TRANSITIONAL CARE, NEIGHBORHOOD DISADVANTAGE, AND HEART FAILURE
HOSPITAL READMISSION: A MODERATED MEDIATION ANALYSIS
Dissertation submitted to
Kent State University College of Nursing
in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
by
Karen S. Distelhorst
May 2020
Dissertation written by Karen Distelhorst MSN, University of Akron, 1994 Ph.D., Kent State University, 2020
Approved by , Chair, Doctoral Dissertation Committee Dana Hansen
, Member, Doctoral Dissertation Committee Lisa Onesko
, Member, Doctoral Dissertation Committee Amy Petrinec
, Member, Doctoral Dissertation Committee Lynette Phillips
, Graduate Faculty Representative Jeffrey Hallam
Accepted by , Director & Associate Dean for Graduate Programs Wendy Umberger
iii
TABLE OF CONTENTS Page LIST OF FIGURES vi
LIST OF TABLES vii
DEDICATION viii
ACKNOWLEDGEMENTS ix
CHAPTER I. BACKGROUND AND SIGNIFICANCE Introduction 1 Background and Significance 3 The Global Burden of Heart Failure 3 Incidence and prevalence 3 Heart failure hospital readmissions 4 A Population Health Approach to Heart Failure 5 Defining population health 5 Population risk factors for heart failure readmission 6 Upstream Factors 7 Defining upstream factors 7 Neighborhood disadvantage 8 Health Care System Strategies for Heart Failure Readmission Reduction 9 Early provider follow-up visits 9 Multidisciplinary transitional care management 10 Nursing and Population Health Management 11 The advancement of population health nursing 11 Care coordination and transition management (CCTM) 13 Upstream factors and the nursing process 15 Statement of the Problem 15 Conceptual Framework 16 Concept Definitions and Epistemic Correlations 16 Upstream factors 18 Healthcare system factors 19 Nursing activities 19 Population factors 20 Population health outcome 20 Study Model 20 Purpose 22
iv
Research Questions 23
II. REVIEW OF THE LITERATURE Introduction 24 Patterns and Trends in Heart Failure Hospital Readmission 24 Causes and Timing 24 Temporal Trends 27 Population Predictors of Heart Failure Readmission 31 Genetic Factors: Age and Race 31 Chronic Disease and Comorbidity 33 Upstream Factors and Heart Failure Readmission 35 Community Socioeconomic Status 35 Neighborhood Disadvantage 37 Hospital Strategies for Readmission Reduction 39 Early Provider Follow-Up 39 Transitional Care Programs 42 Nursing Strategies for Readmission Prevention 45 Significance of the Study 49
III. METHODOLOGY Introduction 51 Methods 51 Research Design 51 Primary Study 52 Current Study 53 Inclusion and exclusion criteria 53 Sample size 53 Outcome and Measures 54 Heart Failure Hospital Readmission 54 Early Provider Follow-Up 54 CCTM Intensity 55 Neighborhood Disadvantage 55 Demographic Variables and Co-Variates 58 Procedure 59 Data Management 60 Missing values 60 Outliers 61 Assumptions 61 Statistical Analysis 63
v
IV. RESULTS Introduction 67 Results 67 Sample Characteristics 67 Comparison of Groups 68 Readmission 68 Early provider follow-up 69 Research Question 1: Relationships Among the Variables 71 Readmission 72 Early provider follow-up 74 Research Question 2 and 3: Mediation 74 Research Question 4: Moderation 75 Summary 77
V. DISCUSSION Introduction 79 Findings 80 Research Question 1: Relationships Among the Variables 80 Relationships 81 Predicting 30-day readmission 82 Predicting early provider follow-up 83 Research Questions 2 – 4: Mediation and Moderation 84 Secondary Findings 85 Implications 86 Nursing Practice 86 Policy 87 Theoretical 88 Research 89 Limitations 90 Conclusion 91
REFERENCES 92
APPENDICES A. Comparison of primary study and current study 112 B. Area Deprivation Index (ADI) Indicators 113 C. The Neighborhood Atlas 114 D. Data details sheet 115 E. Spearman’s rho correlation matrix 117
vi
LIST OF FIGURES
Figure Page
1. Population health nursing scope of practice 13
2. Diagram for the Conceptual Model for Nursing and Population Health 17
3. Adapted study model 22
4. Timeline of U.S. healthcare policy changes 28
5. Univariate outliers 62
6. Simple mediation model in statistical form 64
7. The conditional process model for the study in statistical form 65
8. Relationships among the study variables 71
9. Results of the mediation model 75
10. First stage moderation 77
vii
LIST OF TABLES
Table Page
1. HF Incidence and Aging 4
2. Study Concepts, Variables, and Indicators 18
3. Causes and Timing of HF Hospital Readmissions 25
4. Changes in 30-Day Risk Adjusted Readmission Rates 31
5. Transitional Care Programs – Evidence Table 43
6. Nursing CCTM Impact on Readmissions – Evidence Table 46
7. Sample Characteristics and 30-Day Readmission Status 68
8. Transitional Care and 30-Day Readmission Status 69
9. Patient Characteristics and Early Provider Follow-Up within 14 Days 70
10. Multivariable Analysis of Factors Associated with Readmission 72
11. Multivariable Analysis of Factors Associated with Provider Follow-Up 73
12. Conditional Effects of Early Provider Follow-Up on CCTM Intensity 76
viii
DEDICATION
I would like to dedicate this dissertation to my loving family. To my children, Joanna, Lauren, and Daniel, who have cheered me on from the beginning. I have felt their sincere pride and unconditional love during this journey, and it has motivated me to push through the difficult times. I hope it will be an inspiration for them to follow their dreams, even if it takes a little longer than planned. To my mother and father, who have always believed in my ability to achieve. And to my brother, Mike, and sister, Tammy, who have always been there for me, providing their unwavering support.
ix
ACKNOWLEDGEMENTS
I would like to extend my sincerest gratitude to Dana Hansen for her guidance and instruction throughout this journey. Beginning with my first theory course and closing as my dissertation advisor, she has been kind, insightful, and encouraging, even though the events surrounding us were unpredictable. Also, my deep appreciation goes to Lynette Phillips for her statistical guidance over the years, as I began to formulate my first research questions around population health nursing and through the final analysis. A special thank you as well to Lisa Onesko and Amy Petrinec for their willingness to serve on my dissertation committee and for the expertise they have shared. Thank you to Kathy Chen, who began this dissertation journey with me, and to Pat Vermeersch for taking me under her wing during a period of transition. And finally, thank you to my Cleveland Clinic colleagues, Nancy Albert and Sheila Miller. They have supported my growth as a nurse researcher and gave me the confidence to pursue my dream of earning my PhD. You have all taught me so much and I will be forever grateful.
1
CHAPTER I – BACKGROUND AND SIGNIFICANCE
Introduction
Approximately 6.5 million people in the United States are living with heart failure (HF)
(Benjamin et al., 2017). While evidence-based therapies for the management of chronic HF have improved over time, HF medical expenditures still present a significant burden to the U.S. healthcare system, particularly due to hospitalizations (Bergethon et al., 2016). Further, between
18.5% and 21% of patients hospitalized for HF are readmitted within 30 days (Arora et al., 2017;
Benjamin et al., 2017; Bergethon et al., 2016). The Hospital Readmission Reduction Program
(HRRP) of the Affordable Care Act (ACA) mandates the Centers for Medicare & Medicaid
Services (CMS) to reduce payments to hospitals for excess readmissions to improve quality and reduce costs (CMS, 2019). Heart failure is a major driver of these readmission penalties (Arora et al., 2017; Vidic, Chibnall & Hauptman, 2015) and despite intense efforts by hospitals, only slight improvements in HF readmission rates have occurred since 2009 (Bergethon et al., 2016).
It is known that individual factors, such as black race, older age, male gender, and comorbidities, as well as longer hospital length of stay, are risk factors for HF readmission
(Arora et al., 2017; Ayatollahi et al., 2018; Mirkin, Enomoto, Caputo & Hollenbeak, 2017). Yet beyond these patient and hospital factors, the social and economic conditions of where people live, or “upstream factors,” are also predictive of HF readmission (Akwo et al., 2018; Bikdeli et al., 2014; Meddings et al., 2017). Given the limited success in hospital HF readmission reduction programs (Bergethon et al., 2016), an upstream population health approach that crosses care settings and recognizes both individual and population-level risks is necessary.
Population health is the health outcomes of a group of individuals that are the result of numerous determinants of health, including healthcare, public health, genetics, behavior, social
2 factors, and environmental factors (Kindig & Stoddart, 2003; National Academy of Medicine
[NAM] Roundtable on Population Health Improvement, 2019; Rankin, Ralyea & Sotomayor,
2018). A population is most often defined geographically or geopolitically, but also by disease characteristics and as groups of patients in a practice setting (Fawcett & Ellenbecker, 2015;
NAM, 2015). The focus of population health nursing is often the “high-risk aggregate,” a subgroup in the community that shares a high-risk behavior or condition (Cupp Curley& Vitale,
2016). One such high-risk aggregate is the chronic HF population.
In hospital efforts to reduce the risk of unnecessary hospital readmissions in the HF population, structures for transitional care management are an important component of care.
Transitional care management includes multidisciplinary interventions that promote coordination and continuity of care for patients between healthcare settings to improve outcomes and prevent hospital readmission (Albert et al., 2015; Feltner et al., 2014; Van Spall et al., 2017). Primary care providers have largely driven efforts to improve transitional care and reduce readmissions.
But the registered nurse (RN) in care coordination and transition management (CCTM) is also an integral part of HF readmission reduction efforts, with competencies in support for self- management, advocacy, education, communication, coaching, the nursing process, population health management, collaboration, and care planning (American Academy of Ambulatory Care
Nursing [AAACN], 2016). Further, much of the current research in transitional care and HF readmission reduction focuses on interventions by providers, minimizing the impact of nursing activities, and does not consider the effect of upstream factors on population-specific outcomes.
This study aims to provide evidence of the relationship between early provider follow-up visits,
CCTM activities, neighborhood disadvantage as an upstream factor, and HF hospital readmission.
3
Background and Significance
The Global Burden of Heart Failure
Incidence and prevalence. Heart failure represents a global pandemic affecting 26 million people worldwide (Savarese & Lund, 2017). This complex clinical syndrome presents concurrently with other disease and results in the heart’s inability to function efficiently
(Ponikowsky et al., 2014). Heart failure has many etiologies that range from ischemic injury to the culmination of chronic disease such as hypertension (HTN), diabetes, and obesity (Benjamin et al., 2017; Ponikowski et al., 2014; Savarese & Lund, 2017). According to the American Heart
Association (AHA), 6.5 million people over the age of 20 are living with HF in the United
States, and HF prevalence is projected to increase 46% by 2030, impacting more than 8 million people (Benjamin et al., 2017). This increase in prevalence is largely a result of the aging population and improved treatments that allow people to live longer with HF (Savarese & Lund,
2017).
Heart failure incidence, or new cases, is challenging to determine because HF is a syndrome, not a disease; even with standardized criteria for diagnosis, it is difficult to differentiate between new onset HF and a subsequent episode of acute decompensated HF in medical records (Roger, 2013). The Atherosclerosis Risk in Communities Study (ARIC) study from the National Heart, Lung, and Blood Institute estimates that 1,000,000 new cases are diagnosed annually in the U.S., based on community surveillance data from 2005-2014
(Benjamin et al., 2018). Large cohort studies indicate that HF incidence has been stable overall, but sharply increases with age (see Table 1), approaching nearly 21 per 1,000 population after age 65 (Benjamin et al., 2018; Benjamin et al., 2017), thus making older adults a high-risk population.
4
Table 1
Heart Failure Incidence and Aging
New cases per 1,000 person years Age 65-74 Age 75-84 Age 85 +
Men 9.2 22.3 43.0
Women 4.7 14.8 30.7
Note. Data from “Heart Disease and Stroke Statistics-2017 Update: A Report from the American Heart Association” by E.J. Benjamin et al., 2017, Circulation, 135(10), e146-e603.
Patients of black and Hispanic race have significant disparity when it comes to HF with an incidence per 1,000 person-years of 4.6 and 3.5, respectively, compared to 2.4 for white race
(Benjamin et al., 2017). For black males, the disparity is even greater with 9.1 per 1,000 person- years, compared to 6 per 1,000 person-years for white males (Sharma, Colvin-Adams & Yancy,
2014). Heart failure occurs earlier in life for black adults, with more end-target organ damage due to uncontrolled HTN and chronic kidney disease (Benjamin et al., 2017; Sharma et al.,
2014). Black adults are 20 times more likely to develop HF before age 50 than white adults, and experience a more aggressive disease course (Sharma et al., 2014). This is largely blamed on socioeconomic factors, modifiable risk factors (HTN, obesity, diabetes), and other social determinants of health (Benjamin et al., 2017; Sharma et al., 2014).
Heart failure hospital readmissions. Clinical management of HF has improved due to advancements in medical therapies and interventions that have decreased mortality and hospital length of stay (Ziaeian & Fonarow, 2016). According to the Centers for Disease Control and
Prevention (CDC, 2015), prevalence of HF hospitalizations among all hospitalizations of older adults (65 and older) is 5.1% nationally and slightly higher at 5.5% in Ohio. Yet incidence of HF hospitalizations for all older adults is 17.8 per 1,000 nationally, and higher for Ohio at 25.8 per 1,
000 (CDC, 2015). These hospitalizations for older adults with HF represent a significant cost for
5
Medicare. Readmission within 30 days after discharge for a HF hospitalization is regarded as a reflection of low quality of care and hospitals are held accountable for excessive readmission rates through reduced CMS payments (CMS, 2019). However, there is debate over how much hospitals can control the factors that lead to readmission because all HF populations are not alike.
A Population Health Approach to Heart Failure
Defining population health. Population health is an extremely broad concept with implications that range from a global level down to a group of patients in a practice setting. The term population health is often used as an abbreviated phrase to indicate a variety of practices
(NAM, 2019), including population medicine, population health management, population health improvement, and population-based practice. The terms population health and population health management have also been described as a continuum, beginning with population health management for a group of individuals within a health care system and moving toward a broader collaboration with community organizations to improve the health of a population by addressing upstream factors (Storfjell, Winslow, & Saunders, 2017). This research will focus on population health management at the health system end of the population health continuum.
The Institute for Healthcare Improvement (IHI) includes population health as part of its
Triple Aim, along with patient experience and per capita cost, as a way for health systems to contribute to the overall health of populations while reducing costs (Stiefel & Nolan, 2012). The
IHI framework has been adopted as an organizing strategy by government, public, and private health care organizations around the world (Steenkamer, Drewes, Heijink, Baan & Struijs, 2017;
Stiefel & Nolan, 2012). As a result, there is a wide-spread focus on population health outcomes, but documented strategies for population health management vary (Moore, Peterson, Coffman &
6
Jabbarpour, 2016; Steenkamer et al., 2017). Population health management expands traditional health care of individuals to include evaluation of medical and social determinants of a defined population, in which coordinated programs and activities are supported by a payment model that rewards improvement in population health outcomes (Matthews, Miller, Stroebel & Bunkers,
2017; Zangerle, 2016). Many strategies for population health management have been implemented in response to changing health care policy and regulations, notably managed care organizations, patient centered medical homes, and accountable care organizations (Hewner,
Casucci & Castner, 2016; Matthews et al., 2017; Moore et al., 2016; Steenkamer et al., 2017). In each of these strategies, nurses have been an integral part of a multidisciplinary team.
Population risk factors for heart failure readmission. Despite an extensive amount of research focused on understanding and preventing HF hospital readmissions, little improvement has been made in the reduction of readmission rates overall (Bergethon et al., 2016). Disease complexity and multiple comorbidities are significant and strong individual-level predictors of
HF readmissions (Hewner et al., 2016) and all-cause readmissions (Wang et al., 2016). Further, disparities continue to exist when adding race and socioeconomic status as significant risk factors for HF readmissions (Graham, 2015; Joynt Maddox et al., 2019). Patients of black race have 16 times higher odds of HF readmission compared to white race (Mirkin et al., 2017).
On a broader level, it has been reported that populations with more chronic disease had
16 times the rate of all-cause readmission compared to populations without chronic disease
(Hewner et al., 2016). Social risk factors of populations, such as widespread poverty, disability, housing instability, and living in a disadvantaged neighborhood have been associated with higher readmission rates in HF (Joynt Maddox et al., 2019). Unfortunately, CMS does not account for social risk factors in the risk adjustment process of the HRRP, resulting in higher penalties for
7 hospitals that serve disadvantaged populations (Joynt Maddox et al., 2019). Clearly, not enough consideration is given to the burden of upstream factors as part of the root cause of HF outcomes.
Upstream Factors
Defining upstream factors. In this study, the definition of upstream factor reflects the description of the concept from scholarly literature and professional uses of the term by health organizations and content experts. Upstream factors are conditions related to economic, social, and physical environments that occur outside of the healthcare system, in the communities where people live, and contribute to the root cause of health outcomes for groups of individuals.
(adapted from Braveman, Egerter, & Williams, 2011; Fawcett & Ellenbecker, 2015; MacDonald,
Newburn-Cook, Allen & Reutter, 2013; Stiefel & Nolan, 2012; Williams, Costa, Odunlami &
Mohammed, 2008). These social, economic, and environmental factors, in addition to individual characteristics and behaviors, are broadly referred to as the determinants of health (Office of
Disease Prevention and Health Promotion, 2010; World Health Organization, n.d.). Individual- level factors are likely to have a direct, or downstream, effect on health because they are temporally and spatially close to the health outcome but are also influenced by upstream factors
(Braveman et al., 2011; Robert Wood Johnson Foundation [RWJF], 2017). Upstream factors often have a significant effect on individual behavior because they are grounded in social structures and policies, such as food support programs or tobacco accessibility (Riegelman,
2015). However, upstream factors are largely outside of the control of individuals, occurring in the communities where people live, and thus indirectly contribute to health outcomes for groups of individuals.
The effect of upstream factors differs from individual-level indicators of socioeconomic status (SES) because living in a specific place creates both risks and benefits to health over time
8
(Hughes, Phillips, DeVoe & Bazemore, 2016). Upstream factors represent a set of shared circumstances within a community or neighborhood that create a unique phenomenon greater than the sum of its parts. It is widely understood, in this country and worldwide, that individuals with poverty, unemployment, low education, and inadequate housing are more likely to experience poor health (Bor, Cohen, & Galea, 2017; Chetty et al., 2016; Marmot, 2007; Williams et al., 2008). However, there is also increasing evidence that neighborhood-level SES is associated with health outcomes independently of individual-level SES (Arcaya et al., 2016;
Durfey, Kind, Buckingham, DuGoff & Trivedi, 2019).
Neighborhood disadvantage. Neighborhood disadvantage is defined as the challenge associated with low income, limited education, and substandard living conditions experienced by individuals and their surrounding neighborhood or social network (Kind et al., 2014).
Neighborhood disadvantage has been associated with adverse health outcomes in mental health and chronic disease and is an independent predictor of all-cause mortality and hospital readmissions (Arcaya et al., 2016; Durfey et al., 2019; Hu, Kind & Nerenz, 2018; Hu, Gonsahn
& Nerenz, 2014; Kind et al., 2014). Disadvantaged populations may be segregated into resource poor areas, cut off from opportunities and supports (Durfey et al., 2019), thus perpetuating population health disparities.
Neighborhood socioeconomic disadvantage is an important upstream risk factor for populations with HF; it has been associated with increased risk for incident HF (Akwo et al.,
2018), mortality from HF (Knighton, Savitz, Benuzillo & VanDerslice, 2018), and HF readmission (Bikdeli et al., 2014; Eapen et al., 2015). County-level SES was only modestly associated with 30-day HF readmission (Eapen et al., 2015), and neighborhood SES at the census tract level was associated with 6-month HF readmission even after adjusting for individual-level
9 factors and SES (Bikdeli et al., 2014). Additionally, neighborhood disadvantage at the census block group level (smaller units within a census tract), had strong, independent associations with
30-day all-cause readmissions in HF (Hu et al., 2018; Kind et al., 2014). It is unknown how the effect of neighborhood disadvantage as an upstream factor might impact interventions aimed at improving specific population health outcomes, such as HF readmissions.
Health Care System Strategies for Heart Failure Readmission Reduction
Strategies for HF readmission reduction are complex and require a great degree of coordination between the hospital and early post-discharge outpatient settings. Providers and hospitals together use evidence-based systems of care to identify HF patients appropriate for guideline-directed medical therapy (GDMT), educate the patient and family, and develop a plan of care for transition from inpatient to outpatient care (Yancy et al., 2013). The main components of this collaboration are the early provider follow-up visit and multidisciplinary transitional care management.
Early provider follow-up visits. Current HF guidelines recommend an early provider follow-up within 7-14 days and a telephone follow-up within 3 days of hospital discharge to reduce risk of readmission (Bradley et al., 2013; Yancy et al., 2013). At the first post-discharge visit, and in subsequent visits, providers evaluate response to current GDMT, manage other chronic conditions, address barriers to care, and provide HF education (Yancy et al., 2013). In recent studies, provider follow-up visits within 7 days of discharge have been associated with lower readmission rates for HF (Lee, Yang, Hernandez, Steimle & Go, 2016; Tung, Chang,
Chang & Yu, 2017). When follow-up phone calls by nurses were combined with follow-up provider visits or home visits, readmission rates for primary care practices were reduced
(D’Amore Murray, Powers & Johnson, 2011; Stamp, Machado & Allen, 2014). However,
10 upstream and population factors may impact the occurrence of provider visits and adherence by patients. Early physician follow-up after HF hospitalization was less likely to occur for patients living in rural areas, with low physician density, and lower SES (Kociol et al., 2011). Also, HF patients of black race and female gender were less likely to have scheduled follow-up (Kociol et al., 2011), while black race and drug/alcohol use predicted non-adherence to scheduled follow up visits (Distelhorst et al., 2018). Knowledge of both upstream and population factors may help transitional care managers to address the barriers that prevent adherence to follow-up visits for
HF patients and thus improve readmission rates.
