Behavioral and Pharmacoepidemiological Risk Factors and Mediators for Type Ii Diabetes Mellitus

BEHAVIORAL AND PHARMACOEPIDEMIOLOGICAL RISK FACTORS AND MEDIATORS FOR TYPE II DIABETES MELLITUS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Victoria A. Zigmont, BS, MPH

Graduate Program in Public Health

The Ohio State University

2015

Dissertation Committee:

Susan Olivo-Marston, PhD, MPH, Advisor

Stephen Clinton, MD, PhD

Randall Harris, MD, PhD

Gail Kaye, PhD, RD, LD, PLCC

Abigail Shoben, PhD

Victoria A. Zigmont

2015

ABSTRACT

BACKGROUND: Type II diabetes mellitus (T2DM) is a serious and relevant public health problem. Lifestyle programs like the Diabetes Prevention Program (DPP) can delay a patient’s progression to T2DM. Identifying which patients are likely to enroll in these programs and tailoring recruitment approaches to those with perceived barriers is one way to increase engagement in health promotion. Previous literature on antidepressant use and T2DM has raised concerns that antidepressant use is associated with T2DM, however these studies have been variable in quality. Similarly, while statins are one of the most widely prescribed medications in the United States; concern has been raised that they are associated with incident T2DM. The effect of statin use on glycemic control in nondiabetic patients is currently unclear.

METHODS: Three retrospective cohort studies were conducted among individuals in the

Midwest enrolled in an insurance plan from 2011 through 2014. These studies combined data from medical and pharmacy claims, annual biometric screenings and a health survey.

The goal of the first study was to identify differences between prediabetic patients who did and did not volunteer to enroll in a worksite DPP. Covariates were compared for prediabetics who did and did not elect to participate in the DPP using multivariable logistic regression (n=2,158). Generalized linear mixed models with random intercepts were then used to compare biometric trajectories for the two groups. The goal of studies two and three was to identify if antidepressant or statin users who were members of this ii

insurance cohort were at risk for elevated HbA1c or T2DM development. The second study was restricted to patients with indications for antidepressant use (n=2,063) and the third study was among patients with indications for statin use (n=7,064). The methods were identical for studies two and three. Elevated glycosylated hemoglobin A1c (HbA1c) was compared for nondiabetic antidepressant, or statin, users and nonusers after applying inverse probability weighting with logistic regression for the outcome of elevated HbA1c

(>6.0). To evaluate the risk of T2DM development, Cox proportional hazard models with time varying antidepressant, or statin, use compared incident T2DM diagnoses among antidepressant or statin users and nonusers. Comparisons were also made by duration of use and class for antidepressant, or statin, users and nonusers and intensity of dose was compared for statin users.

RESULTS: The first analysis identified that prediabetics were more likely to express interest in the DPP if they were female, African American, older, free of hypertension, had more doctor visits, or lower self-efficacy to make healthy lifestyle changes. The second analysis found no differences in elevated HbA1c, or new onset T2DM, across antidepressant users, or duration of use. The third analysis found no differences in elevated HbA1c, however new onset of T2DM was highest among statin users who had been taking statins for 2 years or longer; no differences were observed by statin class.

CONCLUSION: Current recruitment strategies are reaching individuals who are not representative of the greater prediabetic population. Targeted recruitment efforts for underrepresented groups are currently underway. A higher prevalence of elevated HbA1c was not observed among nondiabetic users of antidepressants or statins after controlling for baseline differences across groups. Additionally these findings indicate that the

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elevated T2DM risk among statin users may be isolated to a group of patients with higher

T2DM risk, and for antidepressant users, an elevated risk of T2DM was not observed.

DEDICATION

To my loving husband Jason who has been endlessly supportive of my love for research and data. Thank you for being awesome and sticking by me.

To my parents David and Elizabeth, and brother, Alex, for their support of my academic endeavors and for reminding me when it’s time to get back on track.

ACKNOWLEDGEMENTS

There are many people who have helped me in the past years on this journey to getting my PhD. Thank you to the faculty at the Ohio State University College of Public Health; you have been instrumental in sculpting me into the researcher that I am today. Thank you to my advisor Dr. Susan Olivo-Marston, for her continued guidance, encouragement, mentorship and patience throughout this dissertation. Thank you for the academic freedom you have given me throughout this process. To my Committee: Dr. Abigail Shoben, Dr. Randall Harris, Dr. Gail Kaye, and Dr. Steven Clinton, thank you for your time, expertise, guidance and thoughtful feedback throughout this journey. Thank you to Dr. Judy Schwartzbaum for being a wonderful sounding board to brainstorm methodology questions and to Dr. Bo Lu for sharing your causal inference expertise with me. Thank you to Dr. Richard Snow for mentorship and guidance, and for allowing me to apply these projects to practice and to improving patient care. Thank you to my good friend Allahna Esber and my colleagues at OhioHealth who have been there for a much needed coffee break. To my mentors, Dr. Sandra Bulmer, and Dr. Jeffrey Shannon whose guidance and skilled teaching encouraged me to pursue a PhD.

VITA

2003 – 2007 ...... B.S. Chemistry and Physiology &

Neurobiology, University of Connecticut

2008 ...... Organic Chemist, Pfizer Inc. Central

Nervous System Chemistry, Groton, CT

2008 – 2009 ...... High School Science Teacher, Westhill

High School, Stamford, CT

2009 – 2010 ...... Research Assistant Volunteer, Hepatic

Insulin Resistance in Type II Diabetes,

VA Hospital, West Haven, CT

2010 – 2011 ...... Graduate Assistant Coordinator, EHTP

Department of Public Health, Southern

Connecticut State University

2010 – 2012 ...... M.P.H. Health Promotion

Department of Public Health

Southern Connecticut State University

2011 – 2012 ...... Research Assistant, The Rudd Center for

Food Policy and Obesity, New Haven, CT

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2011 – 2012 ...... Graduate Research Fellow, Department of

Public Health, Southern Connecticut

State University

2012 – 2013 ...... Graduate Research Associate and

Program Coordinator, Comprehensive

Comprehensive Cancer Center

The Ohio State University Medical Center

2013 – 2014 ...... Graduate Research Associate and

Teaching Assistant, Division of

Epidemiology College of Public Health

The Ohio State University

2014 to present ...... Clinical Database Informatist, Department

of Clinical Transformation, OhioHealth

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PUBLICATIONS

Zigmont, V.A., Garrett A., Peng J., Seweryn M., Rempala G., Harris R., Holloman C., Karavodin L., Gundersen T., Ahlbom A., Feychting M., Johannesen T., Grimsrud T., and Schwartzbaum J. (2015). Association between Prediagnostic Serum 25- Hydroxyvitamin D Concentration and Glioma Risk among Older Men. Nutrition and Cancer. 67, 1120-30.

Zigmont, V.A. and Bulmer S. M. (2015). The Impact of Caloric Knowledge on College Student's Fast Food Purchasing Intentions. The American Journal of Health Education, 46, 2, 70-78.

Duplantier, A. J., Becker, S. L., Bohanon, M. J., Borzilleri, K. A., Chrunyk, B. A., Downs, J. T., Hu, L. H., El-Kattan, A., James, L. C., Liu, S., Lu, J., Maklad, N., Mansour, M. N., Mente, S., Piotrowski, M. A., Sakya, S. M., Sheehan, S., Steyn, S. J., Strick, C. A., Williams, V. A., & Zhang, L. (2009). Discovery, SAR, and Pharmacokinetics of a novel 3-hydroxyquinolin-2(1H)-one series of potent d-Amino Acid Oxidase (DAAO) inhibitors. Journal of Medicinal Chemistry, 52, 3576-3585.

Bowman, M. D., Holcomb, J. L., Kormos, C. M., Leadbeater, N. E., & Williams, V.A. (2007). Approaches for scale-up of microwave-promoted reactions. Organic Process Reviews and Development, 12, 41-57.

Leadbeater, N. E., Williams, V. A., Barnard, T. M., & Collins, M. J. (2007). Solvent- free, open-vessel microwave-promoted Heck couplings: From the mmole to the mole scale. Synlett, 18, 2953-2958.

Leadbeater, N. E., Williams, V. A., Barnard, T. M., & Collins, M. J. (2006). Open- vessel microwave- promoted Suzuki Reactions using low levels of palladium catalyst: Optimization and scale-up. Organic Process Reviews and Development, 10, 833-837.

Arvela, R. K., Leadbeater, N. E., Sangi, M. S., Williams, V. A., Grandos, P., & Singer, R. D. (2005). A Reassessment of the transition-metal free Suzuki-type coupling methodology. Journal of Organic Chemistry, 70, 161-168.

Leadbeater, N. E., Pillsbury, S. J., Shanahan, E., & Williams, V. A. (2005). An assessment of the technique of simultaneous cooling in conjunction with microwave heating for organic synthesis. Tetrahedron, 61, 3565-3585.

FIELDS OF STUDY

Major Field: Public Health

Specialization: Epidemiology

Minor: Biostatistics

TABLE OF CONTENTS ABSTRACT ...... II DEDICATION ...... V ACKNOWLEDGEMENTS ...... VI VITA ...... VII PUBLICATIONS ...... IX FIELDS OF STUDY ...... X ABBREVIATIONS ...... XIV LIST OF TABLES ...... XV LIST OF FIGURES ...... XVIII CHAPTER 1 LITERATURE REVIEW ...... 1 TYPE II DIABETES MELLITUS ETIOLOGY AND RISK FACTORS ...... 1 TYPE II DIABETES MELLITUS DIAGNOSIS ...... 3 TYPE II DIABETES MELLITUS TREATMENT ...... 4 PREDIABETES ETIOLOGY ...... 4 DIABETES PREVENTION PROGRAMS FOR PREDIABETICS ...... 5 HEALTH PROGRAMS AND SELECTION BIAS ...... 5 PHARMACOEPIDEMIOLOGY AND TYPE II DIABETES MELLITUS ...... 6 AD USE AND SUBSEQUENT TYPE II DIABETES: THE MECHANISM ...... 7 AD USE AND TYPE II DIABETES ...... 8 STATIN USE AND SUBSEQUENT TYPE II DIABETES: THE MECHANISM ...... 11 STATIN USE AND TYPE II DIABETES ...... 12 RESEARCH OBJECTIVES AND HYPOTHESES ...... 14 CHAPTER 2 METHODS ...... 17 STUDY POPULATION ...... 17 HEALTH ASSESSMENT SURVEY ...... 17 PHARMACY CLAIMS ...... 18 MEDICAL CLAIMS ...... 18 BIOMETRIC DATA ...... 18 AIM 1. DETERMINE WHETHER PREDIABETICS WHO EXPRESS INTEREST IN THE DPP ARE DIFFERENT THAN THOSE WHO DO NOT EXPRESS INTEREST ...... 20 STATISTICAL APPROACH FOR AIM 1 ...... 20 POWER ANALYSIS FOR AIM 1 ...... 22 AIMS 2/3. DETERMINE WHETHER ANTIDEPRESSANT/ STATIN USERS ARE AT HIGHER RISK OF T2DM THAN ANTIDEPRESSANT/ STATIN NONUSERS...... 22 IDENTIFICATION OF THE POPULATION WITH MENTAL HEALTH DIAGNOSES USING MEDICAL CLAIMS ...... 22 xi

IDENTIFICATION OF THE POPULATION WITH CARDIOVASCULAR DISEASE USING MEDICAL CLAIMS ...... 23 ASSESSMENT OF PHARMACOEPIDEMIOLOGY (ANTIDEPRESSANT OR STATIN) EXPOSURE VARIABLES ...... 25 ANTIDEPRESSANT CLASS DEFINITIONS ...... 26 STATIN CLASS DEFINITIONS ...... 27 STATIN DOSE ASCERTAINMENT ...... 27 SENSITIVITY ANALYSES ...... 28 EXCLUSIONS FROM THE STUDY POPULATION ...... 28 DIABETES ASCERTAINMENT ...... 29 INVERSE PROBABILITY WEIGHTED- ESTIMATORS ...... 29 LOGISTIC REGRESSION WITH INVERSE PROBABILITY WEIGHTING TO EVALUATE ELEVATED GLYCOSYLATED HEMOGLOBIN (> 6.0)...... 31 EXPOSURE AND ASSESSMENT CLASSIFICATION FOR SURVIVAL ANALYSIS TO EVALUATE TIME TO TYPE II DIABETES MELLITUS ...... 32 TIME-DEPENDENT COVARIATES ...... 33 SURVIVAL ANALYSIS TO EVALUATE TIME TO T2DM ...... 33 POWER ANALYSIS FOR AIM 2 ...... 35 POWER ANALYSIS FOR AIM 3 ...... 35 INSTITUTIONAL REVIEW BOARD ...... 36 CHAPTER 3 ARE PREDIABETICS WHO EXPRESS INTEREST IN THE DPP DIFFERENT THAN THOSE WHO DO NOT EXPRESS INTEREST? ...... 37 INTRODUCTION ...... 37 METHODS ...... 40 RESULTS ...... 43 FACTORS SIMULTANEOUSLY INFLUENCING DPP INTEREST ...... 53 LONGITUDINAL FACTORS ...... 57 DIFFERENCES BETWEEN GROUPS OVER TIME ...... 65 DISCUSSION ...... 74 CHAPTER 4 DETERMINE THE ASSOCIATION BETWEEN ANTIDEPRESSANT USE AND T2DM AMONG PATIENTS WITH INDICATIONS FOR ANTIDEPRESSANT USE...... 82 INTRODUCTION ...... 82 METHODS ...... 83 RESULTS ...... 88 DISCUSSION ...... 110 CHAPTER 5 DETERMINE THE ASSOCIATION BETWEEN STATIN USE AND T2DM AMONG PATIENTS WITH INDICATIONS FOR STATIN USE ...... 117 INTRODUCTION ...... 117 METHODS ...... 119 RESULTS ...... 124 DISCUSSION ...... 153 CHAPTER 6 DISCUSSION OF FINDINGS: CONCLUSIONS AND IMPLICATIONS FOR FUTURE RESEARCH ...... 163 OVERVIEW ...... 163 AIM 1 ...... 164 AIM 2 ...... 165 AIM 3 ...... 167 xii

CONCLUSIONS ...... 169 REFERENCES ...... 170 APPENDIX A: HEALTH ASSESSMENT SURVEY INSTRUMENT ...... 181 APPENDIX B: CDC RISK ASSESSMENT ...... 186 APPENDIX C: DPP RECRUITMENT BROCHURE ...... 188

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ABBREVIATIONS

AD Antidepressant

BMI Body Mass Index

CHD Coronary Heart Disease

CVD Cardiovascular Disease

DPP Diabetes Prevention Program

EMR Electronic Medical Record

GLMM Generalized Linear Mixed Model

HbA1c Glycosylated Hemoglobin or Hemoglobin A1c

HDL High Density Lipoprotein

IPW Inverse Probability Weighting

LDL Low Density Lipoprotein

SSRI Selective Serotonin Reuptake Inhibitor

TCA Tricyclic Antidepressant

T2DM Type II Diabetes Mellitus

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LIST OF TABLES Table 2.1 Summary of International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and CPT Codes Used to Identify the Population of Antidepressant Users ...... 23 Table 2.2 Summary of International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and CPT Codes Used to Identify the Population of Statin Users ...... 24 Table 2.3 Medications Classified as Antidepressants ...... 26 Table 2.4 Medications Classified as Statins ...... 27 Table 2.5 Statin Doses Classified by Moderate and High Intensity by Statin Name ...... 27 Table 2.6 Summary of International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and CPT Codes used to Identify Excluded Patients and Those With the Study Outcome...... 29 Table 3.1 Descriptive Characteristics of Prediabetics With Different Levels Of Interest In The Diabetes Prevention Program ...... 48 Table 3.2 Multivariable Logistic Regression for the Odds Of Being Interested In The DPP Among Prediabetics ...... 55 Table 3.4 Differences in Total Cholesterol, LDL Cholesterol and HDL Cholesterol Between DPP Interested and DPP Not Interested by Year ...... 68 Table 3.5 Differences in Systolic Blood Pressure, Diastolic Blood Pressure and Triglyceride Levels Between DPP Interested and DPP Not Interested by Year ...... 69 Table 3.6 Body Mass Index (BMI) from 2011 - 2014 by Gender and ...... 71 DPP Interest Level ...... 71 Table 3.7 Mean Body Weight (pounds) from 2011 - 2014 by Gender and ...... 72 DPP Interest Level ...... 72 Table 3.8 Mean Waist Circumference (inches) from 2011 - 2014 by Gender and ...... 74 DPP Interest Level ...... 74 xv

Table 4.1 Types of Antidepressants Used by Incident and Prevalent Antidepressant Users ...... 91 Table 4.2 Characteristics of Incident and Prevalent Antidepressant Users ...... 93 Table 4.3 Characteristics of Antidepressant (AD) Nonusers and Incident Antidepressant Users Before and After Propensity Weighting (2013 Data as the Outcome) ...... 95 Table 4.4 Characteristics of Other Antidepressant Users and SSRI Users Before and After Propensity Weighting (2013 Data as the Outcome) ...... 97 Table 4.5 Characteristics of Antidepressant (AD) Nonusers and Incident Antidepressant Users Before and After Propensity Weighting (2014 Data as the Outcome) ...... 98 Table 4.6 Characteristics of Other Antidepressant Users and SSRI Users Before and After Propensity Weighting (2014 Data as the Outcome) ...... 99 Table 4.7 The Prevalence of Elevated Hba1c (>6.0) Among Incident Antidepressant Users and The Prevalence Difference of Elevated Hba1c (>6.0) Between Antidepressant Nonusers and Nonusers After Propensity Score Weighting ...... 100 Table 4.8 The Prevalence of Elevated Hba1c (>6.0) Among Incident SSRI Users and the Prevalence Difference of Elevated Hba1c (>6.0) Between Other Antidepressant Users and SSRI Users After Propensity Score Weighting ...... 101 Table 4.9. Sensitivity Analysis for Incident User Definition. The prevalence of elevated HbA1c (>6.0) among incident antidepressant users and the prevalence difference of elevated HbA1c (>6.0) between incident antidepressant users and nonusers after propensity score weighting ...... 102 Table 4.10. Sensitivity Analysis for Incident User Definition. The prevalence of elevated HbA1c (>6.0) among SSRI users and the prevalence difference of elevated HbA1c (>6.0) between other antidepressant users and SSRI users after propensity score weighting ... 103 Table 4.11 Characteristics of Antidepressant Nonusers and Incident Antidepressant Users ...... 106 Table 4.12 Characteristics of Incident Other Antidepressant Users and SSRI Users ..... 108 Table 4.13. Cox Proportional Hazards Models for T2DM. Comparison between antidepressant users and nonusers or other antidepressant users and SSRI users ...... 109 Table 5.1 Statin Doses Classified by Moderate and High Intensity By Statin Name ..... 121

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Table 5.2 Types of Statins Across Incident and Prevalent Statin Users ...... 126 Table 5.3 Use of Moderate Intensity and High Intensity Statins Among Incident and Prevalent Statin Users ...... 127 Table 5.4 Characteristics of Incident and Prevalent Statin Users ...... 128 Table 5.5 Characteristics of Statin Nonusers and Incident Statin Users Before and After Propensity Weighting (2013 Data as the Outcome) ...... 130 Table 5.6 Characteristics of Moderate Intensity and High Intensity Statin Users Before and After Propensity Weighting (2013 Data as the Outcome) ...... 132 Table 5.7 Characteristics of Lipophilic and Hydrophilic Statin Users ...... 134 Before and After Propensity Weighting (2013 Data as the Outcome) ...... 134 Table 5.8 Characteristics of Statin Nonusers and Incident Statin Users Before and After Propensity Weighting (2014 Data as the Outcome) ...... 136 Table 5.9 Characteristics of Moderate Intensity and High Intensity Statin Users Before and After Propensity Weighting (2014 Data as the Outcome) ...... 138 Table 5.10 Characteristics of Lipophilic and Hydrophilic Statin Users ...... 140 Before and After Propensity Weighting (2014 Data as the Outcome) ...... 140 Table 5.11 Characteristics of Incident Statin Users and Statin Nonusers ...... 145 Table 5.12 Characteristics of Moderate Intensity and High Intensity Statin Users ...... 147 Table 5.13 Characteristics of Lipophilic and Hydrophilic Statin Users ...... 148 Table 5.14 Selected Differences in the Characteristics of Incident Statin Users Across Duration Categories ...... 153

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LIST OF FIGURES Figure 2.1 GLMM for Aim 1, Subaim 1b ...... 21 Figure 3.1. Flow of Patients Who Were Identified as Eligible for the DPP...... 45 Figure 3.2. Multivariable Adjusted Odds Ratios for Interest in the Diabetes Prevention Program...... 56 Figure 3.3. Mean BMI from 2011 – 2014 by DPP Interest Group...... 57 Figure 3.4. Mean Body Weight (lbs) from 2011 – 2014 by DPP Interest Group...... 58 Figure 3.5. Mean Waist Circumference (ins) from 2011 – 2014 by DPP Interest Group. 59 Figure 3.6. Mean Cholesterol (mg/dL) from 2011 – 2014 by DPP Interest Group...... 60 Figure 3.7. Mean LDL Cholesterol (mg/dL) from 2011 – 2014 by DPP Interest Group. 61 Figure 3.8. Mean HDL Cholesterol (mg/dL) from 2011 – 2014 by DPP Interest Group. 62 Figure 3.9. Mean Systolic Blood Pressure (mmHg) from 2011 – 2014 by DPP Interest Group...... 63 Figure 3.10. Mean Diastolic Blood Pressure (mmHg) from 2011 – 2014 by DPP Interest Group...... 64 Figure 3.11. Mean Triglyceride Levels (mg/dL) from 2011 – 2014 by DPP Interest Group...... 65 Figure 3.12. Mean Body Mass Index (BMI) Among Prediabetics by DPP Interest Level and Gender...... 70 Figure 3.13. Mean Body Weight (lbs) Among Prediabetics by DPP Interest Level and Gender...... 71 Figure 3.14. Mean Waist Circumference (inches) Among Prediabetics by DPP Interest Level and Gender...... 73 Figure 4.1 Flow Diagram For Patients With Mental Health Disorders Who Are Antidepressant Users and Nonusers...... 89 Figure 4.2. Sensitivity Analysis for Antidepressant Duration (2014)...... 104

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Figure 5.1. Flow Diagram of Patients With Cardiovascular Disease Classified as Statin Users and Nonusers ...... 125 Figure 5.11 Prevalence Difference of Hba1c Greater Than 6.0 Among Statin Users (Overall) and By Intensity and Type Of Statin Using 2013 and 2014 Outcome Data ... 142 Figure 5.12 Sensitivity Analysis of Prevalence Difference of Hba1c Greater Than 6.0 Among Statin Users (Overall) and By Intensity of Dose and Type of Statin Using 2014 Outcome Data ...... 143 Figure 5.13. Nelson -Aalen Cumulative Hazard Estimates for Time to T2DM Diagnoses Among Incident Statin Users and Statin Nonusers...... 149 Figure 5.14. Association Between T2DM Risk and Statin Use Among Incident Statin Users ...... 150 Figure 5.15 Sensitivity Analysis of the Impact of the Incident Statin Use Definition on Adjusted and Unadjusted Hazard Ratios for T2DM Development Among Statin Users Compared to Nonusers ...... 151 Figure 5.16. Time to T2DM by Statin Duration Categories Among 1,734 Participants 152

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CHAPTER 1

Literature Review

Type II diabetes mellitus (T2DM) is a serious medical condition that affects over 29 million Americans [1]. T2DM is the 7th leading cause of death in the U.S. and this disease increases the risk of cancer and cardiovascular events [2,3]. It is also the leading cause of kidney failure, lower limb amputations, and adult-onset blindness [2,4]. Recent studies of U.S. data estimate that the prevalence of diabetes was 12 – 14% from 2011- 2012, a trend that has increased since 1988 [5]. In addition to these human costs, the estimated financial cost of medical care, disability and premature death was $245 billion in the U.S. in 2012 [6], a cost that will increase as the prevalence of T2DM continues to increase, both globally, and in the U.S. [7].

TYPE II DIABETES MELLITUS ETIOLOGY AND RISK FACTORS

T2DM is a metabolic disorder that predominantly affects adults and has been previously referred to as “adult onset diabetes.” The recent obesity epidemic has changed the age of onset for this disease because overweight and obese children are now being diagnosed with T2DM [8]. T2DM is the most common form of diabetes, affecting between 90-95% of all patients who are diagnosed with diabetes. Type I diabetes diagnoses make up the remaining 5- 10% of diabetes cases [9]. Other types of diabetes include those classified as “other specific types,” which include drug or chemical-induced diabetes, diseases of the exocrine pancreas, genetics, infections, immune related diabetes, and gestational diabetes [9]. Some ambiguity exists in the literature in these categorizations for diabetes

types, and T2DM can also result from medication exposures. Our focus on drug induced T2DM will be limited to antidepressant and statin use.

The major risk factor for T2DM is obesity (and a related sedentary lifestyle). Other less common risk factors associated with T2DM include genetics (a previous family history), physical damage to the pancreas, or among women, a previous diagnosis of gestational diabetes. Obesity is strongly correlated with T2DM because individuals with a high amount of adiposity store fat both in adipose tissue subcutaneously, but also within and around their internal organs. This improper fat storage has been implicated as the reason for insulin resistance, leading to T2DM. Factors contributing to obesity can be attributed to the environment and the fact that the American diet consists of high levels of fat and caloric intake beyond what is expended in physical activity. When energy intake exceeds energy needs, this energy is stored in the form of fat, which is sometimes stored in the organs, contributing to T2DM [9].

In a healthy person, the beta cells of the pancreas are responsible for the secretion of insulin after a meal, which then allows the cells to absorb sugar from the blood and use it for energy at the cellular level. In patients who have Type I diabetes mellitus, also known as “juvenile onset diabetes,” which is believed to be autoimmune in pathology, patients do not secrete insulin. Similar to Type I diabetes mellitus, T2DM is a disorder of the pancreas and abnormal insulin secretion. There is a wide range of variability in the stages of disease, from an under-production of insulin (either none or not enough) to a nonresponse to secreted insulin and subsequent inability of the cells to absorb sugar from the blood (insulin resistance). When this chemical messenger system is not functioning properly, glucose will build up in the blood and cause damage to tissues and organs, leading to detrimental side effects, including cardiovascular disease or vision loss [9].

Signs and symptoms of T2DM include increased thirst, increased hunger, frequent urination, blurred vision, frequent infections, fatigue, cuts and bruises that are slow to

heal, tingling and numbness in the hands and/or feet and recurring skin, gum and bladder infections [9].

TYPE II DIABETES MELLITUS DIAGNOSIS

There are multiple diagnostic tests that can be used to detect a patient’s prediabetic or diabetic status. These methods include a fasting plasma glucose test, random plasma glucose test, oral glucose tolerance test, and glycosylated hemoglobin A1C test. As with any diagnostic test, each has its strengths and weaknesses.

The fasting plasma glucose test is used in patients who have not eaten for at least 8 hours prior to the test. This test includes obtaining a blood sample and subsequent measurement of the glucose concentration in the serum. While this test is preferred by many practitioners due to its low cost and high reliability, patient adherence to fasting prior to the exam may confound the testing results [10]. Serum glucose levels are also influenced by stress levels, time of day, proximity to most recent mealtime, and physical activity levels [11]. The random plasma glucose test is implemented identically to the fasting plasma glucose test, however it does not consider the last time that the patient had a meal, therefore it is limited in that it can be used to diagnose diabetes, but not prediabetes [10].

The oral glucose tolerance test is the most burdensome test for a patient in that a patient must fast for eight hours prior to the test, and then drink a glucose-containing beverage. Blood samples are collected in timed increments to measure a patient’s response to the beverage. While this test is more cumbersome for a patient, it is more diagnostically powerful because it can be used for a diagnosis of gestational diabetes, prediabetes and T2DM [10].

The glycosylated hemoglobin A1C test is used to measure a patient’s average glucose level over the previous 3 months, and it relies on the fact that the hemoglobin A1C molecules in a patient’s red blood cells are exposed to blood glucose and become

glycosylated over time. This test is not reliant on a patient fasting eight hours prior to the test, therefore it is easier to implement and is not affected by a patient’s noncompliance with fasting. The hemoglobin A1c test is now standardized and is the recommended method of diagnosis for T2DM and prediabetes [12].

TYPE II DIABETES MELLITUS TREATMENT

If a patient has been newly diagnosed with T2DM, the first recommendation for diabetes management is through lifestyle modifications (increased physical activity, a healthy diet and a weight loss plan). If detected early enough in the disease process, diabetes can be reversed through weight loss achieved by increased physical activity and dietary modifications [13]. However, some patients are unable to adhere to lifestyle modifications or have too advanced a stage of disease thereby requiring medication for diabetes management [13].

