ADC, but ADC clearly notes 2009; Utah Statute department of agriculture) that all diseases on the Dept of current thru 2009 Agriculture list (online) are to be general session. reported. Vermont Yes. 13 V.S.A. 3504(h) Yes. VT ADC 20 022 009 s Yes, zoonotic diseases only, but Statute current thru (VT) (provides authority for 1203, 1284, 1562, VT ADC explicitly for veterinarian and 1st sess 2009-2010; reporting diseases 20 022 010, 20 022 016, 20 veterinary diagnostic lab VT ADC current associated with WMD) 022 023 (these were all reporting, not physicians. thru Dec 2009 statutes and then were Specific diseases are also listed repealed and reanacted as in separate sections of VT ADC, regulations, declared to be including anthrax, hog cholera, regulations of the glanders, rabies, tubercolusis, Commissioner). VT ADC brucellosis, equine infectious 13 140 007 (animal disease anemia, and chronic wasting. surveillance).

Virginia (VA) Yes. Va Code Ann. § 3.2- Yes. 2 VA ADC 5-30-10 Yes, but online reportable Va Code Ann. 6002 and § 3.2-6001 thru 2 VAC 5030-20. disease list is not in statute or Current thru end of administrative code. ADC 2009 special actually refers to lists no longer session. VA ADC used by the department (verbal current thru confirmation by department of 10/12/2009. this fact). Coded yes because probably sufficient statutory authority.

174

Washington Yes. WA St. 16.36 and Yes. WA ADC (WAC) § Yes, WA ADC lists 14 diseases WA ADC current (WA) 34.05. 16-70-005, WAC § 16-70- and clearly references the use of thru Dec 31 2009, 010, WAC § 16-70-020, the OIE list as the reportable WA St. current thru disease list. No separate disease 2/24/10 list online.

West Virginia Yes. WVC § 19-9-6, Yes. WV ADC 61-1-6 lists Yes, listed in statute (WVC). No West VA Code (WV) Diseases listed WVC § 19- specific reporting reqs for separate disease list online. current thru Feb 9-1 brucellosis and tuberculosis. 2010. Wisconsin Yes. Wis Stats. § 95.22 Yes. ATCP 10.03 (WI Yes, listed in WI ADC. Also an WI ADC s ATCP (WI) (95.24 has enforcement ADC) online disease list available, but current thru Feb laws) there are a number of diseases 2010: statute current listed in WI ADC not listed in thru 2009. the online reportable disease list. Wyoming Yes. Wyoming Statute § No. Yes, but not in WY St. or WY 11.19.101/102 last (WY) 11.19.101, Wyoming ADC. However, clear reference revised in 2008 & Statute § 11.19.102, in statute that the State current thru the Wyoming Statute § Veterinarian shall establish and 2009 general 11.1.103 (Enforcement manage a list of reportable session. Only) diseases, so coded as ―yes‖.

175

As seen in the Table 4.3, there are 5 states without reportable disease lists (9.8% of states): Rhode Island, North Dakota, Kentucky, Delaware, and Colorado. Of these states, 1 state (Delaware) does not have a reportable animal disease list, but does have specific reporting requirements for one or more animal diseases. Kentucky currently does not have a disease list; verbal communication with the Kentucky Department of

Agriculture has confirmed they use the current OIE list and are working on promulgating regulations. Because there is no written reference or authority to use the OIE list, the diseases on this list have not been incorporated for the state of Kentucky. In the case of

North Dakota and Colorado, the state has a disease list online, however, the statute explicitly says that the board is to define diseases in the administrative code—and there is not a list of reportable animal disease in code. Therefore, it is considered here to not have a disease list. Rhode Island—though listing one disease in its administrative code—also states in its statute that a rule is to be promulgated which provides a list reportable animal diseases, yet no rule exists. So again, Rhode Island is considered to not have a disease list. In sum, of the 51 entities being analyzed, 94.1% (48 entities) have one or more reportable animal diseases listed in some manner. This number includes states which list a single disease in a statute or administrative code, but excludes states that state the disease list is to be included in regulation and is not.

In cases where there is only a disease list online but not in a statute or code, but where in the statute/code it explicitly states that the department (or other entity) shall create and maintain a list of reportable animal diseases and provide in some manner, this has been coded as ―yes‖ there is a disease list and ―yes‖ it is in statute/code, because it is

176 directly referenced in the statute/code. This also includes statutes/codes that directly reference the OIE list as their reportable disease list.

Of the 50 states and D.C., 7 states (13.7%) do not have administrative code relating to animal disease reporting. Of these 7 states, all have a statute relating to animal disease reporting. Of the 50 states and D.C., one state—Alaska—does not have a statute relating to animal disease reporting. In the case of Alaska, the statutes cited by the administrative code appear to have been repealed. While it is likely that there is another broad authority-granting statute that provides sufficient authority for the disease reporting required in the administrative code, there are no longer statutes directly relating to animal disease reporting (Burke, 2010). There are two states missing two elements (both a disease list and an administrative code): Colorado and North Dakota (both previously mentioned). No states are missing all three elements.

Of the 50 states and D.C., 10 states (19.6%) have an online disease list which does not list all of the same diseases as the reportable disease list in the administrative code or statute, and 17 states (33.3%) have a comprehensive disease list only online, but this disease list is not codified into a statute or regulation (regardless of whether the state/code gives direct reference to the disease list). This is also noted in Table 4.3. In the case of these 17 states, specific diseases (usually between 1 and 10 diseases) may be codified in the statute/code—usually in individual sections for each disease—but a comprehensive list has not been codified. In 7 of these states, the disease list online is explicitly referenced in some manner by the statute/code of the state.

A summary of descriptive statistics on state variation in disease listing is offered in Table 4.4.

177

Table 4.4: Summary of Reportable Disease Lists

Criteria States States that have one or more reportable animal disease listed 94.1% (48) (excluding jurisdictions that state the disease list is to be included in the regulation and it is not, and the state that is promulgating regulations). States that have administrative codes/regulations related to animal 86.3% (44) disease reporting. States that have a statute related to animal disease reporting. 98.0% (50) (100% if AK is included) States that have an online disease list that doesn‘t match the 19.6% (10) reportable animal disease list in code or statute States that only have an online disease list. 33.3% (17) Of states that only have an online disease list, the percentage of 41.2% (7) states in which the regulation or statute provides an explicit reference to the external disease list.

Animal Diseases Listed as Reportable by State: State Comparison by Number of

Reportable Diseases

This second section presents results from this legal analysis: the animal diseases listed as reportable by state. It also examines the wide variation in these diseases across states. All disease lists have been coded at least twice to ensure accuracy in coding, though inadvertent mistakes may still exist. Please note, as previously mentioned, that broad disease categories like ―any infectious or contagious disease‖ are coded as a single disease entry. Because this coding is literal, requiring little interpretation, and is very time consuming, a second coder only verified a very small-subset of disease coding.

As noted in the previous section, there are 10 states (District of Columbia, Iowa,

Indiana, Louisiana, Maine, Mississippi, Montana, New Hampshire, Pennsylvania, and

Wisconsin) where the state has a disease list in a statute/code as well as online, and the diseases listed in each of these two places are not exactly the same. In one state, there is no disease list (Delaware), but specific diseases are listed in administrative code. In

178 addition, there are 17 states where there is only a reportable disease list online and no comprehensive list in a statute or code. In 7 of these states where there is only an online disease list, this list is explicitly referenced in some manner by the statute/code of the state. In 3 of these states with only online animal disease reporting lists (North Dakota,

Rhode Island, Colorado), where the statute explicitly states that there is to be a rule or administrative code promulgated listing the reportable animal diseases in the state, no such code exists.

Because of the preceding intricacies of animal disease reporting regulation, the analysis of the diseases listed by the 50 states and D.C is approached in the following manner: all diseases from all lists are coded from the state statutes, state regulations, and online disease lists. Where the disease list online does not match the disease list in the statute/regulation, this is noted with an asterix in the subsequent tables. In states where there is only an uncodified reportable animal disease list, these diseases are incorporated into this analysis, and these states are identified in the text and in the tables with an ampersand. Finally, in the three states where there is a disease list but it is not codified and the statute explicitly calls for the codification of the list, these diseases are included in the analysis and tables. However, these states are clearly flagged with a unique pattern in Table 4.3.

As the purpose of this legal analysis is to provide the landscape of animal disease reporting, it seems important to provide the most realistic list possible, which includes the incorporation of disease lists even if they are not codified into law. This analysis is not focused on whether these regulations, statutes, and ―unwritten law‖ can be or are enforced, but rather, to assess what states have regulated, intend to regulate, or consider

179 to be viable animal disease threats via some official mechanism. Furthermore, it is likely that sufficient legal authority may exist to require any reportable animal disease list promulgated by an official government entity to be considered valid.

Table 4.5 shows the basic descriptive statistics from the analysis of the animal diseases listed by states. All numbers are rounded to the nearest whole number. Figure

4.1 provides a chart of all the 50 states and D.C. showing the number of reportable animal diseases. Figure 4.2 provides a visual representation of these data, by mapping the data across all 50 states. This map is not to scale.

There is large variation in the number of reportable animal diseases listed by each state, and as seen in Figure 4.2, this also varies regionally. Figure 4.3 splits the United

States up into four regions (as separated by the US Census). These four regions are West

(WY, WA, UT, OR, NV, NM, MT, ID, HI, CO, CA, AZ, AK), Midwest (IA, IL, IN, KS,

MI, MO, MN, ND, NE, OH, SD,WI), South (AL, AR, DC, DE, FL, GA, KY, LA, MD,

MS, NC, OK, SC, TN, TX, VA, WV), and Northeast (CT, MA, ME, NH, NJ, NY, PA,

RI). As Figure 4.3 shows, the South has the lowest mean and median number of reportable diseases, followed by the East, Midwest, with the West region having the highest.

Table 4.5: Descriptive Statistics of Disease Lists for States

Mean Number of Diseases 76 Median Number of Diseases 88 Range 1-132 Second Quartile 42 Third Quartile 114

180

Figure 4.1: Number of Diseases by State

Number of Diseases Alabama to Mississippi

0 50 100 150

AL 38 AK 122 AZ 107 AR 90 CA 89 CO 32 CT 121 DE 4 DC 28 FL 51 GA 124

HI 105 Number of Diseases ID 86 IL 65 IN 95 IA 89 KS 24 KY 1 LA 23 ME 53 MD 51 MA 58 MI 107 MN 36 MS 117

181

Figure 4.1a: Number of Diseases by State, Continued

Number of Diseases Missouri to Wyoming

0 50 100 150

MT 127 NE 92 NV 113 NH 48 NJ 119 NM 29 NY 124 NC 43 ND 111 OH 37 OK 114 OR 26 PA 132 Number of Diseases RI 79 SC 115 SD 120 TN 20 TX 66 UT 115 VT 25 VA 94 WA 123 WV 53 WI 114 WY 90

182

Figure 4.2 Map of Number of Diseases by State Regulation/Statute (used with permission, Allen, 2011)

Number of Diseases 1 – 43 44 – 89 90 – 114 115 +

183

Figure 4.3 Number of Reportable Animal Diseases by Region

Further Analysis

In addition to the data on the number of diseases in the states‘ reportable animal disease lists, I compare the number of reportable animal diseases listed by state to the total sales (by state) of all animals, including animal products. These data on sales are from the 2007 Agricultural Census, which was released in February 2009, and is the most recent data available (see http://quickstats.nass.usda.gov and https://www.agcensus.usda.gov for details on data and methods employed for data collection). The total sales figure include all domains in that state that sell animals or animal products, and are provided in 2007 U.S. Dollars. I did review the 2002

Agricultural Census Data, and while there are changes in the amount of total sales (by state) beyond inflation from 2002-2007, there is not a significant amount of variation in states‘ position (in total sales) relative to one another. Moreover, I suggest these 2007 data are suitable for comparative analysis as state reportable animal disease lists vary

184 how current they are. In addition, I only have one set of disease lists, so a year to year comparison of the number of diseases is not possible. Crop totals are excluded as this legal analysis focused on animal disease reporting. Animals, such as fish and llamas, are included as well as the typical livestock species such as pigs and cows. This analysis is to see if there is a relationship between the number of diseases listed in each state and the sales of animal products. Given the federal system, it is possible disease lists vary by state because of particular needs within the states. This step explores the relationship between one possibility to explain varying disease lists. The Appendices provide the full list of states with respective total sales. The District of Columbia is removed from this analysis as there are no 2007 Agricultural Census Data for the District.

The results of this analysis are interesting: there does not appear to be any type of linear correlation between the number of animal diseases listed by a state and the total sales (animals and animal products by state) (r= -.045, p=.756). The scatterplot showing the relationship is also provided in Appendix E. Total sales of agricultural products does not appear to be a useful predictor in the number of diseases listed by a state on a reportable animal disease list.

Animal Diseases as Reportable by State: Disease Comparison

After consolidating the diseases as previously discussed, there are 340 distinct diseases, biologic agents, or disease categories that are listed in state disease lists after consolidating the diseases as discussed earlier. These diseases and the states that require their reporting are provided in full in Appendix D. In Tables 4.6 to 4.9, the diseases are listed by the number of states that require their reporting. For example, if three states require the reporting of swine influenza, it is given a value of ―3‖.

185

First, Table 4.6 lists the ―Top 10‖ reportable diseases in the United States. Further analysis and elaboration on what diseases do (and do not) appear on state disease lists comes later in this chapter. This Top 10 does not reflect disease categories such as ―all contagious diseases‖. The number of states that list these diseases—when these broad categories are also considered—are listed in the parentheses next to the number of states which explicitly list these diseases.

Table 4.6: Top 10 Reportable Animal Diseases in the United States

Disease Number of States (No. of States with Broad Categories Included) Brucellosis (All other or all spp, including 49 (50) bovine and porcine and ovine, swine, cervid, and equine) Anthrax 48 (50)

Pseudorabies 48 (49) Rabies 48 (49) Equine Infectious Anemia 48 (49)

Newcastle Disease (including VVND & 46 (48) exotic types) Scrapie 46 (48) Avian Influenza (Highly Pathogenic) 46 (47)* Classical Swine Fever/Hog Cholera 45 (48) Pullorum Disease/Salmonella Pullorum 45 (46) * DC, not included here, does not list HPAI, but does list Novel Influenza A viruses.

There are 89 diseases or disease categories that are only listed by a single state.

Though many of these ―Top 10‖ diseases are readily recognizable to many people

(particularly anthrax, avian influenza, and rabies), there are commonly known diseases in the ―Bottom 89‖ such as animal Diptheria, Cholera, and Cow Pox. Table 4.20 at the end

186 of this chapter lists all of the diseases that were found in reportable animal disease lists during this analysis.

Table 4.7 provides statistics that characterize the distribution of diseases (rounded to nearest percentage). Interestingly, only 4% of the 340 diseases appear in more than 40 state lists. Of all the diseases listed, 35% of them appear 10 or more times. These descriptive analyses are again indicative of wide state-by-state variation in reportable animal disease lists.

Table 4.7: Distribution of Reportable Diseases

Number of States Number of Number of Listing the Disease (Out Diseases Listed by Diseases at Left of 51 Total)* Number of States as Percentage of at Left Total Diseases (n=340) ≥ 10 states 119 35%

≥ 20 states 86 25%

≥ 30 states 51 15%

≥ 40 states 15 4%

* These are cumulative, so diseases listed by more than 40 states are included in the number of diseases listed by more than 30 states.

Further analysis of diseases by animal groups (for example, bovine, equine, and porcine) is conducted. Table 4.8 lists how many diseases are listed by 16 different animal categories to highlight the emphasis of animal disease reporting on specific groups of animals—or, perhaps, across many animal species. Many of these diseases can be found in multiple species and, therefore, are listed under ―multiple species‖. Diseases that are typically found in two species (for example, encephalopathies typically in equine and avian species) are also listed under ―multiple species‖. Diseases that could be, but

187 are rarely found, or have not yet been found in multiple species (for example, scrapie) are listed under the appropriate species group (ovine/caprine for scrapie). Diseases like tuberculosis which are found in multiple species but listed in the disease list separately by species are counted separately by species in this list. Diseases in the ―other‖ category include diseases found in groups not listed here (for example, mink), or are the disease categories (e.g. all exotic diseases). What species group is affected by a given reportable disease was determined through the OIE reportable disease list, the Merck Veterinary

Manual, and the disease lists of the states. Ovine/caprine are combined as this is customary on disease lists.

Table 4.8: Disease Breakdown by Category of Animal

Animal Group Number of Diseases Percentage of Total (n=341) Avian 32 9.4% (Bird) Bee 8 2.3%

Bovine 31 9.1% (Cow) Camelid 1 .3% (Llamas, Camels) Canine 6 1.8% (Dog) Cervid 2 .6% (Deer) Crustacean 8 2.4%

Equine 23 6.7% (Horse) Feline 6 1.8% (Cat) Fish 28 8.2%

Lagomorphs 2 .6% (Rabbits)

188

Animal Group Number of Diseases Percentage of Total (n=341) Mollusc 9 2.7%

Multiple Animal Species 121 35.5% Categories Other Species (Disease 20 5.9% Categories also here) Ovine/Caprine 24 7.0% (Sheep/Goat) Porcine 19 5.6% (Pig)

Diseases found in multiple species are far and above the most common diseases found in reportable animal disease lists. This is followed by avian diseases, bovine diseases, fish diseases, and then ovine/caprine diseases.

In addition to conducting this comparison by animal species, it is useful to also review the frequency with which the diseases in the list developed in Chapter 3 occur.

This list is the foundation for Chapter 5 and Chapter 6. Table 4.9 uses the disease list that is developed in Chapter 3. Again, because all of these diseases are zoonotic, they should—in theory—be reportable in animal disease populations as they pose a threat to public health (refer back to the methodology of constructing this list in Chapter 3). Table

4.9 lists these diseases, along with the number of times they are listed in state reportable animal disease lists, and then the percentage of states which list each of these diseases.

Disease categories in animal disease reporting lists like ―all encephalopathies‖ are not included here because this list of zoonotic diseases selected particular and specific encephalopathies for inclusion, and it is important to make the distinction between all encaphalopathies and a specific disease (biologic agent). In the case of hemorrhagic fevers, there is a note in the table referring to the number of states that list this as a

189 separate category. In the case of diseases like salmonellosis and brucellosis, where there are different species of these biologic agents listed in the animal disease reporting list, any state that listed a species of salmonellosis is counted once. However, salmonellosis species usually called by the disease name (salmonella pullorum and salmonella gallinarum) have not been included under ―salmonellosis‖ in this chart because these diseases are usually called pullorum and Fowl Typhoid, respectively.

Table 4.9: Frequency with which Zoonotic Diseases are Included in State Animal

Disease Reporting Lists

Key Zoonotic Diseases (Chapter 3 List) Number of States That Percentage of List Disease States That List Disease Anthrax 48 94% Blastomycosis 2 4% Bovine Spongiform Encephalopathy (Variant 40 78% Cruetzfeldt Jacob Disease) Bovine Tuberculosis 27 53% Brucellosis (multiple spp.) 49 96% Campylobacter 6 12% Coccidoidomycosis 2 4% Crimean-Congo Hemorrhagic Fever 14 27% Cryptococcosis 0 0% Ebola Hemorrhagic Fever 0 (4 list ―viral 0% (10%) hemorrhagic fevers/disease) Ehrlichiosis/Anaplasmosis 29 57% Eastern Equine Encephalomyelitis 37 73% Glanders 38 75% Hantavirus 3 6% Histoplasmosis 3 6% Highly Pathogenic Avian Influenza 45 88% Japanese Encephalitis 28 55% Lassa Fever 0 (4 list ―viral 0% (10%) hemorrhagic fevers/disease) Leishmaniasis 17 33% Leptospirosis 29 57%

190

Key Zoonotic Diseases (Chapter 3 List) Number of States That Percentage of List Disease States That List Disease Lyme Disease 6 12% Malaria 1 2% Marburg 0 (5 list ―viral 0% (10%) hemorrhagic fevers/disease) Monkeypox 4 8% Newcastle Disease 46 90% Nipah Virus 19 37% Novel Influenza A Viruses 1 2% Plague 20 39% Psittacosis 44 86% Q Fever 35 69% Rabies 48 94% Rocky Mountain Spotted Fever 5 10% Rift Valley Fever 33 65% Salmonellosis 37 (any type)^ 73% Shiga Toxin (producing Escherichia coli) 2^ 4% St. Louis Encephalitis Virus 2 4% Tularemia 32 63% Venezuelan Equine Encephalomyelitis 34 67% West Nile Virus 33 65% Western Equine Encephalomyelitis 33 65% ^ 1 state also lists ―Any disease associated with food-borne illness‖

The results from this analysis are striking: many diseases considered important zoonotic diseases are not listed by states. Of the 41 diseases that are in the disease list constructed in Chapter 3, only 20 of these 41 diseases are listed by more than 50% of the states. Sixteen of the 41 zoonotic diseases are listed by less than 25% of the states.

Diseases such as hemorrhagic fevers are rarely listed (perhaps because they are rarely found in the United States), but even blastomycosis and lyme disease do not typically appear on state disease lists. The omission of many zoonotic diseases may have serious and significant implications for rapid zoonotic disease detection and reporting in the

United States, as subsequent chapters elaborate.

191

Who Has to Report Diseases?

The preceding section provides the results of the legal analysis in terms of what diseases are to be reported. This section reviews state legal practice regarding who is to report these diseases. The Appendices offer a comprehensive look at the animal diseases that are reportable in each state, and who is obligated to report these diseases in each state, per the state‘s code, regulation, or online disease list. These diseases have been coded in nine groups:

Unspecified (Does not mention who is to report),

Veterinarian/Provider Only,

Laboratory Only (or only upon lab confirmation)

Both or Any Person,

Both under only certain conditions,

Both under certain conditions otherwise lab only,

Laboratory (Always) and Veterinarian with Positive Test Results

Veterinarian and Other Officials

Veterinarian (Always) and Laboratory with Positive Test Results.

Table 4.10 summarizes the results of the coding. Please note that states sometimes have separate reportable animal disease lists for providers and laboratories, and therefore the number of states may total more than 50.

Predominately, the results show that states require diseases to be reported by both laboratories and providers/veterinarians or ―any person‖. The next most popular category was only veterinarian only.

192

Table 4.10: Summary of “Who” Has to Report Diseases on Reportable Animal

Disease Lists by State

Who? How Many States Percentage of Categorize in This States Manner? Unspecified 5 9% Vet/provider 10 20% Laboratory 5 9% Both 38 75% Both under certain conditions 1 2% Lab always and vet with positive 1 2% results Vet and other officials 2 4% Vet always and lab with positive 1 2% results

Of the ―Top 10‖ diseases listed previously, who has to report these diseases in the states? Table 4.11 lists the percentage and number of states that require these top 10 diseases to be reported by ―Laboratory and provider or any‖. This is important because it indicates that if, for example, only a laboratory identifies a specific disease, the laboratory may not be obligated to report the disease per the states code, statute, or disease list.

193

Table 4.11: Top 10 Diseases, Who Has to Report

Disease Percentage That List Disease and Requiring Reporting by Both Lab & Provider or “any”* Anthrax 73% (35 of 48 states) Brucellosis (All other or all spp, including bovine and 69% (34 of 49 states) porcine and ovine, swine, cervid, and equine) Pseudorabies 73% (35 of 48 states) Rabies 65% (31 of 48 states) Equine Infectious Anemia 69% (33 of 48 states) Newcastle Disease (including VVND & exotic types) 67% (31 of 46 states) Scrapie 74% (34 of 46 states) Avian Influenza (Highly Pathogenic) 67% (30 of 46 states) Classical Swine Fever/Hog Cholera 67% (30 of 45 states) Pullorum Disease/Salmonella Pullorum 64% (29 of 45 states) *Number of states may not equal 51 because not every state requires the reporting of the disease listed.

Interestingly, as seen in Table 4.11, over 50% of all states require ―any‖ or ―both lab and provider‖ reporting for each of the Top 10 diseases. States may choose ―who‖ has to report on a criterion other than disease/biologic agent. For example, states often list all diseases under the same category, rather than differing ―who reports‖ based on the disease or biologic agent. While most states do list that ‗any or both laboratories and veterinarians‘ must report the diseases on the reportable disease list, there are a number that do not specify who has to report or that specify that either laboratories only or veterinarian only must report.

While this is a descriptive analysis only, this does lead to the question ―what if‖.

For example, what if a veterinarian identified highly pathogenic avian influenza as a presumptive diagnosis and did not report the disease (or suspicion thereof) because only

194 laboratories were required to report once a diagnosis was confirmed? This is certainly a scenario that should be considered by policy-makers.

In addition to the preceding analysis, I also review just the statutes and regulations to determine which states explicitly mention that laboratories must report reportable animal diseases in either a law or a regulation. Laboratories were selected for inclusion in this analysis as they are seen as critical components of effective surveillance and detection, as described in Chapter 3 and again discussed in Chapter 6. However, only 21 states explicitly require laboratory reporting of animal diseases. For example,

Pennsylvania requires ―every diagnostic laboratory in the Commonwealth‖ to report.

Colorado states that laboratories must report positive results for reportable diseases.

These results are pictured in Figure 4.4. Importantly, these results indicate that 30 states do not mention laboratory reporting in their administrative code or statutes on animal disease reporting.

195

Figure 4.4: Does the State Explicitly Mention Reporting of Reportable Animal Diseases by Laboratories in Statute or

Regulation?

No Yes

196

How Fast are Diseases to be Reported?

This section turns to discuss how fast these diseases are to be reported by the parties listed in the previous section. As observed with state comparisons thus far, there are widely varied requirements about how fast diseases are to be reported once they are suspected or detected. Table 4.12 lists the timeliness requirements for each state, and where that timeliness requirement is found (e.g., in statute, regulation, or disease list).

Figure 4.5 shows the fastest reporting category by state. For example, if states list some diseases as ―immediately reportable‖ and some as ―reportable in 48 hours‖ they have been coded as ―0 hours‖ for immediate, since this is the state‘s fastest reporting requirement. Within one day is coded as 24 hours, within two days as 48 hours.

Promptly was coded as 1 hour, simply to separate from ―immediate‖. Some states specify if the within 24 hours means after suspicion, after diagnosis, or both, but this is not shown in Figure 4.5. The most stringent requirement was recorded, regardless of whether it was from a statute, regulation, or disease list for Figure 4.5.

Figure 4.6 then shows, by state, the number of timeliness categorizations. For example, if a state splits their disease list up into three categories—immediate reporting, within 48 hours, and monthly—this is listed as ―3‖. Some states only have one categorization, while others have 3 or 4. This is simply to demonstrate the wide variation in timeliness requirements. This analysis again reviewed statute, regulation, and disease lists. However, one category in a law and three categories in a statute is simply coded as

3: the analysis of timeliness reporting requirements is not additive. While states generally provide specific time requirements, some states have clauses that simply state requirements such as ―promptly‖, as in the case of North Carolina law. This again

197 suggests, similar to other results, that there may be substantial discretion in the timeliness of reporting, in some states.

For both Figure 4.5 and 4.6, if timeliness requirements are only provided for one or two diseases, or if timeliness requirements were different for laboratories and providers, this is not indicated in the figures. Of the 50 states and D.C., 49 provided some type of timeliness requirement. Again, these requirements are further elaborated in Table

4.12, and Figure 4.5 and 4.6 provide illustrative summaries of categories and speed in reporting by state. Of the states and D.C., 44 of 51 require reporting of some or all diseases within 24 hours.

Table 4.12: “How Fast” Do Diseases Need to be Reported: Timeliness Requirements by State

State Timeliness Requirements Alabama None listed in admin code, Code of Alabama, or in disease list. None listed in code. Disease list is delineated into 5 groups. 1) IMMEDIATE REPORTING, 2) END OF THE DAY 3) NEXT WORKING DAY 4) FIFTH WORKING DAY OF EACH MONTH 5) Alaska TENTH WORKING DAY OF EACH MONTH In code. Disease list is delineated into 2 groups. 1) WITHIN 4 HOURS, Arizona 2)BY END OF MONTH Arkansas In code. 1 group. 1) IMMEDIATELY Not listed in code (only listed in disease list w/ clear authority from code). Disease list is delineated into 3 groups. 1) WITHIN 24 HOURS, California 2) WITHIN 2 DAYS, 3) MONTHLY Colorado In statute, immediately In CT ADC for scrapie only, person 1) IMMEDIATELY, laboratory 2) Connecticut within 24 HOURS In Del C. (EIA and bees), immediately; In DE ADC for pseudorabies Delaware "report the facts as they know them", for Scrapie "within 24 hours" District of In DC ADC, 1) WITHIN 2 HOURS 2) WITHIN 24 HOURS, 3) Columbia WITHIN 48 HOURs Florida In code. 1 group. 1) IMMEDIATELY In code. 2 groups.1) IMMEDIATELY, 2)WITHIN 24 HRS or CLOSE Georgia OF NEXT BUSINESS DAY 198

State Timeliness Requirements In code (appendix). Each disease is listed with a separate time requirement. These requirements are in 5 groups. 1) IMMEDIATELY and in less than 24 hours, 2) LESS THAN 24 HOURS, 3) LESS THAN Hawaii 48 HOURS, 4) LESS THAN 72 HOURS, 5) LESS THAN 30 DAYS In IC 25- 211 (for only those diseases listed in code) 1 group. 1) WITHIN 48 HOURS. In statute 212, 1 group. 1) IMMEDIATELY. Veterinarians must report brucellosis and tuberculosis according to statute 212. In ID ADC, foreign animal and reportable diseases are immediately. No time requirement on notifiable diseases. EIA must be reported if positive w/in 24 hours and if negative w/in 48 hours; In Idaho disease list, 1 group. 1) IMMEDIATELY. Illinois In IL ADC. 1 group. 1) IMMEDIATELY Indiana In Indiana Admin Code. 1 group: 1) WITHIN 2 BUSINESS DAYS. Iowa In IA ADC 1) PROMPTLY Kansas In Kansas Statute. 1 group. 1) AT ONCE Kentucky In KY Revised Statutes 1) IMMEDIATELY Louisiana In Louisiana Administrative Code. 1 group. 1) WITHIN 24 HOURS Maine In Maine Revised Statutes. 1 group. 1) IMMEDIATELY In MD Code, 1) WITHIN 48 HOURS; In MD ADC, Hog Cholera, EIA are immediately; Tuberculosis in swine & poultry has not time Maryland requirement; pseudorabies w/in 48 hours, scrapie w/in 24 hours In disease list, 3 groups. 1) IMMEDIATELY, 2) WITHIN 24 HOURS, 3) WITHIN 5 DAYS; Massachusetts General Law 129 Section 8 1) Massachusetts IMMEDIATELY. Michigan Compiled Law, 1 group, 1) IMMEDIATELY. In disease list, in avian disease list, 2 groups: 1) IMMEDIATE, 2) RESULTS FROM PREVIOUS CALENDAR MONTH REPORTED ON FIRST Michigan BUSINESS DAY OF EACH MONTH. Minnesota Statute, 1 group, 1) IMMEDIATELY. In MN ADC, varies by disease from 1) IMMEDIATELY, 2) 2 BUSINESS DAYS, 3) NO TIME Minnesota REQ, 4) 48 HOURS, 5) 14 DAYS FOR NEG TEST. In disease list (of 9 conditions and diseases); 1 group, 1) IMMEDIATELY. No mention of time in Miss Code Ann or Rules of Miss Board of Animal Health. Laboratories required to report positive or Mississippi suspect cases immediately. In disease list, 1 group: 1) WITHIN 24 HOURS OF SUSPICION OR DIAGNOSIS. In MO ADC, only pullorum & typhoid 1) WITHIN 48 Missouri HOURS. Montana Code Annotated, 1 group: 1) IMMEDIATELY. In disease list, Montana 2 groups: 1) IMMEDIATELY, 2) WITHIN 30 DAYS

199

State Timeliness Requirements In disease list, 2 groups: 1) IMMEDIATE, 2) MONTHLY REPORTING. In Admin Code: 2 groups: 1) IMMEDIATE, 2) MONTHLY (or WITHIN Nebraska 30 DAYS). In Statutes: IMMEDIATELY. Nevada For psuedorabies only, within 7 days. New Hampshire NH Rev. Statute 1) IMMEDIATELY In NJ Statutes Annotated: 1 group: 1) WITHIN 48 HOURS. Samein NJ New Jersey Administrative code. In NM Admin Code 1) IMMEDIATELY, in NM Statutes Annotated, 1) New Mexico WIHOUT UNNECESSARY DELAY In state law 1) IMMEDIATELY, in ADC for cervids w/in 24 hours, for typhoid & pullorum w/in 2 days of isolation. In disease list 1) New York IMMEDIATE, 2) MONTHLY North In NC Admin Code, 1) WITHIN TWO HOURS after reasonable Carolina suspicion. NC General Law says 1) "promptly"

North Dakota In disease list, 1) IMMEDIATELY, no mention in ND ADC or Statute. In Ohio Revised Code (statutes), a person must immediately report, a veterinarian must immediately report; specific diseases with laboratory reporting have various lengths (scrapie--24 hours to either and positive test results w/in 7 days; pseudorabies w/7 days, paratb w/in 14 days, EIA Ohio w/in 7 days) Oklahoma In OK Statute and in OK Administrative Code: 1) IMMEDIATELY Oregon In Administrative Rules, 1) IMMEDIATELY Pennsylvania In Consolidated Statutes, 1) IMMEDIATELY

Rhode Island General Law says report in a manner prescribed by regulation (uh, no regulation). Disease list 1) IMMEDIATELY UPON Rhode Island DIAGNOSIS. For RI ADC, scrapie only is IMMEDIATELY reportable. South Carolina SC ST- w/in 48 hours; same in SC ADC. In SD Codified Law 1) SHALL PROMPTLY REPORT. In SD ADC, 1) IMMEDIATELY. In disease list, High morbidity/mortality and zoonoses South Dakota must be reported 1) IMMEdIATELY Tennessee No mention in disease list or TN State Law or TN ADC. In Texas Administrative Code, 1) WITHIN 24 HOURS AFTER DIAGNOSIS. 2) CERTAIN DISEASES--WITHIN 48 HOURS OF Texas COMPLETION OF TESTS. Utah In Admin Code, 1) IMMEDIATELY Vermont In Code of VT Rules, 1) WITHIN 24 HOURS

200

State Timeliness Requirements In disease list, 1) WITHIN 24 HOURS, 2) BY 5th of FOLLOWING MONTH. In Virginia Administrative Code: 1) WITHIN 24 HOURS and 2) BETWEEN FIRST AND TENTH DAY OF EACH MONTH FOR Virginia THE MONTH PRECEDING. Washington In WA Admin code: 1) IMMEDIATELY. West Virginia In WV Code, 1) IMMEDIATELY In Wisconsin Code (also in disease list) 1) WITHIN ONE DAY, 2) Wisconsin WITHIN 10 DAYS Wyoming In Wyoming Statute, 1) IMMEDIATELY.