Multidisciplinary transitional care management. Hospital strategies for readmission reduction are multidisciplinary and include patient education, discharge planning, medication reconciliation, scheduling follow-up appointment before discharge, communication with outpatient providers, and follow-up telephone calls (Bradley et al., 2013; Ziaeian & Fonarow,
2016). Additional system interventions reported as effective in decreasing readmission rates were discharge summaries to primary care physicians, community partnerships, and assigned staff to follow-up on laboratory results after discharge (Bradely et al., 2013). These interventions are often organized as multidisciplinary transitional care management programs that begin in the inpatient setting and extend to the post-acute care period.
Nurses play a prominent role in transitions of care for HF patients at many levels by optimizing the assessment, management, and evaluation of the patient and care during transitions from one setting to another, especially from hospital to home (Albert, 2016). As part of a health care system strategy, transitional care management often focuses on reducing unnecessary and expensive health care utilization, such as ED visits and hospitalization, in the 30-, 60-, and 90- day periods after a transition. Many studies have examined transitional care for patients with HF
11 and the effect on hospital readmission, but there is not consistency in what interventions are included as transitional care activities (Feltner et al., 2014). Regardless, HF transitional care activities have been found to be more effective when bundled together and some studies indicate that the intensity of interventions improves HF readmission outcomes (Feltner et al., 2014;
Stamp et al., 2014). Based on a systematic review of the literature, transitional care interventions for patients with HF have been categorized into eight common themes: planning for discharge, multi-professional collaboration, timely/clear information, medication reconciliation, engaging community, post-discharge monitoring/ education, outpatient follow-up, and advanced care planning (Albert, 2016). These themes are consistent with the competencies for CCTM nursing described by the AAACN (2016) and can be improved upon through a population health management approach.
Nursing and Population Health Management
The advancement of population health nursing. Nursing has deep roots in population- based care with Florence Nightingale’s use of epidemiological principles in the Crimean War and Clara Barton’s founding of the American Red Cross (Cupp Curley & Vitale, 2016). Lillian
Wald, the founder of public health nursing, identified the basic causes of illness for immigrants in New York City, while Mary Breckenridge created the Frontier Nursing Service, resulting in an immediate impact on the population health outcomes of infant and maternal mortality (Cupp
Curley & Vitale, 2016; Storfjell et al., 2017). As nursing became more hospital-based during the middle 20th century, roles and focus changed to medicine-based individual care for most nurses
(Bekemeier, 2008). That trend continues today with 60% of all nurses working in hospitals
(Bureau of Labor Statistics, 2019). Yet regardless of practice setting, nurses still apply a holistic
12 approach to care that recognizes the impact of social and environmental issues on health. For this reason, nurses are an important part of the population health management team.
While health care reform and the ACA have brought population health in to focus for hospital-based and ambulatory nurses, the practice is not new for public health and community health nurses (Hokanson Hawks, 2012). Storfjell and Cruise (1984) first developed a model for community-focused nursing, and later refined it (see Figure 1) as the Population Health Nursing
Model (Storfjell, Winslow & Saunders, 2017). The model identifies three levels of nursing scope of practice in population health and associated nursing activities that progress from basic to advanced (Storfjell & Cruise, 1984; Storfjell et al., 2017). The three levels of population- focused nursing interventions are: 1) serving and coordinating care for individuals and families in the context of their environment; 2) identifying health-related population trends and advocating for solutions; and 3) designing and implementing population-level interventions to improve health (Storfjell et al., 2017). Nurses with advanced education, such as the MSN prepared Clinical Nurse Leader and the DNP prepared Advanced Practice Nurse, can practice at the highest level of population health practice by analyzing trends and developing population- level interventions to improve outcomes (Cupp Curley & Vitale, 2016; Rankin et al., 2018).
However, all nurses in population health practice must consider population factors as context to patient care, regardless of education or practice setting (Storfjell et al., 2017).
In population health practice, the emphasis of care shifts from solely individual disease and treatment to include the social determinants that affect the health and wellness of an entire population (Fawcett & Ellenbecker, 2015; Steenkamer et al., 2017). Population-focused nursing draws upon the tenants of public health, understanding individuals in the context of their social, emotional and physical environments, and integrates it with clinical care to improve the health of
13 communities and populations (RWJF, 2017). Clinical practice settings where nurses have an emerging population-focus include ambulatory care (Ritchie & Leff, 2018), school nursing
(Bergren, 2017; Cowell, 2018), nursing education (Prodoehl-Caniano, 2019), and care management (Hewner, Seo, Gothard & Johnson, 2014; Steaban, 2016). As population health nursing practice continues to evolve, it will be critical to expand nursing knowledge regarding all aspects of the nursing process, including assessment of upstream factors and the identification of population-appropriate interventions, in order to achieve positive outcomes.
Figure 1. Population Health Nursing Scope of Practice. Current population health nursing scope of practice developed out of early community health nursing models. Nursing activities progress from advanced to basic in both models. Adapted from “The Population Health Nursing Model” by J. L. Storfjell , B. W. Winslow and J. S. Saunders, 2017, Catalysts for Change: Harnessing the power of nurses to build population health in the 21st century [White paper].
Care coordination and transition management (CCTM). At the same time as population health nursing was evolving, so was the role of the case manager, dating back to the
14 early 1900’s in the disciplines of public health, nursing, and social work (AAACN, 2016).
Although the role has been rapidly developing over the past 25 years, only recently have the scope and standards of practice for RNs in CCTM been formally defined (AAACN, 2016). Many terms are used to identify the work of CCTM nursing, including care management, case management, transitional care, and transitional care management. Although CCTM is not unique to nursing, nurses are uniquely qualified to bring a population perspective to CCTM.
Nurses have the knowledge and skill to assess, diagnose, identify outcomes, plan, implement, and evaluate interventions for individuals, families, and populations by use of the nursing process (American Nurses Association [ANA], 2015). It is this expertise that makes the RN a pivotal agent of the interprofessional health care team in CCTM.
According to the AAACN (2016), care coordination and transition management are intimately entwined but can be separately defined. Care coordination is the deliberate organization of patient care activities between multiple participants in care, in order to facilitate the appropriate delivery of health care services (AAACN, 2016). In addition, transition management is a critical element of care coordination, providing ongoing support to patients and families over time as they navigate across health care settings, services, and providers (AAACN,
2016). Nurses bring important skills to the role of CCTM, including the ability to apply critical and analytical reasoning and clinical judgment to expedite appropriate health care for complex patients and populations (AAACN, 2016). Population health management is considered an essential competency to CCTM (AAACN, 2016), however, nursing research is needed to gain a fuller understanding the effectiveness of CCTM activities on population-specific health outcomes in HF.
15
Upstream factors and the nursing process. The concept of upstream factors has relevance in population health nursing. Upstream thinking, assessment, and interventions are a way to address the precursors of poor health (Butterfield, 1990; Butterfield, 2017; MacDonald et al., 2013). The assessment of upstream factors can be a valuable part of the nursing process for population health and is necessary for planning interventions that address the health needs of both individuals and groups. (Fawcett & Ellenbecker, 2015). This population-level focus allows nurses to consider actions that will create the conditions to promote, restore and maintain wellness (Fawcett & Ellenbecker, 2015). Nurses can use knowledge of upstream factors at all levels of population health nursing practice (Storfjell et al., 2017): to provide context to interventions for patients and families; to identify trends and plan for appropriate interventions; and to design, implement, and evaluate new population level interventions. Understanding important upstream factors for patients with HF may be useful for nurses involved in transitional care activities to decrease hospital readmissions in this population. However, nursing research that links important upstream factors to nursing activities and population-specific health outcomes is needed.
Statement of the Problem
Despite extensive research aimed at predicting and preventing 30-day hospital readmission in HF patients, rates remain around 20% nationally and studies from a population- based nursing perspective are few. While some evidence supports the effectiveness of multidisciplinary transitional care to decrease HF readmission at the individual level, the impact of nursing activities within the team needs further study to support nursing’s unique contribution to HF outcomes. Additionally, it is unknown how the direct and indirect relationships between
16 early provider follow up, nursing CCTM, and population HF readmission may vary as a result of neighborhood disadvantage.
Conceptual Framework
The Conceptual Model for Nursing and Population Health (CMNPH) guided this study
(see Figure 2). The CMNPH was developed to advance the contributions of the discipline of nursing to population health (Fawcett & Ellenbecker, 2015). Previous population health models
(Kindig, Asada, & Booske, 2008; Stiefel & Nolan, 2012) emphasized the role of providers and the health care system to improve population health outcomes and were not adequate to guide nursing research and practice at the population level. The CMNPH provides a systematic way of understanding population health phenomena and the nursing activities necessary to improve population outcomes (Fawcett & Ellenbecker, 2015). When the Storfjell Population Health
Nursing Model is considered along with the CMNPH, a framework is created that can guide nursing research, which in turn can guide nursing actions in any practice setting.
Concept Definitions and Epistemic Correlations
There are five concepts within the CMNPH: a) upstream factors, b) population factors, c) health care system (HCS) factors, d) nursing activities, and e) population health outcomes. Each concept is broadly defined and has multiple dimensions that allow the model to be applied to many contexts and circumstances. According to the relational propositions of the CMNPH, upstream factors, population factors, and health care system (HCS) factors are all interrelated and also related to nursing activities. Subsequently, nursing activities mediate the relationships of upstream, population, and HCS factors to population health outcomes. Finally, nursing activities and HCS factors are directly related to population health outcomes (Fawcett & Ellenbecker,
2015).
17
Figure 2. Diagram for the Conceptual Model for Nursing and Population Health. Adapted from “A proposed conceptual model of nursing and population health” by J. Fawcett and C. Ellenbecker, 2015, Nursing Outlook, 63(3), 288-298.
As a conceptual model, the CMNPH presents a highly abstract framework that can guide the development of nursing knowledge related to population health. A conceptual model, such as the CMNPH, is intended to provide an overall direction for nursing practice, education, and research (Fitzpatrick & Whall, 2016). The multidimensional concepts of the CMNPH reflect the complex, dynamic nature of population health and are relevant for different populations. A conceptual model is too abstract to be directly applied to nursing practice; the model concepts must first be linked to measurable variables and empirical indicators through nursing research
(Fawcett, 2017). To date, few researchers have utilized the CMNPH as a framework. The conceptual model has been used to support a model for health equity (Butterfield, 2017) and research involving perinatal home nursing (Bloch, Zupan, McKeever & Barkin, 2017).
18
This study will focus on the primary relationships between upstream factors, nursing activities, HCS factors, and population health outcomes. Table 2 summarizes the links between the abstract concepts and dimensions of the CMNPH and the variables of the study. Epistemic assumptions, the relationships between concepts and variables (Bekhet & Zauszniewski, 2008), are supported by empirical evidence and scholarly literature from nursing and public health. This provides consistency between the theoretical and operational aspects of the study to clarify the model and design (Bekhet & Zauszniewski, 2008).
Table 2.
Study Concepts, Variables, and Indicators
CMNPH CMNPH Concept Dimension Variable Empirical Indicator Healthcare System Policies Early Provider IV Provider visit within Factors Follow-up 14 days
Nursing Activities Population-based CCTM Intensity Med Number of CCTM Nursing Processes contacts in 30 days
Upstream Factors Socioeconomic Neighborhood Mod Area Deprivation factors Disadvantage Index
Population Factors Health State Comorbidity CV AHRQ Elixhauser Comorbidity Index
Population Health Population-level HF Hospital DV 30-day readmission Outcome Disease Burden Readmission Note. CMNPH=conceptual model of nursing and population health; CCTM=care coordination transition management; HF=heart failure; IV=independent variable; Med=mediator; Mod=moderator; CV=covariable; DV=dependent variable
Upstream factors. In the CMNPH, upstream factors are the conditions of neighborhoods where people live and includes the dimensions of socioeconomic factors and the physical environment (Fawcett & Ellenbecker, 2015). Examples of upstream factors are poverty
19 rates, unemployment rates, housing availability, and other socioeconomic indicators that can be found from large data bases such as the U.S. Census. Neighborhood disadvantage encompasses many of these factors and can be measured by the Area Deprivation Index (Kind et al., 2014).
The use of a validated index measure, rather than multiple SES indicators, provides a simple measure with established explanatory power (Singh, 2003).
Healthcare system factors. The concept of HCS system factors is defined as the providers, organizations, policies, and payors that serve the health-related needs of populations and have a direct effect on the health of a population (Fawcett & Ellenbecker, 2015). Hospital polices often direct support staff to schedule these early follow-up visits prior to discharge.
Additionally, CMS recognizes the practice and allows providers to bill for transitional care management services, which include an interactive contact (usually by phone) within 3 days of discharge, and a face-to-face visit with a provider within 14 days of discharge (CMS, 2019).
Health system billing databases thus provide an accurate measurement of visit completion and days to visit from discharge.
Nursing activities. Nursing activities in the CMNPH are actions performed by nurses for populations, using multidisciplinary collaboration and nursing practice processes that focus on culturally appropriate disease prevention, wellness promotion, restoration, and maintenance and have a direct effect on the health of a population (Fawcett & Ellenbecker, 2015). These actions encompass all standards of the nursing process including assessment, diagnosis, outcome identification, planning, implementation, and evaluation (ANA, 2015). Nurses in CCTM use the nursing process in working with populations that may defined by geography, by practice setting, or disease process. The CMNPH focuses on nursing practice processes, thus, in lieu of breaking
20 down CCTM activities into specific tasks, the intensity, or number of outreach encounters, will be the measure for this concept.
Population factors. Population factors are the characteristics of individuals (genetic, physiological, and behavioral factors, resilience, and health state) within an aggregated group of persons, that when viewed at the population-level have an indirect effect on the health of that population (Fawcett & Ellenbecker, 2015). Relevant population factors for this study include advanced age, race, gender, and comorbidity as an indicator of health state. Although not part of the study model, these factors will be accounted for through design and statistical control.
Population health outcome. Population health outcomes are the status of the health of a population that is reflected by the dimensions of population-level wellness, disease burden, functional status, life expectancy, mortality, and quality of life (Fawcett & Ellenbecker, 2015).
Population-level disease burden is the incidence and prevalence of chronic disease in a population and the resulting total effect on the population (Fawcett & Ellenbecker, 2015).
Hospitalization for HF is a population health outcome that indicates the burden of HF and reflects ongoing difficulties in managing existing disease in the population (Roger, 2013).
Hospital readmission is an important population-level outcome to study to answer the question of why some hospitals continue to see excessive readmission rates, despite similar interventions applied to readmission reduction efforts. Hospital readmission for this study will be defined as any hospitalization within 30-days of an index admission for heart failure and measured as a dichotomous variable.
Study Model
The study model (see Figure 3) was adapted from the Conceptual Model of Nursing and
Population Health (Fawcett & Ellenbecker, 2015). In the model, early provider follow-up, the
21 independent variable, had a direct relationship to HF hospital readmission, as the dependent variable. Nursing CCTM intensity was the mediating variable between early provider follow-up and HF hospital readmission, while controlling for population factors of race, sex, and comorbidity. The relationships between early provider follow-up, CCTM intensity, and HF hospital readmission were supported by empirical evidence. Early provider follow-up visits are a guideline-recommended practice for the HF population and are associated with lower 30-day readmission rates (Yancy et al., 2013; Lee et al., 2016). Further, nurse case management has been found to be effective in decreasing all-case readmission after hospitalization for HF compared to usual care (Van Spall et al., 2017). However, studies have not specifically examined the indirect effect of provider follow-up on readmission through nursing CCTM activities.
The relationship between neighborhood disadvantage and the other variables was less clear in the existing literature. Risk for HF readmission significantly increases at the individual and population level as neighborhood disadvantage increases (Hu et al., 2018; Joynt Maddox et al., 2019). Understanding the variations in neighborhood disadvantage for a given population could provide insight to the mechanisms of health disparities and facilitate the development of improved treatments and interventions (Kind & Buckingham, 2018). It is unknown if the effectiveness of HF transitional care interventions is conditional on the level of neighborhood disadvantage. Therefore, in the current study model, neighborhood disadvantage was hypothesized to moderate the direct relationship of early provider follow-up and the indirect relationship through CCTM intensity on HF hospital readmission.
22
Figure 3. Study model adapted from the Conceptual Model of Nursing and Population Health. Early provider follow-up represents HCS factors; CCTM intensity represents nursing activities; HF hospital readmission represent population health outcomes; and neighborhood disadvantage represents upstream factors.
Purpose
The purpose of the study was to investigate the relationships between neighborhood disadvantage, early provider follow-up, CCTM intensity, and HF hospital readmission. The study examined the relationship between early provider follow-up and HF hospital readmission and determined if the association was mediated through CCTM intensity. Additionally, the study explored the nature of the relationship of neighborhood disadvantage with HF hospital readmission, through moderated mediation analysis, to determine if the association of early provider follow-up and nursing CCTM intensity on hospital readmission held across levels of neighborhood disadvantage.
23
Research Questions
1. What is the relationship between early provider follow-up, nursing CCTM intensity, neighborhood disadvantage, and hospital readmission in a population of older adults with HF, when controlling for advanced age, race, sex, and comorbidity?
2. Is there a direct relationship between early provider follow-up and HF hospital readmission?
3. Is there an indirect relationship of early provider follow-up on HF hospital readmission through CCTM intensity?
4. Does neighborhood disadvantage moderate the direct relationship of early provider follow-up and the indirect relationship through CCTM intensity on HF hospital readmission?
24
CHAPTER II – REVIEW OF THE LITERATURE
Introduction
There is an extensive body of research examining HF outcomes, including hospital readmission. The literature review focused on three areas, starting with understanding the timing, causes, and trends in HF hospital readmissions. Secondly, studies focused on identifying predictors of HF readmission, including individual and population characteristics that increase risk. Third, researchers aimed to identify and evaluate strategies to reduce HF readmission, specifically those that were related to transition from hospital to home.
Patterns and Trends in Heart Failure Hospital Readmissions
Causes and Timing
Understanding the phenomenon of HF readmission begins with identifying the conditions that patients are most commonly readmitted for, and how soon after discharge the readmissions occur. Four studies examined HF readmission patterns in state, national, and international samples with similar findings. All studies included both younger and older patients and are summarized in Table 3.
In a retrospective cohort study (Ranasinghe et al., 2014), researchers compared the timing and reasons for HF readmission between younger (18-64 years old) and older (65 and older) subjects. Time to readmission for the younger cohorts occurred in a similar pattern as the 65 and older group, with nearly one-third of all readmissions occurring within 7 days. For both age groups, HF was the most common primary readmission diagnosis. In the 65 and older group,
35.6% were readmitted for HF which was slightly higher than other reports (Davis et al., 2017;
Arora et al., 2017). However, with younger age groups, HF was more frequently observed as the
25 primary readmission diagnosis, increasing to 42.2% for ages 55-64, 45.2% for ages 40-54, and
49.6% for ages 18-39, p<.001 for the trend (Ranasinghe et al., 2014).
Table 3
Causes, Timing, And Rates of 30-Day HF Hospital Readmissions
Cause Timing 30-day Study Cohort age HF Non-CV 1-7 days 1-14 days RAR
Ranasinghe et al., ≥18 -- -- 30% 50% 24.4% 2014 ≥65 35.6% 50.4% 23.4% 18-64 -- 40-44% 22.2%
55-54 42.2% 40-54 45.2% 18-39 49.6%
Davis et al., 2017 ≥40a 30.3% 54.8% 34.2% -- 21.4%
Arora et al., 2017 ≥18b 34.5% 50.2% 25% 50% 18.4%
Fudim et al., 2018 ≥18c 46% 36% 33% 50% 11%
Note. CV=cardiovascular. RAR=readmission rate a Mean= 74.7 (SD 14.1); b ≥65 years old = 73.4%; c Mean= 65 (SD 14)
Another retrospective cohort study using the Healthcare Cost and Utilization Project
(HCUP) State Inpatient Databases (California, New York, and Florida) described the readmission patterns of patients 40 years of age and older with a primary discharge diagnosis of
HF for the years of 2007-2011(Davis et al., 2017). The large, all-payer cohort had a mean age of
74.7 (SD 14.1) and the crude 30-day all-cause readmission rate was 21.4%, with only 30.3% of those having a primary readmission diagnosis of HF. The most common non-HF causes of readmission were other cardiovascular conditions (14.9%), pulmonary disease (8.5%), acute
26 infections (7.7%), hematology/oncology conditions (6.7%), and renal/genitourinary conditions
(5/5%). In total, non-cardiovascular conditions were the cause of 54.8% of the readmissions
(Davis et al., 2017).
Similar results on the etiology of HF readmission were reported on a study using the
HCUP National Readmission Data (NRD) from 2013 (Arora et al., 2017). The overall 30-day readmission for the national representative sample from age 18 years was 18.4%, slightly lower than other reported rates. Heart failure accounted for 34.5% of the primary readmission diagnoses, with 15.3% related to other cardiovascular conditions. Non-cardiovascular conditions were the primary cause for 50.2% of HF readmissions, with the most common being pulmonary disease (13.1%) and renal disease (8.9%).
In older cohorts, approximately one-third of HF readmissions were due to acute decompensated heart failure, while non-cardiovascular conditions accounted for over 50% of HF readmission etiologies (Arora et al., 2017; Davis et al., 2017; Ranasinghe et al., 2014). In contrast, a secondary analysis of the ASCEND-HF (Acute Study of Clinical Effectiveness of
Nesiritide and Decompensated Heart Failure) trial data revealed a slightly different pattern of readmission etiology for an international sample of hospitalizations from 2004-2009 (Fudim et al., 2018). Overall, the study sample had a 30-day all-cause readmission of 11%, however, subjects from the North America region were more likely to be readmitted than the other four regions. Unlike previous studies, the ASCEND-HD cohort had higher rates of readmission due to
HF (46%) and other cardiovascular related diagnoses (18%) for all readmitted patients. Findings of this study are limited, however, due to the use of previously collected randomized trial data with specific inclusion and exclusion criteria that may have resulted in bias.