PREDIABETES ETIOLOGY Prediabetes is a condition when serum glucose levels or glycosylated hemoglobin levels are elevated but are not yet high enough for a T2DM diagnosis. Many patients with prediabetes have insulin resistance, a condition where the muscle, bone and liver do not respond appropriately to insulin secretion by the pancreas, so they do not absorb glucose from the bloodstream. This lack of responsiveness results in the pancreas secreting increased levels of insulin to compensate and lower blood glucose levels. Over time, it is possible for the beta cells of the pancreas to stop secreting enough insulin due to the increased demand caused by insulin resistance [10]. In the United States in 2014, prevalence of prediabetes among adults was 37% – 38%, and 49% – 52% were estimated to have either diabetes or prediabetes [5]. Fifteen to thirty percent of these individuals would develop T2DM within five years if they did not improve their lifestyle through weight loss and moderate physical activity. It is recommended that prediabetics lose five to seven percent of their body weight to delay the onset of T2DM [13]. Many previous

studies identified risk and preventive factors for T2DM [14], however two key factors, both modifiable, lend themselves to systematic evaluation in this insurance cohort.

DIABETES PREVENTION PROGRAMS FOR PREDIABETICS

The Diabetes Prevention Program was a major multicenter (27 site) randomized study that sought to compare the effectiveness of modest weight loss (through diet and physical activity) to treatment with metformin to prevent or delay the onset of T2DM [15]. The findings of the study were that the risk of T2DM was lowest among patients who lost a modest amount of weight (those in the weight loss arm). Those in the metformin arm also had reduced risk, but it was not as substantial as those in the weight loss arm [15].

Following the findings of this study, Diabetes Prevention Programs (DPPs) were created for prediabetics in community and worksite settings as a way to delay the onset of, or progression to, T2DM by improving a patient’s health behaviors. The main goal of this program, created by the CDC, is for participants with prediabetes to lose excess body weight by consuming a healthy diet and increasing their time spent engaged in physical activity [16]. DPPs are being implemented across the country in community and worksite settings, yet it is unclear if the participants expressing interest in these programs are different from those who do not elect to participate [17]. If participants in these voluntary prevention programs do not represent the prediabetic population then programs may not be effective at engaging a high-risk population to participate. An evaluation of the characteristics of nonparticipants can be helpful to quantify differences in these two groups and evaluate who is being reached by health promotion programs such as DPPs.

HEALTH PROGRAMS AND SELECTION BIAS

controlled trials evaluating health programs captured a group of engaged participants who did not represent the general population [19]. When wellness programs are executed in real-world settings, selection bias is a concern in describing who ultimately enrolls. Program efficacy may be underestimated because the lowest risk groups in the eligible population enroll and these individuals are not the group that would gain the most benefit from these programs. Alternatively, effectiveness may be overestimated if the lowest risk individuals enroll and stay healthy after programs are completed.

We expect to find that individuals who are more likely to engage in health promotion programs are females, nonsmokers and are generally healthier than the rest of the population [18]. The extent of selection bias among DPP participants has not been previously investigated, possibly because data is not available for those who do not enroll. The use of an existing longitudinal employee cohort will allow us to examine correlates of interest in enrollment and to evaluate trends in risk factor profiles for prediabetics who differ in their interest in enrolling in the DPP. This approach will inform ongoing recruitment efforts, and if necessary, new methods will be developed to recruit and engage higher risk groups.

Pharmacoepidemiology and Type II Diabetes Mellitus

Pharmaceuticals are advantageous for the management of chronic health conditions but this is often not without the cost of side effects. Given the concern regarding increasing T2DM incidence, this proposal will seek to further understand the progression to T2DM among users of antidepressants (ADs) and statins. Seventy percent of Americans take one

prescription drug, and more than half take two, with ADs being the most commonly prescribed pharmaceutical [25]. It is well known that some pharmaceuticals accelerate a patient’s T2DM development due to changes in, and interference with, cellular metabolism pathways [26]. Studies that have examined the relationship between ADs and T2DM have been inconsistent resulting in debate about the direction of this association [27]. The routine prescribing of ADs in the US population (15% of the US population over age forty) [28] warrants further evaluation of this relationship in this occupational cohort. Previous studies in this area have been variable and inconsistent in their results generating a need for further well-designed research to gain insight into the association with subsequent T2DM development [27]. By evaluating incident T2DM among patients within this population of interest, this study seeks to determine if current disease management strategies need to be re-evaluated.

AD USE AND SUBSEQUENT TYPE II DIABETES: THE MECHANISM

Antidepressant use is associated with weight gain, which some researchers attribute to an increased prevalence of T2DM among antidepressant users. Similarly, depression is associated with the development of T2DM, complicating this relationship [29]. Part of this association is due to the effect that having a depressed mood and a lack of energy affects health behaviors, such as reduced physical activity or increased food intake, which can result in a lifestyle that is less healthy and more aligned to obesity development. Sedation, increased appetite, and weight gain can also be side effects of antidepressants [30]. Human studies have observed that tricyclic antidepressants (TCAs) appear to contribute to weight gain and increase blood glucose levels among diabetic patients [31]. One study found that selective serotonin reuptake inhibitors (SSRIs) and related agents (other antidepressants) were associated with metabolic risk factors including increased abdominal obesity and dyslipidemia [32]. A review article summarizing previous studies found a more heterogeneous effect and attributed reduced hyperglycemia, normalized glucose homeostasis and increased insulin sensitivity to some SSRIS, while noradrenergic mechanisms (TCAs) had opposite effects [33].

From a mechanistic perspective, antidepressants are likely acting through multiple interdependent mechanisms that contribute to dyslipidemia, abdominal obesity and hyperglycemia [33]. Zimmerman et al. published a review article summarizing potential mechanisms of antidepressant use and weight gain [30]. From a behavioral perspective, TCAs have been associated with food cravings, in which a patient has an increased appetite for sweet and fatty foods [34]. This increased food intake may contribute to weight gain. Additionally, a patient’s basal metabolic rate may slow down causing them to expend less energy and become more prone to weight gain. Within the central nervous system, the monoaminergic neurotransmitters are associated with appetite control, which include stimulating appetite and identifying feelings of satiety. TCAs are believed to operate through this pathway, as they are the class of antidepressants most consistently associated with weight gain. Antidepressants can be viewed as anti-inflammatory due to their modulation of inflammatory cytokines and their soluble receptors. Thus, changes in receptor levels then lead to changes in metabolism. More specifically, amitriptyline and nortriptyline, (TCAs) were found to increase soluble TNF receptors in the plasma and the concentration of TNF-alpha. Treated subjects first experienced a significant increase in soluble receptor levels followed by an increase in BMI [35].Conversely, the serotonergic system is associated with the downstream effects of promoting satiety and inducing weight loss. Side effects include nausea, which can contribute to weight loss [33]. One caveat is that the long-term weight gain observed in human studies is not explained by this reasoning.

AD USE AND TYPE II DIABETES

An analysis of antidepressants and weight gain using Finnish data found that after using propensity scores to match users to nonusers based on depression related characteristics, users of antidepressants had double the weight gain of nonusers (OR= 1.93 (95% CI 1.48–2.51)) [34]. Weight change was self-reported by participants and the study did not take incident and prevalent use of antidepressants into account in the analysis. The findings of differential weight gain for both users and nonusers of pharmacotherapy 8

highlight the need to measure changes in weight over the course of the study in addition to incident diabetes diagnoses. Ideally, future studies will not rely on self -reported weight from participants, as the association with antidepressant use and weight gain is publically known, and the use of self-reported weights has the potential to induce a systematic bias [34].

An analysis of antidepressant use during the Whitehall study found that antidepressant use was associated with a higher incidence of physician diagnosed T2DM, but not elevated fasting plasma glucose[36]. The authors attribute this difference in findings to the fact that a detection bias may exist where users of antidepressants are more likely to visit their physicians for a T2DM screening [36]. While this is plausible, the authors did not adjust for confounding by indication, and also did not divide out prevalent and incident users of antidepressants. The heterogeneity of this population does not allow a fair examination of the association of antidepressant use with T2DM as prevalent users may be systematically different from incident users[37] .

The integration of both biometric changes and health outcomes after medication initiation in this study will allow us to obtain information about the factors influencing T2DM risk. In addition, because this data is observational, this proposal will address confounding by indication and apply new-user design [37,38]. Despite the effectiveness of a surrogate endpoint (HbA1c) to measure T2DM risk [39], only a few studies on AD use have focused on surrogate endpoints for adverse event monitoring[27]. Monitoring surrogate endpoints is ideal because (1) they are modifiable, and if elevated can be lowered with changes in medications or improvements in lifestyle factors [40]; (2) they provide information about a biological progression to increasing disease severity[41]; and (3) an evaluation of prospectively collected biomarkers eliminates concerns about detection bias if users in this population who are obtaining pharmacotherapy are more likely to see a physician[42,43]. The use of both HbA1c and provider diagnosed T2DM will allow both methods to be compared.

Derijks et al. did a case control study using World Health Organization adverse drug reactions data [44] and measured disturbances in glucose homeostasis. They identified an elevated risk for both hypoglycemia (OR: 1.84, (95%CI: 1.40, 2.42)) and hyperglycemia (OR: 1.52, (95% CI: 1.20, 1.93)) among antidepressant users and this difference in glycemic control was tied to medication class. Other antidepressants were responsible for the side effect of hyperglycemia while SSRIs were associated with hypoglycemia. The use of this adverse drug reaction database prevented the investigators from tight control of confounding factors, since BMI, depression status, comorbid medical conditions and physician visiting patterns were unavailable in this voluntarily reported database.

Knol et al conducted a study using pharmacy claims data and found no association between antidepressant use and subsequent T2DM [45] (AHR= 1.05, (95% CI: 0.88, 1.26)). Their analysis was adjusted for age, sex, and chronic disease score but data regarding BMI and lifestyle factors was unavailable. The initiation of T2DM medication was used as a proxy for a T2DM diagnosis and data was not available for depression diagnoses. It is possible that not all patients with a T2DM diagnosis initiate medication use or that patients had a diagnosis date sometime prior to their treatment with T2DM medications. This is a limitation of the study because there was no access to either survey data with self-reported T2DM or patient medical record data.

Andersohn et al applied a new user design for antidepressant users using medical record data and found an increased risk for T2DM among antidepressant users (IRR: 1.84, (95% CI: 1.35, 2.52)). [46] They identified that this increased risk was isolated to patients using the medications at higher doses over a longer period of time. When doing a subgroup analysis by class of antidepressant, they found the increased T2DM risk held for both TCA users and SSRI users. One limitation of this study was lack of control for depression, or medication use for a non-mental health indication because this data was unavailable.

Pan et al. compared the risk of T2DM after antidepressant use across three large cohort studies: The Health Professionals Follow-up Study (HPFS); The Nurses Health Study I (NHS); and NHS II [47]. The study investigators calculated independent risks for T2DM by study and then pooled together the estimates to identify the overall association. Self reported antidepressant use was measured via surveys collected at several different survey collection times and a Cox proportional hazards model with time varying antidepressant use was used for the analysis. Participants were asked about ever having taken an antidepressant, not if they were a new user. After adjusting for BMI, cholesterol, diabetes risk factors and hypertension the HR for new onset T2DM was 1.17 (95% CI: 1.09, 1.25) for antidepressant users compared to nonusers, but lack of data on dose and duration prevented further exploration of this association.

STATIN USE AND SUBSEQUENT TYPE II DIABETES: THE MECHANISM

Bradford Hill’s Causal Criteria states that for a theory to have causal credibility, it needs to be biologically plausible [48]. Thus, it is meaningful to outline the mechanism underlying statin use and development of diabetes. The “Metabolic Syndrome in Men” (METSIM) study of prolonged statin use among males with metabolic syndrome found that statins increased the risk of diabetes by 46% among statin users compared to nonusers and this was attributed to a 24% decrease in insulin sensitivity and a 12% decline in insulin secretion among individuals using statins [49]. This relationship was dose dependent among atorvastatin and simvastatin users.

The exact mechanism that leads to insulin resistance and decreased insulin secretion is an area of developing research. A review article summarizing the proposed mechanisms of statin treatment and new onset diabetes [50] proposed three major mechanisms: (1) insulin secretion is affected through direct, indirect or combined effects on the calcium channels in pancreatic beta-cells; (2) reduced translocation of glucose transmitter 4 (GLUT4) results in hyperglycemia and hyperinsulinemia; (3) statins decrease important downstream products which lead to reduced intracellular signaling [50]. Other theories

include decreased leptin, which causes the inhibition of beta cell proliferation and insulin secretion and lower adiponectin levels. Downstream effects of lower adiponectin include increased insulin resistance and less effective insulin secretion[50] .

STATIN USE AND TYPE II DIABETES

While statins have been well documented to accelerate T2DM development, the magnitude of this association is still unknown, because of the variation across previous studies. The FDA has updated statin drug labels to report that patients taking statins have a risk of elevated HbA1c and increased fasting serum glucose levels [20]. It is unclear if this recommendation has resulted in changes in physician monitoring of patients to better manage these potential dysglycemic side effects. Additionally, it is not known whether the extensive clinical trial evidence linking statins to T2DM can be generalized beyond trial participants [51] [52]. The populations of patients included in these RCTs have not included low risk patients.

A small but consistent effect of elevated HbA1c ((mean, sd) 0.3%, 0.35 among rosuvastatin group vs. 0.22%, 0.40 among placebo group (p<0.001)) has been observed in the Justification for Use of statins in Prevention: an Intervention Trial Evaluating Rouvastatin, (JUPITER) [53]. Jick et al. performed a nested-case control study among adults ages 30-79 with hyperlipidemia using data from a general practice database [54]. Current statin use was not associated with T2DM (OR: 1.1 (95% CI 0.8, 1.4) when compared to non-exposed subjects[54]. The association was not affected when stratified by the specific statin used, (pravastatin or simvastatin). This study did control for BMI, hypertension, smoking, and healthcare use, but was only able to compare two statin types.

The JUPITER trial enrolled patients without previous cardiovascular disease or diabetes. They were randomly assigned to 20 mg rosuvastatin or placebo and followed up for up to five years. The JUPITER investigators stratified the study participants by the number of 12

diabetes risk factors. Those with one or more major diabetes risk factor had a higher risk of developing diabetes than were those without a major risk factor. In individuals with one or more risk factors, statin use was associated with a 28% increase in diabetes (HR: 1.28, 95% CI: 1.07–1.54, p=0.01). For trial participants with no major diabetes risk factors, statin allocation was associated with no increased risk of diabetes (HR: 0.99, (95% CI= 0.45–2.21)) [53]. The trial also observed a moderate but statistically significant increase in median HbA1c levels among statin users [53]. The applicability to lower risk patients is cited as a study limitation due to the fact that one of the study inclusion criteria was for participants to have elevated C-reactive protein [53].

A meta-analysis of 13 randomized controlled trials (RCTs) comparing statin users to placebo found that statin users had a 9% increased risk for incident T2DM. (OR= 1.09 (95% CI=1.02–1.17)) [55]. The investigators summarized the findings to say that the risk of development of diabetes with statins was highest among older participants, but neither baseline BMI nor changes in LDL-cholesterol concentrations accounted for residual variation in risks [55].

Another meta-analysis of five RCTs investigated if intensity of statin therapy was associated with T2DM incidence and found that intensive statin use was associated with a 12% increased risk of incident T2DM when compared to moderate intensity statin use (OR = 1.12, 95% CI= 1.04-1.22) [56]. The study participants used different statins across the studies, including atorvastatin, pravastatin, and simvastatin and they also had a high risk of future CVD events, which may not be comparable to a more general population of healthcare users using statins for primary prevention.

Observational studies found similar increased risks to randomized controlled trials, but their quality has been variable and lack of control for confounding has affected the ability to generate causal interpretations from this work[55]. One study by Culver et al. used data from the Women's Health Initiative (WHI) and observed that statin use was associated with an increased risk of new-onset T2DM (hazard ratio [HR], 1.48 (95% CI:

1.38. 1.59) [57]. The majority of this study population was comprised of menopausal women and the association between statin use and T2DM was unrelated to previous CVD. Additionally, findings were inconsistent because a class-effect was observed, but no relationship was found to potency or the specific statin taken [57]. This study cites differential access to health care and differential screening rates across statin users and nonusers as a study limitation [57]. Additionally, the study investigators did not exclude prevalent users who likely tolerated the medication better than incident users.

A retrospective longitudinal cohort study was performed in Taiwan among patients with hypertension and dyslipidemia (n=16,027) [58]. This study observed a decreased risk of incident T2DM among fluvastatin users (HR: 0.46, 95% CI: 0.33, 0.61) and an elevated dose response effect of atorvastatin (HR: 1.29, 95% CI: 1.16, 1.44) and pravastatin (HR: 1.34, 95% CI: 1.15, 1.55) on incident T2DM. When the investigators adjusted for sex and age, the association between pravastatin and T2DM disappeared [58]. It is unclear what the study investigators did with the prevalent statin users in this analysis, but they did not incorporate clinically meaningful confounders such as BMI in their analysis.

A retrospective cohort study using a pharmacy claims database compared statin treatment with subsequent anti-diabetic drug use[59]. The comparison group was patients taking other pharmaceuticals during the study period. The investigators observed elevated T2DM risk among statin users (HR: 1.18, (95% CI: 1.15, 1.22)) and these relationships were modified by dose and duration of use for all statins except for fluvastatin. However, the investigators may not have adequately controlled for differences between groups of statin users and nonusers due to limited variables available [59].

Research Objectives and Hypotheses

The long-term goals for this study were (1) to inform ongoing recruitment efforts and if necessary, develop new methods to recruit and engage higher risk groups for the DPP, including the exploration of other program delivery methods; and (2) to evaluate current 14

pharmacotherapy risk factors for T2DM and re-evaluate current patient monitoring strategies as needed. The objectives of this study were two-fold: 1) to determine the extent of selection bias among individuals volunteering to enroll in the DPP; and 2) to assess the risk of T2DM progression among patients who are incident users of pharmacotherapies (ADs and statins). The rationale of this study was that T2DM is largely preventable, and identifying ways to intervene in the natural history of this disease will be valuable for informing future outreach efforts for disease prevention.

The objectives of this study were accomplished through the following three specific aims: 1. Determine whether prediabetics who express interest in the DPP are different from those who do not express interest. Working Hypotheses: 1.1. Prediabetics expressing interest in the DPP will be healthier (fewer comorbid conditions) and more health conscious (less likely to engage in tobacco use or alcohol consumption) than other pre-diabetics in the population (selection bias). 1.2. Prediabetics expressing interest in the DPP will have lower biological risk for T2DM (lower BMI, lower lipid levels) than other prediabetics in the population.

2. Determine the association between antidepressants and T2DM among patients with indications for antidepressant use. Working Hypotheses: 2.1.a. New users of ADs will have higher average HbA1c levels than nonusers of ADs after the medication has been taken for at least a month. 2.1.b. New users of other antidepressants will have higher average HbA1c levels than new users of SSRIs after the medication has been taken for at least a month. 2.2.a. New users of ADs will have a higher incidence of T2DM than nonusers of ADs. 2.2.b. New users of other antidepressants will have a higher incidence of T2DM than users of SSRIs.

3. Determine the association between statins and T2DM among individuals with indications for statin use. Working Hypotheses:

3.1.a. New users of statins will have higher average HbA1c levels than nonusers of statins after the medication has been taken for at least a month. 3.1.b. New users of statins who have a high exposure (high intensity dose) will have a higher average HbA1c level than those who have a low level of exposure (moderate intensity dose). 3.1.c. New users of lipophilic statins will have higher average HbA1c levels than new users of hydrophilic statins after the medication has been taken for at least a month. 3.2.a. New users of statins will have a higher incidence of T2DM than nonusers of statins. 3.2.b. New users of statins classified as lipophilic will have a higher incidence of T2DM than new users of hydrophilic statins. 3.2.c. New users of statins classified as high exposure (high intensity dose) will have a higher incidence of T2DM than new users of lower exposure (moderate intensity dose).

CHAPTER 2

Methods

STUDY POPULATION All three studies used data from annual biometric screenings, medical claims, pharmacy claims and a health assessment. This population included employees (associates) and their spouses who elected to enroll in a health plan and worked at a large hospital system in the Midwest. Associates must have been employed for 32 hours or more per week to be offered the opportunity to enroll in the plan. Associates had the option to enroll their dependent spouse or same sex partner in the insurance plan, as well as other dependent family members (children). As is the case for any health plan, some associates who were eligible may have elected to use their spouse’s employer insurance instead. The health plan covered 28,000 lives in 2014, with roughly 20,000 of these lives being 18 years of age or older (adults). Members were covered for most inpatient and outpatient services including ambulatory care, approved specialists and outside referrals, prescription drugs and laboratory work. Medical services obtained by enrolled members were recorded in a computerized database (through medical claims) with procedures and diagnoses.

HEALTH ASSESSMENT SURVEY Health assessment survey data were utilized to examine correlates of interest in enrolling in DPP among prediabetics. Enrolled employees and spouses completed an optional online health assessment survey in 2014 that took approximately 20 minutes to complete.

This survey collected information on demographics, risk factors, health behaviors, healthcare utilization and past patient medical history (Appendix A. Health Assessment Survey Instrument).

PHARMACY CLAIMS Pharmacy claims from 2009- 2014 were collected for payment purposes for all of the individuals in this cohort. These claims were reflective of all filled prescription medications regardless of the location of purchase, with the one exception that medications obtained during an inpatient hospitalization were not included. Pharmacy claims data included information about the drug name and strength, the quantity of medication obtained, the number of days supply of medication and the quantity of medication obtained.

MEDICAL CLAIMS Medical claims data from 2011- 2014 were collected for payment purposes for all of the individuals in this cohort. These medical claims were reflective of all of the services that a member could obtain including ambulatory care, inpatient and outpatient services, approved specialists, and outside referrals. This data was a reflection of all medical care received for individuals enrolled in the health insurance plan, regardless of care site. The purpose of these administrative claims was for billing and payment for services rendered. Medical claims contained information about patient diagnoses, procedure codes, dates of service, type of medical encounter and servicing provider specialty.

BIOMETRIC DATA Biometric data were collected for health plan enrollees during personal health assessments from 2011- 2014. These measurements were optional for employees to complete, however, those who completed the screenings were incentivized by the employer and received a discount on health plan pricing. Measurements for height,

weight, body mass index (BMI), waist circumference, and lipids (triglycerides and cholesterol) were collected for all four consecutive years.

During 2011 – 2012, blood samples were collected from the biometric screening and the hospital laboratory was used to process the samples. These samples were drawn by phlebotomists, spun at the draw site, and then sent to the lab via courier. Serum from blood samples was analyzed for lipid content (triglycerides and cholesterol) using Roche Analyzers. Glycosylated hemoglobin and lipids (triglycerides and cholesterol) were measured in 2013 and 2014 using point of care testing. A blood sample was obtained via a fingerstick and was used for both lipid and glycosylated hemoglobin testing. The CardioChek System, manufactured by Polymer Technical Systems (Indianapolis, Indiana) was used to obtain total cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides, low density lipoprotein (LDL) cholesterol values in less than two minutes. Glycosylated hemoglobin results were obtained using the “Bayer A1C Now” test. Quality control measures were also implemented to ensure that the screening machines were consistently reporting accurate values. Point of care testing is helpful because patients get immediate test results that are interpreted in the presence of a healthcare professional. This method of testing has been proven to be more accommodating for patients and better for hospital work-flow [60–62].

Non-laboratory measurements obtained during the biometric screening (height, weight, BMI, blood pressure, and waist circumference) were collected using the following protocols from 2011 -2014. Height was measured using a stadiometer and recorded in inches. Patients were asked to remove their shoes and to stand up straight prior to having their height measured. Patients were asked to remove any loose clothing and weight was recorded in pounds using a high capacity scale manufactured by Siltec. Blood pressure was measured from a sitting position, using an automated blood pressure cuff. For readings that were abnormal, the blood pressure measurement was repeated with a manual cuff and the new measurement was recorded.

Body mass index (BMI), was calculated using the standard BMI formula (measured weight (kg))/ height (m2). The computer program automatically calculated BMI eliminating the need for healthcare providers to calculate this measure. Waist circumference was measured at the smallest part of the waist (at the height of a patient’s navel) and recorded in inches using a flexible tape measure.

Aim 1. Determine whether prediabetics who express interest in the DPP are

different than those who do not express interest

STATISTICAL APPROACH FOR AIM 1 For Aim 1, Subaim 1.a, self reported health survey data and measured biometric data from 2014 were used to describe cross sectional differences between the prediabetics who did and did not express interest in participation in the DPP. First, descriptive statistics (T- tests for continuous variables and Pearson’s Chi square test for categorical variables) were used to describe these two groups. Because many variables were examined that could be associated with interest in the DPP (44 total) the P-value was adjusted using a Bonferroni correction for multiple testing. The final significance threshold was set to p< 0.001. In the second part of the analysis a logistic regression model was created to simultaneously adjust for factors that influenced participation in the DPP. The Cochrane Armitage Trend test was implemented to evaluate if increasing levels of an ordinal exposure level were linearly associated with interested in the DPP.

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Nine different models where Y was the outcome variable were fitted. The variables used for Y included: BMI (continuous), waist circumference, body weight, triglycerides, systolic blood pressure, diastolic blood pressure, total cholesterol, LDL cholesterol and HDL cholesterol. Where group2 = The group who was interested in enrolling, and the reference group = those who were not interested in enrolling. Year_2014 is the reference group for the categorical year variable. The random intercept model imposed an exchangeable correlation structure on the model residuals, and the unique identifier for each patient was included in the model to account for multiple measurements on the same individual.

To test the hypothesis that prediabetics expressing interest in the DPP would have a lower biological risk for T2DM (lower BMI, lower lipid levels) during the years prior to 2014, a repeated measures model was fit to account for the correlation within individuals across time. A response profile considering a categorical effect of time was used to allow separate estimates of the difference in means between groups at each time point. This model allowed for any trends across time to be observed, but this method was less powerful than parametric (linear or quadratic) models if the true effect was linear, or quadratic. A mixed model with random intercepts was used which assumes that the correlation structure for the same individual follows an exchangeable structure. A unique identifier was included in the model to account for multiple observations on the same patient. The generalized linear mixed model (GLMM) using a categorical year by group interaction is provided in further detail in Figure 2.1 above. To test for the effect of time, the multivariate Wald test was used to test the significance of the interaction terms in the full model (β5, β6 and β7). A p-value < 0.05 meant that the interaction was statistically significant and there was a difference in the biometric measurement over time between the two groups. This strategy was used for the six models fitted to evaluate if there were time by group trends in biometric outcomes between DPP interest groups.

POWER ANALYSIS FOR AIM 1 The power analysis was calculated in April 2015, prior to the statistical execution of this dissertation. It was assumed that the rate of patients contacting the DPP office would be comparable to the rate of contact from October 2014– March 2015 (interested patients= 400). The anticipated number of interested prediabetics was estimated to be 800, leaving 2,600 prediabetics who were not interested in the DPP. The 2014 biometric screening identified 3,400 prediabetics meeting the inclusion criteria for the DPP. In reality, when the analysis was performed, the number of interested patients was overestimated, and after also incorporating the biometric screening data; the numbers were lower than expected.

Aims 2/3. Determine whether antidepressant/ statin users are at higher risk of

T2DM than antidepressant/ statin nonusers.

IDENTIFICATION OF THE POPULATION WITH MENTAL HEALTH DIAGNOSES USING MEDICAL CLAIMS Patients were identified as members of the population of interest with mental health diagnoses by searching ICD9 diagnosis codes on medical claims from professional, outpatient, and inpatient medical encounters (Table 2.1). This population included individuals with mental health diagnoses or other diagnoses that would justify prescription of an antidepressant.