201

Figure 4.5: Fastest Reporting Category by State (In Hours) (used with permission, Allen, 2011)

Hours 0 1 2 (including DC) 4 24 48 168

202

Figure 4.6: Number of Categorizations for Timeliness of Animal Disease Reporting (by State)

Number of Groups 0 or n/a 1 2 3 (includes D.C.) 4 or more

203

Who are Diseases to be Reported to?

The results of ―who reports‖ and ―how fast‖ have now been discussed. This section examines to whom these disease reports are to be made. In some cases, the statute, the disease list, and/or the administrative code do not indicate the same entity for reporting. For example, in Michigan, the Disease List notes diseases are to be reported to the State Veterinarian or USDA Area Veterinarian while the Administrative Code states diseases are to be reported to the Commissioner. Table 4.13 lists, by state, what entity is listed in state statute or regulation to receive animal disease reports from the parties discussed and per the time requirements presented in previous sections.

In some cases, such as New York, to where or to whom the disease is to be reported varies by disease/biologic agent. New York lists whether each disease is reportable to the

New York Agriculture and Markets, USDA Animal Plant Health Inspection Service,

Department of Health, and/or Cornell University. The entity is listed in Table 4.13, but the corresponding diseases are not listed in this section.

204

Table 4.13: Where Diseases on the Reportable Disease List are to be Reported To

State Report to Who? Found Where? AL State Veterinarian Admin Code State Veterinarian or USDA AR Veterinary Services Admin Code AK State Veterinarian Disease List AZ State Veterinarian Admin Code Board of Animal Health or USDA Department (Admin Code and Code of Veterinary Services; To Regulations); Board of Animal CA Department Health/USDA (Disease List) State Veterinarian or CO USDA State Veterinarian or CO USDA (Disease List); State Veterinarian CO Office; State Veterinarian (Statute) CT State Veterinarian Admin Code Department; Office of State Veterinarian; State Animal Health DE Officials; State Apiarist Admin Code Director of DC Department of DC Health Admin Code FL State Veterinarian Admin Code State Veterinarian or USDA Area GA Veterinarian in Charge Statute HI State Veternarian Admin Code Division (Statute); State Veterinarian's To Division; State Veterinarian's Office (Disease List); Administrator ID Office; Administrator (Admin Code) IL Department of Agriculture Admin Code IN State Veterinarian Admin Code IA State Veterinarian Admin Code KS Livestock Commissioner Statute KY State Veterinarian Statute LA State Veterinarian Admin Code Department of Agriculture, Food, and Rural Resources; Department (Statute and Disease List), ME Commissioner Commissioner (Admin Code) Secretary (Department of Agriculture); Department; State Secretary (Admin Code); Department and Federal Animal Health (Admin Code); Animal Health Officials MD Officials (Disease List) Department of Agriculture and Conservation; Bureau of Animal Bureau (Admin Code); Department MA Health (Statute) State Veterinarian or USDA Area State Veterinarian or USDA Area Veterinarian (Disease List); Director MI Veterinarian in Charge; Director (Statute) 205

State Report to Who? Found Where? MN Board of Animal Health Disease List, Admin Code, and Statute MS State Veterinarian Admin Code MO State or Federal Officials Disease List State Officials (Department of Livestock) and Federal Officials Department (Statute and Admin Code); (USDA APHIS Veterinary State and Federal Officials (Disease MT Services); Department List) State Veterinarian; Department ofr any agent, employee, State Veterinarian (Admin Code); NE appointee thereof Department (Statute) State Veterinarian or Animal Disease & Food Safety; State Veterinarian et al. (Disease List); NV Administrator Administrator (Admin Code) Commissioner, Department of NH Agriculture Statute NJ Department of Agriculture Statute and Admin Code State Veterinarian; Livestock State Veterinarian (Admin Code); NM Board Livestock Board (Statute) NY Agriculture and Market; Cornell; APHIS; Department of Division of Animal Industry (Admin Health; Division of Animal Code); Commissioner (Statute); All NY Industry; Commissioner others (Disease List) NC State Veterinarian Admin Code and Statute State Veterinarian or Area Veterinarian in Charge (USDA APHIS); State Veterinarian or State Veterinarian/AVIC (Disease List); any other agent or representative State Veterinarian/Other Agent ND of the Commissioner (Statute) Director of Agriculture (Statute and Admin Code for Specific Diseases); Licensed Veterinarian (Statute); Director of Agriculture; Licensed Division of Animal Industry (Admin OH Veterinarian; Code) OK State Veterinarian Statute; Code OR Department of Agriculture Admin Code PA Department Statute To the Board; Department of Board (Admin Code); Department RI Environmental Management (Statute) SC State Veterinarian Statute and Admin Code State or Federal Veterinarian; State or Fed Veterinarian (Disease South Dakota Animal Industry List); Animal Industry Board (Statute SD Board Only) TN State Veterinarian Disease List Texas Animal Health TX Commission Admin Code

206

State Report to Who? Found Where? Department of Agriculture and UT Food Admin Code; Statute Commissioner of Agriculture; Livestock Commissioner; Department of Agriculture; All from Admin Code, Varies by VT Livestock Division; The Agency Disease State Veterinarian; Any Veterinarian in Employ of VA Commonwealth Admin Code WA Office of State Veterinarian Admin Code WV Commissioner of Agriculture Statute Animal Health Division; USDA Department (Code); Animal Health State Office of Veterinary Division and USDA Office (Disease WI Services; Department List) WY State Veterinarian Statute

Figure 4.7 illustrates some the information in Table 4.13 in a U.S. map, by graphically showing the states that require reporting to the state veterinarian, a government department (generally with no specific individual or office), another government office/individual, multiple entities, or multiple entities including a federal (rather than state) entity (whether in disease list, code, or statute).

It is interesting to note that some states (13 of 51) require or suggest reporting to a federal entity upon detection of a reportable animal disease, which is not required by federal law (and the enforceability of this reporting is not discussed here). Based on past literature, this does not seem to be the case with human notifiable diseases—so this may be unique to animal diseases in specific states. Often this is a federal office/person in a state, such as the Area Veterinarian in Charge (from the USDA, Animal and Plant Health

Inspection Service) of a region. Nine states have multiple entities listed (when all entities listed for reporting in any statute, regulation, or disease list are included). Many states require reporting to their State Veterinarian as the only requirement or as one of

207 multiple options/requirements for disease reporting. Of all states, 26 states list the State

Veterinarian in their code, statute, or disease list, and one additional state lists the State

Veterinarian‘s Office as the individual/entity to which diseases are to be reported.

208

Figure 4.7: Who are Animal Diseases to be Reported To?

Report to Who? State Veterinarian Department (General) Another Government Office/Entity/Individual (includes D.C.) Multiple Entities Multiple entities, including a Federal Entity

209

Are Diseases Listed by Species?

Moving from the previous section, to whom reportable animal diseases are to be reported, this section presents the results of whether the diseases are listed by species in the disease lists, regardless of whether these lists are online, in code, or in a statute. This criterion adds another level of specificity to disease lists that may help practitioners and the public to identify reportable diseases when they occur. It also is another characteristic that varies across the states‘ animal disease reporting lists. Table 4.14 lists all the states and whether or not the state lists their reportable animal diseases by species in their disease list, administrative code, and/or statute. If diseases are listed un- systematically by species—for example, one or two diseases specify the species of animal—this is not considered to be ―listed by species‖. If the OIE list of reportable animal diseases is used as the state‘s disease list, the state is considered to ―list by species‖ because the OIE does do that in their reportable animal disease list. As seen in

Table 4.14, 32 of 51 States list diseases by animal species. Figure 4.8 shows these results graphically.

Table 4.14: Does the State Reportable Animal Disease List Segregate by Species?

State List by If Yes, Where? Species?

Alabama Yes Disease List Alaska No n/a Arizona Yes Disease List Arkansas Yes Admin Code California Yes Disease List Colorado No n/a Connecticut Yes Disease List Delaware Yes n/a District of Columbia No n/a Florida No n/a

210

List by State Species? Where Georgia No n/a Hawaii Yes Admin Code Idaho Yes Admin Code & Disease List Illinois No n/a

Indiana Yes Admin Code, Lab Only Iowa Yes Admin Code Kansas No n/a Kentucky No n/a Louisiana No n/a Maine Yes Admin Code & Disease List Admin Code, Statute, Disease Maryland Yes List Massachusetts Yes Disease List Michigan Yes Disease List Minnesota Yes Admin Code Mississippi Yes Disease List Missouri Yes Disease List Montana Yes Admin Code & Disease List Nebraska Yes Admin Code & Disease List Nevada Yes Disease List New Hampshire No n/a New Jersey Yes Admin Code New Mexico No n/a New York Yes Disease List North Carolina No n/a North Dakota Yes Disease List Ohio No n/a Oklahoma Yes Admin Code Oregon No n/a Pennsylvania Yes Statute & Disease List Rhode Island Yes Disease List

South Carolina No n/a South Dakota No n/a Tennessee Yes Disease List Texas Yes Admin Code Utah Yes Disease List Vermont No n/a Virginia Yes Disease List 211

List by State Species? Where Washington No n/a West Virginia No n/a

Wisconsin Yes Admin Code & Disease List Wyoming Yes Disease List

212

Figure 4.8: Does the State Reportable Animal Disease List Segregate by Species?

List by Species? If so, Where? None (including D.C.) Disease List Administrative Code & Disease List Administrative Code Administrative Code, Disease List, and Statute Statute and Disease List

213

Provisions for Unknown Diseases

In Chapter 2, the importance and significance of emerging diseases is carefully discussed. Emerging diseases are unknown, and reportable disease lists—which specify diseases that are to be reported—may not capture these diseases if practitioners and laboratories are focused only on known diseases. This section turns to examine if reportable animal disease lists have some type of provision for unlisted and unknown diseases. Table 4.15 presents the results of this analysis. Provisions for unknown diseases include things like ―unexplained increase in dead or diseased animals‖

(California Disease List), or ―unusual acute deaths in livestock‖ (Georgia). Provisions in reportable disease lists such as ―exotic diseases‖ or ―diseases not known in the state‖ are not included here as they are listed in the state disease lists (later in this Chapter and

Appendix E). Figure 4.9 illustrates the data in Table 4.15.

Table 4.15: Does the State List Provisions for Unknown Diseases?

State Provisions? If Yes, Where? Alabama No n/a

Alaska Yes Disease List Arizona No n/a Arkansas No n/a California Yes Disease List Colorado Yes Disease List Connecticut Yes Disease List Delaware No n/a District of Columbia Yes Admin Code Florida Yes Admin Code Admin Code & Disease List, Suspicion of Georgia Yes Bioterrorism Only Hawaii No n/a Idaho No Statute

214

State Provisions? Where?

Illinois No n/a Indiana No n/a Iowa Yes Admin Code Kansas No n/a Kentucky No n/a Louisiana Yes Admin Code Maine Yes Disease List Maryland No n/a Massachusetts No n/a Michigan No n/a Minnesota No n/a Mississippi Yes Disease List Missouri No n/a Montana No n/a Nebraska Yes Admin Code Nevada No n/a New Hampshire Yes Disease List, Neurological Signs in Cattle Only New Jersey No n/a New Mexico No n/a New York Yes Disease List North Carolina No n/a North Dakota Yes Disease List Ohio No n/a Oklahoma Yes Admin Code Oregon Yes Admin Code & Disease List Pennsylvania No n/a Rhode Island No n/a South Carolina Yes Admin Code & Disease List South Dakota Yes Disease List Tennessee No n/a Texas No n/a Utah Yes Disease List, Vesicular Conditions Only Vermont Yes Admin Code Virginia Yes Disease List Washington No n/a

215

State Provisions? Where? West Virginia No n/a Wisconsin No n/a Wyoming No n/a

216

Figure 4.9: Does the State List Provisions for Unknown Diseases?

Provisions for Unknown? If so, Where? None Disease List Administrative Code & Disease List Administrative Code (including D.C.) Statute

217

As Table 4.15 and Figure 4.9 indicate, 29 states (57%) do not provide provisions for unknown diseases in their reportable animal disease lists. Some of the 22 states that do provide provisions limit those provisions to cover only specific symptoms (for example, vesicular or neurological).

Wildlife

As Chapter 2 discusses, wildlife diseases and the reporting of these diseases are primarily left to the states. Animal disease reporting and reportable animal disease lists typically focus on livestock and other domesticated animals. As part of this legal landscape analysis, I also review how many states note or otherwise indicate that animal disease reporting extends beyond livestock to wildlife. While livestock in the United

States may not intermingle with wild animals to the same extent as other areas of the world, many livestock certainly have contact with wild animals, including backyard fowl and cattle grazing on public lands. Subsequently, it may be important to animal disease detection and reporting to also report diseases found in the wildlife population in any given state.

This is not to imply that states which do not mention wildlife in their reportable disease list do not implicitly require/request animal disease reporting in wildlife.

However, many states do explicitly specify that domestic animals and/or livestock are the subject of disease reporting in their statutes or administrative code. Furthermore, many states regulate disease reporting for bees, fish, cervids (deer), and lagomorphs (rabbits).

However, these animal groups are typically domestically raised bees, fish, deer, and rabbits, rather than wild animals. This section only lists states that explicitly note wildlife somewhere in their animal disease reporting requirements.

218

Of the 51 states, 10 states (20%) mention wild animals/wildlife in their reportable animal disease list, statute, or administrative code. Table 4.16 lists these states, and briefly describes the specification relating to wildlife.

Table 4.16: Wildlife Mention in Reportable Animal Disease Lists

The Ten States that Include Mention of Wildlife/Wild Animals Where & What? Disease List, specifies reportable diseases in Alabama wildlife Delaware Admin Code, pseudorabies reportable in wildlife Iowa One separate Admin Code on Chronic Wasting Minnesota Disease List, Rabies Only Statute, Cooperation with Department of Nevada Wildlife Statute, Department of Agriculture to Cooperate in the Case of Wild Animals with Other Pennsylvania Agencies Statute, Confidential Reports Except Regulated South Dakota for Protection of Wildlife Admin Code, Commission yields to Parks & Texas Wildlife Department with Regard to Wildlife Admin Code & Statute, If Department decides disease is a threat to wild animals, the Department of Natural Resources shall be notified. Department of natural Resources also bound to report reportable diseases to Department of Agriculture, Trade, and Consumer Protection, also discusses disease in wild deer and wild fish as related to Wisconsin domesticated deer and fish. Statute, State Veterinarian may report to Wyoming Game and Fish Director if disease threatens wildlife. Game and Fish Department is consulted in development of reportable disease Wyoming list.

Though the key phrases used to search both the web and WestLaw in this dissertation should be sufficient to capture disease reporting in wildlife in statute, code,

219 and online state-produced material, it is possible that the initial search of ―animal‖ and

―disease reporting‖ may have missed wildlife-specific statutes and regulations.

Subsequently, a subset of states were randomly selected (every 10th state) to search more in-depth for wildlife disease reporting requirements to ensure that relevant regulation was not missed. Using the keywords ―wildlife‖, ―disease‖, and ―reporting‖ or ―reportable‖

(both variations were included), additional searches of the web and WestLaw were conducted. Of these states, (Alabama, Georgia, Maryland, New Jersey, South Carolina, and Wisconsin), Alabama already specifies reportable wildlife diseases in their reportable animal disease list and no further information was discovered through additional searches. No further wildlife disease reporting information on provisions for diseases in wildlife was recovered for Georgia, Maryland, or South Carolina. In the case of New

Jersey, an administrative code provision discusses wildlife in relation to quarantine, but there are no additional provisions relating to disease reporting. Wisconsin, as listed in

Table 4.16, does mention wildlife and disease reporting in wildlife in statute and in administrative code, particularly in reference to the development of the disease list and to wild deer and wild fish diseases as related to these diseases being transmitted to domestic populations (for example deer with chronic wasting disease in close proximity to captive deer herds).

Figure 4.10 illustrates the data in Table 4.16. Zoos were not identified as being mentioned in reportable animal disease law or regulation, though some states did note that diseases needed to be reported for wild animals in captivity (see Illinois). The reporting of diseases from zoos and other zoological institutes is another interesting question outside the breadth of this study.

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Figure 4.10: Do Reportable Animal Disease Lists Mention Wildlife?

Wildlife Mention? If so, Where? None (includes D.C.) Disease List Administrative Code Statute Administrative Code & Statute

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It is possible that additional provisions are incorporated into human disease reportable disease requirements, as diseases in wildlife are often relevant only as related to humans and human health (i.e. rabies). That said, wildlife diseases are also important to animals, and this section presents descriptive evidence that most states do not explicitly mention reportable diseases as related to wildlife. In addition, specific wildlife diseases—such as Chronic Wasting Disease—may have more specific reporting requirements that are captured in specific sections of administrative code or state statute.

Additional searches with specific disease agents may capture additional wildlife disease reporting statute/regulation, and this is a key direction for future research. The results presented here are sufficient to present a clear portrait of the disease reporting requirements in the states as related to animals.

Public Health & Animal Disease Reporting

The relevance and salience of animal diseases to public health in the form of zoonoses is highlighted throughout this dissertation. Because of this interest, it is useful to review whether public health issues are mentioned in a state‘s statute, code, or online reportable animal disease list. Table 4.17 offers the results of this analysis, indicating the states where state statute, code, and/or online disease list mentions public health or zoonotic diseases. The language may include a reference to reporting diseases (even just one) to the public health department, notifying local authorities about disease, cooperation with the department of health, and/or zoonotic diseases. This reference or mention should be clear and unambiguously relate to human health officials or in regard to human health, and the words ―public health‖, ―zoonotic disease‖, ―zoonoses‖, or

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―health department‖ are present. The language does not need to provide a directive on about reporting diseases per se, it must only mention public health or zoonotic disease.

Table 4.17: Reference to Human Public Health in Animal Disease Reporting

Statutes, Regulations, and Online Disease Lists

Reference to State Public Health? If Yes, Where? Alabama Yes Disease List Alaska No n/a Arizona No n/a Arkansas No n/a California No n/a Colorado Yes Disease List Connecticut No n/a Delaware No n/a District of Columbia Yes Disease List, Admin Code Florida Yes Statute, Admin Code Georgia Yes Disease List Hawaii No n/a Idaho No n/a Illinois No n/a Indiana No n/a Iowa Yes Statute Kansas No n/a Kentucky No n/a Louisiana No n/a Maine No n/a Maryland Yes Statute Massachusetts Yes Admin Code, Disease List Michigan Yes Statute Minnesota Yes Disease List Mississippi No n/a Missouri Yes Disease List Montana Yes Disease List Nebraska Yes Disease List, Admin Code Nevada No n/a

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Reference to State Public Health? Where? New Hampshire Yes Statute New Jersey No n/a New Mexico No n/a New York Yes Disease List North Carolina Yes Statute, Admin Code North Dakota No n/a Ohio No n/a Oklahoma No n/a Oregon No n/a Pennsylvania Yes Statute Rhode Island Yes Statute, Disease List South Carolina Yes Disease List, Admin Code South Dakota Yes Statute, Disease List, Admin Code Tennessee Yes Disease List

Texas Yes Admin Code, Disease List Utah No n/a Vermont Yes Admin Code Virginia No n/a Washington No n/a West Virginia No n/a

Wisconsin Yes Admin Code, Statute Wyoming Yes Disease List, Statute

Of the 51 states, 27 do not have any mention of public health in their reportable animal disease statute, regulation, or online disease list. However, 24 of these states do mention public health in some way, shape, or form. Figure 4.11 displays this information on a map of the United States.

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Figure 4.11: Do Reportable Animal Disease Lists Mention Public Health?

Public Health Mention, If so, Where? None

Disease List Administrative Code & Disease List (includes D.C.) Statute and Administrative Code Statute Statute and Disease List Administrative Code

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This analysis does not capture whether states‘ human reportable disease lists mention animals. In addition, some states may separate provisions for zoonotic diseases.

In some cases, statutes or code doing either of these things were inadvertently captured in this analysis. For the states of Alaska, Maine, and New Mexico separate sections of statutes or regulations (not in their reportable disease statute or regulation) were identified that referenced zoonotic diseases or animal disease reporting specifically for public health purposes. Generally, diseases that are captured in separate sections of statutes or regulations are already listed in reportable animal disease lists, though other criteria outlined throughout this chapter may be different than for animal disease reporting. More research should be conducted on the extent to which human reportable disease laws and regulations require veterinarian reporting of diseases in animals and/or mention animal disease reporting more generally. This question is outside the scope of this more focused analysis on animal disease reporting requirements. Furthermore, these diseases will be captured in analyses of the human reportable disease lists of states and should not be ―double-counted‖ here (see Council for State and Territorial

Epidemiologists, 2009a, 2009b; Roush, Birkhead, Koo, Cobb, & Fleming, 1999).

Discussion

This chapter presents nine distinct but related analyses which carefully describe the legal landscape of animal disease reporting requirements in the United States. They are summarized in Table 4.18 below. The main point to take away from these analyses, is that there is an incredible amount of variation in state animal disease reporting requirements in the United States.

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Table 4.18: Summary of Legal Landscape Analysis of Animal Disease Reporting in the United States

Element Key Result 1.Law or 48 of 51 (94.1%) of states have a disease list; (44 of 51) 86% have Regulation/Are related administrative code, (17 of 51) 33% only have an online Disease Lists disease list (so list is not codified in law/regulation) Codified 2.Diseases listed a. Mean 76 diseases, Median 88 diseases, Western Region highest (a. State-by-state mean and median. Comparison and b. 340 diseases/biological agents/disease categories; only 35% of b. Disease these are listed by 10 or more states; only 19/41 key zoonoses are Comparison) listed by more than 50% of states. 3. Who has to 38 of 51 (75%) states require both laboratory and provider report diseases & reporting or reporting by ―any person‖; only 20 states have codified laboratory laboratory reporting. reporting in law? 4. How fast are 49 of 51 states have a time requirement; 44 of 51 (86%) require diseases to be reporting of some or all diseases within 24 hours. reported? 5. Who to report 26 of 51 (51%) of states list the State Veterinarian; 13 of 51 (25%) to? require or suggest reporting to a federal entity/individual. 6. Segregated by 32 of 51 (60%) do list by species. Species? 7. Provisions for 22 of 51 (43%) include provisions for unknown (emerging) Unknown diseases. Diseases 8. Mention of 10 of 51 (20%) include a mention of disease reporting in wildlife. wildlife? 9. Mention of 24 of 51 (47%) include some type of public health reference. human disease/public health/zoonoses?

While a map illustrating the comprehensive results above would be nice to provide an overall picture by state, it is very difficult to make a map featuring all of the different characteristics which is why there have been separate maps for each criteria provided throughout this chapter.

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These results provide an interesting picture of animal disease reporting requirements in the United States. Not only do states have different ways of addressing reporting through statutes and regulations, disease lists vary both in their comprehensiveness. Despite the importance of animal diseases to public health and the fact that almost 50% of the states implicitly note this connection by relating animal disease reporting to public health or zoonotic diseases, many zoonotic diseases which are deemed significant zoonoses in Chapter 2 of this dissertation are not present in these reportable animal disease lists.

The topic of ―who reports‖ remains interesting, if enforcement of the law is important or effective disease reporting is to be expected. Some statutes and administrative codes do dictate penalties for non-compliance. The majority of states require that both laboratories and providers must report. However, the question remains of who has to report if only a veterinarian was required by law to report and the animal disease was detected by a farmer, laboratory, or other personnel. Even the 2009 OIE

Terrestrial Animal Health Code notes that farmers and workers who deal with animals are on the front lines and should be supported by veterinarians, as their day-to-day contact is likely to initially discover an animal disease (World Organization for Animal

Health, 2009). However, in many states, as seen by the lack of specification in ―who reports‖ in states‘ statutes and regulation, these individuals are not obligated to report.

That said, the implications of who is legally obligated to report animal diseases is further explored in Chapter 5 and Chapter 6. In particular, while most individuals in the federal government and private sector are under the distinct impression that veterinarians are not legally obligated to report diseases under federal law, in fact, in the United States,

228 federally accredited veterinarians (see 9 CFR Part 161) are bound to report all diagnosed or suspected cases of foreign animal diseases and program diseases (for which APHIS has a disease program) to an Area Veterinarian in Charge (AVIC-APHIS) or a State

Animal Health Official (Code of Federal Regulations, 2009). While this seems to be a good ―catch‖ to ensure veterinarians are reporting animal diseases, federal accreditation for veterinarians is purely voluntary. Typically, veterinarians become federally accredited in order to assist in an animal health emergency. More information can be found at: www.aphis.usda.gov/nvap. If federally accredited veterinarians do not report these diseases as specified in the CFR, they can be suspended as federal veterinarians and their federal accreditation can be revoked. It is estimated that approximately 80% of all veterinarians in the United States are federally accredited, though more recent accreditation numbers were not located (United States Department of Agriculture-Animal and Plant Health Inspection Service, 2006).

This legal analysis presents evidence that states generally provide time requirements, and usually require reporting within 24 hours. Again, the question of enforcement is another issue. States frequently require disease reporting to a state veterinarian. Interestingly, 57% of states do not list provisions for reporting unknown or emerging infections/diseases or biologic agents. States that list specific symptoms—such as vesicular—certainly appear to moving towards more syndromic reporting (like syndromic surveillance), where clinical signs are considered important in addition to actual diagnoses. However, 57% of states have no provisions for unknown animal diseases, suggesting that if an emerging or foreign animal disease not already listed on a

229 reportable disease list emerged in the United States, it is not certain that disease reporting would occur in a timely or effective fashion.

Importantly, though only observationally, this suggests that other reporting parties that are not legally bound to report—such as farm owners—may be extremely fundamental to timely and appropriate disease reporting and outbreak detection (and subsequently an effective response effort) if many veterinarians are obligated to report under federal law. Moreover, this information is certainly publicly available, but does not appear to be publicly known or acknowledged. This may be a topic worthy of further investigation in subsequent research. If nothing else, this added layer of complexity in animal disease reporting surely indicates the lack of a clear picture of who detects and reports both animal diseases and zoonotic diseases in the United States.

Coding Reliability

As noted in Chapter 3, five states were selected at random (starting with the second state on the list, and then choosing every tenth state) to be coded by an additional coder. In total, the second coder recorded results for 45 different data points (nine criteria for each of the five states). Of these 45 data points, 36 or 80% were the same upon first examination (comparing this coding to my own). Of the nine criteria that differed, five were my own coding errors that needed to be corrected, and four were coding errors of the second coder that did not need to be addressed. These errors were re- checked against the source documents, using the coding rules already established. The most onerous criteria—the number of diseases and the names of the diseases on the state disease lists—varied in all but one case between the primary and secondary coder.

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However, they did not differ more than 2 diseases in any instance. The relative

consistency between coders, and repeated coding of the criteria used in this analysis

bolsters the reliability of the coding.

Final Observations

Finally, two important observations should also be noted in this analysis of the

legal landscape. First, some states include clauses in their statutes such as ―any other

disease which has been or may hereafter be adjudged and proclaimed by the

commissioner or the bureau of animal industry of the United States department of

agriculture to be contagious, infectious or otherwise transmissible or communicable‖

(West Virginia Code, Section 19-9-1). Such statements are not incorporated into this

Chapter‘s analysis unless otherwise considered a ―disease category‖—a category which

was carefully discussed earlier in this Chapter. Because of this exclusion, this Chapter

presents clearly what is actually in a state‘s statutes or code and does not extrapolate to

include all diseases hereafter adjudged by the USDA (or any other entity) to be

transmissible or communicable, for example. This may be a limitation to this legal

assessment, but this observation also indicates a limitation to the clarity and effectiveness

of animal disease reporting itself. Further analysis may delve into such issues to better

understand if ‗disclaimers‘ such as this are effective in updating a state‘s statute or

regulation without textual changes to a state‘s code or regulation.

Second, enforcement mechanisms are not discussed in this chapter. Some statutes/regulations do provide enforcement mechanisms and levy penalties for individuals who do not adhere to animal disease reporting requirements. However, this characteristic

231 of animal disease reporting requirements falls outside the scope of this analysis. It is unclear from this chapter and the literature review how enforcement provisions actually impact reporting knowledge or obligation, so the lack of enforcement provisions may not have significant importance. This is a key area for further research, but finding and coding enforcement statutes/regulation will require substantial understanding of the law and exhaustive legal research, as these provisions are often linked to animal disease reporting but not found in the same section of statute or code. Enforcement issues may arise in Chapter 6, during the case studies, but are otherwise outside the scope of this research.

Conclusions & Moving to the Outbreak Database

The purpose of this legal analysis is description rather than explanation. This

chapter thoroughly describes animal disease reporting in the United States, via the

examination of statutes, administrative code, and material produced by states in online

resources (policy guidance). The characteristics of statutes, code, and online material

were specified, carefully coded, and clearly presented.

Throughout this Chapter, directions for future research are suggested. In

particular, additional research could be conducted on enforcement mechanisms in animal

disease reporting statutes. Certainly most individuals—if a disease list is posted on a

departmental website of their respective state—will believe that they need to follow the

reporting requirements on that list. Furthermore, all discussion of the legal landscape is

hypothetical until a disease outbreak actually occurs that tests that legal framework in a

given state(s).

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Topics such as the reporting of wildlife diseases and the development of these legal materials over time also deserve scholarly attention. Also, this legal analysis is not meant to further explain variation in animal disease reporting amongst states. But this is also a worthy topic for further research—particularly to better understand if agricultural lobbies or other factors impact reportable animal disease regulation and law.

This chapter assessess the legal landscape of animal disease reporting, closing the gap in available literature. Prior to this research, there was no attempt to thoroughly assess the requirements for animal disease reporting in the United States, given the federal system of governance and the variation between the states. This chapter not only provides a picture of what reportable animal disease lists look like in the United States, it gives substantial description of other characteristics which are both similar and different amongst the disease lists. In addition, it offers directions for future research and possible explanations—considering federalism and bureaucratic complexity—of why states could have different disease reporting requirements for further investigation and analysis. This chapter lays the foundation for the rest of this dissertation, particularly Chapter 6.

Chapter 5 presents the Outbreak Database, and attempts to fill a significant void in existing research. Records were reviewed to determine who detects and reports zoonotic diseases, and how fast they are detected. The legal analysis reported here indicates that there is a wide variation in the animal diseases that states list as reportable in their legal and official materials. Now that we understand the law, we turn to actual practice to better understand who detects and reports zoonotic diseases and other characteristics associated with these actions—topics not addressed comprehensively in current literature.