27
Overall, these four studies indicate the important role that comorbid conditions have in causing HF 30-day all-cause readmission, especially for older patients. Younger patients with
HF may have greater severity of disease and therefore are more likely to be readmitted for cardiovascular reasons. The data also indicate that there is a vulnerable period within the first 14 days of hospital discharge regardless of age, etiology of readmission, or location. The median time to readmission in a U.S. cohort was 12 days (Davis et al., 2017), and 11 days for an international sample (Fudim et al., 2018); there was no difference in timing for HF vs. non-HF etiologies (Davis et al., 2017; Fudim et al., 2018). In the secondary analysis of data from the
ASCEND-HD trial, 33% of readmissions occurred within 7 days, and two-thirds were within 14 days (Fudim et al., 2018). In U.S. cohorts, half of HF readmissions took place within 13 days
(Arora et al., 2017) and this trend was not affected by age (Arora et al., 2017; Ranasinghe et al.,
2014).
Temporal Trends
When examining HF readmission rates over time, it is important to consider the historical context of readmission reduction efforts in the US. Three important points in time (2009, 2010, and 2012) indicate the potential influence of healthcare policy on HF readmission rates (see
Figure 4). First, to increase transparency and accountability of hospitals for the rising cost of readmissions, CMS initiated public reporting of risk-adjusted unplanned all-cause hospital readmission rates for patients with HF in 2009 (DeVore et al., 2016). That same year, several national campaigns from quality organizations, including the IHI and the AHA, were developed to optimize strategies for transition from hospital to home, setting a goal of reducing 30-day readmissions for patients with HF by 20% nationwide by December 2012 (Bergethon et al.,
2016; White, 2011). The ACA was then passed in 2010, with sweeping reforms for healthcare
28 that would be rolled out over a period of years. As part of the ACA, the HRRP required CMS to assign penalties and reduce payments to hospitals with excessive readmission rates beginning in
2012 (CMS, 2017). The following studies will be reviewed within the context of these historical events.
Figure 4. Timeline of U.S. healthcare policy changes for hospital readmission reduction. CMS = Centers for Medicare and Medicaid Services; ACA = Affordable Care Act; HRRP = Hospital Readmission Reduction Program. Adapted from DeVore, A. D., Hammill, B. G., Hardy, N. C., Eapen, Z. J., Peterson, E. D., & Hernandez, A. F. (2016). Journal of the American College of Cardiology, 67(8), 963-972 and Wasfy, J. H., Zigler, C. M., Choirat, C., Wang, Y., Dominici, F., & Yeh, R. W. (2017). Annals of Internal Medicine, 166(5), 324-331. doi:10.7326/M16-0185
DeVore et al. (2016) assessed the trends of 30-day readmission since the implementation of public reporting by CMS in 2009. A 5% representative sample of patients 65 years and older, who were hospitalized with HF, acute MI, diabetes, and pneumonia, was obtained from
Medicare claims data between 2006 and 2012. Using regression models, adjusted trends for 30- day readmission were estimated and reported as relative changes per year. While post-public reporting readmission rates for HF decreased by 1.8%, there was no significant difference compared to pre-public reporting readmission rates. A limitation of the study, however, was that it did not evaluate individual hospital improvements.
29
Another study described 30-day readmission rate trends using hospital-level data from the AHA Get With The Guidelines® – Heart Failure (GWTG-HF) registry and American
Hospital Association Survey on hospital characteristics. (Bergethon et al., 2016). Hospitals voluntarily participate in GWTG-HF as a quality improvement program that assists in translating
HF guidelines into clinical practice (American Heart Association, 2020). Included in this study were 21,264 patients from 70 hospitals, who were discharged after a HF admission from 2009-
2012. Overall, hospital level 30-day all-cause readmission rate significantly decreased, but only slightly, from 20.0% in 2009 to 19.0% in 2012. The median relative readmission rates for all hospitals between 2009 and 2012 decreased by 5.6%, and only one hospital achieved the goal of
20% relative reduction in 30-day readmission.
The reports from DeVore et al. (2016) and Bergethon et al. (2016) indicated little to no significant change in 30-day all-cause readmission rates for HF associated with CMS public reporting and national campaigns for HF readmission reduction up until 2012. Blecker et al.
(2019) identified a slightly different trend using data from 2008-2015, which included all 3 time periods of public reporting, the ACA enactment, and implementation of the HRRP. Using the
CMS Inpatient Standard Analytic File and Medicare Enrollment Database, the sample included over 3 million hospitalizations for HF for Medicare patients 65 years and older. In this cohort,
30-day all-cause readmission was stable with a mean monthly rate of 26.1% prior to the passage of the ACA in March 2010. In the time between the ACA and implementation of the HRRP, readmission rates decreased by 1.09% (p<.001) per year with a readmission rate of 22.3% in
October 2012. After implementation of the HRRP, readmission rates increased 1.16% (p<.001), thus initial gains were offset by the post HRRP increase (Blecker et al., 2019).
30
Other studies also assessed the association between the HRRP and changes in 30-day HF readmission rates. In a pre-post analysis stratified by hospital performance groups, HF 30-day readmission improved nationally with the passage of the ACA and HRRP (Wasfy et al., 2017).
Overall readmission rates increased by 5.1 per 10 000 discharges per year before the ACA and decreased by 84.7 per 10 000 discharges per year after the law. Additionally, lower performing hospitals had more accelerated improvement after the ACA than hospitals with better performance, with a decrease in 30-day readmission rate from 26.5% in 2010 to 23.3% in 2013
(Wasfy et al., 2017). Similarly, hospital wide efforts to reduce HF readmissions and improve quality have been associated with improvements across all age-insurance groups (Angraal et al.,
2018). Using the HCUP NRD, 30-day all cause HF readmission declined in Medicare, Medicaid, and private insurance groups from 2010 to 2015 (Angraal et al., 2018). Researchers also examined the changes in HF readmissions in the U.S. and Canada and found that rates significantly decreased in both countries from 2005 to 2015 (Samsky et al., 2019). In this retrospective cohort study, 30-day readmission in Canada declined from 19.7% in 2005, to
18.4% in 2012, to 17.6% in 20015 (p<.001), compared to the United States with 21.2% in 2005, to 19.5% om 2012, to 18.5% in 2015 (p<.001). However, there was no significant difference in the acceleration of readmission reduction in the U.S. compared to Canada after the implementation of HRRP penalties in 2012. These findings may suggest that global efforts at readmission reduction for HF were as effective as changes in U.S. healthcare policy (Samsky et al., 2019).
These reports indicate that overall, 30-day all-cause readmission rates for HF have decreased since the introduction and implementation of the HRRP (see Table 4), but not consistently and not with a large effect. While these studies do not infer causality between the
31
HRRP and HF readmission, they demonstrate the potential impact of hospital-led initiatives to improve HF 30-day all-cause readmission rates for the Medicare population and beyond. With the healthcare policy changes related to hospital readmission in the past decade and limited resources, hospitals must identify populations most at risk for HF readmission in order to effectively target the problem.
Table 4
Changes in 30-Day Risk Adjusted Readmission Rates Relative to CMS Policy
Study Level of data Change point 30-day RARR P-value
DeVore et al., 2016 National Public reporting ↓ 0.19
Bergethon et al., 2016 Hospital Public reporting ↓ .001
Wasfy et al., 2017 Hospital ACA ↓ <.001
Blecker et al., 2019 National ACA ↓ <.001
HRRP ↑ <.001
Angraal et al, 2018 National HRRP ↓ .056
Samsky et al., 2019 National HRRP ↓ <.001
Note. RARR=risk adjusted readmission rates; CMS=Centers for Medicare & Medicaid Services; ACA=Affordable Care Act; HRRP=Hospital Readmission Reduction Act
Population Predictors of Heart Failure Readmission
Genetic Factors: Age and Race
In a previously discussed study (Ranasinghe et al., 2014), the younger HF population had a similar pattern of time to readmission compared to the older population. However, the overall
30-day all-cause readmission rate was significantly higher in the 18-64-year-old group (23.4%)
32 compared to the older group (22.0%). Additionally, the youngest subgroup, age 18–39 years, had a higher adjusted risk compared with patients aged 65 years and older (HR 1.12; 95% CI 1.05,
1.20). Another study divided subjects into five age categories to determine if age was an independent predictor of all-cause rehospitalization (Whellan et al., 2016), but found that the rate of 30-day all-cause readmission was similar between the youngest (<45 years) and oldest (≥ 75 years) cohorts (12.2% and 12.5%, respectively), with decreasing risk for the ages between
(Whellan et al., 2016). The <55-year-old cohort had a significantly lower likelihood of death or all-cause readmission for each 10-year increase in age (OR 0.81, 95% CI 0.70, 0.93). However, the 55 years and over cohort had a higher likelihood of death or all-cause readmission for each additional 10 years of age. Overall, both studies support that populations of the youngest and oldest HF patients are most at risk for 30-day all-cause readmission.
As discussed earlier, it is known that patients of black and Hispanic race have significant disparity in HF incidence (Benjamin et al., 2017; Sharma et al., 2014) and HF readmissions
(Graham, 2015; Joynt Maddox et al., 2019; Mirkin et al., 2017). Even when controlling for socioeconomic status, these disparities exist and the reasons are not clear (Durstenfeld,
Ogedegbe, Katz, Park & Blecker, 2016). County-level race has been associated with higher odds of readmission for black and Hispanic communities, compared to white, but a final model that included both race and SES was only modestly associated with 30-day all-cause readmission and did not improve upon risk models based on patient characteristics (Eapen et al., 2015). Lack of access to care has been one hypothesis of the reason for racial and ethnic disparities in HF readmissions. A retrospective cohort study in a single New York City health system explored if access to care eliminates racial/ethnic disparities (Durstenfeld et al., 2016). In this predominantly non-white population with similar healthcare access, disparity in 30- and 90-day
33 readmission were evident even when adjusting for clinical and SES factors. However, only
Hispanic patients had significantly higher risk-adjusted odds of 30-day all-cause HF readmission compared to white patients.
Currently, CMS uses a risk-adjustment model for determination of avoidable hospital readmission that includes patient characteristics of age, gender, and diagnosis codes, but it does not consider population sociodemographic factors (Meddings et al., 2017). One study utilized patient-specific and hospital-level measures of race from the HCUP inpatient databases for
Florida and Washington to develop a risk model for 30-day hospital readmission. In the HF cohort, hospital-level race (% black race) was not significantly predictive when social factors were added to the risk model. However, for patient-specific data, non-white race was a statistically significant readmission predictor in the new model (Meddings et al., 2017).
The population health studies previously discussed (Durstenfeld et al., 2016; Eapen et al,
2015; Meddings et al., 2017) suggest that even though race is a known individual risk factor for
HF readmission, the relationship between race (as percentage of population) and population-level outcomes is more complicated. Healthcare access may not be enough to account for all racial differences in HF readmissions, and some attribution may be due to underlying pathology or cause of HF (Durstenfeld et al., 2016). In addition, the differences in population age as predictors of HF readmission may also be related to more severe HF pathology in younger populations, and with more chronic disease and comorbidity in the older population, and (Ranasinghe et al., 2014;
Whellan et al., 2016).
Chronic Disease and Comorbidity
Davis et al. (2017) explored comorbid conditions that are associated with increased risk for 30-day all-cause readmission in an adult sample (40 years old and up) with a primary
34 discharge diagnosis of HF. Using individual patient comorbidities and secondary diagnoses of
HF from hospital admissions within the previous 2 years, comorbidities were categorized using
HCUP clinical classification software and the Elixhauser comorbidity classification method. In univariate analysis, sepsis within 90-days of the index admission was the acute condition most associated with 30-day readmission. After multivariable adjustment, acute exacerbation of
COPD and clinical sepsis had the strongest association with readmission for acute conditions.
Chronic iron deficiency anemia and chronic kidney disease were the chronic conditions most associated with readmission. Overall, acute clinical conditions present in the prior 90-days to the index admissions were more strongly associated with 30-day readmission than chronic conditions (Davis et al., 2017).
While Davis et al. (2017) explored the association between individual comorbidity and readmission in HF, no population-level studies were found examining comorbidity burden specifically for HF readmission. However, a few population health studies have explored the association of chronic disease and non-condition specific 30-day all-cause readmissions. An observational study examined the association of chronic disease complexity and 90-day post discharge healthcare utilization for Medicaid managed care patients (Hewner et al., 2016).
Chronic disease cohorts were classified as no major chronic illness, chronic illness, and system failure (which included HF and CKD). Chronic disease and system failure cohorts were 2.2 and
4.5 time more likely than non-chronic group to be readmitted in 90-days. (Hewner, et al., 2016).
Another study evaluated the impact of hospital-level population characteristics, including demographic, comorbidity, and socioeconomic factors, on all-cause 30-day readmission rates
(Gohil et al., 2015). This retrospective study included 323 acute care hospitals in California. The
Romano comorbidity index, an indicator of overall illness severity, was calculated for each
35 admission and a mean score generated for each hospital. Hospitals were further categorized into high vs. low readmission rates and a paired-t test was calculated to compare differences.
Hospitals with higher readmission rates had higher mean comorbidity scores, as well as higher proportion of patients who were non-white males, compared to hospitals with lower readmissions (Gohil et al., 2015). CMS does adjust for age, gender, and comorbidity when calculating expected readmission rate for the HRRP (CMS, 2017; Gohil et al., 2015). These factors are therefore important covariates to consider in developing new understanding of the role of upstream factors in HF readmissions.
Upstream Factors and Heart Failure Readmission
Community Socioeconomic Status
In the study previously described, Gohil et al. (2015) examined the impact of population socioeconomic factors from the American Community Survey (ACS) on 30-day readmission rates, controlling for demographics and comorbidity. Zip code level data for percentage of the population living in crowded housing and percentage living in federal poverty area were aggregated to the hospital-level. Hospitals with higher readmission rates had higher percentage of patients living in a federal poverty area and living in crowded housing, compared to hospitals with lower readmissions (Gohil et al., 2015). This study demonstrated a significant but small effect of neighborhood poverty (defined by zip code level data) on general 30-day all-cause readmission. However, other studies specific to HF have examined the impact of neighborhood socioeconomic factors on readmissions at the county, zip code, and census tract levels.
County-level U.S. census data, linked with GWTG-HF data and CMS claims, were examined to determine if community SES was associated with variation in HF 30-day readmission in a sample from 197 hospitals (Eapen et al., 2015). The SES variables from the
36
U.S. census included income, education, home value, and percent of white-collar workers. When controlling for patient and hospital characteristics, higher percentage of HS diploma graduates in a community were associated with lower odds of readmission (Eapen et al., 2015). In a broader, national study of 4073 hospitals in 2,254 counties, researchers utilized data from CMS hospital compare, AHA annual survey, and the U.S. Census to examine the relationship between community factors and hospital readmission for AMI, HF, and pneumonia (Herrin et al., 2015).
County characteristics that were found to be independently associated with increased risk of readmission included the proportion of population never married, Medicare beneficiaries per capita, and low level of education. Rural areas and retirement were associated with lower readmission rates. Overall, 58% of the total variation in readmission was due to the county in which the hospital was located (Herrin et al., 2015).
A retrospective cohort study of Medicare patients hospitalized for HF, AMI, or pneumonia assessed the impact of SDOH, as measured by U.S. census data at the zip code level, on condition-specific readmission (Meddings et al., 2017). The study also aimed to determine which SDOH, (social support, transportation, education, and wealth) would be important predictors of readmission. In the HF cohort, several of the potential predictor variables from the
ACS were highly colinear. Only the proportion of the population married, top quartile of income, and receiving Medicaid remained significant when added to the current risk model for
HF readmissions. However, the SDOH factors did not remain significant in the hypothesized model using census data. This study did not have a nationally representative sample and use of zip code may not have provided the precision to uncover associations (Meddings et al., 2017).
There is some evidence that use of large area-based measures may lead to incorrect estimates of effect and that county- and zip code-level data may not be sensitive enough to
37 improve risk models for HF readmission (Eapen et al., 2015; Herrin et al., 2015; Meddings et al.,
2017). Further, the use of single measures to represent a community’s educational composition, income and employment distributions, or housing conditions may not have the robustness or explanatory power to determine the extent of social disparities in health and mortality (Singh,
2003). However, an index developed and validated by Singh (2003) utilized several key census tract-level indicators in different socioeconomic domains to reflect the multidimensional character of a community’s socioeconomic position. This Area Deprivation Index (ADI) has been further refined as a measure of neighborhood disadvantage by Kind et al. (2014) and validated by others for readmission prediction (Hu et al., 2018) and in the HF population
(Knighton et al., 2018).
Neighborhood Disadvantage
The use of geographically based deprivation indices from census data is not new. Other countries have used the measures to summarize an individual’s SES based on their residential address, as well as to evaluate health policy and distribution of resources (Kind et al., 2014;
Knighton, Savitz, Belnap, Stephenson & Vanderslice, 2016; Singh, 2003). In a retrospective cohort study using a randomized 5% national sample of Medicare patients discharged from the hospital, Kind et al. (2014) evaluated the association between the Singh validated ADI and 30- day hospital readmission. Medicare data were linked to census-block group level data using the patient’s residential ZIP+4. A census block group averages 1,500 people per group, and an ADI score was calculated for every census block group in the US. Based on the distribution of the
ADI scores, researchers sorted the neighborhoods into percentiles by increasing ADI. Pts in the most disadvantaged neighborhoods were more likely to be of black race, have Medicaid, and experience more comorbidity. The most disadvantaged 15% of neighborhoods had a strong and
38 increasing risk of 30-day all-cause readmission for HF, MI, and pneumonia. Living in the top highly disadvantaged neighborhoods places additional burden on the individuals who reside there, beyond their individual circumstances (Kind et al., 2014).
A study at an urban teaching hospital in Michigan explored the effect of neighborhood characteristics, using the ADI, on readmission risk controlling for patient-level clinical and demographic factors (Hu et al., 2014). The retrospective administrative database review included
Medicare patients who were discharged from a single hospital in 2010. The study team geocoded patients’ street addresses to census block groups, linked the readmission to the state ADI file created by Kind et al.(2014), and assigned each patient a neighborhood ADI score based on the census block group where they resided. The mean ADI for all cohorts was 114.7 (range 27.4-
129.2), which was higher than national mean of 100. Heart failure was the most common diagnosis at discharge (7.2% overall), and the more disadvantaged group had significantly more
HF diagnoses (8.4%) than less disadvantaged (6.0%) neighborhoods. With similar results as the national sample in the Kind et al. (2014) study, researchers found for this urban sample that patients in the more disadvantaged neighborhoods had significantly higher risks of 30-day all- cause readmission compared to those living in less disadvantaged neighborhoods. Patients residing in neighborhoods among the top 5% of ADI were 70% more likely to be readmitted than those in lower ADI neighborhoods (Hu et al., 2018).
As evidence has increased for the strong association of social risk factors and hospital readmission, policy related to the HRRP will likely take these factors into account (Joynt
Maddox et al., 2019). The objective of a recent study by Joynt Maddox et al. (2019) was to describe the relationship between social risk factors and readmission, and to determine the impact of adjusting for social risk factors on HRRP penalties for hospitals. Medicare claims data
39 for nearly 3 million beneficiaries hospitalized for HF, AMI, and pneumonia from 2012-2015 were geocoded to the census block group-level to obtain an ADI score for each address. Key social risk variables were residence in a disadvantaged neighborhood and hospital population from a disadvantaged neighborhood. Individual-level social factors of poverty, disability, and housing instability also were identified from CMS files. In the HF cohort, odds of 30-day readmission significantly increased for residence in the most disadvantaged neighborhood and for hospitals with the most population residing in disadvantaged neighborhoods, compared to more affluent neighborhoods. While the effect was small, when all social risk factors were added to the current CMS risk model, potential penalties significantly shifted, decreasing for the most disadvantaged and increasing for some more affluent hospitals (Joynt Maddox et al., 2019).
These studies have provided evidence of the relationship between high neighborhood disadvantage and HF hospital readmission in both national (Kind et al., 2014) and local populations (Hu et al., 2018), even when controlling for individual-level social factors (Hu et al.,
2018; Joynt Maddox et al., 2019). While only the most disadvantaged neighborhoods are associated with higher HF readmission rates, hospitals may be able to use the ADI (Kind et al.,
2014) as one method to identify high risk HF populations. Additional studies are needed to determine if transitional care interventions aimed at readmission reduction are effective for populations across levels of neighborhood disadvantage.
Hospital Strategies for Readmission Reduction
Early Provider Follow-Up
A landmark study by Hernandez et al. (2010) was the first to examine the association between outpatient provider follow-up within 7 days after discharge from a HF hospitalization and 30-day readmission. The study population included over 30,000 patients from 225 hospitals.
40
Medicare claims and registry data from OPTIMIZE-HF and GWTH-HF programs were analyzed at the hospital level to avoid the confounding of individual severity of illness. Early follow-up was defined as an outpatient evaluation and management visit with a physician within 7 day after discharge. The patient-level outpatient claim was aggregated to the hospital level and calculated as a proportion of discharged patients that received early follow-up care. At the time of this study, outpatient follow-up as a central element of transitional care varied significantly across hospitals and the median rate of early follow-up was 38.3%. Hospitals in the lowest quartile for percentage of early follow-up had the highest 30-day readmission rate (23.3%), while patients discharged from hospitals with higher early follow-up rates had a lower risk of 30-day readmission. The trend was similar for 14-day provider follow-up visits (Hernandez et al., 2010).