Table 2.1 Summary of International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and CPT Codes Used to Identify the Population of Antidepressant Users

Condition Code type Codes Indications for Antidepressant Use 296.2x, 296.3x, 298.0, 300.4, 309.0, Depression ICD-9-CM diagnosis 309.1, 309.28, 311 293.84, 300.00 – 300.02, 300.09, Anxiety ICD-9-CM diagnosis 308.3, 309.8 Bipolar Disorder ICD-9-CM diagnosis 296.0, 296.4- 296.8, 301.13 Bulimia Nervosa ICD-9-CM diagnosis 307.51 309.0, 309.1, 309.2, 309.4, 309.9, Adjustment Related Disorder ICD-9-CM diagnosis 308.0 Headache ICD-9-CM diagnosis 784.0, 346.1, 346.2, 307.81 721.2-721.9, 722.1, 722.2, 722.3, Back Pain ICD-9-CM diagnosis 722.5-722.9, 724 Neuropathy ICD-9-CM diagnosis 351, 357 Sleep Related Conditions ICD-9-CM diagnosis 780.5, 307.4, 327.8, 327.0, 327.1 Fatigue ICD-9-CM diagnosis 780.8 Obsessive Compulsive Disorder ICD-9-CM diagnosis 300.3, 301.4 Panic Disorder ICD-9-CM diagnosis 300.01, 300.21 Premenstrual Tension Syndrome ICD-9-CM diagnosis 625.4 Post Traumatic Stress Disorder ICD-9-CM diagnosis 309.81 Smoking Cessation Counseling ICD-9-CM diagnosis V65.42 on substance use and abuse *International Classification of Diseases, 9th clinical modification, where “x” indicates any digit.

IDENTIFICATION OF THE POPULATION WITH CARDIOVASCULAR DISEASE USING MEDICAL CLAIMS Members of the study population included patients with prevalent cardiovascular disease, which was identified by searching ICD9 diagnosis codes on medical claims from professional outpatient, and inpatient medical encounters. This population included individuals with hypertension or hyperlipidemia (primary cardiovascular disease prevention population) or those who had already had a cardiovascular event (secondary cardiovascular prevention population). Cardiac procedure codes for percutaneous cardiac implant (PCI) and valve replacement surgeries were also included since participants with these procedure codes were defined as needing secondary cardiovascular prevention. The specific definitions used for these diagnoses are included in Table 2.2.

Table 2.2 Summary of International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and CPT Codes Used to Identify the Population of Statin Users

Condition Code type Codes Indications for Statin use for Primary Prevention Hypertension ICD-9-CM diagnosis 401.xx-405.xx, 437.2, 459.30 – 459.39, 416.xx Hyperlipidemia ICD-9-CM diagnosis 272.0 -272.2, 272.4 Indications for Statin use for Secondary Prevention Cerebrovascular ICD-9-CM diagnosis 430-437.1, 437.3-438.xx disease Congestive heart 428.xx, 398.91, 402.01, 402.11, 402.91, ICD-9-CM diagnosis failure (CHF) 404.01, 404.03, 404.11, 404.13, 404.91, 404.93 ICD-9-CM diagnosis 410.xx, 411.0, 412.xx, 429.7x, 411.xx, 413.x Acute Myocardial 99217-99226, 99234-99236, 99231-99233, infarction (AMI) & (Inpatient Setting) 99238-99239, 99251-99255, 99261*-99263*, Stable Angina CPT-4 Codes 99291-99300, 99356-99357, 99431*- 99440*, 99460-99465, 99468-99476, 99477-99480 1CD-9 Surgical 00.66, 36.06, 36.07 Percutaneous Procedure Code Coronary Implant 92920-92929, 92933, 92934, 92937, 92938, (Inpatient Setting) (PCI) 92941, 92943, 92944, 92973, 92980, 92982, CPT-4 Codes 92995 1CD-9 Surgical 36.1x, 36.2x Coronary Artery Procedure Code Bypass Graft (Inpatient Setting) 33510-33514, 33516-33519, 33521-33523, (CABG) CPT-4 Codes 33533-33536 414.0x – 414.4, 414.8x, 414.9x, 429.2, 440.xx Ischemic Heart ICD-9-CM diagnosis – 440.9 Disease & Chronic 99201-99205, 99211-99215, 99217-99220, total occlusion of Outpatient CPT-4 99241-99245, 99341-99345, 99347-99350, coronary artery or the codes 99384-99387, 99394-99397, 99401-99404, extremities 99411, 99412, 99420, 99429, 99455, 99456 Stroke ICD-9-CM diagnosis 433.xx, 434.xx Athero-embolism ICD-9-CM diagnosis 444.xx, 445.xx Atherosclerosis /Lower extremity arterial disease/ ICD-9-CM diagnosis 440.1, 440.2x peripherial artery disease *International Classification of Diseases, 9th clinical modification, where “x” indicates any digit.

ASSESSMENT OF PHARMACOEPIDEMIOLOGY (ANTIDEPRESSANT OR STATIN) EXPOSURE VARIABLES Patients who were exposed to 30 or more days of an antidepressant or statin of interest, who started to take the medication after the first 90 days of being enrolled in the insurance plan, and who filled the prescription two or more consecutive times were classified as “incident (or new) users.” Patients who were exposed to less than 30 days of the medication or who did not have any pharmacy claims for the medications in the class being evaluated were classified as nonusers. These medications are outlined in Table 2.3 for antidepressants and Table 2.4 for statins. Additionally, patients whose first date of exposure to the medication was prior to the January 2011 enrollment period (during 2009 or 2010) were classified as prevalent users. Those who had a first claim for a prescription in the 90 days after insurance plan enrollment were classified as prevalent users as a way to be conservative about patients who may have been refilling a prescription that they were taking prior to enrolling in the insurance plan. Sensitivity analyses using 30 days and 60 days as cut points were carried out to examine the impact that this criteria had on final analyses results.

Table 2.3 Medications Classified as Antidepressants

Generic Name Brand Name Class Selective Serotonin Reuptake Inhibitors (SSRIs) Fluoxetine Prozac SSRI Citalopram Celexa SSRI Paroxetine Paxil, Seroxat SSRI Sertraline Zoloft, Lustral SSRI Fluvoxamine Luvox SSRI Escitalopram Lexapro, Cipralex SSRI Tricyclic Antidepressants Clomipramine Anafranil TCAs Trimipramine Surmontil TCAs Amitriptyline Elavil, Endep TCAs Nortriptyline Pamelor TCAs Doxepin Adapin, Sinequan TCAs Other Antidepressants Desvenlafaxine Pristiq SNRI Duloxetine Cymbalta SNRI Trazodone Desyrel SARI Etoperidone Axiomin, Etonin SARI Mianserin Bolvidon, Norval, Tolvon TeCAs Mirtazapine Remeron TeCAs Venlafaxine Effexor SNRI Milnacipran Ixel, Savella SNRI Viloxazine Vivalan NRI Reboxetine Edronax NRI

ANTIDEPRESSANT CLASS DEFINITIONS Antidepressants were divided into selective serotonin reuptake inhibitors (SSRIs) (fluoxetine, citalopram, paroxetine, sertraline, fluvoxamine, and escitalopram), tricyclic antidepressants (TCAs) (clomipramine, trimipramine, amitriptyline, nortriptyline, and doxepin) or other antidepressants (desvenlafaxine, duloxetine, trazodone, etoperidone, mianserin, mirtazapine, venlafaxine, milnacipran, viloxazine, and reboxetine) based upon their mechanism of action (Table 2.3).

STATIN CLASS DEFINITIONS Statins were divided into hydrophilic (fluvastatin, pravastatin and rosuvastatin) or lipophilic (atorvastatin, lovastatin, pitavastatin, and simvastatin) categories based upon their mechanism of action (Table 2.4).

Table 2.4 Medications Classified as Statins

Generic Name Brand Name Class Atorvastatin Lipitor Lipophilic Lovastatin Altoprev, Mevacor Lipophilic Pitavastatin Livalo Lipophilic Simvastatin Zocor Lipophilic Fluvastatin Lescol & Lescol XL Hydrophilic Pravastatin Pravachol Hydrophilic Rosuvastatin Crestor Hydrophilic

STATIN DOSE ASCERTAINMENT Doses were obtained from the ACC/ AHA Guideline for cholesterol treatment to reduce Atherosclerotic Cardiovascular risk in adults [63]. Table 2.5 identifies the different categories utilized to divide doses into treatment intensity categories.

Table 2.5 Statin Doses Classified by Moderate and High Intensity by Statin Name

Statin Name Moderate Intensity High Intensity Atorvastatin 10 - 20 mg 40 - 80 mg Fluvastatin 5 - 40 mg 80 mg Lovastatin 40 mg

Pitavastatin 2 - 4 mg

Pravastatin 40 - 80 mg

Rosuvastatin 20 - 40 mg

Simvastatin 20 - 40 mg 80 mg

SENSITIVITY ANALYSES Sensitivity analyses were performed for all of the main analyses in these studies. First, the effect of changing the definition of incident antidepressant or statin use on the outcome of elevated HbA1c in 2014 was compared. Following this, the effect of changing the definition of incident antidepressant or statin use on the outcome of time to T2DM development was compared. To operationalize this, two more criteria were tested to define incident antidepressant or statin use. New cut points of 30 or 60 days were chosen to classify incident antidepressant or statin use after enrolling in the insurance plan. Those who initiated statin use prior to either 30 or 60 days were considered prevalent antidepressant or statin users. Pharmacy claims did not provide detail regarding the first date of a prescription claim; the investigator can identify this by going back as far in the data as possible to identify the first prescription claim, however when a patient starts a new insurance plan there is no data for use prior to enrollment. These new patients may decide to visit a new physician that is covered by the insurance but has different prescribing patterns relative to a patient’s prior healthcare provider. In this scenario, antidepressant or statin initiation could be classified as incident. Alternatively, a patient could have been taking an antidepressant or statin previously and renewed it sometime at the beginning of insurance enrollment.

EXCLUSIONS FROM THE STUDY POPULATION Individuals were excluded if they had diagnosis codes that would influence T2DM risk (Table 2.6). These excluded diagnoses were polycystic ovarian syndrome (PCOS), steroid induced or gestational diabetes, and pregnancy. Individuals with a diabetes diagnosis within 90 days of enrolling in the insurance plan were also excluded as prevalent diabetics. Patients with PCOS were excluded from the analyses because they are at an elevated risk for T2DM. Use of statins and some antidepressants is contraindicated for pregnant women. Finally, patients with gestational diabetes, steroid induced diabetes or type I diabetes were excluded because these conditions would interact with T2DM.

DIABETES ASCERTAINMENT Diabetes diagnoses were obtained by searching medical claims for a diagnosis code (Table 2.6). Specific claim types (inpatient hospitalizations and evaluation and management) were used to ensure a high accuracy of diagnosis. Evaluation and management claims were those that would reflect a visit with a primary care provider. The first date that an individual had a diagnosis of T2DM was considered the first diagnosis date. These patients were included in the analysis only if they had visited their healthcare provider during the study period, therefore these patient populations were very engaged in their healthcare and this methodology for incident diabetes diagnoses should reflect actual disease incidence.

Table 2.6 Summary of International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and CPT Codes used to Identify Excluded Patients and Those With the Study Outcome.

Condition Code type Codes Exclusion Criteria Polycystic Ovarian ICD-9 CM Diagnosis 256.4x Disease Steroid Induced or ICD-9 CM Diagnosis 249.xx , 251.8x, 648.8x, 962.0x Gestational Diabetes 630.xx-677.xx, 763.89, V22.xx, V23.xx, ICD-9 CM Diagnosis V24.xx, V27.xx, V28.xx, V72.42 Pregnancy ICD-9 Surgical 66.62, 69.0x, 72.xx-75.8x Procedure Code MS-DRG Codes 765-770, 774-782, 789-795 Outcome Diabetes ICD-9-CM diagnosis 250.xx, 357.2, 362.0x, 366.41, 648.0x *International Classification of Diseases, 9th clinical modification, where “x” indicates any digit.

INVERSE PROBABILITY WEIGHTED- ESTIMATORS This analysis utilized existing medical and pharmacy claims data, biometric data and health assessment data. The goal of the analyses was to identify the effect of 29

antidepressant or statin use on the subsequent development of elevated HbA1c or T2DM. The use of observational data prevented being able to take the data as it was and compare the average values between users and nonusers of a pharmaceutical [64]. This was because patients who were users of a pharmaceutical might not be comparable to the patients who were nonusers. Ultimately, if users are older, poorer, sicker or tobacco users, any observed difference in the outcome measured may reflect these underlying differences and not the effects of the pharmaceutical. Ideally, measures of what would happen in the same group if they received the pharmaceutical treatment compared to if they were in the nonuser group would be compared (the counterfactual comparison)[65]. In randomized controlled trials, randomization creates balance between the two groups who are being compared. In observational studies, this randomization can be statistically simulated using inverse probability weighted estimators (IPW) if certain assumptions are met. If an individual has a high predicted probability of being a user of a pharmaceutical, and they were a pharmaceutical user they receive a low weight value, but if they are a user of a pharmaceutical, but had a low probability of being a user, they will receive a higher weight (they are under-represented in the user group).

To implement inverse probability weighting, users were compared to nonusers using variables that were not influenced by treatment, or those that were measured prior to treatment. Following this, a logistic regression model was fit using treatment (for a dichotomous treatment) as the outcome in the model and covariates that were associated with receipt of treatment and developing the outcome. Candidate variables included all variables that confounded the association between treatment and the outcome. Once a model was created, the covariate balance was compared for the two groups before and after weighting. The inclusion of IPW should make the two groups comparable (balanced). If the two groups were not comparable, the model would be refined. To check the fit of the model, the overidentification test was implemented in STATA (it checks that the IPW model fits the data. If there was a violation the p-value would be <0.05). This test is similar to the Hosmer Lemeshow goodness of fit test[64]. Graphically, the two groups can be compared, and any non- overlapping distributions suggest that one or

more baseline covariates are strongly predictive of treatment selection. Once balance was obtained for the two groups, then the final outcome model was fit including the propensity weight estimator in the model with the main effect of treatment to obtain the final estimate for the difference in the two groups. The “Teffects IPW” command (available in STATA 14) was used to simultaneously estimate the propensity score weighted estimators (IPW) and the final outcome model. The standard errors were calculated using the generalized method of moments (GMM) to solve for the ML logit estimates. The final logit model provided coefficients for the average treatment effect, which is the prevalence difference between the treated and untreated groups, and the potential outcome mean, which corresponded to the prevalence of the outcome (HbA1c >6.0) among those who are in the untreated group.

LOGISTIC REGRESSION WITH INVERSE PROBABILITY WEIGHTING TO EVALUATE ELEVATED GLYCOSYLATED HEMOGLOBIN (> 6.0). Two separate analyses were performed to evaluate the effect of incident antidepressant (aim 2) or statin (aim 3) use on glycosylated hemoglobin A1c. These analyses were divided into those who had the outcome of HbA1c measured in 2013, and those who had the outcome of HbA1c measured in 2014. The same logic was applied to both analyses for consistency. Participants were excluded if they were prevalent diabetics so diabetes would not influence HbA1c measurements. These patients had already been identified as diabetic; therefore they were not useful in identifying if HbA1c could be used as an intermediate measurement prior to T2DM diagnosis. Additionally, many factors could influence blood glucose levels among diabetics (medication, physical activity, exercise and behavior changes following a diabetes diagnosis), many of which this study was not designed to evaluate.

To be considered an incident medication user (either antidepressant or statin) the medication use needed to take place prior to the outcome of HbA1c measurement. Differences in incident antidepressant statin users and antidepressant or statin nonusers were compared using t-tests or Pearson Chi Square tests. Propensity scores were

developed using inverse probability weighting to create balance in covariates predicting a patient’s likelihood of receipt of antidepressant or statin treatment for the groups of incident antidepressant or statin users and nonusers. Covariates were included in the propensity score weighted model in an attempt to control for confounding. The balance of descriptive statistics were checked after weighting using Pearson’s Chi square test for categorical variables and t-tests for continuous variables. A final logistic model included propensity weights to account for the probability of antidepressant or statin treatment and a variable for antidepressant or statin treatment group was fit to evaluate the effect of statin treatment on dichotomous HbA1c (≤6.0 vs. >6.0). The overidentification test and graphical evaluation of overlap were used to check the fit of the final propensity model and overlap between the two groups. This methodology was replicated to compare (1) statin user by class: hydrophilic statin users and lipophilic statin users; (2) statin use by dose: incident statin users on high intensity therapy and incident statin users on moderate intensity therapy; (3) antidepressant use by class: selective serotonin reuptake inhibitor users or other antidepressant users. New propensity score models were developed for each of these comparisons.

EXPOSURE AND ASSESSMENT CLASSIFICATION FOR SURVIVAL ANALYSIS TO EVALUATE TIME TO TYPE II DIABETES MELLITUS Participants were followed to the event that occurred first: an incident diagnosis of T2DM; termination of enrollment in the insurance plan; or the end of the follow up period, December 31, 2014. The absolute risk of incident T2DM over the four-year study period was calculated separately for individuals on antidepressant or statin treatment and those with no antidepressant or statin use. To account for the time period prior to incident antidepressant or statin exposure for those who started antidepressants or statins in the middle of the study period, time-dependent exposure variables were created.

TIME-DEPENDENT COVARIATES One of the benefits of the Cox model is the ability to incorporate covariates that change over time [66]. The Cox model works by comparing the covariate pattern for individuals who had an event at a specific time to the individuals who have not yet had the event (or were at risk) at that time. A time-dependent covariate is a predictor in a Cox model whose values vary with time [66]. For example, over the course of a study, patients may gain weight, change marital status, hypertension status or start to use a medication. In the current study investigating pharmacoepidemiology exposures, pharmaceutical use (either incident antidepressant or incident statin use) was modeled as a time-varying covariate in the Cox regression model. For example, a patient may have spent the first 11 months of the study as a statin non-user but then initiated statin use at month 11. If this person was classified as a statin user for the entire study period, the time they spent as a control during the beginning of the study period would not have been included in the analysis. To operationalize this, a new dataset was created where two rows were created for incident antidepressant or statin users. These observations were then split at the date of medication initiation. Thus, each person who spent time as both a control and an antidepressant or statin user has one row of data to correspond to each timeframe of the study. Start, end, exposure, and failure/censor events were re-generated so that they corresponded to the correct exposure time frame. Finally, when the data was analyzed in STATA the unique participant identifier was included so that the analysis took into consideration multiple observations for each individual, and a conventional Cox proportional hazards model was fit for the survival analysis to evaluate time to T2DM.

SURVIVAL ANALYSIS TO EVALUATE TIME TO T2DM Prior to creating the model differences were compared between incident antidepressant or statin users and nonusers using Pearson’s Chi-square for categorical variables and t-tests for continuous variables. Cox proportional hazards models were used to compute hazard ratios with accompanying 95% confidence intervals for the association between incident antidepressant or statin use and incident T2DM. To create the final models, a combination of stepwise selection (p<0.05) was used, purposeful variable selection being 33

careful to consider covariates for inclusion in the model if they changed the exposure of interest (treatment) by more than 20% when removed from the model, or if they were a clinically established confounder that was associated with both the exposure and outcome variables. The inclusion of clinically meaningful interactions between statin or antidepressant use and participant demographic characteristics was included during the model building process (p<0.10).

The same model building process was also used to create the models adjusting for differences between antidepressant or statin doses or statin mechanisms of action. All final proportional hazards models were checked for the assumption of proportional hazards, model diagnostics, collinearity of variables, and the goodness of fit test [66].

Subgroup analyses included examining the effect of antidepressant or statin class and statin dose on T2DM development. Specifically, incident antidepressant users taking SSRI antidepressants, and incident antidepressant users taking other antidepressants were compared. Additionally, incident statin users taking moderate intensity statins were compared to incident statin users taking high intensity statins. Incident statin users taking lipophilic statins were compared to incident statin users taking hydrophilic statins. Cox proportional hazards models were used to compute hazard ratios with accompanying 95% confidence intervals for the association between statin dose (moderate or high) or statin mechanism of action for antidepressant (Other antidepressant or SSRI) or (hydrophilic or lipophilic) and incident T2DM.

A sensitivity analysis was conducted using the final Cox regression models to examine the effect of classifying individuals who initiated an antidepressant or statin 30 and 60 days following enrollment in the insurance plan as incident antidepressant or statin users. These sensitivity analyses were used for the three analyses of time to T2DM: 1) incident antidepressant or statin users vs. nonusers; 2) high intensity statin users vs. moderate intensity statin users; 3) other antidepressant users vs. SSRI antidepressant users or lipophilic statin users vs. hydrophilic statin users.

POWER ANALYSIS FOR AIM 2

Using data from 2014 (the endpoint year), there were a total 2270 individuals over the age of 18 who had ICD-9 codes for depression, anxiety and other indications for antidepressant use. Of these individuals, 1,620 were antidepressant users, and 700 were antidepressant nonusers. The overall utilization rate of antidepressants in this population was calculated to be 9%, and two-fold greater likelihood of antidepressant use was expected among those who progressed to an incident T2DM diagnosis. Previous studies on this topic found that it took approximately one year of antidepressant use to observe an increased risk of T2DM among incident users of antidepressant taking moderate doses [46], and a two-fold increase of T2DM was seen for new users of antidepressant [27]. Based on this sample size of antidepressant nonusers, the power would be at least 0.9999 assuming that these numbers of subjects are antidepressant users and nonusers. A caveat of this power analysis is that the analysis in this dissertation was limited to new users of antidepressants, so the number of antidepressant users was overestimated. However, the large number of individuals in the antidepressant user group compared to the control group implied that there was at least an 80% probability to detect a significantly increased risk of T2DM within one year of the discontinuation of pharmacotherapy (with an α of 0.05 and β of 0.20).

POWER ANALYSIS FOR AIM 3

Using data from 2014 (the endpoint year), there were a total of 2,448 statin users, and 3,676 nonusers of statins with comparable ICD-9 diagnosis codes for cardiovascular risk factors. Roughly 10% of the members in this study population were statin users, and a two and a half-fold greater likelihood of statin use was expected among those who progress to an incident T2DM diagnosis. Based on this sample size of statin nonusers, the power would be at least 0.9985 assuming that this number of subjects were statin users and nonusers. A caveat of this power analysis was this analysis was limited to new users of statins, so the number of statin users was overestimated in this calculation. However, the large number of individuals in the statin user group compared to the control group implied that there was at least an 80% probability to detect a significantly increased risk 35

of T2DM within one year of the discontinuation of pharmacotherapy (with an α of 0.05 and β of 0.20).

SAS version 9.4 (Cary, NC: SAS Institute Inc.) was used for all data management, and Stata 14. (StataCorp. 2015. College Station, TX) was used for data analyses, and generation of figures and tables.

INSTITUTIONAL REVIEW BOARD

This study was approved by the Institutional Review Board at OhioHealth (OhioHealth IRB # OH1-15-0599; Federalwide Assurance #: FWA00014752) and Ohio State University (Federalwide Assurance #: FWA00006378) ceded review to OhioHealth’s IRB.

CHAPTER 3

Are Prediabetics Who Express Interest In The DPP Different Than Those Who Do Not Express Interest?

INTRODUCTION

Background

Type II diabetes mellitus (T2DM) is a serious medical condition that affects over 29 million Americans [1]. T2DM is the 7th leading cause of death in the U.S. and increases the risk of certain types of cancer and cardiovascular events [2,3]. It is also the leading cause of kidney failure, lower limb amputations, and adult-onset blindness [2,4]. In addition to these human costs in 2012 the estimated financial cost of medical care, disability and premature death was $245 billion in the U.S. [6], a cost that will rise as the prevalence of T2DM continues to increase, both globally and in the U.S. [7].

Diabetes Prevention Programs (DPPs) offer the opportunity to delay the onset of T2DM by improving a patient’s health behaviors. The main goal of this program, created by the Centers for Disease Control and Prevention (CDC), is for participants with prediabetes to lose excess body weight by consuming a healthy diet and increasing their time spent engaged in physical activity [16]. The DPP is being implemented across the country in community and worksite settings, however it is unclear if the participants expressing interest in these programs are different from those who do not elect to participate [17].

The RE-AIM Framework [67] is composed of the dimensions of Reach, Efficacy or Effectiveness, Adoption, Implementation and Maintenance. This health theory framework is useful in evaluating the effectiveness of the implementation of a health

promotion program. Reach, the main component of interest for this study evaluates the percentage of potentially eligible participants who participate in a study to those who are potentially eligible and do not elect to participate. Comparing the similarities and differences between these two groups will help to identify how externally generalizable the individuals enrolling in a worksite health promotion program are to the greater population who could have qualified to participate in the program. A review article examining how often the components of RE-AIM are reported when a paper is published in a health promotion journal about a worksite health behavior intervention reported that Reach was only reported 10% of the time [68]. This paper emphasized the need for Reach evaluation so health promoters selecting a worksite health program have transparency around how likely the program translates into a benefit for the new worksite population.

There are often misconceptions about who enrolls in worksite wellness programs [18], despite the fact that they are an effective way to engage employees and decrease their risk for chronic diseases. With an increasing incidence of prediabetes, participation in programs like the DPP can prevent or delay the development of T2DM [13,15,16]. Randomized controlled trials evaluating health programs capture a group of engaged participants who do not represent the general population [19]. When wellness programs are executed in real-world settings, selection bias is a concern in describing who ultimately enrolls. Program efficacy may be underestimated because the lowest risk groups (those who are the most motivated to make lifestyle changes) enroll, and these individuals are not the group that would gain the most benefit from these programs. Alternatively, effectiveness may be overestimated if the healthiest individuals enroll and stay healthy after programs are completed. The extent of selection bias among DPP participants has not been previously investigated, likely because data is not available for those who do not enroll.

The use of an existing longitudinal employee cohort made it possible to examine the correlates of interest in enrollment and to evaluate trends in risk factor profiles for prediabetics who differed in their interest in enrolling in the DPP.

The presence of a larger cohort from which prediabetics are recruited into the DPP will allow for the systematic evaluation of recruitment efforts. While others have examined correlates of enrollment into health programs [18,20–24], the novel component of this approach is the incorporation of risk trajectories (changes in biometric values) to evaluate selection bias into health behavior programs. The long-term goal for this study is to inform ongoing recruitment efforts, and if necessary, develop new methods to recruit and engage higher risk groups for the DPP in the group format. This could include the development of and offering of other program formats (online, offsite or through video conferencing)..

The Diabetes Prevention Program

Wellness programs have the goal of health improvement through lifestyle modifications to decrease a patient’s subsequent disease risk. The DPP is one such program offered by the company worksite and has been proven to delay the onset of T2DM among patients who have prediabetes [10, 16-18]. The DPP was also advertised as a program offered to employees during the new associate onboarding orientation. It was included in the Human Resources publications that advertise associate wellness programming. It was advertised on the company’s intranet. Associates and dependent spouses with glycosylated hemoglobin values in the prediabetic range (5.7-6.4) and a BMI ≥24 were told that they qualified to participate in the worksite DPP and the program would be offered to them free of cost. These patients who were in range for the DPP also received physician referral letters containing a brochure for the DPP to increase their awareness about the free DPP resource (APPENDIX C: DPP Recruitment Brochure). If associates chose to enroll elsewhere it could cost them up to $500. Associates and dependent spouses who were interested in signing up for the program either emailed the DPP email account or called the DPP office where they provided information to the program coordinator who entered their contact information into a database. The content of this program is aligned with the CDC curriculum, and the worksite program is

currently under consideration for accreditation through the CDC. Once classes became available, individuals who were interested were offered the opportunity to enroll in the program on a first-come first-serve basis. The database consisted of individuals who expressed interest in enrolling in the DPP, were enrolled in the insurance plan during 2014, and their biometric screening results in 2014 identified them as being in the prediabetic range.

METHODS

Data Sources

Biometric measurements were collected during annual biometric screenings as described in Chapter 2. Following the biometric screening, individuals falling in the DPP eligibility range met with a health care provider during Point of Care Testing to discuss their test results, which included the receipt of recruitment materials for the DPP. Prediabetic patients were identified using the biometric screening results and later received letters at their home addresses encouraging them to participate in the DPP. Other recruitment efforts included phone calls to prediabetic patients and advertisements on the company intranet.

Longitudinal biometric data was collected at biometric screenings from 2011 to 2014 and these values were used to evaluate patient risk trajectories. Measurements for height, weight, body mass index (BMI), waist circumference, and lipids (triglycerides and cholesterol) were collected all four consecutive years. Blood pressure was measured from a sitting position using an automated blood pressure cuff. For readings that were abnormal, the blood pressure measurement was repeated with a manual cuff and the new measurement was recorded. Waist circumference was measured in inches using a flexible tape measure. A blood sample was obtained via fingerstick, and used for both lipid and glycosylated hemoglobin testing. The CardioChek System, manufactured by Polymer Technical Systems (Indianapolis, Indiana) was used to obtain total cholesterol, HDL

cholesterol, triglycerides, LDL cholesterol and random plasma glucose values in less than two minutes.

Identifying Prediabetics

Health plan members who participated in the 2014 biometric screening were screened for prediabetes and received their test results from a healthcare professional during the point of care biometric screening. The health professional informed the patient of their abnormal values and referred them to worksite wellness and disease management programs that they were eligible for. The DPP was the program that was recommended to prediabetics.

