The Prevalence, Economic, and Social Cost of Methamphetamine

THE PREVALENCE, ECONOMIC, AND SOCIAL COST OF METHAMPHETAMINE

USE; AND THE EFFECTS OF ECONOMIC RECESSIONS, UNEMPLOYMENT

RATES, AND METHAMPHETAMINE ARRESTS ON CHILD ABUSE IN HAWAI`I

A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI`I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

EPIDEMIOLOGY

May 1, 2020

Nancy S. C. Linn

Dissertation Committee:

Eric Hurwitz, Chairperson Andrew Grandinetti Alan Katz Yan Yan Wu Deborah Taira

Keywords: Methamphetamine, meth prevalence, cost of meth use, periodicity of meth arrests

ABSTRACT

This is the first study in Hawai`i to estimate the prevalence and cost of methamphetamine (meth) use and the first investigation of the association of the impact of the periodicity of economic recessions, unemployment rates, and meth arrests on child abuse for the years 2007 to 2017, based on free, open source, and unrestricted data gathered for other purposes. The Hawai`i age-adjusted meth use prevalence was above the national estimates and ranged from 5,050 per 100,000 in 2007 to 3,387 per

100,000 in 2017, with a range from 3,114 to 5,219 per 100,000. The economic and social cost estimates were found to average between 41 to 68 billion dollars (range 15 to 105 billion dollars) when the lost potential from meth use and meth-related impacts were included. The quality-adjusted life-years approach, the Department of

Transportation (DOT) Maximum Abbreviated Injury Scale (range 1-6, 6=death), and the

DOT Value of a Statistical Life were included in the computation of lost potential of meth users and abused children using the RAND and state of Montana approach. When lost potential was not included, the cost of meth use was found to average between 120 to

173 million dollars per year (range 49 to 269 million dollars). The five factors for the cost estimates included: 1) treatment costs, 2) health burden, 3) child endangerment, 4) criminal justice costs, and 5) lost productivity attributable to meth. Meth arrests were found to lag unemployment rates by one year while child abuse lagged about four years behind unemployment and meth arrests, although the Granger causalities were not significant.

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Table of Contents

ABSTRACT ...... iii Table of Tables ...... vii Table of Figures ...... x Table of Acronyms ...... xiii Introduction ...... 16 National Methamphetamine Problem ...... 18 What is Methamphetamine? ...... 33

Effects of Methamphetamine Use ...... 36

Hawai`i Methamphetamine Problem ...... 39 Objective 1. Estimation of the Prevalence of Methamphetamine Users in Hawai`i ...... 43 Objective 1. Introduction ...... 43 Part II Offenses Arrests ...... 55

Methamphetamine Treatment Admissions ...... 57

Wanted Meth Treatment but Did Not Receive ...... 59

Objective 1. Methods ...... 61 Part II Offenses Arrests Methodology ...... 61

Methamphetamine Treatment Admits Methodology ...... 62

Methamphetamine Treatment Wanted but Unavailable Methodology ...... 63

Total Estimated Number of Meth Users in Hawai`i ...... 64

Objective 1. Results ...... 69 Discussion and Public Health Implications ...... 69 Strengths ...... 70 Assumptions and Limitations ...... 70 Objective 2. Estimation of the Economic and Social Cost of Methamphetamine Use in Hawai`i ...... 72 Objective 2. Introduction ...... 72 Factor 1. Cost of Methamphetamine Treatment ...... 76

1.A. Outpatient Drug Treatment Facilities ...... 77 iv

1.B. Inpatient Drug Treatment Facilities ...... 81 Factor 1. Methodology ...... 85 Factor 1. Limitations ...... 86 Factor 2. Health burden ...... 87

2.A. Health burden Associated With Meth Use...... 87 2.B. Health burden associated with meth production ...... 89 Factor 2. Methodology ...... 90 Factor 2. Limitations ...... 91 Factor 3. Meth-related child endangerment ...... 91

3.A. Child Protective Services ...... 92 3.B. Foster Care ...... 94 Factor 3. Methodology ...... 98 Factor 3. Limitations ...... 99 Factor 4. Criminal Justice Costs ...... 100

4.A. Hawai`i Department of Public Safety ...... 101 4.B. Judiciary ...... 103 Factor 4. Methodology ...... 104 Factor 4. Limitations ...... 105 Factor 5. Lost Productivity Attributable to Meth ...... 106

5.A. Premature Mortality Due to Meth ...... 106 5.B. Lost Productivity Associated With Absence for Treatment ...... 109 5.C. Lost Productivity Associated With Meth Use Unemployment ...... 113 Factor 5. Limitations ...... 115 Objective 2. Overall Methodology...... 116 Objective 2. Overall Implications and Discussion ...... 122 Objective 2. Overall Limitations ...... 124 Objective 3. Investigation of the EffectS of Economic Recessions, Unemployment Rates, and Methamphetamine Arrests on Child Abuse in Hawai`i ...... 125 Objective 3. Introduction ...... 125 Objective 3. Methods and Results ...... 138 Objective 3. Discussion ...... 161 Implications for Public Health ...... 163 v

Strengths ...... 164 Limitations ...... 164 Overall Strengths and Limitations ...... 165 Overall Public Health Implications ...... 165 Recommendations ...... 167 References ...... 168

TABLE OF TABLES

Table 1. Drug Testing – Cutoff Levels and Detection Periods for Urinalysis ...... 32

Table 1.1. Estimated Hawai`i Methamphetamine Use Prevalence, 1979-2017 ...... 46

Table 1.2. Hawai`i Methamphetamine Deaths by Year, 2002 - 2017 ...... 51

Table 1.3. Hawai`i Hospital Meth Inpatient Discharges and Emergency Department (ED) Visits, 2007-2016 ...... 53

Table 1.4. Hawai`i Part II Offenses by Age Group, 2007-2017 ...... 56

Table 1.5. Hawai`i ADAM Methamphetamine Inpatient and Outpatient Stays ...... 57

Table 1.6. Hawai`i Methamphetamine Treatment Admits (TEDS-A) by Age Group, 2007-2017 ...... 59

Table 1.7. Discounted Part II Offenses Arrests, 2007-2017 ...... 62

Table 1.8. Hawai`i Discounted Methamphetamine Treatment Admits (TEDS-A) by Age Group, 2007-2017 ...... 63

Table 1.9. Hawai`i Meth Treatment Wanted but Unavailable (23.9%) by Age Group, 2007-2017 ...... 64

Table 1.10. Average Estimated Number of Meth Users in Hawai`i, 2007-2017 ...... 65

Table 1.11. Low Estimate of the Number of Meth Users, 2007-2017 ...... 65

Table 1.12. High Estimate of the Number of Meth Users, 2007-2017 ...... 66

Table 1.13. Age-Adjustment Weights Using the 2000 Projected US Population ...... 66

Table 1.14. Hawai`i Average Prevalence of Methamphetamine Users by Age Group, 2007-2017 ...... 67

Table 1.15. Hawai`i Age-adjusted Meth Prevalence, 2007-2017 ...... 68

Table 1.16. Number and Percentage of Hawai`i Meth Users, 2007-2017 ...... 68

Table 2.1. Number of Hawai`i Treatment Centers ...... 79

Table 2.2. Hawai`i TEDS-A Methamphetamine Treatment Costs, 2007-2017 ...... 80

Table 2.3. Hawai`i Hospital Inpatient Discharges, 2010-2016 ...... 83 vii

Table 2.4. Hawai`i Emergency Department Visits, 2010-2016 ...... 84

Table 2.5. Factor 1: Summary of Hawai`i Methamphetamine Treatment Costs, 2007- 2017 ...... 86

Table 2.6. Value of a Statistical Life (VSL) ...... 89

Table 2.7. Factor 2: Summary of Hawai`i Healthcare and Health Services Attributed to Methamphetamine Use, 2007-2017 ...... 91

Table 2.8. Hawai`i Child Abuse Cases and Estimated Costs of Reduction in Potential (VSL) Due to Meth Use, 2007-2017 ...... 94

Table 2.9. Hawai`i Methamphetamine-related Foster Care, 2007-2017 ...... 97

Table 2.10. Hawai`i Child Protective Service, 2007-2017 ...... 98

Table 2.11. Factor 3: Summary of Hawai`i Meth-related Child Endangerment Costs, 2007-2017 ...... 99

Table 2.12. Hawai`i Department of Public Safety Meth-related Expenditures, 2007-2017 ...... 102

Table 2.13. Hawai`i Judiciary Meth-related Costs, 2007-2017 ...... 104

Table 2.14. Factor 4: Summary of Meth-related Criminal Justice Costs, 2007-2017 . 105

Table 2.15. Lost Potential of Methamphetamine Deaths ...... 109

Table 2.16. Lost Productivity Due to Absence for Outpatient Methamphetamine Treatment, 2007-2017 ...... 110

Table 2.17. Lost Productivity Due to Absence for Hospital Inpatient Treatment for Meth, 2007-2017 ...... 112

Table 2.18. Lost Productivity Due to Absence for Emergency Department Visits for Meth, 2010-2016 ...... 113

Table 2.19. Lost Productivity Due to Meth Use Unemployment for 12.75 Weeks, 2007- 2017 ...... 114

Table 2.20. Factor 5: Summary of Lost Productivity Attributable to Methamphetamine Use, 2007-2017 ...... 115

Table 2.21. VSL Included Summary of Hawai`i Methamphetamine-related Cost Estimates, 2007-2017 (current $ millions) ...... 117

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Table 2.22. No VSL Summary of Hawai`i Methamphetamine-related Cost Estimates, 2007-2017 (current $ millions) ...... 118

Table 2.23. VSL Included Hawai`i Total Methamphetamine Use Cost Estimates, 2007- 2017 ...... 120

Table 2.24. Difference Between VSL and No VSL Hawai`i Total Methamphetamine Use Cost Estimates, 2007-2017 ...... 121

Table 2.25. Lost Productivity Due to Hawai`i Methamphetamine Use, 2007-2017 ...... 122

Table 3.1. Pearson Correlation and Cross-correlation Coefficients for Meth Arrests, Unemployment Rate, BRFSS Depression, and Child Abuse for Hawai`i ...... 140

Table 3.2. Summary of Granger Causality Results ...... 160

TABLE OF FIGURES

Figure 1. Ice Methamphetamine Crystals ...... 17

Figure 2. Most Commonly Reported Drug Threat by Law Enforcement Agencies ...... 19

Figure 3. What drug poses the greatest threat to your area? ...... 20

Figure 4. Agencies Reporting Methamphetamine as the Greatest Drug Threat ...... 21

Figure 5. Drug that Most Contributed to Violent Crime ...... 22

Figure 6. Drug that Most Contributed to Property Crime ...... 23

Figure 7. Drug that Takes Up the Most Law Enforcement Resources ...... 24

Figure 8. Customs and Border Patrol Methamphetamine Seizures along the Southwest Border in 2016 and % Change from 2015 ...... 25

Figure 9. Methamphetamine Trafficking Flows ...... 26

Figure 10. Retail Methamphetamine Price and Purity 1981-2016 in 2016 Dollars ...... 28

Figure 11. Precursors for Amphetamine and Methamphetamine ...... 30

Figure 12. Common Materials Used in Methamphetamine Manufacture...... 31

Figure 13. Eli Lilly Ad for Methamphetamine 1951 ...... 34

Figure 14. Rational Scale of Drug Harm ...... 35

Figure 15. Effects of Methamphetamine Abuse on the Brain ...... 37

Figure 16. Faces of Meth ...... 38

Figure 17. Federal and Hawai`i Methamphetamine Sentences as a Percent of All Drug Sentences 1995 – 2016 ...... 41

Figure 1.1. Hawai'i Age-adjusted Methamphetamine Death Rate per 100,000, 2002- 2018 ...... 50

Figure 1.2. Drug-related Arrest per 100,000 for Oahu, Maui, Hawai`i, and Kauai, 2007- 2017 ...... 54

Figure 1.3. Number of Police per 100,000 for Oahu Maui, Hawai`i, and Kauai, 2007- 2017 ...... 54 x

Figure 1.4. Hawai`i Treatment Methamphetamine Admits (TEDS-A) by Age Group, 2000-2017 ...... 58

Figure 2.1. Hawai`i Consumer Price Index, 1984-2017 ...... 74

Figure 2.2. Hawai`i Public Facility Treatment Admissions by Percent, 1992-2017 ...... 78

Figure 2.3. Number of Hawai`i Meth Labs Destroyed, 2004-2016 ...... 90

Figure 2.4. Distribution of Hawai`i Meth Deaths by 5-Year Age Group, 1999-2018 Combined ...... 107

Figure 2.5. Hawai`i Number of Deaths from Traffic Fatalities and Drugs, 1999-2018 108

Figure 2.6. Total Cost of Methamphetamine Use in Hawai`i, 2007 to 2017 ...... 119

Figure 3.1. Hawai`i Meth Arrests, Unemployment, & Leading Economic Index and US Recessions & Stock Market Crashes ...... 127

Figure 3.2. Hawai`i Adult Civilian Non-institutionalized Percent Depressed, 2006-2016 ...... 134

Figure 3.3. Cross-correlation Analysis: Hawai`i Meth Arrests Lagged by One Year After Unemployment Rate...... 141

Figure 3.4. Cross-correlation Analysis: Hawai`i Child Abuse Lagged by One Year After Unemployment Rate...... 142

Figure 3.5. Cross-correlation Analysis: Hawai`i BRFSS Depression Lagged by One Year After Unemployment Rate ...... 143

Figure 3.6. Cross-correlation Analysis: Hawai`i BRFSS Depression Lagged by One Year After Meth Arrests ...... 144

Figure 3.7. Cross-correlation Analysis: Hawai`i Child Abuse Cases Lagged by Two Years After Meth Arrests ...... 145

Figure 3.8. Cross-correlation Analysis: Hawai`i BRFSS Depression Significantly Negatively Cross-Correlated with Child Abuse Cases and No Lag ...... 146

Figure 3.9. Correlograms of Autocorrelations of Unemployment Rate, Meth Arrests, BRFSS Depression, and Child Abuse with No Differencing ...... 148

Figure 3.10. Correlograms of Autocorrelations of Unemployment Rate, Meth Arrests, BRFSS Depression, and Child Abuse with Differencing = 1 ...... 149

Figure 3.11. Correlograms of Autocorrelations of Unemployment Rate, Meth Arrests, BRFSS Depression, and Child Abuse with Differencing = 2 ...... 150

Figure 3.12. No Differencing vs. Differencing for Cross-correlations between Unemployment Rate and Meth Arrests (left), and Unemployment Rate and Child Abuse (right) ...... 151

Figure 3.13. No Differencing vs. Differencing for Cross-correlations between Unemployment Rate and BRFSS Depression (left), and Meth Arrests and BRFSS Depression (right) ...... 151

Figure 3.14. No Differencing vs. Differencing for Cross-correlations between Meth Arrests and Child Abuse (left), and BRFSS Depression and Child Abuse (right) ...... 152

Figure 3.15. Unemployment Rate and Meth Arrests with Differencing ...... 155

Figure 3.16. Unemployment Rate and Child Abuse with Differencing ...... 155

Figure 3.17. Unemployment Rate and BRFSS Depression with Differencing ...... 156

Figure 3.18. Meth Arrests and Child Abuse with Differencing ...... 156

Figure 3.19. BRFSS Depression and Child Abuse with Differencing ...... 157

Figure 3.20. Meth Arrests and BRFSS Depression with Differencing ...... 157

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TABLE OF ACRONYMS

Acronym Full Name

ACE Study Adverse Childhood Experiences Study

ADAM Arrestee Drug Abuse Monitoring program

ADAD Alcohol and Drug Abuse Division

ADHD Attention Deficit Hyperactivity Disorder

BLS Bureau of Labor Statistics

BRFSS Behavioral Risk Factor Surveillance System

CBHSQ Center for Behavioral Health Statistics and Quality

CBP Customs and Border Protection

CDC Centers for Disease Control and Prevention

CDC Centers for Disease Control Wide-ranging Online Data for WONDER Epidemiologic Research

CEWG Community Epidemiology Work Group

CMEA Combat Methamphetamine Epidemic Act of 2005

CMS Center for Medicare and Medicaid Services

COI cost of illness

CSA Controlled Substances Act

CPI-U Consumer Price Index for All Urban Consumers

DAWN Drug Abuse Warning Network

DEA Drug Enforcement Administration

DoD Department of Defense

DoH Department of Health

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Acronym Full Name

DSM-IV Diagnostic and Statistical Manual for Mental Disorders IV

DTI Quest Diagnostics Drug Testing IndexTM

DUI Driving Under the Influence

DWI Driving While Intoxicated

FDA Food and Drug Administration

GDP Gross Domestic Product

HCUP Healthcare Cost and Utilization Project

HIDTA Hawai`i High Intensity Drug Threat Area

HNTF Hawai`i Narcotics Task Force

International Statistical Classification of Diseases and Related ICD-10 Health Problems-10

LHS Latin hypercube sampling meth methamphetamine

NBER National Bureau of Economic Research

NCS-R National Comorbidity Survey, Replication

NDAA National Defense Authorization Act

NDTA National Drug Threat Assessment

NDTS National Drug Threat Survey

NHIS National Health Interview Survey

NIDA National Institute on Drug Abuse

NIS National Inpatient Sample

NIMH National Institute for Mental Health

NFLIS National Forensic Laboratory Information System

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Acronym Full Name

NPS National Prisoner Statistics

NSDUH National Survey on Drug Use and Health

NSS National Seizure System

N-SSATS National Survey of Substance Abuse Treatment Services

ONDCP United States Office of National Drug Control Policy

P2P phenyl-2-propanone

PCP phencyclidine

PPA phenylpropanolamine

QALY quality-adjusted life-years

RDS Respondent-Driven Sampling

SAMHSA Substance Abuse and Mental Health Services Administration

SAMHDA Substance Abuse and Mental Health Data Archive, Public-use Data PDAS Analysis System

SME Statewide Marijuana Eradication Task Force

TCO Transnational Criminal Organization

TEDS Treatment Episode Data Set

TEDS-A Treatment Episode Data Set - Admissions

tetrahydrocannabinol - principal psychoactive constituent of THC cannabis

UCR Uniform Crime Reports

UNODC United Nations Office on Drugs and Crime

USC United States Code (law)

VSL value of a statistical life

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INTRODUCTION

Methamphetamine (meth) is a major problem in the west, southwest, and central parts of the United States (US) according to the Drug Enforcement Administration

(DEA). (1) Meth, particularly high purity crystal meth also known as ice, poses the greatest drug threat to Hawaiì, Guam, and Saipan. Meth is readily available in most areas of Hawaiì. (2) Hawaiì was one of the first places in the US where the drug made its appearance back in the mid-1980s. Hawaiì has the distinction of leading the US in per capita use of crystal meth or “ice.” (3) Meth is a significant problem in several communities on Oahu, including Ewa Beach, Kalihi, Waianae, and Waipahu. (2)

In Hawai`i, meth has been associated with over 90% of confirmed child abuse cases. (3) Meth is the drug that most contributes to violent and property crime in

Hawai`i. (4) Meth had the highest percentage of any drug (marijuana, cocaine, heroin, opiates, and phencyclidine (PCP)) to be involved in Violent Offenses (24.5%), Domestic

Violence (28%), and Other Offenses (43.6%); marijuana was second in these categories. (5) The economic, public health, societal, and criminal justice impacts to

Hawai`i from the trafficking and abuse of meth are greater than that for any other drug.

The number of annual treatment admissions for methamphetamine abuse in Hawai`i increased by 711.2% from 1992 (295 admits) through 2016 (2393 admits). (6) (7)

However, many users who seek treatment are not admitted because most state-funded treatment programs are operating at maximum capacity. (2)

Hawai`i meth users prefer high purity--averaging over 90%--crystal methamphetamine, which is smoked in glass pipes. Powdered methamphetamine is not commonly abused in Hawai`i. (2) Crystal methamphetamine is a colorless, 16

odorless, smokable form of d-methamphetamine resembling glass fragments, ice shavings, or shiny, bluish-white rocks. (8) It is chemically similar to amphetamine.

Common names for methamphetamine include batu, chalk, crank, crystal, ice, meth, and speed. (3) (9) See Figure 1 for pictures of crystal meth.

Legal methamphetamine is approved by the US Food and Drug Administration

(FDA) for the treatment of attention deficit hyperactivity disorder (ADHD) and exogenous obesity and is marketed in the US and Canada under the trademark name

Desoxyn. (10) (11)

Figure 1. Ice Methamphetamine Crystals

The economic cost of methamphetamine use in Hawai`i is not known. RAND estimated that the economic cost of methamphetamine use in the US reached $23.4 billion (range between $16.2 billion and $48.3 billion) in 2005, the most recent year an analysis was completed. RAND included the burden of addiction, child endangerment, premature death, drug treatment, other health costs, lost productivity, crime, and criminal justice costs. In terms of lost quality of life years, the burden of methamphetamine use was 44,313 years (range between 32,574 and 74,004). (12)

National Methamphetamine Problem

US law enforcement agencies are surveyed annually by the DEA as to the greatest illicit drug threat via the National Drug Threat Survey (NDTS). The 2017

National Drug Threat Assessment (NDTA) reports the results of 2016 National Drug

Threat Survey (n=5155 agencies) and its results are shown in Figure 2 with 44.1% of agencies reporting heroin as their greatest drug threat, 29.8% methamphetamine, 9.3% controlled prescription drugs, and 6.3% fentanyl. (13)

Note that the federal fiscal year is from October to the following September and the Hawai`i state fiscal year is from July to the following June, while other reports are for the calendar year, but all fiscal years will be treated as calendar years. Federal databases use calendar years.

Figure 2. Most Commonly Reported Drug Threat by Law Enforcement Agencies Source: 2016 National Drug Threat Survey (n=5155) (13)

There are US regional and geographic differences in drug threats with methamphetamine in the West compared with heroin in the Northeast which can be seen in Figure 3. The Midwest and Western US had the highest concentrations of respondents who reported methamphetamine as the greatest drug threat in their area.

Figure 4 shows the locations of individual agencies where methamphetamine was reported as the greatest drug threat. Methamphetamine is widely available throughout the US, with the highest availability in the West and Midwest. (13)

Figure 3. What drug poses the greatest threat to your area? Source: 2016 National Drug Threat Survey (n=5155)

Law enforcement agencies assessed that the drug which most contributed to

violent crime was meth, 36.3%, followed by heroin, 25.8%, see Figure 5. (13) As the euphoric effects of meth begin to diminish, users enter a stage called tweaking in which

they are prone to violence, delusions, and paranoia. During the tweaking stage, the

meth user often has not slept for days and, consequently, is extremely irritable. The

tweaker also craves more meth, which results in frustration and contributes to anxiety

and restlessness. In this stage, the abuser may become violent without provocation.

Many users try to buffer the effects of the meth "crash" with other drugs such as cocaine

or heroin. (8)

Figure 4. Agencies Reporting Methamphetamine as the Greatest Drug Threat Source: 2016 National Drug Threat Survey (n=1536 or 29.8%)

Data from surveys of arrestees and the household population in the US suggest there is only modest overlap among demand for the big three expensive illegal drugs

(cocaine/crack, heroin, and methamphetamine). The number of chronic users of these illegal drugs (defined as consuming on four or more days in the previous month) was only about 10% below a naïve estimate obtained by simply summing the numbers of chronic users for each of the three substances, while ignoring polydrug use entirely. In determining which illegal drug contributes the most towards crime, violence, and overdose death in the US, one can usefully think of three more or less separate markets populated at any given time by largely distinct populations of drug users. (14)

The drug which most contributed towards property crime was assessed to be heroin, 38.5%, followed by methamphetamine at 31.9%, see Figure 6. In Hawai`i, 90% of property crime was believed to be drug related. (3)

Figure 5. Drug that Most Contributed to Violent Crime Source: 2016 National Drug Threat Survey (n = 5155)

Figure 6. Drug that Most Contributed to Property Crime Source: 2016 National Drug Threat Survey (n = 5155)

The drug that took up the most law enforcement resources was heroin, 36.1% and then methamphetamine, 30.0% as shown in Figure 7. In the Hawai`i Drug Court, meth was usually the drug of choice but generally it was mixed with alcohol and marijuana. (3)

Figure 7. Drug that Takes Up the Most Law Enforcement Resources Source: 2016 National Drug Threat Survey (n = 5155)

Mexican criminal groups transport crystal and powdered meth from the West

Coast into Hawai`i while Asian criminal groups transport crystal meth from the West

Coast and Asia into Hawai`i, and both groups distribute the drug at the wholesale level.