Early provider follow-up became a standard of care as a recommendation by the
American College of Cardiology Foundation/American Heart Association Task Force on
Practice Guidelines (Yancy et al., 2013). Yet the role of early provider follow-up as a part of readmission risk reduction interventions still is not fully understood. Recent studies have examined more specific characteristics and efficacy of the early follow-up visit and the impact on hospital readmission. Lee et al. (2016) examined whether timing and type of post-discharge follow-up impacted 30-day all-cause readmission in adults with HF. The nested matched case- control study within a large integrated health system compared the type and timing of follow-up care after HF hospitalization. Follow-up (by phone or clinic) within 7 days after discharge was associated with lower adjusted odds of readmission, but first contact after 7 days was not.
Additionally, when the type of contact was a clinic visit, odds were significantly reduced for readmission, and telephone contact trended toward significance (Lee et al., 2016).
41
Wang et al. (2016) further investigated the role of outpatient visits in association with hospital readmission. This single center retrospective observation study compared two types of outpatient visits (clinic and ED/urgent care) for association with 30-day unplanned readmission.
The number of days from hospital discharge to clinic visit was significantly less (Median 6 days) for the readmission group, compared the no readmission group (Median 10 days). Further, clinic follow-up visits were associated with lower risk of readmission only if it occurred after 10 days, and patients with earlier visits experienced more readmissions (Wang et al., 2016). The overall readmission rate for the study population was 11%, which is lower than other reported rates. The study did not account for other interventions that may contribute to readmission risk reduction in the immediate post discharge period.
While evidence about the timing of provider follow-up to reduce risk of HF readmission differed between these two studies, both Lee et al. (2016) and Wang et al. (2016) agree that provider follow-up alone may not be adequate to prevent 30-day hospital readmission. Heart failure patients require a high degree of care coordination and communication between providers during transitions of care (Hernandez et al., 2010; Lee et al., 2016). The American Heart
Association recognizes transition of care in HF as individual interventions and programs with multiple activities that aim to improve care during the shift from one healthcare setting to the next, typically from hospital to home (Albert et al., 2015). In addition to early follow-up visits and telephone contact, HF guidelines for transitions of care recommend multidisciplinary HF disease-management programs for patients at high risk for readmission (Yancy et al., 2013).
There is no consensus, however, on what the structure and content of transitional care programs for HF should be.
42
Transitional Care Programs
In an effort to achieve the goals of readmission reduction and value-based care for HF patients, most hospitals utilize transitional care programs. As previously stated, the structure and content of these programs can vary widely, but common elements exist, including patient education, telephone follow-up, early provider follow-up, medication reconciliation, home visits, and handoff to post-hospital providers (Albert et al., 2015). Studies examining the effect of transitional care programs on HF hospital readmission also vary widely in terms of interventions and design. This review of recent literature examines multidisciplinary team composition and program components, in relation to the effectiveness of the transitional care program on reducing
30-day HF hospital readmission (see Table 5).
Most transitional care programs began with pre-discharge, in-person interaction and included post-discharge telephone contact. When registered nurses were a part of the multidisciplinary team, their role was described as coach (Ong et al., 2016; Hoover, Plamann &
Beckel, 2017) or navigator (Van Spall et al., 2019). Other members of transitional care teams in the studies included APRNs, pharmacists, physicians, social workers, dieticians, and unlicensed community health workers. Three RCTs demonstrated no significant effect of transitional care interventions on 30-day HF readmission (Balaban et al., 2015; Ong et al., 2016; Van Spall et al.,
2019). These studies had the fewest multidisciplinary team members, with nurses functioning as coach (Ong et al., 2016) or navigator (Van Spall et al., 2019). One RCT used unlicensed community health workers as transitional care managers and nurses as part of “usual care”
(Balaban et al., 2015).
In two small quasi-experimental studies, an APRN (Stauffer et al., 2011) and physician
(Ota et al., 2013) were responsible for transitional care management. While these studies
43
Table 5
Transitional Care Programs - Evidence Table
Balaban et al. 2015 Hoover et al, 2017 Ong et al., 2016 Ota et al., 2013 Stauffer et al., 2011 Van Spall et al., 2019 Design RCT QI/NR control RCT Retrospective NR control RCT 30-day readmission NS ↓ NS ↓ ↓ NS Timing Pre-discharge X X X X X Post-discharge X X X X X X Modality In-person X X X X X Phone X X X X X X Team members RN Usual care Coach Coach Navigator APRN X Pharmacist X Physician X X X Unlicensed X Other Components Risk identification X Case management X Office/clinic visit X X Home visit X X X X Telemonitoring X Education X X X X X Med reconciliation X Note. RCT=randomized clinical trial, QI=quality improvement project, NR=non-randomized, NS=no significant findings
44 demonstrated a significant decrease in 30-day HF readmission, design flaws and small sample size limit generalizability. Additionally, use of a licensed independent provider (LIP) for transition management may be an inefficient and costly alternative to nursing CCTM. The most comprehensive model used in the studies also demonstrated a significant and moderate decrease in 30-day HF readmission, with care by an RN, pharmacist and physician (Hoover et al., 2017).
The interventions in this model included telephone contact, home visits, early provider follow- up, and medication reconciliation. The findings are limited by a small overall sample size and very small control group
Several meta-analyses of RCTs dating back to early 2000’s examined the effect of transitional care interventions (TCI) on HF readmission. Type of TCI (telephone follow-up, home visit, and clinic visit), intensity (low, moderate, high), and length of time of TCIs were assessed for impact on acute health care use (Vedel & Khanassov, 2015). Overall, TCIs significantly reduced risk of readmission by 8% compared to usual care. High intensity interventions (home visit with either telephone follow-up or clinic visit) were effective to reduce the risk of readmission and were most effective with older adults >75 years old (Vedel &
Khanassov, 2015). The efficacy of education alone, pharmacist intervention, telemonitoring, telephone support, nurse case management, nurse home visits, and disease management clinics where also examined for effectiveness on all-cause readmission rates (Van Spall et al., 2017).
Nurse home visits were most effective in decreasing all-cause readmission, as well as nurse case management and disease management clinics when compared to usual care, and there was no difference in comparative effectiveness of these 3 interventions. Telephone support, telemonitoring, and pharmacist intervention did not significantly decrease all-cause readmission
(Van Spall et al, 2017). Finally, home visits and multispecialty HF clinics reduced all-cause
45 readmission, but few trials reported 30-day readmission and usual care was heterogenous
(Feltner et al., 2014).
The effectiveness of transitional care programs on HF outcomes, such as hospital readmission, is difficult to quantify because of the vast heterogeneity of program characteristics.
Most of this research has underutilized the skills of the RN in the transitional care models or left nursing out entirely. These studies did not address the skills of nursing care coordination and transition management. Additionally, when telephone contact was made by nurses, it was sometimes described as structured or scripted and did not recognize nursing practice processes.
An additional concern is that some studies relied on non-nurses to lead and implement HF transitional care programs. Nurses must protect our scope of practice and claim responsibility for coordination of care; nursing research must address our unique contribution to HF readmission reduction.
Nursing Strategies for Readmission Prevention
In a review of the literature related to nursing and HF readmission prevention, no studies were found that specifically examined CCTM activities as a primary intervention. However, the components of CCTM, as outlined by AAACN (2016), could be identified in several studies.
These components included support for self-management, advocacy, education and engagement, cross-setting communication, coaching and counselling, nursing process, population health management, teamwork and collaboration, and patient-centered care planning. Three studies examined hospital-based programs that included most or all of the CCTM criteria and utilized a combination of pre-discharge in-person and post-discharge telephone modalities (see Table 6).
All three studies demonstrated real-world effectiveness in decreasing odds of readmission using slightly different implementation models. Hamar et al. (2016) used a collaborative model,
46
Table 6 CCTM Impact on Readmissions - Evidence Table
Hamar et al. 2016 Kripalani et al, 2019 Reese et al. 2019 Intervention model Care Transition Solutions Transition Care Coordinator (TCC) Coordinated Transitional (CTI) Full model Partial model Care (C-TraC) Design Retrospective cohort with Retrospective with concurrent control Prospective with matched matched control historical controls Timing/Modality Pre-discharge/in-person X X X Post-discharge/telephone X X X X CCTM components Self-support management X X X X Advocacy X X X Education and engagement X X X X Cross setting communication X X X X and transition Coaching and counseling X X Nursing process X X X X Population health management X X X X Teamwork and collaboration X X X Patient-centered care planning X X X Findings ↓ odds of RA compared to ↓ odds of RA for both full and partial ↓ odds of RA compared to control intervention control in adjusted model OR 0.56 (95% CI 0.41-0.77) OR 0.512 (95% CI 0.392-0.668) all; OR 0.46 (95% CI 0.24-0.89) full OR 0.536; partial OR 0.482 Limitations Retrospective design Used a CNS as a TCC Convenience sampling of Non-representative sample of control group mostly male veterans Possible historical bias Note. RA=readmission; OR=odd ratio; CI=confidence interval; CNS=Clinical Nurse Specialist
47
Care Transitions Solutions (CTS), in which hospitals outsourced risk prediction, data management, and telephone follow-up. The program augmented the transitional care activities that hospitals already had in place. All components of CCTM were present in the study, but only patients with readmission-sensitive conditions (HF, COPD, pneumonia, and AMI) who were at high risk for readmission received the intervention. The Coordinated Transitional Care (C-TraC) model was a telephone-based, nurse-driven intervention used in both Veterans Administration
(VA) hospitals and non-VA hospitals. Reese et al. (2019) replicated the model specifically for older adult HF and COPD patients in a large VA system. While the C-TraC program also had all components of CCT, it may have demonstrated greater effect than the other studies (see Table 6) by providing specialized training and disease focus for the care coordinator. Kripalani et al.
(2019) utilized a transition care coordinator (TCC) for a multi-component intervention based on the Ideal Transition in Care (ITC) framework. Two models of intensity were compared for this intervention; the full model had most of the CCTM components, while the partial model
(telephone follow-up only) was missing 4 of the 9 components. Interestingly, both the full and partial models were effective to decrease odds of 30-day readmission for adult patients with HF, pneumonia, and COPD (Kripalani et al., 2019).
The programs were also all initiated in the hospital prior to discharge. Hamar et al.
(2016) reported that this improved effectiveness compared to previous studies using the intervention by reducing delayed follow-up. Nurses in both the CTS and C-TraC models participated in, but were not responsible for, the discharge care planning (Hamar et al., 2016;
Reese et al., 2019). The TCC nurses (full intervention) were more active in whole discharge process. The TCC identified patients early in the admission, led daily transition huddles with the care team to develop an evidence-based discharge plan, and provided individualized patient
48 education prior to discharge (Kripalani et al., 2019). In contrast to CTS, the C-TraC and TCC programs had the nurse embedded within the health system with easy access to other providers and services.
The telephone follow-up in these studies provided an efficient, low-cost option for transition management, fully using the skills and competencies of registered nurses. These nurse-driven interventions provide a viable option for hospitals with limited resources or when home visiting is not feasible (Hamar et al., 2016; Reese et al., 2019). The number and intensity of phone calls varied between the studies. Hamar et al., (2016) did not report the actual number of phone calls made, but the intervention planned for four calls over four weeks. Reese et al.
(2019) found that even though weekly calls for four weeks were intended, most patients met goals prior to that time with an average of three calls per patient. The C-TraC nurse spent an average of 23.6 minutes for the in-hospital visit and 45 minutes for the initial phone call (Reese et al., 2019). Kripalani et al. (2019) also did not report number of phone calls, but time spent with the patient in the full TCC intervention ranged from 2-4 hours, and 30-45 minutes for the partial intervention.
These three studies, when considered together, demonstrate that nurse led CCTM interventions effectively reduce the odds of HF hospital readmission for populations of different ages, levels of readmission risk, and veteran status. Additionally, the CCTM interventions take place in conjunction with other transitional care activities, such as home care, provider visits, and social services. Interaction effects with other services were not addressed in any of the studies. It is unknown, if CCTM activities improve the effectiveness of other transitional interventions on
HF hospital readmission, through either direct or indirect relations.
49
Significance of the Study
The assessment of upstream factors is an important part of the nursing process in CCTM, as well as the evaluation of individual and population outcomes. However, research linking relevant upstream factors to nursing activities and population-specific outcomes is lacking.
Population-level data, also referred to as big data, are publicly available from many reliable resources, but access often requires specialized knowledge. There is increasing recognition that knowledge of upstream factors is important for precision of interventions on many levels. In precision medicine, where approaches have been highly individualized at the genetic level, there is a growing consideration of social, behavioral, and environmental factors (Lyles, Lunn,
Obedin-Maliver & Bibbins-Domingo, 2018). In public health, precision is viewed as providing the right intervention to the right population, at the right time (Khoury, Iademarco, & Riley,
2016). The same focus on precision should be true for population-focused nursing interventions.
This study was among the first to examine neighborhood disadvantage as a possible moderator to the indirect effect of early provider follow-up on HF readmission through nursing
CCTM intensity. The examination of moderation and mediation effect together may support targeted intervention based on the moderating variable (Fairchild & MacKinnon, 2009), thus improving precision of transitional care interventions. By establishing if the effectiveness of
CCTM activities differs across levels of neighborhood disadvantage, nursing resources can be efficiently utilized to meet population needs. This study may help to increase the precision of nursing CCTM activities by using knowledge of neighborhood disadvantage. In the long term, studies such as this may also impact policy regarding the incorporation of population data into existing electronic medical records (Bazemore et al., 2016).
50
This proposed study may also contribute to theoretical knowledge that guides nursing practice in the care of populations. For many nurses, thinking at a population-level is a paradigm shift from individual-based assessment and care. As nurses continue to work with individuals and groups experiencing acute and chronic illnesses, a balance between individual-focused and population-based practice is needed with an understanding of individual experiences within the context of the larger community (Fawcett & Ellenbecker, 2015). This research will contribute to that understanding.
51
CHAPTER III – METHODOLOGY
Introduction
The overarching aim of the study was to examine the relationships between early provider follow-up, nursing CCTM intensity, neighborhood disadvantage, and hospital readmission in a population of older adults with HF. The study aims were met through an analysis of secondary data, obtained from a previous, large population health study conducted by this researcher at the Cleveland Clinic Health System (CCHS). For the purpose of this dissertation research, appropriate permissions were obtained, and protocols followed for student research within the Cleveland Clinic Health System.
Approval from the Cleveland Clinic and Kent State University IRBs was obtained prior to beginning the study. A waiver of informed consent was requested since this study used only existing data from a previous retrospective medical record review. The study was deemed exempt since minimal risk was anticipated to the subjects, in terms of confidentiality of data, based on the use of secondary, de-identified data. A data use agreement and permission from the
Cleveland Clinic IRB was obtained for sharing of data with Kent State University faculty.
Shared data were fully de-identified and transferred only on a CCHS approved encrypted USB drive.
Methods
Research Design
This study used a retrospective correlational design to determine the nature of the relationships between early provider follow-up, nursing CCTM intensity, neighborhood disadvantage, and hospital readmission in a population of older adults with HF. The current study used existing data from the study, Health Care Utilization and Neighborhood
52
Disadvantage in Patients with Food Insecurity (Distelhorst et al., 2019). The primary study dataset was broad in scope and appropriate for the current study as it contained data at the individual-, hospital-, and neighborhood-level for the variables of interest. The current study aimed to provide a more focused analysis of the conditional effects of neighborhood disadvantage on outcomes, refining the scope to the older adult HF population. The difference between the primary study and the current study is summarized in Appendix A.
Primary Study
The primary study was a retrospective correlational design that examined healthcare utilization, level of neighborhood disadvantage, and food insecurity in a cohort of adult patients managed within a single health system. The sample was comprised of adult patients (age 18 and older) who received primary care from Cleveland Clinic providers and were hospitalized at a
CCHS hospital during the period of October 1, 2017 – September 1, 2019. Patients discharged from the index hospital admission to the home setting were included in the primary study sample. Because the cross-sectional data of the primary study spanned 2 years, patients were only included for the first index hospitalization. Two of the health system hospitals utilized different documentation parameters from the others, so patients with admissions to those facilities were excluded from the study.
The setting for the primary study was a large health system within Northeast Ohio.
Cleveland Clinic Health System (CCHS) is one of the largest, non-profit health systems in the country, with 18 hospitals and over 210 outpatient locations worldwide (Statistics, 2019). Eleven of the hospitals serve northeast Ohio, and 9 of those were included in the primary study.
Combined, the service area extends into Cuyahoga, Geauga, Lake, Lorain, Medina, Summit,
Portage, and Erie counties. The population served by CCHS in Northeast Ohio has a diverse
53 socioeconomic profile and a substantial amount of health care resources. However, the upstream factors that impact health, such as income, access to healthy food, safe and affordable housing, are not spread equally across and within counties resulting in significant disparities in health outcomes.
Current Study
Inclusion and exclusion criteria. The current study sample was extracted from the primary dataset based on the inclusion criteria of age ≥ 65 years and hospitalized for a primary diagnosis of decompensated HF. Other inclusion criteria for the study were discharge to the home setting and a home address in Ohio, similar to the primary study. Patients were excluded if they were discharged with hospice home services.
Sample size. In order to determine an indirect relationship of early provider follow-up on readmission through CCTM intensity, and if the relationship was conditional on high or low neighborhood disadvantage, a sample size of 500 was needed to achieve 80% power at an alpha of 0.05. According to Fairchild and MacKinnon (2009), when previous interaction effects explain 1%-3% of the variance in a dependent variable, a test of joint significance for moderation of an indirect effect requires a sample size of 500-1000 for .8 power. Previous reports on early provider follow-up (Lee et al., 2016), case management (Hoover et al.,2017), and neighborhood disadvantage (Hu et al., 2018) have reported significant and moderate effect on readmission.
Fairchild and MacKinnon recommend a sample of greater or equal to 500 when previously reported interaction effects are moderate (2009). The final sample size after all exclusions was
1280.
54
Outcome and Measures
Heart Failure Hospital Readmission
The dependent variable for the study was hospital readmission. The operational definition was an unplanned hospital admission at a CCHS hospital arising from acute clinical events of any cause within 30 days after discharge from an index admission where the principle diagnosis was acute decompensated HF. This definition is consistent with the Centers for Medicare &
Medicaid Services (CMS) measure of 30-day all cause HF readmission, which is considered an indication of hospital quality due to the influence of hospital care and the early transition to the non-acute care setting (CMS, 2017). All admission data were originally obtained from the health system internal billing database. A limitation of this method is the potential to miss patient readmissions to hospitals outside of the health system. However, the risk was minimized in the primary study by only including those patients who were active with a CCHS primary care provider. Readmission was calculated first as days from discharge of the index hospitalization to the next hospital admission, then recoded as yes (readmitted in 30 days) or no (not readmitted in
30 days).
Early Provider Follow-Up
The independent variable for the study was early provider follow up (EPF), which was operationally defined as a CCHS primary care office visit completed after discharge from the index hospitalization within 7 or 14 days. In the primary study, dates for office visits were obtained from the health system internal billing database. Days to provider follow-up were calculated from the discharge date of the index hospital admission to the date of the first primary care office visit. For the current study, early provider follow-up within 7 days (EPF-7) and 14
55 days (EPF-14) were calculated from days to provider follow-up and coded as a dichotomous variable: yes = equal or less than 7 and 14 days, no = greater than 7 and 14 days.
CCTM Intensity
Care coordination/transition management intensity was the mediating variable for the study and was operationally defined as the number of contacts made to or on behalf of a patient in the 30-day period after discharge from the hospital. Nurses documented CCTM activity in the
EMR as a patient outreach encounter. This field differs from a simple telephone encounter as it relates to outpatient coordination of care. Because this type of encounter may be used by multiple disciplines, the provider type was identified for each patient outreach encounter and quantified in the primary dataset. For this study, the measure of CCTM intensity (CCTM-I) was calculated as the total number of patient outreach encounters completed by CCHS care coordination nurses for 30 days after hospital discharge. CCTM intensity was also categorized as a dichotomous variable (CCTM-30), indicating if at least one CCTM contact was made during the 30-day period after discharge. Additionally, since national guidelines recommend patient contact within 3 days of hospital discharge, the variable CCTM-3 was coded to indicate yes or no for a 3-day CCTM contact.
Neighborhood Disadvantage
Neighborhood disadvantage was the moderator variable and was operationally defined as residence in an area of high or low socioeconomic deprivation, based on the address of record at the time of the index admission. Neighborhood disadvantage was measured using the 2015 Area
Deprivation Index (ADI) v2.0 (University of Wisconsin School of Medicine and Public Health,
2015). The ADI is a validated, factor-based deprivation index, first developed by Singh (2003) and refined by Kind et al. (2014), that includes 17 indicators of poverty, education, housing, and
56 employment from U.S. census data (see Appendix B). The ADI has a high level of internal consistency with a Chronbach’s alpha of 0.95 at the census tract level (Singh, 2003).
Additionally, the ADI has a high level of test-retest reliability demonstrated by factor analysis of the 17 indicators for different, random subsamples of the U.S. population (Singh, 2003).
Validity was initially tested by comparing factor loadings for each of the indicators at the census tract, zip code, and county levels, which were all similar in magnitude (Singh, 2003). The refined
ADI has been further validated at the census block group level for use in the HF population
(Knighton et al., 2018) and predictive for readmission (Hu et al, 2018; Kind et al., 2014).
Researchers at the University of Wisconsin calculated an ADI score for each census block group, or neighborhood, in the U.S. and created a national database called the
Neighborhood Atlas, which is shared publicly (University of Wisconsin, 2015). Census block groups are the smallest geographic unit used by the U.S. Census Bureau, averaging about 1500 individuals with a minimum of 600 and maximum of 3,000 (United States Census Bureau, n.d.;
Kind et al., 2014). For analytic purposes, raw ADI scores are converted into rankings of disadvantage at the state and national level to allow for easier comparison between neighborhoods (Kind et al., 2014). At the state level, ADI scores are ranked from low to high and divided into 10 equal sections to create deciles for in-state comparison, with 1 being the least disadvantaged and 10 being the most disadvantaged. For national comparison, ADI scores are divided into percentiles, where 1 is the lowest disadvantage and 100 is the highest (Kind &
Buckingham, 2018).
