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The Early Impact of Medicaid Expansion on Health Care Access and Utilization among Individuals with Ambulatory Care Sensitive Conditions

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Title:
The Early Impact of Medicaid Expansion on Health Care Access and Utilization among Individuals with Ambulatory Care Sensitive Conditions
Creator:
Samuels, Shenae K
Place of Publication:
[Gainesville, Fla.]
Florida
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University of Florida
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english
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1 online resource (141 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Health Services Research
Health Services Research, Management, and Policy
Committee Chair:
DUNCAN,R P
Committee Co-Chair:
MAINOUS,ARCH G III
Committee Members:
MARLOW,NICOLE MARGUERITE
MCCARTY,CHRISTOPHER

Subjects

Subjects / Keywords:
access -- ambulatory -- medicaid -- utilization
Health Services Research, Management, and Policy -- Dissertations, Academic -- UF
Genre:
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Health Services Research thesis, Ph.D.

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Abstract:
Since 1965, Medicaid has been a safety net for low-income adults and families, pregnant women, and individuals with disabilities in the United States. However, until 2014, a coverage gap existed for some 2.6 million low-income adults whose income was above Medicaid's eligibility criteria but below eligibility for Marketplace premium tax credits. Consequently, Medicaid's coverage gap generated concern regarding the healthcare and outcomes for poor adults within the coverage gap. In January 2014 under the Affordable Care Act (ACA), states were given the option to expand Medicaid to non-elderly individuals with incomes at or below 138% of the federal poverty level in an effort to improve access to care for low-income adults. As a result, approximately 15.1 million people were enrolled under this Medicaid expansion. Ostensibly, the expansion would provide new enrollees with improved access to primary medical care, and therefore reduce the frequency of emergency department (ED) use for medical circumstances that might better be managed in an ambulatory care setting. Throughout the literature, ambulatory care sensitive conditions (ACSC) have been used as a proxy measure for access to ambulatory care. However, few studies have examined the effects of the state Medicaid expansion on access to care and utilization outcomes among individuals with ACSC. Using a difference-in-differences approach, this study examines the impact of Medicaid expansion on access to care and utilization among low-income individuals with ACSC. In states with Medicaid expansion, Aim 1 determined whether individuals with ACSC had an increased likelihood of insurance coverage, provider access, and routine check-ups, while Aim 2 determined whether changes occurred in ED visits, hospitalizations and length of stay. Results of Aim 1 indicate a 4.19 percentage points increase in the rate of insurance among individuals with ACSC living in expansion states. The documented increase in coverage, however, does not appear to have resulted in changes to the use of the ED. However, when examining ACSC-associated utilization measures among low-income adults, results showed a 23.08 percentage points significant reduction in CHF-associated ED hospitalizations in expansion states. On the contrary, expansion states saw a 15.80 percentage points significant increase in COPD-associated ED hospitalizations. ( en )
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In the series University of Florida Digital Collections.
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Includes vita.
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Includes bibliographical references.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2017.
Local:
Adviser: DUNCAN,R P.
Local:
Co-adviser: MAINOUS,ARCH G III.
Statement of Responsibility:
by Shenae K Samuels.

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THE EARLY IMPACT OF MEDICAID EXPANSION ON HEALTH CARE ACCESS AND UTILIZATION AMONG INDIVIDUALS WITH AMBULA TORY CARE SENSITIVE CONDITIONS By SHENAE K. SAMUELS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSI TY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2017

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2 2017 Shenae K. Samuels

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3 To my parents who inspired my interest i n issues pertaining to healthcare access through their personal struggles as patients in the healthcare system Thank you for your countless sacrifices; none of this would be possible without your invaluable support This di ssertation is dedicated not only to them, but to individuals and families who are faced with diffi culties in accessing healthcare. Lastly, this dissertation is dedicated to my A unt Lorna who faced tremendous difficulties i n accessing needed healthcare. Your untimely death continues to be my motivation in combating healthcare access issues

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4 ACKNOWLEDGMENTS I would like to extend my sincerest gratitude to members of my dissertation committee Dr. Duncan, Dr. Marlow, Dr. Mainous, and Dr. McCarty Thank you all for offering me the support needed to successfully complete my journey as a doctoral student This dissertation would not have been possible without your unwavering guidance I would like to thank each of you for investing in my future as a Health Services Researcher First, I woul d like to thank Dr. Christopher McCarty for agreeing to serve on my dissertation committee and for his expertise on survey research methods. I am thankful that our paths crossed during his survey research methods course and that he expressed such willingn ess to provide support for students in whatever way possible. I would also like to thank Dr. Nicole Marlow for her mentorship and knowledge Her knowledge play ed an integral role in the development of my dissertation I am truly grateful for her kindness throughout my dissertation journey and her belief in my methodological skills; her support has played an integral role in my success. I would also like to thank Dr. Arch Mainous III for allowing me several opportunities to learn and grow as a researcher while challenging me to think outside the box. Last but certainly not least I would like to make a special mention of my committee chair, Dr. R. Paul Duncan. I have had the pleasure of working with Dr. Duncan since the beginning of my graduate studies, in 2011, at the University of Florida. His profound knowledge and impact in the field of health services research focused on health policy, healthcare access and the uninsured have always inspired and continues to inspire me to make my own mark within this field I am honored to have worked with Dr. Duncan, to have him serve as the chair of my committee and to

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5 have him as a mentor. His unwavering support and guidance is reflective of his dedication to the success of his students. My accomplishm ents could not have been possible without his support and mentorship. His influence on my career can never be erased and I am eternall y grateful to him. I would also like to extend my si ncerest gratitude to my parents who have been my s upport since birth a nd continue to relentlessly support my dreams and visions. I would also like to thank my friends and loved ones who provided me with much needed support and companionship. Their companionship and the wonderful times spent with them kept me grounded in time s when I needed it the most. I would also like to thank my fellow PhD students with a special mention of Ara Jo and Ivana Vaughn The companionship and support of my colleagues made a challenging process a bit easier. I look forward to our continued frien dship and collaboration as colleagues. Two other mentors deserve notable mention. I would like to thank Dr. Allyson Hall who I met during my Master of Public Health program and with whom I had the honor of working. I am grateful for her mentorship, guidan ce and friendship throughout the years. I would also like to thank Dr. Keri Norris who I met during my time as a Public Health Summer Fellow at the Centers for Disease Control and Prevention (CDC). She played an integral role in my decision to pursue a PhD and I am thankful for her belief in my capabilities. Finally, I would like to thank the staff and faculty of the Department of Health Services Research, Manageme nt and Policy. They each offered assistance and support during my journey as a doctoral studen t.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ ........ 11 LIST OF ABBREVIATIONS ................................ ................................ ........................... 14 ABSTRACT ................................ ................................ ................................ ................... 16 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 18 Background ................................ ................................ ................................ ............. 18 Attempts to Repeal and Replace the ACA ................................ .............................. 20 Significance ................................ ................................ ................................ ............ 24 2 LITERATURE REVIEW ................................ ................................ .......................... 25 Healthcare Access and Utilization Measures ................................ .......................... 25 Effect of Health Insurance on Access and Utilization of Care ........................... 26 Ambulatory Care Sensitive Conditions ................................ ............................. 28 Effect of Health Insurance on Access and Utilization of Care among Individuals with Ambulatory Care Sensitive Conditions ................................ 30 Public Insurance Expansion and Ambulatory C are Sensitive Conditions ......... 30 Medicaid Expansion and Ambulatory Care Sensitive Conditions ..................... 33 Theoretical Foundation and Conceptual F ramework ................................ ........ 36 Specific Aims ................................ ................................ ................................ .......... 38 Hypotheses ................................ ................................ ................................ ............. 38 3 DATA AND METHODS ................................ ................................ ........................... 41 Data ................................ ................................ ................................ ........................ 42 Aim 1 Data ................................ ................................ ................................ ........ 42 Aim 2 Data ................................ ................................ ................................ ........ 42 Variables ................................ ................................ ................................ ................. 44 Aim 1 Variables ................................ ................................ ................................ 44 Dependent Variables ................................ ................................ ........................ 45 Independent Variable ................................ ................................ ....................... 45 Aim 2 Variables ................................ ................................ ................................ 45 Dependent Variables ................................ ................................ ........................ 46 Independent Variable s ................................ ................................ ..................... 47 Methodology ................................ ................................ ................................ ........... 47

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7 Aim 1 Methodology ................................ ................................ ........................... 47 Aim 1 Econometric Models ................................ ................................ ............... 48 Aim 2 Methodology ................................ ................................ ........................... 48 Aim 2 Econometric Models ................................ ................................ ............... 49 4 RESULTS ................................ ................................ ................................ ............... 51 with Ambulatory Care Sensitive Conditions ................................ ......................... 51 Descriptive Statistics ................................ ................................ ........................ 51 Access to Care Measures ................................ ................................ ................ 52 Sensitive Conditions Associated Utilization Among Low Income Individuals ................................ ........ 54 Descriptive Statistics ................................ ................................ ........................ 54 ACSC Associated Utilization Measures ................................ ........................... 55 5 DISCUSSION ................................ ................................ ................................ ......... 88 Descriptive Findings Discussion ................................ ................................ ............. 88 Aim 1 Discussion ................................ ................................ ................................ .... 90 Aim 2 Discussion ................................ ................................ ................................ .... 93 Strengths and Limitations ................................ ................................ ....................... 97 Future Research and Policy Implications ................................ ................................ 98 6 CONCLUSION ................................ ................................ ................................ ...... 101 APPENDIX A STUDY METHODS: PREVENTION QUALITY INDICATORS .............................. 103 B STUDY METHODS: VARIABLES ................................ ................................ ......... 104 C STUDY METHODS: REGRESSION MODELS ................................ ..................... 108 Aim 1 Regression Models ................................ ................................ ..................... 108 Aim 2 Regression Models ................................ ................................ ..................... 113 D AIM 1 MARGINS PLOTS ................................ ................................ ...................... 116 E AIM 2 MARGINS PLOTS ................................ ................................ ...................... 124 F DISCUSSION SECTION SUPPLEMENTAL TABLES ................................ .......... 130 LIST OF REFERENCES ................................ ................................ ............................. 132 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 141

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8 LIST OF TABLES Table page 4 1 Demographic Characteristics by Medicaid Expansion Status ............................. 56 4 2 Proportion of Individuals with ACSC by Medicaid Expansion Status .................. 58 4 3 Difference in Difference estimates among individuals with ACSC, Insurance % ................................ ................................ ................................ ........................ 59 4 4 Difference in Difference estimates among individuals with ACSC, Usual Source of Care % ................................ ................................ ............................... 60 4 5 Differ ence in Difference estimates among individuals with ACSC, Timely Check Up % ................................ ................................ ................................ ....... 61 4 6 Difference in Difference estimates among individuals with asthma, Insurance % ................................ ................................ ................................ ........................ 62 4 7 Difference in Difference estimates among individuals with asthma, Usual Source of Care % ................................ ................................ ............................... 63 4 8 Difference in Difference estimates among individuals with a sthma, Timely Check Up % ................................ ................................ ................................ ....... 64 4 9 Difference in Difference estimates among individuals with COPD, Insurance % ................................ ................................ ................................ ........................ 65 4 10 Difference in Difference estimates among individuals with COPD, Usual Source of Care % ................................ ................................ ............................... 66 4 11 Difference in Difference estimates among individuals with COPD, Timely Check Up % ................................ ................................ ................................ ....... 67 4 12 Difference in Difference estimates among individuals with diabetes complications, Insurance % ................................ ................................ ................ 68 4 13 Difference in Difference estimates among individua ls with diabetes complications, Usual Source of Care % ................................ ............................. 69 4 14 Difference in Difference estimates among individuals with diabetes complications, Timely Check Up % ................................ ................................ .... 70 4 15 Difference in Difference estimates among individuals with 2 or more ACSC, Insurance % ................................ ................................ ................................ ........ 71 4 16 Difference in Difference estimates among individuals with 2 or more ACSC, Usual Source of Care % ................................ ................................ ..................... 72

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9 4 17 Difference in Difference estimates among individuals with 2 or more ACSC, Timely Check Up % ................................ ................................ ............................ 73 4 18 Demographic Characteristics by Medicaid Expansion Status ............................. 74 4 19 Proportion of Individuals with ACSC by Medicaid Expansion ............................. 76 4 20 Difference in Difference estimates, ACSC associated ED hospitalizations ........ 77 4 21 Difference in Difference estimates, COPD associated ED hospitalizations ....... 78 4 22 Difference in Difference estimates, asthma associated ED hospitalizations ...... 79 4 23 Difference in Difference estimates, CHF associated ED hospitali zations .......... 80 4 24 Difference in Difference estimates, UTI associated ED hospitalizations ............ 81 4 25 Difference in Difference estimates, ACSC associated ED visits ........................ 82 4 26 Difference in Difference estimates, COPD associated ED visits ........................ 83 4 27 Difference in Difference estimates, asthma associated ED visits ....................... 84 4 28 Difference in Difference estimates, UTI associated ED visits ............................ 85 4 29 Difference in Difference estimates, CHF associated ED visits ........................... 86 4 30 Difference in Difference estimates, asthma associated length of stay (LOS) ..... 87 A 1 Technical Specifications for Prevention Quality Indicators (PQI), Source: 2013 2014 Medical Expenditure Panel Survey ................................ ................. 103 B 1 Aim 1 Variables, 2012 2015 Behavioral Risk Factor Surveillanc e System (BRFSS) ................................ ................................ ................................ ........... 104 B 2 Aim 2 Variables, 2013 2014 Medical Expenditure Panel Survey (MEPS) ........ 106 C 1 Logit Regression: Probability of being insured, having a usual source of care and having timely checkup for individuals with ambulatory care sensitive conditions living in expansion states post Medicaid expansion ........................ 108 C 2 Lo git Regression: Probability of being insured, having a usual source of care and having timely checkup for individuals with 2 or more ambulatory care sensitive conditions living in expansion states post Medicaid expansion ......... 109 C 3 Logit Regression: Probability of being insured, having a usual source of care and having timely checkup for individuals with asthma living in expansion states post Medicaid expansion ................................ ................................ ....... 110

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10 C 4 Logit Regression: Probability of being insured, having a usual source of care and having timely checkup for individuals with COPD living in expansion states post Medicaid expansion ................................ ................................ ....... 111 C 5 Logistic Regression: Probability of being insured, having a usual source of care and having timely checkup for individuals with diabetes complications living in expansion states post Medicaid expansion ................................ ......... 112 D 1 Logit Regression: Probability of having had an ACSC associated hospitalization through the ED for low income individuals living in expansion states post Medicaid expansion ................................ ................................ ....... 113 D 2 Negative Binomial Regression: DID estimators for change in ACSC associated ED visits for low income individuals living in expansion states post Medicaid expansion ................................ ................................ .................. 11 4 D 3 Generalized Linear Model: DID estimators for change in asthma associated length of stay (LOS) for low income individuals living in expansion states post Medicaid expansion ................................ ................................ .................. 115 F 1 P roportion of Outcome Measures by Medicaid Expansion Status, Pre Medicaid Expansion ................................ ................................ ......................... 130 F 2 Proportion of Outcome Measures by Medicaid Expansion Status, Post Medicaid Expansion ................................ ................................ ......................... 131