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Table 4.19 Location of Disease Lists on Internet and Date of Lists

Date of List (or Location of Disease List or Code/Statute with Disease List (Current Last Updated), State February 2010) if Available http://avdl.vetmed.auburn.edu/home/policies-and-procedures/reportable- Alabama diseases/list-of-reportable-diseases2 2009 https://www.dec.state.ak.us/eh/docs/vet/Disease%20reporting%209- Alaska 052.pdf N/A 2000 (use 1999 Arizona http://www.azsos.gov/public_services/Title_03/3-02.htm#ARTICLE_4 OIE list) Arkansas http://www.arlpc.org/regs/ReportableDiseases.pdf 2004 http://www.cdfa.ca.gov/ahfss/Animal_Health/pdfs/CA_reportable_disease California _list_05292002.pdf 2006 http://www.colorado.gov/cs/Satellite?blobcol=urldata&blobheader=applica tion%2Fpdf&blobheadername1=Content- Disposition&blobheadername2=MDT- Type&blobheadervalue1=inline%3B+filename%3D688%2F695%2FRepor tableDiseaseList.pdf&blobheadervalue2=abinary%3B+charset%3DUTF- 8&blobkey=id&blobtable=MungoBlobs&blobwhere=1167363899526&ss Colorado binary=true N/A Connecticut Available by request. N/A Delaware http://delcode.delaware.gov/title3/c073/index.shtml N/A District of http://doh.dc.gov/doh/lib/doh/information/epid/dc_communicable_list_08_ Columbia 20_08.pdf 2008 Florida https://www.flrules.org/gateway/ruleNo.asp?ID=5C-20.002 2008

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http://agr.georgia.gov/vgn/images/portal/cit_1210/38/32/46976477RADS Georgia %20Poster%20for%20Web%20-%2012-21-05%20dcs.pdf 2005 http://hawaii.gov/hdoa/admin-rules/subtitle-3-division-of-animal- Hawaii industry/AR-22%20List%20HI%20Report%20Diseases.pdf 2000 http://www.agri.state.id.us/Categories/Animals/animalHealth/healthreporta Idaho ble.php 2009 Illinois http://www.agr.state.il.us/Laws/Regs/Diseased.pdf 2004 Indiana http://www.in.gov/boah/2372.htm N/A Iowa http://www.agriculture.state.ia.us/animalIndustry.asp N/A Kansas http://www.kansas.gov/kahd/laws/reportable_diseases.shtml 2009 Kentucky N/A N/A http://www.ldaf.state.la.us/portal/Offices/AnimalHealthServicesandFoodSa Louisiana fety/VeterinaryHealthDivision/ReportableDiseases/tabid/342/Default.aspx 2007 Maine http://www.maine.gov/agriculture/ahi/diseases/repdis.htm 2005 Maryland http://www.mda.state.md.us/animal_health/diseases/reportable.php N/A Massachusetts http://www.mass.gov/agr/animalhealth/diseases/diseaselist.htm 2007 http://www.michigan.gov/documents/MDA_Reportable_Animal_Diseases _69720_7.pdf ; Avian diseases: http://www.michigan.gov/documents/mda/MIAvianDis2009_284984_7.pd Michigan f 2004 Minnesota http://www.bah.state.mn.us/bah/rules/reportable-diseases.html 2007 http://www.mbah.state.ms.us/disease_programs/reportable_diseases/index. Mississippi html 2008 Missouri http://mda.mo.gov/animals/health/disease/comdisease.php. N/A Montana http://liv.mt.gov/liv/ah/diseases/reportable/report.asp 2007 Nebraska http://www.agr.state.ne.us/division/bai/disease_list.pdf N/A Nevada http://agri.nv.gov/Animal2_Reportable_Diseases.htm N/A New http://www.nh.gov/agric/divisions/animal_industry/documents/diseases.pd Hampshire f 2002 http://www.michie.com/newjersey/lpext.dll?f=templates&fn=main- New Jersey h.htm&cp= 2005

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New Mexico http://www.nmcpr.state.nm.us/nmac/parts/title21/21.030.0004.htm 2001 New York http://www.agmkt.state.ny.us/AI/disease_rep.html 2007 North Carolina http://www.agr.state.nc.us/vet/vetdis.htm 2004 http://www.agdepartment.com/Programs/Livestock/BOAH/ReportableDise North Dakota aseListUpdated1-2008.pdf 2008 Ohio http://codes.ohio.gov/oac/901%3A1-21 2007 Oklahoma http://www.oda.state.ok.us/ais/reportdisease.pdf 2007 http://www.oregon.gov/ODA/AHID/animal_health/cl_reportable_diseases. Oregon shtml#Reportable_diseases 2009 http://www.agriculture.state.pa.us/portal/server.pt/gateway/PTARGS_0_2_ 24476_10297_0_43/AgWebsite/Files/Publications/reportable%20diseases. Pennsylvania pdf N/A Rhode Island http://www.dem.ri.gov/programs/bnatres/agricult/pdf/reprtdis.pdf 2009 South Carolina http://www.clemson.edu/public/lph/ahp/rep_disease.html 2009 South Dakota http://aib.sd.gov/News/2009%20JULY.pdf 2009 Tennessee http://tennessee.gov/agriculture/publications/labguide/lg10.pdf 2009 http://info.sos.state.tx.us/pls/pub/readtac$ext.TacPage?sl=R&app=9&p_dir Texas =&p_rloc=&p_tloc=&p_ploc=&pg=1&p_tac=&ti=4&pt=2&ch=45&rl=2 2009 http://ag.utah.gov/divisions/animal/health/documents/ReportableDiseaseLi Utah st.pdf N/A Vermont N/A N/A Virginia http://www.vdacs.virginia.gov/animals/pdf/033006diseaselist.pdf N/A Washington http://apps.leg.wa.gov/WAC/default.aspx?cite=16-70&full=true# 2006/2007 West Virginia http://www.legis.state.wv.us/WVCODE/Code.cfm?chap=19&art=9#09 2008? http://datcp.state.wi.us/ah/agriculture/animals/disease/reporting- disease/one_day_disease.jsp ; http://datcp.state.wi.us/ah/agriculture/animals/disease/reporting- Wisconsin disease/ten_day_disease.jsp 2006

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http://wlsb.state.wy.us/Animal%20Health/forms/Reportable%20Disease% Wyoming 20List%20Feb%2013%2008.pdf 2008

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Table 4.20: List of Diseases/Disease Categories in State Reportable Animal Disease Lists . Acariosis of Bees . Avian Infectious . Brucellosis (Canine) . Acariapisosis (Honey Laryngotracheitis . Burkholderia (all spp) Bees) . Avian Influenza (High . Camel Pox Virus . Actinobacillosis Path) . Campylobacteriosis . Actinomycosis . Avian Influenza (Low (vibrionic abortion) . Adenovirus (deer) Path) . Canine Ehrlichiosis . African Horse . Avian Polyoma Virus . Canine Distemper Sickness . Avian Pneumonovirus . Canine Influenza . Aflatoxin . Avian Tuberculosis . Canine Parvovirus . African Swine Fever . Babesiosis (any spp). . Capripoxvirus . Akabane Virus . Bacillary . Caprine Disease hemoglobinuria Arthritis/Encephalitis . Aleutian Disease in . Bacterial Kidney . Caseous mink Disease Lymphadenitis . All diseases in . Baculovirus Spp. . Cattle Tick Fever/Tick veterinary diagnostic . Beak and Feather Fever handbook Disease . Centrocestus . Amblyomma (Bont . Bee and Mollusc formosanus tick infection) Diseases . Ceratomyxosis . American Foulbrood . Beef Measles . Channel catfish virus (bees) . Blackleg . Cholera . Ancylostomiasis . Blastomycosis . Chorioptes bovis (Hook worm) . Bluetongue (Domestic . Chorioptes caprae . Anaplasmosis & Exotic) . Chorioptes equi . Anthrax . Bonamiosis (Mollusc . Chronic Wasting . Any Bee Disease infection, all spp, Disease . Any contagious or typically ostrae and . Classical Swine Fever infectious disease exitosa) (Hog Cholera) . Any foreign or exotic . Borna Disease . Clostridium or emerging disease . Bothriocephalosis perfringens (epsilon . Any disease foreign . Botulism toxin) or exotic to the state . Bovine Cysticercosis . Coccidiodomycosis . Any disease . Bovine Genital . Coccidiosis designated by Campylobacteriosis . Contagious Agalactia CDC,OIE or APHIS . Bovine Leukosis . Contagious Bovine declarations . Bovine petechezl fever Pleuropneumonia . Any disease listed by . Bovine Papular . Contagious Caprine Section 71, Title 9 Stomatis Pleuropneumonia CFR (uh, no diseases . Bovine Piroplasmosis . Contagious Ecthyma listed here) . Bovine Spongiform (Soremouth) . Asian Tapeworm of Encephalopathy . Contagious Equine Carp . Boophilus (Southern Metritis . Atropic rhinitis Cattle Tick . Contagious Foot Rot . Aujeszky's Disease Infestation) (Ovine or unsp.) . Avian Chlamydiosis . Bovine Viral Diarrhea . Corona Viral Enteritis (pet birds or poultry) . Brucella melitensis . Cow pox . Avian . Brucellosis (All other . Crayfish plague Encephalomyelitis or All Spp, including . Crimean Congo . Avian Infectious bovine and porcine, Hemmorhagic Fever Bronchitis swine and equine) . Brucellosis (Caprine)

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. Cryptospordiosis . Venezuelan Equine . Fowl Plague (in (clinical case or Encephalomyelitis addition to HPAI) unspecified) . West Nile Virus (not . Fowl Pox . Demodex bovis spp specific, usually . Fowl Typhoid . Demodex ovis equine/avian) . Furunculosis . Dermatophilosis . Equine Herpesvirus . Generalized . Diptheria Infections Demodectic Mange . Disseminated . Equine Infectious (Red Mange) neoplasia bule mussel Anemia . Generalized Sarcoptic . Distemper (dogs or . Equine Influenza (type Mange (any spp) mink) A) . Getah . Dourine . Equine Influenza (All) . Giardiasis . Duck Plague . Equine . Glanders . Duck Viral Enteritis leucoencephalomalaci . Gondieriosis . Duck Viral Hepatitis a . Gyrodactylosis . East Coast Fever . Equine Morbillvirus . Haplosporidiosis . Echinococcosis and/or (Hendra) (nelsoni or costale) Hydatidosis . Equine Piro plasmosis . Hantavirus . Edema Disease . Equine Protozoal . Heartwater . Encephaloymelitis Myeloencephalitis . Heartworm conditions, all spp. . Equine . Hemmorrhagic . Enterovirus Rhinopneumonitis enteritis in swine Encephalomyelitis (including neurologic . Hemmorrhagic . Enteric Redmouth form) Septicemia (any type) . Enteric Septicemia of . Equine Strangles (can . Hendra virus Catfish also be in camelids) . Herpes B Virus . Enzootic Abortion of . Equine Viral Arteritis . Herpesvirosis of Ewes . European Foulbrood Salmonids . Enzootic Bovine (Bees) . Histoplasmosis Leukosis . European Fowl Pest . Horsepox . Ephemeral Fever . Erysipelas (swine) . Hyperkaratosis . Epizootic . Erysipelas (poultry) . Ibaraki Hemorrhagic Disease . Exotic Myiasis . Infectious Bronchitis . Epizootic . Farcy (Avian or unspecified Lymphangitis . Feline calicivirus spp) . Epizootic . Feline . Infectious Bursal Hematopoietic Immunodeficiency Disease Necrosis (fish) Virus . Infectious Bovine . Epizootic Ulcerative . Feline Infectious Rhinotracheitis/ syndrome (fish) Peritonitis Infectious Pustular . Equine erhlichiosis . Feline Leukemia Vulvovaginitis . All Equine . Feline Panleukopenia . Infectious Coryza Encephalomyelitis (Distemper) . Infectious conditions. . Feline Spongiform encephalomyelitis . Equine Encephalopathy (avian) Encephalomyelitis . Foot and Mouth . Infectious (Eastern) Disease haematopoietic . St. Louis . Foreign Arthropod necrosis (including Encephalomyelitis livestock pests and hypodermal, or HNV) . Western Equine disease vectors in fish or crustaceans. Encephalomyelitis . Fowl Cholera . Infectious Keratitis

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. Infectious . Mycoplasma (Avian) . Plague Laryngotracheitis (inc all synoviae) . Porcine Circovirus (other than vaccine . Mycolpasmosis (and Porcine inducedand/or (Avian) (as labeled or Circovirus Type 2) Laryngotracheitis) Mycolplasma . Porcine Cysticercosis . Infectious Pancreatic gallisepticum) . Porcine Influenza Necrosis (fish) . Mycotic Stomatitis . Porcine Dermatits and . Infectious Petechial . Myxomatosis Nephropathy Fever . Nairobi Sheep Disease Syndrome . Infectious Salmon . Necrobacillosis . Porcine Enterovirus Anemia . Neosporosis Type 1/Agent X . Jap Encephalitis Virus . Newcastle Disease . Porcine . Jembrana disease (inc VVND & exotic) Reproductive/Respirat . Juvenile Oyster . Nipah Virus ory Syndrome Disease . Non-endemic . Postweaning . Koi herpesvirus Protozoan and Multisystemic disease Metazoan parasites of Wasting Syndrome . Largemouth Bass fish. . Potomac Horse Fever Virus . Nosemosis of bees . Pox disease . Lead Toxicity . Oncorhynchus masou . Pseudorabies . Leishmaniosis virus (fish) . Psoroptes equi . Leptospriosis (inc . Orinithosis or . Psoroptes bovis Canine) Psittacosis (avian or . Psoroptes ovis . Listeriosis/Listeria/Lis unspecified species) . Psoroptes Scabies Not terellosis . Ovine Epididymitits specified or Multiple . Louping III . Ovine Pediculosis spp. . Lumpy Skin Disease . Ovine Progressive . Psorergates ovis . Lyme Disease Pneumonia . Proliferative kidney . Lymphocytic . Ovine Pulmonary disease choriomeningitis virus Adenomatosis . Pullorum Disease . Maedi-Visna . Pacheco's Disease (Salmonella pullorum) . Malaria . Paracolon Infestation . Q Fever . Malignant Catarrhal . Parafilariasis . Rabbit Hemorrhagic Fever (all forms) . Paramyxovirus Disease . Malignant Edema Infection (other than . Rabies (equine or cattle) Newcastle) . Red Sea bream . Mange (equine) . Paratuberculosis iridoviral disease . Mange (livestock) . Paratyphoid Infection . Rhinotracheitis . Marek's Disease . Perkinsus chesapeaki (Turkey) . Marteiliosis (all spp) . Perkinsosis (marinus . Ricin Toxicosis . Melioidosis and olseni) . Rift Valley Fever . Meningeal Worm . Peste des Petits . Rinderpest . Menangle Virus Ruminants . Rocky Mountain . Microsporidiosis . Pigeon Paramyxovirus Spotted . Mikrocytosis & Pigeon Newcastle Fever/Rickettsia . Monkeypox Disease rickettsii . MSX Disease . Pleistophora ovariae in . Salmonellosis . Mucosal Disease baitfish (including Salmonella . Mycobacteriosis . Piscirickettsiosis enteritidis and infection (fish) . Piroplasmosis ( unsp. typhimurium?) Or spp besides equine)

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. Salmonellosis in . Toxocariasis (Round . Xenohaliotis equine Worms) californiensis Infection . Scabies Unsp. Spp Or . Toxoplasmosis gondi . Yellowhead disease "multiple" spp. . Transmissable . Scabies (Equine) Gastroenteritis . Scabies (Bovine) . Transmissable Mink . Scabies Encephalopathy (Ovine/Caprine) . Transmissible . Scabies in swine or Spongiform other small animals Encephalopathies . Scrapie (sheep/goat or . Trichinellosis unspec spp) . Trichomoniasis . Screwworm (New and . Trichinosis Old World) . Tropical Horse Tick . Sheep Pox and Goat Infestation Pox . Tropileaelaps . Shiga toxin infestation of honey . Shigella toxin bees producing e-coli . Trypanosomiasis (all . Small hive beetle spp) infestation . tuberculosis (multiple . Spherical spp) baculovirosis . tuberculosis (caprine . Spring Viremia of ONLY) Carp . Tuberculosis (cervids) . Staphylococcal . Tuberculosis (bovine) (Enterotoxins and or . Tularemia VISA/VRSA) . Turkey Coronovirus . Streptococcus spp. . Typhus Fever (fish) . Ulcerative . Surra Lymphangitis . Sweating Sickness . Varroosis . Swine Pox . Vesicular or . Swine Vesicular Ulcerative Conditions Disease and/or Disease . Sylvatic Plague . Vesicular Exanthema . T-2 Toxin . Vesicular Stomatitis . Taura Syndrome (all) . Teschen Disease . Vibrio foetus . Tetanus . Viral Encephalopathy . Texas Fever and Retinopathy . Tetrahedral . Viral Hemorrhagic baculovirosis Fevers/Diseases . Theileriosis . Viral Hemorrhagic . Ticks (All except Septicemia (fish) Brown Dog Tick . Wesselsbron Disease Rhipicephalus . Whirling Disease sanguineus) . White Spot disease . Toxic substance . White Sturgeon contamination Iridoviral Disease

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Chapter 5: Development and Analysis of Outbreak Database

This chapter presents the analysis and results of the outbreak database phase of this dissertation, as detailed in the methods presented in Chapter 3. Constructed from various data sources, this database collects information about disease outbreaks for forty-one key zoonotic diseases which were selected using the criteria which Chapter 3 describes. Here I review the development of this outbreak database, and present the results of the analysis to assess key characteristics of zoonotic disease outbreaks in the United States, including who detects these outbreaks, and how fast they are detected.

This chapter first presents general descriptive statistics about the data that were collected. It then further analyzes these data, particularly in light of key research questions presented in Chapter 3. Finally, the concluding section highlights the potential implications of these results, which will be further explored and related to expectations of federalism, bureaucratic complexity, and institutional design in Chapter 6 and in the comprehensive analyses in Chapter 7.

Database Fields

The database consists of 22 different fields, as pictured in Table 5.1. Chapter 3 carefully operationalizes the definition of an “outbreak”, considering the importance of disease cases from a public health, animal health, and security standpoint. The definition based firmly on both the OIE and WHO definitions, which specifically note that an outbreak can be one or more cases clustered spatially or temporally. A secondary coder performed coding on a small subset of outbreaks (10%), to decipher whether they could be coded as outbreaks or not, based on the specific coding rules presented in Chapter 3. A

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PhD post-doc doing work at the National Institutes of Health also coded a small subset of the outbreaks (as to whether they were outbreaks or not) as a secondary coder. There was a high level of correspondence in secondary coding. The coding was the same for 9 of the 10 outbreaks coded (yes it was an outbreak, or no it was not). The one different outbreak was resolved through a minor language change in the coding rules to specify criteria.

The key variables remain “who detects” and “how fast.” However, I also review the data sources for additional variables, including the date of reporting to a state entity, the date of reporting to a federal entity, as well as the date of response to the outbreak. The summary statistics of these additional variables are presented, but no statistical analyses are conducted because of the very small sample sizes. These data did not provide enough information to merit further analysis about how fast zoonotic disease outbreaks are reported. This is a very important question for future research. For more information on variables and coding, see Chapter 3.

Table 5.1: Variables in Outbreak Database

1.Identification 7.State 13.Species of Origin 19.Date of State Number Reporting 2.Outbreak (yes/no) 8.Cases 14.Species Group 20.Date of Federal Affected Reporting 3. Data Source 9.Incidence Rate 15.All Species 21.Date or Response Affected 4.Outbreak Date 10.Case Fatality 16.Who Detects 22.Comments Rate 5.Country Origin 11.Disease 17.Date of First Case 6.Country First 12.CDC A, B, C 18.Date of Initial Detected Agent Recognition

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Results and Analysis: Summary

Please note that no multivariate analysis was planned nor conducted in this phase of research. It may be possible, in future research, to analyze the relationships between the variables discussed here, but that is outside the scope of this research. While a regression could hypothetically be conducted on these data, I opt not do so as this research is not designed for this type of statistical analysis, and there are too many variables not captured in this analysis that are certainly important in detection and reporting. Throughout this chapter, the statistics that are presented typically do not indicate a direction in the relationship between the variables, and the chi-square test (a non-directional test) is frequently used. However, in some cases, there is a clear direction, due to the nature of the variables. For example, detection always occurs after an outbreak in a particular species, so who detects could be dependent on the species in which the outbreak occurs—but the temporal relationship makes the reverse not possible. In these cases, this is noted, but typically a chi-square test is still used because of its appropriateness to the type of data

(nominal), and this implied direction makes the results of the association more powerful since the direction is known. The criterion for statistical significance has been set at α=.05 or 5%, as this is a typical significance level used. More importantly, care has been taken throughout the chapter to discuss the practical importance of the findings beyond just the statistical significance.

Number of Outbreaks in Database

There are 440 outbreaks in the database. In other words, out of the 726 data points gathered, 440 data points were coded as “yes” they were outbreaks. However, many of these outbreaks do not have further information for analysis. For example, they do not

244 specify who detects the outbreak or how fast the outbreak is detected. Therefore, only very brief summary statistics are provided about these data before moving to the outbreaks that have more details that can be analyzed, as is the intention of this Chapter.

Figure 5.1 shows how many outbreaks existed for each year in the database. The outbreaks are relatively evenly distributed, with few outbreaks in 1996 and 1997 (since data collection began in 1998 literature) and slightly more cases in 2006 and 2007.

Figure 5.1: Number of Outbreaks Per Year (n=440)

Figure 5.2 shows the distribution of these outbreaks by disease. If the disease was on the initial list of the 41 zoonoses identified, but not in this figure, there are not any disease outbreaks caused by that zoonotic agent in the United States during 1998-2008 that are captured in this database. The diseases are not included in this figure so that the figure would fit on a single page. The diseases that did not have an outbreak in the database in this period are: Marburg, Ebola, Venezuelan Equine Encephalomyelitis, Crytpococcosis,

Crimean-Congo Hemorrhagic Fever, Nipah, and Rift Valley Fever. In most of these cases, these diseases have not ever been present or are very rarely present (for example, if there is

245 an imported case) in the United States, so it is not particularly surprising so that they didn‟t appear in the data sources.

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Figure 5.2: Distribution of Outbreaks by Disease (n=440)

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Number of Outbreaks with Complete Data to use in Analysis

Even though there are 440 zoonotic disease outbreaks identified, only a subset of those outbreaks has associated data regarding who detected the outbreak and/or how fast the outbreak was detected. Table 5.2 summarizes this subset.

Table 5.2: Subset of Outbreaks with Data for Analysis

Criteria Number of Outbreaks Percentage of Total Outbreaks (n=440) Who Detects 171 36.4%

How Fast 118 25.1%

Who Detects and How Fast 101 21.5%

As displayed in the table, many cases that are coded as “outbreaks” did not have additional information about who detects or how fast outbreaks are detected. Information about who detects appears more frequently than information about how fast outbreaks are detected. In even fewer cases did data on both who detects and how fast exist for the same case. These 101 outbreaks—with both who detects and how fast data—are predominately used for analysis in this chapter. In the appropriate sections, the 171 outbreaks with who detects information are provided just for background information; same for the 118 outbreaks with how fast data. Because part of this analysis is to better understand the relationship between who detects and how fast, it is critical to use the outbreaks with data in both fields for nearly all the statistical analyses. Therefore, for most purposes, the sample size of this outbreak database is n=101. Figure 5.3 shows the distribution of the

101 disease outbreaks with complete data (who detects and how fast) by state.

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Figure 5.3: Disease Outbreaks by State, n=101 (DC=1 outbreak)

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In Figure 5.3, the sample size is n=101 outbreaks, but if you added the number of times each state appears in the analysis, the total would be greater than 101, because many outbreaks occur across state lines in one or more state jurisdictions. In addition to the outbreaks shown on this map, there were outbreaks that spanned 30 or more states that are not pictured on this map. This is defined in this research as a nation-wide outbreak.

California and Texas both have the highest number of outbreaks that emerged in the construction of this database. Most states are represented: only 8 states do not appear in the outbreak database of complete cases. This map is for illustrative purposes only—further analysis on the geographic distribution of cases is provided later in this chapter.

Figure 5.4 shows the number of disease outbreaks (n=101) by year. Figure 5.5 shows the number of disease outbreaks (n=101) by disease. The outbreaks are not evenly distributed by year, the range is from 1 (1996 and 1997) to 15 (2006 and 2007). If the disease is not listed initially in Figure 5.2, it is not listed here. If the zoonoses is listed in

Figure 5.2 (so there was an outbreak in the database), but the value in Figure 5.5 is zero, this implies that there are no outbreaks in the database with both the “who detects” and

“how fast” data.

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Figure 5.4: Disease Outbreaks by Year (n=101)

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Figure 5.5: Disease Outbreaks by Disease (n=101)

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Table 5.3 presents the number of outbreaks in the database that are a CDC A, B, or C agent; these agents are identified by the CDC as potential agents of biological terrorism.

Table 5.3: Disease Outbreaks that are an A, B, C Agent (n=101)

A, B, C Agent? Outbreaks Yes 60% (61 outbreaks) No 40% (40 outbreaks)

Over half of the disease outbreaks in the database are one of the agents listed in the

A, B, C agent list from the CDC (http://www.bt.cdc.gov/agent/agentlist-category.asp).

Again, later sections assess whether this has any meaning for either who detects the outbreak or how fast the outbreaks are detected. Granted, this is a list published by a human health agency, but it is developed by a consortium working group and the agents that are zoonotic on this list are also considered threats to animal health and concerns in that community as well.

Figure 5.6 presents summary information on the number of disease outbreaks by the species group affected. In this Figure, it is clear that the database predominately captures outbreaks in humans, followed by outbreaks in domestic animals. Further analyses are conducted on these data later in the chapter.

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Figure 5.6: Disease Outbreaks by Species Group Affected (n=101)

Over half of the outbreaks in the database (with complete data) are from humans alone. If you include the categories of humans and wildlife, as well as domestic animals and humans, that total reaches 83% (84 outbreaks). Only 17% of the outbreaks (17 outbreaks) exist in domestic animals or wildlife alone. Given the pre-eminence of human health in zoonotic disease literature, this is not necessarily a surprising result.

In Appendix B, the various modes of transmission of these zoonoses are described.

Dr. Larissa May (GW Medical Center) assisted in categorizing these diseases by mode of transmission. Figure 5.7 shows the number of outbreaks by the primary mode of transmission of the disease.

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Figure 5.7: Disease Outbreaks by Type of Transmission

As Figure 5.7 shows, 39 of the disease outbreaks listed (39%) are transmittable through multiple avenues. The two key diseases in this category, which appear in many outbreaks, are salmonellosis and anthrax. Salmonellosis is commonly transmitted through both food and direct contact, and anthrax is commonly transmitted through food, direct contact, and less-frequently in nature, inhalation. Direct contact (32 outbreaks or 32%) accounts for another third of the diseases. Diseases in this category include hantavirus, brucellosis, and HPAI, among others. Importantly, direct contact includes contact with contaminated fluids (such as urine or blood).

Unfortunately, there is not enough information available in the data sources to populate the “incidence” and “case fatality fields” to provide a measure of outbreak severity.

Summary of the Summary Statistics

From this section, it is clear that finding data on who detects zoonotic disease outbreaks and how fast outbreaks are detected is difficult—problems with finding specific

255 information on individual outbreaks has been documented elsewhere (for example, see

Chan et al., 2010). However, a sample size of 101 is sufficient for most statistical analyses, and meets the expectations going into the outbreak database construction (n was expected to be between 100 and 200 outbreaks). We see that most of the zoonotic disease outbreaks captured in this database include humans, and that the most common diseases captured by this database are rabies, anthrax, and salmonella. In addition, most of the diseases are transmittable through multiple pathways.

The next sections present more detailed analyses on the variables in this database.

In particular, analyses are conducted on “who detects”, and the relationships “who detects” has with other variables like “species of affected” and “mode of transmission”. Then, we move to discuss “how fast” zoonotic disease outbreaks are detected, again using statistical analyses to assess whether there is a difference in how fast zoonotic disease outbreaks are detected depending on variables like “species of origin”. We subsequently move to understand the relationship between “who detects” and “how fast” outbreaks are detected, to assess if there is a relationship between those different variables. Finally, we turn to some observational data and analyses on detection and reporting, as well as avenues for future research and a brief prelude of Chapter 6 (Case Studies).

Results and Analysis: Who Detects

Summary Statistics for all Outbreaks with “Who Detects”

First, I present summary statistics (frequency counts) for outbreaks with data on

“who detects” (n=171). However, all further analyses and discussion are based on the outbreaks with data on both “who detects” and “how fast” (n=101). Figure 5.8 illustrates

256 who detects outbreaks for all outbreaks with data on who detects (but not necessarily with

“how fast” information) (n=171). Laboratories focus on human specimen testing detected more outbreaks than any other entity (29%). Next, the „other‟ category captures 34 detections (20%). Table 5.4 explains more about what this „other‟ category captures.

Figure 5.8: Number of Detections by Entity (n=171)

In terms of the other category, Table 5.4 shows the details of the “other categorizations”. Notably, county, city, or district health departments detect 15 outbreaks.

Table 5.4: Who Detects—Other Who Detects? (Other Category, n=34) Frequency Count County, city, or district health department 15 Herd owner 3 Collaboration between multiple entities 7 Other, including correctional institute, 9 Environmental Protection Agencies and others.

While more analysis could be conducted on these data, it is important to move to the 101 cases that have additional data and are the core of the outbreak database.

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Summary Statistics for Outbreak Database (n=101)

From this point on in this section on “who detects,” I use the outbreaks with complete data on “who” and “how fast.” Figure 5.9 offers information on each entity and the number of detections that particular entity makes on outbreaks in the database. Human laboratory detections rank first (33 detections or 33%), followed by physician or clinical

(human) detections (19 detections or 19%), followed by the state health department (16 detections or 16%). Table 5.5 further elaborates the “other” category captures.

Figure 5.9: Number of Detections by Entity (n=101)

Table 5.5: Who Detects—Other

Who Detects? (Other Category, n=14) Number of Outbreaks

County, city, or district health department 5 (36%) Herd owner 1 (7%) Collaboration between multiple entities 4 (29%) Other, including Environmental Protection 4 (29%) Agencies and others.

In the other category, collaboration between multiple agencies in a detection accounts for 29% (4 outbreaks) of the entries. Local health departments, at the county,

258 city, or district level accounts for 36% (5 outbreaks). As the first line of defense, it is expected that local health departments play an important role in outbreak detection.

Who Detects: Species Affected

Intuitively, it would make sense that zoonotic disease outbreaks in animals are detected by one of the animal health entities—a veterinarian, a laboratory that tests animal samples, or a state animal health department, and that disease outbreaks in humans are detected by one of the human health entities. This section analyzes whether this is indeed the case, using the 101 outbreaks in the database.

Chi-squared analyses are conducted. For improved statistical power, the categories are collapsed, so that the animal health entities (veterinarians, lab-animals, state animal health departments) are in one category, and human health entities are in another. The

„other‟ data points are included in either category, as appropriate. For example, “County

Health Department” is placed in the category “human entity”. Notably, n<101 because the

“other” categories could not always be placed in one category or the other (for example, the

EPA or collaborations could not be reliably placed in either the “animal entity” or “human health entity” category).

In terms of species affected, there are six groups in the database—they are listed below in Table 5.6 (seven were initially coded in the database, but there are no “all” entries, with domestic animals, wildlife, and humans in the n=101 data set):

Just as the “who detects” categories are collapsed into animal and human, the

“species affected” categories are collapsed into first (1) humans (humans only) and (2) animals (domestic animals and wildlife). Second, they are collapsed into three categories

(1) humans, (2) animals, and (3) animals and humans. This ensures that frequency counts

259 were higher, as the counts in Table 5.6 were less than or equal to 1 in many cases. Minitab was used to run these statistics, and all statistics thereafter in this chapter.

Table 5.6: Frequency Table (Collapsed)—Who Detects based on Species Affected

Species Humans Humans Humans Domestic Domestic Wildlife Total Affected/ and and Animals Animals Entity Wildlife Domestic and Animals Wildlife Animal 1 0 3 14 0 1 19 Health Entity Human 59 5 9 1 0 0 74 Health Entity Other 5 1 0 1 1 0 8 Total 65 6 12 16 1 1 101

In the first analysis, the independent variables are the two species affected categories (1) humans, (2) animals (wildlife or domestic animals) (n=76). In the second analysis, the independent variables are species affected categories (1) humans, (2) animals

(wildlife or domestic), (3) animals & humans (n=93). In both cases, the chi-square statistic

(χ2=65.986) is high and significant at the p<.001 level. We can reject the null hypothesis that no relation exists between these two variables. These results conform to expectations: animal entities appear to detect outbreaks in animal species; human entities predominately detect outbreaks any time humans are affected. While it is important to note whether a statistic is significant or not, it is more important to understand whether this relationship is practically important. From the contingency table alone, even without the statistic, it is clear that human health entities predominately detect outbreaks when humans are affected.

This is also expected from the literature, and the preeminence of human health in zoonotic disease literature and the authority of human health agencies in policy. In this analysis, the

260 direction of the relationship could be implied (the dependent variable is “who detects” and the independent variable is “species affected”), because it does not make sense to imply that in what species the outbreak occurs is related to what agency detects, as the detection occurs after the outbreak occurs.

Who Detects: Species of Origin

Next, we turn to assess whether there is a relationship between species of origin and who detects the zoonotic disease outbreak. Again, the direction of this relationship could be implied, because the zoonotic disease outbreak is detected after the origin of the outbreak, so “who detects” would again be the dependent variable. However, again, chi- square test is not a directional test and only tests for association between the two variables.

The analysis is conducted as in the previous section: the “animal entity” and

“human health entity” are collapsed from the possible categories, and the “other” category is placed into one of the two categories (either animal entity or human health entity) as appropriate. If this is not possible, the “other” outbreaks are removed from the analysis because of low frequency counts.

Species of origin were categorized into six categories:

1. Domestic animals

2. Wildlife

3. Vector

4. Unknown (not specified in the data)

5. Lab-acquired or intentional

6. Other (including food).

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Again, these categories are collapsed for additional statistical power. First, the domestic animals and wildlife categories are collapsed. The “vector” category is collapsed into

“wildlife” because of low frequencies. The “other” column is collapsed with “lab- acquired/intentional”. Subsequently, 2 categories emerge for a 2x2 contingency table analysis (these are the rows): (1) domestic animals or wildlife, and (2) lab-acquired, intentional, and other, with the columns of (1) animal entity and (2) human entity. The samples size is n<101 because the “other” who detects and “unknown” species of origin entries are removed from the analysis for higher statistical power. Table 5.7 is the contingency table for this analysis

Table 5.7: Contingency Table for Species of Origin and Who Detects (n=72)

Species of Origin Domestic Lab-Acquired, Total Animals or Intentional, or Wildlife Other Animal Health Entity 9 1 10 Human Health Entity 35 27 62 Total 44 28 72

The results of the chi-square (n=91) are statistically significant at a p<.05:

χ2=4.078, p=0.043. These results do not show as strong of an association as the previous results, for the species affected; however there still is a statistical relationship between who detects and the species of origin. That said, the practical implications of this relationship are unclear. On one hand, it is surprising that animal health entities do not detect more outbreaks that originate in domestic animals or wildlife. On the other hand, this may not be so relevant considering public health departments often have purview of salmonellosis and rabies; these zoonoses are a significant number of the outbreaks represented in this database (36 outbreaks or 36%).

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Moreover, substantially more outbreaks in the database affect humans, or humans in combination with another species, than animals alone. It is important to not overstate the practical importance of statistical significance in this case. One key implication of these results is that who detects (animal entity or human health entity) is more closely associated with species affected than species of origin.

Who Detects: Laboratories and Practitioners

This section returns to univariate statistics to describe who detects zoonotic disease outbreaks. Rather than separate animal and human entities, this section categorizes laboratories and providers into two separate categories in order to better understand the role of the laboratory versus the role of a clinical professional (veterinarian or physician) in zoonotic outbreak detection.

Figure 5.10 illustrates the number of outbreaks that are detected by a clinical professional versus those that are detected by a laboratory, with a separate category for those outbreaks that are detected by a state agency. The “other” category is excluded from this chart, except where the “other” entries could be collapsed into one of these three categories (local health departments are not included in Figure 5.10).