Geocoding was used in the primary study to assign a census block group for each patient based on the home address recorded in the billing database. Geocoding is a method in epidemiologic research that is used to investigate the spatial relationships between geographic
57 context and health (Robinson et al., 2010). Many definitions exist, but for this study geocoding is defined as the transformation of locationally descriptive text (residential address) into a valid spatial representation (census block group) using a predefined process (Goldberg, 2008). Using a
Geographic Information System (GIS) program, addresses are converted to a spatial representation that is transformed into a set of latitude and longitudinal coordinates, and then georeferenced to a defined census block group (Robinson et al., 2010). Prior to geocoding in the primary study, addresses were sorted and searched for missing data or obvious data entry errors.
Addresses were then geocoded to census block groups using PROC GEOCODE in SAS 9.4 which links to the U.S. 2018 street lookup data from the Census Bureau. The Ohio ADI dataset was obtained from the Neighborhood Atlas and researchers linked the census block group of each subject to an ADI national percentile rank score of 1 through 100.
In the current study, neighborhood disadvantage was measured as low or high based on the ADI percentile rank. Each subject had a corresponding ADI rank score (1-100) based on place of residence at the time of the index hospitalization. The census block groups in northeast
Ohio are very heterogenous for socio-economic disadvantage compared to other areas of the state, with sharp demarcations between areas of low disadvantage and high disadvantage (see
Appendix C). Kind et al. (2014) reported a non-linear relationship between the ADI and readmission for a national sample, where an independent direct association occurred at the top
15th percentile of disadvantage (ADI range 113.4-129.1). In a study of a single health system known to serve a highly disadvantaged community, Hu et al. (2018) compared the distribution of
ADI values in their cohort to the national distribution to determine the cutoff for low and high distribution (50th percentile). For the current study, subjects were grouped into high and low neighborhood disadvantage areas based on both 85th and 50th percentile rankings, creating the
58 variables ADI-85 and ADI-50. High disadvantage was coded as 1 and low disadvantage as 0 for analysis.
Demographic Variables and Co-Variates
Demographic variables for the sample included age, race, sex, insurance, and comorbid diagnoses at the time of the index admission. Given the known disparities in HF outcomes for patients of non-white race (Benjamin et al., 2017), particularly black males (Sharma et al., 2014) as discussed previously, race and sex were explored in the statistical analysis as potential covariates. Additionally, previous population studies and reviews have confirmed the relationship between multiple chronic conditions with outcomes such as hospital readmission
(Regenstein & Andres, 2014; Trudnak et al, 2014; Elixhauser & Steiner, 2013). In fact, most readmissions after a hospitalization for HF are not due to HF, but due to other non-HF conditions
(Davis et al., 2017). In this study, comorbidity was measured using the AHRQ Elixhauser
Readmission Index, based on the ICD-10 diagnostic codes at the time of the index admission.
The Elixhauser comorbidity measures were first developed in 1998 to predict mortality and hospital resource use based on ICD-9 diagnostic codes from administrative discharge data
(Moore, White, Washington, Coenen & Elixhauser, 2017). Originally the method identified a list of 30 diagnostic codes (later reduced to 29) that could be found in administrative data but did not provide an overall index due to the independent effects of each comorbidity for different populations (Elixhauser, Stiener, Harris & Coffey, 1998). The Elixhauser algorithm has been widely used to identify risk of readmission, although it was not the original intent of the measure.
Consequently, the Elixhauser comorbidities have been used to create a comorbidity index to predict both in-hospital mortality (van Walraven, Austin, Jennings, Quan, & Forster, 2009) and hospital readmission (Moore et al., 2017).
59
The AHQR Elixhauser Comorbidity Index program assigns two index scores based on inpatient records: one for readmission and one for in-hospital mortality (Healthcare Cost and
Utilization Project [HCUP], 2017). The index program transforms the 29 comorbidities variables from the original measures into a single comorbidity index score calculated as a weighted sum of each of the binary comorbidity variables in the administrative data (HCUP, 2017). The resulting comorbidity index score can be used in analyses in place of the 29 individual measures. The index has a c-statistic of 0.634 (95% CI 0.633-0.634) alone but improves slightly when included in a model with other covariates with a c-statistic of 0.646, 95% CI 0.645-0.646 (Moore et al.,
2017). The AHQR Elixhauser Comorbidity Index has demonstrated better performance in predicting hospital readmission than other comorbidity measures designed for mortality outcomes (Buhr et al., 2019; Moore et al., 2017). The c-statistic suggests that the index alone is not a strong predictor of readmission but should be used in a group of covariates (Moore et al.,
2017). In the current study, each subject has an assigned Elixhauser comorbidity index (ECI) based on the administrative data at the time of the index admission, which was considered for a model of potential covariates along with race, sex, age, discharge disposition, and length of stay
(LOS).
Procedure
The existing data set (N=41,566) contained patient demographic and encounter data from the health system billing database and electronic medical record, as well as calculated variables for the primary study. Each record was assigned a subject identification number, linked to a unique patient and index admission date, with associated medical record numbers, demographics,
ICD-10 diagnostic codes, encounter dates, and calculated variables such as presence of food insecurity, comorbidity index, ADI rank score, days to office visit, days to hospital readmission,
60 days to ED admission, and patient outreach encounters for 30 and 90 days. For the purpose of this and other potential secondary analyses, the primary dataset was de-identified by removing all medical record numbers, encounter dates, geocodes, and individual identifiers. The principle investigator of the primary study securely maintains the original data table and master subject list, which links the study subject to the date of the index admission, enterprise identification number, and medical record number.
Data Management
The data were abstracted from the Excel file, AnalysisDataSet_20200107.xlsx, which included 41,566 records. The data details obtained from the existing data set are described in
Appendix D. The data were first sorted by age 65 years and older, then by index admission primary diagnosis codes for acute decompensated HF for a sample of 1,478. The records meeting the initial inclusion criteria were imported into IBM SPSS (version 26) for pre-analysis screening of accuracy, missing values, outliers and the assumptions of normality, linearity, and homoscedasticity.
Missing values. Frequency tables and descriptive statistics were used to identify missing data from each variable. No missing data was identified for the categorical variables of sex, insurance, discharge disposition, early provider follow-up (EPF-7 and EPF-14), and readmission.
The frequency of readmission (n=191, 12.9%) met the criteria for the 90-10 split needed to proceed with analysis (Mertler & Vannatta, 2013). Race had 29 missing/unknown cases. No missing data was identified for the continuous variables of age, LOS, CCTM intensity, and the
Elixhauser comorbidity index score. An apparent geocoding problem in the original data resulted in missing data for ADI scores for 132 cases, which were then removed from the analytic sample for the current study. Frequency of unknown race was then reevaluated, and 23 cases were
61 deleted. Race was then transformed into a new variable (Race2) and recoded to combine the categories of black and other/multiracial into the category of non-white. Combining categories was necessary since the some of the study analysis depends on goodness of fit, which is highly sensitive to adequate frequencies for each cell in the data matrix (Mertler & Vannatta, 2013).
Once these deletions were made, no other missing data was identified.
Outliers. For quantitative variables, outliers were examined using stem & leaf plots and box plots for univariate outliers (see Figure 5), and Mahalanobis distance for multivariate outliers. Extreme outliers for comorbidity index were identified (n=6) for values ≥ 85 and cases were eliminated due to the very small number. Cases were also eliminated for outliers ≥ 6 for provider follow-up (n=19) and CCTM intensity (n=19). A moderate number of extreme outliers
≥ 11 (n=65) were identified for LOS, which were then transformed into a new variable
(LOS_out) using the maximum acceptable value of 10. Additionally, LOS was recoded into a new (LOS_cat) dichotomous variable (1=LOS 4 days or less; 2=LOS 5 days or greater) in order to retain the original values. After deleting cases and transformation, the Mahalanobis distance was 13.216, which was less than the Chi-square critical value at .001(x2=16.266, df=3), indicating no multivariate outliers for the remaining quantitative variables (age, comorbidity index, and ADI).
Assumptions. Univariate normality was assessed for the continuous variables of age,
LOS, comorbidity index, and CCTM-I with histograms, normal QQ plots, and descriptive statistics. Both age (Skewness =.129, SE = .068) and comorbidity index (Skewness = .105, SE =
.068) had slight positive Skewness. Examination of histograms and QQ plots reveal an approximation to a normal distribution. Length of stay (Skewness = 3.499, SE = .068) had severe positive Skewness and a square root transformation was conducted. The Kolmogorov-
62
Figure 5. Univariate Outliers. Box plots were used to evaluate for univariate outliers of the continuous variables.
Smirnov statistic was significant (p< .001) for all 3 variables, indicating non-normal distribution.
CCTM intensity was a count variable with a meaningful zero and the distribution was severely skewed as expected (skewness = 1.464, SE = .068). No further transformation was conducted; however, dummy variables were created (CCTM-0, CCTM-1X, CCTM-2X, CCTM-3X, CCTM-
4X and CCTM-5X) to analyze the data at both the ratio and ordinal levels. For the remaining
63 continuous variables of age and comorbidity, scatter plots indicated multivariate normality and linearity. Homoscedacticiy could not be evaluated or assumed due to the use of a dichotomous outcome variable.
Statistical Analysis
Descriptive analysis began with frequencies and percentages to summarize categorical variables; medians and interquartile range (IQR) were used for continuous variables due to non- normal distribution. Bivariate inferential statistics and logistic regression were used to describe the relationship between early provider follow-up, nursing CCTM intensity, neighborhood disadvantage, and hospital readmissions, including significant covariates (research question 1).
An alpha of 0.05 was used for all statistical tests in the study. Comparison of cases by readmission (yes/no) and by EPF-14 (yes/no) was conducted using Pearson chi-square for categorical variables and Wilcoxon rank sum tests for continuous measures. Spearman’s rho was conducted to explore the strength and directions of the relationships between early provider follow-up, CCTM intensity, neighborhood disadvantage, readmission, age, sex, race, LOS, discharge disposition, and comorbidity. Spearman’s rho was used due to the non-normal distribution of quantitative variables.
Logistic regression was conducted to determine which variables (age, comorbidity, neighborhood disadvantage, EPF at 14 days, and 2 CCTM contacts) were predictors of readmission. It was determined a priori that all variables significant at p < .05 in bivariate analysis would be entered into the multivariable analysis. The forward method using likelihood ratio (LR) to determine model variable selection was used. Early provider follow-up at 7 days was excluded from the analysis due to collinearity with EPF at 14 days.
64
A second logistic regression analysis was completed with variables that were significantly associated (p < .05) with EPF at 14 days to determine which factors predict early provider follow-up. The forward method using the likelihood ratio to determine variable selection was again used. To more fully understand the relationship between CCTM and EPF, three models were created with the variables sex, race, discharge disposition, comorbidity, neighborhood disadvantage, readmission, and LOS. The variables of CCTM intensity, CCTM contact within 30 days, and CCTM contact within 3 days were added separately to each model since they were highly correlated but measured slightly different nuances of CCTM.
Figure 6. Simple mediation model in statistical form. X=early provider follow-up (EPF-14), Y=readmission (RA-30), M=CCTM intensity (CCTM-I)
Logistic regression, ordinary least squares regression, and bootstrap confidence intervals were used to determine the direct and indirect relationships between early provider follow-up,
CCTM intensity, and readmission (research questions 2 and 3). Using the PROCESS macro
(Hayes, 2018), model 4, two regression equations were tested to determine if the association between EPF within 14 days and readmission was mediated by CCTM intensity (see Figure 6).
Ordinary Least Squares regression was used to compute the first equation, since the consequent is a quantitative variable. For the second equation, logistic regression with standardized regression coefficients is used to examine EPF within 14 days and CCTM intensity as predictors
65 of readmission, since the consequent variable (readmission) was dichotomous (Iacobucci, 2012).
Bootstrap confidence intervals from 5000 samples were then used to estimate the indirect effect of EPF on readmission through CCTM intensity.
Figure 7. The conditional process model for the study in statistical form. X=early provider follow-up (EPF-14), Y= readmission (RA-30), M=CCTM intensity (CCTM-I), W=level of neighborhood deprivation (ADI), XW=the product of EPF and ADI.
A moderated mediation analysis was used to determine the association of EPF on readmission through CCTM intensity and verify under what conditions (level of neighborhood disadvantage) EPF and CCTM intensity influence readmission (research question 4). The
PROCESS macro, model 8, was used to estimate the conditional and unconditional direct and indirect effects in a single analysis (see Figure 7). This yields a first stage and direct effect moderation model (Hayes, 2015). A heteroscedacticity consistent standard error and covariance matrix estimator was used to account for the dichotomous variables (Hayes, 2018). Ordinary least squares regression, logistic regression, and bootstrap confidence intervals were used to test
66 for mediation and moderation (Iacobucci, 2012). A path analytic framework for estimating the full model effect produced an index of moderated mediation (Hayes, 2015).
67
CHAPTER IV - RESULTS
Introduction
The purpose of this study was to describe and explore the nature of the relationships between early provider follow-up (EPF), nursing care coordination/transition management
(CCTM) intensity, neighborhood disadvantage, and heart failure (HF) hospital readmission. In this chapter, sample characteristics will be described first, with comparisons between groups for readmission and EPF within 14 days. Bivariate correlations between the variables will also be presented. Finally, the statistical analysis results for each of the research questions will be addressed.
Results
Sample Characteristics
After extraction of cases meeting inclusion criteria from the primary data file, exclusions, and data cleaning, a final study sample of 1280 cases was obtained. The mean age of subjects was 79.6 (SD=8.66), half were female (50.7%), and 25.9% (n=332) were of non-white race.
Given the inclusion criteria of age ≥ 65, most subjects were insured by Medicare (94.9%), with
1.6% insured by Medicaid, and 3.6% private or self-pay. The median length of stay (LOS) was 4 days (interquartile range [IQR]=3, 10) and 31.8% were discharged with home health care.
Subjects’ primary residences were distributed across the west (34.5%), main (18.4%), and east
(47.1%) regions of the health system service area, representing 6 counties. Nearly all of the subjects in the study resided in an urban area (92.0%), and 20.2% lived in the most disadvantaged neighborhoods (n=258). The 30-day readmission rate for the final sample was
13.0%. The rate of EPF within 7 days was 34.1%, and 60.1% of subjects had EPF within 14 days. CCTM intensity ranged from 0 – 5 contacts for the sample, with 46.3% having at least one
68
CCTM contact within 30 days, and 38.8% having a CCTM contact within 3 days after discharge.
Tables 7 and 9 provide a summary of patient characteristics by readmission status and EPF within 14 days
Table 7
Patient Characteristics and 30-Day Readmission Status
Readmission Total No Yes p- N=1280 N=1114 (87.0%) N=166 (13.0%) value Age* 79 73, 94 80 73,94 78 71,93 .031
Sex .718 Female 649 50.7% 567 50.9% 82 49.4% Male 631 49.3% 547 49.1% 84 50.6%
Race .059 white 948 74.1% 835 75.0% 113 68.1% non-white 332 25.9% 279 25.0% 53 31.9%
Length of stay* 4 3,10 4 3,10 4 3,14 .069
Discharge disposition .565 Home 873 68.2% 763 68.5% 110 66.3% Home health 407 31.8% 351 31.5% 56 33.7%
Comorbidity Index* 37 27,62 36 26,62 44 32,65 <.001
Neighborhood .029 least disadvantaged 1022 79.8% 900 80.8% 122 73.5% most disadvantaged 258 20.2% 214 19.2% 44 26.5%
Note. Counts and percentages were used for categorical variables; *medians and interquartile range were used for continuous variables.
Comparison of Groups
Readmission. Six variables (age, comorbidity index, neighborhood disadvantage, EPF within 7 days, EPF within 14 days, and having 2 CCTM contacts) were associated with
69 readmission (p < 0.05; see Table 7 and 8). Patients who were readmitted were more likely to be younger, have more comorbidities, and live in a disadvantaged neighborhood compared to those without a readmission. Patients with readmissions more often had 2 CCTM contacts and fewer provider follow-ups within 7 or 14 days than those without readmissions. Additionally, overall
CCTM intensity was not associated with readmission.
Table 8
Transitional Care and 30-Day Readmission Status
Readmission Total No Yes p- N=1280 N=1114 (87.0%) N=166 (13.0%) value EPF competed within 7 days 438 34.2% 396 35.5% 42 25.3% .009 within 14 days 769 60.1% 683 61.3% 86 51.8% .020 CCTM Intensity* 0 0, 4 0 0, 4 0 0, 4 .143 CCTM contact completed within 3 days 496 38.8% 439 39.4% 57 34.3% .211 within 30 days 592 46.3% 510 45.8% 82 49.4% .383 Number of CCTM contacts None 688 53.8% 604 54.2% 84 50.6% .383 1 contact 271 21.2% 244 21.9% 27 16.3% .097 2 contacts 154 12.0% 124 11.1% 30 18.1% .010 3 contacts 78 6.1% 67 6.0% 11 6.6% .758 4 contacts 52 4.1% 44 3.9% 8 4.8% .597 5 contacts 37 2.9% 31 2.8% 6 3.6% .551 Note. Counts and percentages were used for categorical variables; *median and interquartile range was used for continuous variable. EPF = early provider follow-up; CCTM = care coordination/transition management.
Early provider follow-up. In comparing patients who completed a 14-day EPF to those who did not, 11 variables were significant (p < 0.05; see Table 9). Patients with an EPF within.
70
Table 9.
Patient Characteristics and Early Provider Follow-Up Within 14 Days
Early Provider Follow-up No Yes p-value Age* 80 72, 87 79 73, 86 .159 Sex <.001 Female 293 57.3% 356 46.3% Male 218 42.7% 413 53.7% Race <.001 white 344 67.3% 604 78.5% non-white 167 32.7% 165 21.5% Length of stay* 4 3, 6 4 2, 6 .030 Discharge disposition .008 Home 327 64.0% 546 71.0% Home health 184 36.0% 223 29.0% Comorbidity Index* 38 27, 51 36 26, 47 .027 Neighborhood <.001 least disadvantaged 377 73.8% 645 83.9% most disadvantaged 134 26.2% 124 16.1% 30-day readmission 80 15.7% 86 11.2% .020 CCTM Intensity* 0 0, 1 1 0, 2 <.001 CCTM contact completed within 3 days 150 29.4% 346 45.0% <.001 within 30 days 179 35.0% 413 53.7% <.001 Number of CCTM contacts None 332 65.0% 356 46.3% <.001 1 contact 72 14.1% 199 25.9% <.001 2 contacts 53 10.4% 101 13.1% 0.137 3 contacts 25 4.9% 53 6.9% 0.143 4 contacts 19 3.7% 33 4.3% 0.611 5 contacts 10 2.0% 27 3.5% 0.104 Note. Counts and percentages were used for categorical variables; *median and interquartile range was used for continuous variable. CCTM = care coordination/transition management
71
Research Question 1: Relationships Among the Variables
The first research question was: what is the relationship between early provider follow- up, nursing CCTM intensity, neighborhood disadvantage, and hospital readmission in a population of older adults with HF, when controlling for advanced age, race, sex, and comorbidity? Bivariate correlations among the study variables and covariates ranged from small to moderate for most (see correlation matrix in Appendix E). The directions of the correlations are summarized in Figure 8. Neighborhood disadvantage, comorbidity, and readmission were all positively correlated to each other. Care coordination/transition management intensity and
CCTM contact within 3 days were positively associated with all measures of EPF, but not
Figure 8. Relationships among the study variables. Positive correlations are identified by blue arrows; negative correlations are identified by red arrows. See Appendix E for Spearman’s rho correlation coefficients.
72 associated with readmission. Conversely, having 2 CCTM contacts was positively associated with readmission, but not EPF within 14 days. Both CCTM intensity and EPF within 14 days were negatively associated with neighborhood disadvantage. In contrast, comorbidity was positively associated with CCTM intensity and CCTM contact in 3 days, but negatively associated with EPF within 14 days. Finally, EPF within 14 days was negatively associated with readmission.
Table 10.
Multivariable Analysis of Factors Associated with 30-Day Readmission
Variable B SE Wald df OR 95% CI p-value
EPF within 14 days -.364 .169 4.613 1 .695 .499, .969 .032
CCTM, 2 contacts .511 .227 5.075 1 1.666 1.069, 2.598 .024
Comorbidity Index .023 .006 16.324 1 1.023 1.012, 1.034 <.001
Note. SE = standard error; df = degrees of freedom; OR = odds ratio; CI = confidence interval; EPF = early provider follow-up; CCTM = care coordination/transition management
Readmission. Forward logistic regression was conducted to determine which variables (age, comorbidity, neighborhood disadvantage, EPF within 14 days, and having 2 CCTM contacts) were predictors of readmission. Regression results indicated that the overall model fit of 3 predictors
(comorbidity, EPF within 14 days, and 2 CCTM contacts) was questionable (-2 Log likelihood =
959.195) but statistically reliable in predicting readmission [χ2 (3) = 28.433, p < .001]. The model correctly classified 87.0% of the cases. The regression coefficients are presented in Table 10. The odds ratio for comorbidity indicates a slight increase in the chance of readmission (OR = 1.023, 95% CI =
1.012, 1.034) as the comorbidity index increases. Early provider follow-up decreased the odds of
73
Table 11.