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11 LIST OF FIGURES Figure page 2 1 The Andersen Behavioral Model of Health Services Utilization, Phase 4 ........... 40 2 2 Conceptual Framework ................................ ................................ ...................... 40 D 1 Margins plot indicating the change in insurance among low income nonelderly adults with ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 116 D 2 Margins plot indicating the change in usual source of care among low income nonelderly adults with ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 116 D 3 Margins plot indicating the change in timely checkups among low income nonelderly adults with ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 117 D 4 Margins plot indicating the change in insurance among low income nonelderly adults with asthma by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 117 D 5 Margins plot indicating the change in usual source of care among low income nonelderly adults with asthma by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 118 D 6 Margins plot indicating the change in timely checkups among low income nonelderly adults with asthma by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 118 D 7 Margins plot indicating the change in insurance among low in come nonelderly adults with COPD by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 119 D 8 Margins plot indicating the change in usual source of care among low income nonelderly adults with COPD by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 119 D 9 Margins plot indicating the change in timely checkups among low income nonelderly adults with COPD by Medicaid Expans ion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 120 D 10 Margins plot indicating the change in insurance among low income nonelderly adults with diabetes complications by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ........... 120

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12 D 11 Margins plot indicating the change in usual source of care among low income nonelderly adults with diabetes complications by Medicaid Expansion Status, Pre Pos t Medicaid Expansion ................................ ................................ ........... 121 D 12 Margins plot indicating the change in timely checkups among low income nonelderly adults with diabetes complications by Medicaid Expansion Status, Pre Post Medicai d Expansion ................................ ................................ ........... 121 D 13 Margins plot indicating the change in insurance among low income nonelderly adults with multi ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 122 D 14 Margins plot indicating the change in usual source of care among low income nonelderly adults with multi ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 122 D 15 Margins plot indicating the change in timely checkups among low income nonelderly adults with multi ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ......................... 123 E 1 Margins plot indicating the change in the yearly count of ACSC associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ .............................. 124 E 2 Margins plot indicating the change in the yearly count of asthma associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ .............................. 124 E 3 Margins plot indicating the change in the yearly count of CHF associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ........... 125 E 4 Marg ins plot indicating the change in the yearly count of COPD associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ .............................. 125 E 5 Margins plot indicating the change in the yearly count of UTI associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ........... 126 E 6 Margins plot indicati ng the change in ACSC associated ED hospitalizations among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ................. 126 E 7 Margins plot indicating the change in a sthma associated ED hospitalizations among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ................. 127

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13 E 8 Margins plot indicating the change in CHF associated ED hospitalizations among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ................. 127 E 9 Margins plot indicating the change in COPD associated ED hospitalizations among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ................. 128 E 10 Margins plot indicating the change in UTI associated ED hospitalizations among low income n onelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ................. 128 E 11 Margins plot indicating the change in asthma associated length of stay (LOS) among low income nonelderly adul ts by Medicaid Expansion Status, Pre Post Medicaid Expansion ................................ ................................ ................. 129

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14 LIST OF ABBREVIATIONS ACA Affordable Care Act ACSC Ambulatory Care Sensitive Conditions AECB Acute Exacerbation of Chronic Bronchitis AHCA American Health Care Act AHRQ Agency for Healthcare Research and Quality BCRA Better Care Reconciliation Act BRFSS Behavioral Risk Factor Surveillance System CBO Congressional Budget Office CDC Centers for Disease Control and Prevention CHF Congestive Heart Failure CMS Ce nters for Medicare and Medicaid Services COPD Chronic Obstructive Pulmonary Disease CPI Consumer Price Index DC District of Columbia DID Difference in Differences ED Emergency Department EHBs Essential Health Benefits ER Emergency Room EMTALA Emergency Med ical Treatment and Labor Act FIPS Federal Information Processing Standards FPL Federal Poverty Level GLM Generalized Linear Model HCUP Healthcare Cost and Utilization Project HMO Health Maintenance Organization

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15 IMG International Medical Graduate IOM Instit ute of Medicine IRB Institutional Review Board KFF Kaiser Family Foundation LOS Length of Stay MEPS Medical Expenditure Panel Survey MSE Medical Screening Examination NHIS National Health Interview Survey OR Odds Ratio PCP Primary Care Provider PQI Prevent ion Quality Indicator SID State Inpatient Database UF University of Florida US United States UTI Urinary Tract Infection WHO World Health Organization

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16 Abstract of Dissertation Presented to the Graduate School of the University of Florida Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy THE EARLY IMPACT OF MEDICAID EXPANSION ON HEALTH CARE ACCESS AND UTILIZATION AMONG INDIVIDUALS WITH AMBULATORY CARE SENSITIVE CONDITIONS By Shenae K. Samuels December 2017 C hair: R. Paul Duncan Major: Health Services Research Since 1965, Medicaid has been a safety net for low income adults and families, pregnant women, and individuals with disabilities in the United States However until 2014, a coverage gap existed for so me 2.6 million low income adults whose income wa credits. Consequently, the healthcare and outcomes for poor adults within the coverage gap. In January 2014 under the Affordable Care Act (ACA), states were given the option to expand Medicaid to non elderly individuals with incomes at or below 138% of the federal poverty level in an effort to improve access to care for low income adults. As a result, approximately 15.1 million people were enrolled under this Medicaid expansion Ostensibly, the expansion would provide new enrollees with improved access to primary medical care, and therefore reduce the frequency of emerge ncy department (ED) use for medical circumstances that might bet ter be managed in an ambulatory care setting. Throughout the literature, ambulatory care sensitive conditions (ACSC) have been used as a proxy measure for access to ambulatory care. However,

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17 f ew studies have examined the effects of the state Medicaid expansion on access to care and utilization outcomes among individuals with ACSC Using a difference in differences approach, this study examines the impact of Medicaid expansion on access to care and utilization among low income individuals with ACSC. In states with Medicaid expansion, Aim 1 determined whether individuals with ACSC had an increased likelihood of insurance coverage, provider access and routine check ups, while Aim 2 determined whe ther changes occurred in ED visits, hospitalizations and length of stay R esults of Aim 1 indicate a 4.19 percentage point s increase in the rate of insurance among individuals with ACSC living in expansion states. The documented increase in coverage, howev er, does not appear to have resulted in changes to the use of the ED. However, w hen examining ACSC associated utilization measures among low income adults, results showed a 23.08 percentage point s significant reduction in CHF associated ED hospitalizations in expansion states. On the contrary expansion states saw a 15.80 percentage point s significant increase in COPD associated ED hospitalizations.

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18 CHAPTER 1 INTRODUCTION Background Access to quality healthcare is often at the forefront of many programs, policies and initiatives within the health care sector. However, access to healthcare is a complex concept with several definitions and ways of measurements. In fact, According to the Agency for Healthcare Research and Quality (AHRQ), healthcare access me asures may be focused on health insurance coverage, usual source of care, unmet need, or mental health/substance abuse 1 Since 1965, Medicaid has served as a safety net for low income adults and families, as well as for pregnant women, and individuals with disabilities in t he United States (US). However, despite its reputation as a long standing safety ne t program, a coverage gap existed for some 2.6 million low income adults whose income was above eligibility criteria but below the eligibility for Marketplace pre mium tax credits as established by the Affordable Care Act (ACA) 2 Over the first decade of the 20 th century, ap generated concern s regarding the healthcare and outcomes of poor adults within the coverage gap 2 As such, in January 2014 under the ACA states were given the option to expand Medicaid to non elderly individuals with incomes at or below 138% of the federal poverty level or $16,394 for an individual in 2016 3 As of July 2016, 32 states including the District of Columbia (DC) have chosen to adopt Medicaid expansion 4 enrolling approximately 15.1 million people under Medicaid expansion Of these, 11.9 million were newly eligib le. 5 Through out the literature, health insurance has been shown to increase access, utilization, and to improve health outcomes. As far back as 1974, Aday and Andersen

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19 developed a conceptual model to explain how health care is accessed and utilized. Within the model, insurance coverage is seen as an enabling component and mutable factor in the access and utilization of health care 6 Since then, studies have shown that acce ss to health insurance leads to an increase in health care utilization and improved health outcomes. In fact, a systematic review of studies estimating the causal effects of insurance coverage on utilization and/or health outcomes of the nonelderly found t hat access to health insurance increased the likelihood of having a regular source of care, using preventive and diagnostic services, and having more ambulatory visits. Furthermore, the study found that the insured were less likely to delay care, less like ly to require avoidable hospitalizations, had lower mortality rates and reported better health status 7 Improving access to care, by way of health reform, has been a long standing objective and topic of debate throughout US history especially during times of economic downturn. During 1933 and 1934, the worst years of The Great Depression, unemployment was at its peak of 25%. With a shrinking middle class, a widening gap in healthcare access disparities, and rising uncompens ated hospital care, welfare agencies began assisting in the payment of medical costs for the poor. At that time, t he idea of including some form of health insurance was put forward but was left out of the final Social Security bill 8 In 1937, the Technical Committee on Medical Care assembled with the hopes of advancing comprehensive health re form 8 During this period health reform s inclu ded a state run system with states being given the option to participate. Through the Social Security Act, the federal government provided matching

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20 funds to states with public health expansion, and maternal and child health services I n some ways this ser ved as a precursor to the modern Medicaid program 8 standing program within the US health care system the 2014 Medicaid expansion has been a topic of great polarity among political parties, and decision makers As a consequence, states were given the option to expan d Medicaid by way of a Supreme Court ruling Although public health insurance expansion continues to be a topic of controversy, decision makers are often tasked with enacting mechanisms by which healthcare access may be improved. For example, in 1986, the Emergency Medical Treatment and Labor Act (EMTALA) was enacted as a means of ensuring public access to emergency service s pay T he law required h ospitals participating in Medicare to offer emergency services such as a medical screening examination (MSE) upon request or treatment for an emergency medical condition 9 In more recent times, health ref orm efforts have been focused on repealing and replacing the ACA. Attempts to Repeal and Replace the ACA R ecent health reform efforts are presented in the subsequent paragraphs to provide a context for the changing scheme of healthcare that surrounds the f ocus of this dissertation, particularly as it relates to Medicaid American Health Care Act of 2017 H.R.1628 Reconcil iation Act of 2017 H.R. 1628 (BCR A) are major acts that have been propose d in an effort to repeal and replace the ACA. AHCA was introduced in the House of Representatives on March 7, 2017 without review by the Congressional Budget Office (CBO) and was approved in bo th committees on March 8, 2017 10 AHCA proposed the rev ersal of Medicaid expansion by way of ceasing federal funding for new Medicaid

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21 enrollees who would not be eligible under pre ACA eligibility criteria. Additionally, the AHCA proposed a change in the way Medicaid is funded by moving away from the traditiona l Federal Medical Assista nce Percentages (FMAP) to a per capita cap method of funding. Under the proposed funding mechanism, the federal government would no longer match Medicaid state funding; alternatively, federal spending would be capped at a certain a costs 11 Several amendments were made to the bill in an attempt to garner support. Such amendments included the March 20 th endment that included the state option for states to choose block grants rather than a per capita cap as well as the work requirement for Medicaid benefits ; the April 24 th MacArthur Amendment that would allow ntial health benefits (EHBs) requirements; and the May 3 rd Upton Amendment that included the creation of an $8 billio n fund to lower out of pocket costs for consumers with pre existing conditions living in states that allow insurers to charge higher premiu ms for individuals with pre existing conditions. 10 On May 4, 2017, AHCA passed Congress in a 217 to 213 vote. Within hours and in an effort to garner the necessary 50 votes, the Senate announced a plan to write its own bill for ACA repeal and plac BCRA was released to the public, and on June 26 th the CBO estimated that, compared to the ACA, 15 million additional people would be left uninsured by 2018 a number which would increase to 22 million by 2026. 10 U nder BCR A, the current ACA enhanced Medicaid expansion funding of 90% would continue until 2021 and gradually decrease over 3 years. In 2021, states would

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22 receive 85% in federal matching for Medicaid costs followed by 80% in 2022, and 75% in 2023. Finally in 2024, the federal matching rate would fall anywhere from 50% to 75% depending on the state. In terms of funding, BCRA also proposed a change in Instead a per capita cap would be utilized beginning in 2021 with states given the option of choosing block grant s. inputs would increase based on the medical care component of the consumer price index (CPI) as well as the share of Medicaid enrollees in the various beneficiary categories. However, in 2025, increases would simply be based on the standard inflation rate. 12 Amendments were made to the B C R A through the July 13 th Amendment and the Cruz Amendment, which would allow states to use block grant funding for the Medicaid expansion population. Furthermore, the amendment offered the ability for states to exceed the block grant caps during public health emerge ncies. However, this option would last from 2020 through 2024. 12 The Cruz Amendment would also add $45 billion to address the opioid epidemic H owever, overall, the BCRA pro posed to eliminate the requirement of EHB coverage for the Medicaid expansion population beginning in 2020. Similar to AHCA, the BCRA also proposed a work requirement for Medicaid coverage. 12 Finally under retroactive eligibility, individuals who apply for Medicaid are able to receive benefits for up to three months before if they were eligible during their application period. 12 The BCRA proposed a reduction in the retroactive eligibility from 3 months to one month, as well as elimination of the extension of presumptive eligibility to the Medicaid expansion population. On July 25 th the BCRA f ailed to pass as 60 votes

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23 were needed but the proposal received a Senate vote of 57 43 against a procedural motion. 12 After the Graham and Bill Cassidy proposed the Graham Cassidy plan in a final attempt to repeal and replace the ACA. Similar to the BCRA, the Graham Cassidy plan also included block grant funding. Under the Graham Cassidy plan, states may use no more than 20% of the block grant funding to expand their Medicaid program 13 By 2026, under the Health Care Grant Program federal funding for low income individuals would be equal across states instead of the customary adjust ments for each which would reflect the number of individuals with low income in the state versus the number of low income indiv iduals nationwide clinical risk factors and the actuarial value of coverage. 13 In a ddition to block grant fu nding, the Graham Cassidy plan also proposed per capita caps in Medicaid, which would be based on previous spending patterns for the non expansion Medicaid population in each state. Additionally, separate caps would be established for the elderly, blind an d disabled, children, and non expansion adults. 13 Unlike the BCRA, which proposed the adjustment of per capit a caps based solely on general inflation in 2025, the Graham Cassidy plan also proposed the use of medical inflation to be used for the aged, blind and disabled in 2025. Furthermore, the plan proposed the provision of an additional $8 billion bonus pool to reward states that are able to spend below the per capita limits while keeping satisfactory quality measures. 1 3 However, the Graham Cassidy bill did not make it to the Senate floor for a vote. Common to the BCRA, AHCA, and the Graham Cassidy plan is the proposition capita caps

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24 However, ther e are concerns regarding the ability of block grants and per capita caps to keep up w ith costs. In a previously published work by a colleague A. Hall and myself (2012) block grants were discussed with predicted adverse outcomes of reduced funding for st ates, and an increased number of uninsured. 14 These predictions we re in line with current estimates by the CBO, which predicted an incre ase in the number of uninsured based on all the aforementioned proposed plans. Significance Despite the impending changes to Medicaid, the question of whether or not the expansion of health insurance results in the r ight care at the righ t time and at the r ight place remains, particularly for vulnerable populations such as low income adults with ambulatory care sensitive conditions ( ACSC ) who with proper access to ambulatory care would presumably forgo unnecessary ER visits and preventable ER hospitalizati ons. As such, t he 2014 enactment of Medicaid expansion offers an opportunity to determine the effects of a change in health insurance policy on healthcare access and other healthcare outcomes of interest. The analysis described here is intended to examine one element of that question the impact of Medicaid expansion on the use of hospital services to meet the needs for health conditions that are best managed in an ambulatory care setting.