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Figure 5.10: Who Detects-Entity Type (n=89)

Laboratories detect 41 of 89 outbreaks, or 46%. A veterinarian, physician, or other clinical practitioner detects 30 outbreaks, or 34%. State agencies (either animal health or public health agencies) detect 18 outbreaks, or 20%. Laboratories clearly play a clear role in zoonotic disease outbreak detection, likely in large part because of their diagnostic capabilities and ability to test samples for multiple biologic agents. However, it will be interesting later in the chapter to come back to the question of laboratories to see if they are as fast to detect as other entities, considering laboratory tests are often time consuming to run. In addition, the key role of laboratories is also important considering that in Chapter

4, evidence suggests that 30 states actually do not mention laboratory reporting in their statutes, regulations, or disease lists for animal diseases. Certainly, not all of these diseases are zoonotic, but it is an important point to keep in mind that zoonotic diseases in animal species are not reportable by laboratories in 30 states.

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Who Detects: A, B, C Agents

Next, we turn to analyze whether there is an association between who detects the zoonotic disease outbreak and whether the disease is listed on the CDC‟s A, B, or C agent list, again using the non-directional chi-squre test. Why is this relevant? As seen in the literature on bureaucratic behavior, institutions may not collaborate. So, for example, it may be possible that A, B, and C agents are detected more frequently by a human health entity because the list is created by a human health agency (the CDC). Again, the “who detects” categories are collapsed into “human” and “animal” categories, as explained in previous sections. Table 5.8 is the contingency table for this analysis.

Table 5.8: Contingency Table for Who Detects and A, B, and C Agent (n=93)

A, B, or C Agent Yes No Total Animal Health Entity 13 6 19 Human Health Entity 42 32 74 Total 55 38 93

However, we cannot reject the null hypothesis that there is no association between these two variables. The results of the chi-square test are not statistically significant: χ2=.851, p=0.356. These results would suggest that there may not be evidence to support that there is a relationship between whether a human health entity or an animal health entity detects, and whether the disease is listed on the A, B, C list. Rather than indicating that agencies collaborate (this would be a bit of a stretch), it is more likely to hypothesize (but not conclude, without further evidence) that these agents are of significant concern to both animal entities and human entities involved in outbreak detection activities.

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Summary of Who Detects Zoonotic Disease Outbreaks

We have now reviewed the “who detects” data, and assessed the associations between “who detects” and multiple other variables that are collected in the database.

Purely for illustrative purposes, I return to the 171 outbreaks with “who detects” data to show that the 101 outbreaks in the main database are generally representative of the larger population of outbreaks. Figure 5.11 shows how the 101 outbreaks represented the 171 outbreaks with “who detects” data in the database.

Figure 5.11: Comparing Outbreak Data

The laboratory-human category remains the largest for both sets of outbreaks. In the set of 171 outbreaks, the next largest category is “other” (ranking 4th in the 101 outbreak set), followed by State Health Department (also ranking 3rd in the 101 outbreak 266 set). While there are differences, the 101 outbreak set does typically represent the larger set

(compare the red bars with the purple bars in the graph above). Again, because of the importance of having complete data for further analysis, the 101 outbreak set is used throughout the analysis to allow comparisons for the “who detects” and “how fast” data later in this chapter.

Returning the preceding sections analyzing “who detects” (n=101), let us briefly review the key results. Laboratories (human) detect 33 outbreaks or 33% of the outbreaks in the database. [It is important to note that laboratories that focus on human samples typically do not test animal samples, unless that disease or animal is under the purview of the state public health or health department (for example, rabies). The same goes for laboratories that typically sample animal specimens—they are not typically sent or allowed to receive samples from humans.] The next most frequent category is physician or other clinical practitioner (human), the entity which detects 19 outbreaks or 19%. Clearly, laboratories (human) and physicians are both very important entities in detecting zoonotic disease outbreaks. Intuitively, this makes sense as we typically go to a physician when sick, and the physician sends a sample to a laboratory (perhaps often without a decisive knowledge of which biologic agent is in question). It also makes sense because of the predominance of outbreaks in the database in which humans are affected. Interestingly, in the “other” category, city/county/district health departments appear 5 times (5% of the 101 outbreaks). This indicates that detection can be local, at a department level, as well as at the practitioner or state level. Certainly, local health departments should not be forgotten in a discussion of outbreak detection. When collapsed into sector (either animal or human),

267 human entities detect 74 outbreaks, animal entities detect 19 outbreaks, and the other category captured 8 outbreaks (73%, 19%, and 8% respectively).

When collapsing the categories of “who detects” by entity rather than sector (for example, combining veterinarians with physicians), laboratories detect 41 outbreaks (46% of the outbreaks). Physicians and veterinarians detect 30 outbreaks (34% of the outbreaks), and state agencies detect 18 outbreaks (20%). It is clear that laboratories are truly instrumental in outbreak detection. Again, this is a particularly notable result of this analysis, given that 30 states in Chapter 4 do not mention laboratory reporting in their animal disease reporting law, regulation, or statute.

In terms of whether “who detects” is associated to which species are affected, the results are both statistically and practically significant (i.e., p<.001). What species is affected (animals or humans) is associated with which entity detects the outbreak. Animal entities detect outbreaks in animals more frequently than outbreaks in humans; the same applies to human health entities as well. However, it is interesting to note that human health entities also detect more frequently when humans and other species are affected.

In addition, I review whether there was an association between “who detects” and the species of origin of the outbreak. These variables are related, however, from the chi- square test statistic, it appears that “species affected” is better associated with “who detects” than “species of origin (p=.043, which is less than p<.005).

Finally, an analysis is conducted to evaluate whether “who detects” was associated to whether the outbreak was caused by a CDC A, B, or C list agent. There was not sufficient evidence to reject the hypothesis that these two variables were independent of

268 each other: the results were not statistically significant at p<.005 (χ2=.851, p=0.356). This may indicate that key agents are important, from both an animal and a human perspective.

Next we turn to the second fundamental part of this analysis, “how fast” outbreaks are detected. Similar to this section, the “how fast” analysis assesses how fast outbreaks are detected, and determines whether there are relationships between this and other variables collected in the database.

Results and Analysis: How Fast

In total, there are 118 outbreaks which have “how fast” data. As noted, 101 outbreaks have both “how fast” and “who detects” data. The “how fast” information is most difficult to locate in the literature and records. Indeed, nearly 60 more outbreaks have

“who detects” data, than “how fast”. The “how fast” information is clearly the variable that restricts the inclusion of additional outbreaks in the database.

How Fast: Summary Statistics

This first section presents the summary statistics on “how fast” zoonotic diseases are detected. Again, as with the “who detects” analysis, in this first section, statistics are presented from the n=118 outbreaks—all outbreaks with “how fast” information. This is purely for information, and the n=101 subset of cases is used for all other analysis in this section. Table 5.9 illustrates how fast outbreaks are detected (n=118). The mean, median, and quartiles are presented.

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Table 5.9: How Fast Outbreaks are Detected (n=118)

Statistic Value (in Days)

Minimum 0

Maximum 492

Mean 30.9

Median 12

1st Quartile 6

2nd Quartile 12

3rd Quartile 25.5

The mean and median are significantly different, and both demonstrate the distribution of the data, considering the maximum value is 492, and the mean is only 30.9 days (a histogram is provided for n=101 outbreaks).

Table 5.10 illustrates how fast outbreaks are detected for the subset of outbreaks which contain both “who detects” and “how fast” information (n=101). Again, the mean, median, and quartiles are presented. These statistics are not significantly different from the subset of n=118 outbreaks. The mean is slightly higher, the median is slightly higher, and the third quartile is slightly lower, but not with any statistical or practical significance

(using the t-test for mean difference, p=.948).

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Table 5.10: How Fast Outbreaks are Detected (n=101)

Statistic Value (in Days)

Minimum 0

Maximum 492

Mean 31.7

Median 13

1st Quartile 6

2nd Quartile 13

3rd Quartile 23.3

Figure 5.12 illustrates the distribution of these cases for each unique value (number of days to detection), and Figure 5.13 provides a histogram.

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Figure 5.12: Frequency Distribution of “How Fast” Outbreaks are Detected (n=101)

How Fast are Outbreaks Detected

6 t

n 5

u o C 4

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 20 21 22 23 24 26 27 28 29 31 32 37 39 40 41 42 47 60 00 06 54 56 65 21 22 51 92 1 1 1 1 1 2 2 2 4 Days to Detection

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Figure 5.13: Histogram of “How Fast” Outbreaks are Detected (n=101)

Histogram of Days 80

Mean 31.72 70 StDev 65.72 N 101 60

y 50

n e

u 40

e r F 30

0 0 50 100 150 200 250 300 350 400 450 500 Days

From these statistics and plots, it is clear that there are outliers in the “how fast” data (for example, the point where days=492. The standard deviation is very high (65.72), suggesting that the number of days, on average, it takes to detect an outbreak, deviates a great deal from the mean. This is largely due to the high-value outliers. If every value

≥100 is removed from the data (9 values, then n=92), the standard deviation drops to 12.61, and the distribution appears more normal (see Figure 5.14).

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Figure 5.14: Histogram of “How Fast” Outbreaks are Detected (without Outliers)

(n=92)

Histogram of Days 25

Mean 14.53 StDev 12.61 20 N 92

y 15

e r

F 10

0 0 10 20 30 40 50 60 Days

When tested for a normal distribution (using the Anderson-Darling test), we can reject the hypothesis that the data are in a normal distribution (at p<.05, AD-statistic is 19.897). I expected these data to be more normally distributed. While it might be possible to suggest these data are in another distribution, attempting to find another appropriate distribution

(such as logistic or extreme value) would be based purely on statistics, and have little practical value, as it is not fundamentally important whether these data are normally distributed or not in this analysis, particularly given the small subset of outbreaks these data represent. I am more interested in finding relationships between “how fast” and “other variables.”

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How Fast: Species of Origin

Now we move back to assessing the relationship between “how fast” outbreaks are detected and other variables captured in the outbreak database. First, I analyze whether the median time to detection is statistically different, depending on species of origin.

Similar to previous analyses, species of origin is categorized into:

1. Domestic animals (n=18)

2. Wild animals (n=28)

3. Vector (n=4)

4. Lab-Acquired or Intentional (n=9)

5. Other (n=18)

6. Unknown (n=24)

The Kruskal-Wallis Test is used to compare medians (this is a one-way ANOVA test on ranks), because of the non-normal distribution of the data. This non-parametric test does not rely on the assumption of a particular distribution. The Kruskal-Wallis test is chosen over the Mann-Whitney test because there are more than 2 sets of sample data (6 sets in this case). The Kruskal-Wallis test assumes that the samples from the different populations are independent random samples from continuous distributions.

In this test, the null hypothesis (H0) is that the population medians are equal. For species of origin, we can reject the null hypothesis that these populations have equal medians, at the p<.05 confidence level. Figure 5.15 shows the boxplot of this analysis.

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Figure 5.15: Box Plot of Median Values (Days to Outbreak Detection)

Boxplot of How Fast Outbreaks are Detected by Species of Origin

500

400

300 s

y 30.5

a D 200

9.5

100 7.5 12 11 7

Domestic Lab Other Unkown Vector Wild Species of Origin

While the p-value is statistically significant, suggesting that the medians of these groups are not equal, the practical implications of this significance are not clear. Dunn‟s method to isolate the group or groups that differ from the others (using a multiple comparison procedure) is also performed, and did not yield any significant differences amongst the different groups (for example, lab vs vector or unknown v wild animals). In addition, if the “unknown” values are dropped from the analysis (n=77), the p-value=.095.

In other words, it is no longer significant at the p<.05 level, which has consistently been used in these analyses. In either case, the Kruskal-Wallis test suggests that there are differences in the medians of these populations, however it does not suggest which populations have the greatest distance between them, because Dunn‟s method does not show that there are significant differences in the medians of any particular group versus any

276 other. It does not appear that species of origin as a category provides any explanatory power for how fast the outbreak is detected.

How Fast: Species Affected

Does the median time to detection change depending on the species affected by the outbreak? It would be possible to hypothesize that disease in humans, for example, are detected more rapidly than diseases in wildlife, because humans when sick are more likely to see a physician and wildlife surveillance is often more passive and sick animals are unlikely to be noticed until the disease is more severe or the animal(s) are dead.

Again, as in the previous analyses, the species affected categories are below. There are six groups (seven were initially coded in the database, but there were no “all” entries, with domestic animals, wildlife, and humans in the n=101 data set):

1. Domestic Animals (n=16)

2. Domestic Animals and Humans (n=12)

3. Domestic Animals and Wildlife (n=1)

4. Humans (n=65)

5. Wildlife (n=1)

6. Wildlife Humans (n=6)

Figure 5.16 shows the boxplot of these data with the medians for each group.

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Figure 5.16: Boxplot of How Fast Outbreaks are Detected by Species Affected

Boxplot of How Fast Outbreaks are Detected by Species Affected 500

400

300 s

y a

D 200 18 100 6.5 18 14 20 15 0

ls s e s e s a n lif n lif n a d a d a im m il m il m n u u u A H W H W H d ic d n e st n a lif e a s ld m ls l i o a a W D im im n n A A c ic ti t s es e m m o o D D Species Affected

In this case, the Kruskal-Wallace test has a p-value of .052 (on the H statistic of

10.96), which is significant at the p<.10 level. There is a difference in medians between these groups, but not at the significance level which has been consistently used in this chapter (p<.05). Because of the concern that there are two groups with only 1 outbreak

(domestic animals and wildlife; wildlife), the Kruskal-Wallace test is re-run without those two categories with low sample sizes. The results were an H statistic of 9.96 and a p-value of .019. This p-value is significant at a p<.05 level. It is interesting to note that all three categories which involve disease in humans have a higher median (for example 18 days for domestic animals and humans, versus 6.5 days for domestic animals alone).

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Dunn‟s Method, (a pair-wise multiple comparison procedure) to isolate the group or groups that differ from the others, is again performed after the Kruskal-Wallace. In this comparison, a significant difference is found between the “domestic animals and humans category” versus the “domestic animals” category (Q=2.852 and p-value <.05). This suggests that outbreaks in domestic animals are detected, on the median, more quickly than outbreaks in domestic animals and humans. There was not a statistically significant difference amongst the medians in the other groups. While this difference in medians is statistically significant, there is not an intuitive explanation of why this necessarily would be the case. One could be created, however, in general, I would prefer to have an explanation before trying to explain a statistical result ad-hoc.

How Fast: A, B, and C Agents

Is “how fast” zoonotic disease outbreaks are detected on the median different depending whether or not the agent in question causing the outbreak is an A, B, or C agent on the CDC list? For example, we could hypothesize that outbreaks caused by A, B, C agents may be detected more rapidly because of the bioterrorism concern and additional awareness about these agents. For this statistical analysis, I use the Mann-Whitney Test, given that it is still a non-parametric test, but for two populations, on the equality of medians. This test was not significant at a p<.05 level (p=.127). Therefore, this suggests that we cannot reject the null hypothesis that the medians of these two groups (those that are A, B, C agents and those that are not) are equal.

How Fast: Disease based on Transmission

Now we turn to analyze whether the number of days to detection is significantly different based on transmission categories. For example, are diseases transmitted by direct

279 contact more quickly detected than diseases transmitted by a vector? One could hypothesize this because direct contact may be recognized prior to materialization of symptoms, and detection may be faster because the specific agent or disease may already be suspected.

Figure 5.17 shows the boxplot of the outbreaks (n=101) by mode of transmission.

The six categories of transmission (same as used previously) are:

1. Direct contact (n=32)

2. Environment (n=3)

3. Food-Borne (n=12)

4. Inhalation (n=2)

5. Multiple (n=39)

6. Vector-borne (n=13)

Figure 5.17: Boxplot of How Fast Outbreaks are Detected by Mode of Transmission

Boxplot of How Fast and Mode of Transmission

500

400

300

a D 200

16.5 100 14.5 11 11 10.5 7 0

Direct Contact Environment Food-Borne Inhalation Multiple Vector-Borne Mode of Transmission

280

Returning to the nonparametric Kruskal-Wallis test, because the number of categories is >2, the H statistic is 4.84, with a p-value of .436. This value is not significant.

There is not a difference in medians between these different groups, and the null hypothesis is not rejected. There is not a difference in the median speed of detection based on the mode of transmission.

How Fast: Primary Symptoms

Literature has suggested that detection of diseases depends on symptoms in some cases. For example, some symptoms are more obvious, more disconcerting, or more life- threatening, and clinicians and veterinarians may be alerted more quickly than in cases where the disease presents as “nagging” symptoms or symptoms which resemble more common infections, like the common cold or flu virus. Subsequently, this analysis assesses whether there is a difference in time to detection (in days) among groups of outbreaks which present different primary symptoms. Dr. Larissa May, of the George Washington

University Medical Center, helped to categorize these diseases by symptom. Notably, only the „primary‟ symptom is presented: it is possible that this may be an invalid way to categorize these diseases, if there are two key symptoms. If there were three or more primary symptoms, or if the primary symptom which presents is dependent on the mode of infection (for example, in the case of anthrax which can be respiratory or influenza like if inhaled, gastrointestinal if ingested, or cutaneous if through the skin), these have been coded as “multiple”. The groups and sample sizes are listed below:

1. Cutaneous (n=4)

2. Gastrointestinal (n=26)

3. Influenza-like (n=21)

281

4. Multiple (n=21)

5. Neurological (n=27)

6. Respiratory (n=2).

Figure 5.18 shows the boxplot of these results.

Figure 5.18: Boxplot of How Fast Outbreaks are Detected by Primary Symptom

(n=101)

Boxplot of How Fast Outbreaks are Detected by Primary Symptom

500

400

300

a D 200

100 9.5 10 13.5 12 14 10.5 0

Cutaneous Gastrointestinal Influenza-Like Multiple Neurological Respiratory Primary Symptom

Using the Kruskal-Wallis nonparametric test for multiple groups, the results of this analysis are in some ways surprising. There is no difference in medians between the groups (n=101), suggesting that there is not a relationship between the primary symptom and how fast the outbreak is detected. In this case, we cannot reject the null hypothesis that the medians of these groups are equal (H=1.83, p-value=.872). Granted, because of the small sample sizes, this statistical analysis may lack validity. Therefore, the “respiratory”

282 group (n=2) is removed from the analysis. However, this did not change the results, it still was not significant at the p<.05—the test does not provide evidence that the null hypothesis that the medians of the groups are equal can be rejected.

How Fast: Origin outside the United States

Initially when this research was conceived, one idea was to see if outbreaks that originated outside the United States are detected at the same or at a different speed than those that originated in the United States. For example, if an outbreak is ongoing elsewhere, perhaps the United States would be more vigilant and the outbreak would be detected faster, or an individual who arrives in the United States may realize that they have been exposed to the zoonotic disease prior to arrival and speed detection of the outbreak. In the database with complete data (n=101), 84 outbreaks are reported in the data sources as originating in the United States; 17 outbreaks are reported in the literature as originating outside of the United States. Importantly, these numbers include cases where the product or item was acquired abroad, for example, importing hides from West Africa that contained anthrax. Of the 17 cases that originated outside of the United States, the nations where the outbreak (or source of the outbreak) originated in Nigeria, East Africa, Democratic

Republic of the Congo, Canada, Liberia or Sierra Leone, Thailand, Guatemala, Haiti,

Philippines, Guinea, Burundi or Tanzania, Iraq, El Salvador, Malaysia, Chile, Ghana, and the United Kingdom. Of these 17 outbreaks, 7 were from the African continent, 1 from the

Middle East, 1 from North America, 3 from Central America, 3 from South East Asia, 1 from South America, and 1 from Europe. Figure 5.19 shows these summary statistics of the outbreaks (n=101). However, because of the small sample sizes, these categories are

283 collapsed in the statistical analysis, which just looked at outbreaks that originated in the

United States versus outbreaks which originated in other countries.

Figure 5.19: Number of Outbreaks that Originated in Other Countries by World

Region versus United States

Again, the Mann-Whitney test is used for statistical analysis, because there are only two groups (staying with a nonparametric test). The p-value is .7992, which is not significant. Subsequently, we cannot reject the null hypothesis that the medians of these two groups are equal. How fast outbreaks are detected does not appear to change depending on whether or not the outbreak originated outside of the United States.

How Fast: Region

Finally, the last variable I review in the “how fast” analysis is whether the median speed of detection varied across various geographical regions of the country. In this analysis, the outbreaks are categorized by where the occurred in the United States, by region, as separated by the US Census (and also used in Chapter 4). These regions are:

1. West (WY, WA, UT, OR, NV, NM, MT, ID, HI, CO, CA, AZ, AK) (n=23)

284

2. Midwest (IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, WI) (n=23)

3. South (AL, AR, DC, DE, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN, TX, VA,

WV) (n=25)

4. Northeast (CT, MA, ME, NH, NJ, NY, PA, RI, VT) (n=16)

5. Multiple (Crosses into multiple regions) (n=13).

Figure 5.20 shows a boxplot of how fast outbreaks are detected by region, and the medians.

Notably, the medians appear, at first glance, different. The Midwest, northeast, south, and west regions all appear to detect outbreaks much more quickly than the „multiple‟ group, which has a median of 35 days. In particular, the “multiple” categories median is significantly higher than the others. In part, this could be to the one data point that is a clear outlier (492 days to detection).

Figure 5.20: Boxplot of How Fast Outbreaks are Detected by Region

How Fast Outbreaks are Detected by Region

500

400

300

s 35

a D 200

100 7.5 11 9 11

Midwest Multiple Northeast South West Region

285

Using the Kruskal-Wallis nonparametric test again to assess the differences in medians for multiple groups, the results confirm my suspicion: the H-statistic is 13.78, and the p-value is .008, which is p<.05. Because of the one very clear outlier in the “multiple” group, I removed this outbreak from the analysis (days=492) to see if the results were still statistically significant, which they are, with an H statistic of 10.77 and a p-value of .019, which is still p<.05. Subsequently, the null hypothesis that the medians of these groups are equal can be rejected. It is likely that the region in which the zoonotic disease outbreak occurs is important in how fast (median time) the zoonotic disease outbreak is detected.

This is one of the few statistically significant findings in the analysis. I would contend it is also practically significant, as certain states and regions may in fact be “best practice” states where zoonotic disease outbreaks are detected more quickly, perhaps just because clinicians are more aware (perhaps more travelers, or more high value production animals), or for other reasons, such as that if cases are more dispersed, it may be harder to realize an increase in incidence. Importantly, this was projected in the literature review prior to any statistical analysis. It is also confirmed in Chapter 6.

Beyond just noting that there is a statistically significant difference between the medians of all groups, Dunn‟s method is used to offer multiple comparisons; to assess between which groups this difference is found. There is a significant difference between the multiple and Midwest groups, as well as the multiple and West groups, using a p<.05 level of significance (the Q values were 3.052 and 3.026). This suggests that there is likely a difference in median time to detection when outbreaks are more widespread to when they are in a particular region, and outbreaks in multiple regions are detected more slowly than others. The multiple group does have a smaller sample size than either the Midwest or the

286 west category (n=13 compared to n=23, respectively). It also has a smaller sample size than the south and northeast groups. It is possible that the higher median time to detection for “multiple” outbreaks is observed because of the bias of reporting multistate outbreaks with „problems‟ in detection more frequently than multistate outbreaks that do not have similar issues in the literature. However, Dunn‟s method is designed to handle unequal sample group sizes, so the difference in medians between these groups remains significant

(typically, for nonparametric methods, a Dwass-Steel-Critchlow-Fligner method can be used for groups with equal sample sizes) (Alex Dmitrienko, Christy Chuang-Stein, &

D'Agostino, 2007). Indeed, this does provide evidence to support that if cases are more widespread, detection may take longer, even if that sample group size is smaller than the other groups. It is hopeful that more explanatory power can be added to this statistical finding through the case study analysis.

Summary Discussion: How Fast

The results of the “how fast” analysis are in some ways expected, and in others quite surprising. For example, I had a notion that there may be a more normally distributed data set. I also would have expected different median speeds of detection for either species of origin groups or species affected categories. Neither were significant at the α=.005 level. This does not provide evidence that a zoonotic disease outbreak affecting animals is detected more slowly than zoonoses in people, potentially posing a risk to humans. Nor does it provide evidence that zoonoses that originate in animals are detected more slowly than a zoonoses that originates in people. This is a critically important finding of this research, and has significant policy implications in that perhaps the zoonotic disease

287 detection system is not as „broken‟ as the literature suggests. Chapter 7 will further address these questions and provide policy recommendations based on these statistical findings.

In addition, the median time to detection is not statistically different depending on whether the zoonosis is on the CDC A, B, C agent list or not. Nor is there any difference in the median time to detection based either on mode of transmission of the zoonoses or by primary symptom. Finally, whether or not the outbreak originates outside the United States also does not appear to have a relation to median detection time.

One of the significant findings of this section is that the region in which the zoonotic disease outbreak occurs is likely an important variable in how fast the outbreak is detected, as the medians of these groups are very different. Theoretically, federalism would predict that some states are better than others at detecting zoonotic disease detection, but even then, this difference spread across regions, even beyond states. The key differences are between the “multiple” group and other groups, suggesting that widespread outbreaks are detected more slowly (the median was significantly higher). This has important implications for the United States, particularly as states have different reportable disease laws, and varied infectious disease infrastructure.

Results and Analysis: Relationships between Who and How Fast

Is there a Relationship between “Who” Detects and “How Fast”?

One of the most significant things that this research is investigating is whether or not there is a difference based on “who detects” zoonotic disease outbreaks in “how fast” outbreaks are detected. For example, do laboratories detect diseases faster than clinicians?

Do animal health entities detect faster than human health entities? This analysis is not

288 meant to hypothesize a particular reason for a relationship, but rather to investigate whether any type of association exists. Figure 5.21 shows a boxplot of the medians of each of the different “who detects” groups, presented in the previous section.

1. Laboratory-Animal (n=6)

2. Laboratory-Human (n=33)

3. Other (n=14)

4. Physician or Clinical-Human (n=19)

5. State Animal Health Department (n=2)

6. Veterinarian or Clinical-Animal (n=11)

Figure 5.21: Boxplot of Who Detects versus How Fast Outbreaks are Detected

Who Detects versus How Fast Outbreaks are Detected 500

400

t s

a 300

w 200 o

H 10.5 7.5 100 13 6 28.5 24 10 0

l t t a n r ) n n l) a e n e e a im m th a m n u O m tm tm i A H u r r n y- - (H a a (A r ry l p p l o o a e e a t t c D D ic ra ra i h h n o o lin lt lt li b b a a C a a C e e L L r H H r o l o n a te n a a ia ci im t r si n S a y A in h e r P t te ta e S V Who Detects

Using the Kruskal-Wallis test, again, the results are not statistically significant across these “who detects” outbreaks. The H-statistic is 10.77, and the p-value is .096,

289 which is not significant at the p<.05 level. However, many of these categories have small sample sizes. Therefore, as in the “who detects” analyses, I collapse these categories in two different ways, both of which find their foundations in literature and theory. First, I collapse the “animal” categories and “health” categories, to assess whether there was a difference in how fast zoonotic disease outbreaks were detected in animals versus in humans. As before, the “other” outbreaks were split into animal and human categories, as appropriate. Animal entity (n=19), human entity (n=74), and other (n=8). There was no statistical significance when comparing the median of the outbreaks detected by human entities versus the outbreaks detected by animal industry. The H-statistic is 1.30, and p- value is .522, meaning that at a p<.05 significance level we cannot reject the null hypothesis that the medians of the two groups are equal. If the “other” category is removed

(n=93), the p-value slips to .977, and H-statistic is 0.00. Clearly, there is not a significant difference between the animal and human entities in the median time to outbreak detection.

Is there a difference in how fast laboratories detect versus how fast clinical professionals detect versus state departments of health or animal health? In other words, perhaps it is not “who detects” by sector, perhaps it is “who detects” by the type of entity detecting. For this Kruskal-Wallis test, I again split the detection entities by type, as in the

“who detects” analysis. This resulted in (n=89):

1. State Agencies (n=18)

2. Physician, Veterinarian, or Other Clinician (n=30)

3. Laboratory (n=41).

This sample size is slightly different than the previous because some of the “other” data points did not fit into one of these categories, whereas they did fit within one of the

290 previous (animal or human health) categories. I have presented another boxplot in Figure

5.22 for visual illustration.

Figure 5.22: Boxplot of How Fast by Who Detects (Entity Type)

Boxplot of How Fast by Who Detects (Entity Type)

500

400

300

a D 200

100 28.5 8 11

Clinician Laboratory State Agency Entity Type

The results of this Kruskal-Wallis test are strongly significant, the H-statistic is

13.51, and the p-value is .001, which is significant at the p<.05 level, used in this chapter.

We can reject the null hypothesis that the medians of these groups are equal. The type of entity which detects a zoonotic disease outbreak is related to how fast the outbreak is detected. In addition, using Dunn‟s method to assess the differences between the groups, there is a significant difference (p<.05, using the Q statistic) between state agency and clinician, as well as between state agency and laboratory. As expected from the boxplot, the median time to detection for state agencies is significantly higher than the median detection time for either laboratories or clinicians.

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Summary: Who Detects and How Fast

One of the most significant findings is that the type of entity, but not sector, does appear to change the median time to zoonotic disease outbreak detection (at least the medians are statistically different). This does make sense: clinicians may be able to detect more quickly than other entities, if they are the „first line‟ of defense against disease in both humans and animals. Secondly, laboratories are likely to be faster than state agencies, but possibly slower than physicians because they detect on laboratory diagnostics, which are often submitted by clinicians to find or confirm a biologic agent, and the diagnostics often take time to perform. Finally, state agencies detect outbreaks more slowly, because they are typically gatherers of broader surveillance information, and it is likely that more cases are required before a higher incidence level in space or time triggers some type of notification, which merits investigation and subsequently detection.

While time to detection does appear to be different based on the type of entity who detects, time to detection does not appear to be different based on the sector of the entity that detects. Based on the literature review, a relationship could have been hypothesized, given the pre-eminence of human health entities and the urge for better disease detection in animal populations because of the risk of zoonotic diseases. This has important consequences for the policy recommendations that will be discussed in Chapter 7.

Results: Reporting and Response

When this database was developed, additional fields were added in the event that the data sources yielded additional information about important characteristics. These are

292 times to reporting and response. However, very few outbreaks contain data on these variables, so only limited information is presented here.

Reporting to a State Entity

One field was the date of reporting to a state entity (reporting as assessed in the legal analysis in Chapter 4). There were only 32 outbreaks in the database that contain information on state reporting (from a clinician or laboratory or other entity to the state).

These results are presented in Table 5.11.

Table 5.11: Summary Statistics on How Fast Diseases are Reported to the State (from

Detection) (n=32)

Statistic Value (in Days)

Minimum 0

Maximum 14

Mean 1.625

Median 0

1st Quartile 0

2nd Quartile 0

3rd Quartile 2.25

These outbreaks are distributed as follows, in Figure 5.23.

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Figure 5.23: How Fast are Outbreaks Reported to the State

Days to State Reporting Distribution

18 17

t 10

u o

C 8

6 4 4 3 3 2 2 1 1 1

0 0 1 2 3 4 5 6 14 Days

It is unfortunate that there are not more outbreaks with the dates of reporting.

However, of these 32 outbreaks, the maximum time to reporting is 14 days, the minimum is

0, and the median is 0, suggesting that as soon as an outbreak is detected, it appears to be reported the same day or the next day to a state entity. These results are encouraging—it does not appear from these results as if there is a significant delay in reporting, that could potentially lead to delays in response to the disease outbreak. While it would be technically possible to perform additional analyses on these outbreaks, this is only a tangential part of this analysis and the sample size is relatively small for more advanced statistical analyses.

Furthermore, the outbreaks with information on state reporting sometimes do not have the

“who detects” or “how fast” information, reducing the sample size even further for additional analyses.

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Reporting from the State to Federal Level

Again, this section presents the very bare results which emerge tangentially from the construction of this database. There are very few cases in which the time between state and federal reporting is noted, which is not surprising given the potential sensitivity of states reporting (or not reporting) this information to the federal government, as this reporting is theoretically supposed to exist, but as explained in the legal analysis, is not mandatory. There were only 7 outbreaks with this information, and the median value was 0 days to federal reporting. Additional analysis is not provided because of the small sample size (n=7). This sample size is too small to draw conclusions, other than to say literature that notes how fast diseases are reported from the state to the federal level seems to indicate states report on the same day.

Response

Finally, the last field which is included in this database is “response”, to see if these data could tell us anything about the speed of response related to who detects or the speed of outbreak detection. Again, unfortunately, most of these reports did not provide any information about when response was initiated. In this case “response” can be either federal, state, or local, as not all outbreaks will merit a state or federal response. In addition, federal response is not considered equivalent to federal reporting. There were only 6 outbreaks in the database which included information on how fast response occurred. In addition, it is possible that the response reported in the data source was not the

„first‟ response, but perhaps a large recall, other notable response effort, or depopulation of animals. The median of the number of days to response for these six outbreaks was 5.5.

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Again, additional analyses are not performed because of the extremely small sample size

(n=6), and valid and reliable conclusions cannot really be drawn from this analysis.

Summary Discussion

Summary of Analyses

Because of the large number of different statistical analyses and results, Table 5.12 presents these in one place for review. Previous summary sections discuss these results in more detail.

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Table 5.12: Summary of Key Outbreak Database Results (n=101)

Analysis Statistical Method Results Notes Number of Outbreaks Frequency Counts n=101 (both who detects and how Total number of outbreaks captured fast data) was n=440. Outbreaks with A, B, Frequency Count, 61% (61 outbreaks) C Agent Percentages Species Group Frequency Count, 65 outbreaks (65%) in humans Domestic animals came next, at 16 Affected Percentages alone; 84 outbreaks (83%) included outbreaks (16%) humans and another species Who detects Frequency Counts, 49 outbreaks (49%) by laboratory- Next most frequent was other, percentages human. followed by state health department (34 and 27 outbreaks) Who detects v Species Chi-Square Significant. p<.05 Affected Who detects v Species Chi-Square Significant p<.05, not as strong as who detects of Origin Who detects v Entity Frequency Counts, 41 (of 89 outbreaks, 46%) detected Clinical professions were next with Type percentages by laboratories 30 outbreaks or 34%. Who detects v ABC Chi-Square Not significant. p>.05 (p=.356) Agent How Fast Mean, Median, Mean=31.7, Median=13 Data not normally distributed. Quartiles How Fast v Species of Kruskal-Wallis; Dunn‟s Significant (p<.05), until Dunn‟s method did not find any Origin Method “unknown” values are dropped difference in median between (p=.095). individual groups. How Fast v Species Kruskal-Wallis Not significant, until groups were With three groups (humans, domestic Affected collapsed. animals, domestic animals and humans), significant p<.05 (p=.019).