Meth is typically transported using couriers on commercial flights or via package delivery services. Local independent dealers convert some powdered meth into crystal meth and distribute it at the retail level. Street gangs, local independent dealers, and outlaw motorcycle gangs distribute meth at the retail level. (2)

Most of the meth available in the US is produced clandestinely in Mexico and smuggled across the Southwest border. In all areas of the Southwest border, 2016 meth seizures increased from 2015, ranging from 9% in the San Diego, California area to

1431% in the Big Bend, Texas area. Customs and Border Protection (CBP) meth seizures in 2016 along the Southwest border and the percent change in seizures from 24

2015 are shown in Figure 8. Domestic production continues to occur at much lower levels than in Mexico and seizures of domestic meth laboratories have declined since

2010.

Figure 8. Customs and Border Patrol Methamphetamine Seizures along the Southwest Border in 2016 and % Change from 2015

In 2016, most of the seized domestic laboratories were small-capacity production laboratories, known as the “one-pot” or “shake-and-bake” meth laboratories. Generally, these laboratories are small-scale, easy to conceal, and produce two ounces or less of meth per batch. The ingredients are common household items (e.g. pseudoephedrine or ephedrine tablets, lithium batteries, camp fuel, starting fluid, cold packs, and drain cleaner), which are mixed in a container such as a plastic soda bottle. (13) (15)

A map of the main worldwide meth trafficking routes in 2012-2016 from the

United Nations Office on Drugs and Crime (UNODC) World Drug Report 2018 is shown in Figure 9. (16) As can be seen in the map, the two major seizures areas for meth are the US, and East and Southeast Asia.

The annual DEA National Drug Threat Assessment reports price and purity data lagged by about one and a half years, and provides updates for the previous five years.

Meth was first regulated in the Controlled Substances Act (CSA) as Title II of the

Comprehensive Drug Abuse Prevention and Control Act of 1970. The CSA serves as the legal foundation of the government's fight against drugs of abuse. It was updated in

2016 in Title 21 United States Code (USC) Controlled Substances Act.

Figure 9. Methamphetamine Trafficking Flows Source: United Nations Office on Drugs and Crime: World Drug Report 2018 26

Mexican transnational criminal organizations (TCOs) continued production of

large kilogram quantities of low-cost, high-purity meth indicates an oversupply of meth

in Mexico. Due to this consistently high production, meth prices in the US remain at

record lows and purity remains at record highs. Prices also likely remain low due to increased supply, as more trafficking organizations have become involved in wholesale- level meth trafficking. To counteract the falling price of meth, Mexican TCOs are attempting to expand the US meth market to the East Coast to market the drug to new users. The retail meth price and purity from 1981 to 2016 is shown in Figure 10. (13)

(17) (18)

In 1986, the National Security Decision Directive 221 allows the Secretary of

Defense to support counter narcotics efforts by using military assets such as personnel, ships, aircraft, radar, etc. The 1989 National Defense Authorization Act (NDAA) designated the Department of Defense (DoD) as the lead for Detection and Monitoring agencies to combat trafficking. In response DoD created several Joint Task Forces for implementation of provisions of the NDAA. These timelines are included in the price and purity chart in Figure 10 to inspect for shocks to the price and purity.

Figure 10. Retail Methamphetamine Price and Purity 1981-2016 in 2016 Dollars

The 1996 Comprehensive Methamphetamine Control Act (CMEA) regulates mail order and chemical companies selling precursor chemicals. The CMEA of 2005 added regulations for retail, over-the-counter sales of ephedrine, pseudoephedrine, and phenylpropanolamine products with daily sales limits and 30-day purchase limits, placement of product out of direct customer access, sales logbooks, customer identification (ID) verification, employee training, and self-certification of regulated sellers. (19) The effects of the 2005 CMEA and the 2007 ban on pseudoephedrine in

Mexico may be seen with higher prices and lower purity for methamphetamine a year later. However, beginning in 2008, the pattern reversed as prices began dropping and purity began increasing. Meth producers shifted to using precursors other than

pseudoephedrine. (15) 28

There are many alternative routes to create meth as seen in Figure 11 per

UNODC, Laboratory and Scientific Section. The national average purity was 93.5% and the cost per pure gram of ice was $58 in 2016. (13) The unintended consequence of the shift away from using pseudoephedrine lead to purer meth. One packet of an artificial sweetener such as SplendaTM or Sweet’n LowTM contains one gram. Each gram of pure crystal meth can provide about 30 doses. (8) At the street user level, the price is about $25 per 1/4 gram, with a common user dose of meth of 0.1-1 g/day, and up to 5 g/day in chronic binge use. (20)

The FDA is taking steps to remove phenylpropanolamine (PPA) from all drug products and has requested that all drug companies discontinue marketing products containing PPA. (21) These products may be used to make meth with recipes easily available on the internet and no chemistry training. Common items which many people have at home that are necessary for manufacturing meth include acetone – available as nail polish remover, rubbing alcohol, salt, muriatic acid – used as swimming pool clarifiers, and coffee filters among others. A list may be seen in Figure 12. (22)

Figure 11. Precursors for Amphetamine and Methamphetamine Source: United Nations Office on Drugs and Crime, Laboratory and Scientific Section

The National Forensic Laboratory Information System (NFLIS) collected drug identification results from 906,691 drug cases submitted by federal agencies (DEA and

CBP), all 50 States, and 101 local laboratories in the US in 2016 and analyzed by

March 31, 2017, representing over 98% of laboratories. (23) The top four drugs identified were cannabis/THC (24.13%), meth (20.28%), cocaine (14.82%), and heroin

(11.20%) from 1,552,720 drug reports developed from the drug cases. In the West1 the top four most frequently identified drugs were meth (44.30%), cannabis/THC (17.93%), heroin (12.15%), and cocaine (6.60%). Meth cases reported to NFLIS in 2016 (314,872

1 Alaska, Arizona, California, Colorado, Hawai`i, Idaho, Montana, New Mexico, Nevada, Oregon, Washington, and Wyoming 30

reports) increased by 15.4% over 2015 (272,823 reports) and by 133.4% from 2009

(134,891 reports). They have grown from representing 8% of all cases in 2009 to 20%

of all cases in 2016. Meth reports increased from 2001 through 2005, decreased from

2005 through 2010, and have continued to increase each year since 2011. The

Honolulu Police Department is the sole participant in NFLIS for the state of Hawai`i and performs drug tests for other police departments within the state.

Figure 12. Common Materials Used in Methamphetamine Manufacture. Source: DEA, Methamphetamine: “Meth 101”, DEA Chemical Industry Conference, September 17-18, 2008, Atlanta, GA

Positive tests for meth skyrocketed in the Midwest and South for the combined

(federally mandated and general) US workforce, indicating that meth popularity is spreading beyond the Western regions of the US. (24) The 2017 Quest Diagnostics

Drug Test IndexTM (DTI) annual report announced that the combined US workforce showed a positive drug rate of 4.2% out of over 10 million urinalysis. Of the more than

340,000 positive urine drug tests, 47.35% showed marijuana, 22.44% amphetamines,

7.16% opiates (includes heroin), and 7.10% benzodiazepines, with cocaine at 5.45%.

Often, an employee testing positive for certain illicit drugs can be fired, but they may continue to use illicit drugs within the detection period. Quest Diagnostics is the largest drug test company in the US providing drug testing for current and potential employees.

The drug testing cutoff levels and detection periods for urinalysis are listed in Table 1.

Table 1. Drug Testing – Cutoff Levels and Detection Periods for Urinalysis

Source: ADAM II 2013 Annual Report 32

What is Methamphetamine?

Methamphetamine was synthesized from ephedrine in 1893 by the Japanese chemist Nagayoshi Nagai. (10) Mr. Nagai had previously isolated ephedrine from the plant Ephedra distachya in 1885. Methamphetamine was later synthesized in crystalline form in 1919 by Akira Ogata. During World War II, amphetamine and methamphetamine were used extensively by both the Allied and Axis forces for their stimulant and performance-enhancing effects. (25) (26) Meth and amphetamine were given to Allied bomber pilots to sustain them on long flights, however the pilots became irritable and couldn’t channel their aggression. (27) Meth was formerly legal and advertised, see the Eli Lilly pharmaceutical ad in Figure 13.

Meth is a white, odorless, bitter-tasting crystalline powder that easily dissolves in water or alcohol and is taken orally, by snorting, injection, or smoking. (28) It increases the release and blocks the reuptake of the neurotransmitter dopamine, leading to high levels of the chemical in the brain. Meth’s ability to release dopamine rapidly in reward regions of the brain produces the intense euphoria, or “rush,” that many users feel after snorting, smoking, or injecting the drug. There are no medications at this time approved to treat meth addiction; however, this is an active area of research for NIDA. (9)

Figure 13. Eli Lilly Ad for Methamphetamine 1951

Due to its high potential for abuse, methamphetamine is classified as a Schedule

II drug by the DEA. (29) Drugs, substances, and certain chemicals used to make drugs are classified by the DEA into five distinct categories or schedules depending upon the drug’s acceptable medical use and the drug’s abuse or dependency potential.

Schedule II drugs, substances, or chemicals are defined as drugs with a high potential for abuse, less abuse potential than Schedule I drugs, with use potentially leading to severe psychological or physical dependence. A few examples of Schedule II drugs are: cocaine, methamphetamine, methadone, hydromorphone (Dilaudid), meperidine

(Demerol), oxycodone (OxyContin), fentanyl, Dexedrine, Adderall, and Ritalin. A comparison of the physical harms and dependence caused by various illicit drugs, tobacco, and alcohol, is shown in Figure 14.

Figure 14. Rational Scale of Drug Harm

Effects of Methamphetamine Use

Smoking or injecting meth puts the drug very quickly into the bloodstream and

brain, causing an immediate, intense “rush”. The rush, or “flash,” lasts only a few minutes and is described as extremely pleasurable. Snorting or oral ingestion produces euphoria—a high, but not an intense rush. Snorting produces effects within 3 to 5 minutes, and oral ingestion produces effects within 15 to 20 minutes. The half-life is

around 12 hours. Because the pleasurable effects of meth disappear even before the

drug concentration in the blood falls significantly, users try to maintain the high by taking

more of the drug. In some cases, users indulge in a form of binging known as a “run,”

foregoing food and sleep while continuing to take the drug for up to several days.

Short-term effects of meth includes increased activity and talkativeness, decreased

appetite, and a pleasurable sense of well-being or euphoria. (30) (31) At low doses,

meth also blocks the re-uptake of dopamine, but it also increases the release of

dopamine, leading to much higher concentrations in the synapse (the gap between

neurons), which can be toxic to nerve terminals. (30)

Chronic meth abuse significantly changes how the brain functions, reducing

motor skills and impairing verbal learning. Severe structural and functional changes in

areas of the brain associated with emotion and memory have been demonstrated, which may account for many of the emotional and cognitive problems observed in chronic meth users. Some of these changes persist long after meth abuse is stopped.

Repeated meth abuse may lead to addiction. (28)

Long-term effects include extreme weight loss, severe dental problems, intense itching, anxiety, confusion, violent behavior, paranoia, hallucinations, changes in the

brain's dopamine system, and negative effects on non-neural brain cells called microglia

(holes in the brain). (10) (15) (32) Meth abuse greatly reduces the binding of dopamine to dopamine transporters in the striatum which is important in memory and movement.

The development of holes in the brain despite a 14 months abstinence from methamphetamine abuse even as the dopamine transporters regain function can be seen in Figure 13. (30) (32)

Figure 15. Effects of Methamphetamine Abuse on the Brain

Some of the effects of meth use on physical appearance may be seen in Figure

16 as shown in The Oregonian, a Portland, OR newspaper.

Figure 16. Faces of Meth Source: The Oregonian, May 2005

Hawai`i Methamphetamine Problem

Hawaiì has the largest per capita meth problem in US. (3) Meth poses the greatest drug threat to the Hawaiì due to its association with violent crimes, theft and wide spread availability. Law enforcement agencies in the Hawaiì High Intensity Drug

Threat Area (HIDTA) group consistently report that ice meth is the greatest drug threat in their jurisdictions. There were 2,612 drug-related arrests in 2010, of which 1,334

(51%) were methamphetamine-related. (4; 33) Both Hawai`i male and female urinalysis tests were the highest for meth among the 35 ADAM sites2. (2) (5)

Hawaiì has the nation's highest rate of adults who have tried ice. (3) (34) Two types of meth are available in Hawaiì - crystal and powdered. (2) High purity crystal meth is the most prevalent form available with two primary types of crystal meth. The first type is known as clear, which is white and highly refined. The second type is known as wash, which is brown, less highly refined, and has been washed using acetone and alcohol to improve its appearance. (2) Ice meth is preferred over meth in powder or tablet form in Hawaiì. (4)

The NIDA Community Epidemiology Work Group (CEWG), 2004-2010, reported that meth was the primary drug of abuse in 59% of treatment admissions in Hawaiì, not including alcohol. (34) Hawaiì continues to have the nation’s highest proportion of meth treatment admissions which has increased since 2010. Meth and marijuana are consuming more drug treatment resources in Hawaiì than all other drugs combined.

In Hawai`i, meth has been associated with over 90% of confirmed child abuse cases. (3) Meth is the drug that most contributes to violent and property crime in

2 Honolulu was only included in the ADAM program from 2000 to 2003 39

Hawai`i. (4) Meth had the highest percentage of any drug (marijuana, cocaine, heroin, opiates, and PCP) of involvement in Violent Offenses 24.5%, Domestic Violence 28%, and Other Offenses 43.6%; marijuana was second in these categories. (5) The economic, public health, societal, and criminal justice impact to Hawai`i from the trafficking and abuse of ice meth is greater than that for any other drug.

The estimated cost of methamphetamine use was $23.4 billion across the US in

2005. (12) The Hawai`i Meth Project gave an estimate of the annual health care costs related to methamphetamine use in Hawai`i of more than $700 million in 2005, but provided no details as to their methodology. (35)

Federal drug sentences for meth offenses in Hawaiì have been higher compared the US as a whole since 1995 (18% vs. 7.6%, respectively). The next year, in 1996, federal meth sentences rose to 60.5% in Hawaiì while nationally it was 9.7%. Hawaiì has shown an overall increasing trend for federal meth sentences topping out thus far at

93.5% in 2015 compared to national at 28.5%. See Figure 17 for federal and Hawai`i meth sentences as a percent of all drug sentences between 1995 and 2016. (36)

Literature Search. The author works at a DoD counterdrug agency. Government produced and/or sponsored drug reports and databases as well as reports from private entities such as the RAND Corporation, Abt Associates, and The Lewin Group were known as a consequence of her employment. A scan of the references in the government-sponsored reports yielded additional references and resources. A goal of this dissertation was to use publicly available, free, non- restricted databases to address the three study objectives below. There are government sponsored databases that contain additional data but payment is required. Searches using the terms

“methamphetamine”, “meth” did not reveal any new Hawai`i state or national meth databases beyond already known government sources, but a search for “meth treatment” yielded Hawai`i state prevalence data for certain years from 1979 to 2016, however these data are inconsistent and will be discussed in the Objective 1 section.

Figure 17. Federal and Hawai`i Methamphetamine Sentences as a Percent of All Drug Sentences 1995 – 2016 Source: United States Sentencing Commission

There have been six nationwide and one state study of the costs of illicit drugs in the US:

1. The Economic Cost of Drug Abuse in the United States 1992-2002 (37)

2. The Economic Cost of Methamphetamine Use in the United States, 2005 (38)

3. The Economic Impact of Illicit Drug Use on American Society, 2007 (39)

4. What America’s Users Spend on Illegal Drugs: 1988-1998 (40)

5. What America’s Users Spend on Illegal Drugs: 2000-2010 (41)

6. What America’s Users Spend on Illegal Drugs: 2006-2016 (42)

7. The Economic Cost of Methamphetamine Use in Montana, February 2009 (43)

Studies 2 and 7 will be discussed in the section Objective 2 for methodologies to estimate the economic and social costs of meth abuse in Hawai`i. All drugs were considered in Studies 1, 3, 4, 5, and 6 but are too generic, therefore they are only useful for general background.

Objectives

This dissertation has three objectives: 1) Estimation of the prevalence of methamphetamine users in Hawai`i; 2) Estimation of the economic and social cost of methamphetamine use in Hawai`i; and 3) Investigation of the effects of economic recessions, unemployment rates, and methamphetamine arrests on child abuse in

Hawai`i. Each Objective will be written as an independent section for publication and there may some duplication of figures, tables, and references.

OBJECTIVE 1. ESTIMATION OF THE PREVALENCE OF METHAMPHETAMINE

USERS IN HAWAI`I

Objective 1. Introduction

The number of methamphetamine (meth) users in the US was estimated at

1,011,000 in 2003 with an adult (18+ years) population of 212,622,000 to give an estimated national prevalence of 0.004754 or 475 per 100,000 persons. (38) In 2016, the number of meth users in the US was estimated at 3.2 million with a low estimate of

1.3 million and a high estimate of 5.3 million for a population of 324,118,787. (42)

The Hawai`i Department of Health (DoH), Alcohol and Drug Abuse Division

(ADAD) conducted a series of substance abuse treatment needs assessments of 5,000-

6,000 residents via telephone surveys of adult households in the years 1979, 1985,

1991, 1995, 1998, and 2004, titled variously: Treatment Needs Assessments,

Substance Abuse in Hawai`i, or Hawai`i Adult Survey of Substance Use and Treatment

Needs. (44) (45) The first survey in 1979, estimated meth use prevalence at 6.3% of the adults age 18 and older. All the surveys found very low prevalence for sedatives, stimulants, analgesics, and inhalants. Smoking has always been the most common means of drug ingestion in Hawai`i for meth, marijuana, crack cocaine, and heroin.

The last Hawai`i substance abuse treatment needs assessment phone survey of

5050 people in 2004 showed an estimate of 15,186 persons in need of drug treatment for all drugs. (46) In 2004, the adult sample was restricted to 18-65 years of age with no reason given for the restriction. The percentage needing treatment was reported as

0.7% for 1995, 0.9% for 1998, and then jumped to 23.9% for 2004. The estimates of meth prevalence and treatment needs are not consistent. There were many more 43

persons in need of treatment services for alcohol and drug abuse and dependence than could be treated within the State system of care as treatment admissions for meth increased nearly tenfold from 1991 to 2005. (47)

Crystal meth was already causing problems as far back as 1985, and as of 2003,

Hawai`i had among the highest rates of use across a wide variety of measures.

Dependence on crystal meth or other amphetamines was defined as meeting three of the nine diagnostic criteria of the American Psychiatric Association's Diagnostic and

Statistical Manual of Mental Disorders, 3rd revised edition (DSM-III-R). These criteria should have persisted for at least one month or occurred repeatedly over a longer period. The nine criteria measure substance tolerance and withdrawal, problems in meeting social role expectations, and failed attempts to control substance use. (45) In

1991, lifetime meth use was found to be 3.8%, and by 1998, lifetime use was found to be 11.9% for adults age 18 years and older. (48) Meth use prevalence also includes people who use meth but are not dependent.

The State of Hawai`i obtained its estimate of meth prevalence from the National

Survey on Drug Use and Health (NSDUH) from 2006 to 2011, but from 2012 to 2014, no meth prevalence estimates are given. The 2014 State of Hawai`i Epidemiological

Profile: Selected Youth and Adult Drug Indicators, based on NSDUH 2006-2007, 2008-

2009, and 2010-2011 combined surveys, reported that the adult percentage of ever using methamphetamine was 6.5%, 95% CI (4.8, 8.7) in 2006-2007; 8.9%, 95% CI (6.5,

12.0); and 7.1%, 95% CI (5.1, 9.6) in 2010-2011. (49) The NSDUH questionnaire underwent a partial redesign in 2015. NSDUH says that meth estimates from 2015 onward should not be compared to previous meth estimates. At the state level, the new

NSDUH prevalence estimates for meth are only available for the combined years 2015-

2016 and 2016-2017, which for Hawai`i was 1.00%, 95% CI (0.61, 1.62) and 0.79%,

95% CI (0.47, 1.32) respectively. (50) The Hawai`i NSDUH meth use prevalence for

2015-2016 and 2016-2017 is much lower than all the other surveys and does not seem

plausible compared with previous years’ estimates at 3.8% to 11.9% for Hawai`i state

surveys 1979-1998, and 6.5% to 8.9% using Hawai`i NSDUH data for 2006 to 2011.

See Table 1.1 for a summary of available meth prevalence estimates and other data for

Hawai`i. The exact reasons for the order of magnitude difference in the 2015-2016 and

2016-2017 NSDUH Hawai`i results compared to all previous surveys is unknown. The

Hawai`i state meth prevalence will be estimated for the years 2007 to 2017 and is expected to be greater than 475 per 100,000 in 2007 and 988 per 100,000 in 2016.

Population or general household surveys are the usual method of prevalence estimation, but this method severely underestimates socially undesirable behavior such as illicit drug use. Drug users are less likely to live in households that are included in general household surveys, are less likely to participate in surveys, and also are not likely to report illicit drug use. Indirect methods of estimating the prevalence of socially undesirable behavior may be used instead. An approach is to sample members of these hard-to-reach populations where they congregate such as in jails and in treatment programs. However, samples drawn at collection points are not random samples from the general population. The prevalence of a disease or undesirable behavior can be crucial to secure resources for appropriate responses in terms of treatment and other measures to reduce the harm of illicit drug use. (51)

Table 1.1. Estimated Hawai`i Methamphetamine Use Prevalence, 1979-2017 Hawai`i Users Age Age 18- Age 25- Age 45+ Needs % Male Female Year pop age 18+ Total % 24 years 44 years years Meth Needing (%) (%) 18+ (95% CI) (%) (%) (%) Treatment Treatment 1979 6.3a (46) 1991 3.8 4.9 2.9 5.4 6.8 1.5 (46) (48) 1995 885,002 4.5 11.1 3.2 0.7 (45) 1998 11.9 895,414 15.5 8.5 9.2 14.9 10.2 8100 0.9 (48) (+ 0.5%) 2004 763,938b 5.4 3.3 5.4 5.3 3.1 9164 23.9 (46) 2006-2007 6.5

(52) (4.8, 8.7) 2008-2009 8.9

(47) (6.5, 12.0) 2010-2011 7.1

(52) (5.1, 9.6) 2015-2016 1.00

(53) (0.61, 1.62) 2016-2017 0.79 5 (50) (0.47, 1.32) a Non-prescribed stimulants. b Restricted to be age 18-65 years.

Other approaches to estimating prevalence include link-tracing network sampling or Respondent-Driven Sampling (RDS) (54); internet survey of noninstitutionalized adults (55); Latin hypercube sampling (LHS) and reweighting parameter sets (56); and capture-recapture methods, but these techniques have their own problems and do not apply to this estimation problem. RDS is a form of link-tracing network sampling, in which subsequent sample members are selected from among the social connections of current sample members. The internet survey recruits subjects from a pool of people who have consented to be contacted for public opinion surveys distributed via the internet. LHS is an efficient means of sampling and combining parameters to generate a collection of K sample parameter sets, which gives coverage of the entire distribution for each parameter. Capture-recapture methods involve collating data across a series of different data sources, each of which uniquely records all individuals in the target population observed by that source. (57) The data sources used for this study do not allow for tracking individuals. A study using the logistic regression synthetic estimation approach calculated prevalence for different drugs for California but it found that estimating meth prevalence was less reliable than desired. (58) Thus the logistic regression synthetic prevalence estimation method will not be used because of reliability issues.