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25 CHAPTER 2 LITERATURE REVIEW Healthcare Access and Utilization Me asures Healthcare access may be defined and measured in various ways according to the measures used. medical care under the circumstances in which he or she believes medical care is needed; t hat is obtain medical care. On the other hand, access is sometimes conceived as the realization of potential access through the actual use of medical care as depicted w ithin the Andersen and Aday behavioral mo del of health care utilizat ion (1974) In that framework, access is viewed as being multidimensional; that is, potential and realized. Within this model, p otential access is defined as the presence of enabling resources, such as health insurance, which may increase the likelihood o f healthcare utilization. Realized access is the act of using health services or simply, healthcare utilization 15 Health insurance coverage is associate d with both access and utilization. It may include measures su ch as the proportion of persons with health insurance, with any private health insurance coverage, with only public health insurance coverage, or uninsured all year. Other elements of access inc lude having a usual source of care as indicated by the proportion of persons who have one or more specific source s of ongoing care, with a usual primary care provider, or with very little or no choice in source of care. M easures of healthcare access may al so include difficulties or delays in obtaining health care, and the primary reason for difficulty or delay in obtaining health care; whereas mental health/substance abuse measures may include the proportion of

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26 adults with the various types of mental illnes s/substance abuse who received treatment 1 Like healthcare access, healthcare utilization may be operationalized in various ways depending on the measure used. AHRQ classifies healthcare utilization under eleven categories : specialty care mental health/substance abuse, HIV care, hospital emergency departments, hospitalizations, dental services, home health services, hospice services, nursing home services, and prescription medications 16 Given the multidimensional nature of healthcare access, p otential access, such as the availability of health insurance, plays an integral role in achieving realized access or health care utilization 6,7 As such, interventions targeting improvements in healthcare access o ften include the expansion of health insurance coverage to uninsured populations. Effect of Health Insurance on Access and Utilization of Care Prior to the implementation of the ACA, 48 million nonelderly Americans were without health insurance 17,18 In an effort to reduce the number of individuals without health insurance, the ACA aimed to increase access to health insurance by expanding Medicaid, pr oviding subsidies toward the purchase of private coverage for individuals with incomes up to 400% of the federal poverty level ( FPL ) as well as reforming the health insurance marketplace. Since then, the number of individuals without health insurance has decreased to 29 million or 9.1% of the U.S. population 18 It is well documented that health insurance is an important factor in access ing health care. Data from the National Health Interview Survey (NHIS) showed that more than half of nonelderly adults without health insurance did not have a usual source of care

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27 compared to 10% for insured individuals. Other barriers to health care among nonelderly adults without insurance included delaying care due to cost, going without needed care due to cost and not being able to afford a prescri ption drug 18 A longitudinal study examining the consequences of losing and gaining health insurance coverage on access to care showed that for Medicaid beneficiaries who became uninsured, the proportion of individuals without a usual source of care increased from 12 % to 35% when in dividuals lost their Medicaid coverage. Alternatively, when individuals gained health insurance, the proportion of individuals without a usual source of care was reduced from 33% to 20% 19 This finding indicates the important role that health insurance plays in access to medical care, specifically as it relates t o having a usual source of care. Furthermore, having a usual source of care plays an integral role in the convoluted process of getting the right care at the right time and at the right place. It has been confirmed that by having a usual source of care, pa tients will be better able to get preventive care, and are less likely to delay or forgo needed care. A study by Ayanian et al. (2000) found that uninsured adults were less likely to receive preventive services such as breast cancer screening (64% versus 8 9%) when compared to individuals with health insurance. Results of the study showed that even in terms of basic preventive services such as hypertension screening, the uninsured were less likely to have had their blood pressure screened (80% versus 94%) an d were more likely to go without a routine checkup within the past 2 years (40% versus 18%) 20 Throughout the literature studies have shown that a lack of health insurance not only decreases the likelihood of having a usual source of care, but also

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28 increases the likelihood of delaying needed and preventive care with some reports of cost barriers 20 24 The right care at the right time and right place scenario becomes especially critical for patients with chronic conditions. Chronic conditio ns, such as heart disease, and T ype 2 diabetes, are some of the most common, most costly and o ften preventable conditions among health issues 25 As a result, ensuring the right care at the right time and place involves proper management in which patients have a usual source of care by which they may undergo routine checkups to delay or prevent the progression of disease therefore, decreasing the likelihood of preventable hospitalizations. For uninsured patients with chronic co nditions the goal of preventing avoidable hospitalizations proves to be difficult as more than half of uninsured adults with chronic conditions delay or postpone care when compared to 27% of insured adults with chronic conditions 20,26 Furthermore, for uninsured patients who are hospitalized, studies have shown that a lack of insurance increases mortality risk as well as increase s average length of stay (LOS) 27,28 Some s tudies have cited a lack of insurance as a reason for increases in length of stay, while other studies have found that a lack of insurance reduces length of stay 29,30 I n fact, a recent study found that the expansi on of Medicaid eligibility was associated with shorter hospital length of stay after injury and even lower length of stay for patients who remained uninsured 31 Ambulatory Care Sensitive Conditions Hospitalizations for ambulatory care sensitive conditions (ACSC) are commonly referred to as pr eventable hospitalizations The frequency of such events can be used as a proxy for access to ambulatory care, since as many as 75% occur through the ED 32 34 Billings alongside an advisory panel of primary care ac cess experts developed

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29 the ACSC category and in 1993, the Institute of Medicine (IOM) recommended that ACSC hospitalization be used as an outcome indicator of primary care access 35,36 A CSC include s both acute, and chronic conditions that may be prevented, delayed, controlled or managed with access to appropriate primary care. As such, ACSC are defined as prevention quality indicators (PQIs) by the Agency for Healthcare Research and Quality (AHRQ), and are a set of measures often used to identify ambulatory care sensitive conditions. Acute ACSC include bacterial pneumonia, dehydration, and urinary tract infecti on s (UTI). Chronic ACSC include angina without procedure, perforated appendix, congestive heart failure (CHF ), hypertension, adult asthma, chronic obstructive pulmonary disease (COPD), diabetes short term and long term diabetes complications, as well as uncontrolled diabetes 37 A ng ina pectoris was excluded due to recent evidence regarding concerns of its validity as a PQI 38 Many ACSC ED visits result in hospitalization s that might be avoided given better access to ambulatory care. In fact, a study found that 34% of ACSC ED visits result ed in hospitalizations when compared to non ACSC ED visits (14%) 39 Furthermore, t he association be tween ACSC hospitalization and access to ambulatory care is supported by studies showing that communities with higher access to care have fewer ACSC hospitalizations 40,41 Using data from the Healthcare Cost and Ut ilization Project, a study examining predictors of ACSC admissions in small geographic areas found that ACSC admissions w ere inversely associated with access to local primary care physicians 42 Similar results were found in a study of e mergency department (ED) visits for ACSC among the South Carolina population aged 18 years old and over. Results of the study

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30 found that counties with high ED visits w ere associated with less access to primary health care, as well as no access to community health centers 43 Effect of Health Insurance on Access and Utilization of Care among Individuals with Ambulatory Care Sensitive Conditions Similar to results in the general population, studies have shown negative outcomes from the absence of health insurance among individuals with ambulatory care sensitive conditions (ACSC). Throughout the literature, the absence of health insurance has been cited as a principal contributing factor in the use of emergency departmen ts as alternative sources of care 44 46 Results of a study examining the impact of health insurance on non urgent and ACSC emergency department use found that lack of health insurance was associated with a higher p robability of non urgent or ACSC emergency department use when compared to individuals with private insurance. The article further states that results of their model predict that should the uninsured become insured under the ACA through Medicaid expansio n or insurance exchanges, the result would be a change in ED use for non urgent conditions or ACSC 47 Another study examining patients with ACSC found adverse utilization ou tcomes in terms of hospital length of stay. Results of t he study by Mainous et al. found that lack of insurance was associated with reduced length of stay for both ACSC and non ACSC patients when compared to insured patients The suggestion was therefore t hat lack of insurance may be a stronger determinant of hospital length of stay than the type of condition with which the patient presents 48 Public Insurance Expansion and Ambulatory Care Sensitive Conditions In 2008, the state of Oregon launched a Medicaid lottery. Since then, studie s have investigated the effect of expanding public health insurance on health care use,

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31 health outcomes, financial strain, as well as the well being of low income adults. Through this lottery, investigators found that Medicaid coverage resulted in more out patient visits, hospitalizations, prescription medications and emergency department visits for conditions that were non emergent and treatable by primary care as well as conditions that were emergent and preventable through timely ambulatory care. H owever the study found that Medicaid improved self reported health, increased the diagnosis and treatment of diabetes and decreased the rates of depression 49 51 Another study examining the impact of the Oregon Medicaid lottery on cancer screening rates found that Medicaid coverage led to better health care access and higher recommended cancer screenings, especially among women 52 A study using a difference in difference s model a ssessed the change in access to care, utilization and self reported health among low income non elderly adults living in an expansion state (Kentucky), a private option state (Arkansas) and a non expansion state (Texas) The researchers found that expansio n was associated with increased access to primary care, fewer skipped medications due to cost, reduced emergency department visits, and increased outpatient visits 53 Similar to the Oregon study, this study also fo und that diabetes screening increased. Furthermore, glucose testing among patients with diabetes and regular care for chronic conditions also increased after expansion; therefore, showing the potential impact that expansion may have on the management of ch ronic conditions among low income adults 53 There are important implications to be learned from this study as the study shows that with expansion, newly insured individuals with chronic conditions may be better able to access and utilize the health services that they need in order to manage their

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32 condition and ultimately, avoid preventable hospitalizations. In 2006, there were 4.4 million preventable hospitalizations costing $30.8 billion in hospital costs costs which could have otherwise been avoided with timely and effe ctive ambulatory care or proper self management 54 that expanded health insurance to childless adults with incomes up to 200% of the federal poverty line (FPL), showed that following enrollment into the pro gram, there was a 47.5% decline in ten of the eleven measures of preventable hospitalizations 55 Contrary to results from State Inpatient Databases (SID) from the Healthcare Cost and Utilization Project (HCUP) examined trends in hospital inpatien Medicaid expansion and found no change in preventable admissions, but instead found an increase in the overall number of inpatient admissions 56 dicaid expansion in 2014 presents a unique opportunity for more generalizable studies examining the impact of Medicaid expansion on health care access and utilization. However, to date, very few studies have examined the impact of state Medicaid expansion A recent study using data from the 2010 to 2014 National Health Interview Survey (NHIS) assessed the early effects of the Affordable Care Act Medicaid Expansion on coverage, access, utilization, and health effects and found an increase in Medicaid covera ge, as well as a decrease in lack of coverage. Furthermore, the study found improvements in physician utilization and overnight hospital stays. In terms of health effect s, the study found an increase in the diagnosis of high cholesterol and diabetes 57 The increase in

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33 diabetes and high cholesterol diagnoses in this study provides some insight regarding the potential impact of Medicaid expansion on ambulatory care sensi tive conditions (ACSC) as individuals who gained health care coverage were better able to access care and receive a diagnosis. Consequently, these individuals should be better equipped to delay the progress of disease through the identification of and mana gement of their respective conditions. Despite the potential benefits noted in studies such as those previously discussed some policy makers have voiced concern regarding the expected increase in demand for healthcare with Medicaid expansion and the suppl y of primary care physicians. A study examining emergency department (ED) visits for ACSC based on primary care provider (PCP) density and payer status found that among the insured, ED visits for ACSC ha d a negative correlation with PCP density; that is, the higher the PCP density the lower the number of ED visits for ACSC 58 Furthermore, anoth er study projecting primary care use in the Medicaid expansion population estimated that 2113 additional primary care providers would be needed if all states were to expand Medicaid 59 However, results of a study examining Medicaid expansion in Michigan showed that duri ng the first year following Medicaid expansion, appointment availability for new enrollees increased and wait times stayed within 2 weeks 60 Furthermore, results of a study published in March 2017 showed a 5.4 p ercentage point s increase in primary care appointment availability for patients with Medicaid 61 Medicaid Expansion and Ambulatory Care Sensitive Conditions Despite the controversy that often surrounds Medicaid, stu dies have shown that having Medicaid coverage yields more benefits in terms of having a usual source of care, more health care visits overall, results in timely care and is less likely to result in

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34 d elay or loss of needed care when compared to the uninsur ed 62 64 As such, Medicaid expansion may prove to be beneficial in terms of improving access to care, as well as in reducing preventable ED visits and hospitalizations. However, w hile a mbulatory care sensitive cond itions (ACSC) is a commonly used proxy measure for adequate access to primary care few studies have used ACSC in their evaluation of the ACA Medicaid expansion in terms of improving access outcomes among individuals with ACSC and ultimately, reducing the likelihood of potentially avoidable ED visits and hospitalizations. A study by Torres et al. assessing the impact of the 2014 Medicaid expansion on access to care measures for individuals with chronic disease found that insurance coverage, not having to f orgo a physician visit, and having a check up all increased under Medicaid expansion 65 Given the nature of chronic conditions, some of the conditions examined within the aforementioned study are classified as ACSC They include asthma term and l ong term complications are more appropriate ACSC or PQI measures. While results of this study offer significant insight pertaining to the impact of income adults, a population for which Med icaid expansion was intended. Additionally, the authors were only able to examine data up to 2014 the year in which Medicaid expansion was implemented The aforementioned limitations of the study by Torres et al. brings into question the true impact of Me dicaid expansion on the population for which it is intended that is, for low income adults. In terms of utilization, a n analysis examining the changes in discharges by payer for expansion states and non expansion states showed that, overall regardless of medical condition (all medical condition s asthma, congestive

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35 heart failure, diabetes and surgical), Medicaid expansion states saw a decrease in inpatient hospital stays for all payers, as well as for the uninsured H owever, an increase in inpatient hospi tal stays were seen for Medicaid 66 While results of this study offers insight pertaining to the impact of Medicaid expansion on ACSC, this study did not examine hospitalizations through the ED. M ore than 75% of preventable hospita lization s occur through the ED; therefore, ACSC hospitalizations through the ED may offer a better understanding of the impact of Medicaid ex pansion on access outcomes and potentially a voidable utilization measures. Another study examining the impact of Medicaid expansion on ED utilization for ACSC by high ED utilizers in an urban safety net hospital found that high utilizers were more likely to have ACSC ED visits both before and after Medicaid expansion. Furthermore, the study found that, overall, ACSC ED visits decreased slightly following Medicaid expansion 35 However, r esults of the aforementioned study are limi ted in its generalizability as the findings were regional and may not be applicable to states outside of Maryland. Lastly, a more recent study by Garthwaite et al. examined the impact of Medicaid expansion on the types of ED visits, as well as location, a nd insurance status. The study found that Medicaid expansion states had fewer ED visits when compared to non expansion states. Furthermore, Medicaid expansion states saw a 47.1% decrease in uninsured visit s a 125.7% increase In Medicaid visits following M edicaid expansion, and a decrease in travel time for nondiscretionary conditions 67 indicating that hospitals began attracting Medicaid patients following the implementation of Medicaid expansion. Furthermore, Med icaid expansion states saw a decrease in uninsured ED visits for