How Fast v A, B, C Mann-Whitney Not significant p>.05 (p=.127) Agent

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Analysis Statistical Method Results Notes How Fast v Mode of Kruskal-Wallis Not significant p>.05 (p=.436) Transmission How Fast v Primary Kruskal-Wallis Not significant p>.05 (p=.872) Symptom How Fast v Origin Mann-Whitney Not significant p>.05 (p=.7992) Outside US How Fast v Region Kruskal Wallis; Dunn‟s Significant, p<.05 (p=.008) Difference between the multiple and method Midwest and multiple and West groups at a p<.05 level. Who Detects (by Kruskal Wallis Not significant. With just animal v human entities, Sector) v How Fast removing the other groups, p>.05 (p=.977) Who Detects (by Kruskal Wallis Significant p<.05 (p=.001) Difference at p<.05 between state Entity Type) v How agency and clinician and state agency Fast and laboratory. Reporting to a State Mean; Median Mean=1.625, Median=0 Insufficient sample size for further Entity (n=32) analysis.

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Additional Analyses

What is the denominator?

Certainly, the outbreaks in this database present only a small subset of the zoonotic disease outbreaks in the United States. In order to better estimate how representative these data are, I attempted to estimate the denominator of my numerator—in other words, how many outbreaks occurred over the observed time period. There are a number of significant other reasons why my data sources, in addition to just looking at primarily national reports, as I have done here, may miss a large number of outbreaks, including that outbreaks are not detected and not reported at the state or local level. From both the literature and the outbreak database anecdotes, it appears that underreporting, under-detection, and under- diagnosis is generally suspected in both the veterinary and human medicine professions.

So there are two levels to this analysis: 1) how many outbreaks are detected/reported

(enough to make it into some data source), but are not captured in this analysis, and 2) how many outbreaks occur, but are not detected/reported.

Outbreaks Detected/Reported but not Captured in this Analysis

First, in attempting to estimate the denominator, it is important to note the number of outbreaks from which the outbreaks with complete data emerged. In other words, there are 101 outbreaks with “who detects” and “how fast” data, from a population of 440 outbreaks that are in the database. This means that the outbreak database reflects only

23% of the outbreaks that are coded from the literature. So from these 440 outbreaks collected during data collection, what is the denominator of this number of outbreaks?

Second, we can attempt to estimate this denominator from past literature.

Unfortunately, the Ashford et al. (2003) article that is very similar to this research, uses

299 reports from outbreak investigations worldwide, and does not disaggregate this number between international and domestic outbreaks, so this is not an appropriate comparison.

Similarly, Chan et al. (2010) uses only one data source; a convenience sample of international outbreaks reported through the WHO Disease Outbreak News (Chan et al.,

2010). Because this is mostly international data, and it does not attempt to capture all outbreaks that are detected or reported, so again this comparison has little value.

Third, I did collect some state data to better assess how many cases I missed from looking primarily at national-level literature (NNDSS are disease reported to the national level, MMWR reports are nationally published). Two best practice states are identified in the literature and through preliminary investigation conversations with experts: Minnesota and Georgia. How many zoonotic disease outbreaks have these states reported over the same period of time (1998-2008)? Unfortunately data are not forthcoming, particularly because most summary statistics are provided as the number of cases rather than the number of outbreaks.

In the case of Georgia, a report titled “Vector Borne-and Zoonotic Disease

Summary” provided some estimation, however I had to use cases rather than outbreaks, as that was the only information available. Using these data, there were, between 2002-2006,

917 cases of just 12 zoonoses (in humans only). In the outbreak database, for these same diseases, over the same period, there are only 15 cases that are collected from the data sources that are coded as outbreaks (n=440), or 15/917 or 1.6% (in humans only). While this proportion is not necessarily equivalent to the proportion of outbreaks captured, because the GA cases are not necessarily outbreaks, it does indicate that the denominator of this research is likely quite large. The Georgia report suggests that the number of outbreaks

300 this database captures is quite minimal in comparison to the number of outbreaks that are detected or reported in the United States. Additional information about animal disease outbreaks in Georgia was not located, despite requests to state departments.

In Minnesota, there are even fewer data available publicly. For blastomycosis, 321 cases (again, not outbreaks) were reported in humans from 1999-2008, and 741 in domestic animals for 1998-2008. For the same date range, for the same disease, in the outbreak database (n=440), only 12 cases are captured in humans, and none in animals. This is only

3.7% of the human cases, and 0% of the animal cases. Again, because the 12 cases are involved in outbreaks, but the 321 cases are only cases, this proportion may not hold, but it gives us some additional sense of the denominator of the numerator in the outbreak database—the denominator is quite large, even if the number of cases is reduced significantly if non-outbreak cases are excluded. Minnesota Department of Health also provided, upon request, the number of human cases for three randomly selected diseases:

Anthrax, Brucellosis, and Newcastle disease. There were no human cases of anthrax or

Newcastle reported during 1998-2008 by the Department. While there are no cases of

Newcastle disease in the database either, there is a case of human anthrax during this time period captured in the database, which is interesting, but not investigated further in this research. In terms of brucellosis, the Department reported 23 cases of brucella from 1998-

2008. Only one of these cases is captured in this analysis (4%). This is similar to the results of this analysis reported above. Other Minnesota entities did not respond to requests for assistance.

Unfortunately, additional data from either state were not found publicly. I did submit requests to both these states for key zoonotic diseases, however, at the time of

301 writing, no response had been received. Additional follow up is not planned for this dissertation, as we have some estimate that is reasonable from past literature.

Using these statistics and data just discussed, it appears as if this outbreak database

(these are the outbreaks with complete information) with a sample size of 101, likely captures approximately <2% of known outbreaks. This is based on the calculation that this is only 23% of the outbreaks coded as “yes” in this analysis, and these “yes” outbreaks

(n=440) appear to account for between 0%-5% of cases reported by a state. Granted, this is only for three diseases in two states, however, these percentages are consistently low.

This is likely to vary by state, by disease, by year, and by species affected.

However, this assumption-laden estimation puts the denominator at approximately n=101,000 to n=505,000. This is an important point, and also a limit to the generalizability of this analysis. Moreover, this really indicates the difficulty in collecting information from these outbreaks, as past research at most has captured just over 1,000 outbreaks, and this included outbreaks outside of the United States (Ashford et al., 2003). Even this number, significantly larger than the sample size in this dissertation, is only 10% of the outbreaks that likely occur in the United States.

Outbreaks that Occur, including those that are not Detected or Reported

While we can use some past research to estimate how many outbreaks are detected/reported but are not captured in this analysis, it is even more difficult to assesses how many outbreaks actually occur (regardless of detection and reporting). Anecdotally, an individual at APHIS suggested that approximately 10% of outbreaks are actually reported to an entity. This is likely to vary by disease, as well, considering the “shoot, shovel, shut-

302 up” mentality common with anthrax in livestock, versus other diseases which are more commonly detected and reported, for example rabies in humans.

Doyle et al. (2002) does provide some evidence about how many cases are detected in some manner, but never reported to a system such as a local or state public health agency. Using studies from 1971, 1982, and 1998, the reporting completeness for salmonellosis, using records such as hospital discharge records, ranged from 42-67%.

Unfortunately, this was the only zoonosis in Doyle et al.‟s analysis that was also included in this analysis as well.

In terms of how many cases actually occur and are never detected or reported at all, a 1999 article on foodborne illnesses cited statistics that the underreporting of salmonella has been estimated at ~38 fold, and for ecoli, underreporting is estimated at ~20 fold (Mead et al., 1999). So for the cases of salmonella that are reported, 38 times more cases actually exist. They also note that statistics like these are not available for other diseases, but for diseases that cause severe illness, they use a lower multiplier (in many cases, 2). It is unclear that this multiplier applies to outbreaks as well as cases, but these data were certainly an important benchmark to better understand how many cases go without detection on reporting on a regular basis—which in some cases, is a very, very large number.

Comparisons of these Dissertation Results to Published Research

How do these results about who and how fast outbreaks are detected compare to past literature, in particular, the three key articles which most closely resemble this research? In general, these results are congruous with past results.

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In Ashford et al. (2003), “recognition and reporting” was combined. Health care professionals were the most common source for “recognition and reporting”, accounting for 36.3% of reports. The results in this dissertation also identified clinicians as one of the leading categories, accounting for 19% of detections. Granted, Ashford et al.‟s (2003) sample size was much higher (1,099). In their analysis, health departments were also important, accounting for 30.5% of “recognition and reporting.” Here, in the dissertation, health departments accounted for 16%, which was the fourth leading category. Notably,

Ashford et al. (2003) was looking only at outbreaks in humans, and particularly at potential bioterrorist agents. In Ashford et al., the number of days from the beginning of the outbreak to the “date the problem was first identified by the agency requesting assistance” was 0 to 26 days. While not identical, these times are compatible with my results, which suggest a mean time of 31.2, but a median of 11 days, with a wide range of 0-492. Because

Ashford et al.‟s (2003) time span was until request for assistance, it would make sense that the time in this dissertation was slightly shorter, as it was purely time to recognition, not to requesting assistance.

The Dato et al. (2004) article is also similar to this research. In it, they also review

MMWR articles for a period of one year, for all outbreaks of infectious disease (n=51).

Interestingly, their results were slightly different. Health departments detected 53% of their outbreaks (again, it was only 16% is this dissertation). They categorized 28% of the outbreaks as being recognized by astute clinicians (versus 19% classified to clinicians in the dissertation). Dato et al. (2004) also reviewed how fast outbreaks were detected, and found this information difficult to find in the MMWRs as well. They did not provide days, but ranges for detection. Of the 21 outbreaks with detection information, 9 were detected

304 within one week, six between one week and one month, and six after a month. This seems relatively similar to the results of this research, where there is a significant cluster detected in the first 7 days.

Notably, one of the key differences in this research in comparison to past research is the categorization of “who detects” between a laboratory and a clinician. I specified in my methods that if the clinician detects an infectious disease and suspects the agent, it is classified that the physician detected. However, I classified instances where the physician or veterinarian took routine diagnostic samples, but where a preliminary diagnosis or possible agent was not identified, as laboratory detection when the laboratory produces confirmatory results for a particular biologic agent. Coding is conducted in this manner for reliability, as it was typically stated in the data source whether a laboratory provides a confirmatory diagnosis (in the latter case, the outbreak is coded as clinician detection).

Moreover, laboratories are identified as not having to report in every state, so it was important to assess whether or not they were responsible for detecting diseases. In both

Ashford and Dato et al., it is clear that these laboratory cases, where the laboratory was responsible for the preliminary diagnosis, were grouped into other categories (probably clinician). If I had coded these in the same way, it is likely my results would have been much more analogous to those in both of these other studies.

In Hoffman et al., (2003), the authors found that data reported to the Kansas City

Health Department (of infectious disease cases) through traditional non-electronic reporting came primarily from laboratories (52%), followed by infection control practitioners (34%), which follows more closely to what was found in this dissertation research.

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Finally, in Jajosky and Groseclose (2004), the median delay in national reporting was between 12-40 days. Given the sparse reporting data that this database captures, this does not seem inconsistent with the findings here. While the median time to national reporting was 0 days, this does not incorporate the time from initial case recognition to outbreak detection; this time was included in the Jajoksy and Groseclose (2004) analysis.

Certainly the results in this dissertation, due to the lack of data, do not provide strong evidence for or against these timeliness claims, but they do not contradict them.

Observational Data

Throughout the data collection process, I made some important observations which deserve mention. For example, in many ProMed or MMWR reports, a note was made about the under-reporting of certain diseases, or specific circumstances which made the outbreak or cases unusual. Table 5.13 presents some of the most interesting observations of this analysis, outside the actual data and statistical analyses.

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Table 5.13: Additional Observational Data

Observational Data from the Outbreak Database 1. Lyme disease is frequently noted as overdiagnosed and under-reported in the United States. 2. “Shoot, shovel, and shut-up” is the modus operandi of many ranchers and other

producers in many areas of the country, particularly with anthrax. This leads to significant under-reporting of anthrax in livestock, as well as other diseases, because of confidentiality concerns. 3. Herd owners often do not report, but do handle diseases appropriately, and may even vaccinate if vaccine is commercially available without a veterinarian, so this adds to underreporting of certain diseases. 4. Sometimes rabies infection or exposure is obvious: one woman ran 1.5 miles back to her car with a rabid fox hanging from her arm, stuffed the fox in her trunk, and took it to her physician for rabies confirmation.

5. Sometimes rabies is not obvious: one woman brought a bat into an elementary school and let 90 children touch the dead bat…which was then confirmed by a laboratory to have rabies. 6. Sometimes outbreaks were exacerbated or „cropped up‟ again because animal owners refused to vaccinate. For example, one rancher stated he was “depending on prayer alone” to protect his livestock from anthrax. 7. Physicians sometimes comply with requests not to report, as in one outbreak of hantavirus. The individual later reported their own case to a state health department, because they were worried that they had hantavirus again. 8. Sometimes veterinarians, even state veterinarians, refuse to provide additional information on outbreaks to protect an industry or producer.

Future Research

There are many avenues for further research using similar methods, particularly if additional data sources can be acquired. Further research could be conducted on the select agents of different agencies, and how fast these various agents are detected in outbreaks, and if there is a consistent difference in select agents declared by the USDA versus those declared by HHS versus those that are shared select agents by both agencies. Future research could review additional data on reporting and response, should records become available or sought that contain such information. This research should incorporate data for

307 not only human outbreaks, but for outbreaks in zoo populations, sea life, domestic animals, and wildlife. Moreover, it would be interesting to pursue state-level data to provide additional evidence. Additional zoonotic diseases could also be included in future analyses. Important research should be conducted on how many outbreaks actually occur in the United States in a given year, as this is an important gap in the literature. These are but a few examples of future research that could be pursued, but are outside the scope of this dissertation research.

One key way in which this database could be built upon, however, is for further investigation into a few outbreaks to better understand how the events occurred and the various factors at play in who detects the zoonotic disease outbreak and how fast is detected. This is in the next chapter of this dissertation. The following section introduces

Chapter 6: Case Studies.

Conclusion and Moving to Chapter 6 (Case Studies)

Using a set of zoonotic diseases, this chapter completes research that was missing from the literature: it assesses “who detects” and “how fast” for zoonotic disease outbreaks in the United States. Such analyses have not previously been conducted in the literature on these diseases, or using this variety of data sets. Moreover, Chapter 5 presents research that has not been conducted previously: it assessed if there were differences in how fast zoonotic disease outbreaks were detected, based on who detected the outbreak.

Interestingly, the type of entity appears more important than the sector of the entity, and many variables which may have been hypothesized as significant from previous literature

(such as symptoms or species of origin) are not significant in various “who detects” and

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“how fast” analyses. Despite generalizability concerns and other limitations, this is an important contribution.

In addition to the analyses and evidence presented above, there was another key reason to create an outbreak database: to identify cases for Chapter 6, the case study chapter. This chapter, using outbreaks identified in Chapter 5, reports findings from surveys, legal analyses, and an additional literature review on four outbreaks. Chapter 6 focuses on a series of surveys and interviews with officials involved in zoonotic disease outbreaks. The survey questions are carefully aligned with the overall objectives of this research to ensure Chapter 6 provides meaningful information to elaborate on the statistical analyses in Chapter 5. Together, Chapters 4, 5, and 6 provide key evidence for the construction of policy options and recommendations via a policy analysis in Chapter 7, the conclusion chapter.

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Chapter 6: Case Study Analysis of Selected Outbreaks

The key purpose of this case study analysis is to further understand the practice of detecting and reporting zoonotic disease outbreaks, and the key impediments and facilitators to rapid detection and reporting in a complex and federalized U.S. system. As stated in Chapter 3, the unit of analysis for the case studies is a single outbreak. There is one overarching research goal of this chapter: to identify ways to improve zoonotic disease detection and reporting practice and policy. In order to support and provide evidence for this goal, there are two key objectives of this research: 1) to identify impeding and facilitating factors for rapid zoonotic disease detection and reporting, and

2) to identify whether federalism, institutional design, and bureaucratic behavior are important in rapid zoonotic disease detection and reporting. This chapter connects the theoretical underpinnings of this research that are discussed in Chapter 2, including federalism, institutional design, and germ theory, to zoonotic disease detection practice.

The research design of this chapter, explained in Chapter 3, including the selection of cases and interview questions is based on the objectives stated above.

Chapter Organization and Analysis

This chapter begins by describing the situation of the outbreak through the literature review described in Chapter 3. The slowest outbreaks (called cases here) to detection are discussed first in the chapter, and the fastest cases to detection are discussed second. Second, the interview and survey results are reviewed for all cases. This includes the response rates by sector and outbreak category („slow‟ or „fast‟). Third, this chapter discusses the legal factors that may impact zoonotic disease detection and reporting—

310 these factors are investigated based on their initial identification through the survey/interview step. Finally, the discussion section summarizes the findings, comparing the fastest and slowest cases. In addition, this section returns to the theoretical underpinnings of the dissertation—bureaucracy and institutional design and federalism— and discusses these in relation to the findings of this last phase of research.

Table 6.1 again lists the outbreaks which are reviewed in this case study analysis.

Table 6.1: Outbreaks Selected for Case Studies

Year Disease Fastest or Slowest 2000 (added case) Anthrax Fastest 2003 Salmonellosis Slowest 2003 E. Coli (Shiga producing) Fastest (no interviews/surveys rc‟d) 2006 Salmonellosis Slowest 2006 Rabies Fastest

This research was approved by the GWU Institutional Review Board, Exempt

Review #0011107. The interview/survey questions (both interview and survey questions were the same) are again listed in Table 6.2. These tables are in Chapter 3 and are repeated here for quick reference. The interview questions are specifically targeted to ensure the objectives of this chapter are fulfilled.

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Table 6.2: Interview and Survey Questions

1. What were the most important impediments to rapid detection [recognizing that there is an outbreak] and reporting [reporting this recognition of an outbreak to a state or federal authority] of the zoonotic disease [for example, practitioner education, training, laboratory diagnostics, aware individuals, communication, government programs, policies, law, or any other factor]? 2. What were the most important facilitators for rapid detection and reporting of the zoonotic disease? 3. If in the animal health sector, did you contact or consult human health agencies, sectors, entities, or experts during the outbreak? a. If yes, how would you describe this relationship? 4. If in the human health sector, did you contact or consult animal health agencies, sectors, entities, or experts during the outbreak? a. If yes, how would you describe this relationship? 5. Did federalism (the separation of state and federal government) impact the detection or reporting of this outbreak? a. If yes, how, generally speaking? 6. Did institutional design or bureaucracy (for example, procedures and processes of the entity, communication between entities, chain of authority) impact the detection and reporting of this outbreak? If so, how, generally speaking? 7. Why was this disease reported to a state or federal entity? 8. Did past experiences with zoonotic diseases or outbreaks impact the detection and reporting of this outbreak? If so, how, generally speaking? 9. Are there any factors that we haven‟t already discussed that you feel are important to improve zoonotic disease outbreak detection and reporting in the United States?

Slowest Cases

Background

The slowest cases selected here are very similar. Both are cases of salmonellosis, which is often a foodborne infection. However, neither of these cases were transmitted through human foods. The literature indicated the first outbreak (Samonellosis 2006) was detected 452 days from the first case. This outbreak was caused by agent Salmonella enterica, serotype Schwarzengrund. This serotype is uncommon, causing typically less than half a percentage of all Salmonella cases every year in the United States (Behravesh

312 et al., 2010). The outbreak was reported to be initially recognized on May 8, 2007 and was reported by the CDC to the public on May 16, 2007. Symptoms for this outbreak began on or after January 1, 2006 (Centers for Disease Control and Prevention, 2008b).

The Pennsylvania Bureau of Laboratories identified an increase in the PulseNet reports of the S. Schwarzengrund strain that had the same pulsed-field gel electrophoresis (PFGE) pattern, in reporting three cases to PulseNet. (PulseNet is the national network of laboratories that perform standardized molecular subtyping by PFGE.) PulseNet subsequently identified cases in other states, and notified the CDC OutbreakNet team on

June 9, 2007 (Centers for Disease Control and Prevention, 2008a). Eventually, this outbreak was traced back to contaminated dry dog food. By law, the Federal Food, Drug, and Cosmetic Act requires pet food to be free of harmful biological agents. Ultimately, seventy-nine human patients were identified in 21 states (Behravesh et al., 2010). In this outbreak, Pennsylvania had already investigated a small cluster of cases in 2006—and it should be noted, that if more information was provided in the literature, it is possible that a faster detection date may have been identified—but the Department of Health could not find the origin of the salmonellosis infections and did not characterize the cases as an outbreak.

Epidemiologic information traced the agent to a dog-food manufacturing plant in

Pennsylvania, and unopened bags of dry dog food were found to be contaminated with the outbreak strain (Centers for Disease Control and Prevention, 2008b). Because of the

PFGE pattern, the cases were presumed to be of this common source, as no other connection was made between the individuals with the outbreak strain of Salmonella.

Children appeared to be more susceptible (Behravesh et al., 2010). Eventually, as of

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October 1, 2008, after one previous and unsuccessful attempt at cleaning the plant to prevent future contamination, Mars Petcare US closed the dog-food producing plant in

Pennsylvania permanently. No pet-related illnesses were documented in the CDC or

FDA investigations (the FDA regulates pet food), though S Schwarzengrund was isolated from fecal specimens of dogs in the homes of ill individuals infected with Salmonella

(Centers for Disease Control and Prevention, 2008a; Journal of the American Veterinary

Medical Association News, 2007).

The second „slow‟ outbreak (Salmonellosis 2003) was detected 251 days after the first symptoms presented, according to the data gathered in Chapter 5. Linked to pet rodents, cases were recognized in both humans, mice, and hamsters (Centers for Disease

Control and Prevention, 2005). This outbreak was caused by the agent Salmonella enterica, serotype Typhimurium (Centers for Disease Control and Prevention, 2005).

This serotype was resistant to multiple antibiotics, including ampicillin, chloramphenicol, streptomycin, sulfisoxazole, and tetracycline (Swanson et al., 2007). Symptoms began on or after December 23, 2003, and continued to September 28, 2004 (Centers for Disease

Control and Prevention, 2005). On August 30, 2003, a veterinarian from a pet distributor called the Minnesota Department of Health about two isolations of Salmonella, after approximately 60% of the hamsters from the distributor had died. Based on the identification of two patients in South Carolina and Minnesota, a thorough review of

PulseNet revealed the other cases with a matching PFGE (Centers for Disease Control and Prevention, 2005). According to an epidemiological investigation, the median age of patients was 16 years, and 28 patients were identified with the matching serotype

314 between December 2003 and September 2004 (Centers for Disease Control and

Prevention, 2005; Swanson et al., 2007).

Epidemiologic investigation revealed 28 human cases, of which 59% had primary contact with rodents. The rodents included mice and rats purchased to feed snakes, hamsters, and pet mice and rats. Children are typically more frequently infected by pet rodents because of their close contact with the animals (also see Fuller et al., 2007). The rodents from retail pet stores were traced back to distributors in Georgia, Arkansas, as well as Iowa (Swanson et al., 2007). The pet distributors did give the rodents antimicrobials, typically in drinking water (Bakalar, 2005; Centers for Disease Control and Prevention, 2005). No link was established between these three primary distributors of the rodents with Salmonella, serotype Typhimurium.

In both of these cases, the PFGE subtyping was unusual and the outbreak was characterized by reviewing the PFGE patterns through the PulseNet database. For example, in the Salmonellosis 2003 outbreak, this Salmonella serotype with this PFGE pattern is unusual in PulseNet (approximately .1% of all S. enterica) (Swanson et al.,

2007). If PulseNet had not been in existence, it is unlikely that previous cases in either of these outbreaks would have even been identified. In other words, retrospective identification of the same PFGE patterns would not have been possible.

Furthermore, there were specific factors in common with these two cases which led to the outbreaks going on for an extended period of time prior to detection that are identified through this review. In the first case, the state of Pennsylvania had investigated a “small cluster” of S Schwarzengrund infections in 2006. However, preliminary investigation had not identified the source and epidemiological links had not been made

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(Behravesh et al., 2010). In part, this may be attributed to the fact that, like in the

Salmonellosis 2003 outbreak, many individuals with salmonellosis type symptoms may not seek medical treatment and therefore will not be diagnosed. This leads to underreporting, as discussed in Chapter 5. This was also the case in the Salmonellosis

2003 outbreak. There was a relatively small number of cases, at least those that were detected and reported. Again, it is likely that many human cases went unreported, and even more rodent cases (Dakss, 2005). Underreporting—particularly on top of an already small number of cases—may lead to a lag in detection, because the clusters are not large enough to indicate a problem of concern, and not all salmonellosis cases may be able to be followed up until the origin is found due to time and resource constraints.

Second, in both of these cases, there was a wide distribution of cases, both in space and time. In coordination with the small number of cases, this may also contribute to the lag in detection. These factors impact what could be called the perceived severity or intensity of the outbreak. The severity or intensity of the outbreak was one of the factors that was indicated by the literature in Chapter 3 as important for detection and reporting; number of cases, case fatality, and incidence rates are also discussed in

Chapter 5 (though there was not sufficient information for a thorough analysis of all outbreaks). In the Salmonellosis 2006 outbreak, the report specifically notes the wide, national, distribution of these products, but the relatively small number of cases, stating that the attack rate of this outbreak “appears to be low” (Centers for Disease Control and

Prevention, 2008b). Moreover, salmonellosis typically does not have a high case fatality rate in humans—though in the Salmonellosis 2003 outbreak, the hamsters were not so fortunate. In the Salmonellosis 2003 outbreak, again the cases occurred over an extended

316 period of time. As the MMWR report states, the outbreak cases were “dispersed temporally and geographically” (Centers for Disease Control and Prevention, 2005).

While it is fortunate that neither of these outbreaks was particularly severe in humans, and that salmonellosis typically does not have a high fatality rate in humans, the small number of cases distributed both over large areas and through many months may have also resulted in a delay in the time to detection.

It is interesting to note that by the time the Salmonellosis 2003 outbreak in hamsters was reported, over 60% of the hamsters in that particular hamster population had died. While not documented formally, one may assume that numerous infected hamsters had already been shipped to distributors and on to pet stores and to customers at this point. It is possible that earlier reaction to dying hamsters may have also sped up the time to detection in this instance.

In addition to the two factors—the small number of cases and geographic and temporal distribution of cases—another important factor was identified in relation outbreak response, particularly in the Salmonellosis 2006 outbreak. This outbreak was the first documented instance of a Salmonella outbreak in humans which is attributed to dry dog food and a contaminated pet food plant (Centers for Disease Control and

Prevention, 2008b). While there have been other recalls of pet food and contaminations with Salmonella, no human illnesses have been reported in these instances from dry dog- food (Center for Infectious Disease Research and Policy, 2008; Finley, Reid-Smith, &

Weese, 2006; Reinberg, 2008). Notably, while this disease is considered to be zoonotic, the transmission pathway in this case was food-borne rather than from direct contact with an animal. Subsequent epidemiological investigation suggested that the transmission of

317 the agent from the dry pet food to the human was the result of very specific practices, such as storing pet food in the kitchen area (Behravesh et al., 2010).

Summary of Slowest Cases

PulseNet appeared to be fundamentally important for detecting these outbreaks of salmonellosis, even if the detection was delayed. The outbreaks were not severe in humans, resulting in no human fatalities and relatively few cases. In animals, the outbreak Salmonellosis 2003 did result in numerous fatalities (>600) in pet rodents. In these outbreaks, the low number of human cases in coordination with their distribution both spatially and in time, as well as the possible delay in reporting hamster deaths, may have contributed to the delay in the initial detection of the outbreaks. In addition, the novel transmission pathway may have been a factor that slowed the response in the

Salmonellosis 2006 case.

Fastest Cases

Background

There were three cases that were reviewed for the “fastest cases”. These cases are referred to as Rabies 2006, E. Coli 2003, and Anthrax 2000. These cases were more diverse than the slowest cases, involving a variety of biologic agents and transmission pathways.

The first outbreak, Rabies 2006, was a single case of rabies in an 11 year old boy in California. The boy had recently immigrated from the Philippines in early October,

2006 (Blanton, Hanlon, & Rupprecht, 2007; Centers for Disease Control and Prevention,

2007). The first symptoms were reported on November 15, 2006 and rabies was the

318 suspected diagnosis late the next day, on November 16, 2006. Rabies was confirmed by a laboratory on November 18, 2006. The patient‟s parents were not aware of any animal contact, though siblings recalled the boy had been bitten by a dog approximately 2 years before. No rabies post-exposure prophylaxis was given at that time. The patient did not recover, and was removed from life support on December 13, 2006 (Centers for Disease

Control and Prevention, 2007) .

Rabies is not frequently seen in humans in the United States. In this case, the speed of detection was improved due to confounding risk-factors identified by the clinician, particularly recent immigrant status (Centers for Disease Control and

Prevention, 2007). In fact, of 6 human rabies cases across the entire state of California in

2009, 4 were the result of exposure outside of the United States (California Department of Public Health, 2009). In addition, rabies progresses very rapidly, and though not common in the United States, clinical signs and indicators are typically consistent and can be recognized by alert providers, as they were in this case.

The second outbreak, E. Coli 2003, had little coverage in media, disease reports, or peer reviewed articles (only one report was located on ProMed-mail); no individuals responded to the request to interview. This outbreak was a case of two children in

Pennyslvania, who both were culture-positive for E.Coli 0157:H7 (Escherichia coli)

(ProMed-mail, 2003). The first case presented symptoms on August 2, 2003, and cultures returned positive for E.Coli 0157:H7 on August 3 2003. While both children had exposure to a single petting area at the Philadelphia zoo, a potentially common source of

E. Coli for children, the samples from the animals all cultured negative for E. Coli (for example, see Centers for Disease Control and Prevention, 2001b; E.Coli Suspected in

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Illness of 21 Children," 2000). Though clustered in time and place, the epidemiological investigation did not find a connection. Because of the lack of information, this case is not discussed further in this chapter.

The third case, Anthrax 2000, was a single human case of anthrax (Bacillus anthracis) that occurred in connection with widespread outbreaks of anthrax in cattle across the state of North Dakota, where there was a 10 fold increase in anthrax cases in that year (Centers for Disease Control and Prevention, 2001a). It should be noted that this epizootic is defined by some as an outbreak (Hatfield, 2005; Kirk & Hamlen, 2000); in some cases the distinct clusters of anthrax are not clearly epidemiologically linked. This epizootic in animals affected counties in North Dakota which historically did not see cases of anthrax (Texas Animal Health Commission, 2001). Over 150 cows died; it is not clear from the articles, media coverage, or disease reports if any were infected and recovered, though the high fatality rate of cattle is well documented (Mongoh, Dyer,

Stoltenow, & Khaitsa, 2008). The reason for the spike in anthrax cases in 2000 in North

Dakota was not fully identified, according to many experts (ProMed-mail, 2000).

However, many factors are known to have a role, including, potentially, wildlife migration, temperature, humidity, vaccination status of cattle, and antibiotic use (Mongoh et al., 2008).

These specific herd and human cases are considered as a distinct outbreak, by the definitions provided in Chapter 5. In this case, this specific outbreak was detected rapidly

(2 days) in both humans and in livestock. It is unclear if the herd had been diagnosed with anthrax when the individual contracted the disease, though the risk factors and potential for anthrax were recognized by both the individual and his physician provider,

320 particularly given the ongoing epizootic in other areas of the state. Therefore, the time to detection (2 days) is based on this specific herd outbreak and this single human case of anthrax. This human case was confirmed by the CDC in October, 2000.

It may be important to note that this rapid detection of anthrax occurred even prior to the 2001 attacks using Bacillus anthracis in the United States. In fact, it was the first case of cutaneous anthrax in the United States for eight years (Centers for Disease

Control and Prevention, 2001a). While anthrax is not uncommon in North Dakota cattle, the geographic spread and significantly increased incidence in 2000 was unexpected.

The ongoing epizootic of anthrax in North Dakota during 2000 indicated the presumptive diagnosis of anthrax for the livestock herd. The handling of dead cattle, likely suspected of having anthrax, likely facilitated the presumptive diagnosis of anthrax by the physician who saw the patient.

Summary of Fastest Cases

In the Rabies 2006 outbreak, health providers well aware of rabies symptoms combined with knowledge of patient history (recent immigration from country where rabies is common) led to a rapid detection. Again, in the Anthrax 2000 case, an aware provider, combined with knowledge of the patient‟s history, facilitated the detection of the outbreak. In addition, the knowledge of the ongoing anthrax problem in North

Dakota likely sped the presumptive detection of anthrax in the patient‟s cattle herd.

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Interview and Survey Results

Responses

Twenty-two individuals were identified from the peer reviewed articles and disease-reports, as described in Chapter 3. These individuals were contacted and asked to participate in the study. One email was returned, so in total, 21 individuals received requests to participate. In addition to these individuals, an additional nine individuals were referred by individuals who participated in the study. These nine additional individuals contacted me with their interest and were invited to participate after being asked by an original respondent. In total, 30 individuals were invited to participate in the research.

The response rate was 18/30, or 60%; an additional three individuals replied saying they were not willing (for a variety of reasons) to participate in the research

(21/30, or 70% of the study population provided some type of response). Reasons given for inability to participate included being out of the country and that the outbreak was too long ago to remember. This response rate is reasonable for a survey-type instrument

(Newcomer & Triplett, 2010). Of these 18 respondents, 9 were interviewed by phone and 9 completed the online survey instrument. Because the questions were the same, and notable differences were not observed in how individuals answered the online survey versus the telephone interview, the results from both modes are combined.