Study Population

Health plan enrollees and spouses participated in annual voluntary biometric screenings from 2011 – 2014. For the first part of the analysis, biometric data from 2014 was used to identify prediabetics who qualified for the worksite DPP. Eligibility for the DPP was based upon having a body mass index (BMI) at or above 24 and an HbA1c value from between 5.7 and 6.4, the designated prediabetic range [69].

This study was approved by the Institutional Review Board at Ohio Health and The Ohio State University.

Statistical Analysis

Differences in continuous and categorical variables for prediabetics who were, and were not, interested in the DPP were tested using t-tests and chi-square analyses, respectively. A p-value criteria for significance was set using Bonferroni correction for multiple testing (p<0.001) due to the large number of covariates that were examined. The most important correlates of interest in enrolling in the DPP were evaluated by using a logistic regression model (1= interested in enrolling, 0= not interested in enrolling). Covariates that were of interest included age, gender, ethnicity, job category, education level, depression status, 41

stress levels, sleep, BMI, diet quality, physical activity level, tobacco use, alcohol use, and concurrent comorbid conditions. Clinically meaningful interaction terms (gender X ethnicity, gender X self-efficacy to make healthy lifestyle changes, and gender X fruit and vegetable consumption), were tested for significance in the model with their main effects during the model building process and considered for inclusion at the 0.10 significance level. Covariates that were significant in the univariate analysis (p<0.05) (Table 1) were kept and considered for inclusion in the multivariable logistic regression model, which was built using stepwise selection. Diagnostics, collinearity of variables and the Hosmer Lemeshow Goodness of Fit Test were used to check underlying assumptions and fit of the model. The Cochrane-Armitage trend test was used to analyze whether increasing levels of health behaviors (doctors visits, fruit and vegetable consumption, and self-efficacy) were associated with higher odds of interest in participating in the DPP. Annual biometric measurements (BMI, waist circumference, body weight, systolic blood pressure, diastolic blood pressure, triglyceride levels, HDL cholesterol levels, LDL cholesterol levels and total cholesterol levels) were evaluated to test if there was a difference in trends over time (changes in these biometric values) for prediabetics who were and were not interested in the DPP. Linear mixed models with a random intercept (which assumes an exchangeable correlation pattern) were fit to account for the inter-person correlation in measurements between biometric measurements. Time was modeled categorically. The Multivariate Wald test was used to test if the time by group interaction was significant for each model with the outcome of mean biometric measurement. SAS version 9.4 (Cary, NC: SAS Institute Inc.) was used for all data management, and Stata 14. (StataCorp. 2015. College Station, TX) was used for data analyses and generation of figures and tables.

The data sources (health survey, enrollment file, data from Human Resources regarding employment status and eligibility for enrollment in the insurance plan, and annual biometric screening results) used for this analysis were combined for the population who was enrolled in the insurance plan during the time period of interest and further pared down to those who participated in biometric screening in 2014. The population was

further restricted to prediabetics with biometric values in range for the DPP (individuals with glycosylated HbA1c 5.7 – 6.4 and BMI ≥24), and prediabetic individuals who were interested in the DPP were compared to those who did not express interest in the DPP.

The health assessment contained data regarding many different exposures that are associated with prediabetes and engagement in a voluntary wellness program. These variables included demographics, patient health status, health behaviors with regard to tobacco and alcohol, nutrition, and physical activity, engagement in health improvement, and healthcare use, and stress and sleep variables. Several categories were collapsed to ensure that there were enough cases in each category for univariate analysis. The health assessment questions that were used to measure these behaviors are included in the supplemental materials. (Appendix A Health Assessment Survey).

RESULTS

Subjects

A total of 21,952 individuals were identified who were 18 years of age or older and enrolled in the insurance plan in 2014. 20,409 of these individuals were enrolled in the insurance plan for six months or longer. 1,543 individuals were excluded if they were enrolled in the insurance plan for less than six months in length. 5,389 dependent children were excluded. Dependent children over 18 did not participate in the biometric screening, and therefore, were excluded. 10,627 (70.8%) adults (associates or spouses) were enrolled in the insurance plan for 6 months or longer and participated in the voluntary 2014 biometric screening; 4,393 were associates or spouses who did not participate in the screening. Of the individuals who did participate in the biometric screening, 2,158 (20.3%) were classified as eligible for the DPP based on HbA1c (5.7 – 6.4) and BMI (≥24); 8,431 individuals did not qualify to participate in the DPP (Figure 3.1).

A total of 283 individuals who expressed interest in the DPP were enrolled in the insurance plan for 6 months of time or longer in 2014. Of this group, 28 were excluded because they did not participate in the biometric screening and 38 were excluded because they were interested in the DPP but were not eligible based upon the biometric screening criteria for inclusion in the DPP. Finally, a total of 217 individuals who were interested in the DPP (10%) were compared to a total of 1,941 prediabetics who did not express interest in the DPP.

Figure 3.1. Flow of Patients Who Were Identified as Eligible for the DPP.

Patients 18 years of age and older who were enrolled in the insurance plan for greater than or equal to 6 months were identified using insurance claims. This larger group was then refined to spouses and associates who participated in the 2014 biometric screening and were identified as prediabetic. This group was later broken into patients who did and did not express interest in the DPP. Those who expressed interest in the DPP were composed of those who did and did not enroll in the DPP.

Table 3.1 examines differences between prediabetics with different interest in enrolling in the DPP. Those who were more interested in the DPP tended to be slightly older in age (49.95 vs. 52.16 years old, p =0.004), female (83% vs. 61%, p<0.001), African American (14% vs. 20%, p=0.018), and have a higher level of education (39.6% vs. 31.6%, p =

0.006). Associates (employees on the insurance plan) were more likely to express interest in the DPP than dependent spouses (29.2% vs. 15.2%, p<0.001).

Individuals who were interested in the DPP did not differ from other prediabetics with respect to the number of years enrolled in the insurance plan (p=0.35). Associates who expressed interest in the DPP were no different in the number of years of service to the company (p=0.997), but those who did express interest were more likely to work greater than 36 to 40 hours per work week (p<0.001), and were also more likely to be a secretary, clerk, or staff professional and less likely to be a physician, resident, fellow or a service support associate (p<0.001).

Those with a lower comorbid disease burden were more likely to express interest in the DPP; they were less likely to have reported a personal history of asthma (p=0.018), hypertension (p=0.012), depression (p=0.021), cancer (p=0.015) or high cholesterol (p=0.020). Women who were interested in the DPP were also less likely to have given birth to a baby that was nine pounds or greater (p=0.015), which puts a female at risk for Type II diabetes. Despite this lower comorbid disease burden, those who were interested in the DPP also had slightly lower self-rated health status (p=0.012).

With regard to health behaviors, those who were interested in the DPP were more likely to be tobacco nonusers (p=0.003) but no statistically significant differences were observed in the number of alcoholic drinks consumed per week (p=0.071) or the number of sugary drinks consumed per day (p=0.80). Those expressing interest reported a higher daily consumption of fruits and vegetables (p=0.017) and unhealthy foods (p=0.023). Those who expressed interest in the DPP were not statistically significantly different in the days per week that they engaged in cardiovascular or strength exercise.

Those who were interested in the DPP had lower self-efficacy to make healthy lifestyle changes (p=0.002). These individuals were also more likely to be currently making changes to manage their weight, or planned to make changes in the next month

(p=0.016). Those who were interested in the DPP were no different with respect to interest in getting more cardiovascular exercise (p=0.693), or by level of interest in improving their diet (p=0.586). However, those interested in the DPP were more likely to be interested in getting more strength building exercise (p=0.011) than those who were not interested in the DPP.

Those who were interested in the DPP were more likely to have a primary care physician (p=0.005), to have a higher number of doctor visits (p=0.004), and fewer overnight hospitalizations (p=0.013) and emergency department visits (p=0.015). Those who were not interested in the DPP had slightly more sleep (p=0.057) than those who were interested in the DPP. There were no statistically significant differences with regard to restful sleep (p=0.350) or the amount of stress that influenced health and well-being (p=0.065).

Looking at participants’ biometric screening results, those who were interested in the DPP were less likely to have hypertension (p=0.012) and more likely to have a waist circumference classified as high risk (p=0.007). Interestingly, there were no differences in BMI category (p=0.28) but when mean BMI was compared, those expressing interest were more likely to have a higher BMI (p=0.049) and a higher glycosylated hemoglobin level (p<0.001). There were no differences observed with respect to HDL (p=0.571), LDL (p=0.447), total cholesterol (p=0.774), or triglyceride levels (p=0.508).

Table 3.1 Descriptive Characteristics of Prediabetics With Different Levels Of Interest In The Diabetes Prevention Program DPP Uninterested DPP Interested p- value n=1,941 n=217

Demographics Age (mean, sd) 1,941 49.95 10.81 217 52.16 9.36 p=0.004 Gender (n, %) Male 757 (39.0) 36 (16.6) p<0.001 Female 1,184 (61.0) 181 (83.0) Ethnicity (n, %) White 1,465 (75.4) 156 (71.9) p=0.018

Black 274 (14.1) 44 (20.3)

Other 90 (4.6) 12 (5.5)

Education Level (n, %) High School Graduate or Less 200 (10.3) 15 (6.9) p=0.006

Some College 402 (20.7) 53 (24.4)

College Graduate 613 (31.6) 86 (39.6)

Post Graduate 243 (12.5) 29 (13.4)

Relationship Code (n, %) Associate 1,375 (70.8) 184 (84.8) p< 0.001 Dependent Spouse 566 (29.2) 33 (15.2) Years Enrolled in Insurance 1,941 3.29 1.12 217 3.37 1.12 p=0.355 Plan (mean, sd) Years of Service at Company p=0.997 (mean, sd) 1,328 14.75 11.49 186 14.75 11.1 Standard Hours of Work per

Week (n, %) ≤ 24 hours 81 (4.2) 16 (7.4) p<0.001 > 24 - <32 hours 102 (5.3) 10 (4.6) ≥ 32 - <36 hours 210 (10.8) 18 (8.3) >36 - 40 hours 935 (48.2) 142 (65.4) Job Category (n, %) Clinical Professional 75 (3.9) 9 (4.2) p<0.001 Clinical Technician 118 (6.1) 19 (8.9) Executives/ Directors 43 (2.2) 2 (0.9) Managers/ Supervisors 84 (4.3) 21 (9.7) Nursing 314 (16.2) 35 (16.3) Patient Care 124 (6.4) 35 (16.1) Physicians/ Residents/ Fellows 47 (2.4) 1 (0.5) Secretaries/ Clerks 230 (11.8) 46 (21.2) Service Support 123 (6.3) 8 (3.7) Staff Professional 116 (6.0) 28 (12.9) Support Technician 54 (2.8) 7 (3.2) Continued 48

Table 3.1 Continued Health Status

Presence of Asthma (n, %) No 1,547 (79.7) 182 (83.9) p=0.018

Yes 174 (9.2) 24 (11.1) Presence of Hypertension (n, %) No 1,007 (51.9) 132 (60.8) p=0.012

Yes 935 (48.2) 85 (39.2)

Presence of Depression (n, %) No 1,486 (76.5) 178 (82.0) p=0.021

Yes 239 (12.3) 28 (12.9)

Presence of Cancer (n, %) No 1,616 (83.2) 190 (87.6) p=0.015

Yes 109 (5.6) 16 (7.4) Presence of High Cholesterol (n, %) No 1,196 (61.6) 140 (64.5) p=0.020

Yes 529 (27.2) 66 (30.4) Birth of a Large Baby (≥9 lbs) (n, %) No 925 (47.6) 143 (65.9) p<0.001 Yes 143 (7.4) 28 (12.9) Self-Rated Health (n, %) Fair or Poor 186 (9.6) 21 (9.7) p=0.012 Good 794 (40.9) 108 (49.8) Excellent or Very Good 745 (38.4) 77 (35.5) Hours of Sleep Per Night (n,

%) <5 hours 170 (8.8) 17 (7.8) p=0.057 5 hours - < 8 hours 1,100 (56.6) 141 (65.0) 8 hours or more 672 (34.6) 59 (27.2) Restful Sleep (n, %) Always or most of the time 1,081 (55.7) 128 (59.0) p=0.350 Sometimes, rarely or never 861 (44.3) 89 (41.0)

Stress has affected health and well being in the past year (n, %) Strongly Agree or Agree 106 (5.5) 16 (7.4) p= 0.065 Neutral 403 (20.8) 56 (25.8) Strongly Disagree or Disagree 903 (46.5) 101 (46.6) Continued 49

Table 3.1 Continued Health Behaviors: Tobacco & Alcohol

Tobacco Use (n, %) Never 1,179 (60.7) 154 (71.0) p=0.003

Former or Current Use 546 (28.1) 52 (24.0) Alcoholic Drinks Per Week (n, %) 0 943 (48.6) 121 (55.8) p=0.071

1-3 541 (27.9) 58 (26.7)

4 or more 458 (23.5) 38 (17.5) Health Behaviors: Nutrition & Physical Activity Average Sugary Drinks (daily) (n, %) 0 825 (42.5) 104 (47.9) p=0.80

1 553 (28.5) 64 (29.5)

2 233 (12.0) 26 (12.0)

3 or more 114 (5.9) 12 (5.5) Average Fruits and Vegetables (daily) (n, %) 0-2 523 (26.9) 53 (24.4) p=0.017

3-4 744 (38.3) 90 (41.5)

5 or more 458 (23.6) 63 (29.0) Average Unhealthy Foods (daily) (n, %) 0-1 762 (39.2) 81 (37.3) p=0.023

2 650 (33.5) 84 (38.7)

3 or more 313 (16.1) 41 (18.9) Cardio Exercise (days/ week) 1,725 3.03 1.99 206 2.87 1.89 p=0.278 (mean, sd) Strength Exercise (days/week) 1,725 1.33 1.73 206 1.14 1.55 p= 0.131 (mean, sd) Engagement in Health Improvement Self-efficacy to make healthy lifestyle changes (n, %) Not at all confident 297 (15.3) 50 (23.0) p=0.002 Somewhat confident 636 (32.8) 73 (33.6)

Confident 538 (27.7) 63 (29.0) 254 (13.1) 20 (9. 2) Extremely or Very Confident

Continued

Table 3.1 Continued Interest in making healthy changes to manage weight (n,

%) No need or plans to make 103 (5.3) 9 (4.2) p=0.016 changes

Plan to make changes in the 842 (43.4) 98 (45.2) next month - 6 months Have been making changes 581 (29.9) 81 (37.3) already Interest in getting more cardiovascular exercise (n, %) No need or plans to make 76 (3.9) 8 (3.7) p=0.693 changes

627 (32.3) 72 (33.2) Plan to make changes in the next month - 6 months Have been making changes 139 (7.2) 20 (9.2) already Interest in getting more strength building exercise (n,

%) No need or plans to make 192 (9.9) 19 (8.8) p=0.011 changes Plan to make changes in the 892 (45.9) 116 (53.5) next month - 6 months Have been making changes 180 (9.3) 28 (12.9) already Interest in improving diet (n, %) No need or plans to make 49 (2.5) 4 (1.8) p=0.586 changes Plan to make changes in the 376 (19.4) 50 (23.0) next month - 6 months Have been making changes 214 (11.0) 23 (10.6) already Healthcare Use Presence of a Primary Care

Physician (n, %) 118 (6.1) 8 (3.7) p=0.005 No 1,607 (82.8) 198 (91.2) Yes Continued

Table 3.1 Continued

Doctors Visits (past yr) (n, %) 221 (11.4) 13 (6.0) p=0.004 0 482 (24.8) 55 (25.4) 1 463 (23.8) 58 (26.7) 2 354 (18.2) 50 (23.0) 3-4 205 (10.6) 30 (13.8) 5 or more Overnight Hospitalizations

(past yr) (n, %) 1,593 (82.0) 194 (89.4) p=0.013 0 132 (6.8) 12 (5.5) 1 or more Emergency Department Visits (past yr) (n, %) 1,460 (75.2) 170 (78.3) p=0.015 0 265 (13.7) 36 (16.6) 1 or more Biometric Measurements Presence of Hypertension (n, %) 1,007 (51.9) 132 (60.8) p=0.012 No 935 (48.2) 85 (39.2) Yes Waist Circumference (n, %)

Low Risk <40 M / <35 W 691 (35.6) 57 (26.3) p=0.007

High Risk ≥40 M/ ≥35 W 1,156 (59.5) 153 (70.5)

BMI Category (n, %) 744 (38.3) 75 (34.6) p=0.280 Overweight 1,198 (61.7) 142 (65.4) Obese HDL Cholesterol (mg/dL) 1,941 52.84 17.05 216 53.75 17.19 p=0.461 (mean, sd) LDL Cholesterol (mg/dL) p=0.460 (mean, sd) 1,866 107.60 34.01 206 109.46 35.08 Total Cholesterol (mg/dL) p=0.774 (mean, sd) 1,939 191.00 41.73 216 191.86 39.90 Triglyceride Levels (mg/dL) p=0.632 (mean, sd) 1,936 154.71 84.71 217 151.81 84.66 HbA1c (%) (mean, sd) 1,941 5.91 0.21 217 5.98 0.213 p<0.001 BMI (mean, sd) 1,941 32.45 6.44 217 33.36 6.89 p=0.050 Not all frequencies add up to 100% due to some missing data. aT-test of between group differences was used. bPearson Chi-square test of between group differences was used.

FACTORS SIMULTANEOUSLY INFLUENCING DPP INTEREST In building the model to evaluate which covariates were most important in describing the population who was interested in enrolling in the DPP, several interaction terms were tested for inclusion in the model but none were significant (p>0.10 or all). The final model included gender, age, presence of hypertension, waist circumference, glycosylated hemoglobin A1c percentage, waist circumference (inches), education level, ethnicity, number of doctor visits in the past year, self-efficacy to make health changes, and the average number of daily fruits and vegetables (Table 3.2 and Figure 3.2). Females had 2.40 times the odds of being interested in the DPP compared to males (95% CI: 1.55, 3.72). Every five year increase in age was associated with a 7.7% greater likelihood of expressing interest in the DPP (95% CI: 0.99, 1.17), however, this association was only borderline statistically significant. Prediabetics with hypertension had 29% lower odds of being interested in the DPP compared to those without hypertension (95% CI: 0.50, 0.99).

Prediabetics with a higher risk waist circumference had 44% higher odds of being interested in the DPP (95% CI: 0.98, 2.13) compared to prediabetics with a low risk waist circumference. A 0.01 unit increase in Hemoglobin A1c was associated with 2.5 times the odds of interest in the DPP (95% CI: 1.15, 5.44) among prediabetics (where HbA1c was restricted to 5.7 – 6.4). Education that was greater than high school was associated with greater odds of interest in the DPP. Some college was associated with a 19% increased odds of interest (95% CI: 0.74, 1.92), however this association was not statistically significant. Graduating from college was associated with being 4% lower odds of interest (95% CI: 0.58, 1.61) compared to those who completed high school or less, and graduate education was associated with 79% greater odds of interest in the DPP compared to those with less than some college (95% CI: 0.90, 3.56). There was a significant trend observed across increasing education levels (P-trend= 0.030), although individually the categories were not significant individually.

African American race was associated with 2.23 times the odds of interest in the DPP (95% CI: 1.43, 3.47) compared to Whites, and individuals who self-classified as being an “other race” had 73% higher odds of interest in the DPP compared to Whites (95% CI: 0.84, 3.56). Larger numbers of doctor visits in the past year increased the odds of interest in the DPP among prediabetics compared to those who had zero visits in the previous year. There was a significant trend observed across increasing doctor visits in the last year (P-trend= 0.019), although the categories were not significant individually.

Increased levels of self-efficacy decreased the odds of interest in the DPP among prediabetics compared to those who had the lowest level of self-efficacy (“not at all confident”). Those with the highest confidence level (“extremely confident” or “very confident”) had the lowest odds of interest in the DPP (OR: 0.48, 95% CI: 0.26, 0.91) compared to those who were “not at all confident”. Those who were considered confident had 41% lower odds of interest in the DPP (OR: 0.59, 95% CI: 0.37, 0.95) compared to those who were “not at all confident”. Those who were considered somewhat confident had 39% lower odds of interest in the DPP (95% CI: 0.40, 0.94) than those who were “not at all confident”. There was a significant trend observed across increasing levels of self-efficacy being inversely related to interest in the DPP (P-trend= 0.002).

Increased daily fruit and vegetable consumption was associated with increased odds of interest in the DPP among prediabetics compared to those who had between zero and two servings per day. Consuming 3-4 servings of fruits and vegetables per day was associated with a 7% increased odds of interest in the DPP (95% CI: 0.72 – 1.60) compared to those who consumed between 0-2 servings of fruits and vegetables per day. Consuming five or more servings of fruits and vegetables per day was associated with a 37% increase in odds of interest in the DPP (95% CI: 0.87 – 2.13) compared to those who consumed between 0-2 servings of fruits and vegetables per day. There was a significant trend observed across increasing daily fruit and vegetable consumption (P-trend= 0.020), although individually the categories were not significant individually

Table 3.2 Multivariable Logistic Regression for the Odds Of Being Interested In The DPP Among Prediabetics Covariate OR P Value LB 95% CI UB 95% CI P-Trend Gender

Male Ref

Female 2.40 0.00 1.55 3.72 Age (years) 1.01 0.053 1.00 1.03 Presence of Hypertension

No Ref

Yes 0.71 0.05 0.51 0.995 Waist Circumference

Low Risk <40 M; < 35 W Ref

High Risk ≥40 M; ≥35 W 1.44 0.07 0.98 2.13 Glycosylated Hb1Ac (%) 2.56 0.02 1.18 5.54 Education Level

High School Graduate or Less Ref p=0.030 Some College 1.19 0.47 0.74 1.92 College Graduate 0.96 0.88 0.58 1.61 Post Graduate 0.56 0.10 0.28 1.11 Ethnicity

White Ref

Black 2.23 0.00 1.43 3.47 Other 1.73 0.13 0.84 3.56 Doctor Visits in the Past Year

0 Ref p=0.019

1 1.67 0.15 0.83 3.37 2 1.78 0.11 0.89 3.58 3-4 1.90 0.08 0.94 3.88 5 or more 2.13 0.05 0.99 4.57 Self-Efficacy to make healthy lifestyle changes

Not at all confident Ref p=0.002

Somewhat confident 0.61 0.027 0.40 0.94 Confident 0.59 0.029 0.37 0.95 Extremely or Very confident 0.48 0.023 0.26 0.91 Average fruits and vegetables (daily)

0-2 Ref p=0.02

3-4 1.07 0.74 0.72 1.60 5 or more 1.37 0.18 0.87 2.13 P-trend uses the Cochrane Armitage trend test. OR: The odds ratios were adjusted for gender, age, presence of hypertension, waist circumference, glycosylated hemoglobin A1c, education level, ethnicity, doctor visits in the past year, self- efficacy and average daily fruit and vegetable consumption. 55

Figure 3.2. Multivariable Adjusted Odds Ratios for Interest in the Diabetes Prevention Program. A multivariable logistic regression model was fit with fruit and vegetable consumption, self-efficacy, doctors visits, education level, ethnicity, gender, age, history of hypertension, waist circumference and HbA1c with the outcome of interest in the DPP. All Adjusted Odds Ratios are from the full model containing these covariates, and colors in the figure correspond to different covariates.

LONGITUDINAL FACTORS Longitudinal analysis did not observe any differences in biometric trajectories based on interest in enrolling in the DPP. The biometric measurements considered for this analysis included: waist circumference; BMI; body weight; lipid measurements (triglycerides, HDL cholesterol, LDL cholesterol, total cholesterol); and blood pressure (systolic and diastolic blood pressure). Possible trends were evaluated in these biometric measurement values for the two groups (DPP interested and DPP uninterested) over time (Figures 3.3 – 3.11).

Mean BMI from 2011 - 2014 by DPP Interest Group 35

Body Mass Index 30

29 2011 2012 2013 2014 Year DPP Uninterested DPP Interested

Figure 3.3. Mean BMI from 2011 – 2014 by DPP Interest Group. Overall, the trend is similar between the two groups. Individuals who were interested in the DPP had a higher average BMI than those who did not express interest.

Mean Body Weight from 2011 - 2014 by DPP Interest Group

215 210 205 200 195 190 185 180

Mean Body Weight (Lbs) (Lbs) Weight MeanBody 2011 2012 2013 2014 Year DPP Uninterested DPP Interested

Figure 3.4. Mean Body Weight (lbs) from 2011 – 2014 by DPP Interest Group. Overall, the trend was increasing for both groups and there was no significant difference in body weight by group over the four-year period.

Mean Waist Circumference from 2011 - 2014 by DPP Interest Group 45

39 (Inches)

35 Mean Waist Circumference MeanWaist 2011 2012 2013 2014 Year DPP Uninterested DPP Interested

Figure 3.5. Mean Waist Circumference (ins) from 2011 – 2014 by DPP Interest Group. Overall, the trend was stable over the timeframe, and there was no significant difference in waist circumference between groups.

Mean Cholesterol from 2011 - 2014 by DPP Interest Group 200

195

190

185

180

Mean Cholesterol (mg/dL) (mg/dL) MeanCholesterol 2011 2012 2013 2014 Year DPP Uninterested DPP Interested

Figure 3.6. Mean Cholesterol (mg/dL) from 2011 – 2014 by DPP Interest Group. The trend was similar for both groups over the four-year period but those that were interested in the DPP had slightly higher values. This difference was not statistically significant.

Mean LDL Cholesterol from 2011 - 2014 by DPP Interest Group 120

115

110

105

100 Mean LDL- Cholesterol (mg/dL) (mg/dL) Cholesterol MeanLDL- 2011 2012 2013 2014 Year DPP Uninterested DPP Interested

Figure 3.7. Mean LDL Cholesterol (mg/dL) from 2011 – 2014 by DPP Interest Group. The trend was similar for both groups over the four-year period but those that were interested in the DPP had slightly higher values. This difference was not statistically significant.

Mean HDL Cholesterol from 2011 - 2014 by DPP Interest Group 60

45 Mean HDL- Cholesterol (mg/dL) (mg/dL) MeanHDL-Cholesterol 2011 2012 2013 2014 Year

DPP Uninterested DPP Interested

Figure 3.8. Mean HDL Cholesterol (mg/dL) from 2011 – 2014 by DPP Interest Group. The trend was similar for both groups over the four-year period but those that were interested in the DPP had slightly higher values. This difference was not statistically significant.

6 2

Mean Systolic Blood Pressure from 2011 - 2014 by DPP Interest Group 135

130

(mmHg) 125

120 Mean Systolic Blood Presssure Blood MeanSystolic 2011 2012 2013 2014 Year

DPP Uninterested DPP Interested

Figure 3.9. Mean Systolic Blood Pressure (mmHg) from 2011 – 2014 by DPP Interest Group. Overall, no significant differences in systolic blood pressure were observed between the two groups. The overall trend is that diastolic blood pressure values were roughly stable over the four-year time frame.

Mean Diastolic Blood Pressure from 2011 - 2014 by DPP Interest Group 85

80 (mmHg)

75 2011 2012 2013 2014

Mean Diastolic Blood Presssure Blood MeanDiastolic Year DPP Uninterested DPP Interested

Figure 3.10. Mean Diastolic Blood Pressure (mmHg) from 2011 – 2014 by DPP Interest Group. Overall, no significant differences in diastolic blood pressure were observed between the two groups. The overall trend is that diastolic blood pressure values were roughly stable over the four-year time frame.

Mean Triglycerides from 2011 - 2014 by DPP Interest Group

160

150

140

130

120 Mean Triglycerides (mg/dL) (mg/dL) MeanTriglycerides 110 2011 2012 2013 2014 Year

DPP Uninterested DPP Interested

Figure 3.11. Mean Triglyceride Levels (mg/dL) from 2011 – 2014 by DPP Interest Group. Overall, no significant differences in mean triglyceride levels were observed between the two groups. The overall trend is that average triglyceride values are increasing over time.

DIFFERENCES BETWEEN GROUPS OVER TIME

Body Weight, Body Mass Index and Waist Circumference

Differences in groups with respect to body weight, body mass index and waist circumference from 2011 – 2014 were examined (Table 3.3). With regard to body weight, none of the differences between groups were statistically significant. When the group by time trend term was tested with the outcome of body weight using the multivariate Wald test, it was not significant (p=0.801). Therefore, there was no statistically significant difference in weight loss, or gain, of the two groups with different interest in the DPP over the four-year time frame.