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36 emergent and primary care preventable conditions but saw a substantial increase in Medicaid ED visits for emergent and primary care preventable conditions 67 Authors of thi s study only examined data up to 2014; therefore, it is uncertain whether or not this increase in Medicaid ED visits for emergent and primary care preventable conditions was short term. Results of this study are not generalizable as the authors only examin ed investor owned hospitals, which are inherently different from other hospital types across the nation. Studies examining the impact of Medicaid expansion on access and utilizations measures for individuals with ACSC are limited and results are inconclusi ve Given the previously discussed limitations of formerly published research this study will add to the literature by using nationally representative data to examine the impact of state Medicaid expansion on access to care and utilization for individuals with ACSC thereby contributing more generalizable findings. Furthermore results of this study will build on previous literature by examining ACSC hospitalizations through the ED rather than simply examining all hospitalizations Since 75% of ACSC hospit alizations occur through the ED, b y doing so, results of this study will provide a better understanding of the impact of Medicaid ex pansion on utilization outcomes for conditions that with proper access to ambulatory care would be preventable or avoidabl e Theoretical Foundation and Conceptual Framework The Andersen behavioral model of health services utilization (behavioral model) will serve as the foundation of this study. Developed in the 1960s, the behavioral model has served as a cornerstone model in the fiel d of health services research. The initial model illustrated the way in which predisposing characteristics, enabling resources and need influence the use of health services 15 As illustrated in Figure 2 1 the behavioral

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37 model of health services utilization was later modified to include components such as the environment, and health behavior. Within the environment component, there are elements su ch as the health care system and the external environment that may influence population characteristics such as predisposing characteristics, enabling resources and need. The pathway continues with health behavior, inclusive of personal health practices, and health care utilization which may ultimately feed into the outcomes of perceived health status, evaluated health status and consumer satisfaction 15 Within this model, feedback loops are present to show that the outcomes Fin ally, the model (Figure 2 1) shows that outcomes may be influenced through direct pathways from the environment and population characteristics 15 The beh avioral model captures the convoluted process by which access and utilization are achieved. The behavioral model represents the mechanism by which Medicaid expansion can impact insurance coverage, access to a health care provider, and routine check ups fol lowing implementation of Medicaid expansion. Based on the literature, chan ges in health insurance policy may lead to improvements in acce ss outcomes such as the ones specified in Aim 1 (below) which are expected when controlling for predisposing characteri stics such as age, race/ethnicity, marital status, sex, education, employment and income 53,57 W ith access outcomes of Aim 1 serving as a mediator and should improvements in the access measures be observed a decre ase in ACSC E D vi sits, ACSC h ospitalizations through the ED and ACSC LOS are

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38 expected while controlling for race/ethnicity, age, sex, education, employment, insurance status and marital status (Figure 2 2 ) 34,47,5 3,57 Specific Aims To date, few studies have examined the effects of the state Medicaid expansion on access to care and utilization outcomes among individuals with ambulatory care sensitive conditions (ACSC). The recent implementation of state Medicaid e xpansions under the Affordable Care Act presents a unique opportunity not only to evaluate Medicaid expansion in terms of its intended goal of increasing access to care, but also to examine its effect on ACSC utilization, a well established proxy measure f or access to outpatient care and otherwise preventable hospitalizations As such the aims of the study we re: Aim 1: To determine, among individuals with one or more ambulatory care sensitive conditions (ACSC), whether state Medicaid expansions are associa ted with an increased likelihood of: 1. Insurance coverage 2. Healthcare provider access 3. Routine check ups Aim 2: To determine whether state Medicaid expansions were associated with changes in: 1. ED visits for ACSC 2. ACSC hospitalizations through the ED 3. ACSC as sociated length of stay (LOS) Hypotheses Based on the conceptual framework presented in this study ( Figure 2 2), it was hypo thesized that, following implementation of Medicaid expansion, there would be an increase d likelihood of insurance coverage; access to a health care provider; as well as

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39 an increase d likelihood of timely routine check ups among individuals with ACSC living in expansion states. Furthermore, with improvements in the aforementioned access outcomes and with access outcomes serving as a me diator for Aim 2 outcome measures, it was hypothesized that there would be a decrease in ED visits for ACSC, a decrease in ACSC hospitalizations through the ED as well as a decrease in ACSC associated LOS. As such, the following was hypothesized: Aim 1: M edicaid eligible individuals up to 138% of the Federal Poverty Level (FPL) with one or more ACSC living in expansion states will be more likely to be insured, have access to a healthcare provider, and more likely to have timely routine check ups when compa red to individuals living in non expansion states. Aim 2: When compared to non expansion states, there will be a decrease in the yearly count of ED visits for ACSC, the yearly count of ACSC hospitalizations through the ED as well as a decrease in ACS C ass ociated LOS for Medicaid expansion states following the implementation of Medicaid expansion

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40 Figure 2 1 The Andersen Behavioral Model of Health Services Utilization, Phase 4 Figure 2 2. Conceptual Framework

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41 CHAPTER 3 DATA AND METHODS The pres ent study was approved by the University of Florida (UF) Institutional Review Board (IRB). Expansion and non expansion states were identified through the port. As of October 14, 2016, 19 states chose not to adopt Medicaid Expansion including Alabama, Florida, Georgia, Idaho, Kansas, Maine, Mississippi, Missouri, Nebraska, North Carolina, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Virgin ia, Wisconsin, and Wyoming. Pennsylvania, Indiana, Alaska, Montana and Louisiana were excluded from the analysis due to implementation dates in or after 2015 therefore, providing an insufficient window for post period analysis. Furthermore, since this st udy focuses on Federal expansions only Arizona, Arkansas, Iowa, Michigan, and New Hampshire were excluded from this study as they have approved Section 1115 waivers. As such, a total of 10 states were excluded from the analysis for Aim 1 Additionally, a total of 5 states were excluded f rom the analysis for Aim 2 because they had Section 1115 waivers and/or expanded Medicaid expansion after January 2014 (AZ, IN, LA, MI, and PA) According to state Medicaid expansion eligibility guidelines, participants of Aim 1 and Aim 2 of this study included nonelderly adults aged 18 to 64 years, with incomes at or below 138% of the federal poverty level Aim 1 included individuals with one or more ambulatory care sensitive condition s (ACSC) in addition to the aforementi oned criteria

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42 Data Aim 1 Data The Behavioral Risk Factor Surveillance System (BRFSS) was used to assess insurance coverage, health care provider access, and timely routine check ups. The BRFSS, established in 1984, is a telephone survey that collects stat e data regarding health related risk behaviors, chronic health conditions, and preventive services utilization of U.S. individuals 68 For purposes of this analysis, 2012 2013 BRFSS data w as used to assess the outcomes for the pre Medicaid expansion period and 2015 BRFSS data w as used to assess the outcomes for the post Medicaid expansion period. To assess insurance coverage during th e pre post periods, the following question was analyzed: prepaid plans such as HMOs, or government plans such as Medicare, or Indian Health To assess healthcare provider a was analyzed. Finally, timely routine check ups were determined as respondents who responded to the question: last visited a doctor for a routine checkup with the answer, Aim 2 Data The Medical Expenditure Panel Survey (MEPS) was used to determine if there was a change in the yearly count of ED visits for ACSC, A CSC hospitalizations through the ED and ACSC associated length of stay. To assess the aforementioned Aim 2 outcomes, panels 17, 18, and 19 (that is, years 2013 2014) of MEPS were used for analysis MEPS data for 2013 consist ed of interview rounds 3 5 of p anel 17, as well as interview rounds 1 3 of panel 18 and were used as the pre Medicaid expansion period

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43 MEPS data for 2014 consist ed of interview rounds 3 5 of panel 18, as well as interview rounds 1 3 of panel 19 and were used as the post Medicaid expans ion period. MEPS is a nationally representative survey of non institutionalized civilians in the United States which began in 1996 69 MEPS is composed of two main components the household component and the insurance component. T he household component provides data Interview Survey (NHIS). The household component also contains supplemental data from medical providers while the separate insurance co mponent provides data on employer based health insurance 70 Other components of MEPS include the Medical Prov ider Component, which aims to supplement and/or replace information provided by respondents of the household component and includes health care providers identified by respondents 70 A key advantage of MEPS is the oversampling of blacks, Hispanics and Asians, as well as policy relevant sub groups such as low income households which will be the main focus of this analysis 71 To determine the impact of state Medicaid expansion on ACSC associated ED visits and ED hospitalizations this study used a person level analysis of individuals living in expansion and non expansion states. To examine the change in ED visits, and ED hospitalizations the data was transposed in order to examine the outcomes at the person level rather than at the event level. State FIPS codes, as well a s fully specified ICD 9 codes from MEPS restricted data files were used to identify ACSC utilization within exp ansion and non expansion states. Analysis of the change in ACSC associated length of stay (LOS) was performed at the event level; therefore, data was not transposed for this analysis.

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44 Variables Aim 1 Variables Data from the 2012 2015 Behavioral Risk Factor Surveillance System (BRFSS) was used to measure access outcomes outlined in Aim 1 (Table A 1). Respondents with an ACSC were identified based o n their self report of having been diagnosed with any of the 14 Prevention Quality Indicators (PQI) previously discussed. To identify expansion and non expansion states, the BRFSS state ID variable was used. Currently, 19 states have chosen not to adopt Me dicaid Expansion including Alabama (AL), Florida (FL), Georgia (GA), Idaho (ID), Kansas (KS), Maine (ME), Mississippi (MS), Missouri (MO), Nebraska (NE), North Carolina (NC), Oklahoma (OK), South Carolina (SC), South Dakota (SD), Tennessee (TN), Texas (TX) Utah (UT), Virginia (VA), Wisconsin (WI), and Wyoming (WY) ; the remaining states are in the Medicaid expansion group. Self reported data were used to classify individuals with ACSC. To identify individuals with asthma, the asthma was used. To identify individuals with Chronic Obstructive Pulmonary Disease, was used. Lastly, individuals were classified as having diabetes complications if they responded yes to the question questions : H as a doctor ever told you that diabetes has affec ted your eyes or that you

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45 Dependent Variables Aim 1. 1: HLTHPLN1 is a binary variable that was used to determine access to health insurance. Aim 1. 2: PERSDOC2 is a dummy coded variable that was used to determine whether an individual has a person that they think of as their personal doctor or health care provider. Aim 1. 3: CHECKUP1 is a dummy c oded variable that was used to determine whether an individual has had their recommended routine checkup (i.e. a general physical exam, not an exam for a specific injury, illness or condition within the past year or anytime less than 12 months ago). Indep endent Variable This study used a difference in differences (DID) approach. As such, an interaction term, POST*EXP, was created to test the effect of state Medicaid expansion on the dependent variables of interest. The interaction term included a dummy co ded variable, EXP, indicating Medicaid expansion states (EXP=1). In addition to the EXP variable, the interaction term included a time variable denoting observations occurring in 2015, post Medicaid expansion (POST=1). Aim 2 Variables Data from the 2012 2 014 Medical Expenditure Panel Survey (MEPS) was used to measure the outcomes outlined in Aim 2. Since this project involved analysis of non Medicaid expansion states versus Medicaid expansion states, state IDs were used to determine expansion and non expan sion states. MEPS provided unencrypted state IDs for the top 29 states that could yield accurate estimates. The top 29 states included

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46 Alabama (AL), Arizona (AZ), California (CA), Colorado (CO), Connecticut (CT), Florida (FL), Georgia (GA), Illinois (IL), Indiana (IN), Kentucky (KY), Louisiana (LA), Massachusetts (MA), Maryland (MD), Michigan (MI), Minnesota (MN), Missouri (MO), North Carolina (NC), New Jersey (NJ), New York (NY), Ohio (OH), Oklahoma (OK), Oregon (OR), Pennsylvania (PA), South Carolina (SC) Tennessee (TN), Texas (TX), Virginia (VA), Washington (WA), and Wisconsin (WI). Of the top 29 states, 13 expanded Medicaid (CA, CO, CT, IL, KY, MA, MD, MN, NJ, NY, OH, OR, and WA); 5 states were excluded from the analysis because they had Section 1115 wa ivers and/or expanded Medicaid expansion after January 2014 (AZ, IN, LA, MI, and PA). The remaining11 states were non expansion states (AL, FL, GA, MD, NC, OK, SC, TN, TX, VA, and WI). ED visits for ACSC, ACSC hospitalizations through the ED and ACSC ass ociated length of stay were identified using the fully specified ICD 9 codes for 6 of the 14 PQI; low birth weight (PQI 09) and perforated appendix (PQI 12) were excluded because they do not pertain to adult populations 72 Furthermore, angina pectoris was excluded due to recent evidence regarding the validity of its use as a PQ I 38 Lastly, diabetes complications, and hypertension complications were removed from the analysis due to insufficient sample sizes; leaving 4 of the 14 PQI to be examined (See Table A 1). Dependent Variables Dependent variables included emergency room visits from the MEPS household component events emergency room visits file, as well as hospitalizations through the emergency room measured using the variable, EM ERROOM in the MEPS household component events hospital inpatient stays file. Lastly, ACSC associated length of stay was measured using the number of nights in the hospital variable in the MEPS hospital

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47 inpatient stays file. To determine whether these event s were for ambulatory care sensitive conditions, fully specified ICD 9 CM codes from MEPS restricted data files were used. Independent Variable s This study used a difference in differences (DID) approach. As such, an interaction term, POST*EXP, was create d to test the effect of state Medicaid expansion on the dependent variables of interest. The interaction term included a dummy coded variable, EXP, indicating Medicaid expansion states (EXP=1). In addition to the EXP variable, the interaction term included a time variable consisting of event year to denote observations occurring in 2014, post Medicaid expansion (POST=1). Methodology Consistent with policy evaluations, this study used a difference in differences model. Difference in differences models are co mmonly used in place of randomized control trials, the gold standard of examining causal relationships, as a more pragmatic approach to evaluating the impact of health care policies in observational studies 73 Due to its credibility, ease of implementation and estimation, DiD has become increasingly popular in health policy and medicine 74 Aim 1 Methodology Fifteen separate logit regression s were used to test the probability of having insurance coverage, health care provider access, and timely routine check ups in Medicaid expa nsion states after the Medicaid expansion period among low income individuals with ACSC, low income individuals with multiple ACSC, as well as among low income individuals with diabetes complications, COPD or asthma Models were adjusted for sex, age, empl oyment, race/ethnicity, marital status, education and income

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48 Low income adults were defined as individuals in a household size up to 4 with incomes less than $35,000 according to Federal guidelines used to classify individuals at or below 138% Federal Pov erty Level (FPL) 75 To account for the complex survey design, the svy command in STATA was used Aim 1 Econometric Models The econometric models for the total sample of low income individuals with ACSC, as well as for the sub population of low income individuals with multiple ACSC, diabetes complications, COPD or asthma are presented below. Pr (Health Coverage)= 0 1 Post + 2 Exp + 3 Post 4 5 Age+ 6 7 Race + 8 9 10 Income (3 1) Pr (Access)= 0 1 Post + 2 Exp + 3 Post 4 5 Age+ 6 7 Race + 8 9 10 Income (3 2) Pr (Check Up)= 0 1 Post + 2 Exp + 3 Post 4 5 Age+ 6 7 Race + 8 9 10 Income (3 3) Aim 2 Methodology Five separate negative binomial regression s controlling for sex race/ethnicity, insurance status, and age, were used to determine the change in the yearly count of ED visits for AC SC, COPD, asthma, CHF, and UTI. To determine the probability of having a hospitalization through the ED, five separate logit models were run controlling for sex race/ethnicity, age, insur ance status, education and employment status Lastly, to determine the change in ACSC associated length of stay post M edicaid expansion, five separate generalized linear models (GLM) using a mixed model approach were proposed with r andom effects to accou nt for the clustered nature of the data specifically, repeated measures of participants However, it was determined that the