The determination of the number of people using meth is difficult due to a variety of reasons. Different surveys estimated the number of meth users in various groups, including arrestees in the Arrestee Drug Abuse Monitoring (ADAM) program (5), within households in the NSDUH (50), the National Survey of Substance Abuse Treatment

Services (N-SSATS) (59), and the Substance Abuse and Mental Health Services 47

Administration’s (SAMHSA), Treatment Episode Data Set - Admits (TEDS-A) (60), and

drug-related visits to hospitals and emergency departments (61).

The ADAM program was implemented in the City and County of Honolulu3

(Honolulu) for the years 2000-2003. Arrestees were asked to complete a survey and for

permission to perform a urinalysis for illicit drugs. The Honolulu ADAM data found that

40 to 50 percent of the arrestees tested positive for meth, more than any other national site. In addition, the majority of these arrestees were not picked up for drug offenses but rather for bench warrants and misdemeanor offenses. (46) According to the Center for Behavioral Health Statistics and Quality (CBHSQ) within SAMHSA, NSDUH undercounts the number of meth users by 3.7-fold compared to ADAM. (62)

NSDUH conducts in-person interviews of sample US household residents 12 years and older [individuals living in houses/townhouses, apartments, and condominiums; civilians living in housing on military bases, etc. and individuals in non- institutional group quarters (e.g., shelters, rooming/boarding houses, college dormitories, migratory workers' camps, halfway houses)], but lacks information about individuals who are either homeless, living in short-stay shelters, institutionalized, or in transient living arrangements, i.e., living in different residences throughout the year.

Many heavy users of illegal drugs often find themselves in these more transient circumstances and, consequently, may be missed by NSDUH. The NSDUH survey is self-reported and the survey methodology may cause respondents to answer questions based upon their perception of their interviewer’s desired response. (50)

3 Includes the entire island of Oahu. 48

The Behavioral Risk Factor Surveillance System (BRFSS) is a national system of health-related telephone surveys, but it does not contain questions about meth use. (63)

N-SSATS is a point-prevalence survey on or about March 31 of each year, but it does not report the specific illicit substance. (64) For Hawai`i, only 2017 N-SSATS meth- related data is available. The TEDS-A data gives the number of treatment admissions per year for a specific illicit substance, but not the number of unique individuals. (6)

The number of meth deaths yields another component of meth use prevalence.

The number of meth deaths by year may be found using the CDC Wonder database.

CDC Wonder suppresses data when the number is less than 10. Figure 1.1 shows that the age-adjusted meth death rate is increasing while Table 1.2 contains the number of meth deaths (T46.3) and rate by year for Hawai`i. (65)

The national age-adjusted rate of drug overdose deaths involving psychostimulants with abuse potential (T46.3), which include drugs such as methamphetamine, amphetamine, and methylphenidate, increased from 0.2 per

100,000 in 1999 to 0.8 per 100,000 in 2012. From 2012 through 2018, the rate increased on average by 30% per year to a rate of 3.9 per 100,000 in 2018. (66)

The Hawai`i age-adjusted meth death rate ranged from 2.63 per 100,000 in 2006 to

9.25 per 100,000 in 2017, higher than the national rate. (65)

12 Hawai'i

10 National

8 y = 0.053x2 - 0.4877x + 3.3746 R² = 0.9369 6

Death Rate per 100,000 per Rate Death 2

Year Figure 1.1. Hawai'i Age-adjusted Methamphetamine Death Rate per 100,000, 2002- 2018 Source: CDC Wonder (T43.6)

Table 1.2. Hawai`i Methamphetamine Deaths by Year, 2002 - 2017 Age-Adjusted Year Deaths Rate Per 100,000 2002 20 1.62 2003 29 2.27 2004 41 3.21 2005 44 3.37 2006 34 2.63 2007 38 2.89 2008 31 2.32 2009 43 3.16 2010 48 3.55 2011 54 3.81 2012 48 3.31 2013 63 4.59 2014 71 5.09 2015 100 6.76 2016 129 8.56 2017 133 9.25 2018 163 10.97 Source: CDC Wonder (T43.6)

Hospitalization and Emergency Department (ED) data can provide another

source of meth users, but it is likely that a hospital admission or ED visit would result in

placement in treatment. The International Classification of Diseases, Ninth Revision,

Clinical Modification (ICD-9) classification of 969.72, amphetamines which includes meth, was used to obtain hospital inpatient and ED visits for Hawai`i for the years 2009

– 2016, data is not available for other years. Neither the ICD-9 nor ICD-10 diagnostic

codes discriminate between methamphetamine use, other illicit amphetamine use, and

nonmedical use of prescription amphetamines, but evidence indicates that such codes

primarily represent meth use in Hawai`i. (61) The data for ICD-10 which replaced ICD-9

on October 1, 2015, are available for 2016. (67) The number of Hawai`i hospital

admissions and ED visits for amphetamines/meth (ICD-9, 969.72) are shown in Table

1.3. These data will not be used because of limited data with only six years of data for

hospital admits and four years of data for ED admits, a small number of hospital and ED

admits, and no ages are available. Note that the cells with < 10 are not usable as the

number may range from 1 to 10.

The number of meth-related arrests can provide one aspect of meth prevalence,

but that would not include people arrested for other offenses who are using meth. The

Uniform Crime Reports (UCR) is available through the US Department of Justice and

the State of Hawai`i, Crime Prevention and Justice Assistance Division (68). The UCR

contains the number of arrests by state and local authorities, both nationally and by

state, for various alleged crimes including the possession and manufacture of assorted

illicit drugs such as meth. The number of arrests do not indicate the number of people

arrested since the UCR does not track individuals, and a person may be arrested more

than once per year.

Table 1.3. Hawai`i Hospital Meth Inpatient Discharges and Emergency Department (ED) Visits, 2007-2016 Number Number of ED visits with Discharged from Hospital Admission to ED (no hospital Year Discharges Same Hospital admits from ED) 2007a 2008a 2009b < 10 < 10 < 10 2010 23 22 20 2011a 22 2012a 33 2013 28 26 12 2014 21 20 22 2015b < 10 < 10 < 10 2016 58 55 20 Source: Healthcare Cost and Utilization Project (HCUPnet), Agency for Healthcare Research and Quality, ICD-9 969.72. a No information available. b 10 or less observed values. Due to the transition from ICD-9-CM to ICD-10-CM in October 2015, 2015 statistics were calculated using only quarters 1-3 data. 2016: ICD10 T43.621-T43.625 including subcategories. Note: The hospital discharges include those patients who were admitted through the ED.

The drug-related arrest rate per 100,000 for the four largest counties in Hawai`i

was investigated to determine if the four largest counties differed in arrest rates and is

shown in Figure 1.2. Maui differed from the other counties in having the largest rate of

arrests, but Figure 1.3, set on the same scale as Figure 1.2, shows that the number of

police per 100,000 population on Maui is not significantly different from the other

counties while Kauai has fewer number of police.

Figure 1.2. Drug-related Arrest per 100,000 for Oahu, Maui, Hawai`i, and Kauai, 2007-2017 Source: State of Hawai`i, Crime Prevention and Justice Assistance Division, Uniform Crime Reports

Figure 1.3. Number of Police per 100,000 for Oahu Maui, Hawai`i, and Kauai, 2007-2017 Source: State of Hawai`i, Crime Prevention and Justice Assistance Division, Uniform Crime Report 54

The reason for the differences in drug arrest rates in the four counties may be due to the Taguma effect. Officer Keith Taguma was a 30-year veteran of the Maui

Police Department who retired in December 2014. He was responsible for writing

16.5% of traffic tickets on Maui in 2013 and the most prolific ticket writer ever in the

Maui Police Department. (69) Inspiration or competition of other police officers is speculated.

The following was used to estimate the prevalence of meth users in Hawai`i: Part

II Offenses arrests, TEDS-A meth treatment admits, and the number of users who wanted treatment but were unable to obtain.

Part II Offenses Arrests

The ADAM program tested arrestees, through urinalysis, for marijuana, cocaine, opiates, amphetamine/methamphetamine, barbiturates, benzodiazepines, propoxyphene, phencyclidine, methadone, and oxycodone. (51) Methamphetamine is excreted primarily unchanged, with a small fraction as amphetamine (44% and 6%, respectively). (5) These data provide a “gold standard” of proof of use and are not subject to changes in patterns of “truth telling” regarding drug use over time, by age of the respondent, or stigmatization of the drug. Because 78% of ADAM arrestees never sought treatment for drug or alcohol abuse as indicated in an interview, they are also missing from treatment provider data such as TEDS-A, which collects data on substance abuse treatment admissions. (6)

The ADAM data showed that 40% to 50% of the arrestees tested positive for meth and the majority of these arrestees were not picked up for drug offenses but rather 55

for bench warrants and misdemeanor offenses. (46) Part II Offenses include Violent

(negligent manslaughter, other assault, and sex offenses); Property-related; Drug

Manufacturing/Sale; Drug Possession; Gambling; Alcohol-Related; and Other Offenses, but it does not include traffic-related offenses. The Index offenses are more serious and include murder, rape, robbery, aggravated assault, burglary, larceny-theft, motor-vehicle theft, arson, and human trafficking. (68) The total number of Part II Offenses arrests by age groups for the years 2007 to 2017 is shown in Table 1.4.

Table 1.4. Hawai`i Part II Offenses by Age Group, 2007-2017 Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55+ Total 2007 11,272 7,798 5,693 5,669 5,158 4,542 2,700 2,250 45,082 2008 11,635 7,692 5,344 4,979 4,659 4,075 2,563 2,501 43,448 2009 6,623 4,961 3,481 2,976 2,862 2,652 1,677 1,828 27,060 2010 10,282 7,416 5,535 4,257 4,159 3,855 2,720 3,079 41,303 2011 9,474 7,621 5,870 4,048 4,130 3,941 2,857 3,058 40,999 2012 9,869 7,807 6,204 4,444 4,020 3,673 2,822 3,164 42,003 2013 9,128 7,315 6,387 4,562 3,954 3,710 3,026 3,358 41,440 2014 7,776 6,708 5,727 4,245 3,358 3,070 2,647 3,056 36,587 2015 8,108 6,558 5,779 4,431 3,195 2,998 2,522 3,152 36,743 2016 6,881 6,114 5,402 4,392 2,844 2,816 2,224 3,133 33,806 2017 5,503 5,181 4,476 3,868 2,523 2,267 1,855 2,800 28,432 Source: State of Hawai`i, Crime Prevention and Justice Assistance Division, Uniform Crime Reports

The ADAM results, shown in Table 1.5, includes the percentage of arrestees who tested positive for meth, had treatment for any reason or any time in the past, and the average number of months of meth treatment at an outpatient or inpatient facility in the past year. According to the National Institute of Drug Abuse (NIDA), the recommended length of treatment is three months (70), but the arrestees had three to four months of

treatment in 2001 and an average of one to two months in 2002. It does not seem likely

that there are repeated treatment episodes within the past year for the arrestees.

Table 1.5. Hawai`i ADAM Methamphetamine Inpatient and Outpatient Stays Inpatient Outpatient Last Ave # Last Ave # Ever Ever Year Year Months Year Months (%) (%) (%) Last Year (%) Last Year Male 35.3 28.3 2000 Female 36.8 26.3 Male 32.9 15.8 3.0 29.4 26.6 4.0 2001 Female 48.7 6.9 3.0 41.0 23.6 3.0 Male 39.8 14.9 2.0 31.6 14.7 2.0 2002 Female 44.7 5.6 2.0 28.9 8.3 1.0 Male 25.4 5.8 21.8 3.2 2003a Female 20.8 3.8 26.4 7.7 a In 2003 the data were only available for treatment for any drug or alcohol treatment. Source: Arrestee Drug Abuse Monitoring program (ADAM) Annual Reports

The Part II Offenses arrests was discounted by the ADAM percentages of 40% to

50% who tested positive for meth, and by a non-treatment utilization percentage of

78%. The average of 45% will be used as the point estimate while the low estimate and the high estimate will be discounted by 40% and 50%, respectively.

Methamphetamine Treatment Admissions

The SAMHDA Public-use Data Analysis System (PDAS) website contains 2000-

2017 TEDS-A meth admittance data broken out by age groups. (71) (72) The Hawai`i

TEDS-A data are displayed in Figure 1.4 for 2000-2017 and Table 1.6 for 2007-2017

meth treatment admits by age groups and includes the percentage of admitted patients 57

who had a previous treatment. It can be seen that the number of admits for meth

treatment is increasing over time for ages 50 and above, while the number of admits for

age 18-20 years are decreasing, but admits remain steady for the other age groups.

The number of admits may not be the same as the number of people since a person may enter treatment more than once per year. The National Institute for Drug Abuse

(NIDA) recommends drug treatment for 90 days. (70) Theoretically, a person could

enter treatment multiple times a year, however, the cost of treatment and/or the lack of

income or health insurance becomes a constraining factor for multiple stays.

Figure 1.4. Hawai`i Treatment Methamphetamine Admits (TEDS-A) by Age Group, 2000-2017 Source: SAMHDA Public-use Data Analysis System (PDAS) for TEDS-A

Table 1.6. Hawai`i Methamphetamine Treatment Admits (TEDS-A) by Age Group, 2007-2017 Age (years) Previously Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55 + Total Treated (%) 2007 449 518 508 441 410 276 94 40 2736 62.26 2008 412 458 430 408 340 259 101 44 2452 60.30 2009 389 476 437 364 392 283 121 52 2514 59.23 2010 339 418 405 366 340 270 148 46 2332 59.99 2011 369 458 418 319 375 254 160 90 2443 59.92 2012 419 467 453 358 390 267 155 85 2594 51.92 2013 373 472 437 386 339 313 174 100 2594 52.70 2014 433 472 496 397 323 265 223 126 2735 54.35 2015 356 488 512 409 346 324 238 139 2812 54.09 2016 441 550 534 478 289 281 197 210 2980 54.33 2017 299 343 362 338 239 218 178 143 2163 57.71 Source: SAMHDA Public-use Data Analysis System (PDAS) for TEDS-A Note: The previous treatment could be for any drug or alcohol program for any year.

The year of treatment is not indicated in TEDS-A PDAS and the codebook for the variable does not indicate that the year(s) of previous treatment(s) was recorded. For example, if a person had any type of treatment for any drug or alcohol in 2007, and was admitted for meth treatment in 2017, it would be counted as a previous treatment. The number of previous treatments ranged from one to more than six.

Wanted Meth Treatment but Did Not Receive

Another indicator of meth prevalence are those people who wanted meth treatment but did not receive treatment. The Healthcare Association of Hawai`i4 said

4 Participating hospitals: Castle Medical Center, Sutter Health Kahi Mohala Behavioral Health, Kaiser Permanente Medical Center, Kapiolani Medical Center for Women & Children, Kuakini Medical Center, Molokai General Hospital, North Hawai`i Community 59

that in 2012-2013, 10.3% of adults used illicit drugs, but only 3.5% of residents aged 12

years and older who needed illicit drug and/or alcohol services actually received

treatment. “Multiple key informants recognized that there is a lack of substance abuse services in Hawai`i, with one describing services as fragmented and slow to respond.”

(73) This implies that 96.5% of the 10.3% Hawai`i illicit drug users who needed illicit

drug and/or alcohol services did not receive treatment, but alcohol and drug treatment

needs were not separated. The 96.5% of illicit drug users who wanted treatment but did

not receive it, seems implausibly high and would result in over 40,000 illicit drug or

alcohol users per year wanting treatment.

NSDUH reported that nationally in 2018, among those aged 18 years or older,

11.7% used illicit drugs in the past month and among those who were full-time

employees, 12.6% used illicit drugs in the past month, as did 14.8% among part-time

employees and 23.1% among the unemployed. The Hawai`i NSDUH 2018 report

stated that 16.1% of people with illicit drug use disorder but not including alcohol abuse

received treatment, leaving 83.9% without treatment. However, 79% of users did not

want treatment, leaving 5% who wanted treatment. (74) Compared with the Healthcare

Association of Hawai`i 96.5%, the NSDUH 5% of users who wanted treatment but did

not receive it, seems implausibly low.

Hospital, Pali Momi Medical Center, Rehabilitation Hospital of the Pacific, Shriners Hospitals for Children - Honolulu, Straub Clinic & Hospital, The Queen’s Medical Center, The Queen’s Medical Center – West Oahu, Wahiawa General Hospital, and Wilcox Memorial Hospital. 60

The most recent Hawai`i DoH ADAD survey in 2004 found that 23.9% of meth users wanted treatment but did not receive it and as this percentage seems more credible than either the Healthcare Association of Hawai`i or the NSDUH

Percentages; it will be used.

An alternative method to estimate the prevalence of meth users is from data collected for other purposes such as the Part II Offenses arrests and TEDS-A data, modified by ADAM results and informed by the State of Hawai`i ADAD.

Objective 1. Methods

The number of people was summed over each of the sections Part II Offenses arrests, meth treatment admits, and meth users who wanted treatment but were unable to obtain it. The age-adjusted prevalence was found using the CDC tables.

Part II Offenses Arrests Methodology

Table 1.6 contains the Part II Offenses arrests. The ADAM results informed that a) 40% to 50% of the Part II Offenses arrestees tested positive for meth and b) 78% of

ADAM arrestees never sought treatment for drug or alcohol abuse. The partial estimate of meth users was derived from the positive meth urinalysis average of the Part II

Offenses arrestees (45%) and the 78% who did not seek treatment. The 22% (1-78%) of the Part II Offenses arrests who sought treatment were removed to avoid duplication with the TEDS-A data. The Honolulu ADAM results indicated that 44% of the arrestees were arrested at least once prior to the current arrest, however there was no indication of the year of arrest. The estimated number of meth users was obtained by dividing the 61

number of discounted arrests by half (50%) to account for people with multiple arrests with results shown in Table 1.7.

Table 1.7. Discounted Part II Offenses Arrests, 2007-2017 Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55+ Total 2007 1,978 1,369 999 995 905 797 474 395 7,912 2008 2,042 1,350 938 874 818 715 450 439 7,625 2009 1,162 871 611 522 502 465 294 321 4,749 2010 1,804 1,302 971 747 730 677 477 540 7,249 2011 1,663 1,337 1,030 710 725 692 501 537 7,195 2012 1,732 1,370 1,089 780 706 645 495 555 7,372 2013 1,602 1,284 1,121 801 694 651 531 589 7,273 2014 1,365 1,177 1,005 745 589 539 465 536 6,421 2015 1,423 1,151 1,014 778 561 526 443 553 6,448 2016 1,208 1,073 948 771 499 494 390 550 5,933 2017 966 909 786 679 443 398 326 491 4,997 Source: Hawai`i Uniform Crime Reports and ADAM. Part II Offenses Arrests discounted by 55% negative for meth, 22% who had treatment in past year, and 50% for recidivism.

Methamphetamine Treatment Admits Methodology

Table 1.6 contains the number of meth treatment admits but the year of previous

treatments is not indicated in TEDS-A. For example, if a person had any type of treatment for any drug or alcohol in 2007, and was admitted for meth treatment in 2017, it would be counted as a previous treatment. The number of unique admissions per year was defined as the number without previous treatment plus one half of the number of previously treated people who were admitted in order to avoid over-estimation. The

ADAM arrestees indicated they had one to four months of treatment during the past year. Table 1.8 contains the discounted treatment admissions.

Table 1.8. Hawai`i Discounted Methamphetamine Treatment Admits (TEDS-A) by Age Group, 2007-2017 Age (years) Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55 + Total 2007 309 357 350 304 282 190 65 28 1,884 2008 288 320 300 285 237 181 71 31 1,713 2009 274 335 308 256 276 199 85 37 1,769 2010 237 293 284 256 238 189 104 32 1,633 2011 258 321 293 223 263 178 112 63 1,711 2012 310 346 335 265 289 198 115 63 1,921 2013 275 348 322 284 250 231 128 74 1,910 2014 315 344 361 289 235 193 162 92 1,992 2015 260 356 374 298 252 236 174 101 2,051 2016 321 401 389 348 210 205 143 153 2,170 2017 213 244 258 240 170 155 127 102 1,508 Source: SAMHDA Public-use Data Analysis System for TEDS-A Note: The previous treatment could be for any drug or alcohol program for any year. Number of unique admissions per year was defined as the number without previous treatment plus one half of the number of previously treated people who were admitted.

Methamphetamine Treatment Wanted but Unavailable Methodology

The number of people who wanted treatment but were unable to obtain treatment was found by using the discounted meth treatment admits in Table 1.8. Values in

Table 1.9 were found by using (1 – 23.9%) = [(Table 1.8 cell)/(n + (Table 1.8 cell))] and solving for n.

Table 1.9. Hawai`i Meth Treatment Wanted but Unavailable (23.9%) by Age Group, 2007-2017 Age (years) Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55 + Total 2007 82 94 92 80 75 50 17 7 498 2008 76 85 79 75 63 48 19 8 453 2009 72 89 81 68 73 53 23 10 468 2010 63 77 75 68 63 50 27 9 431 2011 68 85 77 59 69 47 30 17 452 2012 82 91 89 70 76 52 30 17 507 2013 73 92 85 75 66 61 34 19 505 2014 83 91 95 76 62 51 43 24 526 2015 69 94 99 79 67 62 46 27 542 2016 85 106 103 92 56 54 38 40 573 2017 56 64 68 64 45 41 33 27 399 Source: SAMHDA Public-use Data Analysis System for TEDS-A, and Hawai`i Department of Health – Alcohol and Drug Abuse Division

Total Estimated Number of Meth Users in Hawai`i

Table 1.10 displays the sum of the discounted Part II Offense Arrests from Table

1.7, discounted meth treatment admits from Table 1.8, and the number of people who wanted treatment but who were not able to receive treatment from Table 1.9. The same method was used to calculate the low and high estimates and are shown in Tables 1.11 and 1.12.