The breakdown in the individuals who participated in this research is listed in

Table 6.3. Response rates are listed in parentheses. Public health veterinarians who are seated in health departments are listed in the “health” category. Local individuals can include individual practitioners as well as officials at the local or county level. The

322 categories assigned are based on position during the outbreaks described above, or current position if not involved in one of the outbreaks. The response rates for local/health and local/animal categories are much lower than the other categories, so the local and county level officials and frontline providers are under-represented in this survey. Response rates appear to be fairly even for human health and animal health categories. Percentages may not sum to 100% due to rounding.

Table 6.3: Response Information for Interviews and Surveys and as Percentage of

Potential Respondents

Who Local State Federal Total

Human 2 (of 5, 40%) 7 (of 9, 77%) 2 (of 4, 50%) 11 (of 19, 58%) Health Animal 0 ( of 2, 0%) 5 (of 7, 71%) 2 (of 2, 100%) 7 (of 11, 63%) Health Total 2 (of 7, 29%) 12 (of 16, 75%) 4 (of 6, 67%) 18 (of 30, 60%)

Table 6.4 separates the respondents based on whether they worked in a laboratory or played a role in either an official capacity or as a practitioner. Further disaggregation of these results is not possible due to low frequency counts and possible identification of subjects. Again, while there were far fewer numbers of laboratory individuals identified to participate, their response rates are not significantly different. Laboratory individuals who participated in the outbreak were more difficult to identify from the published accounts.

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Table 6.4: Response Information for Laboratories and as Percentage of Potential

Respondents

Who Total

Laboratory 3 (of 6, 50%) Official or 15 (of 24, 63%) Practitioner Total 18 (of 30, 60%)

Table 6.5 further disaggregates the response rate by the fastest and slowest cases.

Because some of the individuals referred by interviewees were not linked directly to either case, they are listed in the “other” column. There were two individuals who were not linked to the outbreaks that are studied here, but for which there are data from the outbreak database on how fast they were detected. These outbreaks are not identified here to maintain confidentiality because they are the only respondents. They were allotted into the “fastest” and “slowest” categories (one for both „fast‟ and „slow‟); these outbreaks were very close to the outliers identified as the case studies (in the same quintile), and for this reason, do not threaten the representativeness of these results. These two additional outbreaks were also the result of biologic agents not involved in the outbreaks identified as „fast‟ or „slow‟, and therefore added more generalizability to the results, since initially 2 of the cases were of the same agent (salmonellosis).

Table 6.5: Response Information for Fastest and Slowest Cases

Total (as Percentage of Total Interviews) Fastest 8 (44%) Slowest 6 (33%) Other 4 (22%)

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Results from Interview and Online Survey Instrument

This section presents the results of the interviews and online surveys. It is important to note that recall-bias is an important limitation to the study, and attributing the findings to a specific outbreak should be done with care. The individuals interviewed have vast amounts of experience, and often, even while discussing one outbreak in particular, explicitly drew on knowledge from other outbreaks to answer the questions. It is for this reason that care is taken to not overstate the distinctions between „fast‟ and

„slow‟ cases. While recall-bias is a potential limitation, it may also benefit the generalizability of the results, as they are drawn from a wider range of experiences. It is also important to note that local and county health officials are under-represented in this research due to the difficulty in identifying them as potential subjects from peer-reviewed articles and outbreak reports. This is a potential limitation to the generalizability of the research.

Impediments and Facilitators

There were a number of key impediments and facilitators that were widely mentioned by the respondents as being important in zoonotic disease outbreak detection and reporting. The top three impediments and top three facilitators are listed in Table

6.6.

The most predominant answer when queried about impediments to detection and reporting of zoonotic disease outbreaks was diagnostic capabilities. Respondents referred to a range of limitations, from limited diagnostic tests that are widely available to the lack of personnel trained to collect and test samples. Other factors that made the “top three” list of impediments included a lack of knowledge or obligation to report the zoonotic

325 disease to a government official. The third impediment identified, is a tie between two factors: (1) the lack of awareness or astuteness among providers to rapidly recognize particular symptoms and consider disease agents which may not be obvious or patterns that may not be apparent for epidemiologists, and (2) funding, which is more self- explanatory. Without funding, training, education, and resources like diagnostics cannot be attained. Of these four impediments that were identified as the most common responses, respondents linked to the „slow‟ outbreaks were more concerned about the latter two as impediments, and respondents linked to the „fast‟ cases were more concerned about the first two, as seen in Table 6.6.

Table 6.6: Top Three Impediments for Outbreak Detection and Reporting

Impediment Total Percentage of For ‘Fast’ For ‘Slow’ all Respondents Outbreaks Outbreaks (n=18) 1.Knowledge/ 44% 38 % 17% Obligation about Reporting 2.Diagnostics 33% 50% 17% 3.Tie: Astute, Aware, 22% 25% 33% Capable Provider 3.Tie: Funding 22% 13% 33%

Table 6.7 provides the top three facilitating factors for rapid outbreak detection and reporting. Similarly, the predominant facilitator was diagnostics capacities and capabilities. However, respondents linked to „fast‟ cases reported this as a facilitator more often than respondents linked to „slow‟ cases. Communication and collaboration between agencies, practitioners and laboratories was also a frequently cited facilitating factor identified by all respondents. Third, astute, aware, and/or capable providers were

326 again listed as an important facilitator for rapid detection and reporting by 33% of respondents: by 2 of 6 of the respondents linked to „slow‟ cases, and by 2 of 8 of the respondents for „fast‟ cases.

Table 6.7: Top Three Facilitators for Outbreak Detection and Reporting

Facilitator Total For ‘Fast’ For ‘Slow’ Percentage of Outbreaks Outbreaks all Respondents (n=18) 1.Diagnostics 44% 63% 50% 2.Communication and 38% 50% 50% Collaboration between Agencies, Practitioners and/or Laboratories 3.Astute, Aware, Capable 33% 25% 33% Provider

Discussion of Impediments and Facilitators

As would be expected given the results in Chapter 5 (laboratories detected the most outbreaks), some aspect of diagnostic testing and laboratory capacity was identified by 89% of respondents as either a facilitating factor (if good) or as an impediment (if lacking), particularly for detecting, confirming, and reporting a zoonotic disease outbreak

(the separation of these steps was not further explored in this survey). The need for good diagnostics for rapid detection was also identified in Chapter 2 as fundamentally important for biosurveillance activities, from the theoretical framework of germ theory in public health practice. In the survey responses, the comments regarding the diagnostics

„category‟ ranged from noting the automatic laboratory reporting of results, to actually having the appropriate diagnostic tests for confirming and reporting the disease, to having the funding to serotype cases. In many of these cases, the respondent listed one aspect of

327 diagnostics as an impediment (such as getting samples), and another as a facilitator (such as the availability of a good diagnostic assay). For the individuals who responded in reference to a „slow‟ case, 3 of 6 cited laboratory capacities during the outbreak as a facilitator.

In particular, the respondents linked to a „slow‟ outbreak cited the PulseNet system as particularly important for detecting outbreaks. This system is specifically for food-borne disease causing pathogens, like salmonellosis and e-coli. It can help identify outbreaks using standardized fingerprinting via pulsed-field gel electrophoresis (PFGE).

Notably, none of the other respondents replied in reference to another food-borne outbreak, so perhaps laboratory capabilities as a facilitator is unique to these pathogens rather than the „slow‟ cases. Certainly PulseNet is unique not to the „slow‟ cases, but to the type of pathogen in question.

There are some differences between the responses of those linked to „slow‟ versus

„fast‟ cases, in addition to the diagnostics factor which was just discussed. The knowledge and obligation to report was predominately reported as an impediment by respondents linked to „fast‟ cases rather than those linked to „slow‟ cases. Arguably, this may be a result of the disease agents and their reporting mechanisms rather than how fast the outbreak was detected. In other words, both of the „slow‟ outbreaks were food-borne and reported through electronic laboratory reporting pathways, rather than the fast outbreaks which were reported by providers.

From these results we can conclude that appropriate diagnostics and diagnostic laboratory capabilities are critical to timely zoonotic disease detection and reporting, and that many respondents feel as if the capabilities are not currently sufficient. This

328 confirms the results of Chapter 5 and findings regarding “who detects.” Further exploration of what specific capabilities and needs exist would be an important next step.

While detection and reporting was not disentangled in this research, diagnostics could be important for both detection (confirming the agent) and reporting (reporting confirmed results to an official).

Though communication and collaboration was cited equally as a facilitator by both respondents linked to „slow‟ and „fast‟ outbreaks, it was cited more frequently by individuals in the animal health sector than individuals in the human health sector as an important facilitating factor for detection and reporting of zoonotic outbreaks. For example, one respondent cited that good communication was “critical in zoonotic disease outbreak detection, but also response.” Of the seven respondents from the animal health sector, 57% cited this as an important facilitator. Only 27% of the human health sector respondents (n=11) cited this as an important facilitator. Certainly the sample sizes are small, but this remains an important finding, particularly in the consideration of policy recommendations.

Of the factors cited as impediments and facilitators, there are three that may be linked to federalism or institutional design, which we will discuss later. First is the knowledge or willingness to report diseases that are reportable, second is the communication and collaboration between agencies and groups, and third is funding.

While certainly all the factors may in some way be connected to federalism and the design of our bureaucratic institutions, the other two impediments and facilitators—the diagnostic capacity and aware/astute providers appear to be more a function of technical abilities, training, and education (perhaps also allocation of resources) than specifically

329 related to institutional design, bureaucratic behavior, or federalism. Federalism, institutional design, and bureaucratic behavior are discussed in a later section of this

Chapter.

Collaboration between Animal Health and Human Health

Considering the interesting finding that more animal health than human health individuals responded that collaboration and communication was important, the next question provided important insights into which groups though that collaboration and communication between the sectors had gone well, or not as well. Comments on the collaboration between human health and animal health appeared to be very diverse— from responses like the relationship was “miserable” and “contentious” to, “we worked great together” and “there were and are no issues.” However, nearly all respondents (16 of 18, or 89%) noted that they felt as if this issue varied dramatically by state, and some states were known for working much more successful collaboration between their animal health sector and human health sectors than were others.

Of seven respondents from the animal health sector, four (57%) indicated that they had a good working relationship with colleagues, agencies, and entities in the public health sector. For example, one respondent stated that the relationship was “great, I feel completely comfortable to call my colleagues at any time, even just to chat and get a second opinion.” The other two respondents that reported interaction indicated in different ways that they felt they had tried to open avenues for collaboration and cooperation, but that it wasn‟t always successful, that it took time and patience, or simply that they realized that a lack of cooperation can slow and cause problems in outbreak characterization and reporting. Of the 11 respondents from the public health/human

330 health sector, seven (63%) respondents characterized the relationship between the sectors as “very good”. The other three respondents from the human health sector indicated problems in the relationship, and another respondent did not suggest interaction occurred.

The individuals that reported that there were problems in the relationship between the animal health and public health sector were predominately linked to the „slow‟ cases

(four of six respondents, or 67% of those reporting a problem). Interestingly, most of these respondents reported that the critical problems were really in the response to the outbreak, rather than in the detection and reporting of the outbreak. Though this sample size is small, more individuals did report interaction between the sectors—perhaps because of the population surveyed (those involved in zoonotic diseases)—than in past research (Lipton, Hopkins, Koehler, & DiGiacomo, 2008)

Despite findings indicating that problems certainly exist, some were respondents suggested that there were very good working relationshisp, and many were very enthusiastic about the model of cooperation and collaboration they had enjoyed in their state. In outbreaks, many provided observations of bilateral press releases and publications to ensure that one message was going to the public. Many noted that they worked together regularly, and had developed important working relationships. The respondents who identified the relationships in a negative light were not as willing to give examples, though remarks were made about others “being secretive about mutually important information” and not working towards a common goal successfully.

Results and Discussion of Federalism

One of the most surprising findings of the interviews and online surveys was the overwhelming response, when specifically asked, that federalism did not play a role in

331 the detection or reporting of the outbreak. Only one individual suggested that federalism did play a role in detection and reporting of a zoonotic disease outbreak (6%), and this respondent did not suggest a negative or positive role of federalism, simply that it did impact the detection and reporting because of federal requirements for certain diseases.

However, an additional three individuals (split between the „slow‟, „fast‟, and „other‟ cases) did suggest that while federalism was not important in detection and reporting, it was critically important in response efforts. Most of the respondents who suggested federalism impacted response replied that the problem typically emerged in the collaboration between state and federal agencies, typically involved „policy territory‟

(i.e., who had authority), and that federal to federal or state to state (inter and intrastate) communication and collaboration was less problematic. Federal-state problems in communication may well relate not only to federalism, but institutional design and the impact of federalism on bureaucratic behavior, that is, conflicting objectives among the agencies.

The separation of state and federal powers, the separation of government between local and state or federal powers also emerged as a concern among respondents. Four individuals (22%), equally distributed across „slow‟ and „fast‟ cases suggested that not enough deference, communication, and resources are provided to individuals at the local level, including both practitioners in the field as well as local public health officials.

Local-level individuals and officials are certainly the front line of any detection and reporting of disease outbreaks, and challenges they face in communicating to higher level government officials about a variety of subjects certainly emerges as a concern from this research.

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From these results, it appears that the separation of state and federal powers is not a significant concern to those involved in detecting and reporting zoonotic disease outbreaks. However, federalism may be important in response to zoonotic disease outbreaks. The results from this survey/interview instrument do seem to indicate that respondents identify federalism as important in practice, even though they may not state it explicitly.

Results and Discussion of Institutional Design and Bureaucratic Behavior

In comparison to federalism, more respondents suggested that bureaucracy and institutional design do play a role in the rapid detection and reporting of the disease outbreak. Fifty percent of respondents suggested that institutional design and bureaucratic behavior played a role. Of those, two specifically noted that it was more important in the response phase than in the detection and reporting phase. For example, one respondent stated that bureaucratic processes impacted “how the epidemiological investigation was executed.” Of the 9 respondents who suggested that institutional design and bureaucracy was important, 33% of these respondents are in the animal health sector and 67% in the human health sector. Of the 9 respondents, 33% were from the „slow‟ cases, 22% from the „other‟ cases, and 44% from the „fast‟ cases. While the sample sizes are quite small, it is interesting to note that more respondents linked to „fast‟ cases rather than „slow‟ cases replied that institutional design and bureaucracy impacted detection and reporting.

Perhaps even more interesting is the fact that of the nine respondents mentioning institutional design or bureaucracy, four (44%) noted that they felt as if the institutional design and bureaucracy was a positive factor in practice: three suggested it was negative, and two others thought it was both. In all, 67% of the nine respondents thought that

333 institutional design/bureaucracy did or could play a positive role in zoonotic disease detection, reporting, or response. Some specifically cited their own state system as a

„model‟ of a bureaucracy that was effective for practice, because of “centralized disease reporting” or a “streamlined system”. However, concerns were also noted about “siloed” bureaucracies and as one respondent stated “misguided budget priorities”. These responses confirm the suggestions in Chapter 2, that „compartmentalization‟ can be problematic in bureaucracies. Some states may be leaders in zoonotic disease detection and reporting practice through the development of effective institutions and bureaucracies. However, in zoonotic disease detection and reporting practice, there remains some concern about the stove-pipe nature of bureaucracies and the inability or unwillingness to realign priorities with actual threats to animal health or public health.

Results and Discussion of Reporting

From Chapters 2 and 4, it is clear that there are different points of view on reportable diseases and the effectiveness of reportable disease lists in actually getting diseases reported to a state entity. This was definitely noted from the responses to this instrument. When asked why the disease case was reported to a state or federal entity, 11

(61%) respondents noted it was because the disease was on a reportable disease list. Five individuals specifically cited that it was “state law” to report. Four respondents (22%) suggested it was the “urge” or “obligation” from a professional or ethical standpoint to report, in addition to or instead of the fact that the disease was reportable by law. In terms of reporting to the federal level, one individual noted that it was reported to the federal level because of the desire for assistance, two others suggested it was just a matter of practice or policy (not necessarily law) that the federal authority is notified. In

334 addition to these comments, three respondents of the 18 respondents (17%) noted that individuals are unlikely to want to report or to actually report to federal authorities, and prefer to report either locally or to the state (these numbers and percentages are not mutually exclusive because respondents often cited more than one reason or discussed more than one thing).

Four respondents (22% of all respondents) also noted that laboratory submissions were automatically reported. These responses were distributed relatively equally over

„fast‟ and „slow‟ cases and across human and animal health respondents. However, the electronic reporting remarks predominately came from those involved with the „slow‟ cases, which makes sense, considering the disease agents involved in those cases were part of the PulseNet system, as discussed.

In addition to noting that disease cases were reported because the disease was listed on state reportable disease lists, a subset of respondents voiced strong criticism of reportable disease lists and stated they were not effective and did not work. Three individuals (17%) contended that reportable disease lists were not effective and as one respondent put it, “largely ignored”. There was more than one suggestion that better active or syndromic surveillance could catch those cases at the state level without relying on practitioner (physician or veterinarian) reporting.

Results and Discussion of Past Experiences

When asked if past experiences impacted how zoonotic disease outbreaks were detected or reported, 12 respondents said „yes‟ (67%), 4 said „no‟ (22%), and 2 said „I don‟t know‟ (11%). Of those that said yes, 58% were from the human health sector. Of those that said yes, 41% were involved with the „slow‟ cases, in comparison to 33% from

335 the „fast‟ cases (the remainder were the „other‟ cases). This provides slight evidence that, despite being „slow‟, the slower cases valued past experiences with outbreaks more than those involved in „fast‟ outbreaks. It is interesting that some individuals did say that past experiences didn‟t play a role, though in many cases, this was interpreted to mean that the detection and reporting was simply “routine” and because it was not out of the ordinary, usual protocols were followed. Certainly, past experiences, development of effective processes, and institutionalization of relationships did seem important to the majority of respondents.

Results and Discussion of Other Factors

Table 6.8 lists the three factors most commonly cited by respondents as other important factors for the improvement of zoonotic disease outbreak detection and reporting practice in the United States. These responses are not further disaggregated by

„slow‟ and „fast‟ cases because this was an open-ended question and not one attributable to the case, and was presented to the respondents as a general query. One of the most critical factors discussed by nearly all those interviewed (this was one interesting difference between the two modes), was that seven of nine interviewees (78%) remarked on the importance of interpersonal relationships in cross-sectoral collaboration—knowing the person on the other side of the phone, being able to call them not only during an outbreak, but as needed for professional expertise or just a second-opinion. The importance of interpersonal relationships was also mentioned by survey respondents three times. In all, 67% of respondents specifically cited how important interpersonal relationships were in practice. Certainly this call for better collaboration and cooperation

336 via relationships is not new, and is also related to institutional design and bureaucracy

(for example Kahn, 2006; Keusch, Pappaioanou, Gonzalez, Scott, & Tsai, 2009).

The second most common improvement suggested was the education and training of the public, as well as of practitioners about diseases, reporting, and how to deal with a potential zoonotic disease case. Finally, resources were reported by three respondents as important to improve zoonotic disease detection and reporting practice in the United

States, particularly for diagnostics and better surveillance. Other factors which were identified by respondents included trust of government and regulatory actions (two respondents), sharing data (one respondent), and surveillance systems (two respondents).

The implementation of these recommendations will be complex, but can start in veterinary and medical curriculums, to encourage more awareness of zoonotic diseases and relationships between the sectors.

Table 6.8: Top Three Recommendations for Improving Practice

Improvements to Practice Total Percentage of all Respondents (n=18) 1. Interpersonal Relationships 67% and Communication 2. Education-Practitioner and 22% Public 3. Resources 16%

Further Observations

One of the most important results of the interviews and surveys was the strong contention from the respondents linked to „slow‟ cases that these outbreaks were really indications of best practices and demonstrative of how the system works—not how it doesn‟t. They provided evidence for such claims, including that if PulseNet and

337 electronic laboratory reporting didn‟t exist, these outbreaks never would have been identified at all. In other words, without these systems that have developed over time, many outbreaks, especially foodborne, may not have been detected. The long time to detection in the „slow‟ cases clearly appears to represent a success in identification of the outbreak, rather than a failure to quickly detect. Time to detection may be important, but it certainly is not the only important characteristic; success in detection should not be understated.

In addition, respondents pointed out that we can‟t prevent disease, and never will be able to. For this reason, it may not be important to capture every disease or detect every outbreak—perhaps preventing the spread and amplification of disease should be the focus, rather than preventing bacteria or viruses in general. This was an important take-away from these interviews and surveys—what do we want to detect? How important is it to detect every outbreak? Conversely, other respondents suggested detecting as many cases as possible is critical to preparing for and rapidly detecting emerging diseases. The idea that we need to develop baseline data to which we can compare disease trends over time also emerged, though two respondents also suggested that aggregated data—often all that is available in large electronic reporting systems— may not be all that useful because they may not tell the whole story. One other respondent noted that this data is great, but “no one ever has the time to make it useful.”

However, in order to rapidly identify changing trends or bioterrorism events, the argument can be made that it is critically important to have baseline trend levels on specific disease agents. We detect disease not for the sake of detection, but to inform science, policy, and to prepare for an emerging disease outbreak or bioterrorism event in

338 the future. That said, it is important to remember that disease will always be in our world, and our obsession with detection should not result in misaligned priorities for resource allocation.

Discussion of Instrument, Respondent Categories, and Results

While respondents linked to the „fast‟ and „slow‟ cases did voice some different perspectives, in many cases, there were not striking differences. Moreover, sometimes respondents would reply that for a certain question, the best case they could remember as an example was not the case in question, but a different zoonotic disease outbreak.

Perhaps this reflected recall-bias, as most of these individuals have participated in dozens upon dozens of outbreaks. So while it is important to not overstate the „fast‟ and „slow‟ case differences, the lessons drawn by the survey and interview results are important.

Interestingly, the respondents linked to „fast‟ cases did report more frequently that the lack of diagnostics was an important impediment to timely detection and reporting; they also reported that diagnostics was an important facilitating factor more frequently than the respondents linked to „slow‟ cases.

In terms of the differences between the sectors, one of the most important differences was the citation of communication and collaboration as an important facilitating factor by animal health respondents far more than human health respondents

(57% to 27%). Responses were otherwise quite similar between the sectors in most cases, including the percentage of respondents reporting that they enjoyed a good relationship with the other sector (57% animal health and 63% human health).

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Legal Analysis

The third segment of these case studies is a brief legal analysis. The purpose of the legal analysis is to assess the impact of laws and regulations and legal issues on disease detection and reporting that were identified through the interviews and survey.

Because reportable disease lists (and their problems) have already been discussed extensively in Chapter 4 as well as in Chapter 2, the focus here is on the other laws and statutes which were identified specifically by respondents.

Overall, legal aspects or specific laws and regulations did not regularly emerge either the interviews or surveys. In terms of federalism, this provides additional evidence to confirm the responses that federalism did not play a role in the detection or reporting of outbreaks: no laws or legal issues were identified here to suggest otherwise. With the help of Stephanie David and Taylor Burke, both from the Department of Health Policy,

GWU, the statutes, regulations, and issues identified by the respondents were reviewed, with a specific eye towards how they impact detection and reporting.

Confidentiality

Three respondents noted that confidentiality and/or privacy as a concern for veterinarians. Confidentiality, described typically in public health as a “person‟s claim to keep private the secrets exchanged in the course of that relationship” seems to be the issue in question here (rather than privacy) (Gostin, 2008, p. 316) While the relationship between patients and physicians is protected, the relationship between veterinarians and their clients can be exposed, often more readily, including by the use of Freedom of

Information Act (FOIA) requests, as was revealed in the interviews and survey. The

AVMA has compiled a list of the provisions for the confidentiality of veterinary patient

340 records for the states, and while many states do require client authorization to release, the provisions of exceptions to this requirement for authorization are typically quite broad

(American Veterinary Medical Association, 2011). In addition, some states do not require client authorization for sharing or releasing the information at all.

Certainly, the lack of confidentiality between a veterinarian and his or her client, or the possibility that the records may be released under a broad „exception‟ provision, is identified in this research as a concern for the reporting of zoonotic diseases. For example, in a large production agriculture operation, reporting may mean that the veterinarian loses the client‟s business, if the reporting results in a quarantine and loss of product movement. It remains unclear how significant of a barrier this is to zoonotic disease reporting in animal populations from these case studies. In addition, it is possible this concern is more relevant when discussing reporting from the state to the federal level rather than from the practitioner level to the state, if federal response is a concern for practitioners because of the repercussions, particularly related to production agriculture.

This legal issue should be explored further (for other legal issues applying to veterinarians, see Babcock, Marsh, Lin, & Scott, 2008).

Specific State Laws and Regulations

Because revealing the state in question could enable the identification of the respondents, the specific state laws will only be discussed generally. In particular, laws or regulations from two jurisdictions were identified, one for a „slow‟ case and one for a

„fast‟ case. In both cases, they were cited by the respondents as facilitators and benefits to the timely detection and reporting of zoonotic disease outbreaks. The first law was a reportable disease regulation which very broadly required the reporting of specific

341 diseases by veterinarians, as well as other diseases which may become zoonotic. The usefulness of including a broad clause such as this is apparent, as other diseases could be easily incorporated under such a clause without the full revision of the regulation in the event of a disease outbreak. Such clauses were relatively frequently observed in Chapter

4, and in an emerging disease outbreak, such clauses may be important to require the reporting of a disease not specifically listed. In covering the unknown, this type of provision ensures that rapid rule-making will not be required in an emerging disease event (see Roush, Birkhead, Koo, Cobb, & Fleming, 1999).

Another regulation was an administrative code first promulgated over 80 years ago: as discussed in Chapter 2, infectious diseases have long been a concern of officials.

In many ways, despite the changes in technology and science, it is interesting to note that the regulation has not been altered dramatically. However, its provisions are quite broad in granting authority to the state department of health. The second piece of legal material, from the same jurisdiction, was a law also enacted over five decades ago.

Again, the intertwined nature of human health and animal health is apparent even in this law, as surprisingly, its definitions not only cover the isolation and quarantine of people, but also of animals, in the same clause. Moreover, it provides broad authority for reportable diseases, including any diseases which may be a significant threat to public health (exact language excluded here so as not to be identifying). Again, this expansive language includes not only known diseases, but also emerging infections. In these cases, the broad authority, long history of the law or code, and the clear indication that the health of humans and animals is intertwined is viewed as a facilitator toward the timely reporting of zoonotic diseases. However, it does raise the question—can reporting clauses

342 ever be so broad that the result is data-overload in reporting systems? Certainly, the more prominent concern is under-reporting, but it may be foreseeable that too much reporting could be just as much of an impediment to outbreak detection as not enough.

Food Safety Modernization Act (FSMA)

Finally, the last legal aspect identified in this research is the FSMA. This act is new, and was recently passed into law, though it remains unfunded (as of February

2011). If implemented, it would provide improved surveillance and detection systems for foodborne illness. This act did not impact the disease outbreaks of this chapter, as those were between 1998-2008. Nor has it been funded, so its consequences are not yet fully known. However, more than one respondent suggested it may impact how foodborne diseases are detected, reported, and responded to (U.S. Congress, 2011).

Of the provisions which would improve detection and reporting for foodborne diseases in the FSMA, there are many. For example, Section 205 provides for the coordination of Federal, State, and local foodborne surveillance systems. Section 203 calls for laboratory integration, including common methods, cooperative work, and collaborative relationships. The CDC, as well as localities and states are also to receive additional grant funding to improve such capabilities. Also, “Centers of Excellence” are to be established under the Public Health Service Act (42 U.S.C. 280g et seq.) for food safety (see Section 399V-5 of the FSMA). All of these sections, in theory, would improve detection and reporting for zoonotic diseases that are also foodborne.

There are other provisions which may change the dynamic amongst federal agencies, and between industry and public health officials. The FSMA has been interpreted as giving more authority to the FDA, particularly for recall authority, and

343 relationships between the CDC, USDA, and FDA will need to continue to improve under the new regulation for improved surveillance and detection. In terms of public health and industry, manufacturers will be subject to additional preventative controls and inspections

(see Sections 101, 104, and 201 as examples).

Certainly this is but a cursory overview of an important new policy that could impact detection and reporting of zoonotic foodborne diseases. Prompted primarily be the Salmonella enteritidis outbreak in 2010 in the United States, in which there was miscommunication amongst federal agencies, it will be interesting to see not only the effects of this new policy, but if it takes a similar miscommunication to prompt disease surveillance and reporting reform and evolution for other diseases, not just foodborne infections.

Discussion of Case Studies

Key Findings for Case Studies

Table 6.9 presents the key findings from the review of media coverage, outbreak reports, and peer-reviewed articles of this case study phase of research. Many of these factors characterizing the cases could be considered impediments (low attack rate), facilitators (immigrant history), or both (rare agent in humans). Certainly, the impediments cited in this section were often confirmed in the interviews and surveys— i.e. the geographically and temporally dispersed cases did make detecting the outbreak more difficult in the „slow‟ cases, and in the „fast‟ cases, astute providers were critical for the rapid detection and reporting of the disease.

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Table 6.9: Key Findings from Literature for Outbreaks

Case Key Factors Severity of Outbreak Salmonellos -First time dry dog-food implicated <100 cases (humans), no case is (2006) in human illness from Salmonella. fatality -Low attack rate. -Rare serotype. Salmonellos -Low attack rate. >600 cases (animals + people), is (2003) -Temporally and geographically no human case fatality, dispersed cases. approximately 60% hamster -No link between distributors. case fatality Rabies -Recent immigrant. 1 case (human), 100% case (2003) -No recent reported suspicious fatality animal contact. Anthrax -First case of cutaneous anthrax 1 case (human), >5 cases (2000) since 1992. (animal) in outbreak, >150 cases -Outbreak part of large epizootic in in epizootic >90% animal case North Dakota. fatality -Dead cattle contact.

However, it is important to note the interviews and surveys identified factors that have been discussed in the literature, as noted in Chapter 2. For example, the importance of diagnostic capabilities and capacities, the knowledge about disease reporting, the communication and collaboration between agencies, and the provider being aware and capable to quickly and rapidly recognize key symptoms or suspect unusual agents were noted by respondents. Some of these factors were identified both as impediments and as facilitators: their absence slows rapid zoonotic disease outbreak detection and reporting, while their presence speeds it.

Table 6.10 summarizes the key interview and survey findings categorized by the speed of outbreak detection. As suggested by the “who detects” findings of Chapter 5, as well as germ theory in Chapter 2, diagnostics were by far the most commonly cited factor that either impedes (if lacking) or facilitates (if good) the rapid detection and reporting of

345 an outbreak. Collaboration occurred widely for all respondents, though the respondents linked to „slow‟ cases were more likely to report difficulties in collaboration with the other sector, particularly in the response phase of the outbreak. Nearly all respondents

(89%) noted that inter-sectoral collaboration and communication varied widely by state in the United States.

Federalism was not widely viewed as important by respondents, while institutional design/bureaucracy was much more so. The question of why disease outbreaks were reported resulted in similar answers among all the outbreaks. In terms of past experiences, respondents from the „slow‟ cases were slightly more likely to say it was important (41% versus 33% for „fast‟ cases and 25% for the others). Reporting, while cited by most respondents, certainly does not work for every state or every agency, as was indicated in the responses. While often citing state law as the reason diseases are reported, reporting also appears to ride on the provider‟s moral, ethical, or scientific obligation to report—an intangible, rather than tangible factor.

As Table 6.10 displays, it seems that while some factors are slightly different across the three categories of cases, respondents gave answers that were more similar than they were different. While respondents linked to „slow‟ cases indicated that those cases were indeed best practice, it appears as if the „lessons learned‟ from this research can be drawn more generally across all zoonotic disease outbreaks. As such, the generalizability of this research is bolstered the respondents‟ ientification of many facilitators and impediments for both the „fast‟ and the „slow‟ cases.

Responses to the last question of the survey instrument, asking respondents to identify improvements for practice in zoonotic disease outbreak detection and reporting

346 are not disaggregated by outbreak category, as respondents were not asked to refer to the specific outbreak (though it is possible that some remained focused on the outbreak in question). The most common result for identified for improvement was “interpersonal relationships”—from 66% of respondents.

Table 6.10: Key Interview/Survey Findings Compared by Outbreak Type

Outbreak Surveys/Interviews Fastest (n=8) -Impediments: Reporting, diagnostics -Facilitators: Diagnostics, communication and collaboration -Collaboration: More likely to respond it was good to very good. -Federalism: Not important. -Institutional Design/Bureaucracy: Most likely to respond it‟s important. -Why Report: Disease list, state law. -Past Experience: Less likely to say important.

Slowest (n=6) -Impediments: Astute providers, funding -Facilitators: Communication and collaboration -Collaboration: More likely to respond it was problematic to it took some work (particularly in response). - Federalism: Not important. -Institutional Design/Bureaucracy: Relatively likely to respond it‟s important. -Why Report: Disease list, state law. -Past Experience: More likely to say important.

Other (n=4) -Impediments: No majority factor -Facilitators: Astute providers -Collaboration: Mixed. - Federalism: Not important. -Institutional Design/Bureaucracy: Least likely to respond it‟s important. -Why Report: Disease list, state law. -Past Experience: Less likely to say important.

Specific Discussion on Theoretical Factors

One of the most important points of this phase of research was to test if the factors identified in the literature are important not only in theory, but in practice. Here, the

347 results are clearly mixed, particularly from the surveys and interviews. Federalism really doesn‟t seem to constitute an impediment for detection and reporting practice for zoonotic diseases, at least on the basis of responses when subjects were specifically queried about the separation of state and federal government powers specifically in reference to detection and reporting (not response). However, federalism may play an important role in response—and this role should be investigated in future research.

Granted, multiple respondents noted that individuals would be even less likely to report if they had to report to the federal level, but that this reporting pathway is currently not law

(and therefore was not identified as a factor impacting the outbreak). Though the separation of the state and federal powers may not have appeared as important to respondents, the separation of local powers from the state level was acknowledged as an issue, particularly in complaints that not enough deference is granted to the local level officials and practitioners, who are the front line responders to zoonotic disease outbreaks.