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49 data was unable to sustain the original analyses based on errors stating that the even af ter multiple attempts at specifying the 76 As such, panel 18, which consisted of individuals with two consecutive years of data, were dropped from the dataset leaving only asthma associated LOS to be analyzed As such, GLM with gamma distribution and log link was used to determine the change in asthma associated length of stay among low income individuals controlling for sex race/ethnicity, age, insurance status, and education. A variable denoting family income as a percentage of the poverty level was used to sub s et the population to low income adults with incomes at or below 138% FPL. T o account for the complex survey design, svy command in STATA was used for all analyses Aim 2 E conometric Models The econometric models to examine each ACSC associated utilization measure among low income individuals is presented below. ED visits for ACSC= 0 1 Post + 2 Exp + 3 Post 4 Sex + 5 6 Insurance 7 Race (3 4) ACSC ED Ho spitalization = 0 1 Post + 2 Exp + 3 Post 4 Sex 5 6 7 Race + 8 Education 9 In surance (3 5)

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50 Asthma Length of Stay = 0 1 Post + 2 Exp + 3 Post 4 Sex + 5 6 Race + 7 8 In surance (3 6)

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51 CHAPTER 4 R ESULTS The aims of this dissertation were to assess the early impact of the 2014 Medicaid expansion on health care access and utilization among individuals with presented in t his chapter. A ll results presented are based on weighted estimates. For Aim 1, th e final sample was 37,989 (unweighted) and 12,807,975 (weighted ) from the Behavioral Risk Factor Surveillance System. For Aim 2, t he final sample size for the analysis of ED v isits and hospitalizations through the ED was 44,954 (unweighted) and 163 752 148 (weighted) from the Medical Expenditure Panel Survey (MEPS) T he final sample size for the analysis of length of stay was 5,889 (unweighted) and 20,313,665 (weighted) from th e Medical Expenditure Panel Survey (MEPS). A statistical significance level at or below 0.05 was used for all results within this study. with Ambulatory Care Sensitive Conditions D escriptive Statistics The total population of individuals living in expansion states was 5,316,635 for years 2012 2015. In non expans ion states, the total population was 7,491,340 for years 2012 2015. Table 4 1 presents descriptive statistics, which were u sed to examine the distribution of demographic characteristics between non expansion and expansion states. Low income adults in expansion states were more likely to be in the youngest age category of 18 29 years old (13.0% versus 8.8 % ). Furthermore, low in come a dults in expansion states were more likely to be classified as non White (39.6% versus 37.6%). Low income adults were also more likely to be a college graduate or higher (7.6% versus 6.5 %) and less likely to be married (26.0% versus 32.1 %) However, in

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52 terms of employment, low income adults in expansion states were more likely to be unemployed (15.2% versus 12.5% ) but less likely to report being unable to work (43.2% versus 48.1%). Table 4 2 presents the proportion of diabetes complications, COPD, an d asthma among low income adults with ACSC in expansion and non expansion states. In expansion states, low income adults with ACSC were significantly less likely to have COPD (45.5% versus 50.0 % ). However, low income adults with ACSC were significantly mor e likely to have asthma (73.5% versus 65.9 % ). Furthermore, non expansion states had a higher proportion of low incomes adults with 2 or more ACSC when compared to expans ion states (28.6% versus 25.5%). Access to Care Measures Results of the logit regressio n models are presented in Appendix C and margins plots are presented in Appendix D Tables 4 3 to 4 5 present the adjusted mean difference s in insurance rates before and after the implementation of Medicaid expansion, as well as the DID results S ignific ant increases in insurance rates, usual source of care, and timely check ups were observed among individuals with ACSC. Among individuals with ACSC, non expansion states saw a 7.00 percentage point s increase in insurance rates and a 2.61 percentage points increase in timely check ups. Expansion states showed a significantly grea ter increase in insurance rates ( a 11.19 percentage point s increase ) while usual source of care increased by 3.04 percentage point s Tables 4 6 to 4 8 present the adjusted mean diff erences and DID results for access outcome measures among individuals with asthma. Si gnificant increases in insurance r ates, and usual source of care were observed among individuals with

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53 asthma. Similar to the general ACSC population, among individuals wit h asthma, non expansion states saw a 7.43 percentage point s increase in insurance while expansion states saw a n 11.49 percentage points increase in in insurance rates. Furthermore, expansion states saw a 4.92 percentage point s i ncrease in usual source of c are. N o significant change s in timely check ups were observed for individuals with asthma. Among individuals with COPD, non expansion states saw a 6.15 percentage point s increase in insurance rates while expansion states saw a 9.10 percentage point s increa se in insurance rates. Furthermore, expansion sta tes saw a 0.28 percentage points increase in usual source of care and a 5.70 percentage point s increase in timely check ups (Tables 4 9 and 4 11). Non expansion states saw a 9.45 percentage point s increase in insurance rates among individuals with diabetes complications (Table 4 12). However, a change in insurance rates was not observed among individuals with diabetes complications living in expansion states nor was a change in rates of usual source of care and timely check ups observed in expansion and non expansion states. Results in Tables 4 15 to 4 17 show significant increases in insurance rates, and timely check ups among individuals with two or more ACSC. Among individuals with ACSC co mor b id ity, non ex pansion states saw a n 8.40 percentage point s increase in insurance rates while expansion states saw a 7.11 percentage point s increase in in insurance rates. In regards to timely check ups, expansion states saw a 7.07 percentage point s increase Lastly th e DID model measured the changes in each access outcome measure of expansion states from before to after the implementation of Medicaid expansion

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54 relative to non expansion states. Results of the DID model showed that following Medicaid expansion, the chan ge in insurance rates was 4.19 percentage points significantly higher in expansion states than non expansion states (Table 4 3), while there were no significant differences in the changes for usual source of care (Table 4 4 ) Aim 2 mpact on Ambulatory Care Sensitive Conditions Associated Utilization Among Low Income Individuals Descriptive Statistics The total population of individuals living in expansion states was 66,718,626 for the years 2013 2014. In non expansion states, the tot al population was 97,033,522 for years 2013 2014. Table 4 18 presents descriptive statistics, which were used to examine the distribution of demographic characteristics between non expansion and expansion states. Overall, non expansion states were more lik ely to have low income adults with incomes at or below 138% FPL (24.4% versus 22.3%). For purposes of this study, the population was limited to low income adults. Low income adults in non expansion states were more likely to be Non Hispanic White (57.1% ve rsus 54.9%). In terms of employment, non expansion states were more likely to have individuals who were unemployed (79.1% versus 75.7%). Lastly, individuals living in non expansion were more likely to be uninsured (11.7% versus 7.6%). Table 4 19 presents t he proportion of individuals with any ambulatory care sensitive condition, chronic obstructive pulmonary disease, asthma, congestive heart failure, and urinary tract infection. In expansion states, low income adults were significantly more likely to have a sthma (49.7% versus 42.7%). However, low income adults living in expansion states were significantly less likely to have congestive heart failure (7.3% versus 2.8%).

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55 ACSC Associated Utilization Measures Results of the regression models are presented in A ppendix D and margins plots are presented in Appendix E. Tables 4 20 to 4 24 present the adjusted mean differences in hospitalizations through the ED before and after the implementation of Medicaid expansion, as well as results of the DID results Signifi cant increa ses of 17.25 percentage points and 17.97 percentage points were observed for ACSC associated ED hospitalizations in expansion states and non expansion states, respectively (Table 4 20 ). Expansion states also saw an 11.29 percentage points signif icant increase in asthma associated ED hospitalizations (Table 4 22 ) and a 12.37 percentage point s decrease in CHF associated ED hospitalizations (Table 4 23 ). Further the change in CHF associated ED hospitalizations following Medicaid expansion was 23.08 percentage point s lower for expansion states than non expansion states (Table 4 23 ). However, the change in COPD associated ED hospitalizations was 15.80 percentage point s higher for expansion states than non expansion states (Table 4 21 ). Results indicat e no significant changes in UTI associated ED hospitalizations. In terms of ED visits, results showed an 85.44 percentage point s significant reduction in ACSC associated ED visits in non expansion states (Table 4 25 ). However, no other significant changes in ED visits were observed for any ACSC or subsets of ACSC (Tables 4 25 to 4 29 ). Lastly, results of the LOS models showed no significant changes in asthma associated length of stay (LOS) (Table 4 30 ).

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56 Table 4 1. Demographic Characteristics by Medicaid Expansion Status Characteristics Medicaid Expansion Status Non Expansion States (n=7,491,340) Expansion States (n=5,316,635) Age (years)*** 18 29 656,815 (8.8%) 691,477 (13.0%) 30 39 996,837 (13.3%) 837,406 (15.8%) 40 49 1,411,327 (18.8%) 1,019,834 (19.2%) 50 59 2,878,245 (38.4%) 1,776,612 (33.4%) 60 64 1,548,116 (20.7%) 991,306 (18.6%) Gender Male 2,598,405 (34.7%) 1,887,644 (35.5%) Female 4,892,935 (65.3%) 3,428,991 (64.5%) Race/ethnicity*** Non Hispanic White 3,159,120 (62.3%) 2,122,557 (60.4%) Non Hispanic Black 1,107,423 (21.8%) 614,347 (17.5%) Hispanic 524,103 (10.3%) 560,206 (15.9%) Other 281,121 (5.5%) 216,659 (6.2%) Income Less than $10,000 1,892,130 (25.3%) 1,458,086 (27.4%) Less than $15,000 1,755,060 (23.4%) 1,211,399 (22 .8%) Less than $20,000 1,856,626 (24.8%) 1,294,804 (24.4%) Less than $25,000 1,552,110 (20.7%) 1,033,926 (19.4%) Less than $35,000 435,414 (5.8%) 318,421 (6.0%)

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57 Table 4 1. Continued Education** Less than high school 2,189,030 (29.2%) 1,537,673 (28. 9%) High school graduate 2,731,138 (36.5%) 1,772,160 (33.4%) Some College 2,080,686 (27.8%) 1,600,150 (30.1%) College graduate or higher 483,538 (6.5%) 401,549 (7.6%) Employment** Employed 1,247,530 (24.4%) 863,636 (24.4%) Unemployed 636,748 (12.5%) 538,193 (15.2%) Retired/homemaker/student 770,129 (15.1%) 614,202 (17.3%) Unable to Work 2,457,566 (48.1%) 1,530,630 (43.2%) Marital Status*** Married 2,397,111 (32.1%) 1,373,859 (26.0%) Not Married 5,076,990 (67.9%) 3,913,569 (74.0%) Family Compos ition Mean number of adults (95% CI) 1.7 (1.7, 1.7) 1.6 (1.6, 1.7) Mean number of children (95% CI) 56.9 (55.8, 58.0) 55.5 (54.0, 56.9) Note: All estimates were calculated using survey weights ***Indicates significant differences at P < 0.001 ** Indi cates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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58 Table 4 2. Proportion of Individuals with ACSC by Medicaid Expansion Status ACSC Medicaid Expansion Status Non Expansion States Expansion States Total Diabe tes Complications Yes 1,110,345 (73.0%) 433,285 (70.5%) 1,543,630 (72.3%) No 409,754 (27.0%) 181,222 (29.5%) 590,976 (27.7%) COPD*** Yes 3,668,131 (50.0%) 2,357,003 (45.4%) 6,025,134 (52.0%) No 3,674,882 (50.0%) 2,839,832 (54.6%) 6,514,715 (48.1%) Asthma*** Yes 4,884,913 (65.9%) 3,868,777 (73.5%) 8,753,690 (69.1%) No 2,522,336 (34.1%) 1,392,511 (26.5%) 3,914,847 (30.9%) Multi ACSC 2,139,464 (28.6%) 1,352,258 (25.5%) 3,491,723 (27.3%) All Others 5,340,807 (71.4%) 3,958,264 (74.5%) 9,299,072 (72.7%) Note: All estimates were calculated using survey weights ***Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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59 Table 4 3 Difference in Difference estimates among individuals with ACSC Insurance % Insurance % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 78.96 90.15 11.19 (8.32, 14.06)*** Non Expansion States 72.20 79.20 7.00 (4.57, 9.43)*** Difference in Differences (95% CI) a,b 4.19 (0.42, 7.96)* a Estimates based on adjusted regression models controlling for gender, rac e/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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60 Table 4 4 Difference in Difference estimates among individuals with ACSC, Usual Source of Care % Usual Source of Care % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 83.66 86.70 3.04 (0.26, 5.83)* Non Expansion States 81.61 83.61 2.00 ( 0.28, 4.29) Difference in Differences (95% CI) a,b 1.04 ( 2.56, 4.64) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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61 Table 4 5 Difference in Difference estimates among individuals with ACSC, Timely Check Up % Timely Check Up % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 72.36 75.66 3.30 ( 0.13, 6.73) Non Expansion States 70.29 72.91 2.61 (0.00, 5.19)* Difference in Differenc es (95% CI) a,b 0.69 ( 3.60, 4.98) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significan t differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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62 Table 4 6 Difference in Difference es timates among individuals with a sthma, Insurance % Insurance % Pre Medicaid Expansion Pos t Medicaid Expansion Difference (95% CI) a,b Expansion States 79.14 90.64 11.49 (8.19, 14.79) *** Non Expansion States 71.64 79.06 7.43 (4.34, 10.51) *** Difference in Differences (95% CI) a,b 4.07 ( 0.48, 8.61) a Estimates based on adjusted regression m odels controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 I ndicates significant differences at P < 0.05

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63 Table 4 7 Difference in Difference es timates among individuals with a sthma, Usual Source of Care % Usual Source of Care % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion Sta tes 81.67 86.59 4.92 (1.40, 8.44) ** Non Expansion States 80.50 82.15 1.65 ( 1.34, 4.63) Difference in Differences (95% CI) a,b 3.28 ( 1.35, 7.90) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital statu s, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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64 Table 4 8 Diff erence in Difference es timates among individuals with a sthma, Timely Check Up % Timely Check Up % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 71.82 74.67 2.85 ( 1.38, 7.08) Non Expansion States 68.62 71.64 3.02 ( 0.23, 6.26 ) Difference in Differences (95% CI) a,b 0.17 ( 5.51, 5.18) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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65 Table 4 9 Difference in Difference estimates among individuals with COPD, Insurance % I nsurance % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 82.13 91.23 9.10 (5.36,12.81) *** Non Expansion States 75.38 81.53 6.15 (3.01, 9.29) *** Difference in Differences (95% CI) a,b 2.95 ( 1.93, 7.83) a Estima tes based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates signi ficant differences at P < 0.01 Indicates significant differences at P < 0.05