Table 1.10. Average Estimated Number of Meth Users in Hawaiì, 2007-2017 Age (years) Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55+ Total 2007 2,369 1,820 1,441 1,379 1,262 1,037 556 430 10,294 2008 2,406 1,754 1,318 1,234 1,118 944 539 478 9,790 2009 2,242 1,743 1,316 1,128 1,095 956 563 551 9,593 2010 2,104 1,672 1,329 1,071 1,031 916 608 581 9,313 2011 1,990 1,743 1,400 992 1,057 917 643 617 9,358 2012 2,124 1,807 1,513 1,115 1,071 895 640 635 9,800 2013 1,949 1,723 1,528 1,160 1,010 942 693 682 9,688 2014 1,764 1,612 1,462 1,111 886 783 670 652 8,939 2015 1,751 1,601 1,486 1,155 880 825 663 681 9,042 2016 1,614 1,579 1,440 1,211 765 753 571 743 8,677 2017 1,235 1,218 1,112 983 658 594 486 620 6,897 Source: Hawaiì Uniform Crime Reports, TEDS-A, ADAM, NSDUH, Healthcare Association of Hawaiì, and Hawaiì Department of Health – Alcohol and Drug Abuse Division

Table 1.11. Low Estimate of the Number of Meth Users, 2007-2017 Age (years) Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55+ Total 2007 2,150 1,667 1,330 1,268 1,162 949 503 386 9,414 2008 2,179 1,605 1,213 1,137 1,027 865 489 429 8,944 2009 1,379 1,198 932 788 795 666 370 332 6,460 2010 1,904 1,527 1,222 988 950 840 555 522 8,508 2011 1,804 1,595 1,285 914 976 840 588 557 8,559 2012 1,932 1,655 1,392 1,028 992 823 585 574 8,980 2013 1,772 1,581 1,403 1,071 932 870 634 616 8,880 2014 1,611 1,481 1,350 1,027 821 723 618 592 8,224 2015 1,594 1,473 1,374 1,069 818 766 613 620 8,326 2016 1,480 1,460 1,335 1,125 710 698 528 682 8,018 2017 1,127 1,116 1,024 908 609 550 449 566 6,348 Source: Hawaiì Uniform Crime Reports, TEDS-A, ADAM, NSDUH, Healthcare Association of Hawaiì, and Hawaiì Department of Health – Alcohol and Drug Abuse Division

Table 1.12. High Estimate of the Number of Meth Users, 2007-2017 Age (years) Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55+ Total 2007 2,589 1,971 1,552 1,489 1,363 1,126 608 473 11,172 2008 2,633 1,905 1,421 1,331 1,209 1,024 589 526 10,638 2009 1,637 1,391 1,067 905 907 769 435 403 7,515 2010 2,305 1,816 1,438 1,154 1,112 991 661 642 10,119 2011 2,174 1,892 1,514 1,072 1,137 993 699 676 10,158 2012 2,317 1,959 1,634 1,202 1,149 966 695 697 10,618 2013 2,128 1,866 1,652 1,249 1,087 1,015 752 747 10,496 2014 1,915 1,743 1,573 1,193 952 843 722 712 9,651 2015 1,910 1,729 1,599 1,241 942 883 711 743 9,759 2016 1,748 1,699 1,545 1,297 821 808 615 804 9,337 2017 1,342 1,318 1,198 1,059 707 638 521 675 7,459 Source: Hawaiì Uniform Crime Reports, TEDS-A, ADAM, NSDUH, Healthcare Association of Hawaiì, and Hawaiì Department of Health – Alcohol and Drug Abuse Division

The Master List of 2000 US projected population and age-adjustment weights from the CDC, National Center for Health Statistics, shown in Table 1.13 was used to

derive the age-adjustment weights for the age groups (75)

Table 1.13. Age-Adjustment Weights Using the 2000 Projected US Population Population in Adjustment Age (years) thousands Weight 18–24 26,258 0.128810 25–29 17,722 0.086936 30–34 19,511 0.095712 35–39 22,180 0.108805 40–44 22,479 0.110272 45–49 19,806 0.097159 50–54 17,224 0.084493 55+ 58,671 0.287813 Total 203,851 1.0 Source: CDC, National Center for Health Statistics (Klein & Schoenborn, 2001)

The cell numbers in Tables 1.10, 1.11, and 1.12 were divided by the Table 1.13

standard 2000 population and multiplied by the age-adjustment weights. The average

age-adjusted prevalence by age groups is shown in Table 1.14.

Table 1.14. Hawaiì Average Prevalence of Methamphetamine Users by Age Group, 2007-2017 Age (years) Year 18-24 25-29 30-34 35-39 40-44 45-49 50-54 55+ 2007 0.01162 0.00893 0.00707 0.00676 0.00619 0.00509 0.00273 0.00211 2008 0.01180 0.00861 0.00646 0.00605 0.00549 0.00463 0.00265 0.00234 2009 0.01100 0.00855 0.00645 0.00553 0.00537 0.00469 0.00276 0.00270 2010 0.01032 0.00820 0.00652 0.00525 0.00506 0.00449 0.00298 0.00285 2011 0.00976 0.00855 0.00687 0.00487 0.00519 0.00450 0.00315 0.00303 2012 0.01042 0.00886 0.00742 0.00547 0.00525 0.00439 0.00314 0.00311 2013 0.00956 0.00845 0.00750 0.00569 0.00495 0.00462 0.00340 0.00335 2014 0.00865 0.00791 0.00717 0.00545 0.00435 0.00384 0.00329 0.00320 2015 0.00859 0.00785 0.00729 0.00567 0.00432 0.00405 0.00325 0.00334 2016 0.00792 0.00775 0.00706 0.00594 0.00375 0.00369 0.00280 0.00365 2017 0.00606 0.00597 0.00545 0.00482 0.00323 0.00291 0.00238 0.00304 Source: Hawaiì Uniform Crime Reports, TEDS-A, ADAM, Hawaiì Department of Health – Alcohol and Drug Division, and CDC National Center for Health Statistics (Klein & Schoenborn, 2001)

The prevalence was summed across age groups to obtain the yearly age- adjusted meth use prevalence in Table 1.15. The same methods were used to compute the low and high prevalence estimates. The adult population of Hawai`i was obtained using the American Community Survey from the State of Hawai`i, Department of

Business, Economic Development & Tourism, Census website and was used to compute the number and percentage of meth users in Table 1.16.

Table 1.15. Hawaiì Age-adjusted Meth Prevalence, 2007-2017 Average Low Age- Age- High Age- Low Estimate High adjusted adjusted adjusted Year Estimate (Total from Estimate Prevalence Prevalence Prevalence Table 1.14) per 100,000 per 100,000 per 100,000 2007 0.04618 0.05050 0.05481 4,618 5,050 5,481 2008 0.04387 0.04803 0.05219 4,387 4,803 5,219 2009 0.03169 0.04706 0.03687 3,169 4,706 3,687 2010 0.04174 0.04568 0.04964 4,174 4,568 4,964 2011 0.04199 0.04591 0.04983 4,199 4,591 4,983 2012 0.04405 0.04807 0.05209 4,405 4,807 5,209 2013 0.04356 0.04753 0.05149 4,356 4,753 5,149 2014 0.04034 0.04385 0.04734 4,034 4,385 4,734 2015 0.04085 0.04436 0.04788 4,085 4,436 4,788 2016 0.03933 0.04257 0.04580 3,933 4,257 4,580 2017 0.04618 0.03387 0.03659 3,114 3,387 3,659 Source: Hawaiì Uniform Crime Reports, TEDS-A, ADAM, Hawaiì Department of Health – Alcohol and Drug Division, and CDC National Center for Health Statistics (Klein & Schoenborn, 2001)

Table 1.16. Number and Percentage of Hawaiì Meth Users, 2007-2017 Low Average High % % % Adult Number Number Number Low Average High Year Population of Meth of Meth of Meth Meth Meth Meth Users Users Users Users Users Users 2007 997,623 46,070 50,380 54,680 4.62 5.05 5.48 2008 1,003,594 44,028 48,203 52,378 4.39 4.80 5.22 2009 1,004,822 31,843 37,048 47,287 3.17 3.69 4.71 2010 1,059,960 44,243 48,419 52,616 4.17 4.57 4.96 2011 1,070,453 44,948 49,144 53,341 4.20 4.59 4.98 2012 1,074,505 47,332 51,651 55,971 4.41 4.81 5.21 2013 1,096,828 47,778 52,132 56,476 4.36 4.75 5.15 2014 1,111,207 44,826 48,726 52,605 4.03 4.39 4.73 2015 1,120,397 45,768 49,701 53,645 4.09 4.44 4.79 2016 1,120,792 44,081 47,712 51,332 3.93 4.26 4.58 2017 1,121,541 34,925 37,987 41,037 3.11 3.39 3.66 Source: American Community Survey, Hawaiì Uniform Crime Reports, TEDS-A, ADAM, Hawaiì Department of Health – Alcohol and Drug Division, and CDC National Center for Health Statistics (Klein & Schoenborn, 2001) 68

Objective 1. Results

The estimated lifetime meth prevalence in Hawai`i was expected to be greater than the national estimates of 475 per 100,000 persons in 2003 and 988 per 100,000 in

2016. The Hawai`i age-adjusted meth use prevalence was above the national estimates and found to range from 5,050 per 100,000 in 2007 to 3,387 per 100,000 in

2017. Prevalence declined from 2007 to 2017 and followed the declining Part II

Offenses arrests. The arrests pattern followed the unemployment rate. However, the unemployable, unwilling to work, homeless, etc. sector persists despite a strong economy and may account for an increasing meth death trend since 2006. (76)

Discussion and Public Health Implications

These annual prevalence estimates are the first calculated evidence-based estimates of lifetime meth users in the state of Hawai`i using data collected for reasons other than prevalence estimation. The prevalence estimates were obtained using no cost, open-source, unrestricted data. The trends in meth prevalence estimates are useful for public policy since NSDUH underestimates meth use because many chronic drug users either live outside traditional households or refuse to answer questions regarding socially undesirable behaviors.

Treatment beds and treatment counseling sessions are known to be insufficient as shown by the varying meth arrests over time while the number of treatment admissions remain fairly steady in Hawai`i. These annual estimates may be used for forecasting upcoming needs for treatment and requesting legislative funding to increase 69

availability of treatment. While the effects of meth impact the individual user, methamphetamine use has a social and economic impact on society with associated criminal justice, familial, and health care costs.

Strengths

The first evidence-based estimates of the Hawai`i state annual meth prevalence from 2007-2017 were found using publicly available meth-related data collected for other purposes. Trend analyses may be used for forecasting upcoming needs for treatment and demands on the criminal justice system. The approach used to estimate meth prevalence uses data that have been collected for other purposes and provides next to no cost estimates.

Assumptions and Limitations

It is expensive and time-consuming to conduct probability sampling of the population of Hawai`i for meth users as the target population is small, 3,387 per

100,000 in 2017, and the behavior is stigmatized. The ADAM arrestee meth use prevalence of 40% to 50% was based on data from 2000-2003 and is likely an underestimate as meth use has increased since that time based on treatment admissions and Drug Enforcement Agency publications. According to the Center for

Behavioral Health Statistics and Quality (CBHSQ) within SAMHSA, NSDUH undercounts the number of meth users by 3.7-fold compared to ADAM. (62) These assumptions are reasonable, for example, assuming that not all chronic meth users are arrested. 70

Only adult users of meth were included, however an examination of the number of juvenile users in Figure 1-1 show that the numbers in Hawai`i are negligible compared with the number of adult users. There is an unknown number of meth users who did not obtain treatment and who were not arrested for any Part II Offenses. The number of occasional and recreational (weekend) meth users could not be included due to the data used which had a greater probability of including chronic meth users. The prevalence trend follows the Part II Offenses arrests trend, instead of the increasing meth death rate trend. However the increasing meth death rate trend could be due to meth users getting older.

OBJECTIVE 2. ESTIMATION OF THE ECONOMIC AND SOCIAL COST OF

METHAMPHETAMINE USE IN HAWAI`I

Objective 2. Introduction

There has been no specific study of the burden of the economic, social, and public health costs of methamphetamine (meth) use in Hawai`i. In addition, many of the

nation-wide studies exclude Hawai`i for information on the cost of treatment by drug and service setting. The per capita national cost burden of meth use is an underestimate in

Hawai`i due to the higher cost of living here. The cost of living in Honolulu in each year

2008 to 2019 is about 18.5% higher than the national average according to the Bureau of Economic Analysis. (77)

The Economic Cost of Methamphetamine Use in the United States, 2005 report by RAND (RAND report) published in 2009, was the first national estimate of the economic cost of methamphetamine use. RAND estimated that the economic cost of meth use in the US reached $23.4 billion in 2005. This figure included the estimable costs associated with 1) drug treatment and other health costs; 2) the intangible burden of addiction, premature death, and lost productivity; 3) crime and criminal justice costs;

4) child endangerment; and 5) harms resulting from production. A lower-bound

estimate of $16.2 billion and an upper-bound estimate of $48.3 billion was provided, reflecting the uncertainty in estimating the costs of meth use. The degree of uncertainty, as indicated by these lower and upper bounds, varied considerably across the five cost components mentioned above, with some categories showing much greater uncertainty than others. (12)

The State of Montana produced The Economic Cost of Methamphetamine Use in

Montana in 2009 (Montana report) focusing on estimating the costs in the five areas used by the RAND report and had an estimated total cost of slightly more than $200 million in 2008. (78) Both the RAND and Montana reports indicated that other potential

harms of methamphetamine could not be included due to a lack of availability of reliable

information. All estimated costs were stated to likely be an underestimate in both

studies.

The Economic Impact of Illicit Drug Use on American Society used 2007 data

and was a comprehensive assessment of societal costs attributable to illicit drug use,

but it did not involve monetization of intangible losses associated with reduced quality of

life and only addressed the consequences of illicit drug use as they relate to crime,

health, and productivity. It used a cost of illness approach (COI); but the COI approach

does not consider intangible costs related to pain and suffering.

The Economic Cost of Drug Abuse in the United States, 1988-1998 report only

included the street value of the illicit drugs in its economic costs. (79) The report did not

involve monetization of intangible losses associated with reduced quality of life and

addressed only the consequences of illicit drug use as they relate to crime, health, and

productivity.

The annual cost burden of the economic, social, and public health impacts of

meth use in Hawai`i was estimated for each year for the 11-year period 2007-2017 for

adults age 18 years and over. It usually takes two to three years for data release and

the 2018 data has not yet been released as of March 2020. The Consumer Price Index

for All Urban Consumers (CPI-U) represents changes in prices of all goods and services 73

purchased for consumption by urban households. The CPI-U includes expenditures by urban wage earners and clerical workers, professional, managerial, and technical workers, the self-employed, short-term workers, the unemployed, retirees and others not in the labor force and covers 93% of the total population. (80) Medical care includes prescription drugs and medical supplies, physicians' services, eyeglasses and eye care, and hospital services. Figure 2.1 shows the Hawai`i CPI-U and Medical Care Index for

1983-2017 and it can be observed that medical costs have been rising more rapidly than the consumer costs as a whole.

Figure 2.1. Hawai`i Consumer Price Index, 1984-2017 Source: Bureau of Labor Statistics, Hawai`i Medical Care Index not available for 2003, 2005, and 2006

The five cost factors used in the RAND report and the Montana report were used as guides to estimate the economic and social cost of methamphetamine use in Hawai`i:

1. Meth treatment;

Users can recover from methamphetamine addiction if they have ready

access to effective treatments that address the multitude of medical and

personal problems resulting from their long-term use of the drug. (15)

2. Health burden attributable to meth use;

Drug abuse and addiction increase a person’s risk for a variety of mental and

physical illnesses related to a drug-abusing lifestyle or the toxic effects of the

drugs themselves. (28)

3. Meth-related child endangerment;

Programs designed to prevent child maltreatment build stronger, healthier

children; and reduce the burdens on state services such as education, law

enforcement, corrections, and mental health. (31)

4. Criminal justice costs attributable to meth use;

Hawai`i has the distinction of being the No. 1 per capita for crystal meth or

“ice” use in the United States. (40)

5. Lost productivity attributable to meth use.

People between the ages of 21 and 50 years who used meth were 97% more

likely to be unemployed than their peers, and unemployed for an average of

12.75 weeks. (12)

All data used in calculating the costs in Hawai`i are free, publicly available, non- restricted data from 2007-2017. This type of data was chosen so that it would be transparent, widely available, and easily obtainable for resource-constricted general public and public health researchers. Note that neither RAND nor Montana included dental costs and estimated dental costs will not be used here. Dental needs and costs are extremely variable, and there is no good estimate of dental needs due to meth use in Hawai`i.

Studies have found that $1 invested in substance abuse treatment saves taxpayers $7 in future costs; that $1 invested in treatment could save $11.54 in combined medical and social costs; for every $1 spent on substance abuse treatment

$5.60 was returned in reduced welfare, food stamps, Medicaid, crime courts, and imprisonment. (81)

Factor 1. Cost of Methamphetamine Treatment

A. Outpatient drug treatment facilities

B. Inpatient drug treatment facilities

The cost of treatment does not include the cost of private drug treatment services

provided by private physicians or psychologists, other private licensed treatment

counselors, and private facilities, as these data are not publicly available. It also does

not include federal services such as treatment at Tripler Army Medical Center or the

Veterans Administration as these data are also not publicly available and the costs are

borne by the federal government. Thus, the cost of treatment in Hawai`i will be an underestimate.

1.A. Outpatient Drug Treatment Facilities

The Treatment Episode Data Set – Admissions (TEDS-A) was used as the measure of the number of meth treatment at public facilities. TEDS-A tracks treatment admissions to publicly funded treatment facilities for abuse of alcohol, illicit drugs, prescription drugs, and other reasons for persons age 12 years and older. TEDS-A is an admission-based system but admissions do not represent individuals. An individual admitted to treatment twice within a calendar year would be counted as two admissions, however, the cost will be the same for either two people or one person treated twice.

For reasons of confidentiality, data about clients’ unique identification are unavailable, thus TEDS-A is unable to follow individual clients through a sequence of treatment episodes. TEDS-A does not include data on facilities operated by Federal agencies

(Bureau of Prisons, Department of Defense, and the Veterans Administration). (6)

From 1992 to 2001, alcohol was the primary reason for treatment admissions to facilities in Hawaiì. In 2005, Hawaiì ranked second in the nation for the percentage of drug related treatment admissions that were meth related. By the first half of 2012, meth started to rank first in drug related treatment admissions, above marijuana and alcohol. (82) (83) There were more substance abuse admissions to publicly funded treatment facilities for meth/amphetamine as the primary drug (2,093, 30.7%) in Hawaiì than admissions for cocaine, heroin, and other opiates combined as shown in Figure

2.2. The number of treatment admissions for methamphetamine use in Hawai`i

increased by 711.2% from 1992 (295 admits) through 2016 (2393 admits). (6) (7)

However, many users who seek treatment were not admitted because most state-

funded treatment programs are operating at maximum capacity. (2) Marijuana was in

second place (1,986, 29.1%). (60) The public health concern is that neither the number

of treatment facilities nor beds have risen in a substantive manner over the last 20

years, thus decisions must be made to prioritize which substance of abuse is more

important to treat. In addition, even if the rate of addiction remains the same over time, with population increases the number of people needing treatment will also increase.

Figure 2.2. Hawai`i Public Facility Treatment Admissions by Percent, 1992-2017 Source: SAMHSA TEDS-A

Table 2.1 shows the number of treatment centers in Hawai`i. Most people do not pay the full cost of treatment which is mainly bourne by their insurance carrier or

Medicaid.

Table 2.1. Number of Hawai`i Treatment Centers Private for Total Substance Meth Profit Abuse Treatment Treatment Treatment County Total Centers Only Centers Free Hawai`i 57 52 2 6 3 Kauai 10 10 0 0 0 Maui 5 5 0 0 0 Oahu 72 72 3 11 3 Total 144 139 5 17 6 Source: SAMSHA - Treatment Centers, 2016

Information on the cost of each treatment episode must be inferred based on information on the number of treatment episodes and typical per episode costs from other sources. In 2018, the cost of a non-hospital inpatient stay ranged between $200 to $900 per day while outpatient sessions were between $100 to $500 per session. The cost of detoxification was between $600 to $1,000 per day. Luxury rehabilitation began at $35,000/month. Generally, for residential or outpatient treatment, participation for less than 90 days is of limited effectiveness, and treatment lasting significantly longer is recommended for maintaining positive outcomes for an average cost of $12,000-

$60,000 for 60-90 days in 2006. (67) The 2006 costs were converted to 2007-2017

costs using the inflation calculator from the Bureau of Labor Statistics5. The per capita national cost burden of meth use is an underestimate in Hawai`i due to the higher cost of living here as the cost of living in Honolulu is about 18.5% higher than the national average. (77) Table 2.2 contains the number of treatment admissions and the low, average, and high costs of treatment.

Table 2.2. Hawai`i TEDS-A Methamphetamine Treatment Costs, 2007-2017 Low Current Average Current High Current Total Year Year Year Year Admits (2006 $12,000 (2006 $36,000 ($60,000 per (TEDS-A) per 90 days) per 90 days) 90 days) (current $) (current $) (current $) 2007 2,736 32,832,000 98,496,000 164,160,000 2008 2,452 29,424,000 88,272,000 147,120,000 2009 2,514 30,168,000 90,504,000 150,840,000 2010 2,332 27,984,000 83,952,000 139,920,000 2011 2,443 29,316,000 87,948,000 146,580,000 2012 2,594 31,128,000 93,384,000 155,640,000 2013 2,594 31,128,000 93,384,000 155,640,000 2014 2,735 32,820,000 98,460,000 164,100,000 2015 2,812 33,744,000 101,232,000 168,720,000 2016 2,980 35,760,000 107,280,000 178,800,000 2017 2,669 32,028,000 96,084,000 160,140,000 Source: TEDS-A, Agency for Healthcare Research and Quality, and Bureau of Labor Statistics CPI calculator.

According to the National Institute on Drug Abuse (NIDA) there is a 40%- 60% relapse rate after drug treatment and relapse is a normal part of recovery. For meth addiction, no Food and Drug Administration (FDA) approved medications are currently

5 https://data.bls.gov/cgi-bin/cpicalc.pl 80

available to assist in treatment, so treatment usually consists of behavioral therapies.

Patients typically require long-term or repeated episodes of care to achieve the ultimate

goal of sustained abstinence and recovery. (70) Naltrexone is an off-label6 drug that some doctors are using to treat meth addiction, but it only works in about half the

patients. (84) A double-blind, placebo-controlled, randomized clinical trial which just

finished in 2019 used Naltraxone and Bupropin (also off-label) but the data analysis has

yet to be finished (NCT03078075). (85)

1.B. Inpatient Drug Treatment Facilities

Inpatient health care costs includes hospital admissions induced by the use of methamphetamine, incremental costs of caring for patients admitted for another cause but whose conditions are exacerbated by meth use, emergency department care of meth patients not admitted to the hospital, hospital inpatient care of suicide attempts to which meth use is a likely contributor, and health administration and support. (12)

A 2006 Oregon study of ED visits over a 20-week period found an average of

17.65 meth-related visits per week with hospital charges averaging $133,181 per week and $7,546 per person per week, for an estimated total of $6.9 M in annual charges.

Oregon meth-related ED patients were more likely to be male and uninsured. The top four medical conditions associated with meth-related visits were mental health (18.7%),

6 Off-label prescribing is when a physician prescribes a drug that the US Food and Drug Administration (FDA) has approved to treat a condition different than your current condition. This practice is legal and common. In fact, one in five prescriptions written today are for off-label use. (132) 81

trauma (18.4%), and skin infections (11.1%). Consumer prices including rent in

Honolulu are 21.48% higher than in Portland, Oregon. (86)

The Healthcare Cost and Utilization Project (HCUPnet) is a part of the Agency for

Healthcare Research and Quality (AHRQ) which collects and disseminates health care

utilization, access, charges, quality, and outcomes data from across the US from public

and private institutions. The purchase of other datasets under AHRQ allows for more

detailed data access. HCUPnet provides free on-line limited health care statistics and

information for hospital inpatient, emergency department (ED), and ambulatory care

settings from the HCUP Nationwide Databases (NIS, KID, NEDS, and NRD7) and the

State Databases (SID, SASD, and SEDD) for those States that have agreed to

participate. (61)

The International Classification of Diseases, Ninth Revision, Clinical Modification

(ICD-9) classification of 969.72, amphetamines which includes meth, was used to obtain

HCUP hospital inpatient and ED visits for Hawai`i which was available 2010-2016.

Neither the ICD-9 nor ICD-10 diagnostic codes discriminate between methamphetamine

use, other illicit amphetamine use, and nonmedical use of prescription amphetamines, but evidence indicates that such codes primarily represent methamphetamine use. (61)

In Hawai`i, meth is much more common than amphetamines.

7 NIS - National Inpatient Sample, KID - Kids' Inpatient Database, NEDS - Nationwide Emergency Department Sample NRD - Nationwide Readmissions Database SID - State Inpatient Databases SASD - State Ambulatory Surgery and Services Databases SEDD - State Emergency Department Databases 82

ICD-10 replaced ICD-9 on October 1, 2015. (67) The annual aggregate costs for

Hawai`i hospital inpatient stays for amphetamines/meth (ICD-9, 969.72 and ICD-10,

T43.621-T43.625) are shown in Table 2-3. The average cost per day in Hawai`i for

hospital services has increased from 1991 to 2016 as shown in Figure 2-3 and is a real

increase since all of the costs have been converted to 2016 dollars. (87) The hospital

costs for years 2007 to 2009, and 2015 were estimated by converting the 2010-2014

and 2016 costs to 2007, 2008, 2009, and 2015 dollars and finding the average costs for

that particular year.