For body mass index, all of the differences between groups were statistically significant. When the group by time trend term was tested with the outcome of body mass index using the multivariate Wald test, it was not significant (p=0.974). Therefore, there was no difference in the change in BMI of the two groups with different interest in the DPP over the four-year time frame.

With regard to waist circumference, none of these differences between groups were statistically significant. When this group by time trend term was tested using the multivariate Wald test and waist circumference as the outcome, it was not significant (p=0.595). Therefore, there was no statistically significant difference in the change in waist circumference for the two groups with different interest in the DPP over the four- year time frame.

Table 3.3 Differences in Body Weight, Body Mass Index and Waist Circumference between DPP Interested and DPP Not Interested by Year Body Weight Difference between groups LB: 95% CI UB: 95% CI 2011 0.24 -6.15 6.64 2012 -0.59 -6.93 5.75 2013 -0.54 -6.86 5.77 2014 0.61 -5.62 6.83 Body Mass Index 2011 0.85 -0.09 1.79 2012 0.95 0.03 1.88 2013 0.92 0.00 1.84 2014 0.89 0.00 1.78 Waist Circumference 2011 0.26 -0.74 1.27 2012 -0.06 -1.03 0.90 2013 0.30 -0.65 1.26 2014 0.47 -0.42 1.36

Total Cholesterol, LDL Cholesterol and HDL Cholesterol

Differences in groups with respect to total cholesterol, LDL cholesterol and HDL cholesterol from 2011 – 2014 were examined (Table 3.3). None of the differences in cholesterol levels between groups were statistically significant. When the group by time trend term was tested using the multivariate Wald test, it was not significant (p=0.758), therefore, there was no statistically significant difference in the change in total cholesterol levels for the two groups with different interest in the DPP over the four-year time frame.

None of the differences in LDL cholesterol between groups were statistically significant. When the group by time trend term was tested using the multivariate Wald test, it was not significant (p=0.837), therefore, there was no statistically significant difference in the change in LDL cholesterol levels for the two groups with different interest in the DPP over the four-year time frame.

None of the differences in HDL- cholesterol between groups were statistically significant. When the group by time trend term was tested using the multivariate Wald test, it was not significant (p=0.347), therefore, there was no statistically significant difference in the change in HDL cholesterol levels for the two groups with different interest in the DPP over the four-year time frame.

Table 3.4 Differences in Total Cholesterol, LDL Cholesterol and HDL Cholesterol Between DPP Interested and DPP Not Interested by Year

Total Cholesterol Difference between groups LB: 95% CI UB: 95% CI 2011 2.94 -3.17 9.04 2012 1.62 -4.26 7.50 2013 2.51 -3.27 8.29 2014 0.34 -4.97 5.66 LDL - Cholesterol 2011 2.49 -2.79 7.77 2012 0.92 -4.16 5.99 2013 2.27 -2.71 7.26 2014 0.68 -3.98 5.33 HDL- Cholesterol 2011 2.23 -0.20 4.66 2012 2.22 -0.14 4.58 2013 1.45 -0.88 3.78 2014 0.84 -1.35 3.03

Systolic Blood Pressure, Diastolic Blood Pressure and Triglycerides

Differences in groups with respect to systolic blood pressure, diastolic blood pressure and triglyceride levels were examined from 2011 – 2014 (Table 3.5). None of the differences in systolic blood pressure between groups were statistically significant. When the group by time trend term was tested using the multivariate Wald test, it was not significant (p=0.096), therefore, there was no statistically significant difference in the change in systolic blood pressure levels for the two groups with different interest in the DPP over the four-year time frame.

For diastolic blood pressure, the difference between groups was statistically significant for 2014, but not for 2013 or 2012. When the group by time trend term was tested using the multivariate Wald test, it was not significant (p=0.294), therefore, there was no statistically significant difference in the change in diastolic blood pressure levels for the two groups with different interest in the DPP over the four-year time frame. 68

None of these differences in triglyceride levels between groups were statistically significant. When the group by time trend term was tested using the multivariate Wald test, it was not significant (p=0.557), therefore, there was no statistically significant difference in the change in triglyceride levels for the two groups with different interest in the DPP over the four-year time frame.

Table 3.5 Differences in Systolic Blood Pressure, Diastolic Blood Pressure and Triglyceride Levels Between DPP Interested and DPP Not Interested by Year

Systolic Difference between groups LB: 95% CI UB: 95% CI Blood Pressure 2011 1.33 -1.22 3.88 2012 0.78 -1.62 3.17 2013 0.64 -1.71 2.98 2014 -1.61 -3.68 0.45 Diastolic Blood Pressure 2011 -0.27 -1.98 1.43 2012 -1.14 -2.74 0.45 2013 -0.36 -1.91 1.20 2014 -1.76 -3.12 -0.40 Triglycerides 2011 -10.46 -23.16 2.23 2012 -5.82 -18.00 6.36 2013 -8.52 -20.46 3.41 2014 -2.57 -13.38 8.25

Descriptive statistics for the two groups, after stratifying by gender indicated that prediabetic males who were interested in the DPP were more likely to have gained weight during the previous time period compared to males who were not interested in the DPP. This assessment was speculative because the small sample sizes for men precluded any formal statistical testing of this association because the analysis was underpowered.

Additionally, more variability was observed for males compared to females due to smaller cells sizes. (Figures 3.12 – 3.14 and Tables 3.6 – 3.8).

Mean BMI from 2011 - 2014 by DPP Interest Group & Gender 35

27 Body MassBody Index

25 2011 2012 2013 2014 Year DPP Interested Males DPP Interested Females DPP Uninterested Males DPP Uninterested Females

Figure 3.12. Mean Body Mass Index (BMI) Among Prediabetics by DPP Interest Level and Gender.

Women who were uninterested in the DPP had an overall lower BMI compared to women who were interested in the DPP. Males who were interested had a larger increase in their body weight during 2011 – 2014 compared to men who did not express interest in the DPP. The reader is cautioned to interpret the trends in this figure as exploratory due to the smaller numbers of participants who are male relative to females. (Raw numbers are provided in table 3.6).

Table 3.6 Body Mass Index (BMI) from 2011 - 2014 by Gender and DPP Interest Level Interested in the DPP Not Interested in the DPP LB UB LB UB Males n BMI n BMI 95% CI 95% CI 95% CI 95% CI 2011 19 29.11 27.06 31.15 394 30.50 29.94 31.05 2012 26 29.50 27.92 31.08 453 30.37 29.85 30.89 2013 28 30.68 28.44 32.92 533 30.75 30.28 31.22 2014 40 31.20 29.62 32.78 808 31.42 31.03 31.82 Females 2011 117 33.39 31.99 34.79 696 31.69 31.18 32.20 2012 132 32.92 31.61 34.23 782 31.96 31.48 32.44 2013 138 33.44 32.26 34.63 908 32.00 31.56 32.43 2014 185 33.84 32.80 34.88 1,259 33.15 32.77 33.52

Mean Body Weight from 2011 - 2014 by DPP Interest Group & Gender 230

220

210

200

190

180 Body Weight (lbs) (lbs) Weight Body 170 2011 2012 2013 2014 Year DPP Interested Males DPP Interested Females DPP Uninterested Males DPP Uninterested Females

Figure 3.13. Mean Body Weight (lbs) Among Prediabetics by DPP Interest Level and Gender.

Women who were uninterested in the DPP had an overall lower body weight compared to other women who were interested in the DPP. Males who were interested had a larger increase in their body weight during 2011 – 2014 compared to men who did not express interest in the DPP. The reader is cautioned to interpret the trends in this figure as exploratory due to the smaller numbers of participants who are male relative to females. (Raw numbers are provided in table 3.7).

Table 3.7 Mean Body Weight (pounds) from 2011 - 2014 by Gender and DPP Interest Level Interested in the DPP Not Interested in the DPP Body LB UB Body LB UB Males n n Weight 95% CI 95% CI Weight 95% CI 95% CI 19 204.16 187.20 221.11 397 212.65 208.68 216.63 2011 26 207.15 194.31 220.00 453 212.39 208.51 216.27 2012 28 215.46 199.11 231.82 536 215.28 211.80 218.75 2013 38 220.00 206.41 233.59 754 219.62 216.50 222.74 2014 Females 117 198.46 189.56 207.37 697 186.97 183.83 190.11 2011 131 194.42 186.59 202.25 781 189.64 186.64 192.64 2012 138 198.52 190.97 206.06 908 190.61 187.87 193.36 2013 174 203.28 196.34 210.22 1,150 196.46 193.97 198.95 2014

Mean Waist Circumference from 2011 - 2014 by DPP Interest Group & Gender 45

Waist Circumference (in) Waist 35 2011 2012 2013 2014 Year DPP Interested Males DPP Interested Females DPP Uninterested Males DPP Uninterested Females

Figure 3.14. Mean Waist Circumference (inches) Among Prediabetics by DPP Interest Level and Gender.

No overall differences were observed in the waist circumference trend between groups over time due to the overlapping confidence intervals. The reader is cautioned to interpret the trends in this figure as exploratory due to the smaller numbers of participants who are male relative to females. (Raw numbers are provided in table 3.8).

Table 3.8 Mean Waist Circumference (inches) from 2011 - 2014 by Gender and DPP Interest Level Interested in the DPP Not Interested in the DPP Waist Waist LB UB LB UB Males n Circum- n Circum 95% CI 95% CI 95% CI 95% CI ference -ference 2011 18 39.00 36.74 41.26 352 39.87 39.34 40.39 2012 26 39.27 37.31 41.23 428 39.76 39.07 40.45 2013 27 41.41 39.13 43.69 497 40.36 39.89 40.84 2014 38 41.58 39.95 43.21 755 40.92 40.52 41.33 Females 2011 111 39.20 38.03 40.36 646 38.21 37.75 38.66 2012 127 38.31 37.12 39.50 730 38.09 37.55 38.63 2013 131 39.47 38.45 40.49 858 38.80 38.39 39.20 2014 179 40.27 39.33 41.21 1,210 39.53 39.16 39.90

DISCUSSION

In summary, prediabetics who expressed interest in enrolling in a worksite national DPP (group delivered, in person, one-year in length) were systematically different than those who did not express interest. Current recruitment strategies were effective at engaging individuals who were not representative of the greater prediabetic population. Participants who expressed interest in the DPP differed across demographic characteristics, including being more likely to be female, slightly older and more educated. These findings agree with previous literature by Anderson & Spilman [70] [71]. Associates were also more likely to express interest, which could reflect convenience of class locations to work locations as a company employee and the fact that employees are likely more exposed to marketing than spouses who do not use the company intranet.

Zavela and Cottrell observed that during a worksite program offered to university employees, non-intenders (those who did not intend to participate in the health programs) were on average, five years older than those who elected to participate [18]. Therefore they recommended future participant engagement strategies focus on older members of 74

the population. Kristal et al. demonstrated that in a worksite nutrition program offered to men, found that those who enrolled were slightly older than non-enrollees [72]. Tardash et al. summarized prediabetic recruitment into a DPP program at a worksite [73]. They compared nonparticipants to participants and found that nonparticipants were slightly younger than participants, however a limitation of their study was the use of a mailed survey (only six replies were obtained from the 18 who elected not to participate, and only five could be analyzed due to complete consent documentation.) This study, which found that prediabetics who were older in age were more likely to express interested in enrolling in the DPP aligns well with the findings of Zavela & Cotrell and Kristal et al. It is possible that this specific worksite population contains prediabetics who are older in age and view themselves as being at higher risk for diabetes so they elected to enroll in the DPP, however, this needs to be tested in future studies.

It was hypothesized that prediabetics who expressed interest in the DPP would have a lower biological risk for T2DM (fewer comorbid conditions) and more health conscious behaviors (less likely to engage in tobacco use or alcohol consumption) compared to other prediabetics in the population. The current study observed that individuals who expressed interest in the DPP had fewer comorbid conditions and less tobacco use, however there was no difference in alcohol consumption between the two groups. This relationship may not be straight forward, because although those expressing interest had a lower comorbid disease burden, they also had higher glycosylated hemoglobin A1c values, therefore they may have more risk factors for prediabetes. Those expressing interest consumed higher levels of fruits and vegetables which is corroborated with a study by Kristal et al. where male participants were recruited into a worksite nutrition program and those who enrolled consumed greater amounts of fruits and vegetables [72].

The univariate analysis found that tobacco nonusers were more likely to express interest in the DPP, however when the multivariable model was constructed, smoking status was not included in the final model because it was not statistically significant. Shepard et al. and Conrad et al found that those who participated in worksite fitness populations were

more likely to be non-smokers than nonparticipants and both of these studies focused on describing the characteristics of participants and nonparticipants but did not perform a multivariable adjustment[74] [75]. Alexy at al. observed that smokers were less likely to participate in worksite wellness program focused on increasing physical activity, however they concluded that smokers have poorer health and this may be a barrier to participation in a physical activity program [76]. Smoking status was self-reported, therefore health care workers may not truthfully report this due to the social stigma surrounding smoking and its proven health risks. Potential under-reporting may be why smoking status did not remain in our full multivariable model describing the population of prediabetics who were interested in enrolling in the DPP.

The univariate analysis found that those who expressed interest were more likely to intend to engage in strength training activities, but there were no differences in levels of participation in physical activity (either cardiovascular or aerobic) between the two groups. This population had no measurable differences in physical activity between those who did and did not express interest, however, Baun et al. found that during their fitness program implementation, employees enrolling in health programs may already be fit [77]. The fitness program implemented did not restrict enrollment to prediabetics, which is one notable difference with this study.

A review of previous literature comparing the demographic makeup of participants and nonparticipants in a health behavior program did not observe that African Americans were more likely to participate. One study described recruitment into a DPP from a worksite setting and was unable to investigate racial differences because the study population was too homogenous [73]. It is possible that African Americans in this employee healthcare worker population understand that they have a higher risk of developing diabetes, or have a family member who has had diabetes, and this successful communication of health risks or their family experiences, has made these individuals more engaged in the Diabetes Prevention Program. Future studies should use focus

groups to better understand patient perceptions of diabetes risk and reasons for participating in the DPP.

Davis et al. observed that participants were more likely to have higher levels of job stress than non-participants [78]. The current study did not find stress to be a significant factor differing between prediabetics who did and did not express interest. Evans, et al. and Morgan et al. both found that individuals who believed in their ability to improve their health were more likely to participate [79,80]. The current study did not echo the same findings, as it was found that prediabetics with higher self-efficacy to adopt a healthy lifestyle behavior were less likely to participate. Part of this observed difference could be explained by different worksite settings (General Mills Corporation versus a healthcare industry) and underlying knowledge about nutrition and physical activity. Those who expressed interest in the DPP were more likely to be currently making changes to manage their weight or planned to make changes in the next month. This finding agrees with the transtheoretical model [81] because individuals at a higher stage of change (the preparation or action phases) were more likely to express interest in the DPP. Those who were interested in participating in the DPP also had lower self-efficacy to make healthy lifestyle changes, which agrees with the Health Belief Model because these individuals may be more comfortable participating in a program to improve their health instead of implementing changes independently [82].

Previous studies examined demographic characteristics, behavioral variables and psychosocial factors [83], however to the best of our knowledge, none have investigated comorbid diseases, healthcare use, and differences in biometric results. Individuals expressing interest in the DPP had a lower comorbid disease burden; they were less likely to report a personal history of asthma, hypertension, depression or cancer or to have been hospitalized overnight in the past year. One study by Alexy et al. reported that nonparticipants had poorer perceived health status (this is a different construct than comorbid disease) [76]. Additionally, participants cited comorbid conditions such as hypertension or obesity as barriers to program participation [76].

Those who expressed interest in the DPP were more likely to have visited the doctor in the last year and had lower self-rated health. The association between doctors visits and interest in the DPP is not straight-forward to interpret. Individuals may either visit the doctor more frequently due to a medical necessity, or because they have anxiety about their health status, and not simply because they are more engaged in their health status. Additionally, the health behavior model aligns with this finding because patients with lower self-rated health may perceive themselves as susceptible to the disease, and thus choose to enroll in the DPP due to this acknowledgement of perceived susceptibility. There were no differences observed in use of a primary care provider.

Overall, there were no statistically significant differences in longitudinal trends of biometric values for these two groups. This approach to evaluate reach has not been used before, but may be more telling when there are more years of biometric data available for analysis.

Strengths and Limitations

Strengths of the current study include the large sample size (n= 2,159) of prediabetics with a similar risk factor profile to other working populations in the United States. The use of biometric screenings to identify prediabetic patients meeting eligibility criteria for the DPP ensured that prediabetic classifications were reliable. Specifically, the use of glycosylated hemoglobin is a more stable measure of blood sugar and prediabetic status compared to blood glucose levels that fluctuate throughout the day. Point of care testing has been adopted by the hospital system as a way to raise awareness about potential prediabetes, but this testing is not considered appropriate for the diagnosis of prediabetes. Patients with elevated results have been recommended to see their healthcare provider for further testing, and are also encouraged to enroll in the DPP if they fall in the prediabetic range. Since the first biometric testing session was offered (2013), the Bayer A1c Now Test has been used, therefore we would not expect bias in testing to affect the final outcomes of our study because all patients were affected equally regardless of group. The

data that was available to describe prediabetics who were and were not interested in enrolling in the DPP was collected prior to enrollment; therefore enrollment did not bias these variables. Additionally, the depth of information available in our study was much more descriptive then previous studies which have relied on mailed surveys with low response rates [73] or have relied on questionnaire data but not measured biometric data [76,78–80,84]. All prediabetics in this study received the same “dose” of recruitment in terms of mailed letters and biometric screening results. It cannot be determined how often different groups used the company intranet, so exposure to these advertisements likely differ across individuals.

Limitations of the current study include the use of self-reported health behaviors, which can be susceptible to social desirability bias. However the amount of social desirability bias inherent in this type of data collection should not differ systematically between the two groups of prediabetics being compared. The DPP allows some individuals to be risk- assessed into the DPP because they have specific risk factors that elevate their risk for T2DM (Appendix B: DPP CDC Risk Assessment). These individuals may not have participated in the biometric screening, and were not included in this analysis if that is the case. It is possible that those who completed the biometric screening are systematically different than those who did not, however no statistically significant differences were observed in the average age or the gender distribution for these two groups.

The HbA1c screening test (Bayer A1c Now) was recently compared to other point of care tests (Axis Shield Afinion and Siemens DCA Vantage), and found that it is less valid (it underestimated the HbA1c in the standard reference solution by roughly 0.4% HbA1c) than the other two tests[85]. One limitation of this particular study was no quality control for the number of different samples used to compare the testing devices, specifically, the Bayer A1c Now test was evaluated using 13 samples, compared to the other tests Axis Shield Afinion (n=34) and Siemens DCA Vantage (n=254). The number of tests performed correlated closely with how accurate the test was. However, as of March 2014, these are the only three NSGP (National Glycohemoglobin Standardization Program)

certified HbA1c Point of Care devices available in the US for use in Healthcare facilities[85]. Additionally, quality controls were used for A1c testing during the biometric screening to ensure that the test values obtained from the biometric values were accurate and precise.

The variables that were collected during the biometric screening were available for all associates and spouses because this was a criterion for inclusion in this study. All spouses and associates were offered the option to complete the health assessment survey, however only 88% of the study population that was enrolled in 2014 completed this survey. These individuals who did and did not take the survey were not different in age, enrollment time in the insurance plan, or time working at the company, but were more likely to be female (p=0.013). This finding is consistent with the past literature about how gender influences health engagement and the likelihood of completing a survey. The findings of this survey cannot be generalized to the worksite population because females were over-represented in the survey.

Another limitation was there was no data available to describe the relationship between the spouse, or covered dependent (married or cohabitating) or about the employment status or occupation for the spouses or covered dependents. Family members or spouses may be more similar to one another and this correlation may have influenced the study findings. Similarly, people who are socially connected (friends or coworkers) may have similar correlations in health behaviors and perceptions about enrolling in the DPP [86]. While the associates that were enrolled in this insurance plan were all working 36 or more hours per week, it is possible that covered spouses or dependents may be retired or unemployed, and have more time during the work day may present fewer barriers to enrolling in a Diabetes Prevention Program.

The final limitation of this study is that it compared individuals who did not express interest in the Diabetes Prevention Program and those who did express interest. Intention does not always predict action; therefore those who did express interest may not actually

enroll in the program. In fact, only 117 of the 217 individuals who expressed interest (54%) actually did enroll in the program. There are many reasons why individuals may elect not to enroll in the program after initially expressing interest, however this data was not collected in the current study. Timing of class meetings, capacity and class location were anecdotally cited as programmatic barriers to enrollment. Because these barriers represented logistic challenges that can be overcome as the program is deployed this study did not focus on those factors. Additional lifestyle coaches have been hired to offer more classes at preferred times as requested by participants. This work is ongoing.

CHAPTER 4

Determine the Association Between Antidepressant Use and T2DM Among Patients With Indications for Antidepressant Use

INTRODUCTION

Background

Antidepressants are used by roughly 15% of Americans over the age of 40 [28]. Antidepressants use increased by nearly 400% between the timeframe from 1989 – 1994 and 2005- 2008, among all Americans [87]. Currently, they are the third most frequently prescribed drug taken by Americans 18-44 years old [87].

Previous studies on the association between antidepressant use and T2DM have been variable and inconsistent in their findings. Although most studies have been observational in design, differing sources of data have been utilized, including pharmacy claims data [45,88], medical records [46] and self-reported survey data [36,47,89]. For comparability of groups, observational studies necessitate analytical methods to control for confounding by indication. One approach to address this difference in groups is through matching, or the use of a propensity score, which is a more parsimonious way to obtain comparable groups that allows for a causal interpretation if underlying assumptions are met. Although one observational study used propensity scores [90], the majority of studies have been reliant on the correct specification of the regression model used [36,88,91], to control for confounding. Studies have also differed in their control of important confounding factors. For example, many have not incorporated weight changes [45,46,88,91,92], and several 82

using claims data have not limited the antidepressant users being studied to incident medication users [45,91,93], potentially over-representing patients who tolerate antidepressants well, thus introducing prevalent user bias[37].

The present study used a large retrospective sample of adults without a diagnosis of diabetes to: (1) assess the association between exposure to antidepressant medications with elevated glycosylated hemoglobin (HbA1c); and (2) assess the association between exposure to antidepressant medications and new onset T2DM.

METHODS

Data Sources

This study was a retrospective cohort of employees and dependent spouses enrolled in an insurance plan in the Midwest. The sources of data utilized for this analysis included yearly biometric screening data (collected annually between 2011 and 2014), a health survey (collected in 2014), medical claims data (collected at every medical encounter for billing purposes between 2011 and 2014) and pharmacy claims data (collected for all outpatient prescriptions between 2009 and 2014).

Study Population

To be considered a member of the study population, individuals must have had continuous enrollment in the insurance plan as early as January 2011 and for a minimum enrollment length of 12 months (365 days). The study limited its eligible population to those with diagnosis codes that are indicated for the use of the antidepressant medication. Individuals were also excluded as prevalent diabetics if they developed T2DM during the first 90 days of enrollment in the insurance plan.

Mental Health Study Population

Antidepressants may be utilized for the treatment of a mood or anxiety disorder, or for other indications including headache, back pain, neuropathy, sleep related conditions, fatigue, and post traumatic stress disorder. Patients with these conditions had a diagnosis code in their medical claims that reflected these conditions (Table 2.2). The categories of antidepressants were divided into selective serotonin reuptake inhibitors (SSRIs), tricyclic antidepressants (TCAs), and other commonly prescribed antidepressants (classified as other antidepressants) [94]. These medications have different mechanisms of action, but have all been proven to be efficacious for the treatment of depression, [95,96] and for use among patients with other indications [97]. Not all patients with these diagnoses were using antidepressants for disease management, which allowed a comparable reference group of antidepressant nonusers to be obtained.

Antidepressant Exposure

Participants were classified as antidepressant users if they had two or more consecutively filled prescriptions for the medication of interest and they obtained more than 30 pills during the study period. Antidepressant users were then classified as prevalent or incident users of the medication. Individuals who initiated antidepressant use prior to January 2011 were considered prevalent antidepressant users because the medical claims data started on January 2011, therefore inclusion of these individuals would result in an immortal person time bias. This analysis focused on incident users of antidepressants due to bias associated with classifying prevalent users as members of the exposure group [37,38]. This analysis of incident users allowed for the inclusion of pre- exposure biomarkers in these analyses. Individuals were also considered to be prevalent antidepressant users if they had a prescription for an antidepressant within 90 days of enrollment on the insurance plan. This assumption was made because medication use during this period most likely corresponded to the renewal of an existing prescription from a previous physician. Individuals were classified as incident antidepressant users if they initiated antidepressant use more than 90 days following enrollment in the insurance

plan.

Antidepressant Category Definitions

Antidepressants were divided into selective serotonin reuptake inhibitors (SSRIs) (fluoxetine, citalopram, paroxetine, sertraline, fluvoxamine, and escitalopram), tricyclic antidepressants (TCAs) (clomipramine, trimipramine, amitriptyline, nortriptyline, and doxepin) or other antidepressants (desvenlafaxine, duloxetine, trazodone, etoperidone, mianserin, mirtazapine, venlafaxine, milnacipran, viloxazine, and reboxetine) based upon their mechanism of action (Table 2.2).

Outcome Ascertainment

New onset T2DM was assessed using inpatient medical claims or claims with a CPT code for evaluation and management services (an appointment with a primary care physician) containing T2DM diagnosis codes. (Table 2.6)

Antidepressant Use and Elevated Glycosylated Hemoglobin Values

To evaluate the association between antidepressant use and elevated HbA1c, diabetics were excluded from the analysis. This was done because if a patient had already been identified as diabetic a prediabetic HbA1c was no longer informative for this population. Additionally, once patients obtained a T2DM diagnosis they likely changed their behaviors or started medication for T2DM management. These factors would influence HbA1c measurement.

HbA1c values were collected in 2013 and 2014, and the timing between these measurements and the index date of antidepressant use differed by user, therefore two separate analyses were conducted; one using HbA1c in 2013 and the second using HbA1c in 2014 as the outcome. Individuals who had an HbA1c level <6.0 were

compared to those with a value ≥ 6.0, which indicated an elevated risk of prediabetes [98]. This model was estimated using an ordinary logistic regression equation that included the Inverse Probability of Treatment Weight (IPTW). The model included all variables associated with prediabetic HbA1c and antidepressant use. This statistical approach was employed to minimize residual confounding in terms of available variables.

Statistical Analysis

Descriptive statistics were compared for incident antidepressant users and prevalent antidepressant users, for antidepressant nonusers and incident antidepressant users, and for TCA, SSRI, and other antidepressant users,. Covariates of interest at baseline were compared (gender, age, waist circumference, BMI, ethnicity, education level, and number of office visits and inpatient hospitalizations in the baseline year). Continuous covariates were compared using t-tests and categorical covariates were compared using Pearson’s Chi square test. Descriptive statistics were also compared after weighting using the same statistical tests to check that covariate balance was obtained for the two groups after weighting was utilized. Once the final model was built it was checked to verify that the overlap assumption and the overidentification test for covariate balance were not violated.

Subaim 2.a. examined having an elevated HbA1c in either 2013 or 2014 among nondiabetic incident antidepressant users and nonusers. Diabetic individuals were removed from the analysis if they developed diabetes prior to their biometric screening in 2013 or 2014. Individuals with a missing glycosylated hemoglobin measurement in the year being evaluated were also excluded from the analysis.

The propensity score model used to evaluate the risk of elevated HbA1c (>6.0) among incident antidepressant users compared to nonusers and was identical for the model

evaluating HbA1c in 2013 and 2014. The model included age, gender, BMI in the baseline year, education, ethnicity and number of office visits in baseline year.

Following the development of propensity scores, the overidentification test was used to check the fit of the models. All models failed to reject the null hypothesis, and therefore there was not evidence of a lack of fit. Graphically, overlap was evaluated between the two groups compared after propensity weighting and there were no obvious violations to the overlap assumption.

A sensitivity analysis was conducted using the final logistic model to examine the effect of classifying individuals who initiated antidepressant use 30 and 60 days following enrollment in the insurance plan as incident antidepressant users.

Time to T2DM

Participants were followed to the event that occurred first: an incident diagnosis of T2DM; termination of enrollment in the insurance plan; or the end of the follow up period, December 31, 2014. The absolute risk of incident T2DM over the three-year study period was calculated separately for individuals on antidepressant treatment and among those with no antidepressant use. To account for the time period prior to incident antidepressant exposure for those who started antidepressants in the middle of the study period, time-varying exposure variables were created, and participants were split between groups. Cox proportional hazards models was used to compute hazard ratios with accompanying 95% confidence intervals for the association between antidepressant use and incident T2DM. Clinically meaningful interactions between antidepressant use and participant demographic characteristics were tested for inclusion during the model building process (p<0.10). Stepwise selection was used to build the final Cox proportional hazards model (p<0.05) to adjust for differences between antidepressant users and nonusers. The final model was checked for the assumption of proportional hazards, diagnostics and the goodness of fit test. Subgroup analyses included examining the effect of antidepressant class on T2DM development.