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66 Table 4 10 Difference in Difference estimates among individuals with COPD, Usual Source of Care % Usual Source of Care % Pre Medicaid Expansion Post Medicaid Expansion Diff erence (95% CI) a,b Expansion States 89.56 89.84 0.28 ( 2.62, 3.17) Non Expansion States 85.27 87.94 2.68 ( 0.07, 5.42) Difference in Differences (95% CI) a,b 2.40 ( 6.38, 1.59) a Estimates based on adjusted regression models controlling for gender, r ace/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant difference s at P < 0.05

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67 Table 4 11 Difference in Difference estimates among individuals with COPD, Timely Check Up % Timely Check Up % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 74.36 80.06 5.70 (1.46, 9.95) ** Non E xpansion States 72.23 76.28 4.05 (0.53, 7.57) Difference in Differences (95% CI) a,b 1.65 ( 3.87, 7.17) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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68 Table 4 12 Difference in Difference estimates among ind ividuals with diabetes complications Insurance % Insurance % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 84.57 89.95 5.39 ( 1.68, 12.45) Non Expansion States 80.06 89.51 9.45 (4.48, 14.42)*** Difference in Dif ferences (95% CI) a,b 4.07 ( 12.65, 4.52) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates si gnificant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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69 Table 4 13 Difference in Difference estimates among individuals with diabetes complications Usual Source of Care % Usu al Source of Care % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 92.42 91.60 0.81 ( 0.57.5.84) Non Expansion States 91.55 94.19 2.64 ( 0.57, 5.84) Difference in Differences (95% CI) a,b 3.45 ( 10.05, 3.15) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicate s significant differences at P < 0.01 Indicates significant differences at P < 0.05

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70 Table 4 14 Difference in Difference estimates among individuals with diabetes complications Timely Check Up % Timely Check Up % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 83.45 88.52 5.07 ( 1.69, 11.82) Non Expansion States 84.26 87.36 3.10 ( 2.07,8.26) Difference in Differences (95% CI) a,b 1.97 ( 6.45, 10.39) a Estimates based on adjusted regression models controllin g for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates signifi cant differences at P < 0.05

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71 Table 4 15 Difference in Difference estimates among individuals with 2 or more ACSC, Insurance % Insurance % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 86.06 93.16 7.11 (3.20, 1 1.02)*** Non Expansion States 77.25 85.65 8.40 (4.41, 12.38)*** Difference in Differences (95% CI) a,b 1.29 ( 6.86, 4.27) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupatio n, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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72 Table 4 16 Difference in Difference e stimates among individuals with 2 or more ACSC, Usual Source of Care % Usual Source of Care % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 91.71 93.02 1.32 ( 1.82, 4.45) Non Expansion States 88.66 90.83 2.17 ( 0. 89, 5.22) Difference in Differences (95% CI) a,b 0.85 ( 5.20, 3.50) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using sur vey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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73 Table 4 17 Difference in Difference estimates among individuals with 2 or more ACSC, Timely Check Up % Timely Check Up % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 77.00 84.07 7.07 (1.62, 12.52)* Non Expansion States 74.23 80.35 6.12 (1.61, 10.63)** Difference in Differences (95% CI) a,b 0.65 ( .6.0 7, 7.96) a Estimates based on adjusted regression models controlling for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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74 Table 4 18 Demographic Characteristics by Medicaid Expansion Status Characteristics Medicaid Expansion Status Non Expansion States (n=97,033,522) Expa nsion States (n=66,718,626) Income** Low Income 97,033,522 (24.4%) 66,718,626 (22.3%) All Others 300,836,848 (75.6%) 233,107,280 (77.7%) Age (years) 18 29 10,461,745 (19.1%) 7,795,887 (20.0%) 30 39 8,202,813 (15.0%) 7,065,585 (18.1%) 40 49 9,449 ,622 (17.3%) 7,212,095 (18.5%) 50 59 18,833,352 (34.4%) 11,571,221 (29.7%) 60 64 7,808,473 (14.3%) 5,317,076 (13.6%) Gender** Male 34,629,638 (35.7%) 26,653,840 (39.9%) Female 62,403,885 (64.3%) 40,064,786 (60.1%) Race/ethnicity*** Non Hispanic W hite 55,431,859 (57.1%) 36,637,715 (54.9%) Non Hispanic Black 17,687,573 (18.2%) 9,971,647 (14.9%) Hispanic 16,679,433 (17.2%) 14,320,457 (21.5%) Other 7,234,658 (7.5%) 5,788,807 (8.7%) Education Less than high school 35,331,937 (39.2%) 23,220,829 (3 7.1%) High school graduate or GED 23,217,542 (25.7%) 16,080,593 (25.7%) Some College or Associate degree 24,822,771 (27.5%) 17,388,730 (27.8%) College graduate or higher 6,818,991 (7.6%) 5,901,687 (9.4%)

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75 Table 4 18 Continued Employment* Employed 1 7,233,772 (20.9%) 13,954,277 (24.3%) Unemployed 65,060,742 (79.1%) 43,355,801 (75.7%) Marital Status Married 23,876,401 (28.6%) 16,292,776 (28.0%) Not Married 59,464,130 (71.4%) 41,814,165 (72.0%) Insurance Status*** Insured 85,696,943 (88.3%) 61, 654,531 (92.4%) Uninsured 11,336,580 (11.7%) 5,064,095 (7.6%) Note: All estimates were calculated using survey weights ***Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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76 Table 4 19 Proportion of Individuals with ACSC by Medicaid Expansion ACSC Medicaid Expansion Status Non Expansion States Expansion States Total Any ACSC Yes 32,111,890 (99.9%) 23,512,574 (99.9%) 55,624,465 (99.9%) No 23,357 (0.1%) 16,637 (0.1%) 39,994 (0.1%) COPD Yes 6,430,335 (20.0%) 5,417,916 (23.0%) 11,848,252 (21.3%) No 25,704,912 (80.0%) 18,111,295 (77.0%) 43,816,207 (78.7%) Asthma* Yes 13,721,034 (42.7%) 11,697,221 (49.7%) 25,418,255 (45.7%) No 18,414,213 (57.3%) 11 ,831,991 (50.3%) 30,246,204 (54.3%) CHF** Yes 2,349,858 (7.3%) 652,465 (2.8%) 3,002,323 (5.4%) No 29,785,389 (92.7%) 22,876,746 (97.2%) 52,662,136 (94.6%) UTI Yes 9,610,664 (29.9%) 5,744,972 (24.4%) 15,355,635 (27.6%) No 22,524,583 (70.1%) 17,7 84,240 (75.6%) 40,308,824 (72.4%) Note: All estimates were calculated using survey weights ***Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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77 Table 4 20. Difference in Difference estimates, ACSC associated ED hospitalizations ACSC associated ED hospitalizations % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 40.64 57.89 17.25 (2.15, 32.35)* Non Expansion States 53. 14 71.10 17.97 (5.67, 30.26)** Difference in Differences (95% CI) a,b 0.72 ( 18.70, 17.27) a Adjusted for gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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78 Table 4 21 Difference in Difference estimates, COPD associated ED hospitalizations COPD associated ED hospitalization s % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 10.51 18.57 8.06 ( 1.71, 17.84) Non Expansion States 22.60 14.86 7.74 ( 19.81, 4.33) Difference in Differences (95% CI) a,b 15.80 (1.21, 30.39)* a Adjusted for gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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79 Table 4 22 Difference in Difference estimates, asthma associated ED hospitalizations Asthma associated ED hospitalizations % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 14.57 25.86 1 1.29 (1.68, 20.90)* Non Expansion States 20.16 26.27 6.11 ( 4.40, 16.63) Difference in Differences (95% CI) a,b 5.17 ( 7.98, 18.33) a Adjusted for gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calc ulated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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80 Table 4 23 Difference in Difference estimates, CHF associated ED hospitaliza tions CHF associated ED hospitalizations % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 12.93 0.55 12.37 ( 22.94, 1.81)* Non Expansion States 4.68 15.38 10.70 ( 3.64, 25.05) Difference in Differences (95% CI) a ,b 23.08 ( 39.77, 6.38)** a Adjusted for gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant di fferences at P < 0.01 Indicates significant differences at P < 0.05

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81 Table 4 24 Difference in Difference estimates, UTI associated ED hospitalizations UTI associated ED hospitalizations % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 9.54 12.47 2.93 ( 7.53, 13.39) Non Expansion States 8.49 15.26 6.77 ( 1.59, 15.14) Difference in Differences (95% CI) a,b 3.84 ( 14.75, 7.07) a Adjusted for gender, race/ethnicity, age, insurance status, education, and employ ment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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82 Table 4 25 Difference in Difference est imates, ACSC associated ED visits ACSC associated ED visits % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 201.54 86.34 115.20 ( 251.51, 21.11) Non Expansion States 146.90 61.47 85.44 ( 160.82, 10.05)* Diff erence in Differences (95% CI) a,b 29.76 ( 179.31, 119.79) a Adjusted for gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0. 001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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83 Table 4 26 Difference in Difference estimates, COPD associated ED visits COPD associated ED visits % Pre Medicaid Expansion Post Medicaid Expansion D ifference (95% CI) a,b Expansion States 26.67 94.77 68.10 ( 945.70, 387.81) Non Expansion States 387.83 108.89 278.95 ( 11.32, 147.53) Difference in Differences (95% CI) a,b 347.05 ( 344.67, 1038.77) a Adjusted for gender, race/ethnicity, age, insura nce status, education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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84 Table 4 27 Difference in Difference estimates, asthma associated ED visits Asthma associated ED visits % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 360.44 92.66 267.78 ( 565.37, 298.13) Non Expansion States 72.74 51.87 20.87 ( 87.86, 46.12) Difference in Differences (95% CI) a,b 246.90 ( 554.74, 60.93) a Adjusted for gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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85 Table 4 28 Difference in Difference estimates, UTI associated ED visits UTI associated ED visits % Pre Medicaid Exp ansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 362.33 87.59 274.74 ( 681.54, 132.06) Non Expansion States 904.17 78.78 825.40 ( 1944.78, 294.99) Difference in Differences (95% CI) a,b 550.66 ( 360.90, 1462.21) a Adjusted fo r gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significan t differences at P < 0.05

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86 Table 4 29 Difference in Difference estimates, CHF associated ED visits CHF associated ED visits % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 35.76 565.80 530.04 ( 619.05, 1679.1 3) Non Expansion States 550.72 69.04 481.69 ( 1199.86, 236.48) Difference in Differences (95% CI) a,b 1011.73 ( 550.72, 2574.17) a Adjusted for gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calc ulated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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87 Table 4 30 Difference in Difference estimates, asthma associated length of s tay (LOS) Asthma associated LOS % Pre Medicaid Expansion Post Medicaid Expansion Difference (95% CI) a,b Expansion States 306.69 327.10 20.41 ( 143.79, 184.61) Non Expansion States 378.36 331.97 46.39 ( 199.53, 106.75) Difference in Differences (95% CI) a,b 66.80 ( 157.19, 290.78) a Adjusted for gender, race/ethnicity, age, insurance status, education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant di fferences at P < 0.01 Indicates significant differences at P < 0.05

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88 CHAPTER 5 DISCUSSION Descriptive Findings Discussion Overall, d escriptive findings showed that there were more individuals living in non expansion states when compared to expansion st ates. Results of Aim 1 descriptive findings showed that low income adults in expansion states fa red favorably in some instances, as they were less likely to be unemployed, and more likely to have a college degree or higher. Based on the Andersen model, pre disposing characteristics influences one s enabling resources. This becomes especially true within the US health care system where the primary mechanism of health insurance coverage is through employer sponsored health insurance. As such, e mployed individu als, as well as individuals with a college degree or higher are more likely to be insured as they are able to attain health insurance through their employers. Aim 1 descriptive findings also showed that expansion states were more likely to have low incom e individuals between the age of 18 and 29 years old. Prior to the ACA, young adults were more likely to be uninsured, and less likely to receive needed medical care 77 However, provisions within the ACA ha ve enabled young adul ts to have better acces s to health care Within the ACA, young adults are allowed to stay on their six which has drastically reduced the rate of uninsurance among moderate and high income young adults. For example, one study found that the rate of uninsurance among young adults decreased from above 30 % in 2009 to 19 % in the second quarter of 2014. Results further illustrated no significant change in health insurance coverage among low income young adults

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89 between 2010 and 2013. Howe ver, following the 2014 Medicaid expansion, there was a decline in uninsurance from 39.6 % to 30.7% among low income young adults 78 Results of that study indicate that despite the implementation of the ACA provision designed to improve access among y oung adults, low income young adults did not experience a reduction in uninsurance rates until the implementation of Medicaid expansion in expansion states These results indicate that for low income young adults living in an expansion state may enable th em to better access the enabling resource of health insurance when compared to low income young adults in non expansion states. L ow income individuals in expansion states were more likely to classify themselves as non White, and more likely to be u n married Results of a previous study found that non Whites and individuals who were unmarried experienced larger gains in coverage under the ACA, and Medicaid expansion 79 T herefore, non White and unmarried individuals living in expansion states may also benefit in terms of improved access to care measures Using Aim 2 data, for low income individuals, regardless of whether they had an ACSC or not, the results demonstrated similar descriptive findings as the descriptive findings of Aim 1. Aim 2 descriptive findings showed that low income individuals living in expansion states were more likely to be employed, and more likely to be non White. Lastly, descriptive findings of Aim 2 indicate that individuals in non expansion states were more likely to be uninsured and more like ly to be of low income. These findings highlight the coverage gap that often exists in non expansion states where individuals have incomes above Medicaid eligibility but below the criteria for Marketplace premium tax credits 2 therefore, leaving them uninsured.