Though respondents did not explicitly cite federalism, their indication of the importance of state reportable disease lists does, implicitly, implicate federalism and the value that is placed on having individual disease reporting lists (whether for humans or animals) for each state. State reportable disease lists were also identified as an important positive consequence of the separation of state and federal powers, and the ability of states to act as sovereign governments in Chapter 2. Moreover, though respondents overwhelmingly did not identify federalism as a factor that impacted disease detection and reporting, they almost all identified their states as “models” for some aspect of disease detection and reporting. The acknowledged their states for the collaboration

348 between the animal health and human health sectors, the integrated and efficient state health department, or the effective laboratory systems. These „best practices‟ may be an important result of federalism, as each state is allowed to proceed in creating their own systems and institutions, as discussed in Chapter 2. The implicit findings here suggest the benefits of federalism to zoonotic disease detection and reporting practice should not be overlooked, as they are fundamentally important. Moreover, these observations suggest that individuals understand the importance of being able to create and form their own processes, procedures, policies, and laws within their states, even though they don‟t identify this capability as related to federalism.

When respondents were asked specifically about federalism, they said it was not important, yet in open ended comments, respondents suggested it was. For example, respondents stated that “our state processes work exceptionally well, perhaps better than other states.” This is in contrast to institutional design and bureaucratic behavior, which is more clearly indicated as an important factor both explicitly and implicitly by respondents. However, institutional design and bureaucratic behavior can either be a facilitating or impeding factor: well structured bureaucracy or carefully designed institutions can facilitate rapid zoonotic disease outbreak detection and reporting, whether through streamlining of processes, clear pathways of communication, or systems that foster disease reporting and analysis. Institutions that remain siloed (or compartmentalized, as explained in Chapter 2) without clear communication pathways continue to have issues with cooperation and collaboration, as well as lack clear mechanisms of how the different sectors should work together. Institutional design and bureaucratic behavior can be an impediment to rapid disease detection and reporting.

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Being identified as both a potential impediment and a potential facilitator indicates the importance of institutional design for the improvement of zoonotic disease detection and reporting practice. The relative weight of this factor, against other factors identified in this research, was not further investigated in the surveys and interviews. However, from the number of individuals who suggested that institutional design/bureaucratic behavior is important (67%), it certainly is more important than many of the other factors that were identified. Moreover, the comments about coordination among agencies, and overlapping policy interests also confirm the answers to the question asking if institutional design/bureaucracy is important. This finding is further transformed into potential policy recommendations in Chapter 7.

Interestingly, respondents did not comment on enforcement of reportable disease lists in the interviews or surveys. While underreporting of diseases is widely recognized, and a minority of survey respondents suggested that reporting is a problem, enforcement of reportable disease statutes and regulations for either animals or people never became a topic of conversation. Perhaps this is because in a practical sense, this would be nearly impossible to monitor. In addition, this concern may have also been captured when respondents recommended more training and education for practitioners, so they understand not only that they are supposed to report, but why reporting is important for public and animal health. From respondents‟ perspectives, it certainly appears that enforcement mechanisms may not be the solution to the problem of underreporting.

Though many individuals suggested that individuals report diseases because „it‟s in the law‟, the sentiment seemed to be that most of them also felt an obligation to public health to report, and did not report because they feared the consequences from a legal

350 standpoint. The question of how to enforce reportable disease statutes and regulations also leads to a possible argument in support of electronic/automatic laboratory reporting: when a sample is identified, it is reported. While this may delay detection of an outbreak until a confirmation of a biologic agent, it also removes one selective human interaction

(the reporting from the laboratory to the state or local official) from the equation.

Legal

This Chapter also discusses the legal aspects that were identified directly by interview and survey respondents. This brief overview of the legal aspects also uncovers another concern among particularly veterinary practitioners—confidentiality.

With increasing intensive production agriculture, the consequences of confidentiality (or lack thereof) between a veterinarian and a client, particularly when the value of the products or animals reaches in the millions merits more study.

Certainly, while some of the statutes and regulations identified by respondents apply more to response, the statutes and regulations do demonstrate that the connection between human and animal health has been long-recognized. Moreover, these statutes highlight that broad reporting provisions for diseases may be fundamentally important for wider authority, particularly in an emerging disease event. Further research on what types of disease lists are useful in practice, for example, if some provisions are more effective than others, and other aspects of reportable disease lists like time requirements

(and those identified as important in Chapter 4) should be conducted. Reportable disease lists, whether policy or regulation, are indicated by this research to be a vital instrument in disease reporting, though a vocal minority of respondents and past literature identified in Chapter 2 explains some of the issues with relying on these lists for disease reporting.

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Finally, only time will tell what changes the FSMA will bring. While it certainly has the potential to bring important improvements in foodborne disease detection, reporting, and even response, it also has the potential to raise tensions between states and the federal government and perhaps even bring important issues of jurisdiction into the courts. These potential consequences are considered in Chapter 7.

Conclusion

The next chapter provides policy recommendations and a policy analysis, based on a set of criteria. Based on questions driven by the theoretical framework of this research, this chapter provides evidence that federalism seems to be implicitly important even if not readily identified by respondents as critical for disease detection and reporting. Institutional design and bureaucracy seem to be critical not only in theory, but also in practice. The benefits of good institutional design were readily acknowledged by the respondents in this survey, while the negative consequences of a poor structure or highly complex bureaucracy on zoonotic disease detection, reporting, and response are also apparent. Most importantly, these case studies identified many of the same factors important to zoonotic disease detection and reporting as were suggested in Chapter 2.

Federalism (particularly in terms of „best practice‟ states), institutional design, bureaucratic behavior, and germ theory all do play a role in practice.

There are no easy solutions to improve zoonotic disease detection and reporting, and one relevant question that has emerged from this phase of the dissertation research: how important is detecting every case or every outbreak? How can we be sure we capture what will prepare us for an emerging disease, or indeed, identify an emerging disease in the early stages? Moreover, with the difficulties in the response phase of

352 zoonotic disease outbreaks identified in this research, what is the point of detecting and reporting if we don‟t have the resources to respond? The final chapter of this dissertation explores some of these complex issues, drawing on recommended improvements provided by the case study phase of this research to guide policy recommendations.

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Chapter 7: Policy Recommendations and Conclusions

This dissertation conducts important research on zoonotic disease detection and reporting practice in the United States, using federalism and theories of institutional design and bureaucratic behavior to inform and guide the research. Chapter 1 presents an overview of this dissertation, Chapter 2 offers a comprehensive review of both the theoretical and empirical literature. Chapter 3 identifies the methods used in this dissertation. Chapter 4 provides analyses of laws and regulations on animal disease reporting in the United States. Then, Chapter 5 creates and analyzes an outbreak database, identifying who detects zoonotic disease outbreaks and how fast they are detected. Chapter 6 presents the results of case studies, connecting theory to practice and identifying factors facilitating and impeding detection and reporting practice. This final chapter presents conclusions and policy recommendations based on this dissertation research.

This chapter is organized into five sections. First, the purpose of this research is reviewed and summarized, including the strengths and potential limitations of the findings. Second, the key results of Chapters 4, 5, and 6 are summarized and discussed briefly, with particular reference back to the research questions guiding this dissertation.

Third, this chapter presents a policy analysis that assesses key policy recommendations based on important criteria which impact the feasibility and the viability of each policy option. Fourth, this chapter presents avenues for future research.

At the start of this research, I envisioned having a great deal of evidence about how many problems existed in the zoonotic disease detection and reporting system in the

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United States, particularly given the federalized system of governance and complex bureaucracies. While problems certainly exist, and concerns remain, overall, this dissertation provides evidence that zoonotic disease outbreaks are detected relatively quickly, particularly by diagnostic laboratories and practitioners. Moreover, the case studies—driven heavily by the theoretical framework of federalism and institutional design—highlight the fact that even cases that are detected slowly are not „worst cases‟ but important examples of best practices in electronic reporting and advances in diagnostic subtyping. Certainly, there remain critical concerns about the effectiveness and appropriateness of state-reportable disease lists. But respondents—while always offering ways to improve zoonotic disease detection and reporting practice—cited how the

„system‟, however fragmented in a federalized, complex governance system, has in most cases succeeded. Institutional design and federalism in many cases have facilitated rapid disease detection and reporting, which is an important finding to improve this public policy. This chapter summarizes these important findings.

Summary of Research

Zoonotic diseases are a significant threat to public health. From the work of John

Gaunt who used statistics in the 17th century, to William Farr and John Snow who followed the London cholera epidemic in the 19th century, we have long been interested in detecting and responding to infectious disease outbreaks as quickly as possible (Nelson

& Masters Williams, 2007; Nelson & Sifakis, 2007; Susser & Susser, 1996). Now called biosurveillance, these activities (including surveillance, detection, and reporting) work to identify infectious disease cases in a rapid and efficacious manner. Zoonoses are a

355 particularly relevant concern, because nearly 75% of the most recent emerging infectious diseases are indeed zoonoses—passed from animals to humans (Chomel, Belotto, &

Meslin, 2007; Taylor, Latham, & Woolhouse, 2001). Increasing the likelihood of zoonotic disease emergence and resurgence are the growing interactions of humans, domestic animals, and wildlife with faster travel, growing trade, urbanization, and intensified agricultural production (Blancou, Chomel, Belotto, & Meslin, 2005; Daszak,

Cunningham, & Hyatt, 2000). Indeed, SARS and West Nile virus—both significant public health threats in the United States, are zoonotic. Other diseases frequently cited as public health and bioterrorism concerns are also zoonotic, including anthrax, hemorrhagic fevers, tularemia, and others.

In the United States, biosurveillance activities are regulated by local, state, and federal agencies. Federalism, the separation of state and federal powers, means that authority falls to both the states and the federal government per the Constitution. Federal authorities can regulate public health, typically through the commerce clause or through the power to tax. The states‟ authorities to regulate bioseurveillance-related activities are primarily based in the 10th Amendment: all powers not delegated to the federal government are reserved for the states. In the case of public health, authority for public health regulation comes primarily through states‟ police powers (Gostin, 2008). Under these police powers, states can and do mandate disease reporting and also offer other protections to the public from infectious disease threats, such as quarantine and isolation authorities. Subsequently, because of federalism, different states have different regulations regarding the detection and reporting of diseases. States voluntarily report diseases to the federal government.

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In addition to the direct consequences of federalism, federalism has resulted in institutions (state agencies) that are designed by each state, as sovereign governments.

The design of these institutions is influenced by various stakeholders, but more importantly, their design influences the role of agencies in policy and intergovernmental relations (Anderson, 2006; Wilson, 1989). Many different agencies are involved in public health regulation at both the state and federal level, including departments of interior, environment, and agriculture. Institutional design and theories of bureaucratic behavior often indicate competition and problems in coordination across agencies (Kettl,

2004; Weingast, 2005). For zoonotic diseases, this is a particular concern, as coordination is required for effective surveillance, detection, and reporting of diseases that affect both the human and the animal population.

Despite the importance of biosurveillance activities and zoonotic diseases to public health, little research has been conducted assessing the practice of zoonotic disease outbreak detection in the United States. First, though reportable human diseases have been analyzed, reportable animal disease lists had not been reviewed, yet it is important to understand the animal side of the zoonotic disease reporting equation in a federal system where different states have different requirements (Council for State and

Territorial Epidemiologists, 2009a, 2009b; Roush, Birkhead, Koo, Cobb, & Fleming,

1999). Subsequently, the regulations and statutes from the 50 states and D.C. are reviewed in the first phase of this dissertation. Second, though the timeliness of disease detection is very important to public health as indicated by germ theory, research had not been completed specifically on zoonoses, and little research has examined who actually detects the zoonotic outbreaks, and how fast they are detected (Ashford et al., 2003; Dato,

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Wagner, & Fapohunda, 2004). This dissertation creates an outbreak database, identifies zoonotic disease outbreaks and characterizes them by who detected them and how quickly they are initially recognized. Third, despite the fact that federalism, institutional design, and bureaucratic behavior may certainly impact zoonotic disease detection, past literature had not connected the dots between theory and practice, or investigated whether these factors are beneficial or problematic for practice. The third and final phase of this dissertation uses a case study approach with expert interviews to reveal how federalism, institutional design, and bureaucratic behavior influence zoonotic disease outbreak detection practice.

This dissertation makes important contributions to inform more complex models on disease detection and for further stakeholder research on disease reporting, and to provide suggestions for improving policy. As with any empirical research, there are potential limitations to validity and reliability of the findings offered here. These limitations are carefully explained in Chapter 3, and mitigated as best possible given practical constraints.

The key limitation to the legal analysis in Chapter 4 is the possibility that state regulations and statutes describe law, but not necessarily practice. For Chapter 5, the most significant threat to validity is that the results may not be widely generalizable. In

Chapter 6, limitations to interviewee recall present potential limitations to the findings.

The generalizability of this research is limited to U.S. practice only. Despite these limitations, this mixed-methods approach using content analysis, statistical analyses, and case studies with interviews create a comprehensive examination of zoonotic disease

358 detection and reporting practice in the United States. This research identifies important factors that both impede and facilitate rapid zoonotic disease detection and reporting.

Returning to the Research Questions and Key Findings from Chapters

The dissertation addresses two primary research questions, with the first research question having three subsidiary questions. These questions are repeated with summarized answers here.

1. What is the current state of practice in zoonotic disease outbreak detection in the

United States?

a. What is the current variation in state law with respect to animal disease

reporting?

b. Who detects zoonotic disease outbreaks in the United States in animals and

people?

c. How fast are zoonotic disease outbreaks recognized in animals and people?

2. What elements are needed for improved zoonotic disease outbreak detection in the

United States?

The key results from each chapter are described in Table 7.1. The results from

Chapter 4 answer research question 1a. Findings demonstrate that federalism does play a role in disease reporting, as indicated through the wide variation in reportable animal disease lists and associated requirements (such as who to report to and how fast the disease case needs to be reported). States act as sovereign entities using the authority of their police powers to set differing requirements. Many different biologic agents/diseases

359 are listed on these lists—340 in total. Of the 50 states and D.C., 48 (94%) have one or more reportable animal disease listed in some manner. Thirty-three percent of states (17 states) have online disease lists, though only 41% of the states (7 states) explicitly mention this online external list in an administrative code or regulation. The most common diseases found on state lists include brucellosis, anthrax, pseudorabies, rabies, and equine infectious anemia. Many high consequence zoonotic diseases are listed, though many are not, particularly those that are not seen frequently in the United States or are considered emerging infectious diseases, including Ebola, Lassa, and Rift Valley fever. Additional variations by state were found in terms of who diseases are to be reported to, whether public health was mentioned, or if emerging infections are in some way captured by a broad disease category. In conclusion, there is a wide variation in state reportable animal disease lists, and though most list the frequent threats to production agriculture, as well as key zoonoses, from a One Health standpoint, key zoonoses are often absent from these lists.

One hundred and one outbreaks are analyzed in Chapter 5, with findings addressing research questions 1.b. and 1.c. Sixty one percent of these outbreaks are caused by agents on the CDC A, B, or C lists of potential bioterrorism agents. More than

80% of the outbreaks occurred in humans and another species. The median number of days to reporting is found to be 13, and outbreaks are most commonly detected by diagnostic laboratories. There are many „slow‟ outliers—outbreaks that took a significant period of time to detect. However, respondents and interviewees consulted in the case studies suggest that these are not failures, but successes in detecting zoonotic disease outbreaks that otherwise would have been undetected if particular systems and processes

360 had not existed. Laboratories and clinicians also detect zoonotic disease outbreaks more quickly than state agencies, and the difference is statistically significant, using a Kruskal-

Wallis test and Dunn‟s method. The mode of transmission and primary symptom of the biologic agent did not impact the time to detection. The time to reporting in a state entity was short (1.625 days on average, median of 0 days). These data are the first specifically analyzing zoonotic disease outbreaks in the United States.

Four case studies are reported in Chapter 6 to address research question 2.

Importantly, though „slow‟ outbreaks to detection are used as case studies to identify improvements, these cases actually yielded many instances of best practices; there are not significant differences in the lessons-learned between the „slow‟ and „fast‟ cases.

Chapter 6 results show, as suggested in Chapter 5 from the „who detects results‟, that diagnostics are critically important in detection and reporting of outbreaks and are frequently cited as both a key impediment (if lacking) and a key facilitating factor (if present). Other factors that respondents think are important in impeding or facilitating zoonotic disease outbreak detection and reporting include astute providers, knowledge about reporting, and communication/collaboration between entities. In addition, the case studies confirm that institutional design and bureaucratic behavior is important, and particularly, that it can have positive effects. When they were asked, respondents did not explicitly say that federalism impacted detection and reporting, but their responses about

„best practices‟ at the state level and the importance of state reportable disease lists (as discussed in Chapter 4) indicate that federalism does impact disease detection and reporting practice, confirming literature reviewed in Chapter 2. Respondents overwhelmingly cited that establishing interpersonal relationships are the best way to

361 improve zoonotic disease detection and reporting practice. Other factors cited include better training, education, and additional resources for diagnostics and logistics, particularly at local levels.

Together, these three chapters, drawing on the theoretical and empirical literature reviewed here, create a comprehensive picture of zoonotic disease detection and reporting practice in the United States. The links between the chapters, theory, and practice, are illustrated in Figure 7.1.

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Table 7.1: Key Results from Empirical Chapters

Chapter Key Results

Chapter 4 -Most states have a disease list. (Legal Analysis) -Median, 88 diseases per state list -86% of states require reporting of some or all diseases within 24 hours. -43% have a reference for unknown/emerging diseases. -There is variation in who has to report (providers, laboratories or any person) There is wide variation in disease lists over the 50 states. Many zoonoses are absent from a majority of lists, particularly those not typically present in the United States (i.e., hemorrhagic fevers). Chapter 5 -Median time to detection: 13 days (Outbreak Database) -Most common detectors: laboratories—human health (49%) -Animal entities detect more frequently in animals, human entities more frequently in people - Many factors are not significant in time to detection, including symptoms, species affected, and mode of transmission. The relationship between who detects the outbreak and how fast the outbreak was detected was statistically significant; state agencies tended to detect significantly slower than clinicians and laboratories. Chapter 6 -Diagnostics was the most frequently cited factor as a (Case Studies) facilitating factor. -Lack of knowledge or obligation to report was the biggest impediment. -Federalism was not explicitly identified as important by respondents (only 6% of respondents mentioned it). -Institutional design and bureaucratic behavior were both identified as important. -While reportable disease lists were frequently cited by respondents, there were strong opinions against their usefulness. A large majority of respondents (66%) suggested that interpersonal relationships were needed for improvement in zoonotic disease detection and reporting practice; federalism was implicitly as a positive facilitating factor because of “best practices” and state-by-state successes.

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The pyramid in Figure 7.1 is the „theoretical framework‟ repeated from Chapter 2.

The addition on the right hand side of the figure are the key results of this dissertation research, with arrows demonstrating the relationships. The value of germ theory

(importance of timely detection of zoonotic disease outbreaks to public health) on the very bottom, and the complexity of the „problem‟ of zoonotic diseases at the very top now are better explained by the theoretical frames in the middle of the pyramid, as highlighted in this dissertation. On the lower level of the circles, federalism seems to play an important role in zoonotic disease detection and reporting because of the states‟ ability to design their own institutions, processes, and best practices as sovereign governments with authority, via their police powers, over reportable disease lists and detection processes. The separation of state and federal government systems also impacts institutional design of agencies and offices, as well as behavior within the bureaucracies, which also affects zoonotic disease detection and reporting. The behavior of bureaucracies is complex, but processes developed for disease detection and reporting often are beneficial to the timely identification and reporting of outbreaks, as indicated by survey respondents; a lack of collaboration or coordination across agencies was suggested as a negative result of federalism. Thus federalism, institutional design, and bureaucratic behavior impact zoonotic disease detection and reporting practice, though not always in the ways suggested in the literature.

The top three circles are the results of the research which describes the actual practice of disease detection and reporting. The legal aspect, which emerges as a potential issue from federalism, has shown on the animal-side to be widely variable, as was predicted in Chapter 2. How fast zoonotic diseases are detected, influenced by both

364 federalism and institutional design and bureaucratic behavior, is identified as 13 days, though many factors thought to influence time to detection, such as type of disease or primary symptoms, did not. However, „how fast‟ is impacted by „who detects‟, the third aspect of practice as highlighted by this dissertation. Who detects, flowing from the theoretical frames of institutional design and cooperation, is identified primarily as diagnostic laboratories, which also is confirmed in the survey/interview instrument.

In sum, the theoretical frameworks used, which emerge from the U.S. governmental system, as expected, are important in the practice of zoonotic disease detection and reporting in the United States. Moreover, these theoretical frameworks guided the direction of this research, probing to connect the theories of governance and reviewing zoonotic disease detection as a policy problem. These theories guided both the angle and approach in this dissertation, and this research is the first recognized attempt to explicitly link these theories directly to zoonotic disease detection and reporting practice in the United States. This resulted in important findings on practitioners perceptions of federalism and bureaucracy that otherwise would not have been discovered.

This research successfully provides evidence of not only the legal environment of animal disease reporting, but evidence on how quickly zoonotic disease outbreaks have been detected in the past, corroborated with input from practitioners actually involved on the important factors in zoonotic disease detection and reporting. This unique approach and the policy recommendations presented below are a significant contribution to furthering improvements in zoonotic disease detection practice in the United States.

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Figure 7.1: A Model Relating Empirical Results to Expectations from Literature

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Policy Analysis

The background and policy problems involved in zoonotic disease detection and reporting are clearly explored in Chapter 2 of this dissertation. This policy analysis 1) provides a problem statement, 2) discusses key stakeholders and interests, 3) identifies criteria, and 4) evaluates five policy options based on these criteria. This policy analysis is based more on the health-policy approach (Wilensky and Teitlebaum, 2007) than a strict rational policy analysis approach (Weimer and Vining, 2005), and has been adapted to discuss policy options where the background and theory have already been presented.

The policy options considered here are focused on states, because at the time of this writing, primary authority for zoonotic disease detection and reporting remains at the state level under the authority of the states‟ police powers. If federal entities become more involved or further their authority in the realm of zoonotic diseases, these recommendations will still be important because even if policy and regulation is promulgated by the federal government, the front-line of defense against bio-threats are the local responders who are responsible for reporting and detection of disease threats.

Many improvements—even if federally-driven or funded—will need to be implemented at the state and local level. The choice to focus on state action is the result of not only separation of state and federal powers, as comprehensively discussed earlier, but also because of the insights provided at the state level reported in Chapter 6.

Because this research does not have the imperative or need to produce one recommendation, the benefit of this policy analysis is to carefully evaluate the strengths and weaknesses of these policy options. Certainly much more research could be conducted on specific analyses, including cost-benefit analyses from both a qualitative or

367 quantitative perspective. Just as this dissertation‟s scope excluded questions of improving technical surveillance systems, like syndromic surveillance systems, as well as legal enforcement of detecting and reporting diseases, and response at both the state and federal level, the focus of these options is on detection and reporting of zoonses, employing the empirical evidence gained in this research.

Problem Statement

What actions should States take in the next fiscal year to improve zoonotic disease detection and reporting in their State?

Exploring the Problem

As suggested in Chapter 2, zoonotic diseases are a critical threat to public health.

Emerging and re-emerging diseases are predominately zoonotic, and despite the fact that many diseases are not present in the United States currently (for example, Rift Valley

Fever, Lassa, and others), the United States has had many important zoonotic disease outbreaks that have threatened public health in the last decade. These outbreaks include

West Nile, monkeypox, and anthrax, among others. Despite the threat of zoonotic diseases, the system for zoonotic disease detection and reporting in the United States is fragmented, with a significant separation between the animal health sector and human health sector. In addition, the processes are complicated by the wide variation in state law and policy guidance, as well as by the design of institutions (agencies) and the bureaucratic behavior of agencies in the United States, which have overlapping policy jurisdictions and competing policy interests.

Empirical evidence and theoretical literature is documented in Chapter 2. In short, past research has called for improved collaboration between sectors, a more

368 integrated animal and human health detection and reporting system, as well as improved communication between local, state, tribal, and federal governments. With the fiscal crisis in 2011, policy recommendations that are fiscally responsible will be of critical importance, as states and localities face restricted budgets (National Association of

County and City Health Officials, 2010; State of California, 2010; Texas Health and

Human Services Commission, 2011).

Key Stakeholders

This section presents six key stakeholders, and briefly indicates their interests in zoonotic disease detection and reporting. This overview is not meant to be exhaustive.

As expressed throughout this research, there is typically not a primary authority at the state level (or federal level) for zoonotic disease detection and reporting. While different state agencies have different interests, there may also be interests within state agencies that compete or collide, for example, between diagnostic laboratories and practitioners.

Because the institutional design of every state is different, the interests represented here are those generally presented in any given state in the union. Certainly, there are going to be minor differences: for example, in some states the board of animal health monitors reportable animal diseases, in others, it is the department of agriculture.

1. Department of Health or Public Health

The Department of Health has primary jurisdiction over human diseases, as well

as often over diseases and problems such as rabies. In states, health departments

frequently have more resources, in comparison to other agencies. Departments of

health have many responsibilities and interests, including analyzing data on

reportable diseases, conducting epidemiological investigations, and tracking the

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status of ongoing outbreaks, among other activities. Departments of health, as

executive agencies, have authority granted to them via the state legislatures. They

typically exercise this power via agency regulations. Agency regulations must go

through a notice and comment period, which can be a long process, so rapid

policy changes are not possible. Health departments also are responsible for

public outreach and education, particularly to vulnerable populations who may be

most at risk. Legal liability and preventing serious public health threats are

concerns to state health departments, who are also subject to intense pressures

from powerful physician groups. However, public health often does not receive

as much funding as other health interests in a state health department (for

example, Medicaid), and infectious diseases often receives less resources than

other interests within a public health budget (Trust for America's Health, 2010).

While most of the expertise is human-disease related, many health agencies have

a zoonotic disease division, and nearly every state has a public health veterinarian

on staff (some states share this as the same post as the State Veterinarian). There

are many competing issues in a state health department.

2. Department of Agriculture

Similar to state health departments, there are also many interests within state

agricultural departments. They must not only encourage production, but protect

consumers while also considering the potential economic impact (in addition to

animal and human health impacts) from a zoonotic disease outbreak. Production

agriculture is a significant economy in many states, and a zoonotic disease

outbreak can have significant negative implications on industry, which state

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agriculture departments frequently try to protect (State of Connecticut, 2003).

Departments of agriculture have an interest in preventing widespread zoonotic

disease outbreaks to avoid the negative consequences to both animal health

(directly impacting industry), and human health (both directly and indirectly

impacting industry). Moreover, given the increasing need for food production,

these departments have an interest in improved production and husbandry

practices to sustain higher levels of production in the absence of disease. Of state

departments, agricultural departments are most likely to retain veterinarians and

veterinary knowledge on staff. Like other departments, agriculture departments

are facing far tighter budgets (Kee, 2010).

3. Department of the Environment or Interior

Infectious disease detection and reporting is rarely a first-line priority for

departments of environment or interior, that also have a wide policy jurisdiction

involving natural resources, land-use, water-quality, and a myriad of other issues.

Some states focus on infectious disease in wildlife more than others, or have very

specific disease interests, for example, Wyoming that continues to have problems

with brucellosis in wildlife populations (Wyoming Game and Fish Department,

2011). Typically zoonotic diseases are not as much of a concern to departments,

that focus instead on diseases that will significantly impact the health, stability,

and well-being of animal populations. In states where wildlife and domestic

animals interact frequently, state departments of the environment again are

focused on the habitat of the wildlife and conservation of resources more so than a

zoonotic disease threat. However, these departments frequently have the only

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wildlife resources, in terms of ability to detect diseases in the wildlife through

sample collection, testing, and diagnosis, and the only supply of personnel trained

in wildlife biology and disease.

4. Private Diagnostic Laboratories

While there also public diagnostic laboratories (both animal—veterinary

diagnostic laboratories) and human diagnostic laboratories, these are under the

jurisdiction of the state. This stakeholder group is the private sector of diagnostic

laboratories. These laboratories may not work „in the public interest‟ like state

agencies, and like any private entity, are generally for-profit. In some cases, these

diagnostic laboratories may have additional assays that can test for particular

agents, and associated expertise. Some are also interested in developing new,

improved technology. However, in terms of zoonotic disease detection and

reporting, these laboratories historically have less of a vested interest in the

outcome of the test, as they are businesses, rather than entities created to protect

public or animal health. More laboratories recently have become interested in

public-private laboratory partnerships to bolster public capacity and technology,

and in the interest of protecting health (for example, see Biomérieux, 2011; Effler,

leong, Tom, & Nakata, 2002).

5. Individual Practitioners: Physicians and Veterinarians

Physicians and veterinarians both focus on the health and wellbeing of their

patients—whether human or animal. Detecting zoonotic diseases is part of

practice, but many may not have the time or resources to stay up-to-date on

salient disease threats (particularly those practitioners do not deal with infectious

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disease on a regular basis). Both have ethical and moral obligations, and the

patient-physician relationship is incredibly important for privacy and

confidentiality. Veterinarians likely do not enjoy as much legal protection for

confidentiality in their relationships with animal owners, but they value these

relationships as well. Practitioners of either type may not report diseases at the

request of the patient or client, though from the interview and survey responses,

the obligation to report for the public good is important to veterinarians and

physicians alike.

6. Patients/Clients/Individuals

Finally, patients and clients are stakeholders, both in an individual and a

collaborative sense. Individually, patients may present with a zoonotic disease, or

have animal(s) with a zoonotic disease. They may have hunted wildlife that has a

zoonotic disease. Collectively, individuals‟ health represents public health. The

clients with production agriculture operations play a significant role in ensuring

intensive agriculture products are free of infectious diseases. Together, we all

have an interest in ensuring zoonotic diseases are not a significant threat to our

individual health, and also to the public health of the United States. However,

patients and clients also have privacy and confidentiality concerns, and sometimes

economic considerations that may impede disease reporting.

Criteria

There are four criteria that I have used to evaluate the policy options. First is political feasibility, which includes cost and financial feasibility, given the fiscal problems of many states. Second is effectiveness in improving detection. Third is

373 effectiveness in improving reporting. Fourth is overall impact on „One Health‟ in the

United States—indicating the impact on the actual protection of public health and animal health, based on improved disease detection and reporting. These criteria capture not only the importance of the policy option being practically attainable, but the value of improving detection and reporting, noting—as was recognized in the surveys and interviews—that detecting every single case of zoonotic disease is not only impossible, it may not be in our best interest given limited time and resources.

Policy Options

Each of the options discussed here is an incremental improvement rather than a comprehensive overhaul of the zoonotic disease outbreak detection and reporting system in the United States. Moreover, the first four options are not mutually exclusive—while mutual exclusive options are often important in policy analyses, this modified analysis specifically is tailored to providing broad discussion about the options rather than recommending a single option.

Option 1: Facilitate a more integrated and comprehensive diagnostics system, e.g. public- private memoranda of understanding, and thorough assessment and redress of critical diagnostic test gaps.

While stakeholders would broadly support such an initiative, it would require some funding and also the political capital to bring together different sectors (public and private as well as animal and human) to reliably evaluate the diagnostic capabilities and proceed with memoranda of understanding to ensure that diagnostic tests occur as rapidly as possible in the appropriate lab. This option—identifying weaknesses of the current state diagnostic capabilities and developing policy to work together in an outbreak—

374 would improve detection and reporting, as well as public health and animal health at a moderate level.

Option 2: Develop web-based communication (for example, through a Sharepoint site), both inter-state and intra-state, specifically for practitioners, epidemiologists, and others involved in zoonotic disease detection and reporting to share case knowledge, offer open- source updates, and promote electronic communication to foster collaboration.

Depending on the existing information technology infrastructure, this option may require less resources than other options if a dashboard database can be built upon existing technology. While most stakeholders would support such an attempt in theory, it may be difficult to get busy individuals to take the time to share information and communicate online, so it is important to encourage collaborations. Moreover, patients and clients may have privacy concerns about wider sharing of case information, even if identifying data are removed. In terms of improving detection and reporting, this option has more of an indirect effect that may be experienced over time, as those involved gain knowledge, information, and trust. Similarly, animal health and human health would be improved indirectly from better knowledge of zoonotic threats and rapid sharing of information in an outbreak, including emerging diseases.

Option 3: Develop a reportable zoonotic disease list and associated outreach materials, that apply to zoonotic infections in both animals and humans, considering emerging disease threats, require all individuals (e.g. practitioners, laboratories, physicians, producers) to report to a state public health veterinarian‟s office.

This option may be more difficult to reach consensus with the stakeholders, though fiscally, it would be possible to achieve through developing of policy guidance

375 and focused education materials. Different parties, including the state health, agriculture, and environmental agencies, as well as practitioners, will have different interests in having diseases included, as well as different tactics and preferences for who to target for outreach activities, as well as various levels of interest in proceeding with such a list.

This option is unlikely to have a significant effect on detection, but may improve reporting of zoonotic disease simply by improving the knowledge and awareness of zoonoses amongst stakeholder groups. Its impact on animal and human health would be low to moderate, depending on how well the option raised knowledge about the growing threat of zoonotic disease threats, and how much it encouraged stakeholders to acknowledge the factors that facilitate the emergence and resurgence of zoonotic diseases and to take measures to protect public health and animal health.

Option 4: Provide funding for completing and addressing local-level critical gap assessments to assess problems at local and county levels in the detection and reporting of zoonotic diseases, including examining outreach to producers, logistics for laboratory testing, communication gaps between the sectors, and specific zoonotic threats that can be feasibly addressed.

This option will be the most expensive, and for this reason, likely the least feasible. There may also be concerns at the state level of delegating too much authority or responsibility to the local level. Practitioners and individuals would likely appreciate such a move, as local agencies work within their communities, and interpersonal relationships are more easily made and maintained amongst groups that live together on a daily basis. This option would definitely improve zoonotic disease detection, particularly as more resources are made available for sample collection, diagnostic testing,

376 epidemiological investigation, and the like. In terms of reporting, this may increase reporting to the local level, but reporting to the state level will still rest upon the obligation (both legal and ethical) of the local agencies to report. Animal health and public health would be significantly impacted in a positive way as gaps are identified and addressed to improve public health and animal health on a local level—by individual, production unit, and community.

Option 5: Instead of implementing one of the prior policy options, states could opt to not actively pursue new policy initiatives. It is possible that improved zoonotic disease outbreak detection and reporting may be encouraged through other initiatives, such as those aimed specifically at mitigating foodborne-infection risks and encouraging healthier production approaches.