Table 2.3. Hawai`i Hospital Inpatient Discharges, 2010-2016 Number of discharges (Includes those Length of Stay Aggregate Costs Cost to Year admitted through ED) in Days (mean) (current $) charge ratio 2007 301,169b 2008 314,060b 2009 < 10 314,154b 2010 23 5.2 331,288 0.452 2011 22 5.4 224,458 0.401 2012 47 3.4 324,568 0.388 2013 28 3.7 321,201 0.414 2014 21 3.0 216,953 0.469 2015a < 10 6.9 349,130b 0.452 2016 58 3.6 630,415 0.370 2017 362,772b Source: Healthcare Cost and Utilization Project (HCUPnet), Agency for Healthcare Research and Quality, ICD-9 969.72 for 2009-2015 and ICD-10 T43.621-T43.625 for 2016. a Due to the transition from ICD-9-CM to ICD-10-CM in October 2015, 2015 statistics were calculated using only quarter 1-3 data. b Estimated: Average of 2010-2014 and 2016 costs, adjusted for inflation. Note: The hospital discharges includes those patients who were admitted through the ED. Cost is the actual cost. Charge is the amount charged to patients or insurance companies and updated without regard to the real changes in costs of each service. (88)

HCUPnet does not include costs or charges for ED visits in the free version of

their website. Charges are often updated by hospitals uniformly and without regard to

the real changes in costs of each service. (88) A study of 2011 ED charges in California found a wide range in charges for the same ED visit level, ranging from $156 to $6,662 with an average of $1,112 ($588-$1,720). (89) The average cost for an ED visit in

2013-2014 was found to be $1,486 ($164, $4,893), albeit for syncope. (90) This ED

cost will be used for 2013 and 2014 with inflation adjustment for 2010 and 2016. The years with missing number of discharges from the ED (2007, 2008, 2009, 2011, 2012, and 2017) will not be estimated because the discharge numbers vary too much and there is more missing data than available data. The costs are small compared to the treatment costs but will still contribute to an underestimate of the overall costs.

Table 2.4. Hawai`i Emergency Department Visits, 2010-2016 Low Costs Average Costs High Costs Number ) Year Discharged (2013 $164) (2013 $1,486) (2013 $4,893 (current $) (current $) (current $) 2007a 2008a 2009 < 10 2010 20 3,086 28,003 92,084 2011 2012 2013 12 1,968 17,856 58,716 2014 22 3,608 32,736 107,646 2015b < 10 2016 20 3,375 30,618 100,680 Source: Healthcare Cost and Utilization Project (HCUPnet), Agency for Healthcare Research and Quality, ICD-9 969.72 for 2009-2015 and ICD-10 T43.621-T43.625 for 2016. a No information available. b 2015 statistics were calculated using only quarter 1-3 data. 84

RAND identified nine primary conditions that were considered to be meth-induced:

fetal dependence, drug-induced neuropathy, drug-induced mental health disorders, mental health and drug screens, poisoning by psychostimulant drugs, skin infections, bacterial skin infections, other skin inflammation, and chronic skin ulcers. Although it is possible to suffer many of these skin conditions in the absence of meth use, RAND elected to include skin conditions in their assessment of meth-induced costs because they have been widely cited as a common consequence of meth use. (20) Meth users are particularly prone to skin infection (ICD-9, 686), lesions (ICD-9, 709.9) (e.g., excoriations and ulcers), and, consequently, cellulitis (ICD-9, 682), potentially stemming from delusion-induced scratching, needle marks, and chemical burns. However, none of the meth-induced skin conditions can be used due to the small sample size in

Hawai`i.

Factor 1. Methodology

The cost of treatment for TEDS-A may be found in Table 2.2, for hospital

inpatient drug treatment facilities in Table 2.3, and for ED visits in Table 2.4. The total

cost for meth treatment is the sum of the tables is shown in Table 2.5.

Table 2.5. Factor 1: Summary of Hawai`i Methamphetamine Treatment Costs, 2007-2017 Year Low Estimate Average Estimate High Estimate Treatment Costs Treatment Costs Treatment Costs (Current $) (Current $) (Current $) 2007 33,814,652 100,841,590 167,868,556 2008 31,634,363 94,274,994 156,915,625 2009 32,436,010 96,679,696 160,923,382 2010 30,913,144 92,095,577 153,317,197 2011 32,781,488 97,895,525 163,009,561 2012 35,905,143 107,066,293 178,227,443 2013 36,471,208 108,783,173 181,120,110 2014 38,935,252 116,393,652 193,898,053 2015 40,118,206 119,656,385 199,194,536 2016 43,357,514 128,832,234 214,349,743 2017 32,148,598 95,720,249 159,291,901 Source: TEDS-A and Healthcare Cost and Utilization Project (HCUPnet), Agency for Healthcare Research and Quality

Factor 1. Limitations

• HCUPnet contains data from both public and private hospitals whereas TEDS-A

contains data for publicly funded treatment centers.

• The free HCUPnet does not contain costs for ED visits.

• Amphetamines and meth data are combined for TEDS-A and HCUPnet, however

meth is dominant in Hawai`i.

• TEDS-A does not include data on facilities operated by Federal agencies (the

Bureau of Prisons, the Department of Defense, and the Veterans Administration),

but these costs are not borne by the State of Hawai`i except indirectly.

• TEDS-A data are updated after being first published and are not stable until about

four years after the initial numbers are disseminated.

• Emergency Department costs are not included for the years 2007, 2008, 2009,

2015, and 2017 due to lack of data for estimation.

• The costs from the meth treatment factor will be an underestimate due to these

limitations.

• The treatment costs are heavily influenced by the TEDS-A treatment costs. The

number of hospital stays and ED visits are about 1% of the number of TEDS-A visits.

Factor 2. Health burden

A. Health Burden Associated With Meth Use

a. Treatment for adverse health effects

b. Quality-adjusted life-years (QALYs)

B. Health Burden Associated With Meth Production

a. No clean-up cost will be added due to meth not being manufactured

in Hawai`i in any appreciable quantity.

b. Arrests related to meth production is included in Factor 4, Criminal

Justice Costs Attributable to Meth.

2.A. Health burden Associated With Meth Use

Drug abuse and addiction increase a person’s risk for a variety of mental and physical illnesses related to a drug-abusing lifestyle or the toxic effects of the drugs themselves. Additionally, the dysfunctional behaviors that result from drug abuse can 87

interfere with a person’s normal functioning in the family, the workplace, and the

broader community. (91) Addiction and drug dependence reduces the quality of life.

(38)

The psychological effects of long-term meth use include increased risk of memory loss, deficits in thinking and motor skills, increased distractibility, weight loss, mood disturbances, anxiety, depression, insomnia, reduced concentration and poor memory, psychosis or psychotic behavior (paranoia, hallucinations, and repetitive motor activity), homicidal or suicidal thoughts, and aggressive or violent behavior. (70) (92)

The RAND quality-adjusted life years (QALYs) approach will be used to estimate the total health burden of meth use. The reduction in well-being was calculated using the Department of Transportation (DOT) Maximum Abbreviated Injury Scale (MAIS) in conjunction with the DOT value of a statistical life (VSL) shown in Table 2.5. MAIS range from “1” with a factor of 0.003 (minor injury) to “5” with a factor of 0.593 (critical injury) and will be multiplied by the low and high VSL to obtain the intangible costs of meth use as the lower and upper estimates, respectively. A MAIS of “6” is unsurvivable and equal to the VSL. Estimates covering the entire range of potential disabilities are unobtainable. (93)

Table 2.6. Value of a Statistical Life (VSL) Average Low VSL VSL High VSL (current (current (current Year millions $) millions $) millions $) 2007 3.2 5.8 8.4 2008 3.2 6.0 8.4 2009 3.2 6.0 8.4 2010 3.2 6.0 8.4 2011 3.2 6.2 8.4 2012 5.2 9.1 12.9 2013 5.2 9.1 13.0 2014 5.2 9.2 13.0 2015 5.4 9.4 13.4 2016 5.4 9.6 13.4 2017 5.4 9.6 13.4 Source: Dept of Transportation VSL unadjusted for age. VSL guidance not updated since 2016.

2.B. Health burden associated with meth production

Meth is no longer produced in quantity in Hawai`i, see Figure 2.4 for a Drug

Enforcement Agency (DEA) presentation on the decreasing number of Hawai`i meth labs destroyed. No costs such as meth lab cleanups will be assigned for the health burden due to meth production. The number of arrests for small meth production is included in the Uniform Crime Reports arrests in Factor 4.

Figure 2.3. Number of Hawai`i Meth Labs Destroyed, 2004-2016 Source: DEA presentation on CMEA, https://www.deadiversion.usdoj.gov/mtgs/pharm_awareness/conf_2017/jan_2017/elkhol y.pdf

Factor 2. Methodology

RAND identified a “best estimate” using weighted score of 0.141 to indicate the reduction in well-being in calculating addiction. (38) The reduction in QALYs due to meth use was multiplied by the estimated number of meth users from 2007 to 2017 developed in Objective I and is shown in Table 2.7. The low estimate of the VSL was multiplied by the low estimate of the number of meth users, the average VSL multiplied by the average number of meth users, and the high VSL by the high estimate of the number of meth users.

Table 2.7. Factor 2: Summary of Hawai`i Healthcare and Health Services Attributed to Methamphetamine Use, 2007-2017 Average Low # Average # High # of Low VSL High VSL VSL Year of meth of meth meth (current (current (current users users users millions $) millions $) millions $) 2007 46,070 50,380 54,680 20,787 42,621 64,763 2008 44,028 48,203 52,378 19,865 40,779 62,036 2009 31,843 37,048 47,287 14,367 31,343 56,007 2010 44,243 48,419 52,616 19,962 42,328 62,319 2011 44,948 49,144 53,341 32,956 63,057 97,021 2012 47,332 51,651 55,971 34,704 66,274 102,595 2013 47,778 52,132 56,476 35,031 67,626 103,520 2014 44,826 48,726 52,605 34,131 64,582 99,391 2015 45,768 49,701 53,645 34,848 67,275 101,356 2016 44,081 47,712 51,332 33,563 64,583 96,987 2017 34,925 37,987 41,037 26,592 51,419 77,536 Source: Dept of Transportation VSL unadjusted for age and RAND QALY weight of 0.141.

Factor 2. Limitations

• There may be other substances and/or alcohol involved in the meth-related death.

However, it would be difficult to ascertain which drug was directly responsible for the

death or whether it was a multiple drug interaction.

• Other data used by RAND are restricted or for fee and thus not used in this paper.

• The Department of Transportation VSL numbers are supposed to be updated every

year but they have not been updated since the guidance for 2016.

Factor 3. Meth-related child endangerment

A. Child protective services

B. Foster care 91

Child abuse prevention interventions were shown to be remarkably cost effective

in a Michigan study, often costing only a small fraction of the expense of treatment.

Some costs were directly related to abuse (e.g. hospital costs) while other costs were

indirect (e.g., troubles in school, involvement with the juvenile justice system, increased

mental health problems) and other costs were delayed (e.g. special education costs);

approximately 30% of abused children have some type of language or cognitive

impairment; over 50% of abused children have socioemotional problems; approximately

14% of abused children exhibit self-mutilation or other self-destructive behavior; over

50% of abused children have difficulty in school, including poor attendance and misconduct; over 22% of abused children have a learning disorder (from Daro, 1988, p.

154). It was estimated that one quarter of all children from abusive households will receive some special education services for at least one year between kindergarten and twelfth grade. Programs designed to prevent child maltreatment serve society in several ways: they build stronger, healthier children; they reduce the burdens on state services such as education, law enforcement, corrections, and mental health; and they free money for more life-enhancing projects. (94)

3.A. Child Protective Services

The State of Hawai`i has child abuse data from 1970 onwards but child abuse

numbers due to drugs are only available from 2011 to 2016 from the Department of

Human Services Databooks as shown in Table 2.8. The types of maltreatment reported

by the state include physical abuse, neglect, medical neglect, sexual abuse,

psychological abuse, and threatened harm. The regression line based on the percent of 92

child abuse due to drugs from 2011 to 2017 was found and used to estimate the percent

of child abuse cases due to drugs for 2007 to 2010.

Percent of child abuse cases due to drugs = 2.1429 * (Year - 2006) + 30.243 (Eq. 2.1)

This percentage was then multiplied by the number of confirmed child abuse case to

find the estimated number of child abuse due to drugs cases for 2007 to 2010 as shown

in Table 2.8. The “Cost of Reduction in Potential Due to Meth” column is calculated by

Cost Estimate of Reduction in Potential Due to Meth = (Child Abuse Cases due to Meth

Use) X (QALY weight of 0.141) X (DOT VSL from Table 2.5) (Eq. 2.2)

The state of Montana, which also has a huge meth problem, tracked drug-related

out-of-home placements beginning in September 2006 that identified the type of drug involved. The Montana average of 29% with a range of (25.8%, 44%) was used to

attribute meth-related child abuse as it is the only state that has a publicly available

report. (78)

Table 2.8. Hawai`i Child Abuse Cases and Estimated Costs of Reduction in Potential (VSL) Due to Meth Use, 2007-2017 Child Abuse Child Average Confirmed Cases Abuse Low VSL VSL High VSL Child Due to Cases due Estimate Estimate Estimate Abuse Drugs to Meth (current (current (current Year Cases (%) Use millions $) millions $) millions $) 2007 2082 23.8a 496a 224 420 587 2008 1850 34.5a 639a 288 541 757 2009 2174 36.7a 797a 360 674 944 2010 1575 38.8a 611a 276 534 724 2011 1424 31 417 306 535 758 2012 1392 36.5 519 381 666 951 2013 1329 35.4 482 353 625 884 2014 1406 40.2 565 430 749 1,068 2015 1568 41.8 656 499 888 1,239 2016 1355 41.1 583 421 749 1,045 2017 1297 46.0 547 416 740 1,034 Source: Hawai`i State Department of Human Services Databooks, RAND QALY weight of 0.141, Montana average 29% (25.8%, 44%), and Department of Transportation VSL a Estimated.

3.B. Foster Care

Children are placed in foster care when a child protective services worker and

the family court system have determined it is not safe for them to remain home.

Displacement from their family and disruption of their usual routine and familiar

surroundings is traumatizing for many children. (95)

The Financial Audit of the Department of Human Services, State of Hawai`i

report for 2007 to 2009 contains various line items for federal expenditures given to

Hawai`i that have “Foster Care” or “Child Welfare” or “Social Services Block Grant” in

the title. For the years 2007 to 2009, Table 2.9 “Foster Care” costs will consist of the

sum of “Foster Care – Title IV-E”8 and “ARRA9 - Foster Care – Title IV-E”. The Child

Protective Services costs in Table 2.10 will be the sum of “Children’s Justice Grant to

States”, “Child Welfare Services – State Grants”, “Social Services Block Grant”, and

“Child Abuse and Neglect State Grants”.

The US Department of Health and Human Services, Office of Community

Services Division of Social Services, Administration for Children & Families started to produce an annual Social Services Block Grant report in 2010 that included national and state data, including combined state and federal expenditures for foster care and for child protective services. (96) From 2010 to 2017, Hawai`i spent a low of $0 in 2015 to a high of $6.9 million in 2007 (current year funds) on foster care, and a low of $15.2 million in 2015 to a high of $55.7 million in 2014 on protective services (Child Abuse).

See Table 2.9 for expenditures for foster care and Table 2.10 for protective services for children. (96) (97) The Social Services Block Grants Annual Reports do not include the reasons for the huge differences in budget allocations. The number of foster care cases did not drop precipitously in the years with low budget allocations.

The number of children in foster care is available through the State of Hawai`i,

Department of Human Services Databooks for 2007-2016, in 2017 these data were available in the Child Abuse and Neglect in Hawai`i report. (98) The State of Hawai`i did not have statistics that attribute foster care placement due to meth-related issues nor even due to a general drug-related category until 2015. In 2007, Montana foster

8 Title IV-E Title IV-E provides foster care maintenance payments to states on behalf of each child who has been removed from the home, as well as adoption assistance and reimbursement for administrative and training expenses. 9 American Recovery and Reinvestment Act of 2009 95

care placement due to meth was 44% and 25.8% in 2008 for an average of 29%.

Although alcohol was the primary substance in almost 37% of all foster care cases,

meth was the next highest substance. (78) The Montana range of 25% to 44% with an

average of 29% was used to attribute meth-related foster care placement as it is the

only state that has a publicly available report. Montana estimated the share of court

and administration costs related to child removal because of meth to be $3,845 per child

per year based on estimates from a variety of national sources in 2005. The court and

administration costs will be considered in the Crime Factors section because these

costs are non-separable for Hawai`i.

Costs of Meth-related Protective Services for Children = ((25.8%, 29%, or 44%)

Montana meth-related Child Abuse Cases) X (Protective Services for Children)

(Eq. 2.3)

The average length of stay in foster care was 13.9 months in FY 2016 according

to the Child Welfare Information Gateway. (99) The state of Hawai`i had not increased the stipend for foster care in 24 years, but as a result of a lawsuit in 2013, the state increased the rates in 2014 and 2016 with adjustments in the future for inflation. (100)

Table 2.9. Hawai`i Methamphetamine-related Foster Care, 2007-2017 Low Average High Annual Average Meth- Meth- Meth- Budget Low Meth- Meth- Number related related related Child related related High Meth- in Number Number in Number Foster Foster Foster related Foster in Foster Foster in Foster Care Care Care Foster Care Year Care Care Care Care (current $) (current $) (current $) (current $) 2007 4,129 1065 1197 1817 23,442,461 6,048,155 6,798,314 10,314,683 2008 3,522 909 1021 1550 21,723,679 5,604,709 6,299,867 9,558,419 2009 3,086 796 895 1358 22,700,906 5,856,834 6,583,263 9,988,399 2010 2,672 689 775 1176 1,676,073 432,427 486,061 737,472 2011 2,355 608 683 1036 3,479,398 897,685 1,009,025 1,530,935 2012 2,315 597 671 1019 4,304,869 1,110,656 1,248,412 1,894,142 2013 2,180 562 632 959 4,085,230 1,053,989 1,184,717 1,797,501 2014 2,231 576 647 982 3,978,743 1,026,516 1,153,835 1,750,647 2015 2,386 616 692 1050 0 0 0 0 2016 2,597 670 753 1143 552,780 142,617 160,306 243,223 2017 2,688 694 753 1183 16,963,656 4,376,623 160,306 7,464,009 Source: Financial Audit of the Dept. of Human Services, State of Hawai`i, 2007, 2008, 2009; Social Services Block Grants Annual Reports, Administration for Children & Families; Montana average 29% (25.8%, 44%) meth-related foster care.

Table 2.10. Hawai`i Child Protective Service, 2007-2017 Average Low Meth- High Meth- Meth- Number Protective related related related of Child Services for Protective Protective Year Protective Abuse Children Services for Services for Services for Cases (current $) Children Children Children (current $) (current $) (current $) 2007 4,129 45,224,802 11,667,999 13,115,193 19,898,913 2008 3,522 40,048,458 10,332,502 11,614,053 17,621,322 2009 3,086 46,700,724 12,048,787 13,543,210 20,548,319 2010 2,672 65,394,255 16,871,718 18,964,334 28,773,472 2011 2,355 47,662,099 12,296,822 13,822,009 20,971,324 2012 2,315 49,715,583 12,826,620 14,417,519 21,874,857 2013 2,180 50,152,508 12,939,347 14,544,227 22,067,104 2014 2,231 55,726,162 14,377,350 16,160,587 24,519,511 2015 2,386 15,274,631 3,940,855 4,429,643 6,720,838 2016 2,597 20,592,129 5,312,769 5,971,717 9,060,537 2017 1,297 15,920,070 4,107,378 4,616,820 7,004,831 Source: Financial Audit of the Dept. of Human Services, State of Hawai`i, 2007, 2008, 2009; Social Services Block Grants Annual Reports, Administration for Children & Families 2010-2017; Montana average 29% (25.8%, 44%) meth-related foster care).

Factor 3. Methodology

The sum of the reduction in potential (VSL) of children who were abused due to

meth in Table 2.8, the cost of meth-related Foster Care in Table 2.9, and the cost of meth-related Child Protective Services in Table 2.10 , was found for each of the years

2007-2017 and is shown in Table 2.11.

Table 2.11. Factor 3: Summary of Hawai`i Meth-related Child Endangerment Costs, 2007-2017 Low Total Average High Total Cost Total Cost Cost Year (current $) (current $) (current $) 2007 241,716,154 439,913,507 617,213,596 2008 303,937,211 558,913,920 784,179,741 2009 377,905,621 694,126,473 974,536,718 2010 293,304,145 553,450,395 753,510,944 2011 319,194,507 549,831,034 780,502,259 2012 394,937,276 681,665,931 974,768,999 2013 366,993,336 640,728,944 907,864,605 2014 445,403,866 766,314,422 1,094,270,158 2015 502,940,855 892,429,643 1,245,720,838 2016 426,455,386 755,132,023 1,054,303,760 2017 424,484,000 744,777,126 1,048,468,840 Source: Reduction in Potential (VSL) Table 2.8, Foster Care Table 2.9, and Child Protective Services Table 2.10

The summary costs in Table 2.11 are dominated by the reduction in potential

(VSL) of children who were abused due to meth, rather than the costs for Foster Care and Child Protective Services. Not all abused children evidence problems and some who do may do so for reasons unrelated to their abusive history. There are no Hawai`i

data that address these specific concerns.

Factor 3. Limitations

• The meth-related court and administration costs associated with placing a child into

the foster care system used the Montana average estimate of 29% with a low of

25.8% and a high of 44% for meth-related child abuse since Hawai`i does not track

meth-related child abuse.

• Summary costs (Table 2.11) are dominated by the reduction in potential (VSL) of

children who were abused due to meth, rather than the costs for Foster Care and

Child Protective Services.

• There may have been other expenditures on foster care and child protective

services that were not included, thus these total costs are likely to be an

underestimate of the true costs.

• RAND cites past national studies that found approximately 80% of the children who

enter the foster care system due to their caretakers' substance abuse, are severely

neglected. No data are available for Hawai`i.

Factor 4. Criminal Justice Costs

A. Hawai`i Department of Public Safety

i. Law enforcement – Police and sheriffs' departments

ii. Correctional expenditures for offenders with meth-related crimes

iii. State Narcotics Enforcement Division

B. Judiciary

The 2017 National Drug Threat Assessment (NDTA) reported the results of 2016

National Drug Threat Survey (n=5155 agencies) with 44.1% of agencies reporting heroin as their greatest drug threat, 29.8% methamphetamine, 9.3% controlled prescription drugs, and 6.3% fentanyl. (13) However, there were regional differences in illicit drug use. The Midwest and Western US had the highest concentrations of

100

respondents who reported meth as the greatest drug threat in their area. Hawai`i has the distinction of being the Number 1 per capita for crystal meth or “ice” use in the

United States. (3) Law enforcement agencies assessed that the drug which most contributed to violent crime was methamphetamine, 36.3%, followed by heroin, 25.8%.

Meth, particularly high purity crystal methamphetamine also known as ice, poses the greatest drug threat to Hawai`i, Guam, and Saipan. Meth is readily available in most areas of Hawai`i. (2)

Meth-induced violent and property crimes were those generally attributable to actions of people under the influence of meth or in need of meth due to parole and probation violations for meth offenses.

4.A. Hawai`i Department of Public Safety

Meth arrests are associated with law enforcement, judiciary, property and violent crime, and incarceration costs. RAND did not consider the effect of meth use on every type of offense (e.g., vandalism) or community corrections violation, the costs associated with meth-related convictions (e.g., denial of some welfare benefits, denial of student aid, removal from public housing) and neither will this paper. It was noted that quantifying these consequences is important for understanding the full costs of meth use, but that data limitations prevent their inclusion. (38) In Hawai`i, the Department of

Safety, Narcotics Enforcement Division is responsible for enforcing controlled substance and regulated chemical laws and pursuing the appropriate use of pharmaceuticals.