Multivariable Analysis of Factors Associated with 14-Day Provider Follow-Up
Model 1 Model 2 Model 3 Factor OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value Sex, male 1.613 1.276, 2.038 <.001 1.644 1.300, 2.077 <.001 1.589 1.256, 2.009 <.001 Race, non-white .677 .503, .910 .010 .660 .490,.889 .006 .667 .495, .897 .007 Discharge disposition, .777 .606, .995 .046 ------.775 .605, .994 .045 Home health care Comorbidity Index .992 .984, 1.000 .049 .991 .983, .999 .028 .991 .984, .999 .029 Neighborhood .701 .510, .965 .029 .704 .511, .971 .032 .673 .490, .926 .015 disadvantage 30-day readmission .700 .498, .984 .040 .704 .499, .992 .045 ------CCTM intensity 1.269 1.154, 1.396 <.001 ------CCTM contact, 30 days ------2.292 1.806, 2.908 <.001 ------CCTM contact, 3 days ------2.129 1.666, 2.722 <.001
Model fit -2 LL = 1644.056 -2 LL = 1625.217 -2 LL = 1635.558 χ2 = 86.538, p ≤ .001 χ2 = 96.879, p ≤ .001 χ2 = 86.538, p ≤ .001 Note. OR = odds ratio; CI = confidence interval; EPF = early provider follow-up; CCTM = care coordination/transition management; LL = log likelihood ratio
74 readmission (OR = .696, 95% CI = .499, .969), and having 2 CCTM contacts slightly increased the odds of readmission (OR=1.666, 95% CI = 1.069, 2.598).
Early provider follow-up. Logistic regression was completed with 10 variables across three models (sex, race, discharge disposition, comorbidity, neighborhood disadvantage, readmission, LOS, CCTM intensity, CCTM contact within 30 days, and CCTM contact within 3 days) to determine which significantly predicted EPF within 14 days. All three models were significantly reliable in predicting the outcome but had questionable fit with high -2 log likelihood ratios (see Table 11). The models correctly classified 62.7% - 63.7% of the cases.
Patients of non-white race, female, and discharged with home healthcare (model 1 and 3 only) had lower odds of having an EPF within 14 days. Higher comorbidity and neighborhood disadvantage also decreased the odds of EPF, but neighborhood disadvantage had a greater effect. In all 3 models, CCTM increased the odds of EPF within 14 days. CCTM intensity, ranging from 0-5 contacts, increased odds slightly for EPF (OR = 1.269, 95% CI 1.154, 1.396).
For patients with at least one CCTM contact within 30 days after discharge, odds of having an
EPF within 14 days increased 2.3 times from not having a follow-up (OR = 2.29, 95% CI 1.806,
2.908). When CCTM contact was within the first 3 days of discharge, odds of EPF still significantly increased, but were attenuated slightly (OR = 2.129, 95% CI 1.666, 2.722) and readmission no longer remained significant in the model.
Research Questions 2 and 3: Mediation
Research questions 2 and 3 were: Is there a direct relationship between early provider follow-up and HF hospital readmission? Is there an indirect relationship of early provider follow-up on HF hospital readmission through CCTM intensity? Using the PROCESS macro, model 4, two regression equations were tested to determine if the association between EPF
75 within 14 days and readmission is mediated by CCTM intensity (see Figure 9). In the first equation, using ordinary least squares regression, EPF was significantly related to CCTM intensity (a = .334, SE = .075, p < .001, 95% CI = .187, .480). The second logistic regression equation examined EPF and CCTM intensity as predictors of readmission. Early provider follow-up was directly associated with decreased readmission (c' = -.430, SE = .169, p = .011,
95% CI = -.760, -.098), but CCTM intensity was not (b = .116, SE = .060, p = .053, 95% CI = -
.002, .233). The bootstrap confidence intervals from 5000 samples indicated that the indirect effect of EPF on readmission through CCTM intensity was not significant (ab = .039, SE = .022,
95% CI = -.002, .085).
Figure 9. Results of the mediation model. X = early provider follow-up within 14 days; Y = readmission; M = CCTM intensity. * significance < .05, ** significance < .001
Research Question 4: Moderation
The fourth research question was: Does neighborhood disadvantage moderate the direct relationship of early provider follow-up and the indirect relationship through CCTM intensity on
HF hospital readmission? In this moderated mediation analysis, the area deprivation index (ADI)
76 rank was the moderating variable representing neighborhood disadvantage. A first stage and direct effect moderation model was tested using two equations. In the first equation of the analysis, the relationship between EPF within 14 days and CCTM intensity was moderated by neighborhood disadvantage (b = -.006, SE = .003, p = .002, 95% CI = -.012, -.001). The conditional effects were significant at ADI values less than 80 (see Table 12). Patients who had early follow-up appointments and who resided in the least disadvantaged areas had more CCTM contacts than patients who lived in the most disadvantaged neighborhoods (see Figure 10).
Table 12.
Conditional Effects of Early Provider Follow-Up on CCTM Intensity (First Stage Moderation)
ADI score Effect SE t p-value 95% CI 27.00 .528 .107 4.945 <.001 .319, .738 54.00 .357 .073 4.897 <.001 .214, .500 79.53 .195 .099 1.962 .050 .000, .389 80.20 .190 .101 1.895 .058 -.007, .388 88.00 .141 .116 1.210 .226 -.088, .370 Note. ADI = area deprivation index; SE = standard error; CI = confidence interval
In the second equation, the direct effect of EPF within 14 days on readmission was not moderated by neighborhood disadvantage (b = .001, SE = .007, p = .881, 95% CI = -.012, .014).
Bootstrap confidence intervals from 5000 samples confirmed the absence of an indirect effect of
EPF on readmission through CCTM intensity. The index of moderated mediation (Index = -
.001, SE = .001, 95% CI = -.002, .000) did not support the hypothesis that the direct and indirect relationships between EPF within 14 days, CCTM intensity, and readmission were moderated by neighborhood disadvantage.
77
1.2
1
0.8
EPF in 14d 0.6 • NoPFU_14
• Yes.00 CCTM CCTM Intensity 0.4 1.00
0.2
0 0 10 20 30 40 50 60 70 80 90 100 Area Deprivation Index rank
Figure 10. First stage moderation. Visual representation of the first stage conditional effect of neighborhood deprivation (ADI rank) on CCTM intensity (CCTM-I) by the occurrence of early provider follow-up within 14 days (PFU_14: .00=no, 1.00=yes). CCTM = care coordination/transition management; ADI = area deprivation index.
Summary
The relationships between early provider follow-up, CCTM intensity, hospital readmission, and neighborhood deprivation are complex. Living in a highly disadvantaged neighborhood and high comorbidity were associated with higher readmissions. Patients who had an EPF within 14 days had 30% decreased odds of being readmitted compared to patients without an EPF. However, neighborhood disadvantage and comorbidity were associated with decreased EPF within 14 days. Additionally, comorbidity was associated with higher CCTM intensity, but higher neighborhood disadvantage was associated with less CCTM intensity.
78
Early provider follow-up was directly associated with decreased readmission in patients with HF and was not conditional on level of neighborhood disadvantage. Nursing CCTM intensity did not contribute to an indirect effect of EPF within 14 days on readmission.
However, EPF within 14 days was positively associated with CCTM intensity and moderated by neighborhood disadvantage. Patients who had early follow-up appointments and lived in areas of low to moderate neighborhood disadvantage (below the 80th percentile) had more CCTM contacts.
79
CHAPTER V - DISCUSSION
Introduction
Heart failure (HF) hospitalizations represent a significant burden to the U.S. health care system (Bergethon et al., 2016), which is compounded when 18.5% to 21% of patients are readmitted within 30 days (Arora et al., 2017; Bergethon et al., 2016). Hospital readmission has been considered an indicator of poor quality of care, bringing into question if the care and treatment during the transition from hospital to home was well managed. With excessive readmission rates (as defined by CMS), hospitals are at risk for financial penalties and reduced
Medicare reimbursement. But for patients with HF, readmission may mean the worsening of the disease process or difficulty managing their HF and other comorbidities. Further, disparities are known to exist for HF and HF hospital readmission in populations of black race and lower socioeconomic status (Gohil et al., 2015), and living in a highly disadvantaged neighborhood adds to the risk of poor outcomes in HF (Hu et al., 2016; Kind et al., 2014).
Providers, hospitals, and nurses bring different perspectives to the problem of HF hospital readmissions. To be successful in reducing readmissions, an integrative approach is needed that accounts for care across health care settings, as well as the upstream factors that impact health overall. Early provider follow-up (EPF) after discharge from a HF hospitalization, usually within
7 - 14 days, is known to reduce readmissions (Lee et al., 2016; Wang et al., 2016). But there is little evidence of how nursing interventions related to care coordination and transition management (CCTM) may compliment or improve outcomes when combined with EPF.
Additionally, it is unknown how upstream factors such as neighborhood disadvantage interact with EPF and nursing CCTM interventions to impact HF hospital readmissions.
80
The purpose of this study was to examine the relationship between EPF, nursing CCTM intensity, and hospital readmission in a population of older adults with HF, and to determine if an interaction exists with neighborhood disadvantage. Also, the relationships between patient characteristics (comorbidity, age, sex, and race), hospitalization factors (length of stay and discharge disposition), and the primary study variables were analyzed. Key findings supported and further illuminated previous research reports on the direct relationship between EPF within
14 days and decreased readmission, but CCTM intensity did not further add to the relationship as hypothesized. Neighborhood disadvantage did not moderate the direct effect of EPF within 14 days on readmission, indicating that the association is independent of where patients live. An unexpected finding was that CCTM intensity predicted increased readmission, while EPF predicted decreased readmission. Additionally, the positive relationship between EPF and CCTM intensity was dependent on the level of neighborhood disadvantage.
In this chapter, key findings will first be discussed related to the research questions followed by secondary findings that add to the understanding of the relationships between the variables. Findings will then be related back to the conceptual model from a population health perspective and implications for research, practice, and policy will be discussed. Limitations of the study will also be addressed. The conclusion will summarize the findings and highlight the important contributions to the body of nursing knowledge related to population health and nursing CCTM.
Findings
Research Questions 1: Relationships Among the Variables
The first research question was broad in scope to determine the relationships between early provider follow-up, nursing CCTM intensity, neighborhood disadvantage, and hospital
81 readmission in a population of older adults with HF, when controlling for advanced age, race, sex, and comorbidity. Several themes emerged from this question related to the strength and direction of the relationships, predicting the outcome of hospital readmission, and prediction of completing an EPF within 14 days of hospital discharge. Additionally, comorbidity was found to be a consistent, significant covariate in many of the relationships.
Relationships. For elderly patients with HF, living in a more disadvantaged neighborhood and a higher comorbidity index were associated with increased readmission. This is consistent with previous individual-level studies (Davis et al., 2017; Hu et al., 2018) and population health studies (Hewner et al., 2016; Kind et al., 2014). What was new in this study was that more neighborhood disadvantage and comorbidity were associated with less early provider follow-up within 14 days. In contrast, comorbidity was associated with higher CCTM intensity, but more neighborhood disadvantage was associated with less CCTM intensity. This may be due to the nature of the work of nursing CCTM and the lack of telephone and transportation resources in disadvantaged neighborhoods.
A study by Hoyer et al. (2018) also found that care coordination was associated with higher comorbidity and that patients had socioeconomic barriers to self-care and engagement.
Coordination of care is just one part of CCTM competencies as described by AAACN (2016).
Patients with HF often have complex comorbidities that require more nursing intervention such as support for self-management, advocacy, education, coaching, and care planning. In this study,
CCTM was conducted by phone, consistent with other nursing transition management studies
(Hamar et al., 2016; Kripalani et al., 2019; Reese et al., 2019). In determining neighborhood disadvantage, the indicators of the Area Deprivation Index (ADI) include the percentage of housing units without a telephone (Kind et al., 2014). It is likely, then, that patients living in
82 highly disadvantaged neighborhoods may not have consistent phone access, which may impair their ability to access follow-up care.
Living in a disadvantaged neighborhood may also mean limited access to transportation, which may contribute to fewer provider follow-up visits. Highly disadvantaged neighborhoods have a higher percentage of housing units without a motor vehicle (Kind et al., 2014). Follow-up appointments are often made prior to hospital discharge, but scheduling appointments in this manner may reflect provider’s perceptions of readmission risk rather than patient needs
(Distelhorst et al., in press). Vulnerable patients often depend on neighborhood supports in the initial discharge period (Kind et al., 2014). If those supports are lacking, patients may be less likely to complete a follow-up appointment. It is unclear why higher comorbidity was associated with less early provider follow-up. Patients with HF may be seen by cardiologists, or in the case of multimorbidity, other specialists, in the immediate post-discharge period, resulting in fewer primary care office visits within 14 days.
Predicting 30-day readmission. In the multivariable analysis, only early provider follow-up within 14 days, having 2 CCTM contacts, and comorbidity remained significant in predicting readmission. Patients who had an early provider visit had 30% decreased odds of being readmitted compared to patients without an early provider visit. Previous reports found that early provider follow-up decreased odds of readmission by 18% - 46% (Lee et al., 2016;
Tung et al., 2017). Our population had a lower overall readmission rate (13%) than reported in other national studies (Arora et al, 2017), but similar to a single health system study (Lee et al.,
2016). Comorbidity had a small effect on odds of readmission and neighborhood disadvantage did not remain significant. For the elderly HF population, individual-level comorbidity may be a better predictor of readmission than the population-level variable of neighborhood disadvantage.
83
This finding is not consistent with other studies that found the highest-level of neighborhood disadvantage was positively related to hospital readmission for Medicare eligible patients (Hu et al., 2018; Kind et al., 2014).
In this study, patients with 2 CCTM contacts had 1.7 times greater odds of readmission.
This is different from previous case-controlled studies on nurse-led transitional care programs reported decreased odd of readmission ranging from 44% - 54% (Hamar et al., 2016; Kripalani et al., 2019; Reese et al., 2019). In this study, CCTM nurses were responsible for a population of patients defined by primary care providers. They were not directly involved in discharge planning but did have full access to the inpatient discharge plan of care and were responsible to ensure plans were followed through. Additionally, all patients received a transition management call, and high-risk patients received ongoing care coordination. A study by Hoyer et al. (2018) stratified all discharged patients into levels of care coordination: a single follow-up telephone call for patients at low risk for readmission, and ongoing case management for high risk patients.
Both groups were associated with lower all-cause readmission rates in the general patient population. In our sample of elderly patients with HF, we did not differentiate between type of
CCTM contacts. However, our findings did indicate a difference between 1 CCTM contact and 2 contacts: one contact was associated with increased early provider follow-up, and 2 contacts increased odds of readmission. Further study would be needed to differentiate outcomes related to levels of transition management and care coordination in the elderly HF population.
Predicting early provider follow-up. Early provider follow-up within 14 days is an important transitional care intervention for readmission reduction. Ten factors were positively associated with having an early provider visit, and seven remained important predictors in a multivariable analysis. Non-white, female patients who were discharged with home health care
84 had lower odds of completing an EPF within 14 days. In previous reports, women and patients of non-white race (Distelhorst et al., 2018; Kociol et al.,2011) were also less likely to have an early provider follow-up. Comorbidity only slightly decreased the odds of completing an EPF, but neighborhood disadvantage decreased the odds of having an EPF within 14 days by approximately 30%.
Having a telephone follow-up within 3 days of hospital discharge is a reasonable intervention based on HF guidelines for transitional care (Yancy et al., 2013). In this study,
CCTM contact within 3 days of discharge was associated with 2 times greater odds of having an early follow-up appointment compared to those without contact. Additionally, we found that any
CCTM contact within 30 days after hospital discharge, regardless of the timing, increased odds of EPF within 14 days by a similar magnitude. It is unclear if 3 days is the most appropriate timing for initial CCTM contact in order to increase the likelihood of having an EPF. Previous studies have examined the impact of post-discharge telephone contact for patients with HF on hospital readmission (Hamar et al., 2016; Lee et al, 2016; Reese et al, 2019), but did not examine the effect on early provider follow-up.
Research Questions 2 – 4: Mediation and Moderation.
Research questions 2 and 3 addressed the direct and indirect relationships between EPF within 14 days and readmission, with CCTM intensity as the hypothesized mediator. Research question 4 explored further if these relationships were dependent on where patients lived, as measured by neighborhood disadvantage. Using a moderated mediation analysis, all three questions were examined simultaneously. This allowed for a more complete analysis to examine the relationships of the variables (EPF, CCTM, and readmission), and how they are contingent
85 upon, or moderated by, different contexts or circumstances (Hayes, 2018), such as neighborhood disadvantage.
In this study population, there was no evidence of an indirect relationship between early provider follow-up and readmission through CCTM intensity. Additionally, the direct relationship between early provider follow-up and readmission was significant but was not moderated by neighborhood disadvantage. Previous studies have reported a strong and independent association between neighborhood disadvantage and readmission (Hu et al., 2017;
Jencks et al., 2019; Kind et al., 2014), but have not examined the interaction between neighborhood disadvantage and transitional care interventions. The moderated mediation analysis performed in this study, however, revealed unexpected findings related to the relationship between EPF within 14 days and CCTM intensity, and the interaction with neighborhood disadvantage.
Secondary Findings
In the analysis, we tested for first stage and direct effect moderation as described by
Hayes (2015). First stage moderation examined the relationship between early provider follow- up and CCTM intensity to determine if an interaction with neighborhood disadvantage existed.
We found that early provider follow-up was positively related CCTM intensity and moderated by neighborhood disadvantage. Patients who had early provider follow-up appointments and lived in areas of low to moderate neighborhood disadvantage (below the 80th percentile, indicating better socioeconomic status) had more CCTM contacts. As discussed earlier, patients that lived in higher disadvantaged neighborhoods had less EPF within 14 days and CCTM intensity. It may be that for this study population, current transitional care interventions were not reaching patients living in the most disadvantaged neighborhoods. Hoyer et al. (2018) also identified a similar
86 phenomenon in their study, in which the most vulnerable patients were the hardest to reach and thus not engaged in care coordination interventions.
Implications
Nursing Practice
The study findings highlight the importance of assessment of upstream factors, such as neighborhood disadvantage, as part of a population health nursing assessment for the older adult
HF population. In order to develop appropriate population-based nursing interventions, it is important to identify what upstream factors interact with the interventions and ultimately impact outcomes of interest (Bazemore et al., 2016). Based on our findings, neighborhood disadvantage is an important upstream factor that may negatively influence participation in the transitional care interventions of early provider follow-up and nursing CCTM in the elderly HF population.
Because patients from highly disadvantaged neighborhoods were less likely to have an early provider follow-up and CCTM contact, and more likely to have a readmission, CCTM nurses and providers must collaboratively identify ways to engage this segment of the older adult
HF population. First, nursing interventions need to address those factors that contribute to neighborhood disadvantage. It is not reasonable to expect the traditional process of transition management (making an appointment prior to discharge, calling within 3 days, and having a provider visit within 14 days) to be effective when a population has significant challenges related to phone access, transportation, education, income, and more. Care coordination and transition management may be more effective when meeting this population where they are. If patients were stratified by level of neighborhood disadvantage, among other risk prediction measures,
CCTM nurses could implement a more precise plan of care.
87
For patients being discharged to homes that are in highly disadvantaged neighborhoods, interventions could include first CCTM contact in the hospital, providing provide a single contact number so the patient or family can initiate contact after discharge if needed. Further, the
CCTM nurse could evaluate the best method of CCTM contact, either telephone or home visit, and the best plan for early provider follow-up. In previously discussed studies related to CCTM impact on readmission (see Table 5), the timing and method (pre-discharge/in-person vs post- discharge/telephone) of the programs varied, none utilized home visits, and all were effective in reducing readmissions. The report by Kripalani et al. (2019) described a full model where
CCTM contact was initiated in hospital, and a partial model where first contact was by phone post-discharge. Both were effective in readmission reduction, but the researchers did not assign the models based on any patient or population characteristics. Additional research is needed to determine the most effective timing and method for CCTM interventions for the population of older adults with HF from disadvantaged neighborhoods.
Policy
Population-level data are not often available to nurses and providers in the medical record, although neighborhood context has been proposed as an important indicator for providers at the point of care (Bazemore et al, 2016; Hughes et al., 2016; Kind et al., 2014). The technology for HIPAA-compliant geocoding linking the patient’s address to important population data currently exists (Hughes et al., 2016), but may not be widely used. Without integration of population-level data into the medical record, it would be difficult to incorporate upstream factor assessment into the CCTM nursing process. As the evidence for the relationship between upstream factors, health care interventions, and population health outcomes increase,
88 policies will be needed to support the inclusion of important population data into the medical record.
Individual-level social determinants of health are increasingly being assessed and documented in electronic medical records. The National Academies of Sciences, Engineering, and Medicine (2019) has recommended increased integration of social care into the delivery of health care. National health policy is currently being developed with bills that address collecting and analyzing information related to social determinants of health, improving existing programs to reduce disparities, and enhancing coordination of services (H.R. 4621, 2019; S. 1323, 2019; S.
2721, 2019). This study provides evidence of the importance of both individual-level and population-level social determinants of health, since both independently impact health care equity and outcomes. As support at the local and national level grows for the inclusion of social determinants in the medical record, the argument must be made to include population-level data as well. Additionally, we identified a possible gap in care based on patients living in the most disadvantaged neighborhoods. Future funding opportunities should be pursued to establish and test transitional care programs targeting populations from highly disadvantaged neighborhoods.
Innovative, nurse-led transitional care programs will be needed to reach the most vulnerable patients, decrease health disparities, and improve population outcomes.
Theoretical
Our findings provide partial support for the propositions of the CMNPH (Fawcett &
Ellenbecker, 2015) for the population of older adults with HF. As seen in Figure 9, neighborhood disadvantage, comorbidity, and early provider follow-up are inter-related and directly associated with CCTM. Given the interaction found for neighborhood disadvantage with early provider follow-up and CCTM, the lack of the desired effect of CCTM on readmission presents an
89 opportunity for developing population-based nursing interventions to fill the gap. In this study,
CCTM may not have mediated the relationship between early provider follow-up and readmission because the CCTM and early follow-up interventions themselves were not reaching the patients in the most disadvantaged neighborhoods.
The CMNPH (Fawcett & Ellenbecker, 2015) provides insight into the factors that influence population-level nursing activities and outcomes but may be inadequate to guide
CCTM nurses in the development of population health related nursing interventions. Hospitals are increasingly focused on population health management, driven by external policy forces that impact reimbursement based on outcomes for the population of patients they serve. Nurses are often the bridge between individual care and population outcomes, reconciling both population- level and individual-level assessment, interventions, and outcomes. A model that combines the relational propositions of the CMNPH and the levels of practice from the Population Health
Nursing Model by Storfjell et al. (2017) could provide a framework for guiding population-based nursing care in CCTM and across all levels of population health.