SAS version 9.4 (Cary, NC: SAS Institute Inc.) was used for all data management, and Stata 14. (StataCorp. 2015. College Station, TX) was used for data analyses, and generation of figures and tables.

RESULTS

Subjects

A total of 18,007 individuals were identified who were 18 years of age or older and enrolled in the insurance plan anytime from 2011 to 2013. 17,264 of these individuals were enrolled in the insurance plan for six months or longer. 4,699 individuals with no medical claims during the study period were excluded, and 743 individuals were excluded for enrollment that was less than six months. 12,565 of the adults (associates or spouses) were enrolled in the insurance plan for 6 months or longer and had 1 or more medical or pharmacy claim during the study period. 10,502 were classified as ineligible to be included in this study population because they did not have any diagnoses of mental health disorders during the study period. The 2,063 individuals with mental health disorders (15.4% of the population with medical and pharmacy claims) were included in the study population, and 167 were excluded for polycystic ovarian syndrome (PCOS) (n=18), pregnancy (n=9) or T2DM prior to, or at baseline (n=143) or T2DM during the first 90 days of the enrollment period (n=12). This narrowed the final study population down to 523 incident (new antidepressant users), 463 antidepressant nonusers (controls) and 898 prevalent antidepressant users. The patient flow is presented in Figure 4.1.

Figure 4.1 Flow Diagram For Patients With Mental Health Disorders Who Are Antidepressant Users and Nonusers. Patients were first identified if they were 18 years of age or older and enrolled in the insurance plan between 2011 – 2013 for one or more years. Patients with mental health disorders were identified as the population of interest. Once excluded patients were removed, these patients were divided into incident antidepressant users, antidepressant nonusers and prevalent antidepressant users.

Patients that were diagnosed with diabetes at the same time they were diagnosed with a mental health disorder (allowing them to enter the study) were excluded from the analyses comparing incident antidepressant users and nonusers, but as expected these numbers of excluded patients were not balanced by antidepressant category. Prevalent diabetics were not excluded from the descriptive statistics comparing incident and prevalent antidepressant users, as the category of prevalent antidepressant use was not included in the analyses. 0.22% of prevalent antidepressant users (n= 9) had a diabetes diagnosis in the first 90 days following insurance enrollment, compared to 0.38% of incident antidepressant users (n= 2) and 1% of antidepressant nonusers (n= 1).

Table 4.1 provides the distribution of the types of antidepressants used in the study population across incident and prevalent antidepressant users. Incident and prevalent antidepressant users varied slightly in their patterns of antidepressant use when examining antidepressants by type. Antidepressant use by category was comparable across antidepressant type. SSRI use was most common for both types of users (77.5% for incident users, and 74% for prevalent users), followed by other antidepressants (2.9% vs. 1.4%) and tricyclic antidepressants (19.7% vs. 22.6%). Within categories of antidepressant classes, specific antidepressants are comparable. More variability was seen within the tricyclic antidepressant user group due to the smaller cell sizes.

Table 4.1 Types of Antidepressants Used by Incident and Prevalent Antidepressant Users

Incident Antidepressant Prevalent Antidepressant Users Users n=524 n=907 n % n % Selective Serotonin 406 77.5% 671 74.0% Reuptake Inhibitors (SSRIs) Fluoxetine 84 20.7% 127 18.9% Citalopram 116 28.6% 148 22.1% Paroxetine 16 3.9% 62 9.2% Sertraline 93 22.9% 178 26.5% Fluvoxamine 2 0.5% 7 1.0% Escitalopram 95 23.4% 149 22.2% Tricyclic Antidepressants 15 2.9% 31 1.4% Clomipramine 1 6.7% 1 3.2% Trimipramine 0 0

Amitriptyline 7 46.7% 21 67.7% Nortriptyline 5 33.3% 7 22.6% Doxepin 2 13.3% 2 6.5% Other Antidepressants 103 19.7% 205 22.6% Desvenlafaxine 4 3.9% 16 7.8% Duloxetine 23 22.3% 65 31.7% Trazodone 43 41.7% 56 27.3% 0 0 Etoperidone Mianserin 0 0

Mirtazapine 4 3.9% 7 3.4% Venlafaxine 29 28.2% 60 29.3% Milnacipran 0 1 0.5%

0 0 Viloxazine Reboxetine 0 0

Characteristics of Incident and Prevalent Antidepressant Users

The characteristics of incident and prevalent antidepressant users were compared (Table 4.2). There were roughly twice as many prevalent antidepressant users than incident antidepressant users, and prevalent antidepressant users were approximately six years older in age. There were no observable differences in gender, ethnicity, education level, BMI, waist circumference, mental health diagnoses or inpatient hospitalization between prevalent and incident antidepressant users. Prevalent users had slightly higher triglyceride levels and approximately four more office visits during the baseline year compared to incident antidepressant users.

Table 4.2 Characteristics of Incident and Prevalent Antidepressant Users

Incident Prevalent Antidepressant Users Antidepressant Users n=524 n=907 Age (mean, sd) 37.93 11.22 43.17 11.07 Gender (n, %)

Female 399 (76.2) 727 (80.1) Male 125 (23.8) 180 (19.9) Ethnicity (n, %)

White 311 (89.1) 576 (92.0) Black 24 (6.9) 23 (3.7) Other 14 (4.0) 27 (4.3) Education Level (n, %)

HS Graduate or Less 17 (7.1) 24 (5.4) Some College 64 (26.6) 115 (25.7) College Graduate 126 (52.3) 239 (53.4) Post Graduate 34 (14.1) 70 (15.6) BMI^ (mean, sd) 28.59 7.53 29.73 7.26 Waist Circumference^ (mean, sd) 35.72 8.45 36.79 6.65 Triglycerides^ (mean, sd) 110.68 68.34 130.12 95.29 Mental Health Diagnoses (n, %)

Anxiety 69 (16.0) 99 (16.8) Depression 170 (13.1) 295 (10.9) Bulimia Nervosa and Bipolar Disorder 34 (32.4) 81 (32.5) Panic Disorder, Obsessive Compulsive 73 (5.4) 139 (8.9) Disorder Adjustment Related Disorder 62 (13.9) 80 (15.3) Premenstrual tension syndrome and post 32 (11.8) 60 (8.8) traumatic stress disorder Other Indication 84 (6.1) 153 (6.6) Number of Office Visits^ (mean, sd) 7.31 12.11 11.68 13.52 Inpatient Hospitalization^ (n, %) Yes 29 (5.5) 65 (7.2) No 495 (94.5) 842 (92.8) ^ Measured in the baseline year (2011). *Other Indications are defined as fatigue, sleep related conditions, back pain, headache, neuropathy or smoking cessation counseling on substance use and abuse.

The propensity score model used to evaluate the risk of elevated HbA1c (>6.0) among incident ‘other antidepressant’ users compared to incident ‘SSRI antidepressant’ users was also identical across years and included gender and a high risk waist circumference prior to antidepressant use. The smaller sample sizes of patients taking specific types of antidepressants prevented the use of a propensity model with more covariates. However, in evaluating the balance between groups before and after applying propensity weights the covariates were balanced between the two groups. The balance in groups can be observed before and after weighting in Tables 4.5 – 4.8. Additionally, the overidentification test was not violated (p > 0.05) and there was overlap when the distribution of propensity weighting was examined visually for the two groups.

The characteristics of antidepressant nonusers and antidepressant users were compared when using HbA1c measured in 2013 (Table 4.3). Antidepressant users were more likely to be female (84.4 vs. 73.7, p=0.011) and had slightly more office visits (7.56 vs. 5.83, p=0.058) compared to antidepressant nonusers. There were no statistically significant differences in age, ethnicity, education level, high -risk waist circumference, or BMI at baseline. After weighting, the groups were much more balanced, as supported by the non- significant p-values after propensity weighting.

Table 4.3 Characteristics of Antidepressant (AD) Nonusers and Incident Antidepressant Users Before and After Propensity Weighting (2013 Data as the Outcome)

Before Propensity Weighting After Propensity Weighting Incident AD Incident AD AD Nonusers AD Nonusers Users Users p- n=225 / n=195/ p- n=225 n=195 value (wt) n = 225 (wt) n = 232 value Age (mean, sd) 38.38 12.08 37.88 19.8 0.669 37.77 12.09 37.9 9.69 0.939 Gender (n, (%))

Female 166 (73.7) 146 (84.4) 0.011 173 (76.9) 180 (77.6) 0.894 Male 59 (26.2) 27 (15.6) 53 (23.6) 52 (22.4)

Ethnicity (n, (%)) White 161 (86.6) 120 (88.9) 0.15 198 (88.0) 197 (84.9) 0.869 Black 16 (8.6) 5 (3.7) 16 (7.1) 22 (9.5)

Other 9 (4.8) 10 (7.4) 12 (5.3) 13 (5.6)

Education Level (n, (%)) HS Graduate or Less 15 (10.3) 7 (6.4) 0.142 19 (8.4) 18 (7.8) 0.901 Some College 28 (19.2) 26 (23.9) 49 (21.8) 48 (20.7)

College Graduate 72 (49.3) 63 (57.8) 116 (51.6) 114 (49.1)

Post Graduate 31 (21.2) 13 (11.9) 41 (18.2) 53 (22.8)

High Risk Waist Circumference (n, (%)) Yes 126 (57.0) 78 (62.4) 0.328 130 (57.8) 133 (57.3) 0.649 No 95 (43.0) 47 (37.6) 95 (42.2) 100 (43.1)

First BMI (mean, 28.04 6.73 28.36 7.76 0.690 28.38 7.93 28.9 6.65 0.597 sd)^ Number of Office 5.83 8.67 7.56 9.47 0.058 6.54 10 6.48 6.56 0.959 Visits ξ (mean, sd) ^ Variable was measured prior to antidepressant use for antidepressant users, or at baseline for antidepressant nonusers. The propensity-weighted model was adjusted for age, gender, BMI in the baseline year, education, ethnicity and number of office visits in baseline year. ξ Variable was measured in the baseline year (2011).

The characteristics of other antidepressant users and SSRI users were compared when using HbA1c measured in 2013 (Table 4.4). Other antidepressant users were less likely to be female (73.7 vs. 85.4, p=0.011) and to have a higher waist circumference (63.2 vs. 42.1, p=0.018 than SSRI users. There were no statistically significant differences in age, ethnicity, education level, first BMI or the number of office visits for these two groups. A propensity model was developed using age, gender, and baseline waist circumference. After weighting, the groups were more balanced, as supported by the non-significant p- values after propensity weighting.

Table 4.4 Characteristics of Other Antidepressant Users and SSRI Users Before and After Propensity Weighting (2013 Data as the Outcome)

Before Propensity Weighting After Propensity Weighting

Other AD Other AD SSRI Users SSRI Users Users Users p- n=193 / n=49/ p- n=193 n=49 value (wt) n = 193 (wt) n = 193 value Age (mean, sd) 37.04 11.17 38.27 10.82 0.491 36.86 14.7 36.52 7.14 0.878 Gender (n, (%))

Female 161 (73.7) 42 (85.2) 0.011 166 (73.8) 166 (71.6) 0.98 Male 32 (26.2) 7 (14.3) 27 (12.0) 27 (11.6)

Ethnicity (n, (%)) White 141 (75.8) 35 (85.4) 0.383 141 (91) 144 (85.7) 0.413 Black 5 (2.7) 3 (7.3) 6 (3.9) 17 (10.1)

Other 7 (3.8) 3 (7.3) 7 (4.5) 7 (4.2)

Education Level (n, (%)) HS Graduate or Less 6 (4.1) 3 (2.8) 0.322 6 (4.9) 7 (5.9) 0.454 Some College 31 (21.2) 7 (6.4) 35 (28.5) 28 (23.7)

College Graduate 70 (47.9) 15 (13.8) 68 (55.3) 53 (44.9)

Post Graduate 14 (9.6) 7 (6.4) 14 (11.4) 29 (24.6)

High Risk Waist Circumference (n, (%)) Yes 98 (63.2) 16 (42.1) 0.018 96 (61.9) 83 (52.9) 0.649 No 57 (36.8) 22 (57.9) 59 (38.1) 74 (47.1)

First BMI (mean, sd)^ 27.98 7.32 30.38 8.09 0.072 28.19 9.54 28.69 4.73 0.709 Number of Office Visits ξ 6.61 8.79 7.9 11.72 0.398 6.4 10.66 5.5 6.617 0.634 (mean, sd) ^ Propensity weighted model was adjusted for gender and baseline waist circumference. ξ Variable was measured in the baseline year (2011).

The characteristics of antidepressant nonusers and antidepressant users were compared when using HbA1c measured in 2014 (Table 4.5). Antidepressant users had a higher level of education (29.5 vs. 19.8 with some college, p=0.033) and were slightly (but not statistically significant) more likely to be White (90.9 vs. 85.2, p=0.07) compared to antidepressant nonusers. There were no statistically significant differences in age, ethnicity, education level, high-risk waist circumference, or BMI at baseline. After weighting, the groups were much more balanced, as supported by the non-significant p- values after propensity weighting. 97

Table 4.5 Characteristics of Antidepressant (AD) Nonusers and Incident Antidepressant Users Before and After Propensity Weighting (2014 Data as the Outcome)

Before Propensity Weighting After Propensity Weighting AD Incident AD AD Incident Nonusers Users Nonusers AD Users n=251 n=251 p- n=251 / wt n n=251/ wt n = p- value = 335 335 value Age (mean, sd) 38.47 11.08 37.4 11.58 0.29 37.34 11.15 37.4 11 0.967 Gender (n, (%))

Female 191 (76.1) 201 (80.1) 0.281 257 (76.7) 258 (77.0) 0.942 Male 60 (23.9) 50 (19.9) 78 (23.3) 77 (23.0)

Ethnicity (n, (%)) White 202 (85.2) 211 (90.9) 0.07 297 (88.7) 298 (88.7) 0.651 Black 23 (9.7) 10 (4.3) 24 (7.2) 23 (6.8)

Other 12 (5.1) 11 (4.7) 14 (4.2) 15 (4.5)

Education Level (n, (%)) HS Graduate or Less 21 (10.7) 10 (5.3) 0.033 27 (8.1) 25 (7.4) 0.998 Some College 39 (19.8) 56 (29.5) 77 (23.0) 77 (22.9)

College Graduate 100 (50.8) 98 (51.6) 174 (51.9) 175 (52.1)

Post Graduate 37 (18.8) 26 (13.7) 57 (17.0) 58 (17.3)

High Risk Waist Circumference (n, (%)) Yes 126 (57.0) 81 (61.4) 0.42 132 58.7) 127 (54.7) 0.649 No 95 (43.0) 51 (38.6) 94 41.8) 104 (44.8)

First BMI (mean, sd)^ 28.43 6.89 28.56 (7.6) 0.854 28.58 7.33 28.67 6.83 0.908 Number of Office Visits 5.75 8.01 6.51 9.48 0.329 6.39 9.64 6.38 9.19 0.993 ξ (mean, sd) ^ Propensity weighted model was adjusted for age, gender, BMI in the baseline year, education, ethnicity and number of office visits in baseline year. ξ Variable was measured in the baseline year (2011).

There were no differences observed in the characteristics of other antidepressant users and SSRI users (Table 4.6). More specifically, there were no statistically significant differences in age, gender, ethnicity, education level, first BMI or the number of office visits for these two groups. The proportion with a high-risk waist circumference was not statistically significant, but there were slightly more individuals in the other antidepressant group with a high-risk waist circumference (64.5 vs. 48.6, p=0.079) than in the SSRI group before weighting. A propensity model was developed using age,

gender, and baseline waist circumference. After weighting, the groups were much more balanced, as supported by the non-significant p-values after propensity weighting.

Table 4.6 Characteristics of Other Antidepressant Users and SSRI Users Before and After Propensity Weighting (2014 Data as the Outcome)

Before Propensity Weighting After Propensity Weighting

Other AD Other AD SSRI Users SSRI Users Users Users p- n=199 / wt n n=48/ wt n = p- n=199 n=48 value =175 175 value Age (mean, sd) 37.09 11.61 38.67 11.36 0.398 36.64 15.47 38.25 7.49 0.475 Gender (n, (%))

Female 156 (78.4) 40 (83.3) 0.448 144 (82.3) 143 (81.7) 0.928 Male 43 (21.6) 8 (16.7) 31 (17.7) 32 (18.3)

Ethnicity (n, (%)) White 170 (85.4) 39 (84.8) 0.188 151 (86.3) 140 (80) 0.328 Black 7 (3.5) 3 (6.5) 5 (2.9) 16 (9.1)

Other 6 (3.0) 4 (8.7) 7 (4.0) 7 (4.0)

Education Level (n, (%)) HS Graduate or Less 7 (3.5) 3 (7.9) 0.283 6 (3.4) 7 (4.0) 0.514 Some College 42 (21.1) 12 (31.6) 38 (21.7) 36 (20.6)

College Graduate 84 (42.2) 15 (39.5) 75 (42.9) 54 (30.9)

Post Graduate 19 (9.5) 8 (21.1) 19 (10.9) 32 (18.3)

High Risk Waist Circumference (n, (%)) Yes 89 (64.5) 18 (48.6) 0.079 84 (60.9) 68 (52.7) 0.311 No 49 (35.5) 19 (51.4) 55 (39.9) 61 (47.3)

First BMI (mean, sd)^ 28.16 7.48 29.97 8.29 0.195 28.38 7.93 28.96 6.65 0.597 Number of Office Visits 6.17 9.18 6.71 9.76 0.719 6.37 11.59 6.03 6.23 0.844 ξ (mean, sd) ^ Propensity weighted model was adjusted for age and baseline waist circumference. ξ Variable was measured in the baseline year (2011).

The Prevalence of Elevated Hba1c Comparing Incident Antidepressant Users to Antidepressant Nonusers After Propensity Weighting

Table 4.7 displays the prevalence differences obtained after comparing incident antidepressant users to nonusers using logistic regression and HbA1c (>6.0) measured in either 2013 or 2014 as the outcome. There were no significant differences in the probability of elevated HbA1c between antidepressant users and nonusers for either 2013 or 2014.

Table 4.7 The Prevalence of Elevated Hba1c (>6.0) Among Incident Antidepressant Users and The Prevalence Difference of Elevated Hba1c (>6.0) Between Antidepressant Nonusers and Nonusers After Propensity Score Weighting

Coefficient LCL (95% CI) UCL (95% CI) 90 days (n= 226) 2013 ATE 0.51% -5.8% 6.85% PO Mean (untreated) 5.69% 2.03% 9.36% 90 days (n= 335) 2014 ATE 1.35% -4.05% 6.77% PO Mean (untreated) 6.99% 3.42% 10.5% Models were adjusted for age, gender, baseline BMI in the baseline year, education, ethnicity and number of office visits in baseline year. ATE is the prevalence difference of HbA1c >6.0 between incident antidepressant users and antidepressant nonusers. PO Mean is the prevalence of HbA1c >6.0 among antidepressant nonusers

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The Prevalence of Elevated HbA1c Comparing Incident Other Antidepressant Users to SSRI Users After Propensity Weighting

Table 4.8 shows the prevalence differences obtained after comparing incident other antidepressant users to incident SSRI users using logistic regression and HbA1c (>6.0) measured in either 2013 or 2014 as the outcome. There were no significant differences in the probability of elevated HbA1c between other antidepressant users and SSRI users for either 2013 or 2014.

Table 4.8 The Prevalence of Elevated Hba1c (>6.0) Among Incident SSRI Users and the Prevalence Difference of Elevated Hba1c (>6.0) Between Other Antidepressant Users and SSRI Users After Propensity Score Weighting

Coefficient LCL (95% CI) UCL (95% CI) 90 days (n= 193) 2013 ATE (Other vs. SSRI) 4.2% -7.07% 15.5% PO Mean (SSRI Users) 6.7% 2.7% 10.76% 90 days (n= 190) 2014 ATE (Other vs. SSRI) -4.10% -12.69% 4.50% PO Mean (SSRI Users) 10.47% 5.49% 15.45% Models were adjusted for gender and waist circumference at baseline. ATE is the prevalence difference of HbA1c >6.0 between other antidepressant users and SSRI users. PO Mean is the prevalence of HbA1c >6.0 among SSRI Users

Sensitivity Analysis for Incident Antidepressant Definition Effect on the Prevalence of Elevated HbA1c

When the definition of incident antidepressant use was changed, the results did not differ (Table 4.9). Overall, the potential outcome mean in the untreated group was statistically significant. For the 90 day definition of antidepressant exposure in 2013, the prevalence of elevated HbA1c was 5.69% among SSRI users (95% CI: 2.03%, 9.36%). When other

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antidepressants users were compared to SSRI users they were no different with regard to the prevalence of elevated HbA1c. In 2013 the difference in the prevalence of elevated HbA1c between other antidepressant users compared to SSRI users was 1.35% (95% CI: -4.05%, 6.77%). Similar patterns were observed across definitions and years.

Table 4.9. Sensitivity Analysis for Incident User Definition. The prevalence of elevated HbA1c (>6.0) among incident antidepressant users and the prevalence difference of elevated HbA1c (>6.0) between incident antidepressant users and nonusers after propensity score weighting

Coefficient LCL (95% CI) UCL (95% CI) 30 days (n= 245) 2013 ATE 3.0% -3.99% 10.0% PO Mean (untreated) 5.87% 2.16% 9.58% 30 days (n= 346) 2014 ATE -0.63% -4.57% 5.84% PO Mean (untreated) 7.06% 3.49% 10.63% 60 days (n= 232) 2013 ATE 1.29% -5.1% 7.72% PO Mean (untreated) 5.7% 2.05% 9.35% 60 days (n= 322) 2014 ATE 0.65% -4.74% 6.04% PO Mean (untreated) 7.0% 3.46% 10.55% 90 days (n= 226) 2013 ATE 0.51% -5.8% 6.85% PO Mean (untreated) 5.69% 2.03% 9.36% 90 days (n= 335) 2014 ATE 1.35% -4.05% 6.77% PO Mean (untreated) 6.99% 3.42% 10.5% Models were adjusted for education level, ethnicity, baseline BMI, number of office visits, gender and age. ATE is the prevalence difference in HbA1c >6.0 between incident antidepressant users and antidepressant nonusers. PO Mean is the prevalence of HbA1c >6.0 among antidepressant nonusers

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When the definition of incident antidepressant use was changed based on the timing of use and enrollment in the study for other antidepressant users and SSRI users, the results did not differ (Table 4.10). Overall, the prevalence of elevated HbA1c in the SSRI users was statistically significant (in 2013 using the 90 day definition the prevalence of elevated HbA1c was 6.7% (95% CI: 2.7%, 10.76%). However, when this group was compared to those who were treated with other antidepressants, they were no different. The difference in the prevalence of HbA1c between other antidepressant users and SSRI users was 4.2% however it was not statistically significant (95% CI: -7.07%, 15.05%).

Table 4.10. Sensitivity Analysis for Incident User Definition. The prevalence of elevated HbA1c (>6.0) among SSRI users and the prevalence difference of elevated HbA1c (>6.0) between other antidepressant users and SSRI users after propensity score weighting

Coefficient LCL (95% CI) UCL (95% CI) 30 days (n= 215) 2013 ATE 6.08% -5.34% 17.5% PO Mean (SSRI Users) 6.71% 2.88% 10.55% 30 days (n= 208) 2014 ATE -4.5% -12.45% 3.43% PO Mean (SSRI Users) 10.29% 5.54% 15.04% 60 days (n= 202) 2013 ATE 6.58% -5.6% 18.7% PO Mean (SSRI Users) 7.15% 3.08% 11.2% 60 days (n= 190) 2014 ATE -4.09% -12.69% 4.50% PO Mean (SSRI Users) 10.47% 5.49% 15.45% 90 days (n= 193) 2013 ATE 4.2% -7.07% 15.5% PO Mean (SSRI Users) 6.7% 2.7% 10.76% 90 days (n= 190) 2014 ATE -4.10% -12.69% 4.50% PO Mean (SSRI Users) 10.47% 5.49% 15.45% Models were adjusted for gender and waist circumference at baseline. ATE is the prevalence difference in HbA1c >6.0 between other antidepressant users and SSRI users. PO Mean is the prevalence of HbA1c >6.0 among SSRI users

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The Effect of Antidepressant Duration on HbA1c in 2014

Antidepressant exposure was categorized by duration. The categories were defined as 0 days (controls), 31 – 183 days, 184 – 729 days, and 730 days – 1370 days. It could be clinically possible that an association with elevated HbA1c exists for longer-term antidepressant use and this relationship may vary across durations of exposure [46] . The median values for HbA1c were comparable across the different durations of exposure to antidepressants. Less variability was observed for those in the 31 – 183 day exposure group due to a smaller sample size for this group (Figure 4.2). Additionally, if antidepressant users were pooled ignoring their duration of use, their HbA1c values were not systematically different from those of antidepressant nonusers.

Figure 4.2. Sensitivity Analysis for Antidepressant Duration (2014). Observations were included if education level, ethnicity, baseline BMI, number of office visits, gender and age were not missing. (0 days (nonusers) n= 251, 31- 183 days, n=36, 184-729 days n=116, and 730 – 1370 days n=108). The definition of incident antidepressant use was 91 days after study enrollment.

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Time to Type II Diabetes Mellitus Among Antidepressant Users

Time-varying exposure variables were created where an antidepressant-exposed control was classified as a member of the control group until the date when exposure to the antidepressant started. For tables 4.14 and 4.15, the members of the population in the two groups were not independent, because an individual could be both a case and a control using this definition.

Comparison of the characteristics of antidepressant nonusers to incident antidepressant users illustrated that the only statistically significant difference between the two groups was in the time exposed to antidepressants, which was only present among incident antidepressant users (Table 4.11). No differences were observed for age, gender, ethnicity, or education level.

It is important to note that there were no differences in waist circumference, BMI or the proportion of each group with a high-risk waist circumference. In evaluating subsequent T2DM development, these groups were very similar at baseline, so the likelihood of an unequal covariate distribution between groups is unlikely. There were also no differences observed in the distribution of number of mental health diagnoses or the number of office visits in the baseline year of this study.

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Table 4.11 Characteristics of Antidepressant Nonusers and Incident Antidepressant Users

AD Nonusers Incident AD Users n=986 n=522 p-value Time Using Antidepressants 0 0 529.07 357.91

Age (mean, sd) 38.26 11.74 37.91 11.22 0.571

Gender (n, (%)) 0.217 Female 723 (73.3) 398 (76.3) Male 263 (26.7) 124 (23.8)

Ethnicity (n, (%)) 0.664 White 591 (87.4) 311 (89.4) Black 55 (8.1) 24 (6.9) Other 30 (4.4) 13 (3.7)

Education Level (n, (%)) 0.757 HS Graduate or Less 43 (9) 17 (7) Some College 116 (24.3) 63 (26.3) College Graduate 245 (51.3) 126 (52.5) Post Graduate 74 (14.5) 34 (14.2)

First Waist Circumference (mean, sd)^ 35.6 7.26 35.59 7.86 0.974

First BMI (mean, sd)^ 28.68 7.32 29.03 7.68 0.447

High Risk Waist Circumference (n, (%)) 0.684

Not High Risk (< 35 W, <40 M) 424 (57.9) 213 (56.7)

High Risk (>=35 W, >=40 M) 308 (42.1) 163 (43.4)

Number of Mental Health Diagnoses (n, 0.09 (%)) 1 777 (78.8) 388 (74.3)

2 138 (14) 95 (18.2)

3 71 (7.2) 39 (7.5)

ξ Number of Office Visits (mean, sd) 6.84 10.85 7.31 12.13 0.444 ^ First waist circumference, and first BMI refers to the waist circumference or BMI measurement prior to antidepressant exposure. For antidepressant nonusers the first recorded measurement was used. ξ Variable was measured in the baseline year (2011).

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The Cox proportional hazards model that compared antidepressant users and nonusers included the treatment variable for antidepressant use or nonuse and BMI, gender, and waist circumference. This model passed the proportional hazards test (p=0.10). Inclusion of age in the model failed the Proportional Hazards test, so it was removed.