(101) The Department of Public Safety is also responsible for law enforcement, corrections, treatment programs for inmates, and victim assistance compensation. (102) 101

Meth-related expenditures are estimated by multiplying the percentage of meth-related arrests by the Department of Public Safety expenditures. The number and percentage of meth arrests, are shown in Table 2.12. Meth arrests include the manufacturing, sale, or possession of meth.

Table 2.12. Hawai`i Department of Public Safety Meth-related Expenditures, 2007- 2017 Dept of Public Estimated Meth- Meth- % Meth- Total Safety related Year related related Arrests Expenditures Expenditures Arrests1 Arrest (current $) (current $) 2007 50,271 1248 2.48 378,409,000 9,394,172 2008 48,227 665 1.38 414,463,000 5,715,012 2009 47,541 615 1.29 464,897,000 6,014,002 2010 46,968 773 1.65 538,110,000 8,856,222 2011 47,084 952 2.02 471,459,000 9,532,516 2012 48,382 1057 2.18 502,002,000 10,967,222 2013 48,130 1145 2.38 451,946,000 10,751,676 2014 42,887 1379 3.22 533,727,000 17,161,600 2015 42,712 1328 3.11 504,343,000 15,681,015 2016 38,691 1172 3.03 485,985,000 14,721,109 2017 43,119 1221 2.83 269,812,983 7,640,290 Source: Uniform Crime Reports; Hawai`i Dept. of Public Safety Annual Reports. 1 Manufacturing, sale, and possession.

The costs associated with the production of meth such as lab cleanup will not be considered for Hawaiì as meth is generally not produced in Hawaiì. (103) The number of small scale meth labs in Hawaiì has decreased from three per year in 2006 to zero since 2014 as shown in Figure 2.7. Large-scale production facilities are not located in

102

Hawai`i and most of the methamphetamine available in Hawai`i is imported. Meth is transported into the state from Mexico via the continental United States, primarily through California, Nevada, Arizona, and Washington. (104)

The Hawai`i HIDTA is federally funded and coordinates working relationships between 24 federal, state, and local drug enforcement task forces within the state of

Hawaiì. There are 16 federally funded task forces in the Hawaiì HIDTA region which will not be included in costs for the state of Hawaiì. The Statewide Marijuana

Eradication Task Force (SME) and the Hawai`i Narcotics Task Force (HNTF) are non-

HIDTA funded and are included within the Hawai`i Department of Public Safety. (104)

4.B. Judiciary

The state of Hawai`i judiciary is a unified state court system that functions under one administrative head, the Chief Justice of the Hawai`i Supreme Court. It has nine different court divisions but does not have a specific drug-related court nor are cases classified as meth-related. The expenditures for the judiciary system are found in the

Annual Judiciary Reports and the total judiciary expenditures, including courts, jails, and prisons, may be found in Table 2.13. (105) The meth-related judiciary expenditures were found by apportioning by the percent of meth-related arrests.

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Table 2.13. Hawai`i Judiciary Meth-related Costs, 2007-2017

Total Judiciary Meth-related % Meth-related Year Expenditures Judiciary Costs Arrest (Current $) (Current $) 2007 132,106,969 2.48 3,276,253 2008 152,142,233 1.38 2,099,563

2009 155,857,508 1.29 2,010,562 2010 142,521,668 1.65 2,351,608 2011 139,427,174 2.02 2,816,429

2012 142,034,247 2.18 3,096,347 2013 144,474,260 2.38 3,438,487 2014 159,839,263 3.22 5,146,824 2015 165,932,703 3.11 5,160,507 2016 168,852,965 3.03 5,116,245 2017 173,325,168 2.83 4,905,102 Source: State of Hawai`i, Annual Judiciary Reports (General Fund, Special Fund, and Revolving Fund)

Factor 4. Methodology

The sum of the costs of meth-related Hawai`i Department of Public Safety, Table

2.12, and the costs of meth-related Judiciary Division, Table 2.13, was found for each of the years 2007-2017 and is shown in Table 2.14.

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Table 2.14. Factor 4: Summary of Meth-related Criminal Justice Costs, 2007-2017 Year Costs (current $) 2007 12,670,425 2008 7,814,575 2009 8,024,564 2010 11,207,830 2011 12,348,945 2012 14,063,569 2013 14,190,163 2014 22,308,424 2015 20,841,522 2016 19,837,354 2017 9,810,204 Source: Uniform Crime Reports; Hawai`i Dept. of Public Safety Annual Reports and Hawai`i Annual Judiciary Reports (General Fund, Special Fund, and Revolving Fund)

Factor 4. Limitations

• The effect of meth use was not considered on every type of offense (e.g., vandalism)

or community corrections violation, the costs associated with meth-related

convictions (e.g., denial of some welfare benefits, denial of student aid, removal from

public housing) due to data shortfalls.

• The percentage of meth-related (manufacturing, sale, or possession) arrests used to

attribute meth costs may be an underestimate for Judiciary costs since the Hawai'i

ADAM results showed that while 40% - 50% tested positive for meth, the majority of

arrestees were not picked up for drug offenses, but for bench warrants and

misdemeanor offenses.

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Factor 5. Lost Productivity Attributable to Meth

Productivity losses were attributed to premature mortality, lost earnings

associated with absence for treatment, and unemployment.

A. Premature Mortality Due to Meth

B. Lost Productivity Associated With Absence for Meth Treatment

C. Lost Productivity Associated With Meth Use Unemployment

5.A. Premature Mortality Due to Meth

The breakdown by age groups of meth-related deaths (T43.6) in Hawai`i is

shown in Figure 2.5, for the years between 1999 and 2018 combined for 5-year age

groups.10 The Centers for Disease Control and Prevention (CDC) Wide-ranging Online

Data for Epidemiologic Research (WONDER) multiple-cause-of-death datafile attempts

to identify those who died of the immediate consequence of use (e.g., overdoses) as

well as those who died from complications of long-term substance abuse. The greater number of meth deaths at older ages is consistent with the use of meth at older ages.

There may be other substances and/or alcohol involved in the meth-related death, but it is difficult to determine which substance was the only cause or if it was a combination of various substances. Note that CDC WONDER does not calculate age-adjusted rates when the data are grouped by age group.

10 The multiple-cause-of-death output suppresses the number of deaths between 0 and 9. The small population of Hawai`i would lead to suppression of most data if the years were not combined. 106

250 225 200 175 150 125 227 100 194 164 75 142 Number Deaths of 130 50 88 25 53 29 44 37 0

Age Group (years) Figure 2.4. Distribution of Hawai`i Meth Deaths by 5-Year Age Group, 1999-2018 Combined Source: CDC WONDER Multiple Case of Death, (T43.6)

Since 2000, the number of deaths where meth was involved was higher than cocaine, heroin, or fentanyl and has been on an increasingly upward trend in Hawai`i.

Meth-related deaths started sharply increasing in 2013 and overtook traffic fatalities, obtained from the National Highway Traffic Safety Administration Fatality Analysis

Reporting System, in 2015 and continued through 2018. The number of Hawai`i deaths from methamphetamine, cocaine, heroin, fentanyl compared to traffic vehicle fatalities between 1999 and 2018 are shown in Figure 2.6.

The number of meth-related suicides in Hawai`i for the years 2007-2018 was suppressed by CDC WONDER indicating there were between 0 to 9 meth-related

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suicides annually with the actual number unknown, therefore meth-related suicides cannot be included in the estimation of premature mortality costs.

180 Traffic Fatalities Meth Deaths T43.6 160 Cocaine Deaths T40.5 Heroin Deaths T40.1 Fentanyl T40.4 140

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100

Number Deaths of 60

Year Figure 2.5. Hawai`i Number of Deaths from Traffic Fatalities and Drugs, 1999-2018 Source: CDC Wonder and National Highway Traffic Safety Administration - Fatality Analysis Reporting System. Notes: Drug deaths between 0-9 deaths suppressed by CDC WONDER. Too many unstable data points to use Death Rate.

The benefit of preventing a fatality is measured by what is conventionally called value of a statistical life (VSL). It is not the valuation of life as such, but the valuation of reductions in risks. There are several approaches to calculating VSL and there are

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several US federal agencies that calculate VSL. The Department of Transportation

(DOT) VSL from Table 2.6 was used to place a value on premature mortality due to meth-related deaths in Table 2.15. The DOT does not have any reliable method for estimating the overall probability distribution of the average VSL. (93)

Table 2.15. Lost Potential of Methamphetamine Deaths Number of Meth- Average related Low VSL VSL High VSL Year Deaths (millions $) (millions $) (millions $) 2007 38 122 228 319 2008 31 99 186 260 2009 43 138 258 361 2010 48 154 298 403 2011 54 281 491 697 2012 48 250 437 624 2013 63 328 580 819 2014 71 383 667 951 2015 100 540 960 1,340 2016 129 697 1,238 1,729 2017 133 718 1,277 1,782 Source: Dept of Transportation VSL unadjusted for age and CDC WONDER Meth- related deaths (T43.6),

5.B. Lost Productivity Associated With Absence for Treatment

RAND and Montana identified two potential sources of absenteeism: missed work due to time in treatment and missed work due to other reasons. NIDA indicates that treatment for less than 90 days (or 12.75 weeks) is of limited effectiveness, and treatment lasting significantly longer is recommended for maintaining positive outcomes.

(70) Persons in meth-related treatment miss work for treatment, but for Hawai`i there are no data on whether these persons were full-time or part-time workers. The Hawai`i

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minimum wage and the average annual income were obtained from the State of Hawai`i

Data Book, Section 12 (106), to find estimates for lost income while in treatment and is shown in Table 2.16. Meth users are assumed to work mainly at minimum wage jobs and the average income was used as the high estimate of lost productivity. A person is assumed to have had a job prior to treatment and will be returning to a job.

Table 2.16. Lost Productivity Due to Absence for Outpatient Methamphetamine Treatment, 2007-2017 Average Total Estimate Number Number of Weekly Low (Ave of High of Meth Weeks Income, Average Estimate, Minimum Estimate, Admits Absent Minimum Weekly Minimum Wage & Average (TEDS- Due to Wage Income Wage Ave Wage) Income Year A) Treatment (current $) (current $) (current $) (current $) (current $) 2007 2146 27,576 290 759 7,997,069 14,463,664 21,564,510 2008 2149 27,615 290 782 8,008,249 14,801,452 21,953,647 2009 1958 25,160 290 795 7,296,487 13,649,463 20,178,561 2010 1815 23,323 290 802 6,763,598 12,734,222 19,031,364 2011 1891 24,299 290 816 7,046,812 13,437,541 20,265,658 2012 2062 26,497 290 834 7,684,043 14,891,145 22,336,718 2013 2033 26,124 290 843 7,575,975 14,799,274 22,701,799 2014 2107 27,075 290 869 7,851,736 15,689,934 24,421,605 2015 2247 28,874 310 902 8,950,925 17,497,614 26,737,278 2016 2364 30,377 340 926 10,328,316 19,228,894 28,129,472 2017 2163 27,795 370 955 10,283,984 18,413,889 25,737,753 Source: TEDS-A meth, State of Hawai`i Data Book minimum wage and average income

For Hawai`i, the mean length of stay at a hospital and the number of hospital inpatient discharges was available for the years 2010-2015 and 2016. The mean days of stay was rounded up to the next whole day as it was assumed that no one would go to work on the same day of discharge. The daily minimum wage and the average

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income was used to generate the low and high estimates of lost productivity in current dollars for hospital inpatient stays. The average of the minimum wage and the average income was used as the average estimate. Table 2.17 contains the lost productivity costs for meth-related hospital inpatient treatment. The average of the available data was used for 2007-2009 and 2017.

The number of discharges from the ED is available for 2010, 2013, 2014, and

2016. There are too few data points to estimate data for the missing years. All ED visits were counted as one day missed of work. Table 2.18 contains the productivity costs for ED visits.

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Table 2.17. Lost Productivity Due to Absence for Hospital Inpatient Treatment for Meth, 2007-2017 Low Average Productivity Productivity High Daily Days Cost of Cost of Hospital Productivity Income, Daily Number of Mean absent Hospital Stay, Stay (Ave of min Cost Hospital Minimum Average Hospital Days of due to Minimum wage & ave Stay, Average Wage Income Inpatient Stay in Hospital Wage income) Income Year (current $) (current $) Discharges Hospital Stay (current $) (current $) (current $) 2007 58 156 39a 4.5a 5 11,310 20,865 30,420 2008 58 159 39a 4.5a 5 11,310 21,158 31,005 2009 58 160 39a 4.5a 5 11,310 21,255 31,200 2010 58 163 33 5.2 6 11,484 21,879 32,274 2011 58 167 36 5.4 6 12,528 24,300 36,072 2012 58 169 47 3.4 4 10,904 21,338 31,772 2013 58 174 38 3.7 4 8,816 17,632 26,448 2014 58 180 21 3.0 3 3,654 7,497 11,340 2015 62 185 39a 6.9 7 16,926 33,716 50,505 2016 68 185 58 3.6 4 15,776 29,348 42,920 2017 74 191 39a 4.5a 5 14,430 25,838 37,245 Source: TEDS-A meth, State of Hawai`i Data Book income & minimum wage, and HCUPnet. a Estimate, average of available data used.

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Table 2.18. Lost Productivity Due to Absence for Emergency Department Visits for Meth, 2010-2016 Low Average High Productivity Productivity Productivity Daily Cost of ED Cost of ED Cost ED Income, Daily Visit, Visit (Ave of Visit, Minimum Average Number Minimum min wage & Average Wage Income Discharged Wage ave income) Income Year (current $) (current $) from ED (current $) (current $) (current $) 2007 58 156 2008 58 159 2009 58 160 < 10 2010 58 163 20 1,160 2,210 3,260 2011 58 167 2012 58 169 2013 58 174 12 696 1,392 2,088 2014 58 180 22 1,276 2,618 3,960 2015 62 185 < 10 2016 68 185 20 1,360 2,530 3,700 2017 74 191 Source: State of Hawai`i Data Book income & minimum wage, and HCUPnet.

5.C. Lost Productivity Associated With Meth Use Unemployment

The Hawaiì DoH ADAD survey in 2004 found that 23.9% of meth users wanted treatment but did not receive it. (47) The number of treatment spaces have not significantly increased over the years to meet the increasing demand. RAND determined that people between the ages of 21 and 50 years who used meth were 97% more likely to be unemployed than their peers, and unemployed for an average of 12.75 weeks. The number of people who wanted treatment but were unable to obtain it was obtained from Table 1.9 in Objective 1. This number is an underestimate because not all meth users want treatment. The Hawaiì minimum wage and the average annual income obtained from the State of Hawaiì Data Book, Section 12 (106), was multiplied 113

by 12.75 weeks to obtain the average meth-related lost wages per meth user and is shown in Table 2.19.

Table 2.19. Lost Productivity Due to Meth Use Unemployment for 12.75 Weeks, 2007-2017 Average Number of Low Lost Lost Income High Lost Meth Weekly Average Income, (Ave of Min Income, Users Who Minimum Weekly Minimum Wage & Ave Average Wanted Wage Income Wage Wage) Income Year Treatment (current $) (current $) (current $) (current $) (current $) 2007 498 290 759 1,841,355 3,330,313 4,819,271 2008 453 290 782 1,674,968 3,095,802 4,516,637 2009 468 290 795 1,730,430 3,237,098 4,743,765 2010 431 290 802 1,593,623 3,000,407 4,407,191 2011 452 290 816 1,671,270 3,186,939 4,702,608 2012 507 290 834 1,874,633 3,632,909 5,391,185 2013 505 290 843 1,867,238 3,647,552 5,427,866 2014 526 290 869 1,944,885 3,886,417 5,827,949 2015 542 310 902 2,142,255 4,187,763 6,233,271 2016 573 340 926 2,483,955 4,624,540 6,765,125 2017 399 370 955 1,882,283 3,370,303 4,858,324 Source: TEDS-A, State of Hawaii 2004 Treatment Needs Assessment, State of Hawai`i Data Book income & minimum wage.

Combining Tables 2.15, 2.16, 2.17, 2.18, and 2.19 yields the Factor 5 productivity loss due to meth use in Table 2.20.

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Table 2.20. Factor 5: Summary of Lost Productivity Attributable to Methamphetamine Use, 2007-2017 Low Average High Productivity Productivity Productivity Year Loss Loss Loss (current $) (current $) (current $) 2007 9,849,856 17,815,070 26,414,520 2008 9,694,626 17,918,598 26,501,549 2009 9,038,365 16,908,074 24,953,887 2010 8,370,019 15,759,016 23,474,492 2011 8,730,891 16,649,271 25,005,035 2012 9,569,830 18,545,829 27,760,299 2013 9,453,053 18,466,430 28,159,020 2014 9,801,934 19,587,133 30,265,805 2015 11,110,646 21,720,053 33,022,394 2016 12,830,104 23,886,550 34,942,946 2017 12,181,415 21,811,307 30,635,104

Factor 5. Limitations

• Not all persons who wanted meth treatment were able to obtain treatment.

• Meth users may earn a minimum wage and are less likely to achieve an average

wage.

• It was assumed everyone entering treatment retains his or her job and cannot work

when in intensive or residential therapy.

• No information on individuals who lose their jobs because of treatment – either

because they miss too much work or are fired for being drug users.

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Objective 2. Overall Methodology

A summary of the estimated costs was calculated for each of the five factors according to the procedures described in each section with the productivity cost estimates summarized in Table 2.21. The average cost estimate for Factor 4, Criminal

Justice, was used for both low and high estimates. The overall productivity cost estimates chart is shown in Figure 2.7. The estimated costs were dominated by the

VSL for Child Abuse and Meth deaths, resulting in startling numbers for the economic and social costs of meth use. A summary of the costs without the VSL may be seen in

Table 2.22.

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Table 2.21. VSL Included Summary of Hawai`i Methamphetamine-related Cost Estimates, 2007-2017 (current $ millions) 1. Treatment 2. Healthcare 3. Child endangerment 4. Criminal justice 5. Lost productivity Year Low Average High Low Average High Low Average High Low Average High Low Average High 2007 34 101 168 20,787 42,621 64,763 242 440 617 - 13 - 10 18 26 2008 32 94 157 19,865 40,779 62,036 304 559 784 - 8 - 10 18 27 2009 32 97 161 14,367 32,159 57,175 378 694 975 - 8 - 9 17 25 2010 31 92 153 19,962 42,328 62,319 293 553 754 - 11 - 8 16 23 2011 33 98 163 32,956 63,057 97,021 319 550 781 - 12 - 9 17 25 2012 36 107 178 34,704 66,274 102,595 395 682 975 - 14 - 10 19 28 2013 36 109 181 35,031 67,626 103,520 367 641 908 - 14 - 9 18 28 2014 39 116 194 34,131 64,582 99,391 445 766 1,094 - 22 - 10 20 30 2015 40 120 199 34,848 67,275 101,356 503 892 1,246 - 21 - 11 22 33 2016 43 129 214 33,563 64,583 96,987 426 755 1,054 - 20 - 13 24 35 2017 32 96 159 26,592 51,419 77,536 424 745 1,048 - 10 - 12 22 31

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Table 2.22. No VSL Summary of Hawai`i Methamphetamine-related Cost Estimates, 2007-2017 (current $ millions) 1. Treatment 2. Healthcare 3. Child endangerment 4. Criminal justice 5. Lost productivity Year Low Average High Low Average High Low Average High Low Average High Low Average High 2007 34 101 168 18 20 30 - 13 - 10 18 26 2008 32 94 157 16 18 27 - 8 - 10 18 27 2009 32 97 161 18 20 31 - 8 - 9 17 25 2010 31 92 153 17 19 30 - 11 - 8 16 23 2011 33 98 163 13 15 23 - 12 - 9 17 25 2012 36 107 178 14 16 24 - 14 - 10 19 28 2013 36 109 181 14 16 24 - 14 - 9 18 28 2014 39 116 194 15 17 26 - 22 - 10 20 30 2015 40 120 199 4 4 7 - 21 - 11 22 33 2016 43 129 214 5 6 9 - 20 - 13 24 35 2017 32 96 159 8 5 14 - 10 - 12 22 31

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Figure 2.7 shows the cost of meth use in Hawai`i from 2007 to 2017. The dip in the high cost estimate in 2009 may be attributed to Factor 2: Healthcare and Health

Services which is in turn dependent on the lower number of Part II offenses arrests in

2009.

130,000 120,000 110,000 100,000 y = 3850.7x + 61,961 90,000 R² = 0.4289 80,000 70,000 60,000 y = 2475.6x + 41,421 R² = 0.4617 50,000 40,000 30,000 20,000 y = 1609.6x + 18,648 10,000 R² = 0.4645 0 Estimated Cost (current $ millions) (current Cost Estimated

Year High Ave Low Linear (High) Linear (Ave) Linear (Low) Figure 2.6. Total Cost of Methamphetamine Use in Hawai`i, 2007 to 2017

The State of Hawai`i Budget in Brief for each of the state fiscal years (July-June) is listed below for comparison. (107)

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Table 2.23. VSL Included Hawaiì Total Methamphetamine Use Cost Estimates, 2007-2017 Low Average High State of Year (current (current (current Hawaiì Budget $ million) $ million) $ million) ($ million) 2007 21,102 43,192 65,587 11,500 2008 20,235 41,458 63,011 12,100 2009 14,810 40,821 45,047 12,700 2010 20,321 43,001 63,261 10,948 2011 33,345 63,734 98,002 11,221 2012 35,177 67,095 103,790 10,870 2013 35,477 68,408 104,651 11,080 2014 34,668 65,507 100,732 11,758 2015 35,445 68,330 102,855 12,081 2016 34,088 65,511 98,310 12,633 2017 27,070 52,291 78,784 13,143 2018 14,254 2019 14,377 2020 15,475 2021 15,699 Ballotpedia, Historical Hawaii budget and finance information 2007-2011; State of Hawaiì Budget in Brief: 2011-2013, 2013-2015, 2015-2017, 2017-2019, 2019-2021

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Table 2.24. Difference Between VSL and No VSL Hawai`i Total Methamphetamine Use Cost Estimates, 2007-2017 State of Low Average High Hawai`i Year (current (current (current Budget $ million) $ million) $ million) ($ million) 2007 56 131 207 11,500 2008 49 120 191 12,100 2009 49 122 194 12,700 2010 50 119 188 10,948 2011 54 127 200 11,221 2012 60 140 220 10,870 2013 60 141 223 11,080 2014 71 158 246 11,758 2015 72 162 253 12,081 2016 76 173 269 12,633 2017 54 126 207 13,143 2018 14,254 2019 14,377 2020 15,475 2021 15,699

The lost potential of children who are abused, the meth users, and the deaths due to meth is shown in Table 2.25 which is the difference between Tables 2.23 and

2.24.

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Table 2.25. Lost Productivity Due to Hawai`i Methamphetamine Use, 2007-2017 State of Low Average High Hawai`i Year (current (current (current Budget $ million) $ million) $ million) ($ million) 2007 65,531 43,061 20,878 11,500 2008 62,962 41,338 20,027 12,100 2009 44,998 40,699 14,601 12,700 2010 63,210 42,881 20,118 10,948 2011 97,948 63,607 33,129 11,221 2012 103,730 66,956 34,938 10,870 2013 104,591 68,267 35,235 11,080 2014 100,661 65,348 34,401 11,758 2015 102,783 68,167 35,170 12,081 2016 98,234 65,338 33,796 12,633 2017 78,283 51,845 26,672 13,143 2018 14,254 2019 14,377 2020 15,475 2021 15,699

Objective 2. Overall Implications and Discussion

This study is the first in the state of Hawaiì to estimate the public health burden of meth use based on treatment, health care, criminal justice, child endangerment, and productivity costs particular to Hawaiì for each year from 2007 to 2017. As Hawaiì has the highest per capita meth use rate in the nation, meth is a significant economic burden on our state and impacts a wide range of government agencies and consumes significant public resources. The lost potential of the children who are abused because of meth use, the adults who use meth, and deaths due to meth use is larger than the

State of Hawai’i’s annual budget.