Research
In this study, we identified important upstream factors that influence the provision of transitional care for the elderly population with HF. The next step would be to conduct prospective studies that address specific CCTM nursing interventions in the context of neighborhood disadvantage, utilizing the CMNPH (Fawcett & Ellenbecker, 2015) and the
Population Health Nursing Model (Storfjell et al., 2017) as a guiding framework. Transitional care by providers and nurses can be tailored to population factors, such as HF disease burden, and upstream factors, such as neighborhood disadvantage, to evaluate the impact on individual and population health outcomes. Future CCTM nursing research would build on these
90 individual-level interventions, identifying trends and designing population-level interventions to improve outcomes, also guided by the CMNPH and Population Health Nursing Model framework.
Limitations
A limitation of the study was that the sample was from a single health system, which could result in a threat to external validity related to context-dependent mediation (Shadish,
Cook, & Campbell, 2001). Policies and practices related to transitional care activities may be specific to this health system, thus the association between early provider follow-up and nursing
CCTM may not occur in other health systems. However, nursing practices at the time of the study were evidence-based and followed the AACN CCTM model.
Additionally, use of retrospective data in the primary study from medical records and billing data may result in limitations related to accuracy and matching to study variables.
However, use of administrative data for dates of billed office visits is considered an accurate measure and a robust research tool in population-based research (Gavrielov-Yusim & Friger,
2013). In contrast, the dates of CCTM encounters were obtained from medical record documentation since they are not directly billed and thus may have missing data. This limitation was minimized as CCHS has a robust care coordination program with established protocols and accountability.
Finally, the readmission data from the original study may have limitations due to the use of only the health system administrative data base for identification of hospital readmissions.
Patients may have had readmissions outside of the health system, but this was also be minimized by including only patients who received primary care services within the health system.
91
Conclusion
In summary, this study has contributed to the advancement of population health nursing theory and provided the groundwork for validation of the CMNPH. We have supported the propositions of the model by linking more concrete variables and empirical indicators to the model concepts and demonstrating the relationships between the variables. The CMNPH, along with the Population Health Nursing Model (Storfjell et al., 2017) can guide future CCTM research.
The interaction between neighborhood disadvantage and transitional care interventions in the older adult HF population is important new knowledge. This finding raises awareness of a possible gap in care, where interventions known to improve HF readmission outcomes are not reaching all populations equally. The elderly HF population living in highly disadvantaged neighborhoods have specific characteristics and needs, many that are outside of individual control, that make achieving population health goals challenging. Assessment of both individual- level and population-level data is needed to inform interventions and plan for transitional care programs to address this care gap. The addition of upstream factor assessment can be made possible with population-level data available in EMR, but practice and policy changes are needed for this to be a standard of care. Nurses and providers must continue to collaborate in research and practice to meet population health outcomes, with each discipline bringing unique perspective and skill to the team.
92
References
Akwo, E. A., Kabagambe, E. K., Harrell, F. E., Jr., Blot, W. J., Bachmann, J. M., Wang, T. J., . .
. Lipworth, L. (2018). Neighborhood deprivation predicts heart failure risk in a low-
income population of blacks and whites in the southeastern united states. Circulation:
Cardiovascular Quality and Outcomes, 11(1), e004052.
doi:10.1161/CIRCOUTCOMES.117.004052
Albert, N. M. (2016). A systematic review of transitional-care strategies to reduce
rehospitalization in patients with heart failure. Heart and Lung: Journal of Acute and
Critical Care, 45, 100-113. doi:10.1016/j.hrtlng.2015.12.001
Albert, N. M., Barnason, S., Deswal, A., Hernandez, A., Kociol, R., Lee, E., . . . White-Williams,
C. (2015). Transitions of care in heart failure: A scientific statement from the American
heart association. Circulation: Heart Failure, 8, 384-409.
doi:10.1161/HHF.0000000000000006
American Academy of Ambulatory Care Nursing [AAACN]. (2016). Scope and Standards of
Practice for Registered Nurses in Care Coordination and Transition Management.
Pitman, NJ: Author.
American Heart Association (2020). Get With The Guidelines – Heart Failure. Retrieved from
https://www.heart.org/en/professional/quality-improvement/get-with-the-guidelines/get-
with-the-guidelines-heart-failure
American Nurses Association [ANA]. (2015). Nursing: Scope and standards of practice (3rd
ed.). Silver Springs, MD: Author.
Angraal, S., Khera, R., Zhou, S., Wang, Y., Lin, Z., Dharmarajan, K., . . . Krumholz, H. M.
(2018). Trends in 30-Day Readmission Rates for Medicare and Non-Medicare Patients in
93
the Era of the Affordable Care Act. American Journal of Medicine, 131(11), 1324-1331 .
doi:10.1016/j.amjmed.2018.06.013
Arcaya, M. C., Tucker-Seeley, R. D., Kim, R., Schnake-Mahl, A., So, M., & Subramanian, S. V.
(2016). Research on neighborhood effects on health in the United States: A systematic
review of study characteristics. Social Science and Medicine, 168, 16-29.
doi:10.1016/j.socscimed.2016.08.047
Arora, S., Patel, P., Lahewala, S., Patel, N., Patel, N. J., Thakore, K., . . . Gopalan, R. (2017).
Etiologies, trends, and predictors of 30-day readmission in patients with heart failure.
American Journal of Cardiology, 119(5), 760-769. doi:10.1016/j.amjcard.2016.11.022
Ayatollahi, Y., Liu, X., Namazi, A., Jaradat, M., Yamashita, T., Shen, J. J., . . . Yoo, J. W.
(2018). Early readmission risk identification for hospitalized older adults with
decompensated heart failure. Research in Gerontological Nursing, 11(4), 190-197.
doi:10.3928/19404921-20180322-01
Balaban, R., Galbraith, A., Burns, M., Vialle-Valentin, C., Larochelle, M., & Ross-Degnan, E.
(2015). Patient Navigator Intervention to Reduce Hospital Readmissions among High-
Risk Safety-Net Patients: A Randomized Controlled Trial. Journal of General Internal
Medicine, 30(7), 907-915. doi:10.1007/s11606-015-3185-x)
Bazemore, A. W., Cottrell, E. K., Gold, R., Hughes, L. S., Phillips, R. L., Angier, H., . . . DeVoe,
J. E. (2016). "Community vital signs": Incorporating geocoded social determinants into
electronic records to promote patient and population health. Journal of the American
Medical Informatics Association, 23, 407-412. doi:10.1093/jamia/ocv088
Bekemeier, B. (2008). "Upstream" nursing practice and research. Applied Nursing Research, 21,
50-52. doi:10.1016/j.apnr.2007.11.002
94
Bekhet, A.K. & Zauszniewski, J.A. (2008). Theoretical substruction illustrated by the theory of
learned resourcefulness. Research and Theory for Nursing Practice: An International
Journal, 22(3), 205-214.
Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R., . . . Muntner, P.
(2017). Heart disease and stroke statistics-2017 update: A report from the American
Heart Association. Circulation, 135(10), e146-e603.
doi:10.1161/CIR.0000000000000485
Benjamin, E. J., Virani, S. S., Callaway, C. W., Chamberlain, A. M., Chang, A. R., Cheng, S., . .
Muntner, P. (2018). Heart Disease and Stroke Statistics-2018 Update: A Report from the
American Heart Association. Circulation, 137(12), e67-e492.
doi:10.1161/CIR.0000000000000558
Bergethon, K. E., Ju, C., DeVore, A. D., Hardy, N. C., Fonarow, G. C., Yancy, C. W., . . .
Hernandez, A. F. (2016). Trends in 30-day readmission rates for patients hospitalized
with heart failure: Findings from the get with the guidelines-heart failure registry.
Circulation: Heart Failure, 9(6). doi:10.1161/CIRCHEARTFAILURE.115.002594
Bergren, M. D. (2017). School nursing and population health: past, present, and future. Online
Journal of Issues in Nursing, 22(3). doi:10.3912/OJIN.Vol22No03Man03
Bikdeli, B., Wayda, B., Bao, H., Ross, J. S., Xu, X., Chaudhry, S. I., . . . Krumholz, H. M.
(2014). Place of residence and outcomes of patients with heart failure: Analysis from the
Telemonitoring to Improve Heart Failure Outcomes Trial. Circulation: Cardiovascular
Quality and Outcomes, 7, 749-756.
95
Blecker, S., Herrin, J., Li, L., Yu, H., Grady, J. N., & Horwitz, L. I. (2019). Trends in Hospital
Readmission of Medicare-Covered Patients with Heart Failure. Journal of the American
College of Cardiology, 73(9), 1004-1012. doi:10.1016/j.jacc.2018.12.040
Bloch, J. R., Zupan, S., McKeever, A. E., & Barkin, J. L. (2017). Perinatal Nurse Home Visiting
Referral Patterns Among Women with Diabetes and Hypertension in Philadelphia.
Journal of Obstetrics, Gynecology & Neonatal Nursing, 46(1), 29-39.
doi:10.1016/j.jogn.2016.08.009
Bor, J., Cohen, G. H., & Galea, S. (2017). Population health in an era of rising income
inequality: USA, 1980–2015. The Lancet, 389(10077), 1475-1490. doi:10.1016/s0140-
6736(17)30571-8
Bradley, E. H., Curry, L., Horwitz, L. I., Sipsma, H., Wang, Y., Walsh, M. N., . . . Krumholz, H.
M. (2013). Hospital strategies associated with 30-day readmission rates for patients with
heart failure. Circulation: Cardiovascular Quality and Outcomes, 6(4), 444-450.
doi:10.1161/CIRCOUTCOMES.111.000101
Braveman, P., Egerter, S., & Williams, D. R. (2011). The social determinants of health: Coming
of age. Annual Review of Public Health, 32, 381-398. doi:10.1146/annurev-publhealth-
031210-101218
Buhr, R. G., Jackson, N. J., Kominski, G. F., Dubinett, S. M., Ong, M. K., & Mangione, C. M.
(2019). Comorbidity and thirty-day hospital readmission odds in chronic obstructive
pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices.
BMC Health Services Research, 19(1). doi:10.1186/s12913-019-4549-4
Butterfield, P. G. (1990). Thinking upstream: Nurturing a conceptual understanding of the
societal context of health behavior. Advances in Nursing Science, 12(2), 1-8.
96
Butterfield, P. G. (2017). Thinking Upstream: A 25-Year Retrospective and Conceptual Model
Aimed at Reducing Health Inequities. Advances in Nursing Science, 40(1), 2-11.
doi:10.1097/ANS.0000000000000161
Bureau of Labor Statistics. (2019) Occupational Outlook Handbook, Registered Nurses. U.S.
Department of Labor. Retrieved from https://www.bls.gov/ooh/healthcare/registered-
nurses.htm on February 05, 2020.
Centers for Disease Control [CDC]. (2015). Heart Disease and Stroke Maps and Data [online].
Retrieved March 18, 2019, from National Center for Chronic Disease Prevention,
Division for Heart Disease and Stroke Prevention https://www.cdc.gov/dhdsp/maps/
Centers for Medicare and Medicaid Services [CMS]. (2017). Measure Information Form.
Retrieved from https://www.cms.gov/Medicare/Medicare-Fee-for-Service-
Payment/PhysicianFeedbackProgram/Downloads/2015-ACR-MIF.pdf
Centers for Medicare and Medicaid Services [CMS]. (2019). Hospital Readmission Reduction
Program (HRRP). Retrieved from https://www.cms.gov/medicare/medicare-fee-for-
service-payment/acuteinpatientpps/readmissions-reduction-program.html
Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., . . . Cutler, D. (2016). The
Association Between Income and Life Expectancy in the United States, 2001-2014.
JAMA, 315(16), 1750-1766. doi:10.1001/jama.2016.4226
Collecting and Analyzing Resources Integral and Necessary for Guidance for Social
Determinants Act of 2019, H.R. 4621, 116th Cong. (2019-2020).
Cowell, J. M. (2018). Population health: school health services and school nursing. The Journal
of School Nursing, 34(1), 10. doi:10.1177/1059840517748720
97
Cupp Curley, A. L., & Viatale, P. A. (2016). Population-based Nursing: Concepts and
competencies for advanced practice (2nd ed.). New York, NY: Springer Publishing.
D'Amore, J., Murray, J., Powers, H., & Johnson, C. (2011). Does telephone follow-up predict
patient satisfaction and readmission? Population Health Management, 14, 249-255.
doi:10.1089/pop.2010.0045
Davis, J. D., Olsen, M. A., Bommarito, K., LaRue, S. J., Saeed, M., Rich, M. W., & Vader, J. M.
(2017). All-Payer Analysis of Heart Failure Hospitalization 30-Day Readmission:
Comorbidities Matter. The American Journal of Medicine, 130(1).
doi:10.1016/j.amjmed.2016.07.030
DeVore, A. D., Hammill, B. G., Hardy, N. C., Eapen, Z. J., Peterson, E. D., & Hernandez, A. F.
(2016). Has Public Reporting of Hospital Readmission Rates Affected Patient
Outcomes?: Analysis of Medicare Claims Data. Journal of the American College of
Cardiology, 67(8), 963-972. doi:10.1016/j.jacc.2015.12.037
Distelhorst, K., Bena, J. F., Morrison, S. & Albert, N. M. (in press). Factors Associated with Pre-
versus Post-Discharge Scheduling for Early Follow-up Appointments. Journal of
Cardiovascular Nursing.
Distelhorst, K., Burchill, C., Adams, K., Lopez, R., Crisafi, J. & Tang, A. (2019). Health Care
Utilization and Neighborhood Disadvantage in Patients with Food Insecurity.
Unpublished manuscript. Cleveland Clinic Health System, Cleveland, Ohio.
Distelhorst, K., Claussen, R., Dion, K., Bena, J. F., Morrison, S. L., Walker, D., . . . Albert, N.
M. (2018). Factors associated with adherence to 14-Day office appointments after heart
failure discharge. Journal of Cardiac Failure, 24(6), 407-411.
doi:10.1016/j.cardfail.2018.04.006
98
Durfey, S. N. M., Kind, A. J. H., Buckingham, W. R., DuGoff, E. H., & Trivedi, A. N. (2019).
Neighborhood disadvantage and chronic disease management. Health Services Research,
54 Suppl 1, 206-216. doi:10.1111/1475-6773.13092
Durstenfeld, M. S., Ogedegbe, O., Katz, S. D., Park, H., & Blecker, S. (2016). Racial and Ethnic
Differences in Heart Failure Readmissions and Mortality in a Large Municipal
Healthcare System. JACC: Heart Failure, 4(11), 885-893. doi:10.1016/j.jchf.2016.05.008
Eapen, Z. J., McCoy, L. A., Fonarow, G. C., Yancy, C. W., Miranda, M. L., Peterson, E. D., . . .
Hernandez, A. F. (2015). Utility of socioeconomic status in predicting 30-day outcomes
after heart failure hospitalization. Circulation: Heart Failure, 8(3), 473-480.
doi:10.1161/CIRCHEARTFAILURE.114.001879
Elixhauser, A., Steiner, C., Harris, D. R., & Coffey, R. M. (1998). Comorbidity Measures for
Use with Administrative Data. Medical Care, 36(1), 8-27.
Elixhauser, A., & Stiener, C. (April 2013). Readmissions to U.S. Hospitals by Diagnosis, 2010.
Rockville, Maryland: Agency for Healthcare Research and Quality Retrieved from
https://hcup-us.ahrq.gov/reports/statbriefs/sb153.pdf.
Fairchild, A. J., & MacKinnon, D. P. (2009). A general model for testing mediation and
moderation effects. Prevention Science, 10(2), 87-99. doi:10.1007/s11121-008-0109-6
Fawcett, J. (2017). Applying Conceptual Models of Nursing: Quality Improvement, Research,
and Practice. New York, NY: Springer.
Fawcett, J., & Ellenbecker, C. H. (2015). A proposed conceptual model of nursing and
population health. Nursing Outlook, 63(3), 288-298.
doi:http://dx.doi.org/10.1016/j.outlook.2015.01.009
99
Feltner, C., Jones, C. D., Cené, C. W., Zheng, Z.-J., Sueta, C. A., Coker-Schwimmer, E. J. L., . . .
Jonas, D. E. (2014). Transitional care interventions to prevent readmissions for persons
with heart failure: A systematic review and meta-analysis. Annals of Internal Medicine,
160, 774-784. doi:10.7326/M14-0083
Fitzpatrick, J.J. & Whall, A.L. (2016). Conceputal Models of Nursing: Global Perspectives (5th
ed.). Boston, MA: Pearson.
Fudim, M., O'Connor, C. M., Dunning, A., Ambrosy, A. P., Armstrong, P. W., Coles, A., . . .
Mentz, R. J. (2018). Aetiology, timing and clinical predictors of early vs. late
readmission following index hospitalization for acute heart failure: insights from
ASCEND-HF. European Journal of Heart Failure, 20(2), 304-314.
doi:10.1002/ejhf.1020
Gohil, S. K., Datta, R., Cao, C., Phelan, M. J., Nguyen, V., Rowther, A. A., & Huang, S. S.
(2015). Impact of Hospital Population Case-Mix, Including Poverty, on Hospital All-
Cause and Infection-Related 30-Day Readmission Rates. Clinical Infectious Diseases, 61,
1235-1243. doi:10.1093/cid/civ539
Goldberg, D. W. (2008). A Geocoding Best Practices Guide. North American Association of
Central Cancer Registries, Inc. http://doi.org/10.1.1.119.714
Graham, G. (2015). Disparities in cardiovascular disease risk in the United States. Current
Cardiology Reviews, 11, 238-245.
Hamar, B., Rula, E. Y., Wells, A. R., Coberley, C., Pope, J. E., & Varga, D. (2016). Impact of a
Scalable Care Transitions Program for Readmission Avoidance. American Journal of
Managed Care, 22(1), 28-34.
100
Hayes, A. F. (2015). An Index and Test of Linear Moderated Mediation. Multivariate Behavioral
Research, 50(1), 1-22. doi:10.1080/00273171.2014.962683
Hayes, A.F. (2018). Introduction to Medication, Moderations, and Conditional Process Analysis.
New York: Guilford Press.
Healing Communities through Health Care Act, S. 2721, 116th Cong. (2019-2020).
Healthcare Cost and Utilization Project [HCUP]. (June 2017). HCUP Elixhauser Comorbidity
Software. Agency for Healthcare Research and Quality, Rockville, MD. www.hcup-
us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp.
Hernandez, A. F., Greiner, M. A., Fonarow, G. C., Hammill, B. G., Heidenreich, P. A., Yancy,
C. W., . . . Curtis, L. H. (2010). Relationship between early physician follow-up and 30-
day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA,
303(17), 1716-1722.
Herrin, J., St. Andre, J., Kenward, K., Joshi, M. S., Audet, A. M. J., & Hines, S. C. (2015).
Community factors and hospital readmission rates. Health Services Research, 50.
doi:10.1111/1475-6773.12177
Hewner, S., Casucci, S., & Castner, J. (2016). The roles of chronic disease complexity, health
system integration, and care management in post-discharge healthcare utilization in a
low-income population. Research in Nursing and Health, 39(4), 215-228.
doi:10.1002/nur.21731
Hewner, S., Seo, J. Y., Gothard, S. E., & Johnson, B. J. (2014). Aligning population-based care
management with chronic disease complexity. Nursing Outlook, 62(4), 250-258.
doi:10.1016/j.outlook.2014.03.003
101
Hokanson Hawks, J. (2012). The Affordable Care Act: Emphasis on population health. Urologic
Nursing, 32(5), 233-234.
Hoover, C., Plamann, J., & Beckel, J. (2017). Outcomes of an Interdisciplinary Transitional Care
Quality Improvement Project on Self-Management and Health Care Use in Patients with
Heart Failure. Journal of Gerontological Nursing, 43(1), 23-31. doi:10.3928/00989134-
20160901-01
Hoyer, E. H., Brotman, D. J., Apfel, A., Leung, C., Boonyasai, R. T., Richardson, M., . . .
Deutschendorf, A. (2018). Improving Outcomes After Hospitalization: A Prospective
Observational Multicenter Evaluation of Care Coordination Strategies for Reducing 30-
Day Readmissions to Maryland Hospitals. Journal of General Internal Medicine, 33(5),
621-627. doi:10.1007/s11606-017-4218-4
Hu, J., Gonsahn, M. D., & Nerenz, D. R. (2014). Socioeconomic status and readmissions:
Evidence from an urban teaching hospital. Health affairs (Project Hope), 33, 778-785.
doi:10.1377/hlthaff.2013.0816
Hu, J., Kind, A. J. H., & Nerenz, D. (2018). Area deprivation index predicts readmission risk at
an urban teaching hospital. American Journal of Medical Quality, 33(5), 493-501.
doi:10.1177/1062860617753063
Hughes, L. S., Phillips, R. L., DeVoe, J. E., & Bazemore, A. W. (2016). Community vital signs:
Taking the pulse of the community while caring for patients. Journal of the American
Board of Family Medicine: JABFM, 29, 419-422. doi:10.3122/jabfm.2016.03.150172
Iacobucci, D. (2012). Mediation analysis and categorical variables: The final frontier. Journal of
Consumer Psychology, 22(4), 582-594. doi:10.1016/j.jcps.2012.03.006
102
Jencks, S. F., Schuster, A., Dougherty, G. B., Gerovich, S., Brock, J. E., & Kind, A. J. H. (2019).
Safety-Net Hospitals, Neighborhood Disadvantage, and Readmissions Under Maryland's
All-Payer Program: An Observational Study. Annals of Internal Medicine, 171(2), 91-98.
doi:10.7326/M16-2671
Joynt Maddox, K. E., Reidhead, M., Hu, J., Kind, A. J. H., Zaslavsky, A. M., & Nerenz, D.