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90 Aim 1 Discussion Aim 1 examined the early impact of Medicaid expansion on health care access among low income individuals with ACSC. Res ults of this aim indicate that Medicaid expansion was associated with a significant increase in insurance rates among low income individuals with ACSC living in expansion states However, no other significant changes for usual source of care and timely check ups were observed among individuals with ACSC and its subsets. These results are similar to a recent study by Torres et al. which reported a 4.9 percentage points increase in insurance coverage and no changes in having a personal physician among nonelderly patients with chronic disease following Medicaid expansion. 65 Results pertaining to timely check ups were also similar to a study which found no sign ificant increase in timely check ups among low income adults in Medicaid expansion states. 80 Therefore while Medicaid expansion achieved its goal of improving a ccess to health insurance, it did not impact usual source of care nor did it impact realized access through improvements in timely check ups. explain the results of A im 1. The ceiling effect often occ urs when the independent variable no longer has an effect on the outcome measure. For instance, p rior to the implementation of Medicaid expansion, data from this study showed that a bout 83% of low income respondents with ACSC in both expansion and non expa nsion states had a usua l source of care (Table F 1 ) With such high proportions of individuals with a usual source of care prior to Medicaid expansion, we are less likely to see a statistically significant increase in these proportions. T a lso indicates that prior to Medicaid expansion, about 70% of low income respondents with ACSC living in both expansion and non expansion

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91 states had timely check ups ( Table F 1 ) which falls above the national median of 67.7% 81 Results of Aim 1 show evidence to support the multi faceted nature of the Andersen model and are consistent with previous research that has foun d that access to health insurance does not equate to health care access. Results suggest that simply changing one aspect of the model, the enabling resource of health insurance, may not be sufficie nt to impact realized access. For example, a 2016 study by Baker, et al. proposed adequate provider capacity as a mechanism by which access to care and effective healthcare delivery are impacted 82 This suggests the need to consider the environmental factor of the number of full time equivalent primary care providers per 1000 population or simply the supply of provider s available to meet the demands of newly insured Medicaid enrollees In fact, s tudies have suggested that Medicaid expansion may have played a role in provider capacity issues such as longer wait times for appointments, and may res ult in the need for 2113 additional primary care providers if all states were to expand Medicaid. 59,83 Furthermore, a mixed methods study found that healthcare access issues were more often expressed among low income families with public insurance 84 Evidence suggests that the access to care issues fa ced by Medicaid beneficiaries are more pronounced when seeking specialist care 85 In addition to insufficient supply of provider s to meet increased demand for healthcare, l ow provider participation may als o play a significant role in creating inadequate provider capacity for Medicaid beneficiaries In fact, a stu dy showed that approximately 29.9% of office based physicians, and 33 % of primary care physicians did not accept new Medicaid patients in 2011 to 2 012 53,86

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92 Furt hermore, the CDC estimates that, in 2013, only 68.9% of physicians were accepting new Medicaid patients compared to 83.7% who were accepting new Medicare patients, and 84.7% who were accepting new p rivately insured patients 87 L ow provider participation may be due to a myriad of factors such as low reimbursement rates, payment delays, and concerns about the time it takes to manage patients with complex ne eds especially given the low reimbursement rates. With past provisions of the ACA focused on increasing Medicaid payments for some primary care services to 100 % of Medicare rates in 2013 and 2014 86 m uch of the literature has focused on how changes in willingness to accept Medicaid patients. While the l iterature shows that increas ed Medicaid provider payments may be o accept new Medicaid patients p articularly, after the ACA M edicaid reimbursement increase where one study observed an increase in Medicaid appointment availability from 58.7% to 66.4% 88 Furth er factors m ust be taken into consideration, such as admi nistrative burdens, as such factors may make some providers more or less responsive to changes in reimburs ement 89 Literature suggests that salari ed providers providers who regularly participate in Medicaid providers in Health Maintenance Organizations (HMOs) or hospital based practices, as well as providers perceived to be less desirable such as international medical graduates (IMGs) may be less responsive to reimbursement increases 90 Therefore, future initiatives need to not only consider the expansion of health insurance accessibility but also to consider the inclusion of incentives designed to encourage different provider groups to accept newly insured patients such as Medicaid patients.

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93 Aim 2 Discussion Aim 2 examined the early impact of Medicaid expansion on ACSC associated utilizati on among low income individuals. Results of this aim indicate d that, following Medicaid expansion, there was a 23.08 percentage point s differential reduc tion in CHF association ED hospitalizations. However, the re was a 15.80 percentage points differential increase in COPD associated ED hospitalizations following Medicaid expansion. According to a report by the Engelberg Center for Health Care Reform at Brookings, the treatment of chronic conditions, such as CHF, benefit from a three pronged approach that focuses on patient behavior physician or practice level interventions and public policy or population health strategies 91 Elements of physician interventions and public policy strategies have been frequently studied in studies of nonelderly and/or low income adult s For example, one such study examined the impact of Medicaid expansion on access to care, and utilization among low income adults living in an expansion state, a private option state and a non expansion state. Results of that study coincided with previou s studies which showed that insurance expansion was associated with increased primary care access increased outpatient visits and increased prescription medications 19,49,53,92 Although physician intervention (us ual source of care, and timely check ups), and Medicaid expansion as a public policy strategy are the focus of this study, the plausible role of patient behavior in the observ ed impact of Medicaid expansion will be discussed as a factor in the opposing res ults of COPD associated and CHF associated ED hospitalizations. In particular, the subsequent paragraphs focus on medication adherence to explain results pertaining to COPD associated and CHF associated ED hospitalizations. Furthermore, the role of

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94 provide r level factors such as the ED referrals by PCPs will be discussed. Additionally, the subsequent paragraphs address similar yet distinct characteristics between COPD and asthma and their role in the opposing results of significant increases in ED hospitali zations for COPD with no significant changes found for asthma associated ED hospitalizations. health care provider 93 Medication adherence is a critical factor in chronic disease management as it greatly affects the success of treatment and ultimately, the likelihood of adverse health outcomes such as hospitalization, worsened morbidity, and death. For example, a study showed that risk of hospitalization among patients with CHF was more than twice that of the general population 94,95 Furthermore, studies have also shown that poor medication and disease management adherence among patients with COPD lead s to emerge ncy hospitalization 96,97 Results of Aim 2 showed a 15.80 percentage point s differential increase in COPD associated ED hospitalizations while a 23.08 percentage points differential reduction in CHF association ED hospitalizations was found following Medicaid expansion. Despite evidence in the literature which points to an increase in primary care access, and increased prescription medication, it is important to note the differences in the rate of medication adhere nce between patients with CHF and patients with COPD. COPD patients are particularly vulnerable to medication non adherence as their treatment regimen includes the use of multiple medications, often including aerosolized medications that require daily use ranging from 2 to 6 times in a day and requires the

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95 use of different devices such as wearing oxygen in addition to implementing behavioral and lifestyle changes 95 As such, medication adherence rates for COPD patients are about 40% to 60% on average as compared to medication adherence rates for CHF p atients with adherence rates as high as 98% 95,98 Furthermore, it is estimated that as many as 85% of COPD patients use their inhaler in correctly 99 Therefore, while some evidence suggests that gaining insurance may lead to improved access to care among low income adults 19 patient behavior such as medication non adherence may hinder the full potential of public policy strategies such as Medicaid expansion Multiple studies have shown that medication adherence facilitates lowering health care use and costs 95,100 In addition to greater issues of medication adherence, COPD patients also experience acute exacerbations of chronic bronchitis (AECB) or COPD exacerbations which often leads to frequent ED visits 101 particularly, among low income COPD patients, as well as those who are younger than 65 years old. 102 T herefore, high medication n on adherence and the risk of AECB may serve as plausible factors for the observed increase in COPD ED hospitalization s On the other hand, the high rates of medication adherence among patients with CHF (upward of 98% versus upwa rd of 60% for COPD) may have facilitated the observed reduction in CHF ED hospitalizations. In fact, medication adherence has been shown to reduce health care use and costs among individuals with chronic vascular diseases such as CHF. 95 In addition to the role of patient behavior, provider level behaviors must be considered. Studies have shown that most patients who v isit the ED for non emergent causes were sent by their usual source of care sometimes without the consultation of a

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96 physician or by an automated message. 103,104 Furthermore, research suggests that EDs have become an increasingly vital source of hospital admissions due to office based bypass administrative barriers such as the difficulty of receiving non elective admissions 104 Results of this study and findings from previous research highlight the need to include prov ider level interventions in efforts to reduce preventable hospitalizations. In addition to patient behavior and provider level factors, it is impo rtant to note the role that disease pathology plays a role in the results of this study. This is particularly important when observing the opposing results of asthma and COPD, respiratory conditions with similar disease characteristics. Asthma and COPD are both respiratory conditions with reduced rate of pulmonary airflow. Despite this similarity, there are distin ct differences that influence the management and progression of disease. 105 Asthma differs in its age of onset which typically occurs during childhood COPD occurs primarily among older adults with a history of smoking. History of smoking differs am ong COPD and asthma patients with COPD patients reporting greater pack years therefore, putting them at risk for poorer lung function 105 In fact, smoking cessation has been shown to reduce the risk of COPD exacerbations which are often asso ciated w ith increased ED visits. 106 A notable key difference that influences disease management and progression of asthma and COPD is the reversibility of airway obstruction. In asthma, airway obstruction is generally fully reversible or nearly fully reversible where as COPD is characterized by airway obstruction that is not fully reversible even with adequate access and disease management As such, COPD is progressive in nature and is

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97 marked by declining lung function. 105 Consequently, al though asthma is more prevalent, the progressive nature of COPD which is marked by COPD exacerbations over the natural course of the disease 10 7 as well as the irreversibility of COPD airway obstruction contributes to a larger number of hospitalizations, and poorer disease prognosis 105 The observed in crease in ED hospitalization for COPD and the irreversibility of COPD airflow obstructi on may suggest the need to revisit the inclusion of COPD as a condition that is sensitive to access to adequate ambulatory care. Finally in recent times, the Centers for Medicare and Medicaid Services (CMS) ha ve placed significant focus on reducing hospit al readmissions due to the cost burden on the health care system. Since 2009, CMS has publicly reported risk standardized readmission rates in an effort to reduce hospital readmission rates. Acute heart failure is among the conditions that are publicly rep orted by CMS 108 Since these initiatives were implemented under the assumption that readmission rates serve as an indicator of quality of care, it is possible that the observed reduction in CHF is a reflection of such initiatives rather than the 2014 Medicaid expansion. Strengths and Limitations To the best of my knowledge, this is the first study to examine the early impact of Medicaid expansion on access and utilization among low income nonelderly adults with ambulatory care sensitive conditions, inclusive of individuals with diabetic complications. Furthermore, this study uses nationally esults add to a growing body of literature evaluating the 2014 Medicaid expansion, and in general, the effect of health insurance on access to care among a vulnerable population of low income adults with ACSC, as well as its impact on utilization measures that are often avoidable with proper access to ambulatory care.

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98 limitation s should be noted. First, the observational nature of this study c annot provide evidence of a causal relationship. Second, the study included self reported data that may introduce recall bias However, despite the use of self reported data, this study inc ludes some self reported data that is supplemented by data provided by the providers and hospitals that served respondents of the household component of MEPS. Third, this study used the first ICD 9 CM code to classify ACSC associated events. However, the f irst ICD 9 CM code may not accurately reflect the primary diagnosis of some patients 72 Lastly, these findings may not present the full extent of changes under the 2014 Medicaid expansion because at the time of this study, the latest available data was 2015 and 2014 for BRFSS and ME PS, respectively. As such, more time may be needed to examine the true impact of Medicaid expansion on the outcome measures of interest. Furthermore, the changes reflected might include external initiatives that were hospital readmission rates that were implemented prior to January 2014. Future Research and Policy Implications Fu ture research should examine patients, the appropriat e ness of ED referrals by office based physicians, as well as medication adherence in the context of Medicaid expansion. This is especially critical as research suggests that providers may be unwilling to accept Medicaid; therefore, creating a barrier to he althcare access despite the availability of healthcare coverage. This is particularly true for providers faced with the decision of accepting patients with ACSC in light of low reimbursement r ates, high administrative workl oad, in addition to the complexit y of treating patients with ACSC or comorbidities. Furthermore, research has shown that physicians may play a critical role in ED visits for non emergent cases

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99 and ED hospitalizations as most ED visits for non emergent cases were initiated by the physician partially due to administrative barriers for non elective admissions or simply their inability to see patients. Lastly, medication adherence in the context of health reform should be examined as adherence plays a critical role in alleviating healthcare u tilization and costs; partic ularly among patients with ACSC, such as those with chronic conditions whose treatment success relies on adherence to o ften complex treatment regimens. Furthermore, this study suggests that the observed increase in ED hospitali zations for COPD may be partially due to the progressive nature of COPD and the irreversibility of COPD airflow obstruction which may suggest the need to revisit the inclusion of COPD as a condition that is sensitive to access to adequate ambulatory care. T his study attempted to evaluate the early impact of Medicaid expansion on ACSC associated LOS; however, the data could not sustain the entirety of the analysis. Future research should examine the impact of Medicaid expansion on ACSC associated LOS as res earch has shown that insurance status affects LOS; however, the direction of the effect remains inconclusive. Results of this study add to a growing body of literature that indicates that the expansion of health coverage may not be enough in improving acce ss to care. F uture policy initiatives for low income adults, including those with ACSC, should not only focus on the expansion of health insurance coverage. Rather, future policies should include erable and complex populations while accounting for provider mix in each state as d ifferent provider types such as salaried providers may be less responsive to such incentives. Furthermore,

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100 medication non adherence should be considered as an important fact or in the improvement of healthcare outcomes. This may be achieved through a greater focus on simplified drug regimens, patient education, case management, and pharmaceutical counseling. 100 These considerations are especially critical as recent evidence suggests that low access to primary care may not be the primary drive of ACSC hospitalizations. 109 Therefore, future policy initiatives should think outside the realms of simply increase healthcare covera ge and primary care access.

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101 CHAPTER 6 CONCLUSION This study examined the early impact of Medicaid expansion on access to care among individuals with ACSC, as well as its impact on ACSC associated utilization measures. Results of this study indicate that following Medicaid expansion, there was a 4.19 percentage point s differential increase in insurance coverage among individuals with ACSC living in expansion states. Furthermore, results of the study indicate a 15.08 percentage points differential increase in COPD ED hospitalizations, whereas a 23.08 percentage points differential reduction in CHF ED hospitalizations was observed. While Medicaid expansion achieved its goal of increasing insurance coverage, results of this study indicate that other considera tions accept Medicaid patients, as well as medication adherence are especially important among low income individuals with ACSC, as well as to achieve improvements in ACSC associated utilization measures. These considera tions align with recommendations for a three pronged approach that focuses on patient behavior physician or practice level interventions and public policy or population health strategies when addressing chronic disease treatment. Given the changing schem e of health care, results of this study provide evidence pertaining to the effect of health insurance expansion and offer s insight in to current and future health reform initiatives Most importantly, results suggest the need for decision makers to consider other factors that may play integral roles in addressing access to care issues particularly, among low income individuals and individuals with ACSC.

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102 well as medication adherence in the context of health reform, such as Medicaid expansion.