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The rate of meth use has increased over the years and therefore the number of meth users has also increased; however, the number of treatment centers and available spaces have remained fairly static, leading to unfulfilled needs for treatment. The increasing meth death rate is attributed to negative lifestyle changes of chronic meth users, and an increases in the purity of meth and decreasing price since 2007 shown in

Table 10. The increased resource requirements for meth-related treatment, health care, child abuse and neglect attributable to meth, criminal justice system, and law enforcement costs in addition to decreases in quality of life and reduced productivity results in tangible and intangible costs to both the meth user and the public.

Public health prevention programs to control communicable diseases (e.g. measles) and chronic (e.g. diabetes) before they manifest, and social problems (e.g. homeless) are cost effective, often costing only a small fraction of the expense of subsequent treatment and follow on consequences. Increased emphasis and funding for 1) prevention programs to discourage meth use and 2) meth treatment programs would be expected to have both economic and social benefits.

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Objective 2. Overall Limitations

• The Healthcare Association of Hawai`i states that 10.3% of adults use drugs, but

only 3.5% who needed treatment received treatment.

• NSDUH states that 5% of drug users could not obtain treatment in 2016-2017.

• The meth-related court and administration costs associated with placing a child

into the foster care system used the Montana estimate of 29% (25.8%, 44%) for

meth-related child abuse since Hawai`i does not track meth-related child abuse.

• For fee and restricted data were not used and may provide a better estimate of

the costs.

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OBJECTIVE 3. INVESTIGATION OF THE EFFECTS OF ECONOMIC RECESSIONS,

UNEMPLOYMENT RATES, AND METHAMPHETAMINE ARRESTS ON CHILD

ABUSE IN HAWAI`I

Objective 3. Introduction

When people lose their jobs, they are more likely to turn to illegal drug use. A review of 28 studies in 12 different countries11 found that both economic recessions and individual unemployment led to an increase in illegal drug use. (108) The systematic review found that most evidence supported the hypothesis that drug use increases in times of recession because unemployment increases psychological distress which in turn increases drug use. (108) For the most part it was because people were so psychologically distressed by their job loss that they turned to drugs to cope with their frustrations. (108) (109) A decrease in employment rates (increase in unemployment rates) was found to be associated with illegal drug use. (110) (111) Unemployed individuals did not stop using drugs because they lacked money to buy them, instead, people in dire financial straits simply switched to cheaper drugs. (108) Both drug selling and drug use among youth were found to be higher when the economy is weaker. (110)

As the unemployment rate increased by 1 percentage point in a given county in the US, the opioid-death rate rose by 3.6 percent, and emergency-room visits rose by 7 percent.

It was suspected that the dominant factor linking macroeconomic conditions to adverse drug outcomes is that the fatal and near fatal abuse of opioids often (and increasingly

11 Argentina, Australia, Brazil, France, Jamaica, New Zealand, Norway, Sweden, Switzerland, United Kingdom, United States, and Vietnam 125

over time) reflects a physical manifestation of mental health problems that have long been known to rise during periods of economic decline. (112)

The periodic increase and decrease of methamphetamine (meth) arrests in

Hawaiì stresses public infrastructure with its surge requirements for short-term increases and subsequent decreases in law enforcement, the judiciary system, child and social welfare, and imprisonment resource requirements. In addition, cyclical demands on the public health system for treatment services for meth addiction are difficult because of the fluctuating demands in the number of treatment spaces and therapists. The number of treatment facilities does not increase with increased meth use. The number of meth arrests and the unemployment rate in Hawaiì between 1976 and 2016 is shown in Figure 3.1. As can be seen, the Hawaiì meth arrests and the unemployment rate have similar periodicities.

The relationship between the unemployment rate and meth arrests in Hawai`i was investigated using cross-correlation and Granger causality. Cross-correlation is a measure of similarity of two time series as a function of the displacement (lag) of one relative to the other. (113) Cross-correlation is useful for determining the time delay or lag between two time series. The Granger causality test is a statistical hypothesis test for determining whether one time series can forecast another. It tests for dependencies between two or more time series to reject the null hypothesis of statistical independence. The differenced (aka stationary or pre-whitened) time series is used to test the direction of causality, however Granger causality may not be true causality.

(114)

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Figure 3.1. Hawai`i Meth Arrests, Unemployment, & Leading Economic Index and US Recessions & Stock Market Crashes Source: Uniform Crime Report, Bureau of Labor Statistics, Federal Reserve Banks of Philadelphia & St. Louis, and National Bureau of Economic Research

The Business Cycle Dating Committee of the National Bureau of Economic

Research (NBER) defines a recession as a significant decline in economic activity

spread across the economy and can last from a few months to more than a year. The

Committee does not have a fixed definition of economic activity. It examines and

compares the behavior of various measures of broad activity: real gross domestic

product12 (GDP) measured on the product and income sides, economy-wide employment, and real income. The Committee also may consider indicators that do not

12 GDP is a monetary measure of the market value of all the final goods and services produced in a period of time, often annually. 127

cover the entire economy, such as real sales and the Federal Reserve's index of

industrial production. (115)

Literature Search. Initially, the number of meth arrests was obtained as a factor

in the determination of the prevalence of meth users. (116) An examination of the

periodicity of the meth arrests leads to a recollection of the recession in 2008. A Google

search for recessions led to the National Bureau of Economic Research which

determines the start and end of recession periods for the US. Subsequently, a search

for “Hawai`i unemployment rates” resulted in the Bureau of Labor Statistics website.

(115) A search for “Hawai`i economic indicators” yielded the Federal Bank of

Philadelphia website for the monthly Hawai`i Economic Index. (117) A search on

Google for “recession drug use” resulted in the International Journal of Drug Policy special edition on recessions, business cycles, and drug use.

Nine out of the last ten recessions were found to coincide with increases in

unemployment rate. (118) Figure 3.1 includes the US recessions and stock market

crashes with the number of meth arrests and the unemployment rate in Hawai`i.

However, the national unemployment rate and the Hawai`i unemployment rate may

differ in the timing of unemployment cycles and thus the Hawai`i Leading Economic

Index was added as shown in Figure 3.1. (117)

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The seasonally adjusted leading index for Hawai`i predicts the six-month growth rate of the state's coincident index13. The Leading Economic Index is comprised of nonfarm payroll employment, the unemployment rate, average hours worked in manufacturing and wages and salaries, state-level housing permits (1 to 4 units), state initial unemployment insurance claims, delivery times from the Institute for Supply

Management (ISM) manufacturing survey, and the interest rate spread between the 10- year Treasury bond and the 3-month Treasury bill. (117) Leading indicators are indicators that usually, but not always, change before the economy as a whole changes.

They are useful as short-term predictors of the economy. From an examination of recessions and stock market crashes in Figure 3.1, not all recessions or stock market crashes yielded a rise in unemployment rate. The leading indicators did show a corresponding increase in the unemployment rate.

Depression is one of the most common mental disorders in the US Current research suggests that depression is caused by a combination of genetic, biological, environmental, and psychological factors. Risk factors include: personal or family history of depression; major life changes, trauma, or stress; and certain physical illnesses and medications. Depression affects people in different ways and there is no

13 The coincident index combines four state-level indicators to summarize current economic conditions in a single statistic. The four state-level variables in each coincident index are nonfarm payroll employment, average hours worked in manufacturing by production workers, the unemployment rate, and wage and salary disbursements deflated by the consumer price index (US city average). The trend for each state’s index is set to the trend of its gross domestic product (GDP), so long-term growth in the state’s index matches long-term growth in its GDP. 129

"one-size-fits-all" for treatment. Depression is usually treated with medications, psychotherapy, or a combination of the two. (119)

According to the CDC, 10.4% of physician visits have depression indicated on the medical record. (120) The National Institute for Mental Health (NIMH) defines possible depression as experiencing some of the following signs and symptoms most of the day, nearly every day, for at least two weeks:

• Persistent sad, anxious, or “empty” mood

• Feelings of hopelessness, or pessimism

• Irritability

• Feelings of guilt, worthlessness, or helplessness

• Loss of interest or pleasure in hobbies and activities

• Decreased energy or fatigue

• Moving or talking more slowly

• Feeling restless or having trouble sitting still

• Difficulty concentrating, remembering, or making decisions

• Difficulty sleeping, early-morning awakening, or oversleeping

• Appetite and/or weight changes

• Thoughts of death or suicide, or suicide attempts

• Aches or pains, headaches, cramps, or digestive problems without a clear

physical cause and/or that do not ease even with treatment

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There are many types of depressive disorders such as persistent depressive disorder (dysthymia), postpartum depression, psychotic depression, seasonal affective disorder, bipolar disorder, disruptive mood dysregulation disorder (diagnosed in children and adolescents) and premenstrual dysphoric disorder (PMDD). Not everyone who is depressed experiences every symptom. Some people experience only a few symptoms while others may experience many. Several persistent symptoms in addition to low mood are required for a diagnosis of major depression, but people with only a few – but distressing – symptoms may benefit from treatment of their “subsyndromal” depression.

The severity and frequency of symptoms and how long they last will vary depending on the individual and his or her particular illness. (119)

There are three measures of Hawai'i statewide depression that are also part of national assessments, (1) the Behavioral Risk Factor Surveillance System (BRFSS)

Anxiety and Depression Optional Module, (2) the Center for Medicare and Medicaid

Services (CMS) which provides data on chronic diseases for Medicare and Medicaid recipients of all ages, and (3) the household-based National Survey on Drug Use and

Health (NSDUH).

BRFSS is the nation's premier system of health-related landline telephone and cell phone surveys that collect data about US residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. (121)

The optional Anxiety and Depression Module assesses the prevalence of anxiety and depressive disorders in the general population at the state level. This module is composed of the Patient Health Questionnaire (PHQ-8) which has been validated against the nine diagnostic criteria for a depressive disorder in the Diagnostic and 131

Statistical Manual for Mental Disorders (DSM-IV). In 2011, BRFSS included cellular

phone numbers in the sampling frame in response to the growing number of people who

use cellular phones for their telephone needs and the weighting methodology was

changed to be in line with other national surveillance systems. Thus data from 2011

forward should not be directly compared with data from 2010 and before. Hawai`i

started participating in BRFSS in 1986. (121)

The Center for Medicare and Medicaid Services (CMS) provides data on chronic

diseases for Medicare and Medicaid recipients of all ages. (122) Depression is

considered to be a chronic disease by CMS and is available for 2007-2014 in Hawai`i.14

CMS uses the International Statistical Classification of Diseases and Related Health

Problems-10 (ICD-10) codes for depression, but it does not separate out the various types of depression. The CMS overall depression percentage is lower than the BRFSS percentage, however note that the population base is different for CMS clients and

BRFSS respondents. The prevalence of depression for those less than 65 years of age is higher than for those who are 65 years and older in CMS.

The National Survey on Drug Use and Health (NSDUH) data for adult (age 18+

years) depression in Hawai`i is available from 2010-2016. According to NSDUH only

31.7% of adults with a mental illness received mental services from 2011 to 2015. In

2016, 31.6% of the selected NSDUH sample did not complete the interview. Reasons

14 CMS: On October 1, 2015 the conversion from the 9th version of the International Classification of Diseases (ICD-9-CM) to version 10 (ICD-10-CM) occurred. Regardless of when a claim was submitted for payment, services that occurred prior to October 1, 2015, use ICD-9 codes. Chronic conditions identified in 2015 are based upon ICD-9 codes for the first ¾ of the year (January-September) and ICD-10 codes for the last quarter of the year (October-December). 132

for non-response to interviewing include: refusal to participate (22.2%); respondent unavailable or no one at home (4.5%); and other reasons such as physical/mental incompetence or language barriers (4.6%). Adults and adolescents with major depressive episode may disproportionately fall into these non-response categories.

While NSDUH weighting includes non-response adjustments to reduce bias, these adjustments may not fully account for differential non-response by mental illness status.

Adult depression questions derived from the National Comorbidity Survey, Replication

(NCS-R) were introduced in 2004, but not all states were included. The NSDUH prevalence of major depressive episode was highest among adults reporting two or races (10.5%). Hawai`i has 23.8% of its population reporting two or more races. (121)

(123) (124)

Figure 3.2 shows the available depression prevalence estimates from the

BRFSS, CMS Medicare/Medicaid, and NSDUH adult civilian noninstitutionalized people in Hawai`i. Only the depression data were used although mental illness data are available because mental illness encompasses non depression data. There is no overlap between the BRFSS and NSDUH data as can be seen in Figure 3.2. The CMS

Medicare/Medicaid depression data does not include measurement error. The CMS

Medicare/Medicaid data are only from people who have either Medicare or Medicaid while the NSDUH percent depressed is lower than the CMS Medicare/Medicaid data which does not seem plausible as people who have Medicare or Medicaid are usually sicker and older than the general population.

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The BRFSS depression data were used since these data have the longest span of years and the missing data for years 2007 and 2009 were estimated using the average of the adjacent years.

Figure 3.2. Hawai`i Adult Civilian Non-institutionalized Percent Depressed, 2006- 2016

The Adverse Childhood Experiences (ACE) Study is a long term, in-depth analysis of over 17,000 adult Americans, matching their current health status against adverse childhood experiences that occurred on average a half-century earlier. The number of categories of adverse childhood exposures showed a graded relationship to the presence of adult diseases including ischemic heart disease, cancer, chronic lung

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disease, skeletal fractures, and liver disease (p < 0.001). The seven categories15 of adverse childhood experiences were strongly interrelated and persons with multiple categories of childhood exposure were likely to have multiple health risk factors later in life. Persons who had experienced four or more categories of adverse childhood exposure, compared to those who had experienced none, had 4- to 12-fold increased health risks for alcoholism, drug abuse, depression, and suicide attempt; a 2- to 4-fold increase in smoking, poor self-rated health, ≥ 50 sexual intercourse partners, and sexually transmitted disease; and a 1.4- to 1.6-fold increase in physical inactivity and severe obesity. (125)

Child abuse was found to lead to a lifetime prevalence of depressive disorders of

23%. Childhood emotional abuse increased risk for lifetime depressive disorders, with adjusted odds ratios of 2.7 [95% confidence interval (CI), 2.3-3.2] in women and 2.5

(95% CI, 1.9-3.2) in men. A strong, dose-response relationship between the ACE score and the probability of lifetime and recent depressive disorders was found (p<0.0001).

Exposure to ACEs is associated with increased risk of depressive disorders up to decades after their occurrence. Early recognition of childhood abuse and appropriate intervention may thus play an important role in the prevention of depressive disorders throughout the life span. (125)

The number of child abuse cases have been tracked by the Hawai`i Child

Protective Services since 1970, but the number of child abuse cases with drugs as a

15 Psychological abuse, physical abuse, contact sexual abuse, exposure to substance abuse, mental illness, violent treatment of mother or stepmother, and criminal behavior in the household. 135

factor have only been identified since 2013. (126) The type of illicit drug is not

identified. A child is counted each time he/she was found to be a victim. There may be

several reports for one child or several children in one report. Since 2013, 23 factors16

precipitating the abuse incident for confirmed child victims have been recorded, but only

up to six factors may be recorded for each case. The percent figure is based on the total number of child victims (duplicated count) and not the total number of factors. The child abuse drug factor percentages for 2013-2017 are 40.2, 41.8, 40.8, 46.0, and 42.2, respectively.

Note that this is not an ecological study as there is no unexposed or control

group since everyone in Hawai`i would be exposed to US recession periods and stock

market crashes with no exposure differences except for the likelihood of unemployment.

Based on the review of previous literature above, the variables of interest are

listed below:

• Recession is considered to be a latent explanatory variable and was measured by

the Unemployment Rate which is available for Hawai`i for the years 1976-2017.

• Psychological Distress is considered as a latent variable and was measured by the

BRFSS Depression Rate available for the years 2006-2016 with interpolation for

2007 and 2009.

16 Alcohol abuse, Broken family, Chronic family violence, Drug abuse, Family discord, Heavy continuous child care responsibility, Inability to cope with parenting responsibility, Inadequate housing, Incapacity due to handicap/chronic illness, Insufficient income/misuse of income, Lack of tolerance to child's behavior, Loss of control during discipline, Mental health problem, Mental retardation, Missing, New baby in home/pregnancy, Normal authoritarian discipline, Parental history of abuse as a child, Physical abuse of spouse/fighting, Police/court record (excluding traffic), Recent relocation, Social isolation, and Unacceptable child rearing method. 136

• Meth Use was measured by Meth Arrests which are available from 1976 to 2017.

Meth Arrests is an undercount of people who use meth since not all meth users are

arrested. The occasional, recreational, and weekend users are less likely to be

arrested.

• Child Abuse Due to Drugs numbers were not used since there are only five years of

data from 2013-2017 (40.2%, 41.8%, 40.8%, 46.0%, and 42.2%, respectively), but

Child Abuse numbers, available from 1970-2017 were used instead. Although the

number of cases would be different between Child Abuse Due to Drugs and the total

Child Abuse numbers, the pattern is likely to be the same and for this analysis, only

the pattern matters.

The relationship between unemployment, meth arrests, depression, and child abuse in Hawai`i was investigated using cross-correlation and Granger causality because of the periodicity and time series nature of the unemployment, meth arrests, and child abuse data. Cross-correlation assumes there is no auto-correlation in the time series data, meaning no regular periodicity. (127) It is of interest to determine the strength of relationship and time lag between unemployment, meth arrests, depression, and child abuse.

The Pearson correlation coefficient indicates the degree of linear relationship between two variables. Spearman’s rho rank correlation measures the rank correlation between two variables. Kendall’s tau rank correlation measures the ordinal association between two variables. Goodman and Kruskal's gamma measures the strength of association of cross tabulated data for two ordinal variables. None of these types of 137

correlation coefficients are the correct measure of association for time series data.

However, the Pearson correlation coefficients between the four variables will be computed for comparison to cross-correlation coefficients.

Objective 3. Methods and Results

The relationship between the unemployment rate, meth arrests, depression, and child abuse in Hawai`i was investigated via cross-correlation and Granger causality to determine the time lag in years between the variables and the strength of their association. The Pearson correlation coefficients and the largest cross-correlation coefficients of Unemployment Rate, Meth Arrests, BRFSS Depression, and Child Abuse are shown in Table 3.1. The cross-correlation results are shown in Figures 3.3 – 3.8 and provide the lag between the initiating events and subsequent events based on the largest cross-correlation coefficient. BRFSS Depression was most negatively and strongly associated with Child Abuse (Pearson r = -0.758, p < 0.007; cross-correlation r

= -0.758, s.e. = 0.302, no lag) and Meth Arrests (Pearson r = -0.650, p < 0.030; cross- correlation r = -0.665, s.e. = 0.316, 1 year lag) contrary to previous studies which showed a positive association. Unemployment Rate was significantly negatively associated with Child Abuse (Pearson r = -0.401, p < 0.008; cross-correlation r = -0.401, s.e. = 0.154, no lag) also contrary to previous studies.

The Unemployment Rate association with BRFSS Depression was strong with a cross-correlation of r = 0.601, s.e. = 0.354, for a lag of three years while the Pearson correlation was small and negative (r = -0.132. p = 0.698). Surprisingly, the

138

Unemployment Rate correlation with Meth Arrests was moderate (Pearson r = 0.229, p

= 0.144; cross-correlation r = 0.296, s.e. = 0.169, one year lag) despite the displaying the most similarity in the shape of the time series data. Meth Arrests was moderately and positively associated with Child Abuse (Pearson r = 0.319, p < 0.040; cross- correlation r = 0.392, s.e. = 0.158, two year lag). The lagged data are depicted as a green dotted line in Figures 3.3 - 3.7 for convenience. No data was lagged in Figure 3.8 as there was no best lag.

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Table 3.1. Pearson Correlation and Cross-correlation Coefficients for Meth Arrests, Unemployment Rate, BRFSS Depression, and Child Abuse for Hawai`i Unemployment Meth BRFSS Child Rate Arrests Depression Abuse 0.296 0.601 -0.401 Cross-correlation Unemployment 0.169 0.354 0.154 Standard Error Rate 42 11 42 N (1 years) (3 years) (0 years) (Lag in Years)

-0.665 0.392 Cross-correlation Pearson Correlation 0.229 0.316 0.158 Standard Error Meth Arrests Sig. (2-tailed) 0.144 11 42 N N 42 (1 year) (2 years) (Lag in Years)

-0.758 Cross-correlation Pearson Correlation -0.132 -0.650 BRFSS 0.302 Standard Error Sig. (2-tailed) 0.698 0.030 11 N Depression N 11 11 (0 years) (Lag in Years)

Pearson Correlation -0.401 0.319 -0.758 Child Abuse Sig. (2-tailed) 0.008 0.040 0.007 N 42 42 11 Note: Pearson correlation coefficients in lower left triangle and Cross-correlation coefficients in upper right triangle. The Cross-correlation coefficients are the largest in absolute value with the given lag.

