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Transitional Care, Neighborhood Disadvantage, and Heart Failure

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Transitional Care, Neighborhood Disadvantage, and Heart Failure 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
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