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103 APPENDIX A STUDY METHODS: PREVENTION QUALITY INDICATORS Table A 1. Technical Specifications for Prevention Quality Indicators (PQI), Source: 2013 2014 Medical Expenditure Panel Surve y Medical Conditions Corresponding ICD 9 Codes Chronic Obstructive Pulmonary Disease (COPD) 4910 4911 49120 49121 4918 4920 4928 494 4940 4941 496 Asthma 49300 49301 49302 49310 49311 49312 49320 49321 49322 49381 49382 49390 49391 49392 Congestive h eart failure; nonhypertensive 39891 4280 4281 42820 42821 42822 42823 42830 42831 42832 42833 42840 42841 42842 42843 4289 Urinary Tract Infections 59010 59011 5902 5903 59080 59081 5909 5950 5959 5990

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104 APPENDIX B STUDY METHODS: VARIABLES Table B 1. A im 1 Variables, 2012 2015 Behavioral Risk Factor Surveillance System (BRFSS) Variable Definition Data Source Dependent (Outcomes) Health Care Coverage (HLTHPLN1) Dummy coded variable that was used to determine access to health insurance. 2012 2015 BR FSS Health Care Access (PERSDOC2) Dummy coded variable that was used to determine whether an individual has a person they think of as their personal doctor or health care provider. 2012 2015 BRFSS Routine Check Up (CHECKUP1) Dummy coded variable that was used to determine whether an individual has had their recommended routine checkup (i.e. a general physical exam, not an exam for a specific injury, illness or condition within the past year or anytime less than 12 months ago). 2012 2015 BRFSS Inde pendent Variable Post Medicaid Expansion (POST*EXP) Interaction term developed based on the state variable, _STATE and interview year variable, IYEAR 2012 2015 BRFSS Explanatory Variables Sex (SEX) Dummy coded variable indicating Male or Female 2 012 2015 BRFSS

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105 Table B 1. Continued Age (_AGEG5YR) Fourteen level categorical age variable 2012 2015 BRFSS Employment Status (EMPLOY1) Categorical variable indicating employment status 2012 2015 BRFSS Race/Ethnicity (_RACEGR3) 5 Level Race /ethnicity variable 2012 2015 BRFSS Marital Status (MARITAL) Categorical marital status variable 2012 2015 BRFSS Education (EDUCA) Categorical education level variable 2012 2015 BRFSS Income (INCOME2) Categorical household income from all sources 201 2 2015 BRFSS

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106 T able B 2. Aim 2 Variables, 2013 2014 Medical Expenditure Panel Survey (MEPS) Variable Definition Data Source Dependent (Outcomes) Emergency Department Visits ( ER TOTAL ) Total number of emergency room visits 2013 2014 MEPS HC Event File s, Emergency Room Visits File Hospitalizations th rough the Emergency Department ( EMERROOM) Binary variable that I dentifies hospital stays originating from the Emergency Department 2013 2014 MEPS HC Hospital Inpatient Stays File ICD 9 CM Code for Co nditions and/or Procedure (ICD9CODX) Fully specified ICD 9 codes. Used to identify Ambulatory Care Sensitive Conditions 2013 2014 MEPS HC Medical Conditions File or the encrypted Fully Specified ICD 9 codes (if approved by MEPS) Person ID (DUPERSID) Use d to append person level information such as the Emergency room visit events to the Medical Conditions File 2013 2014 MEPS Household Component Files Independent Variable Post Medicaid Expansion (POST*EXP) Interaction term developed based on the confid ential and non public use state variable, STATE COUNTY FIPS CODES, the event date month and year (ERDATEMM, ERDATEYR) in the Emergency Room Visits File and the event start date month and year (IPBEGMM, IPBEGYR) in the hospital inpatient stays file 2013 20 14 State/County FIPS codes, MEPS HC Event Files, Emergency Room Visits Files, Hospital Inpatient Stays File Table B 2 Continued

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107 Explanatory Variables Sex (SEX) Categorical variable indicating Male or Female 2013 2014 MEPS HC Full Year Consolidated Files Age (AGELAST) Continuous age variable 2013 2014 MEPS HC Full Year Consolidated Files Race/Ethnicity (RACETHX) 5 Level Race/ethnicity variable 2013 2014 MEPS HC Full Year Consolidated Files Education ( EDUYRDG ) Categorical education level vari able 2013 2014 MEPS HC Full Year Consolidated Files Insurance Status (INSCOV13/INSCOV14) Categorical insurance status variable 2013 2014 MEPS HC Full Year Consolidated Files Employment (EMPST31/EMPST41/EMPST53) Categorical employment status variable 2 013 2014 MEPS HC Full Year Consolidated Files Poverty Level ( POVLEV13/POVLEV14 ) Family Income as a percentage of poverty line 2013 2014 MEPS HC Full Year Consolidated Files

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108 APPENDIX C STUDY METHODS: REGRESSION MODELS Aim 1 Regression Models Table C 1 Logi t Regression: Probability of being insured, having a usual source of care and having timely checkup for individuals with ambulatory care sensitive conditions living in expansion states post Medicaid expansion Individuals with ACSC Difference in Di fferences Unadjusted (95% CI) a Adjusted (95% CI) a,b Insurance Status Uninsured Ref Ref Insured 0.57 (0.32, 0.82 )*** 0.54 (0.24, 0.83 )*** Usual Source of Care No Usual Source of Care Ref Ref Usual Source of Care 0.07 ( 0.19, 0.32 ) 0.11 ( 0. 19, 0.41 ) Timely Check Up Untimely Check up Ref Ref Timely Check up 0.02 ( 0.18, 0.22 ) 0.05 ( 0.19, 0.28 ) a Adjusted for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weigh ts *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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109 Table C 2 Logi t Regression: Probability of being insured, having a usual source of care and having tim ely checkup for individuals with 2 or more ambulatory care sensitive conditions living in expansion states post Medicaid expansion Individuals with Difference in Differences Unadjusted (95% CI) a Adjusted (95% CI) a,b Insurance Status Uninsured Ref Ref Insured 0.44 ( 0.06, 0.94 ) 0.22 ( 0.35, 0.79) Usual Source of Care No Usual Source of Care Ref Ref Usual Source of Care 0.18 ( 0.34, 0.70) 0.06 ( 0.66, 0.54) Timely Check Up Untimely Check up Ref Ref Timely Check up 0.07 ( 0.31, 0.44) 0.11 ( 0.35, 0.57) a Adjusted for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calc ulated using survey weights ***Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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110 Table C 3. Logit Regression: Probability of being insured, having a usual sour ce of care and having timely checkup for individuals with asthma living in expansion states post Medicaid expansion Individuals with Asthma Difference in Differences Unadjusted (95% CI) a Adjusted (95% CI) a,b Insurance Status Uninsured Ref Ref Insured 0.64 (0.34, 0.94 )*** 0.56 (0.21, 0.91 )** Usual Source of Care No Usual Source of Care Ref Ref Usual Source of Care 0.19 ( 0.11, 0.50) 0.29 ( 0.08, 0.66) Timely Check Up Untimely Check up Ref Ref Timely Check up 0.04 ( 0.20, 0.29) 0.00 ( 0.29, 0.29) a Adjusted for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights ***Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0. 01 Indicates significant differences at P < 0.05

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111 Table C 4 Logit Regression: Probability of being insured, having a usual source of care and having timely checkup for individuals with COPD living in expansion states post Medicaid expansion Individual s with COPD Difference in Differences Unadjusted (95% CI) a Adjusted (95% CI) a,b Insurance Status Uninsured Ref Ref Insured 0.44 (0.05, 0.83 )* 0.48 (0.03, 0.93 )* Usual Source of Care No Usual Source of Care Ref Ref Usual Source of Care 0.1 2 ( 0.48, 0.25) 0.23 ( 0.66, 0.21) Timely Check Up Untimely Check up Ref Ref Timely Check up 0.03 ( 0.31, 0.24 ) 0.12 ( 0.20, 0.44) a Adjusted for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were ca lculated using survey weights ***Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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112 Table C 5 Logistic Regression: Probability of being insured, having a usual source of care and having timely checkup for individuals with diabetes complications living in expansion states post Medicaid expansion Individuals with Diabetes Complications Difference in Differences Unadjusted (95% CI) a Adjusted (95% CI) a,b I nsurance Status Uninsured Ref Ref Insured 0.14 ( 0.49, 0.77) 0.29 ( 1.08, 0.50) Usual Source of Care No Usual Source of Care Ref Ref Usual Source of Care 0.32 ( 1.05, 0.41) 0.55 ( 1.53, 0.44) Timely Check Up Untimely Check up Ref Ref Timel y Check up 0.14 ( 0.44, 0.72) 0.18 ( 0.53, 0.89) a Adjusted for gender, race/ethnicity, age, marital status, education, occupation, and income b All estimates were calculated using survey weights ***Indicates significant differences at P < 0.001 ** Indica tes significant differences at P < 0.01 Indicates significant differences at P < 0.05

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113 Aim 2 Regression Models Table D 1 Logi t Regression: Probability of having had an ACSC associated hospitalization through the ED for low income individuals living in expansion states post Medicaid expansion ED Hospitalizations Difference in Differences Unadjusted (95% CI) a Adjusted (95% CI) a,b ACSC associated No Ref Ref Yes 0.58 (0.11, 1.06)* 0.84 (0.26, 1.41)** COPD associated No Ref Ref Yes 0.72 ( 0.06, 1.51) 0.63 ( 1.52, 0.26) Asthma associated No Ref Ref Yes 0.37 ( 0.90, 0.16) 0.42 ( 0.34, 1.19) CHF associated No Ref Ref Yes 0.84 ( 0.28, 1.96) 2.11 ( 1.97, 6.19) UTI associated No Ref Ref Yes 0.35 ( 0.33, 1.04) 0.72 ( 0.27, 1.70) a Adjusted for gender, race/ethnicity, age, insurance status education, and employment status b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indica tes significant differences at P < 0.05

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114 Table D 2 Negative Binomial Regression: DID estimators for change in ACSC associated ED visits for low income individuals living in expansion states post Medicaid expansion ED Visits Difference in Differences Unadjusted (95% CI) a Adjusted (95% CI) a,b ACSC associated No Ref Ref Yes 0.83 ( 1.32, 0.35)** 0.87 ( 1.42, 0.32)** COPD associated No Ref Ref Yes 0.84 ( 2.05, 0.03) 1.27 ( 2.90, 0.36) Asthma associated No Ref Ref Yes 0.27 ( 0.8 7, 0.33) 0.34 ( 1.30, 0.63) CHF associated No Ref Ref Yes 1.04 ( 2.23, 0.15) 2.08 ( 3.95, 0.20)* UTI associated No Ref Ref Yes 1.56 ( 2.40, 0.72)*** 2.44 ( 3.54, 1.34)*** a Adjusted for gender, race/ethnicity, insurance status and age b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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115 Table D 3 Generalized Linear Model: DID estimators fo r change in asthma associated length of stay (LOS) for low income individuals living in expansion states post Medicaid expansion Length of Stay Difference in Differences Unadjusted (95% CI) a Adjusted (95% CI) a,b Asthma associated No Ref Ref Y es 0.11 ( 1.13, 0.91) 0.20 ( 0.49, 0.88) a Adjusted for gender, race/ethnicity, insurance status age, and education b All estimates were calculated using survey weights *** Indicates significant differences at P < 0.001 ** Indicates significant differenc es at P < 0.01 Indicates significant differences at P < 0.05

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116 APPENDIX D AIM 1 MARGINS PLOTS Figure D 1. Margins plot indicating the change in insurance among low income nonelderly adults with ACSC by Medicaid Expansion Status, Pre Post Medicaid Expa nsion Figure D 2. Margins plot indicating the change in usual source of care among low income nonelderly adults with ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion

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117 Figure D 3. Margins plot indicating the change in timely checkups amo ng low income nonelderly adults with ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion Figure D 4. Margins plot indicating the change in insurance among low income nonelderly adults with asthma by Medicaid Expansion Status, Pre Post Medic aid Expansion

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118 Figure D 5. Margins plot indicating the change in usual source of care among low income nonelderly adults with asthma by Medicaid Expansion Status, Pre Post Medicaid Expansion Figure D 6. Margins plot indicating the change in timely ch eckups among low income nonelderly adults with asthma by Medicaid Expansion Status, Pre Post Medicaid Expansion

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119 Figure D 7. Margins plot indicating the change in insurance among low income nonelderly adults with COPD by Medicaid Expansion Status, Pre Po st Medicaid Expansion Figure D 8. Margins plot indicating the change in usual source of care among low income nonelderly adults with COPD by Medicaid Expansion Status, Pre Post Medicaid Expansion

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120 Figure D 9. Margins plot indicating the change in ti mely checkups among low income nonelderly adults with COP D by Medicaid Expansion Status, Pre Post Medicaid Expansion Figure D 10. Margins plot indicating the change in insurance among low income nonelderly adults with diabetes complications by Medicaid Expansion Status, Pre Post Medicaid Expansion

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121 Figure D 11. Margins plot indicating the change in usual source of care among low income nonelderly adults with diabetes complications by Medicaid Expansion Status, Pre Post Medicaid Expansion Figure D 12. Margins plot indicating the change in timely checkups among low income nonelderly adults with diabetes complications by Medicaid Expansion Status, Pre Post Medicaid Expansion

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122 Figure D 13. Margins plot indicating the change in insurance among lo w income nonelderly adults with multi ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion Figure D 14. Margins plot indicating the change in usual source of care among low income nonelderly adults with multi ACSC by Medicaid Expansion Status Pre Post Medicaid Expansion

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123 Figure D 15. Margins plot indicating the change in timely checkups among low income nonelderly adults with multi ACSC by Medicaid Expansion Status, Pre Post Medicaid Expansion

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124 APPENDIX E AIM 2 MARGINS PLOTS Figure E 1. Margins plot indicating the change in the yearly count of ACSC associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion Figure E 2. Margins plot indicating the change in the yearly count of as thma associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion

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125 Figure E 3. Margins plot indicating the change in the yearly count of CHF associated ED visits among low income nonelderly adults b y Medicaid Expansion Status, Pre Post Medicaid Expansion Figure E 4. Margins plot indicating the change in the yearly count of COPD associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion

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126 F igure E 5. Margins plot indicating the change in the yearly count of UTI associated ED visits among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion Figure E 6. Margins plot indicating the change in ACSC associated ED hospitalizations among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion

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127 Figure E 7. Margins plot indicating the change in asthma associated ED hospitalizations among low income nonelderly adults by Medicaid Exp ansion Status, Pre Post Medicaid Expansion Figure E 8. Margins plot indicating the change in CHF associated ED hospitalizations among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion

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128 Figure E 9. Margins plot ind icating the change in COPD associated ED hospitalizations among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion Figure E 10. Margins plot indicating the change in UTI associated ED hospitalizations among low incom e nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid Expansion

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129 Figure E 11. Margins plot indicating the change in asthma associated length of stay (LOS) among low income nonelderly adults by Medicaid Expansion Status, Pre Post Medicaid E xpansion

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130 APPENDIX F DISCUSSION SECTION SUPPLEMENTAL TABLES Table F 1 Propo rtion of Outcome Measures by Medicaid Expansion Status Pre Medicaid Expansion Outcome Measure (%) Medicaid Expansion Status Non Expansion States Expansion States Total Ins urance Status *** Yes 72.68 77.05 74.50 No 27.32 22.95 25.50 Usual Source of Care Yes 83.04 83.51 83.24 No 16.96 16.49 16.76 Timely Check ups Yes 70.28 70.92 70.55 No 29.72 29.08 29.45 Note: All estimates were calculated using survey weights ***Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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131 Table F 2 Propo rtion of Outcome Measures by Medicaid Expansion Status P ost Medicaid Expansio n Outcome Measure (%) Medicaid Expansion Status Non Expansion States Expansion States Total Insurance Status*** Yes 79.97 89.93 84.08 No 20.03 10.07 15.92 Usual Source of Care Yes 84.44 85.71 84.97 No 15.56 14.29 15.03 Timely Check ups Yes 73.74 7 4.74 74.15 No 26.26 25.26 25.85 Note: All estimates were calculated using survey weights ***Indicates significant differences at P < 0.001 ** Indicates significant differences at P < 0.01 Indicates significant differences at P < 0.05

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141 BIOGRAPHICAL SKETCH Shenae Kimberly Samuels was born in January 1989 in St. Andrew, Jamaica an d moved to Florida in June 2 014. Shenae obtained a Bachelor of Science in Health Education from the University of Florida in May 2010. After finishing her b achelor s degree, Shenae became interested in the influence of health policy on adverse health behav iors and health outcomes. As a result of this interest, Shenae later pursued and obtained a Master of Public Health with a concentration in public health management and p olicy in May 2013. Through her Master of Public Health studies, Shenae became further interested in health disparities research and its intersection with health policy. In 2014, Shenae began her PhD studies in health services re search at the University of Florida where she further explored her interests in health disparities research This ultimately led to her dissertation topic focused on the impact of Medicaid expansion on access to care and utilization among individuals with ambulatory care sensitive conditions.