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350 10 9 300 8 250 7

200 6 5 150 4 100 3 2 Rate Unemployment 50 Number of Meth Arrests Number of Meth 1 0 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Year Meth Arrests Lagged Meth Arrests Unemployment Rate

Cross Std Cross-correlation Lag Correlation Error

-7 -0.185 0.169 -7 -6 -6 -0.095 0.167 -5 -5 -0.106 0.164 -4 -0.132 0.162 -4 -3 -0.161 0.160 -3 -2 -0.109 0.158 -2 -1 0.079 0.156 -1 0 0 0.229 0.154 Lag 1 1 0.296 0.156 2 2 0.216 0.158 3 3 0.08 0.160 4 4 -0.112 0.162 5 5 -0.288 0.164 6 6 -0.372 0.167 7 7 -0.431 0.169

0 1 -1 0.2 0.4 0.6 0.8

-0.8 -0.6 -0.4 -0.2

Figure 3.3. Cross-correlation Analysis: Hawai`i Meth Arrests Lagged by One Year After Unemployment Rate

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Cross Std Cross Correlation Lag Correlation Error -7 -7 0.138 0.169

-6 -0.028 0.167 -6 -5 -5 -0.18 0.164 -4 -4 -0.284 0.162 -3 -0.357 0.160 -3 -2 -0.376 0.158 -2 -1 -0.385 0.156 -1 0 0 -0.401 0.154 1 -0.341 0.156 1

Lag 2 2 -0.265 0.158 3 -0.188 0.160 3 4 -0.098 0.162 4 5 0.003 0.164 5

6 0.09 0.167 6 7 7 0.14 0.169 0 1 -1

0.2 0.4 0.6 0.8 -0.8 -0.6 -0.4 -0.2 Figure 3.4. Cross-correlation Analysis: Hawai`i Child Abuse Lagged by One Year After Unemployment Rate

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14 12 10 8 6 Rate (%) 4 2 0 2004 2006 2008 2010 2012 2014 2016 2018 Year Unemployment Rate BRFSS Depression Lagged BRFSS Depression

Cross Std Lag Correlation Error -7 0.133 0.500 -6 0.099 0.447 -5 -0.142 0.408 -4 -0.397 0.378 -3 -0.582 0.354 -2 -0.444 0.333 -1 -0.279 0.316 0 -0.132 0.302 1 0.093 0.316 2 0.43 0.333 3 0.601 0.354 4 0.494 0.378 5 0.191 0.408 6 0.112 0.117 7 0.036 0.500

Figure 3.5. Cross-correlation Analysis: Hawai`i BRFSS Depression Lagged by One Year After Unemployment Rate

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350 14 300 12 250 10 200 8 150 6 100 4 50 2 Number of Meth Arrests Number of Meth 0 0 Rate BRFSS Depression 2004 2006 2008 2010 2012 2014 2016

Year

Meth Arrests BRFSS Depression Lagged BRFSS Depression

Cross Std Lag Correlation Error -7 0.273 0.500 -6 0.337 0.447 -5 0.300 0.408 -4 0.061 0.378 -3 -0.297 0.354 -2 -0.483 0.333 -1 -0.519 0.316 0 -0.650 0.302 1 -0.665 0.316 2 -0.388 0.333 3 0.072 0.354 4 0.241 0.378 5 0.200 0.408 6 0.335 0.447 7 0.441 0.500

Figure 3.6. Cross-correlation Analysis: Hawai`i BRFSS Depression Lagged by One Year After Meth Arrests

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350 4500 300 4000 3500 250 3000 200 2500 150 2000 1500

100 Cases Abuse 1000 50 500 Number Confirmed of Child Number of Meth Arrests Number of Meth 0 0

Year Meth Arrests Child Abuse Lagged Child Abuse

Cross Std Lag Correlation Error -7 0.364 0.169 -6 0.327 0.167 -5 0.312 0.164 -4 0.241 0.162 -3 0.191 0.160 -2 0.178 0.158 -1 0.215 0.156 0 0.319 0.154 1 0.375 0.156 2 0.392 0.158 3 0.385 0.160 4 0.356 0.162 5 0.257 0.164 6 0.13 0.167 7 -0.05 0.169

Figure 3.7. Cross-correlation Analysis: Hawai`i Child Abuse Cases Lagged by Two Years After Meth Arrests

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14 2500 12 2000 10 8 1500 6 1000 4 500 2 Depression Rate Rate (%) Depression 0 0 2005 2007 2009 2011 2013 2015 2017

Year CasesAbuse Number Child of

BRFSS Depression Child Abuse

Cross Std Lag Correlation Error -7 0.436 0.500 -6 0.381 0.440 -5 0.126 0.408 -4 -0.079 0.378 -3 -0.353 0.254 -2 -0.541 0.333 -1 -0.705 0.316 0 -0.758 0.302 1 -0.508 0.316 2 -0.207 0.333 3 -0.094 0.354 4 0.22 0.378 5 0.293 0.408 6 0.372 0.447 7 0.295 0.500

Figure 3.8. Cross-correlation Analysis: Hawai`i BRFSS Depression Significantly Negatively Cross-Correlated with Child Abuse Cases and No Lag

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Granger causality was used to test the strength and direction of the bivariate associations between Unemployment Rate, Meth Arrest, and Child Abuse. The BRFSS

Depression data were available only for 11 years, 2006-2016 and so no meaningful time series analysis could be performed. Granger causality requires stationarity (no autocorrelation) of the time series data. (113)

First, the presence of autocorrelation was tested. Autocorrelation is the correlation of a data series with itself as a function of the time lag between the values. If autocorrelation is present, the series may not be comprised of independent values and cross-correlations may be spurious. (127) The autocorrelation of Unemployment Rate,

Meth Arrests, and Child Abuse were all significant using the Box-Lyung statistic (128), p

< 0.0001, p < 0.002, and p < 0.0001, respectively, indicating that these data should be differenced. Only two of the Box-Lyung statistics for BRFSS Depression were significant and the rest were not, suggesting differencing should be performed. The correlograms for Unemployment Rate, Meth Arrests, BRFSS Depression, and Child

Abuse with no differencing (original data) shown in Figure 3.9 also indicates that differencing is needed as the correlograms have smooth patterns.

Auto-correlation may be removed by differencing the time series data where each data point is subtracted from its successor but results in one less data point.

Differencing removes the ups and down of a time series, eliminates trend and seasonality, and consequently stabilizes the mean of the time series. The differenced time series is also called stationary or prewhitened time series. The differenced variable is indicated by “d” appended after the variable.

147

Figure 3.9. Correlograms of Autocorrelations of Unemployment Rate, Meth Arrests, BRFSS Depression, and Child Abuse with No Differencing

Autocorrelation analyses were performed after differencing the data by 1 for

Unemployment Rate, Meth Arrests, BRFSS Depression, and Child Abuse. An inspection of the correlograms in Figure 3.10 shows that the autocorrelation coefficient function (ACF) with differencing is more random for Meth Arrests, BRFSS Depression, and Child Abuse compared with no differencing. The Box-Lyung statistic was significant

for Unemployment Rate at p < 0.0001, indicating more differencing is needed. The Box-

Lyung statistic was not significant at α = 0.05 for Meth Arrests, indicating no more

differencing was needed. The first two lags for BRFSS Depression and Child Abuse

was significant at time lags 1 and 2, p < 0.05 but not significant at all other time lags,

148

with the mixed results indicating that one additional differencing is needed for a decision on the number of differencings.

Figure 3.10. Correlograms of Autocorrelations of Unemployment Rate, Meth Arrests, BRFSS Depression, and Child Abuse with Differencing = 1

After differencing the data by 2, “second-order differencing” for all the variables to check that Meth Arrests, BRFSS Depression, and Child Abuse should only have differencing = 1. The Box-Lyung statistic was not significant for Unemployment Rate at

α = 0.05. The Box-Lyung statistic was significant for Child Abuse at p < 0.001, but upon inspection of the correlogram in Figure 3.11, it can be seen that Child Abuse has been over differenced. The results indicate that Unemployment Rate should be differenced

149

by 2, while Meth Arrests, BRFSS Depression, and Child Abuse should be differenced by

Figure 3.11. Correlograms of Autocorrelations of Unemployment Rate, Meth Arrests, BRFSS Depression, and Child Abuse with Differencing = 2

The cross-correlations were performed with the differenced time series and are shown in Figures 3.12 – 3.14 for a comparison between the cross-correlation correlograms with no differencing and differencing. The correlograms indicate that the cross-correlation between the differenced time series yield correlograms with a more random scatter compared with the undifferenced time series. Note that other methods or statistics may also be used.

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Figure 3.12. No Differencing vs. Differencing for Cross-correlations between Unemployment Rate and Meth Arrests (left), and Unemployment Rate and Child Abuse (right)

Figure 3.13. No Differencing vs. Differencing for Cross-correlations between Unemployment Rate and BRFSS Depression (left), and Meth Arrests and BRFSS Depression (right)

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Figure 3.14. No Differencing vs. Differencing for Cross-correlations between Meth Arrests and Child Abuse (left), and BRFSS Depression and Child Abuse (right)

The differenced time series are displayed in Figures 3.15 – Figures 3.20 and shows that the residuals are random. See Figures 3.3 – 3.8 above for the undifferenced time series with the lag shifts.

The differenced time series then can be used to test the strength of association and direction of Granger causality. The Granger causality test is a statistical hypothesis test for determining whether one time series is can forecast another. (114) It investigates causality between two time series but Granger causality may not be true causality. It tests for dependencies between two or more time series, to reject the null hypothesis of statistical independence. (129) The null hypothesis for the test is that lagged x-values do not explain the variation in y or that x(t) does not Granger-cause y(t).

(130) Note that BRFSS Depression has only 11 observations and differencing reduced 152

it to 10, while the differenced Unemployment Rate has n= 40, Meth Arrests has n=41, and Child Abuse has n=46. BRFSS Depression was not used in further analyses due to the small number of observations. In Figure 3.3, Meth Arrests lags Unemployment by one year, although statistically the association is not significant as shown in Table 3.2 below.

Let Xt and Yt be two stationary time series with zero means, then the original

Granger causality model is (114)

Eq. 3.1

= 𝑚𝑚 + 𝑚𝑚 +

𝑋𝑋𝑡𝑡 � 𝑎𝑎𝑗𝑗 𝑋𝑋𝑡𝑡−𝑗𝑗 � 𝑏𝑏𝑗𝑗 𝑌𝑌𝑡𝑡−𝑗𝑗 𝜀𝜀𝑡𝑡 𝑗𝑗=1 𝑗𝑗=1

= 𝑚𝑚 + 𝑚𝑚 +

𝑌𝑌𝑡𝑡 � 𝑐𝑐𝑗𝑗 𝑋𝑋𝑡𝑡−𝑗𝑗 � 𝑑𝑑𝑗𝑗 𝑌𝑌𝑡𝑡−𝑗𝑗 𝜂𝜂𝑡𝑡 𝑗𝑗=1 𝑗𝑗=1

To apply the causality model, the following regressions were conducted on the below univariate restricted model and unrestricted bivariate unrestricted model and applying an F-test.

Eq. 3.2

Restricted model ( : = 0): = 𝑚𝑚

𝐻𝐻0 𝑏𝑏𝑗𝑗 𝑋𝑋𝑡𝑡 � 𝑎𝑎𝑗𝑗 𝑋𝑋𝑡𝑡−1 𝑗𝑗=1

Unrestricted model ( : = 0): = 𝑚𝑚 + 𝑚𝑚

𝐻𝐻𝑎𝑎 𝑛𝑛𝑛𝑛𝑛𝑛 𝑎𝑎𝑎𝑎𝑎𝑎 𝑏𝑏𝑗𝑗 𝑋𝑋𝑡𝑡 � 𝑎𝑎𝑗𝑗 𝑋𝑋𝑡𝑡−1 � 𝑏𝑏𝑗𝑗 𝑌𝑌𝑡𝑡−1 𝑗𝑗=1 𝑗𝑗=1

153

Y is said to “Granger cause” time series X, if and only if regressing for X in terms of both past values of X and Y is statistically significantly more accurate than doing so with past values of X only, i.e. the parameter estimates for , = 1, … , are statistically

𝑗𝑗 significant. The first-order autoregressive model (one 𝑏𝑏lag,𝑗𝑗 = 1) 𝑚𝑚was first tested. If the one lag is statistically significant, higher-orders (more lags)𝑚𝑚 will be added to the model.

All times series variables were differenced so that the stationary assumption in Granger causality test were met. Intercepts were included in the analysis because the differenced times series do not all have mean zeroes.

The Granger test statistic is the F-statistic where

F = [(RSS_restricted – RSS_unrestricted)/m] / [RSS_unrestricted / (n-k)] and is distributed as F(m, n-k) where

m = maximum number of lags

n = number of cases

k = number of parameters

Since the time series are stationary and autocorrelations are accounted by lagged observations, linear regression was used to find the residual sum of squares

(RSS). IBM Statistics SPSS version 26 was used to perform the regression analyses with the constant included.17

17 There are other software that can perform all the steps to obtain stationarity and multiple lags in one analysis. 154

250 5 200 4 150 3 2 100 1 50 0 0 -1 -50 -2 -100

-3 Unemployment Rate (d2) Number of Meth Arrests (d1) Arrests Meth of Number -150 -4 -200 -5 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Year Meth Arrests (d1) Unemployment Rate (d2) Figure 3.15. Unemployment Rate and Meth Arrests with Differencing

4 800 3 600 2 400 1 200 0 0 -1 -200 = 2 -2 -400 -3 -600 -4 -800 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 DifferencingAbuse 1 = Child

Unemployment Unemployment Rate Differencing Year Unemployment Rate (d2) Child Abuse (d1) Figure 3.16. Unemployment Rate and Child Abuse with Differencing

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4.0 3.0 2.0 1.0

Rate (%) 0.0 -1.0 -2.0 -3.0 -4.0 2006 2008 2010 2012 2014 2016 Year Unemployment Rate (d2) BRFSS Depression (d1)

Figure 3.17. Unemployment Rate and BRFSS Depression with Differencing

250 800 200 600 150 400 100 50 200 0 0 -50 -200 -100 -400 -150 -200 -600 Child Abuse DifferencingAbuse 1 = Child Meth Arrests Differencing = 1 = Differencing Arrests Meth -250 -800 1970 1980 1990 2000 2010 2020 Year

Meth Arrests (d1) Child Abuse (d1)

Figure 3.18. Meth Arrests and Child Abuse with Differencing

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2.0 800 1.5 600 1.0 400 0.5 200 0.0 0 -0.5 -200 -1.0 -400 -1.5 -600 Child Abuse DifferencingAbuse 1 = Child -2.0 -800 2004 2006 2008 2010 2012 2014 2016 2018 Year BRFSS Depression Differencing = 1 BRFSS Depression (d1) Child Abuse (d1) Figure 3.19. BRFSS Depression and Child Abuse with Differencing

200 2.0 150 1.5 100 1.0 50 0.5 0 0.0 -50 -0.5 -100 -1.0 -150 -1.5

Meth Arrests Differencing = 1 = Differencing Arrests Meth -200 -2.0 2005 2007 2009 2011 2013 2015 2017 2019 Year RFSS Depression Differencing = 1 Meth Arrests (d1) BRFSS Depression (d1)

Figure 3.20. Meth Arrests and BRFSS Depression with Differencing

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Note that the X and Y have been differenced and are stationary but are denoted

with an added d# as a reminder that it is the differenced value that was used. The

Granger causal relationships between Unemployment, Meth Arrests, and Child Abuse

were investigated as follows, where d = differenced:

Hypothesis 1

H0 = Unemployment does not Granger-cause Arrests

H1 = Unemployment Granger-causes Arrests

1a) Restricted: Arrests_d1 = lag(Arrests_d1)

1a) Unrestricted: Arrests_d1 = lag(Arrests_d1) + lag(Unemployment_d2)

1b) Restricted: Unemployment_d2 = lag(Unemployment_d2)

1b) Unrestricted: Unemployment_d2 = lag(Unemployment_d2) + lag(Arrests_d1)

Hypothesis 2

H0 = Arrests does not Granger-cause Abuse

H1 = Arrests Granger-causes Abuse

2a) Restricted: Abuse_d1 = lag(Abuse_d1)

2a) Unrestricted: Abuse_d1 = lag(Abuse_d1) + lag(Arrests_d1)

2b) Restricted: Arrests_d1 = lag(Arrests_d1)

2b) Unrestricted: Arrests_d1 = lag(Arrests_d1) + lag(Abuse_d1) 158

Hypothesis 3

H0 = Unemployment does not Granger-cause Abuse

H1 = Unemployment Granger-causes Abuse

3a) Restricted: Abuse_d1 = lag(Abuse_d1)

3a) Unrestricted: Abuse_d1 = lag(Abuse_d1) + lag(Unemployment_d2)

3b) Restricted: Unemployment_d2 = lag(Unemployment_d2)

3b) Unrestricted: Unemployment_d2 = lag(Unemployment_d2) + lag(Abuse_d1)

Table 3.2 contains a summary of the Granger causality results. Each hypothesis

is associated with a set of four equations. Since only the RSS were wanted, the other

coefficients from the regression were not reported for this application of Granger

causality. If one set of equations are significant, then there is unidirectional Granger

causality. If both sets are significant, then there is bidirectional Granger causality (or

feedback). If neither set are significant, then the time series are independent. All of the

null hypotheses failed to be rejected, although the Granger causality of Unemployment

Rate on Child Abuse was the closest to significance (F(1, 36) = 2.707, p = 0.109).

159

Table 3.2. Summary of Granger Causality Results H Dependent Variable Independent Variable m n k RSS F(m, n-k) p Conclusion 1a_r Arrests_d1 lag(Arrests_d1) 1 40 192,448 lag(Arrests_d1) + Fail to 1a_un Arrests_d1 1 40 3 183,326 1.75 0.174 lag(Unemployment_d2) reject H0 1b_r Unemployment_d2 lag(Unemployment_d2) 1 40 26.18 lag(Unemployment_d2) 1b_un Unemployment_d2 1 40 3 25.477 1.01 0.399 ns + lag(Arrests_d1) 2a_r Abuse_d1 lag(Abuse_d1) 1 40 3,529,747 lag(Abuse_d1) + Fail to 2a_un Abuse_d1 1 40 3 3,419,186 2.00 0.131 lag(Arrests_d1) reject H0 2b_r Arrests_d1 lag(Arrests_d1) 1 40 192,448 lag(Arrests_d1) + 2b_un Arrests_d1 1 40 3 177,067 0.854 0.473 ns lag(Abuse_d1) 3a_r Abuse_d1 lag(Abuse_d1) 1 39 3,529,728 lag(Abuse_d1) + Fail to 3a_un Abuse_d1 1 39 3 3,264,313 2.707 0.109 lag(Unemployment_d2) reject H0 3b_r Unemployment_d2 lag(Unemployment_d2) 1 39 26.18 lag(Abuse_d1) + 3b_un Unemployment_d2 1 39 3 26.17 0.0124 0.998 ns lag(Unemployment_d2)

160

Objective 3. Discussion

The Unemployment Rate and Meth Arrests time series (see Figure 3.3) overlap most closely when the Meth Arrests lags Unemployment Rate by one year, implying that it takes about one year after the Unemployment Rate starts to increase before Meth

Arrest starts to increase. This was true for the 1970’s and the 1980’s recessions, but not for the Great Recession 2007 – 2009 where the Meth Arrests increased and decreased in synch with the Unemployment Rate.

The 1970’s recession started in November 1973 and ended in March 1975. It started as a result of the members of the Organization of Arab Petroleum Exporting

Countries (OPEC) proclaiming an oil embargo in October 1973 which lasted until March

1974, and a stock market crash January 1973 - December 1974. The 1973 Oil

Embargo strained a US economy that had grown increasingly dependent on foreign oil.

(131) A sign of the times were long lines to purchase gasoline.

There were two recessions in the 1980’s. The first recession was short, January

1980 - July 1980 (6 months) caused by the Federal Reserve gradually raising interest rates up to 20% in June 1981 to fight inflation. The second recession was from July

1981 to November 1982 (16 months) caused by rising interest rates resulting in less availability of consumer credit and subsequent diminished demand for goods and housing which led to job losses. Note that Hawai`i’s meth problem started to increase in the 1980’s.

The Great Recession, December 2007- June 2009 (18 months), arose from the collapse of the US housing bubble and the subprime mortgage crisis with bank collapse.

The US housing bubble burst during 2006 and homeowners began to default on their 161

mortgage payments in large numbers starting in 2007. Unemployment lasted longer than the previous two recession periods and unemployment benefits were extended twice. Unemployment benefit usually lasts for 26 weeks. Employment levels returned to pre-recession levels May 2014, 5 years later.

The Child Abuse numbers lagged the Unemployment Rate by (see Figure 3.4) four years and the Granger causality statistic was not significant. Observation of the

Unemployment Rate and Child Abuse numbers through the next recession to 5 years after is needed to obtain more data points. The Child Abuse numbers also lagged Meth

Arrests by four years and the Granger causality statistic was not significant. Other studies have found an effect of unemployment on psychological distress which led to child abuse. (108) (112)

The BRFSS Depression rate was most negatively and strongly associated with

Child Abuse (r = -0.758, p < 0.007) and Meth Arrests (r = -0.650, p < 0.030), contrary to previous studies. However there were only 10 data points (one was lost to differencing) for BRFSS Depression which is too few data points to truly perform time series analysis.

162

Implications for Public Health

This study shows associations between downturns in the economy (proxy was unemployment rate) to meth use (proxy was meth arrests) and child abuse to provide an early warning of subsequent societal and public health impacts to prepare for mitigation efforts. Note that child abuse due to drugs was not used because data were only recorded for five years from 2013-2017 and the type of drug was not specified.

Nine out of the last ten recessions were found to coincide with increases in unemployment rate at the national level and the same relationship was seen in Hawai`i.

(118) This implies that unemployment rates are a good proxy for recessions, but other indicators of economic downturns that arise before a recession would be even better to find associations with human behavioral consequences.

The surge requirements for short-term increases and subsequent decreases in law enforcement, judiciary system, child and social welfare, and imprisonment poses fluctuating demands on social, judicial, and economic support structures. Increased meth use leads to an increased demand for treatment and child abuse. Although it may be difficult to increase the number of treatment facilities, it may be possible to hire additional healthcare workers for evening and weekend hours, and use community sites for outpatient visits. As suggested in other research (125), early recognition of childhood abuse and appropriate intervention may mitigate or prevent the occurrence of alcoholism, drug abuse, depression, suicide attempt, smoking, domestic violence, extreme number of sexual partners, sexually transmitted disease, ischemic heart disease, cancer, chronic lung disease, skeletal fractures, liver disease, physical inactivity, and severe obesity (leading to diabetes) throughout the life span. 163

A suggestion is proposed for the Hawai`i Child Protective Services to add a checkbox that indicates parent or guardian meth use to child abuse intake forms and publish that data. These data should be published with the yearly reports in order to assist other agencies in preparing for mitigation efforts. In addition, since meth is the primary illicit drug in use in Hawai'i, all state agencies that are not currently noting if meth use was a factor in using agency services or resources should do so.

Strengths

• Data are for the entire state of Hawai`i.

• First investigation of the factors related to the periodicity of meth arrests and child

abuse in Hawai`i.

• Associates unemployment to drug use and child abuse to provide an early warning

of subsequent societal and public health impacts to prepare for mitigation efforts.

Limitations

• Data are at the population level as it is infeasible in the US to follow an individual

throughout life and constantly obtain metrics.

• Not all data were available for the same length of time. Any analyses involving

BRFSS Depression was limited to 11 years.

164

OVERALL STRENGTHS AND LIMITATIONS

Strengths. Comprehensive list of free, non-restricted meth-related data for the state of Hawai`i. It provides the first estimate of the prevalence of meth users, the first estimate of the cost of meth use, and the first investigation of the factors related to the periodicity of meth arrests and child abuse in Hawai`i using data gathered for other purposes. Associations of unemployment to drug use and child abuse may provide an early warning of subsequent societal and public health impacts to prepare for mitigation efforts.

Limitations. Estimates contain wide lower and upper bounds due to the nature of the data. Interpolations and extrapolations are used due to unavailability of data. The data is at the population level as it is infeasible in the US to follow an individual throughout life and constantly obtain metrics. Not all data was available for the same length of time.

OVERALL PUBLIC HEALTH IMPLICATIONS

Public policy on the meth-related problems in Hawai`i may be better developed on the basis of better information disseminated by this dissertation. Meth use causes not only public health burdens for treatment and other health issues, but also for the public safety system (e.g. law enforcement, judiciary, prisons), and the child and social welfare system. This dissertation includes a comprehensive list of free, publicly

165

available, non-restricted meth-related data for the state of Hawai`i which may be used to inform future research and provide justification for changes in public policy.

This dissertation, provides first estimation of: 1) evidence-based prevalence of meth users in Hawai`i; 2) the cost of the burden of meth use based on treatment, healthcare, criminal justice, child endangerment, and productivity costs particular to

Hawai`i, and 3) an investigation of the factors related to the periodicity of meth arrests in

Hawai`i. Many users who seek treatment are not admitted because most state-funded treatment programs are operating at maximum capacity. The increasing meth death rate is attributed to negative lifestyle changes of chronic meth users, and increasing meth purity and decreasing price since 2007 as shown in Figure 10. Violence associated with the distribution and use of methamphetamine is a serious concern for law enforcement officials and healthcare professionals in Hawai`i.

Inspection of factors that are linked to an increase in meth arrests and by implication, an increase in meth use, may provide an early warning of societal and public health impacts to prepare for mitigation efforts. This can inform state policy makers and the Hawai`i state legislature to prepare for a surge in personnel and funding requirements for meth-related primary (treatment and healthcare costs), secondary

(public safety and judiciary systems) and tertiary (child abuse, child protective services, foster care, child psychological and medical care) impacts. Future research may focus in-depth on meth areas touched upon here such as meth-related treatment, healthcare, child abuse and neglect attributable to meth, public safety and judiciary systems, in addition to decreases in quality of life and reduced productivity on tangible and intangible costs to both the meth user and the public. 166

RECOMMENDATIONS

Hawai`i has the highest per capita of meth users in the nation which impacts all local residents. Governmental and private institutions need to increase their tracking of services that are related to meth use so that interventions can be targeted by time and place.

• Reinstitute the ADAM arrestee urinalysis program in Hawai`i.

• Indicate if meth use was the reason for placing a child into foster care or under

protective services

o The Hawai`i Child Protective Services should add a checkbox that indicates parent or guardian meth use to child abuse intake forms and

publish these data.

o These data should be published with the yearly reports in order to assist other agencies in preparing for mitigation efforts.

167

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