(2019). Adjusting for social risk factors impacts performance and penalties in the hospital
readmissions reduction program. Health Services Research, 54, 327-336.
doi:10.1111/1475-6773.13133
Khoury, M. J., Iademarco, M. F., & Riley, W. T. (2016). Precision public health for the era of
precision medicine. American Journal of Preventive Medicine, 50(3), 398-401.
doi:10.1016/j.amepre.2015.08.031
Kind, A. J. H., & Buckingham, W. R. (2018). Making Neighborhood-Disadvantage Metrics
Accessible — The Neighborhood Atlas. New England Journal of Medicine, 378(26),
2456-2458.
Kind, A. J. H., Jencks, S., Brock, J., Yu, M., Bartels, C., Ehlenbach, W., . . . Smith, M. (2014).
Neighborhood socioeconomic disadvantage and 30-day rehospitalization: A retrospective
cohort study. Annals of Internal Medicine, 161,765-774. doi:10.7326/M13-2946
Kindig, D., & Stoddart, G. (2003). What is population health? American Journal of Public
Health, 93, 380-383. doi:10.2105/AJPH.93.3.380
Kindig, D. A., Asada, Y., & Booske, B. (2008). A population health framework for setting
national and state health goals. JAMA, 299, 2081. doi:10.1001/jama.299.17.2081
Knighton, A. J., Savitz, L., Belnap, T., Stephenson, B., & VanDerslice, J. (2016). Introduction of
an Area Deprivation Index Measuring Patient Socioeconomic Status in an Integrated
103
Health System: Implications for Population Health. eGEMs (Generating Evidence &
Methods to improve patient outcomes), 4(3), Article 9. DOI:
http://dx.doi.org/10.13063/2327-9214.1238
Knighton, A. J., Savitz, L. A., Benuzillo, J., & VanDerslice, J. A. (2018). It takes a village:
Exploring the impact of social determinants on delivery system outcomes for heart failure
patients. Healthcare (Amsterdam, Netherlands), 6(2), 112-116.
doi:10.1016/j.hjdsi.2017.06.001
Kociol, R. D., Greiner, M. A., Fonarow, G. C., Hammill, B. G., Heidenreich, P. A., Yancy, C.
W., . . . Hernandez, A. F. (2011). Associations of patient demographic characteristics and
regional physician density with early physician follow-up among Medicare beneficiaries
hospitalized with heart failure. American Journal of Cardiology, 108, 985-991.
doi:10.1016/j.amjcard.2011.05.032
Kripalani, S., Chen, G., Ciampa, P., Theobald, C., Cao, A., McBride, M., . . . Speroff, T. (2019).
A transition care coordinator model reduces hospital readmissions and costs.
Contemporary Clinical Trials, 81, 55-61. doi:10.1016/j.cct.2019.04.014
Lee, K. K., Yang, J., Hernandez, A. F., Steimle, A. E., & Go, A. S. (2016). Post-discharge
follow-up characteristics associated with 30-day readmission after heart failure
hospitalization. Medical Care, 54(4), 365-372.
Lyles, C. R., Lunn, M. R., Obedin-Maliver, J., & Bibbins-Domingo, K. (2018). The new era of
precision population health: Insights for the All of Us Research Program and beyond.
Journal of Translational Medicine, 16(1), 211. doi:10.1186/s12967-018-1585-5
104
MacDonald, S. E., Newburn-Cook, C. V., Allen, M., & Reutter, L. (2013). Embracing the
population health framework in nursing research. Nursing Inquiry, 20, 30-41.
doi:10.1111/nin.12017
Marmot, M. (2007). Achieving health equity: from root causes to fair outcomes. Lancet, 370,
1153-1163. doi:10.1016/S0140-6736(07)61385-3
Matthews, M. R., Miller, C., Stroebel, R. J., & Bunkers, K. S. (2017). Making the paradigm shift
from siloed population health management to an enterprise-wide approach. Population
Health Management, 20(4), 255-261. doi:10.1089/pop.2016.0064
Meddings, J., Reichert, H., Smith, S. N., Iwashyna, T. J., Langa, K. M., Hofer, T. P., &
McMahon, L. F., Jr. (2017). The Impact of Disability and Social Determinants of Health
on Condition-Specific Readmissions beyond Medicare Risk Adjustments: A Cohort
Study. Journal of General Internal Medicine, 32(1), 71-80. doi:10.1007/s11606-016-
3869-x
Mertler, C.A. & Vannatta, R.A. (2013). Advanced and multivariate statistical methods. Glendale,
CA: Pyrczak Publishing.
Mirkin, K. A., Enomoto, L. M., Caputo, G. M., & Hollenbeak, C. S. (2017). Risk factors for 30-
day readmission in patients with congestive heart failure. Heart and Lung, 46(5), 357-
362. doi:10.1016/j.hrtlng.2017.06.005
Moore, M., Peterson, L., Coffman, M., & Jabbarpour, Y. (2016). Care coordination and
population management services are more prevalent in large practices and patient-
centered medical homes. Journal of the American Board of Family Medicine, 29(6), 652-
653. doi:10.3122/jabfm.2016.06.160180
105
Moore, B. J., White, S., Washington, R., Coenen, N., & Elixhauser, A. (2017). Identifying
Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative
Data: The AHRQ Elixhauser Comorbidity Index. Medical Care, 55(7), 698–705.
National Academy of Medicine Roundtable on Population Health Improvement [NAM]. (2019).
Working Definition of Population Health. Retrieved from
http://nationalacademies.org/HMD/Activities/PublicHealth/PopulationHealthImproveme
ntRT.aspx
National Academies of Sciences, Engineering, and Medicine. (2019). Integrating Social Care
into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health.
Washington, DC: The National Academies Press. https://doi.org/10.17226/25467.
Office of Disease Prevention and Health Promotion. (2010). Healthy People 2020. Retrieved
from Washington, DC: https://www.healthypeople.gov/2020/about/foundation-health-
measures/Determinants-of-Health
Ong, M. K., Romano, P. S., Edgington, S., Aronow, H. U., Auerbach, A. D., Black, J. T., . . .
Better Effectiveness After Transition-Heart Failure Research, G. (2016). Effectiveness of
Remote Patient Monitoring After Discharge of Hospitalized Patients with Heart Failure:
The Better Effectiveness After Transition -- Heart Failure (BEAT-HF) Randomized
Clinical Trial. JAMA Internal Medicine, 176(3), 310-318.
doi:10.1001/jamainternmed.2015.7712
Ponikowski, P., Anker, S. D., AlHabib, K. F., Cowie, M. R., Force, T. L., Hu, S., . . . Filippatos,
G. (2014). Heart failure: preventing disease and death worldwide. ESC Heart Failure,
1(1), 4-25. doi:10.1002/ehf2.12005
106
Prodoehl-Caniano, D. (2019). Integrating population health concepts into community health
simulation experiences. Nurse Educator, 44(2), 97.
doi:10.1097/NNE.0000000000000572
Ranasinghe, I., Wang, Y., Dharmarajan, K., Hsieh, A. F., Bernheim, S. M., & Krumholz, H. M.
(2014). Readmissions after Hospitalization for Heart Failure, Acute Myocardial
Infarction, or Pneumonia among Young and Middle-Aged Adults: A Retrospective
Observational Cohort Study. PLoS Medicine, 11(9), e1001737. doi:10.1371/journal.
pmed.1001737
Rankin, V., Ralyea, T., & Sotomayor, G. (2018). Clinical Nurse Leaders forging the path of
population health. Journal of Professional Nursing, 34(4), 269-272.
doi:10.1016/j.profnurs.2017.10.008
Reese, R. L., Clement, S. A., Syeda, S., Hawley, C. E., Gosian, J. S., Cai, S., . . . Driver, J. A.
(2019). Coordinated-Transitional Care for Veterans with Heart Failure and Chronic Lung
Disease. Journal of the American Geriatrics Society, 67(7), 1502-1507.
doi:10.1111/jgs.15978
Regenstein, M., & Andres, E. (2014). Reducing hospital readmissions among Medicaid patients:
A review of the literature. Quality Management in Health Care, 23(4), 203-225.
doi:10.1097/QMH.0000000000000043
Riegelman, R. (2015). Public health 101: Healthy people, healthy populations (2nd ed.).
Burlington, MA: Jones & Bartlett.
Ritchie, C. S., & Leff, B. (2018). Population health and tailored medical care in the home: The
roles of home-based primary care and home-based palliative care. Journal of Pain and
Symptom Management, 55(3), 1041-1046. doi:10.1016/j.jpainsymman.2017.10.003
107
Robert Wood Johnson Foundation [RWJF]. (2017). Catalysts for change: Harnessing the power
of nurses to build population health in the 21st century [Executive Summary]. Retrieved
from https://www.rwjf.org/en/library/research/2017/09/catalysts-for-change--harnessing-
the-power-of-nurses-to-build-population-health.html
Robinson, J. C., Wyatt, S. B., Hickson, D., Gwinn, D., Faruque, F., Sims, M., … Taylor, H. A.
(2010). Methods for retrospective geocoding in population studies: The Jackson heart
study. Journal of Urban Health, 87(1), 136–150. http://doi.org/10.1007/s11524-009-
9403-2
Roger, V. L. (2013). Epidemiology of heart failure. Circulation Research, 113, 646-659.
doi:10.1161/CIRCRESAHA.113.300268
Samsky, M. D., Ambrosy, A. P., Youngson, E., Liang, L., Kaul, P., Hernandez, A. F., . . .
McAlister, F. A. (2019). Trends in Readmissions and Length of Stay for Patients
Hospitalized With Heart Failure in Canada and the United States. JAMA Cardiology.
doi:10.1001/jamacardio.2019.0766
Savarese, G., & Lund, L. H. (2017). Global Public Health Burden of Heart Failure. Cardiac
Failure Review, 3(1), 7-11. doi:10.15420/cfr.2016:25:2
Shadish, W.R., Cook, T.D., & Campbell, D.T. (2001). Experimental and Quasi-experimental
Designs for Generalized Causal Inference. Belmont, CA: Wadsworth Cengage Learning.
Sharma, A., Colvin-Adams, M., & Yancy, C. W. (2014). Heart failure in African Americans:
Disparities can be overcome. Cleveland Clinic Journal of Medicine, 81(5), 301-311.
doi:10.3949/ccjm.81a.13045
Singh, G. K. (2003). Area Deprivation and Widening Inequalities in US Mortality, 1969-1998.
American Journal of Public Health, 93(7), 1137-1143.
108
Stamp, K. D., Machado, M. A., & Allen, N. A. (2014). Transitional care programs improve
outcomes for heart failure patients: An integrative review. The Journal of Cardiovascular
Nursing, 29(2), 140-154. doi:10.1097/JCN.0b013e31827db560
Steaban, R. L. (2016). Health care reform, care coordination, and transformational leadership.
Nursing Administration Quarterly, 40(2), 153-163.
doi:10.1097/NAQ.0000000000000158
Steenkamer, B. M., Drewes, H. W., Heijink, R., Baan, C. A., & Struijs, J. N. (2017). Defining
population health management: A scoping review of the literature. Population Health
Management, 20(1), 74-85. doi:10.1089/pop.2015.0149
Stiefel, M., & Nolan, K. (2012). A Guide to Measuring the Triple Aim: Population Health,
Experience of Care, and Per Capita Cost [White paper]. Paper presented at the IHI
Innovation Series, Cambridge, Massachusetts.
Storfjell, J. L., & Artress Cruise, P. (1984). A model of community-focused nursing. Public
Health Nursing, 1(2), 85-96.
Storfjell, J. L., Winslow, B. W., & Saunders, J. S. D. (2017). Catalysts for Change: Harnessing
the Power of Nurses to Build Population Health in the 21st Century [White paper].
Accessed at https://www.rwjf.org/en/library/research/2017/09/catalysts-for-change--
harnessing-the-power-of-nurses-to-build-population-health.html
Trudnak, T., Kelley, D., Zerzan, J., Griffith, K., Jiang, H. J., & Fairbrother, G. L. (2014).
Medicaid admissions and readmissions: understanding the prevalence, payment, and most
common diagnoses. Health Affairs, 33(8), 1337-1344. doi:10.1377/hlthaff.2013.0632
109
Tung, Y. C., Chang, G. M., Chang, H. Y., & Yu, T. H. (2017). Relationship between early
physician follow-up and 30-day readmission after acute myocardial infarction and heart
failure. PloS One, 12(1), e0170061. doi:10.1371/journal.pone.0170061
UNDERSTAND Act, S. 1323, 116th Cong. (2019-2020).
United States Census Bureau (n.d.). Census Block. Retrieved from
https://www.census.gov/glossary/#term_Censusblock
University of Wisconsin School of Medicine Public Health. (2015). Area Deprivation Index v2.0.
Downloaded from https://www.neighborhoodatlas.medicine.wisc.edu/ June 5, 2019
Van Spall, H. G. C., Lee, S. F., Xie, F., Oz, U. E., Perez, R., Mitoff, P. R., . . . Connolly, S. J.
(2019). Effect of patient-centered transitional care services on clinical outcomes in
patients hospitalized for heart failure: The PACT-HF randomized clinical trial. Jama,
321(8), 753-761. doi:10.1001/jama.2019.0710
Van Spall, H. G., Rahman, T., Mytton, O., Ramasundarahettige, C., Ibrahim, Q., Kabali, C., . . .
Connolly, S. (2017). Comparative effectiveness of transitional care services in patients
discharged from the hospital with heart failure: A systematic review and network meta-
analysis. European Journal of Heart Failure. doi:10.1002/ejhf.765 van Walraven, C., Austin, P. C., Jennings, A., Quan, H. & Forster, A. J. (2009). A modification
of the Elixhauser comorbidity measures into a point system for hospital death using
administrative data. Medical Care, 47(6), 626-633.
Vedel, I., & Khanassov, V. (2015). Transitional care for patients with congestive heart failure: A
systematic review and meta-analysis. Annals of Family Medicine, 13(6), 562-571.
doi:10.1370/afm.1844
110
Vidic, A., Chibnall, J. T., & Hauptman, P. J. (2015). Heart failure is a major contributor to
hospital readmission penalties. Journal of Cardiac Failure, 21(2), 134-137.
doi:10.1016/j.cardfail.2014.12.002
Wang, H., Johnson, C., Robinson, R. D., Nejtek, V. A., Schrader, C. D., Leuck, J., . . . Zenarosa,
N. R. (2016). Roles of disease severity and post-discharge outpatient visits as predictors
of hospital readmissions. BMC Health Services Research, 16(1), 564.
doi:10.1186/s12913-016-1814-7
Wasfy, J. H., Zigler, C. M., Choirat, C., Wang, Y., Dominici, F., & Yeh, R. W. (2017).
Readmission rates after passage of the hospital readmissions reduction program: A pre-
post analysis. Annals of Internal Medicine, 166(5), 324-331. doi:10.7326/M16-0185
Whellan, D. J., Stebbins, A., Hernandez, A. F., Ezekowitz, J. A., McMurray, J. J., Mather, P. J., .
. . O'Connor, C. M. (2016). Dichotomous relationship between age and 30-day death or
rehospitalization in heart failure patients admitted with acute decompensated heart
failure: Results from the ASCEND-HF trial. Journal of Cardiac Failure, 22(6), 409-416.
doi:10.1016/j.cardfail.2016.02.011
Williams, D. R., Costa, M. V., Odunlami, A. O., & Mohammed, S. A. (2008). Moving upstream:
How interventions that address the social determinants of health can improve health and
reduce disparities. Journal of Public Health Management and Practice, 14 Suppl, S8-17.
doi:10.1097/01.PHH.0000338382.36695.42
White, N. W. (2011). Hospital to Home initiative – reviewing the goals and challenges. U. S.
Cardiology, 8(2), 84-87.
World Health Organization. (n.d.). The Determinants of Health. Retrieved from
https://www.who.int/hia/evidence/doh/en/
111
Yancy, C. W., Jessup, M., Bozkurt, B., Butler, J., Casey, D. E., Drazner, M. H., . . . Wilkoff, B.
L. (2013). 2013 ACCF/AHA guideline for the management of heart failure: A report of
the American College of Cardiology Foundation/American Heart Association task force
on practice guidelines. Circulation, 128. doi:10.1161/CIR.0b013e31829e8776
Zangerle, C. M. (2016). Population health: The importance of social determinants. Nursing
Management, 47, 17-18.
Ziaeian, B., & Fonarow, G. C. (2016). The prevention of hospital readmissions in heart failure.
Progress in Cardiovascular Diseases, 58(4), 379-385. doi:10.1016/j.pcad.2015.09.004.
112
Appendix A
Comparison of primary study and current study
Primary Study Current Study Purpose Describe the rate of HCU and food Explore the relationships between insecurity, and the distribution of early provider follow-up, CCTM neighborhood disadvantage. intensity, HF hospital readmission, and Compare groups with and without food neighborhood disadvantage. insecurity for HCU and neighborhood disadvantage Design Descriptive, comparative Descriptive, correlational Convenience sample Convenience sample Population CCHS provider patient panel CCHS provider patient panel Adult Older adult with HF Variables HCU – total number of inpatient and HF hospital readmission – ED admissions within 90 days of index hospitalization for any cause within admission (DV) 30-days of an index admission for acute decompensated HF (DV)
Neighborhood disadvantage – based on Neighborhood disadvantage – based on population income, education population income, education employment, and housing quality; employment, and housing quality; measured by ADI (IV) measured by ADI (Moderator)
Primary care visits – number of office Early provider follow-up – days to visits in 90 days after index admission primary care visit; yes = within 7 days (Co-variable) of discharge from index admission (IV) Patient outreach encounters – number CCTM intensity – number of patient of care management contacts in 90 outreach encounters by care days after index admission (co- management within 30 days of variable) discharge (Mediator)
Food Insecurity – measured by Hunger vital signs screening (IV)
Statistical Descriptive statistics Linear regression/Conditional process analysis Cox regression analysis analysis (Mediation and moderation) Note. HCU=healthcare utilization; CCTM=care coordination transition management; HF=heart failure; CCHS=Cleveland Clinic Health System; ED=emergency department; IV=independent variable; DV=dependent variable; ADI=Area Deprivation Index
113
Appendix B Area Deprivation Index (ADI) Components and Factor Score Coefficients
Census Data Block Group Component Factor Score Coefficienta Percentage of population aged ≥ 25yr with <9 yr education 0.0849
Percentage of population aged ≥ 25yr with at least a high school diploma -0.0970
Percentage of employed persons aged ≥16 yr in white collar occupations -0.0874
Median family income -0.0977
Income disparityb 0.0936
Median home value -0.0688
Median gross rent -0.0781
Median monthly mortgage -0.0770
Percentage of owner-occupied housing units (home ownership rate) -0.0615
Percentage of civilian labor force population aged ≥16 yr unemployed 0.0806 (unemployment rate)
Percentage of families below the poverty level 0.0977
Percentage of population below 150% of the poverty threshold 0.1037
Percentage of single-parent households with children aged <18 yr 0.0719
Percentage of occupied housing units without a motor vehicle 0.0694
Percentage of occupied housing units without a telephone 0.0877
Percentage of occupied housing units without complete plumbing (log) 0.0510
Percentage of occupied housing units with >1 person per room (crowding) 0.0556
Note. a All coefficients are multiplied by -1 to ease interpretation. Greater ADI means greater disadvantage. b Income disparity defined by Singh (2003) as the log of 100 X ratio of the number of households with <$10,000 annual income to the number of households with ≥$50,000 annual income. Adapted from “Neighborhood socioeconomic disadvantage and 30-day rehospitalization: A retrospective cohort study” by A. J. H. Kind et al., 2014, Annals of Internal Medicine, 765-774.
114
Appendix C The Neighborhood Atlas: Rankings of Ohio neighborhoods by socioeconomic status disadvantage
Adapted from University of Wisconsin School of Medicine Public Health. 2015 Area Deprivation Index v2.0. Downloaded from https://www.neighborhoodatlas.medicine.wisc.edu/ June 5, 2019
115
Appendix D Data Details from Primary and Current Study Indicator from primary study Description Value Current study inclusion
Encounter_Key Study ID - single link to all encounters Summary_Discharge_Status_Desc Discharge disposition from Home, home health care All encounter Encounter_Type_Description Type of encounter Inpatient, outpatient Inpatient only
DateAD Encounter Admission date x
DateDC Encounter Discharge date x AgeGroup Age group at time of encounter 1=18-64; 2=65 or older 2=65 or older
CURRENT_PCP Name of PCP ContractType Provider contract type employed; external; no PCP; employed only quality alliance Diagnosis_Code Primary encounter diagnosis ICD-10 codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.00 to 428.99 Hospital Location of encounter Avon, CC, Euclid, Fairview, All Hillcrest, Lutheran, Marymount, Median, South Pointe LOS Length of stay x
AgeAtAdmission Age at encounter x
Gender Sex male; female All
116
Indicator from primary study Description Value Current study inclusion Race Race white; black; hispanic; asian; All american indian/alaskan native; other; unknown/unspecified ADI Area Deprivation Index percentile rank score 0-100 x ADI_level ADI 85th percentile or greater 1=low; 2=high x
CCF_Payor_Classification insurance class anthem blue cross; Medicaid; All medical mutual; Medicare; other; other managed care Diagnosis_Description Description of primary x diagnosis
Comorbidity_index Elixhauser comorbidity x readmission index PostEncounter_Type Encounters within 30 days of inpatient; emergency; office inpatient; office visit; discharge from index visit; outreach encounter outreach encounter admission Readmission_date inpatient admission within 30 only one value per 30-day period x days of the index admission Days_readmission days to readmission difference between the x readmission_date and DateDC (1-31 [31=any value >30]) Outreach_date date of outreach encounter Count_outreach number of outreach encounters 0-k within 30-day period x
Office_date date of office visit x Days_office days to first office visit difference between the x office_date and DateDC (0-30)
117
Appendix E Spearman’s Rho Correlation Matrix