Certainly the status-quo is feasible, as it does not require action by state legislatures or executive agencies. However, the status-quo also, as demonstrated in this dissertation, has significant problems in ensuring diseases are rapidly reported. Lack of action will not improve our capacities for better detection. In the short-term, continuing with the status quo will not have a significant impact on public health or animal health.

However, in the long-term, continuing the status quo could have negative consequences for both public health and animal health, as zoonotic diseases may continue to pose a bigger threat without improved disease detection and reporting systems and processes.

Improvements have long been called for (Beatty, Scott, & Tsai, 2008; Institute of

Medicine, 1988, 2003; Keusch, Pappaioanou, Gonzalez, Scott, & Tsai, 2009; Lederberg,

2002); the status quo is not desired by executive agencies or other stakeholder groups, though fiscal limitations may prevent action.

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Table 7.2: Comparing the Policy Options based on a Set of Criteria

Option Political Effectiveness Effectiveness Effectiveness Feasibility in Improving in Improving in Improving (Including Detection Reporting Public and Financial) Animal Health 1.Diagnostic Moderate Moderate to Moderate. Moderate to Capacity feasibility high high. 2.Web-Based Moderate Low to Low to Moderate, over Collaboration feasibility. moderate, over moderate, over time. Tool time. time. 3.Reportable Moderate to Low. Low to Low to Zoonotic Disease high moderate. moderate. Lists feasibility. 4.Local/County Low Moderate to Moderate. High. Critical Gap feasibility. high. Assessments 5.Status Quo High None to low. None to low. None to feasibility negative.

Recommendations

Based on the empirical evidence in this dissertation, the first four of these policy options would have significant merits if implemented to improve zoonotic disease outbreak detection and reporting practice in the United States. For example, option 1

(identification and redress of gaps) would be important, as diagnostic laboratories play a critical role in timely detection (as seen in Chapter 5), reporting (as evidenced in Chapter

4 and Chapter 6), as well as in improving zoonotic disease detection and reporting

(Chapter 6). Option 2 would improve interpersonal connections, and intersectoral collaborations (as identified in Chapter 6). Option 3 would encourage states to refocus on zoonotic diseases as a threat to public health, and allow states to propose policies and/or regulations that best suit their state, yet ensures that all stakeholders are responsible for reporting (Chapter 4 and Chapter 6). Option 4 would encourage disease detection and reporting on the front-lines, bolstering laboratory resources (Chapter 5 and

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Chapter 6). These are but only some of the ways that these options address some of the problems and opportunities identified in this dissertation.

Because of the importance of diagnostic testing in all aspects of zoonotic disease detection and reporting, I would suggest Option 1 as the highest priority for implementation. Diagnostic laboratories are important in detecting and reporting zoonotic disease outbreaks, and there is a need for appropriate diagnostic assays to ensure that diseases can be identified, typed, and actually detected. Option 1 has the best feasibility, both politically and financially, along with the best improvement in detection, reporting, and One Health. Moreover, there are different components of Option 1 which can be conducted incrementally—for example, the assessment and prioritization of gaps can occur prior to directing additional funding towards specific laboratory capabilities.

Additional targeted surveillance may be needed for certain populations, such as in the zoo population where there currently appears to be very limited formalized or systematic surveillance initiatives. Moreover, Option 1 provides the best opportunity to enable public-private-academic partnerships, which have been shown to be critically important in many aspects of protecting „One Health‟. Public-private-academic collaboration can bring together the strengths and resources of these different sectors to improve diagnostics in the public interest. Private laboratory network capacity, including laboratory response networks, may play a critical role in increasing our capability to test samples for a wide range of diseases as well as increasing our surge-capacity during an outbreak.

Based on the evidence on federalism and institutional design presented in Chapter

6, states do have some best practices and specific processes that can enable both

379 intersectoral collaboration and communication, as well as rapid detection, reporting and response. In order to implement the chosen option here, or any of the other options, I would suggest starting with the states with excellent laboratory diagnostic laboratories and networks, particularly those that may have explored a public-private model of response, in an emergency or outbreak situation. States with excellent IT capabilities, including those with the ability to filter large quantities of electronic data or share data with emergency managers in outbreak situations should be identified and used as examples to improve future practice in other, similarly situated states.

Thoughts Moving Forward

The overwhelming recommendation from those involved in zoonotic disease detection and reporting was better interpersonal relationships between individuals from separate sectors (as well as from different levels of government). One key to improving zoonotic disease detection, in the era of budgetary cuts and fiscal problems, is to use existing infrastructure or cost-effective infrastructure to create pathways for improved communication and collaboration at every opportunity. For example, online databases and websites can encourage communication; sharing case stories, best practices, and interesting anecdotes can foster working relationships without the need for substantial resources. Many states already have electronic reporting systems that, with the addition of the proper interface, could serve as communication pathways. Many states also run both table-top exercises and full-scale exercises that require the involvement of different groups: states need to take full advantage of these opportunities to not only exercise policy guidance and resource deployment, but to introduce individuals with common backgrounds and common interests, so when the need comes to make a phone call from

380 an animal health agency to a human health agency, no introductions need to be made.

Certainly this is likely easier said than done, but building relationships is done one person at a time. While additional training and travel is likely not going to be possible during this budget crisis, our information technology is now sufficient enough to ensure we can build relationships, at least electronically, across agencies and sectors prior to zoonotic disease outbreaks to speed detection and reporting of outbreaks.

Second, further research needs to be conducted to assess what works in practice for those actually detecting and reporting cases of zoonotic disease. Regulation and policy guidance should not be created for these individuals to simply use, it should be created to be useful to these individuals. As seen in the case of reportable diseases lists, there are strong feelings in practice that these lists are misguided and totally ineffective.

But respondents to the survey also noted that these lists, in many cases, are why diseases are reported to the county or state in the first place. If more was done to provide individuals who report with the „rewards‟ of their reporting, for example quarterly updates on disease threats in particular areas, new transmission pathways of which they should be concerned, growing trends in particular animal or human populations, they may be more likely to report. Incentives certainly have been discussed by many to encourage reporting: while in this budget climate financial or resource incentives do not seem to be a possibility, we should think of other tools that could benefit providers, such as those mentioned above (for example, see Keusch et al., 2009). Moreover, as time and resources are stretched to a maximum, efficiency is key to improve detection and reporting. Practitioners should have to report once, to one agency. Agencies, ideally,

381 should be able to enter the data once, for all to see. Removing overlapping systems and efforts may be difficult, particularly to start, but efficiencies can be obtained.

Many of these incremental changes have been widely discussed. However, this dissertation also highlights that the ability of states to design and implement their own systems, institutions, and bureaucracies is fundamentally important to improving zoonotic disease detection and reporting. States are diverse, in populations of animals, populations of humans, human-wildlife interactions, laboratory systems, resources, and in many other ways relevant to zoonotic detection and reporting. That said, the lack of consistency across state regulation for reporting zoonotic diseases may be a critical threat to the public health security of this country and could have significant public health implications during an actual disease event. There are both arguments for and against standardization of detection and reporting practice. Certainly, all states are not the same, nor do they face the same disease threats or problems. However, in order to have a strong defense and response to both natural and intentional disease threats, some standardization is critical. While this does not inherently mean a one size fits all detection system in the United States in the answer, standardized information technology systems from best practice states, consistent case definitions, and common standards of practice from states that deal with certain diseases most frequently are not „one size fits all‟ recommendations. Consistency, transparency, and common scientific knowledge are not enemies of state sovereignty, and will help to ensure public and animal health in this country.

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Future Research

The research conducted in this dissertation provides an important foundation for future research on zoonotic disease detection and reporting, but the need for additional research remains significant. Certainly, there are minor variations of this research that could be conducted with reasonable ease. For example, other zoonotic diseases could be examined. The geographic scope could be enlarged, similar to analyses conducted in

Chan et al. (2010). Additional research could be conducted on the role of enforcement mechanisms in disease reporting. More data sources could be located for a larger sample size. A survey could be undertaken to assess producer knowledge of zoonotic diseases.

Further research on state wildlife laws for trade could be performed (Keusch et al., 2009).

There are many different avenues which could be explored in future research, and others have also been mentioned in previous chapters. However, I propose three general research areas which I suggest are fundamentally important to secure the public‟s health, animal health, and our economic and national security.

First, I believe the practical implications of not reporting zoonotic diseases should be explored. Yes, rapid detection and reporting is essential for response. Yes, the United

States has a legal obligation to report diseases to international governing bodies.

However, does the lack of reporting have significant implications for public health? For animal health and production agriculture? Under-reporting—and the vast amount of under-reporting which occurs—has been mentioned repeatedly in this dissertation. While we can barely grasp the denominator, in other words, how many disease outbreaks actually go undetected for all the outbreaks we do detect, perhaps the more important question is, why is it important? More direct links need to be made between the lack of

383 reporting and significant economic, security, and health implications. While perhaps this sounds obvious, there is a huge gap in empirical research that provides evidence of the importance of detection and reporting in concrete, quantifiable, measures. Moreover, and perhaps more importantly, these consequences should be reviewed by region (including the world), by disease, and by species. For example, I would argue—as would others— that a lack of reporting of an emerging disease that has caused a significant outbreak is much more practically important than a lack of reporting of a single case of salmonellosis for a horse.

Research on the costs of incomplete detection is critical for multiple reasons, and certainly reflects the International Health Regulations (2005) movement towards the need to report “public health events of international concern” and away from an obsession with specific diseases on a list. As resources shrink, we must prioritize what is important to us. Spreading our resources for disease detection more thinly is probably not the answer.

Focusing our limited resources on high risk regions, production units, or populations may be a more effective way to defend ourselves, our animals, our security, and our economy from disease threats. Similarly, effective syndromic surveillance systems and other new technology may allow us to cover more ground with less resources.

Specifically, such research could begin by examining evidence of specific disease outbreaks, modeling through econometric and cost-benefit analyses the economic costs of delayed reporting and/or detection. Similarly, research could be conducted assessing the costs and benefits of reporting diseases (or small outbreaks) that have few implications for public health or animal health. In addition, corresponding empirical data could be collected on the social impacts of detecting and reporting disease outbreaks. There is no

384 doubt that such research will be a significant challenge, in terms of operationalizing key variables, and finding time and resources. However, as we (in the United States) face significant budget cuts, research of this sort may be critically important as we try to allocate our fewer resources more efficiently.

Second, I think additional research should be conducted to highlight promising practices—both in terms of policies and practice—that could be emulated across the country. This may not necessarily be lessons from inside the United States: I think it would be useful to look internationally as well as domestically, to identify how other nations and localities are effectively handling disease detection and reporting, and even training related to these subjects. Case studies of localities, states, provinces, or countries that have proven and largely successful disease detection and reporting programs could be performed. For example, are states with particular characteristics—such as large numbers of production animal operations—more likely to have aware practitioners or robust reporting systems? The most important part of this potential research would be to emerge with policies, plans, or programs that are actually implementable—in other words, socially, politically, and economically feasible and palatable. This would require not only the identification of best practices through empirical research, but the analysis of how these promising practices could be implemented, given the federal and complex bureaucratic system of the United States, located in a complex socio-political environment.

Third, and finally, I maintain that more primary research on zoonotic diseases will be required to successfully protect animals and humans from biological threats.

Certainly, this type of research is quite tangential from the research performed in this

385 dissertation. Yet without further knowledge of why we should care about zoonotic diseases and where they are likely to emerge and persist, it will be difficult to devote public monies to improve our detection and reporting systems. Certainly, the significant challenge that remains is translating this primary research into effective, implementable public policy. For example, we know that the bushmeat market and consumption of bushmeat leads to emerging diseases and is a threat to public health. Yet, what, can we in the United States, do?

Integrating valid and reliable scientific (technical virological and bacteriological) research with public policy and public policy research, has, and will continue to be, a significant challenge. This research is complex and multifaceted. Indeed, it involves addressing fundamental questions about development and human nature that are far beyond the scope of this dissertation. Bioterrorism concerns must also be considered. But addressing technical questions about the emergence of new diseases, the persistence of known diseases, and the potential purposeful introduction of diseases is critical to face the “zoonotic disease problem” of detection and reporting in the United States.

Conclusion

Table 7.3 presents the final results responding to the research questions from this dissertation research.

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Table 7.3: Results Linked to Research Questions

Chapter Key Results

1.What is the current Practice is characterized by a separation of federal and state state of practice in powers, in a complex bureaucratic system where many zoonotic disease stakeholders are involved. outbreak detection in the United States? a. What is current A wide variation in state law exists with respect to animal variation in state law disease reporting requirements—many different biologic with respect to animal agents are listed, with different time requirements for disease reporting? reporting, and various legal and policy approaches (statutes, regulations, and online disease lists) to creating reportable disease lists. b.Who detects zoonotic Diagnostic laboratories (human) detect most of the disease outbreaks in outbreaks, followed by clinicians and state health the United States in departments; local and county health departments also play animals and people? an important role in detection. c.How fast are zoonotic A median time to detection is 13 days for the 101 outbreaks disease outbreaks reviewed; time to detection is impacted by the type of entity recognized in animals detection (clinicians and diagnostic laboratories are faster and people? that state agencies). 2.What elements are For improved zoonotic disease detection (and reporting) in needed for improved the United States, improved diagnostic capabilities are zoonotic disease critical, collaboration, and interpersonal relationships detection in the United among stakeholders are the most important factor. States?

This research provides an important contribution to public health literature and practice: it has illuminated important details about zoonotic disease outbreak detection practice, as well as in zoonotic disease outbreak reporting. Most importantly, this dissertation provides a description of zoonotic disease detection practice that were previously not documented in the literature, including the fact that significant zoonotic diseases are indeed omitted from many reportable disease lists. This research has provided the first zoonotic-disease specific evidence of how fast zoonoses are detected, and who detects them. It has discussed potential challenges identified in the literature, and also identified

387 how some of these challenges actually affect zoonotic disease detection and reporting practice (like the design of specific institutions). This dissertation has highlighted the importance of diagnostic laboratories, as well as astute practitioners, and knowledge about zoonoses. While sometimes detection is delayed, these cases are not necessarily

„worst case‟ scenarios, and can yield important lessons for improved detection and reporting. This dissertation also presents policy options that could be adopted by states and localities to improve zoonotic disease detection and reporting practice in the United

States.

In addition, this dissertation presents a significant contribution to the field of public policy and public administration. In addition to framing a perceived public health issue as a public policy problem, this is the first time that the impact of the U.S. system of governance on zoonotic disease detection and reporting practice has been studied to investigate how to improve zoonotic disease outbreak detection in the United States.

This research reminds us that federalism and institutional design play an important role in many fields of public policy, and can be an important lens to help us understand the details of the problem at hand. Moreover, it is an important reminder that there are important characteristics of our U.S. system of governance—like federalism—can also be simultaneously impeding factors, depending on the policy problem or perspective.

While much work remains to be done, we should continue to focus on emerging disease threats and cooperation amongst levels of government and various sectors to improve our biosurveillance and ultimately protect our „One Health‟. Certainly this research has highlighted that there are existing best practices, but it has also identified, echoing previous research with empirical data, that important vulnerabilities in our

388 zoonotic disease detection and reporting system do exist and are critical threats to the health and security of the United States.

389

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Appendix A: Brief Synopses of Key Organization and Entities

Appendix A provides brief descriptions of domestic and international agencies and organizations involved in zoonotic disease surveillance and detection. This appendix provides information not otherwise provided in the dissertation that may be useful to readers, but is not intended to be a thorough explanation of these entities and their endeavors. There are many private and non-profit organizations also now involved in zoonotic disease surveillance and detection, but these are not covered in this Appendix.

U.S. Agencies

Department of Homeland Security:

The Department of Homeland Security (DHS) is involved in zoonotic disease detection through multiple avenues, including the Office of Health Affairs, and funding for Centers of Excellence specifically focused on zoonotic diseases, such as the National

Center for Foreign Animal and Zoonotic Disease Defense (FAZD) (see http://fazd.tamu.edu). DHS also operates the foreign animal disease research center at

Plum Island, and is the lead in the creation of the National Bio and Agro-defense Facility

(NBAF), intended to replace Plum Island with new laboratory facilities. For more on

NBAF, please see: http://www.dhs.gov/files/labs/editorial_0762.shtm (current January

2011).

Department of Defense:

The Department of Defense (DoD) is involved in worldwide veterinary and health missions. DoD operates a Veterinary Food Analysis and Diagnostic Laboratory, and also

418 provides veterinary care in the military (for example, http://www.veterinaryservice.army.mil/animal.html [current January 2011]). DoD also operates one of the most prominent disease laboratories in the United States, the U.S.

Army Medical Research Institute of Infectious Diseases (USAMRIID). A link to

USAMRIID is located here: http://www.usamriid.army.mil.

Department of the Interior:

In particular, the Fish and Wildlife Services (FWS) in the Department of the

Interior (DOI) is primarily responsible for diseases, including zoonoses, in wildlife populations. FWS works to observe and rapidly detect emerging and known infectious disease threats in wildlife species. For example, FWS conducts disease surveillance and observation for avian diseases in migratory birds: http://www.fws.gov/migratorybirds/avianhealth.html (current as of January 2011).

Department of Health and Human Services:

The Department of Health and Human Services (HHS) is involved in zoonotic disease detection and surveillance through multiple organizations, including the Centers for Disease Control (CDC), Food and Drug Administration (FDA), National Institutes of

Health (NIH). The CDC performs research and detection activities focusing on zoonoses, the FDA handles many food-borne zoonotic disease issues, and the NIH performs and funds primary research on zoonoses. These organizations have many centers, branches, and entities which deal with zoonotic diseases. A link to these operating divisions is here: http://www.hhs.gov/open/contacts/index.html#od (current as of January 2011).

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Department of Agriculture:

The Department of Agriculture (USDA) performs research as well as zoonotic disease detection and surveillance in livestock and other domestic production animals.

The USDA is responsible for the National Animal Health Reporting System, as well as primary research through the National Veterinary Services Laboratories. The Animal and

Plant Health Inspection Service is primarily responsible for most of the disease detection, surveillance, and research of zoonoses in animal populations. A link to APHIS is: http://www.aphis.usda.gov. In particular, the sections on animal health and emergency preparedness and response are important resources for information on zoonotic disease preparedness and response efforts in animals.

U.S. Agency for International Development

The U.S. Agency for International Development (USAID) also does a great deal of work surrounding infectious disease detection, surveillance, and prevention internationally. USAID has also funded research on the transmission of emerging infectious diseases as well as neglected tropical diseases. More information on their work in infectious diseases is available here: http://www.usaid.gov/our_work/global_health/id/index.html# (current January 2011).

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International Organizations

World Health Organization

The World Health Organization (WHO) is the primary international authority for human infectious disease surveillance and detection, and is the United Nation’s authority for public health. It partners with other entities for actual surveillance and detection activities, and also coordinates with regional centers and wildlife partners on zoonotic disease surveillance and detection. The WHO is also responsible for the key international legal instrument governing public health emergencies of international concern, the

International Health Regulations, revised in 2005. Information on Veterinary Public

Health in the WHO is here: http://www.who.int/zoonoses/vph/en/ and information on the

IHRs is available here: http://www.who.int/topics/international_health_regulations/en/.

World Organization for Animal Health

The animal counterpart of the WHO is the World Organization for Animal Health

(OIE). The OIE maintains reports on zoonoses occurring in member states, and publishes the list of notifiable animal diseases. It provides international standards for veterinary services worldwide, including for diagnostic testing. Diseases notifiable to the OIE are available here: http://www.oie.int/eng/maladies/en_classification2011.htm?e1d7 (current

January 2011).

U.N. Food and Agriculture Organization

The Food and Agriculture Organization (FAO) is the United Nation’s authority on food and agriculture issues. FAO has several initiatives on zoonotic diseases, including

421 on avian influenza, foodborne zoonoses, and veterinary public health. A key FAO program is also the Emergency Prevention System for Transboundary Animal and Plant

Pests and Diseases System. Their website on livestock and human health is here: http://www.fao.org/ag/againfo/themes/en/human_health.html, and on poultry and human health here: http://www.fao.org/ag/againfo/themes/en/poultry/human_health.html

(current January 2011).

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Appendix B: List of Diseases under Consideration for Inclusion

Appendix B provides the list of the diseases that are under consideration for inclusion in this dissertation research. The method for including and excluding these diseases is provided in Chapter 3; this Appendix provides the full listing of which list the disease or biologic agent was on (for example, the OIE reportable disease list), and the full listing of all diseases considered.

Key

Yellow: Listed by CDC and OIE notifiable list.

Blue: Listed by CDC or OIE notifiable list and CDC list of Category A, B, or C agents.

Green: Listed by CDC or OIE and contained in wildlife disease of interest list.

Orange: Mentioned in key conceptual literature as a problematic or relevant zoonoses.

Strikethrough: Disease was excluded because though included in previous category, it is not zoonotic.

Grey: Identified by physician for inclusion.

Note: Citations are included where the disease was included on the basis of literature, where the disease was excluded as non-zoonotic, or where a key article cited the disease as a significant zoonoses.

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Table B.1: List of Diseases under Consideration for Dissertation

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Notifiable List List (World Organization Organization Agriculture: (Centers for (CDC) (Centers for Organization for Animal for Animal Animal and Plant Disease Control Disease Control and for Animal Health, Health, 2004, Health Inspection and Prevention, Disease Prevention, 2007) Health, 2006) 2004, 2005) 2005) Service, 2009b) 2009a)

Acarapisosis of honey bees x

Acariosis of bees x

Acquired Immunodeficiency Syndrome x

African horse sickness x X

African swine fever x X

American foulbrood of honey bees x x

Anthrax x x x x

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Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Atrophic rhinitis of swine x

Aujesky's disease x x

Avian chlamydiosis x x

Avian infectious bronchitis x x

Avian infectious laryngotracheitis x x

Avian mycoplasmosis (M. gallisepticum) x x

Avian mycoplasmosis (M. synoviae) x

Avian tuberculosis x

Bacterial kidney disease x

Blastomycosis

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Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Bluetongue (United States Department of Agriculture: Animal and Plant Health Inspection Service, 2009a) x x x

Bonamia exitiosa (In Mollusc) x x

Bonamia ostreae (In Mollusc) x x

Botulism, foodborne (Centers for Disease Control and Prevention, 2009b) x x

Botulism, infant (considered under botulism) x x

Botulism, other (considered under botulism) x x

Bovine anaplasmosis x x

426

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Bovine babesiosis x x

Bovine cysticercosis x

Bovine genital camplyobacteriosis x x

Bovine spongiform encephalopathy (C. Brown, 2004; Gibbs, 2005) x x

Bovine tuberculosis (Buxton, 2006) x x

Bovine viral diarrhea x

Brainerd Diarrhea

Brucellosis (Brucella abortus) x x x x

Brucellosis (Brucella canis) x

427

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Brucellosis (Brucella melitensis) x x x

Brucellosis (Brucella suis) x x x x

Buruli Ulcer

California serogroup virus disease x

Candidiasis

Camelpox x

Campylobacter

Caprine arthritis/ encephalitis x x

Chancroid x

Chlamydia trachomatis x

428

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Cholera (Merck & Co, 2008) x x

Chronic Wasting Disease x

Ciguatoxin

Classical swine fever (World Health Organization, 2009) x x x

Coccidioidomycosis x

Contagious agalactia x x

Contagious bovine pleuropneumonia x x

Contagious caprine pleuropneumonia x x

Contagious equine metritis x x

Crayfish plague x

429

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Crimean Congo haemmorrhagic fever x

Cryptococcosis

Cyclosporiasis x

Dermatophilosis x

Dermatophytes

Diptheria x

Dourine x x

Duck virus enteritis x

Duck virus hepatitis x x

Ebola Hemorrhagic Fever (all strains) (Daszak et al., 2000; Warfield et al., 2006) X

430

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Echinococcosis/hydatidosis x x

Ehrlichiosis/ Anaplasmosis (Daszak et al., 2000) x

Enterovirus encephalomyelitis x

Enzootic abortion of ewes x x

Enzootic bovine leukosis x x

Epizootic haematopoietic necrosis x x

Epizootic lymphangitis x

Epizootic ulcerative syndrome x

431

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Equine encephalomyelitis (Eastern) x x x x

Equine encephalomyelitis (Western) x x x x

Equine infectious anemia x x

Equine influenza x x

Equine piroplasmosis x x

Equine rhinopneumonitis x x

Equine viral arteritis x x

European foulbrood of honey bees x x

432

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Foot and mouth disease (Merck & Co, 2008; World Health Organization, 2009) x x x

Fowl cholera x x

Fowl pox x

Fowl typhoid x x

Giardiasis

Glanders x x X

Gonorrhea

Gyrodactylosis x

Heaemorrhagic septicaemia x x

Haemophilus influenzae x

Hansen Disease (Leprosy) x

433

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Hantavirus pulmonary syndrome (Centers for Disease Control and Prevention, 2009a; Daszak et al., 2000) x x

Heartwater x x

Hemolytic uremic syndrome x

Hepatitis A acute x

Hepatitis B (acute & perinatal infection) x

Hepatitis B chronic x

Hepatitis C acute x

Hepatitis C chronic x

434

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Highly pathogenic avian influenza (and low pathogenic per Chapter 2.7.12 of the Terrestial Animal Health Code) x x x

Histoplasmosis

Horse Mange x

Horse pox x

Human Immunodeficiency Virus (HIV) x

Infectious bovine rhinotracheitis/ infectious pustular vulvovaginitis x x

Infectious bursal diseaes (Gumboro) x x

435

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Infectious haematopoietic necrosis x x

Infectious hypodermal and haematopoietic necrosis x

Infectious pancreatic necrosis x

Infectious salmon anemia x

Influenza associated pediatric mortality x

Japanese encephalitis x x

Lassa Fever (Warfield et al., X 2006)

Legionellosis x

Leishmaniosis x x

436

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Leptospirosis x x

Listeriosis x

Lumpy skin disease x x

Lyme Disease x

Maedi-visna x x

Malaria x

Malignant catarrhal fever x x

Marburg (Daszak et al., X 2000; Warfield et al., 2006)

Marek's disease x x

Marteilia refringens (In Mollusc) x x

Marteilia sydney (In Mollusc) x

437

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Measles x

Melioidosis x

Meningococcal disease x

Mikrocytos mackini (in Mollusc) x x

Mikrocytos roughley (in Mollusc) x

Monkeypox (Bernard & Anderson, 2006)

MSX disease (Haplosporidium nelsoni) x

Mycobacterium Species (Non TB)

Myxomatosis x x

438

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

New world screwworm x x

Newcastle disease (Daszak et al., 2000) x x

Niarobi sheep disease x x

Nipah virus encephalitis (Daszak et al., 2000) x x

Nocardiosis

Nosemosis of bees x

Novel influenza A virus infections (U.N. Food and Agriculture Organization et al., 2008) x

439

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Old world screwworm x x

Oncorhynchus masou virus disease x

Ovine epididymitis x x

Ovine pulmonary adenomatosis x

Paratuberculosis x x

Perkinsus marinus (in Mollusc) x x

Perkinsus olseni (in Mollusc) x x

Pertussis x

Peste des petits ruminants x x

440

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Plague (Daszak et al., 2000) x x X

Poliomyelitis, paralytic x

Poliovirus infection, nonparalytic x

Porcine brucellosis x

Porcine cysticercosis x x

Porcine reproductive and respiratory syndrome x x

Powassan virus disease x

Psittacosis x X

Pullorum disease x x

441

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Q fever x x x X

Rabbit haemorrhagic disease x x

x (human and Rabies animal) x x x

Red sea bream iridoviral disease x

Rocky Mountain Spotted fever x

Rift Valley fever (Warfield et al., 2006) x x

Rinderpest x x

Rubella (and congenital) x

Salmonella enteritidis

442

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Salmonellosis x x x x

Scrapie x x

Severe Acute Respiratory Syndrome x

Sheep pox and goat pox x x

443

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Shiga toxin producing Escherichia coli x x

Shigellosis x

Small hive beetle infestation x

Smallpox x

Spherical baculovirosis x

Sporotrichosis

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Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Spring viraemia of carp x x

Surra x x

St. Louis encephalitis virus disease x

Staphylococcus (food poisoning)

Streptococcal (Group A and toxic-shock) x

Streptococcus pneumoniae (drug resistant and not) x

Swine vesicular disease x x

Syphilis (primary, secondary, neuro, latent, congenital stillbirth) x

Taura syndrome x x

445

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Tetanus x

Tetrahedral baculovirosis x

Theileriosis x x

Toxic shock syndrome (not streptococcal) x

Transmissible gastroenteritis x x

Trichinellosis x x x

Trichomonosis x x

Tropilaelaps infestation of honey bees x

Trypanosomosis (tsetse- transmitted) x x

Tularemia x x x x x

446

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Turkey rhinotracheitis x

Typhoid x

Vancomycin x

x (morbidity & Varicella death only)

Varroosis of honey bees x x

Venezuelan equine encephalomyelitis x x x

Vesicular stomatitis x x

Vibrio parahaemolyticus

Vibrio vulnificus

447

Wildlife Diseases of Interest Category A, B, or (USDA-WS) C Bioterrorism OIE Old OIE A Old OIE B (United States Agent? Human Nationally Reportable List (World List (World Department of

Vibrosis x

Viral haemorrhagic septicaemia x x

West Nile fever (West Nile disease-humans) x x

White spot disease x x

Xenohaliotis californiensis (in Mollusc) x

Yellow Fever x

Yellowhead disease x x

448

Appendix C: Primary Symptoms and Transmission Pathway of Selected Zoonoses

Appendix C lists the diseases/biologic agents in this dissertation with the key symptoms observed and by the primary transmission pathway. These categories are intended to be general, and may not capture clinical nuances observed with these biologic agents. Dr. Larissa May provided assistance with these categorizations. These categorizations were used in Chapter 5 for further analysis of data in the outbreak database.

Table C.1: Zoonotic Disease with Primary Symptoms and Mode of Transmission

Mode of Disease Primary Symptoms Transmission Dependent on type of infection; Influenza-like; Respiratory Distress (inhalational); Gastrointestional (Gastrointestinal); skin lesion Food; Inhalation; Anthrax (cutaneous) Direct Contact Blastomycosis Respiratory Inhalation Bovine Spongiform Encephalopathy (variant Cruetzfeld Jacob Disease) Neurological Food Food, Inhalation, Bovine Tuberculosis Respiratory; Other Direct Contact Brucellosis Influenza-like Direct Contact Campylobacter Gastrointestinal Food Coccidioidomycosis Respiratory; Other Inhalation Crimean-Congo Hemorrhagic Fever Hemorrhagic; Influenza-like Vector Respiratory; neurological (immunocompromised patients, Cryptococcosis mainly) Inhalation Unknown; Direct Ebola Influenza-like; Hemmorhagic Contact Ehrlichiosis/Anaplasm osis Influenza-like Vector

449

Mode of Disease Primary Symptoms Transmission Eastern Equine Encephalomyelitis Neurological Vector Direct Contact; Indirect Contact Localized infection; Respiratory; (secretions); Glanders Septicemia Inhalation Indirect Contact Hantavirus Influenza-like; hemorrhagic (feces, urine) Histoplasmosis Respiratory Inhalation Highly Pathogenic Avian Influenza Influenza Direct Contact Japanese Encephalitis Encephalitis Vector Direct Contact; Indirect Contact Lassa Influenza-like; hemorrhagic (feces, urine) Leishmaniasis Cutaneous Vector Indirect; Leptospirosis Influenza-like Environmental

Lyme Influenza-like; cutaneous; varied Vector Malaria Influenza-like Vector Direct Contact; Marburg Hemorhagic; Influenza-like Indirect Contact

Monkeypox Cutaneous; Influenza-like Direct Contact Direct Contact; Newcastle Infleunza-like Indirect Contact Nipah Influenza-like Direct Contact Novel Influenza A Influenza Varied Respiratory (Pneumonic); Plague Cutaneous (Bubonic); Septicemia Vector Direct Contact; Psittacosis Respiratory Indirect Contact Food; Inhalation; Q Fever Influenza-like; Respiratory Direct Contact Rabies Neurological Direct Contact Rocky Mountain Septicemia; Influenza-like; Spotted Fever cutaneous Vector Vector; Food; Rift Valley fever Influenza-like; hemorrhagic Direct Contact Salmonellosis Gastrointestinal Food; Direct

450

Mode of Disease Primary Symptoms Transmission Contact Shiga Toxin Producing E-Coli Gastrointestinal Food St Louis Encephalitis Neurological Vector

Trichinosis Gastrointestinal; Influenza-like Food

Cutaneous; Influenza-like; Vector; Direct Tularemia Respiratory (Varied) Contact; Food Venezuelan Equine Encephalomyelitis Neurological; Influenza-like Vector

West Nile Neurological; Influenza-like Vector Western Equine Encephalomyelitis Neurological; Influenza-like Vector

451

Appendix D: Additional State Data on Agriculture and Results of Statistical Analysis

In Appendix D, you will find state by state agricultural data illustrating the sale of animals and animal products in 2007 US dollars (most recent full data at time of writing

Chapter 4 in 2010). This information is graphed for comparison. These data are available from “2007 Agricultural Census” https://www.agcensus.usda.gov, using the data tool provided on the site.

In addition, Appendix D contains the Minitab scatterplot illustrating the lack of correlation between the number of diseases listed by each state and the sales of animals or animal products by state in millions 2007 US $.

452

Figure D.1: Scatterplot of Number of Diseases vs Animal Total

Scatterplot of Number of Diseases vs Animal Total

140 PA MT NY CT WA SD GA ANKJ MS 120 UTSC OK WI NV ND AZ MI HI

s IN 100 VA NE e WY AR IA CA s ID

a RI e

s 80

i D

IL TX

f o

r 60 MWE V MDFL e NH MO b NC m OH AL

u MN 40 CO N NM VT OR KS LATN 20 DE KY 0

0 2000 4000 6000 8000 10000 12000 14000 16000 Animal Total

453

Figure D.2: Animal Totals by State Including Products in 2007$

454