Comparison of the characteristics of incident other antidepressant users to incident SSRI users revealed that there were no statistically significant differences in time exposed to antidepressants, age, (regardless of a linear or categorical modeling of this variable), gender, ethnicity, or education level (Table 4.12). Other antidepressant users had a smaller waist circumference (34.89 inches vs. 37.36 in) and BMI (28.58 vs. 30.78) compared to SSRI users. This pattern was also reflected in the gender specific cutoffs for a high-risk waist circumference. (Those with a high-risk waist circumference was 38.6% vs. 58.7% comparing other antidepressant users to SSRI users). There were no differences observed in the distribution of number of mental health diagnoses or the number of office visits in the baseline year of this study.

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Table 4.12 Characteristics of Incident Other Antidepressant Users and SSRI Users

Incident Other AD Incident SSRI Users Users n=406 n=101 p-value Time Using Antidepressants 538.13 355.73 510.5 372.47 0.489 Age (mean, sd) 37.93 11.21 38.27 11.22 0.789 Age (n, (%)) 0.841

<30 281 (28.5) 154 (29.5)

30 - 39 286 (29) 286 (28.4)

40 - 49 224 (22.7) 125 (23.9)

≥50 195 (19.8) 95 (18.2)

Gender (n, (%)) 0.697

Female 306 (75.4) 78 (77.2)

Male 100 (24.6) 23 (22.8)

Ethnicity (n, (%)) 0.164

White 243 (90) 60 (86.9)

Black 20 (7.4) 4 (5.8)

Other 7 (2.6) 5 (7.3)

Education Level (n, (%)) 0.891

HS Graduate or Less 13 (6.9) 3 (6.4)

Some College 47 (24.9) 13 (27.7)

College Graduate 103 (54.5) 23 (48.9)

Post Graduate 26 (13.8) 8 (17)

First Waist Circumference 34.89 6.73 37.36 6.74 0.007 (mean, sd)^

First BMI (mean, sd)^ 28.58 7.58 30.78 7.98 0.027 High Risk Waist Circumference (n, (%)) 0.002

Not High Risk (< 35 W, <40 M) 178 (61.4) 31 (41.3)

High Risk (≥35 W, ≥40 M) 112 (38.6) 44 (58.7)

Number of Mental Health Diagnoses (n, (%)) 0.417 1 308 (75.9) 71 (70.3)

2 71 (17.5) 20 (19.8)

3 27 (6.7) 10 (9.9)

Number of Office Visits ξ 7.11 12.17 7.66 12.05 0.685 (mean, sd) ^ First waist circumference, and first BMI refer to the first waist circumference or BMI measurement prior to antidepressant exposure. For antidepressant nonusers the first recorded measurement was used. ξ Variable was measured in the baseline year (2011). 108

A total of 18 antidepressant users developed T2DM over the study period, and 34 individuals in the antidepressant nonuser group developed T2DM over the study period. A total of 17 other antidepressant and SSRI antidepressant users developed T2DM during the study period. Of these, 17.7% were in the SSRI group (n=3) and 92.3% were in the other antidepressant user group (n=14).

The Cox proportional hazards model of the time to T2DM, adjusting for gender baseline BMI and baseline waist circumference and comparing incident antidepressant users to antidepressant nonusers found an 14% lower risk of T2DM among the control group, but this difference was not statistically significant (HR: 0.865, 95% 0.307, 2.434) (Table 4.13). Comparison of other antidepressant users to SSRI users found an elevated hazard for T2DM among other antidepressant users after adjusting for gender, baseline BMI and baseline waist circumference, however this association was not statistically significant (HR: 2.35, 95% CI: 0.418, 13.167).

Table 4.13. Cox Proportional Hazards Models for T2DM. Comparison between antidepressant users and nonusers or other antidepressant users and SSRI users

Incident Antidepressant users versus antidepressant nonusers (n = 1,063) HR LCL (95% CI) UCL (95% CI) Adjusted (A) 0.865 0.307 2.434 Unadjusted 0.856 0.294 2.486

Other AD Users versus SSRI Users (n = 357) HR LCL (95% CI) UCL (95% CI) Adjusted (B) 2.346 0.418 13.167 Unadjusted 2.755 0.502 15.127

Models A and B were both adjusted for gender, baseline BMI and baseline waist circumference

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DISCUSSION

In summary, this analysis of elevated glycosylated hemoglobin among nondiabetic incident antidepressant users found no statistically significant increased risk of HbA1c among antidepressant users after propensity score weighting. In comparisons of other antidepressant use and SSRI use, no statistically significant association was observed with elevated HbA1c values. In evaluating time to T2DM, incident antidepressant users were not statistically significantly different from nonusers of antidepressants. Similarly, no associations were found for other antidepressant users compared to SSRI users and subsequent T2DM development.

The study population was limited to all individuals with coded mental health diagnoses to prevent screening bias, which could occur if study participants had differences in willingness to visit a physician. It is foreseeable that patients with mental health diagnoses may be more likely to see their physician and have a subsequent T2DM diagnosis identified in medical claims compared to those without a mental health diagnosis[43].

Glycemic Control for Nondiabetic Any Antidepressant Users

The Whitehall II study of British civil servants found an association of patient reported T2DM among antidepressant users compared to nonusers during the 18-year study period that included four examinations [36]. When participants were systematically screened for undiagnosed T2DM using fasting plasma glucose levels or elevated glucose levels, there was no observed association [36]. These findings of no change in elevated blood sugar are comparable to the current findings of no association between T2DM and antidepressant use. The smaller sample sizes between types of antidepressants used in the Whitehall II Study precluded the investigators from doing analyses by antidepressant class or duration of use [36].

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A Finnish study examined the relationship between antidepressant use and glycemic control in a sample of nondiabetic patients with depression, and had mixed findings [99]. Depression status was not associated with corrected insulin response, oral glucose tolerance testing or glucose levels, however an association with increased insulin resistance using the homeostasis model assessment of insulin resistance (HOMAIR). Part of this variation in findings could be explained by a lack of control for depression status, which is associated with obesity (and a mediator for insulin resistance). The investigators sought to determine if antidepressant use modified the association between depression and glycemic control, but no modification was identified. Additionally, the investigators may have been underpowered to see an association (out of 4,419 study participants 147 were antidepressant users) [99].

Glycemic Control Among Nondiabetic Other Antidepressant and SSRI Users

The current analysis is not directly comparable to several other studies finding an association between SSRI antidepressant use and a subsequent reduction in HbA1c levels among diabetic patients [45] [44] due to removal of the diabetic patients. Elevated HbA1c was viewed as an opportunity to identify patients that were at high risk for T2DM development and were being missed by current physician screening practices.

Derijks et al. examined (1) the association of antidepressant use and hypoglycemia and (2) the association of antidepressant use and hyperglycemia [44] using data from the World Health Organization adverse drug reaction database. The investigators found antidepressant use was associated with hypoglycemia (OR: 1.84, (95% CI: 1.40, 2.42) among SSRI antidepressants. Additionally, antidepressant use was associated with hyperglycemia (ROR: 1.52 (95% CI: 1.20, 1.93) and this was most pronounced among other antidepressants (those with an affinity for the 5-HT2c receptor, histamine-1 receptor and norepinephrine uptake receptor). Systematic differences in this study design and our study are the use of adverse drug reports, which may be most commonly reported for more severe cases, and underreported among those with minimal side effects. The

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investigators had data available on age, gender, diabetic co-medication and the reporting year, but other clinically relevant confounders were unavailable (such as BMI, mental health conditions, waist circumference or race).

New Onset T2DM

The Diabetes Prevention Program Outcomes Study (DPPOS) found that antidepressant use was associated with the development of diabetes using annual OGTT or fasting blood sugar, however this population was composed of overweight prediabetics, and had higher levels of comorbid conditions than our study population[100,101]. Although this study was a randomized control trial to compare three groups: a lifestyle intervention, metformin treatment and placebo, antidepressant treatment was not randomized and all of the data collected about exposure variables were self reported by participants. The current study did not have HbA1c values prior to antidepressant use therefore it is challenging to know how exactly how comparable the population is to the DPPOS, but the DPPOS had a higher risk average BMI relative to the current study population. It is possible that patients with more comorbid conditions (metabolic syndrome) are more likely to progress to T2DM and antidepressant use can correlate with this.

Pan et al. compared antidepressant use and time to T2DM in three large cohort studies: the Health Professionals Follow-up Study, and the Nurses Health Study (NHS) I and II [47]. The investigators found an elevated risk for T2DM among antidepressant users after adjusting for BMI, age, history of hypertension, and cholesterol (HR: 1.17 (95% CI: 1.09, 1.25). The study investigators were unable to evaluate differences by dose or duration of use and their study differed from the current study because patients were not included in the study on a basis of a mental health diagnosis at baseline. Mean ages for these three cohorts were 56.4, 61.3 and 38.1 years. The average age in the current study was most similar to the NHS II, but NHS II differed from the current study in the use of self- reported antidepressant medication use, self-reported T2DM and gender differences. Additionally, Pan et al. included prevalent antidepressant users in the analysis, while the

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current study removed them to control for the bias that could be included if some patients have come to tolerate a medication compared to the new users [37].

A review article summarizing previous studies of antidepressant effects on glycemic control by McIntyre et al. [33] summarized short-term studies of antidepressant use and explained the mechanisms of action between antidepressant heterogeneity. Short-term studies of antidepressant use were found to be heterogeneous in study populations, and have not observed an association with T2DM. In describing mechanisms of action across antidepressant classes, SSRIs reduce hyperglycaemia, normalize glucose homeostasis and increase insulin sensitivity, while some noradrenergic antidepressants (ex. desipramine, which was classified as a tricyclic antidepressant in this study) exert opposite effects.

A small number of cases of T2DM was observed among SSRI and Other Antidepressant users. Pan et al. [47] found an increased risk for T2DM among SSRI users (pooled multivariate adjusted HR: 1.10 (95% CI: 1.00, 1.22)) and among other antidepressant users (pooled multivariate adjusted HR: 1.26 (95% CI: 1.11, 1.42), but in comparing these two groups there was not a measurable difference in T2DM risk. This is compatible with the current study findings due to the comparisons across antidepressant users and not to nonusers as Pan et al. did. One more relevant difference between the current study and the one by Pan et al. is their combination of TCA users with other antidepressant users. This collapsing of categories makes it challenging to identify how the distribution of antidepressant categories differed from this study. Their subgroup analyses showed that TCA only users were responsible for the observed elevated diabetes mellitus risk in the TCA and other antidepressant category.

Limitations and Strengths

Some of the limitations of this study include a lack of information about mail away prescriptions or medications that were filled and were being taken by a patient prior to enrolling in the insurance plan. A sensitivity analysis was performed at several different

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cutoffs evaluating how results were influenced if incident users starting a medication prior to 2011 were excluded and considered to be prevalent users. This change in definitions did not change the results of the analyses.

A second limitation was that variables for all the risk factors that can influence development of T2DM were not available in the secondary data used for this analysis. Some potentially important covariates that were unavailable included family history of T2DM, HbA1c prior to antidepressant use, and exercise or dietary habits at baseline. The ability to measure HbA1c would have allowed a better understanding in the differences in risk across antidepressant users at baseline. For example, knowing how many antidepressant users were prediabetic could have allowed us to better estimate baseline risk. The use of the health assessment survey data allowed the inclusion of survey data that was more descriptive than what has been done in prior studies using observational data and medical claims.

Results from laboratory tests that would be collected during a primary care visit were not available for this analysis. The absence of these values prevented having test results that a physician would use to diagnose a patient with T2DM. The availability of biometric screening values for all study participants regardless of disease status allowed prevention of detection bias or preferential data collection on individuals who were sick and seeking medical care. Initially it was sought to examine differences in tricyclic antidepressant users, but the smaller cell sizes (n=15) prevented these analyses. The Cochrane Review recommendations to report antidepressant use by type (other antidepressant, SSRI or TCA) for ease of comparison to other studies was the justification behind removal of TCA users in analyses of antidepressant by type [94] .

A more complex model that incorporated propensity scores for analyses of time to T2DM could not be used because of the use of time varying exposures to fairly account for study time among participants who started antidepressant exposure after baseline. The group of antidepressant users and nonusers were not statistically significantly different at baseline,

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which means this method of statistical adjustment was a reasonable approach to control for confounding between groups. The comparison of other antidepressant users to SSRI users found differences at baseline: SSRI users had a larger waist circumference; a higher BMI and a larger proportion had a high-risk waist circumference at baseline.

The current study examined relatively short term use of antidepressants (3.66 years or less) compared to those who would be using the medication for a lifetime, therefore these findings will not be representative of long term exposure to antidepressants. Antidepressant users in this study started to use an antidepressant and then continued its use until the end of the study period. As more health systems convert data to electronic medical records observational studies using these sources of data will be more reflective of more applicable exposure levels to different medications.

In addition, the analysis was limited to patients who had a coded diagnosis for a mental health condition or a coded indication for AD use as a method of obtaining a comparable reference population. Anecdotal conversations with physicians have uncovered that a primary care physician may be less likely to provide a diagnosis code for a mental health disorder (depression) than a psychiatrist due to the stigma around mental health conditions. This under-coding would have limited the pool of mental health patients that were included in our analysis. The current sample was still relatively large, and the use of diagnosis codes provided a fair comparison between users and nonusers.

The use of pharmacy data to measure exposure to medication use assumes that patients who fill their prescriptions are continuously taking their medications. To address this,

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patients were required to fill their medication more than two times during the study period. 80% of incident antidepressant users were classified as taking their medication for half a year or longer, which may be considered a surrogate marker for taking the medication.

Although the study time was not as long as some other cohort studies, it is plausible that enough time passed for users of antidepressants to observe a difference in risk of T2DM development. Previous studies on this topic have found that it will take roughly one year of AD use to see an increased risk of T2DM among incident users of ADs taking moderate doses [46], and a two-fold increase of T2DM may be seen for new users of AD[27] after one year of medication use.

Study strengths included a large patient population (approximately 1,000 patients) with the availability of data to describe all of a patient’s healthcare use during the study period, including exposure to pharmaceuticals. The study was set up as a retrospective cohort, and the presence of exposure prior to the outcome allowed a stronger causal association than in a different study design. The use of an observational study reflected “real world” physician care patterns and patient risk factors. The availability of biometric measurements provided the ability to adjust for pre- antidepressant use values and the presence of survey data allowed the incorporation of clinically meaningful demographic factors in the analysis.

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CHAPTER 5

Determine the Association Between Statin Use and T2DM Among Patients With Indications For Statin Use

INTRODUCTION

Background

Cardiovascular disease is the leading cause of morbidity and mortality in the United States and roughly one out of three Americans dies each year from heart disease or stroke [102]. An analysis from the National Health and Nutrition Examination Survey (NHANES) 2011-2012 estimated that 34.2% of adults ages 40 – 59 have cholesterol levels < 200 mg/dL, and only 23.9% of adults ≥60 have total cholesterol values in this range [102]. Statins inhibit the enzyme HMG-COA, (or 3-hydroxy-3-methylglutaryl- coenzyme A) which is responsible for endogenous cholesterol synthesis in the liver, thus improving the lipoprotein profile in patients with hyperlipidemia[103]. Statins improve a patient’s lipoprotein profile by reducing the serum cholesterol concentration of low density lipoprotein (LDL) cholesterol, increasing high density lipoprotein (HDL) cholesterol and decreasing triglyceride levels[104].

Several large clinical trials have consistently found that statin therapy decreases the occurrence of coronary heart disease (CHD) events (including acute myocardial infarction) by 24-37%, independent of baseline heart disease[11,105,106]. Statins have also been proven to be effective in preventing first and second heart attacks, and first and second strokes, therefore they are indicated for the primary and secondary prevention of 117

cardiovascular disease[107]. Recent estimates from NHANES using 2011 – 2012 data observed that 27.9% of adults aged 40 -59 used a cholesterol lowering medication in the previous thirty days, and of these, 83% used a statin[108]. Simvastatin was the most frequently used statin. As age increased, so did the prevalence of cholesterol lowering medication use. For 60 – 74 year olds cholesterol lowering medication was used by 43.3% of adults, it was used by 47.6% of adults ages ≥75 years old [108].

A small but consistent effect of elevated HbA1c has been observed in the Justification for use of statins in prevention: an Intervention Trial Evaluating Rosuvastatin, (JUPITER) [53]. The JUPITER trial enrolled patients with elevated C-reactive Protein levels, but without previous cardiovascular disease or diabetes and randomly assigned them to rosuvastatin 20 mg or placebo and followed up for up to 5 years.

Sattar et al conducted a meta-analysis of randomized controlled trials of statin use and the risk of incident T2DM found a modest association (HR=1.09; (95% CI: 1.02 – 1.17))[55]. A meta-analysis by Priess et al. combined the results from five RCTs that followed patients for longer than one year, and compared intensive statin use and moderate statin use[56]. The pooled analysis resulted in a dose response relationship[56]. Observational studies of statin use in large cohort studies and retrospective studies using medical records data have not consistently aligned with the findings of randomized, placebo controlled trials [59,109,110]. Most of these differences can be tracked back to limitations in study design or lack of control for differences in groups. Several previous studies of statin use and subsequent T2DM have not offered equal opportunities for screening to both exposure groups (statin users and nonusers), leading to unadjusted confounders and potential biases in study findings[57]. Individuals using statins may have a higher risk for T2DM than nonusers due to increased incidence of metabolic syndrome and other indications for statin use [109], therefore observational studies should account for these differences in the analysis.

The present study used a large retrospective sample of adults without a diagnosis of diabetes to: (1) assess the association between exposure to statin medications and 118

elevated glycosylated hemoglobin (HbA1c); and (2) assess the association between exposure to statin medications and new onset T2DM.

METHODS

Data Sources

Study Population

To be considered a member of the study population, individuals must have had continuous enrollment in the insurance plan as early as January 2011 and for a minimum enrollment length of 12 months (365 days). The study limited its eligible population to those with diagnosis codes that were indicated for the use of the statin medication (Table 2.2). Individuals were also excluded as prevalent diabetics if they developed T2DM during the first 90 days of enrollment in the insurance plan.

Cardiovascular Disease Study Population

Statins may be utilized for the secondary or primary prevention of cardiovascular disease [107]. Patients who had these indications for statin use were identified using diagnosis codes in their medical claims to reflect these conditions. Not all patients with these diagnoses were users of statins for disease management, which allowed us to compare statin users with statin nonusers.

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Statin Exposure

Participants were classified as statin users if they had two or more filled prescriptions for the medication of interest and they obtained more than 30 pills during the study period. Statin users were then classified as prevalent or incident users of the medication. Individuals who initiated statin use prior to January 2011 were considered prevalent statin users because the medical claims data started on January 2011. Therefore, inclusion of these individuals would result in an immortal person time bias [37,38]. Individuals were also considered to be prevalent statin users if they had a prescription for a statin within 90 days of enrollment on the insurance plan. This assumption was made because medication use during this period most likely corresponded to the renewal of an existing prescription from a previous physician. Individuals were classified as incident statin users if they initiated statin use more than 90 days after enrollment in the insurance plan.

Statin Category (Class) Ascertainment

Classes of statins were divided by their mechanism of action of the statin: hydrophilic statins (pravastatin, rosuvastatin, or fluvastatin); or lipophilic statins (simvastatin or atorvastatin).

Statin Dose Ascertainment

Doses were obtained from the ACC/ AHA Guidelines for cholesterol treatment to reduce Atherosclerotic Cardiovascular risk in adults [63]. Table 5.1 below identifies the different categories utilized to divide doses into treatment intensity categories.

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Table 5.1 Statin Doses Classified by Moderate and High Intensity By Statin Name

Statin Name Moderate Intensity High Intensity Atorvastatin 10 - 20 mg 40 - 80 mg Fluvastatin 5 - 40 mg 80 mg Lovastatin 40 mg Pitavastatin 2 - 4 mg Pravastatin 40 - 80 mg Rosuvastatin 20 - 40 mg Simvastatin 20 - 40 mg 80 mg

Outcome Ascertainment

Statin Use and Elevated Glycosylated Hemoglobin A1c Values

To evaluate the association between statin use and subsequent elevated HbA1c, diabetics were excluded from the analysis. This was done because if a patient had already been identified as diabetic, an elevated HbA1c (in prediabetic range) was no longer informative for this population. Additionally, once patients obtained a T2DM diagnosis they likely changed their behaviors or started medication for T2DM management. These factors would influence subsequent HbA1c measurements.

HbA1c values were collected in 2013 and 2014, however the timing between these measurements and the index date of statin use differed by user. To account for this, two separate analyses were conducted; one using HbA1c in 2013 and the second using HbA1c in 2014 as the outcome. Individuals who had a HbA1c level <6.0 were compared to those with a value >6.0, which indicated an elevated risk of prediabetes.

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Statistical Analysis

This model was estimated by using an ordinary logistic regression equation that included the Inverse Probability of Treatment Weight (IPTW). The model included all variables associated with prediabetic HbA1c and statin use.

Descriptive statistics were compared for incident statin users and prevalent statin users, for statin nonusers and incident statin users, for lipophilic and hydrophilic statin users, and finally for low intensity statin users and high intensity statin users. Covariates of interest at baseline were compared (gender, age, LDL-cholesterol, HDL-cholesterol, triglycerides, waist circumference, BMI, ethnicity, education level, number of office visits, number of inpatient hospitalizations and if a patient took a statin for primary or secondary prevention of cardiovascular disease. Variables describing healthcare utilization (office visits and inpatient hospitalizations) were measured for the baseline year (2011). Continuous covariates were compared using t-tests and categorical covariates were compared using Pearson’s Chi square test. Descriptive statistics were also compared after weighting using the same statistical tests to check that covariate balance was obtained for the two groups after weighting was utilized. Once the proposed final model was built it was checked to verify that the overlap assumption and the overidentification test for covariate balance were not violated [64].

A sensitivity analysis was conducted using the final logistic model to examine the effect of classifying individuals who initiated statin use 30 and 60 days following enrollment in the insurance plan as incident statin users.

Time to T2DM

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period was calculated separately for individuals on statin treatment and among those with no statin use. To account for the time period prior to incident statin exposure for those who started statins in the middle of the study period time varying exposure variables were created, and participants were split between groups.

Subaim 3.a. examined having an abnormal HbA1c among nondiabetic incident statin user and statin nonusers. Individuals were excluded from the analysis if they developed diabetes prior to their biometric screening date or were missing the value for their glycosylated hemoglobin measurement in the year of interest (2013 or 2014). Separate analyses were carried out for 2013 and 2014.

The propensity score model was used to evaluate the risk of elevated HbA1c (>6.0) among incident statin users compared to nonusers and was consistent for the model evaluating HbA1c in 2013 and 2014. The model included gender, age, LDL cholesterol level, triglyceride level, and BMI at baseline; ethnicity, use of a statin for primary or secondary prevention, education level and the number of office visits taking place in 2011. The propensity score model that was used to evaluate the risk of elevated HbA1c (>6.0) among moderate intensity incident statin users compared to high intensity incident statin users included age, gender and having a high-risk waist circumference prior to using a statin.

Cox proportional hazards models were used to compute hazard ratios with accompanying 95% confidence intervals for the association between statin use and incident T2DM. Clinically meaningful interactions between statin use and participant demographic characteristics were tested for inclusion during the model building process (p<0.10). Stepwise selection was used to build the final Cox proportional hazards model (p<0.05) to adjust for differences between statin users and nonusers [64]. The final model was checked for the assumption of proportional hazards, diagnostics and the goodness of fit test [66]. Subgroup analyses included examining the effect of statin class and dose on T2DM development.

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SAS version 9.4 (Cary, NC: SAS Institute Inc.) was used for all data management, and Stata 14. (StataCorp. 2015. College Station, TX) was used for data analyses and generation of figures and tables.

RESULTS

Subjects

A total of 18,007 individuals were identified who were 18 years of age or older and enrolled in the insurance plan anytime from 2011 to 2013. 17,264 of these individuals were enrolled in the insurance plan for six months or longer. 4,699 individuals with no medical claims during the study period were excluded (this step also excluded dependent children) and 743 individuals were excluded for enrollment that was less than six months in length. 12,565 of the adults (associates or spouses) were enrolled in the insurance plan for 6 months or longer and had one or more medical or pharmacy claim during the study period. 5,501 were classified as ineligible to be included in this study population because they did not have any diagnoses of cardiovascular disease during the study period. The 7,064 individuals with cardiovascular disease (56% of the population with medical and pharmacy claims) were included in the study population, and 709 were excluded for polycystic ovarian syndrome (PCOS) (n= 31), pregnancy during enrollment (n=12), T2DM prior to, or at baseline (n=308), or T2DM during the first 90 days of the enrollment period (n=358). This narrowed the final study population down to 817 incident (or new statin users), 4,050 statin nonusers (controls) and 1,488 prevalent statin users. The patient flow is presented in Figure 5.1.

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Figure 5.1. Flow Diagram of Patients With Cardiovascular Disease Classified as Statin Users and Nonusers Patients were first identified if they were 18 years of age or older and enrolled in the insurance plan between 2011 – 2013 for one or more years. Patients with CVD were identified as the population of interest. Once excluded patients were removed, these patients were divided into incident statin users, statin nonusers and prevalent statin users.

Patients that were diagnosed with diabetes at the same time they were diagnosed with primary or secondary cardiovascular disease (allowing them to enter the study) were excluded from the analyses comparing incident statin users and nonusers, but as expected these numbers of excluded patients were not balanced by statin category. Prevalent

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diabetics were not excluded from the descriptive statistics comparing incident and prevalent statin users, as the category of prevalent statin use was not included in the analyses. 13.1% of prevalent statin users (n= 225) had a diabetes diagnosis in the first 90 days following insurance enrollment, compared to 6.0% of incident statin users (n= 52) and 2.0% of statin nonusers (n= 81).

Table 5.2 provides the distribution of the types of statins used in the study population across incident and prevalent statin users. Incident statin users were more likely than prevalent statin users to be prescribed a hydrophilic statin. Additionally, atorvastatin was used more often among incident statin users while simvastatin was used less frequently among incident statin users compared to prevalent statin users. There were no measurable differences for other statins.

Table 5.2 Types of Statins Across Incident and Prevalent Statin Users Incident Statin Users Prevalent Statin Users n=869 n=1,713 n % n % Hydrophilic 221 25.4% 358 20.9% Fluvastatin 0 0 2 0.1% Pravastatin 141 16.2% 199 11.6% Rouvastatin 80 9.2% 157 9.2% Lipophilic 648 74.6% 1355 79.1% Atorvastatin 381 43.8% 408 23.8% Lovastatin 14 1.6% 48 2.8% Pitavastatin 1 0.1% 1 0.1% Simvastatin 252 29.0% 898 52.4%

Table 5.3 provides the distribution of the doses of statins used in the study population across incident and prevalent statin users. The breakdown of moderate and high intensity statin use was comparable between incident and prevalent statin users. Atorvastatin was

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most frequently prescribed statin for high intensity statin use in both groups, and high intensity simvastatin was more frequent among prevalent statin users.

Table 5.3 Use of Moderate Intensity and High Intensity Statins Among Incident and Prevalent Statin Users

Incident Statin Use Prevalent Statin Use n=869 n=1,713 Moderate Intensity High Intensity Moderate Intensity High Intensity n=778 (89.5) n=91 (10.5) n=1,492 (87.1) n=221 (12.9) n % n % n % n % Fluvastatin 0 (0.0) 0 (0.0) 1 (50.0) 1 (50.0) Pravastatin 141 (100.0) 0 (0.0) 199 (100.0) 0 (0.0) Rosuvastatin 80 (100.0) 0 (0.0) 157 (100.0) 0 (0.0) Atorvastatin 295 (77.4) 86 (22.6) 273 (66.9) 135 (33.1) Lovastatin 14 (100.0) 0 (0.0) 48 (100.0) 0 (0.0) Pitavastatin 1 (100.0) 0 (0.0) 1 (100.0) 0 (0.0) Simvastatin 247 (98.0) 5 (2.0) 813 (90.5) 85 (9.5)

Characteristics of Incident and Prevalent Statin Users

There were roughly twice as many prevalent statin users as incident statin users, and prevalent statin users were overall older in age. There were no observable differences in gender, ethnicity or education level, or triglyceride levels or BMI. Prevalent statin users had lower LDL cholesterol, but statin use likely influenced this variable. The lack of biometric values prior to statin initiation for prevalent users impaired the ability to compare changes between groups and control for baseline covariates prior to statin initiation. Prevalent and incident statin users were similar in the frequency of primary and secondary prevention statin use, and prevalent users had more office visits and were also slightly more likely to have had an inpatient admission.

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Table 5.4 Characteristics of Incident and Prevalent Statin Users

Incident Statin Users Prevalent Statin Users n=869 n=1,713 Age (mean, sd) 49.43 9.48 52.95 9.11 Gender (n, %)

Female 430 49.5% 853 49.8% Male 439 50.5% 860 50.2% Ethnicity (n, %)

White 527 83.4% 944 83.5% Black 73 11.6% 126 11.1% Other 32 5.1% 61 5.4% Education Level (n, %)

HS Graduate or Less 58 13.2% 113 14.1% Some College 120 27.4% 222 27.6% College Graduate 183 41.8% 293 36.5% Post Graduate 77 17.6% 175 21.8% ξ BMI (mean, sd) 31.54 6.88 30.96 6.5 ξ LDL-C (mean, sd) 128.45 36.93 100.6 36.67 ξ HDL-C (mean, sd) 51.65 15.63 52.47 14.61 ξ Triglycerides (mean, sd) 39.33 6.08 39 7.18 Cardiovascular Events (n, %)

No Event Yet 775 (89.2) 1,558 (91.0) Congestive Heart Failure 13 (1.5) 19 (1.1) Ischemic Heart Disease 10 (1.2) 37 (2.2) Cerebrovascular Disease 17 (2.0) 36 (2.1) Stroke or Athleroembolism 0 (0.0) 0 (0.0) Athlerosclerosis 6 (0.7) 8 (0.5) Acute Myocardial Infarction 15 (1.7) 28 (1.6) Percutaneous Coronary Implant 28 (3.2) 22 (1.3) CABG 5 (0.6) 5 (0.3) Type of Prevention (n, %)

Primary Prevention 819 (94.3) 1,631 (95.2) Secondary Prevention 50 (5.7) 82 (4.8) ξ Number of Office Visits (mean, sd) 7.3 9.54 10.08 11.65 Inpatient Admission ξ (n, %) Yes 54 (6.2) 116 (6.8) No 815 (93.8) 1597 (93.2) ξ Data uses values from the baseline year (2011).

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The propensity score model used to evaluate the risk of elevated HbA1c (>6.0) among hydrophilic incident statin users compared to lipophilic incident statin users included age, gender and a high risk waist circumference prior to statin use. The smaller sample sizes of patients taking high intensity statins or hydrophilic statins prevented the use of a propensity model with more covariates. However, in evaluating the balance between groups before and after applying propensity weights the covariates were balanced between the two groups. The balance in groups can be observed before and after weighting in Tables 5.5 – 5.19.

Following the development of propensity scores, the fit of the models was checked by using the overidentification test. All models failed to reject the null hypothesis, and therefore there was not evidence of a lack of fit. Graphically, overlap was evaluated between the two groups being compared after propensity weighting and there were no obvious violations to the overlap assumption.

Statin nonusers were compared to statin users before and after propensity weighting for the analysis using 2013 data as the outcome (Table 5.5). Statin users were slightly older (48.9 vs. 45.9 p<0.001) in age and more likely to be male (50.1 vs. 34.5, p<0.001). There were no differences in ethnicity, education level, having a high-risk waist circumference, BMI or the type of prevention a statin was used for. Statin users had higher LDL cholesterol (174.7 vs. 127.9 p<0.011) and higher triglyceride levels (177.0 vs. 133.5, p<0.001) and fewer physician visits in 2011 (5.8 vs. 7.34, p<0.001) than statin nonusers. After propensity weighting was applied to the data, the groups were balanced in the distribution of all listed covariates.

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Table 5.5 Characteristics of Statin Nonusers and Incident Statin Users Before and After Propensity Weighting (2013 Data as the Outcome)

Before Propensity Weighting After Propensity Weighting Statin Incident Statin Statin Incident Nonusers Users Nonusers Statin Users n=1,820 / n=387/ p - p- n=1,820 n=387 weighted n = weighted n = value value 1047 1068 Age (mean, sd) 45.97 10.22 48.95 9.47 <0.001 46.76 12.74 46.46 5.93 0.761 Gender (n, (%)) Female 1,193 (65.6) 193 (49.9) <0.001 669 (63.8) 694 (64.9) 0.80 Male 627 (34.5) 194 (50.1) 379 (36.2) 375 (35.1)

Ethnicity (n, (%))

White 1,314 (84.6) 275 (86.5) 0.253 926 (88.4) 959 (89.7) 0.651 Black 172 (11.1) 26 (8.2) 79 (7.5) 60 (5.6)

68 (4.4) 17 (5.4) 43 (4.1) 50 (4.7) Other Education Level (n, (%))

HS Graduate or 132 (10.7) 37 (14.2) 0.164 113 (10.8) 108 (10.1) 0.981 Less Some College 308 (24.8) 64 (24.5) 238 (22.7) 247 (23.1)

College Graduate 579 (46.7) 106 (40.6) 502 (47.9) 525 (49.2) Post Graduate 221 (17.8) 54 (20.7) 194 (18.5) 188 (17.6) High Risk Waist

Circumference (n, (%)) Yes 1012 (56.1) 149 (47.0) 0.33 466 (44.5) 454 (42.4) 0.657 No 791 (43.9) 132 (53.0) 581 (55.5) 616 (57.6) First LDL-C 127.97 79.2 174.76 119.79 <0.001 133.70 112.57 140.0 49.30 0.31 (mean, sd)^ First Triglyceride 133.53 83.59 177.09 123.69 <0.001 137.04 118.61 139.98 49.3 0.636 Level (mean, sd)^ First BMI 30.52 6.96 30.34 5.94 0.69 30.1 8.82 30.1 3.71 0.983 (mean, sd)^ Type of Prevention (n, %) Primary 1,760 (96.7) 368 (95.1) 0.121 32 (3.1) 28 (2.6) 0.719 Prevention Secondary 60 (3.3) 19 (4.9) 1015 (96.9) 1041 (97.4) Prevention Number of Office Visits ξ 7.34 9.82 5.8 7.82 0.004 8.68 13.2 8.8 5.07 0.897 (mean, sd) ^ First LDL, First Triglyceride Level and first BMI refer to the LDL, Triglyceride or BMI measurement prior to statin exposure. For statin nonusers the first measurement was used. ξ Variable was measured in the baseline year (2011).

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Moderate intensity statin users were compared to high intensity statin users before and after propensity weighting using 2013 data as the outcome (Table 5.6). High intensity statin users were more likely to be male (75.0 vs. 47.3, p<0.001) than statin nonusers. There were no differences in age, ethnicity, education level, having a high-risk waist circumference, or BMI. High intensity statin users had higher LDL cholesterol (217.0 vs. 169.2, p=0.017) and higher triglyceride levels (216.5 vs. 171.3, p=0.031) and were more likely to be taking the statin for secondary prevention (11.5 vs. 3.7, p=0.13) than moderate intensity statin users. After propensity weighting was applied to the data, the groups were more balanced in the distribution of covariates, but type of prevention was still unbalanced between groups.

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Table 5.6 Characteristics of Moderate Intensity and High Intensity Statin Users Before and After Propensity Weighting (2013 Data as the Outcome)

Before Propensity Weighting After Propensity Weighting Moderate Moderate High High Intensity Intensity Intensity Intensity p- n=351 n=52 p- n=351 n=52 value (wt) n =295 (wt) n =277 value Age (mean, sd) 48.9 9.39 48.7 9.95 0.850 49.05 12.60 48.60 5.12 0.819 Gender (n, (%)) Female 185 (52.7) 13 (25) <0.001 144 (48.8) 115 (41.5) 0.483 Male 166 (47.3) 39 (75) 151 (51.2) 162 (58.5)

Ethnicity (n, (%))

White 252 (86.3) 37 (88.1) 0.559 212 (84.8) 200 (88.1) 0.564 Black 26 (8.9) 2 (4.7) 26 (10.4) 13 (5.7)

Other 14 (3.8) 3 (7.1) 12 (4.8) 14 (6.2)

Education Level (n, (%))

HS Graduate or 31 (13.2) 7 (17.5) 0.722 0.953 Less 26 (13.2) 37 (18.8) Some College 60 (25.5) 9 (22.5) 50 (25.4) 56 (28.4)

College 97 (41.3) 14 (35) Graduate 80 (40.6) 80 (40.6) Post Graduate 47 (20.0) 10 (25) 41 (20.8) 38 (19.3) High Risk Waist

Circumference (n, (%)) Yes 134 (51.9) 22 (57.9) 0.492 155 (52.7) 163 (58.8) 0.520 No 124 (48.1) 16 (42.1) 139 (47.3) 114 (41.1) First LDL-C 169.24 103.30 217.05 191.51 0.017 175.54 141.33 172.46 71.64 0.881 (mean, sd)^ First Triglyceride 171.29 108.09 216.46 194.52 0.031 176.72 146.37 170.58 71.84 0.766 Level (mean, sd) First BMI 30.28 5.96 30.54 5.06 0.796 30.29 7.80 30.15 2.68 0.895 (mean, sd)^ Type of Prevention (n, (%)) Primary 338 (96.3) 46 (88.5) 0.013 283 (96.3) 238 (86.2) 0.025 Prevention Secondary 13 (3.7) 6 (11.5) 11 (3.7) 38 (13.8) Prevention Number of Office Visits ξ 5.99 8.24 5.06 6.85 0.440 5.46 9.57 5.93 3.17 0.701 (mean, sd) ^ First LDL, First Triglyceride Level and first BMI refer to the LDL, Triglyceride or BMI measurement prior to statin exposure. For statin nonusers the first measurement was used. ξ Variable was measured in the baseline year (2011).

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Hydrophilic and lipophilic statin users were compared before and after propensity weighting for the analysis using 2013 data as the outcome (table 5.7). Lipophilic statin users were more likely to be male (53.8 vs. 41.8, p=0.04) than hydrophilic statin users. There were no differences in age, ethnicity, education level, having a high-risk waist circumference, or BMI, LDL- cholesterol levels, triglyceride levels or presence of a previous cardiovascular event. After propensity weighting was applied to the data, the groups were balanced across covariates.

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Table 5.7 Characteristics of Lipophilic and Hydrophilic Statin Users Before and After Propensity Weighting (2013 Data as the Outcome) Before Propensity Weighting After Propensity Weighting Hydrophilic Lipophilic Hydrophilic Lipophilic p - n=98 n=305 p- n=98 n=305 value n (wt)= 291 n (wt) = 290 value Age (mean, 49.10 8.75 48.89 9.68 0.848 50.35 6.36 48.61 12.25 0.207 sd) Gender (n, (%))

Female 57 (58.2) 141 (46.2) 0.04 142 (48.7) 143 (49.3) 0.957 Male 41 (41.8) 164 (53.8) 149 (51.2) 147 (50.7)

Ethnicity (n, (%))

White 78 (86.7) 211 (86.5) 0.214 225 (84.9) 304 (89.1) 0.610 Black 10 (11.1) 18 (7.4) 32 (12.1) 23 (6.7)

Other 2 (2.2) 15 (6.2) 8 (3.0) 14 (4.1)

Education Level (n, (%)) HS Graduate (14.1) 27 (13.7) 0.74 34 (14.8) 26 (11.3) 0.871 or Less 11 Some College 20 (25.6) 49 (24.9) 57 (24.8) 45 (19.6)

College (35.9) 83 (42.1) 81 (35.2) 78 (33.9) Graduate 28 Post Graduate 19 (24.4) 38 (19.3) 58 (25.2) 39 (17.0) High Risk Waist

Circumference (n, (%)) Yes 39 (54.9) 117 (52.0) 0.666 157 (54.0) 151 (52.1) 0.772 No 32 (45.0) 108 (48.0) 134 (46.0) 139 (47.9) First LDL-C 171.14 110.25 176.37 121.03 0.741 170.93 80.92 177.54 151.54 0.705 (mean, sd)^ First Triglyceride 171.04 110.87 178.52 125.84 0.648 171.41 81.77 180.27 158.29 0.618 Level (mean, sd) First BMI 30.37 5.97 30.30 5.82 0.922 30.18 3.91 30.12 7.34 0.943 (mean, sd)^ Type of Prevention (n, %) Primary 92 (96.8) 287 (95.7) 0.613 285 (97.9) 277 (95.5) 0.418 Prevention Secondary 3 (3.2) 13 (4.3) 6 (2.1) 13 (4.5) Prevention Number of Office Visits ξ 5.37 7.79 6.03 8.17 0.48 4.71 5.22 6.04 9.14 0.180 (mean, sd) ^ First LDL, First Triglyceride Level and first BMI refer to the LDL, Triglyceride or BMI measurement prior to statin exposure. For statin nonusers the first measurement was used. ξ Variable was measured in the baseline year (2011).

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Statin nonusers were compared to statin users before and after propensity weighting for our analysis using 2014 data as the outcome (Table 5.8). Statin users were slightly older in age (45.6 vs. 48.9 p< 0.011) and more likely to be male (48.9 vs. 34.8, p<0.001) than statin nonusers. There were no differences in ethnicity, education level, having a high- risk waist circumference, BMI, the type of prevention a statin may be used for and number of physician visits between the groups. Statin users had higher LDL cholesterol (175.7 vs. 125.7 p<0.001) and higher triglyceride levels (174.9 vs. 132.8, p<0.001) than statin nonusers. After propensity weighting was applied to the data, the groups were balanced in the distribution of all listed covariates.

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Table 5.8 Characteristics of Statin Nonusers and Incident Statin Users Before and After Propensity Weighting (2014 Data as the Outcome)

Before Propensity Weighting After Propensity Weighting

Statin Incident Statin Incident Nonusers Statin Users Nonusers Statin Users p- n=1,983 n=319 p- n=1,983 n=319 value n (wt) =1,141 n (wt) = 1,139 value Age (mean, sd) 45.6 10.2 48.9 9.5 <0.001 46.8 12.9 46.5 6.0 0.770 Gender (n, %) Female 1,292 (65.2) 163 (51.1) <0.001 748 (60.4) 741 (60.3) 0.943 691 (34.8) 156 (48.9) 420 (33.9) 421 (32.8) Male Ethnicity (n, %) White 1,571 (84.6) 252 (79.0) 0.519 1029 (83.2) 1034 (83.0) 0.879 200 (10.8) 29 (9.1) 91 (7.3) 79 (6.2) Black 86 (4.6) 18 (5.6) 48 (3.7) 50 (3.9) Other Education Level (n, %) HS Graduate or 152 (9.9) 30 (9.4) 0.454 116 (9.3) 111 (9.0) 0.960 Less 397 (26.0) 63 (19.7) 273 (21.6) 272 (21.3) Some College College 703 (46.0) 103 (32.3) 555 (44.9) 535 (43.1) Graduate 277 (18.1) 51 (16.0) 225 (18.3) 244 (19.7) Post Graduate High Risk Waist Circumference

(n, %) Yes 968 (54.1) 115 (49.6) 0.189 605 (53.0) 644 (56.5) 0.44 No 820 (45.9) 117 (50.4) 537 (47.0) 495 (43.5) First BMI 30.3 7.0 30.3 6.2 0.891 29.9 8.8 30.1 3.8 0.818 (mean, sd)^ First LDL-C 125.7 74.4 174.7 121.6 <0.001 132.4 107.1 139.1 44.7 0.238 (mean, sd)^ First Triglyceride 132.8 84.2 174.9 122.2 <0.001 136.4 119.7 139.2 44.7 0.625 Level (mean, sd) Type of Prevention (n, %) Primary 1,925 (97.1) 305 (95.6) 0.476 1138 (91.8) 1137 (91.3) 0.665 Prevention Secondary 58 (2.9) 14 (4.4) 31 (2.4) 25 (1.9) Prevention Number of Office Visits ξ 7.1 10.5 6.7 8.2 0.505 8.8 12.8 8.7 5.8 0.872 (mean, sd) ^ First LDL, First Triglyceride Level and first BMI refers to the LDL, Triglyceride or BMI measurement prior to statin exposure. For statin nonusers the first measurement was used. ξ Variable was measured in the baseline year (2011).

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Moderate intensity statin users were compared to high intensity statin users before and after propensity weighting for the analysis using 2014 data as the outcome (Table 5.9). High intensity statin users were more likely to be male (66.7 vs. 47.3, p=0.034) than statin nonusers. There were no statistically significant differences in age, ethnicity, education level, having a high-risk waist circumference, BMI, LDL cholesterol, triglyceride levels, the type of previous cardiovascular events or the number of physician visits in 2011. After propensity weighting was applied to the data, the groups were balanced in the distribution of the covariates in table 5.9.

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Table 5.9 Characteristics of Moderate Intensity and High Intensity Statin Users Before and After Propensity Weighting (2014 Data as the Outcome)

Before Propensity Weighting After Propensity Weighting Moderate Moderate High High Intensity Intensity Intensity Intensity p- n=298 n=33 p- n=298 n=33 value n (wt) =241 n (wt)=238 value Age (mean, sd) 48.8 9.48 49.2 10.06 0.818 48.38 13.12 49.23 4.07 0.672 Gender (n, (%)) Female 157 (52.7) 11 (33.3) 0.035 121 (50.2) 113 (47.5) 0.805 Male 141 (47.3) 22 (66.7) 120 (49.8) 125 (52.5)

Ethnicity (n, (%))

White 233 (83.2) 28 (84.8) 0.394 186 (81.9) 211 (90.6) 0.208 Black 31 (11.1) 1 (3.0) 28 (12.3) 6 (2.6)

(5.7) 2 (6.1) (5.7) (6.9) Other 16 13 16 Education Level (n, (%))

HS Graduate or 28 (12.2) 4 (12.1) 0.711 23 (12.3) 31 (13.5) 0.835 Less Some College 62 (27.1) 5 (15.2) 46 (24.6) 51 (22.3)

College 93 (40.6) 14 (42.4) 78 (41.7) 114 (49.8) Graduate Post Graduate 46 (20.1) 6 (18.2) 40 (21.4) 33 (14.4) High Risk Waist

Circumference (n, (%)) Yes 109 (50) 13 (50) 1.0 121 (50.2) 118 (49.6) 0.962 No 109 (50) 13 (50) 120 (49.8) 120 (50.4) First LDL-C 197.5 171.79 108.57 192.3 0.283 176.48 150.72 172.62 73.43 0.893 (mean, sd)^ 7 First Triglyceride 198.0 173.5 112.9 195.83 0.331 177.64 155.73 171.59 73.45 0.833 Level (mean, 4 sd) First BMI 30.21 6.16 30.5 6.03 0.812 29.99 8.11 29.20 2.70 0.572 (mean, sd)^ Type of Prevention (n, (%)) Primary 287 (96.3) 30 (90.9) 0.144 234 (97.1) 222 (93.3) 0.297 Prevention Secondary 11 (3.7) 3 (9.1) 7 (2.9) 16 (6.7) Prevention Number of Office Visits ξ 6.62 8.40 7.18 8.60 0.715 6.37 11.23 6.80 3.69 0.809 (mean, sd) ^ First LDL, First Triglyceride Level and first BMI refer to the LDL, Triglyceride or BMI measurement prior to statin exposure. For statin nonusers the first measurement was used. ξ Variable was measured in the baseline year (2011). *Propensity weighting adjusted for sex and waist circumference prior to initiating a statin.

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Hydrophilic and lipophilic statin users were compared before and after propensity weighting for the analysis using 2014 data as the outcome (table 5.10). Lipophilic statin users were more likely to be male (53.3 vs. 37.7, p=0.013) and White (86.7 vs. 79.0, p=0.042), compared to hydrophilic statin users. There were no differences in age, education level, having a high-risk waist circumference, BMI, triglyceride levels, LDL- cholesterol levels, presence of a previous cardiovascular event or number of doctors visits. After propensity weighting was applied to the data, the groups were balanced across covariates.

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Table 5.10 Characteristics of Lipophilic and Hydrophilic Statin Users Before and After Propensity Weighting (2014 Data as the Outcome) Before Propensity Weighting After Propensity Weighting Hydrophilic Lipophilic Hydrophilic Lipophilic p - n=85 n=246 p- n=85 n=246 value n (wt)= 291 n (wt) = 290 value Age (mean, 49.74 9.27 48.50 9.61 0.301 49.91 7.05 48.02 11.91 0.222 sd) Gender (n, (%))

Female 53 (62.4) 115 (46.8) 0.013 123 (50.6) 122 (50.0) 0.952 Male 32 (37.7) 131 (53.3) 120 (49.4) 122 (50.0)

Ethnicity (n, (%))

White 64 (79.0) 197 (86.7) 0.042 182 (77.4) 193 (83.9) 0.457 Black 14 (17.3) 18 (7.8) 38 (16.2) 22 (9.6)

Other 3 (3.7) 15 (6.5) 15 (6.4) 15 (6.5)

Education Level (n, (%)) HS Graduate 8 (11.7) 24 (12.6) 0.512 26 (13.2) 24 (12.4) 0.689 or Less Some College 16 (23.5) 51 (26.8) 36 (18.3) 50 (25.8)

College 26 (38.2) 81 (42.6) 84 (42.6) 81 (41.8) Graduate Post Graduate 18 (26.5) 34 (17.9) 51 (25.9) 39 (20.1) High Risk Waist

Circumference (n, (%)) Yes 26 (42.6) 96 (52.5) 0.183 157 (54.0) 151 (52.1) 0.772 No 35 (57.4) 87 (47.5) 134 (46.0) 139 (47.9) First LDL-C 185.97 127.81 170.85 117.49 0.382 184.88 93.63 172.40 148.49 0.537 (mean, sd)^ First Triglyceride 181.32 128.49 174.37 122.54 0.699 181.06 94.67 176.04 154.38 0.809 Level (mean, sd) First BMI 29.98 6.14 30.32 6.15 0.705 29.74 4.18 30.13 7.62 0.666 (mean, sd)^ Type of Prevention (n, %) Primary 1925 (97.1) 317 (95.8) 0.206 238 (97.9) 235 (96.3) 0.557 Prevention Secondary 58 (2.9) 14 (4.2) 5 (2.1) 9 (3.7) Prevention Number of Office Visits ξ 6.78 9.87 6.63 7.86 0.896 6.85 7.92 6.34 9.05 0.750 (mean, sd) ^ First LDL, First Triglyceride Level and first BMI refer to the LDL, Triglyceride or BMI measurement prior to statin exposure. For statin nonusers the first measurement was used. ξ Variable was measured in the baseline year (2011). *Propensity weighting adjusted for sex and waist circumference prior to initiating a statin.

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The Prevalence of Elevated HbA1c Among Incident Statin Users by Intensity of Dose and Statin Class After Propensity Weighting

Figure 5.11 shows the prevalence differences obtained after comparing incident statin user categories using logistic regression and HbA1c (>6.0) measured in either 2013 or 2014 as the outcome. There were no significant differences in the probability of elevated HbA1c between hydrophilic and lipophilic statin users for either 2013 or 2014. When comparing moderate and high intensity statin users, estimates obtained in 2013 (PD: 0.051 (95% CI: -0.102, 0.203) and 2014 were different from one another (PD: 0.121 (95% CI: -0.001, 0.243). While the magnitude of the association was in the same direction across years, it was larger in the group of statin users with HbA1c measured in 2014 than it was for 2013.

Statin use comparing users to nonusers was similar in magnitude across 2013 (PD: 0.051 (95% CI: -0.018, 0.120) and 2014 (PD: 0.061 (95% CI: 0.003, 0.119), but this association was only statistically significant in the 2014 population. The 2014 population was the analysis that was not balanced after weighting with regard to statin use for primary or secondary prevention.

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Figure 5.11 Prevalence Difference of Hba1c Greater Than 6.0 Among Statin Users (Overall) and By Intensity and Type Of Statin Using 2013 and 2014 Outcome Data

Sensitivity Analysis for Incident Statin Definition Effect on the Prevalence of Elevated HbA1c

A sensitivity analysis was conducted to evaluate the effect of changing the definition of incident statin use on the propensity weighted logistic regression using the outcome of having HbA1c greater than 6.0 (measured in 2014). All of these analyses kept the propensity weighed models the same as the previous analyses. The definitions for incident statin use defined statin use as being incident either 30, 60 or 90 days following enrolling in the study. For all definitions of incident statin use (either 30, 60 or 90 days after study enrollment) incident statin use was statistically significantly associated with higher odds of HbA1c greater than 6.0 comparing incident statin users to nonusers. Similarly, for high intensity statin use compared to moderate intensity statin use, this association held regardless of the definition of incident statin use (either 30, 60 or 90 days following enrolling in the study). Finally, findings comparing lipophilic statin users to 142

hydrophilic statin users were consistently not significant when comparing across definitions of 30, 60 or 90 days for incident statin use. (Figure 5.12).

Figure 5.12 Sensitivity Analysis of Prevalence Difference of Hba1c Greater Than 6.0 Among Statin Users (Overall) and By Intensity of Dose and Type of Statin Using 2014 Outcome Data Measures of effect are prevalence differences of HbA1c >6.0 using 2014 outcome data. 90 was defined as statin initiation 90 days after the enrollment date in the insurance plan or the date of obtaining a cardiovascular diagnosis for statin use. 60 is defined as statin initiation 60 days after the enrollment date in the insurance plan or the date of obtaining a cardiovascular diagnosis for statin use. Multivariable logistic regression models were fit including for the inverse probability of treatment weights and treatment with the outcome of HbA1c >6.0.

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Time to T2DM Among Statin Users

Table 5.11 compares the characteristics of statin users and nonusers in the time to T2DM analysis. This data includes time varying treatment with statins; statin users were included in the non-user group prior to their statin use. The average amount of time that incident statin users used a statin for was 575.52 days. Incident statin users were slightly older in age (49.3 vs. 45.87, p<0.001) and more likely to be male (50.1 vs. 38.5, p<0.001) than statin nonusers. No statistically significant differences were observed for the two groups in ethnicity, education level, BMI, high-risk waist circumference or the number of physician office visits in 2011. Statin users had a slightly larger waist circumference (39.1 vs. 38.0, p=0.043), higher LDL cholesterol (176.02 vs. 129.17, p<0.001), a higher triglyceride level (177.18 vs. 135.65, p<0.001) a larger number of cardiovascular related diagnoses (7.33 vs. 6.61 p<0.001) and were more likely to be using a statin for secondary cardiovascular disease prevention (5.0 vs. 3.3, p<0.001).

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Table 5.11 Characteristics of Incident Statin Users and Statin Nonusers

Statin nonusers Incident Statin Users n=4,050 n=817 p-value Time Using Statins 0 0 575.52 368.33 <0.001 Age (mean, sd) 45.87 10.54 49.3 9.53 <0.001 Gender (n, (%)) <0.001 Female 2492 (61.5) 408 (49.9) Male 1158 (38.5) 409 (50.1) Ethnicity (n, (%)) 0.514 White 2393 (82.6) 499 (83.7) Black 377 (13.0) 68 (11.4) Other 127 (4.5) 29 (4.9) Education Level (n, (%)) 0.692 HS Graduate or Less 224 (10.8) 52 (12.6) Some College 545 (26.4) 110 (26.7) College Graduate 938 (45.4) 177 (43.0) Post Graduate 361 (17.5) 73 (17.7) First Waist Circumference (mean, sd)^ 38.05 6.95 39.11 5.94 0.002 First BMI (mean, sd)^ 30.72 7.16 31.23 6.47 0.124 First LDL-C (mean, sd)^ 129.17 79.12 176.02 117.60 <0.001 First Triglyceride Level (mean, sd) 135.65 85.31 177.18 120.34 <0.001 High Risk Waist Circumference (n, (%)) 0.559 Not High Risk (< 35 W, <40 M) 1,343 (45.2) 209 (43.7) High Risk (>=35 W, >=40 M) 1,631 (54.8) 269 (56.3) Total Number of CVD Diagnoses 6.61 (4.06) 7.33 (5.23) <0.001 Type of Prevention (n, (%)) Primary Prevention 3916 (96.7) 768 (94.0) <0.001 Secondary Prevention 134 (3.3) 49 (6.0) Number of Office Visits ξ (mean, sd) 7.56 11.00 7.03 8.92 0.195 ^ First LDL, First Triglyceride Level, first waist circumference, and first BMI refers to the LDL, Triglyceride, waist circumference or BMI measurement prior to statin exposure. For statin nonusers the first recorded measurement was used. ξ Variable was measured in the baseline year (2011).

Over the course of the study period, there were a total of 670 new diagnoses of T2DM. 73.9% (n=495) of these took place in statin nonusers and 26.1% (n=175) took place in statin users. There were a total of 175 new diagnoses of T2DM among the 817 statin users. Of these, 17 took place among those using high dose statins, and 158 took place

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among those using moderate dose statins. Types of statins included lipophilic or hydrophilic statins. A total of 204 statin users were hydrophilic statin users, and 21.1% (n=43) of these were diagnosed with T2DM during the study period. A total of 132 (21.5%) of the 613 lipophilic statin users were diagnosed with T2DM during the study period. Patients were followed for a total of 14,980.9 person years.

The characteristics of moderate intensity and high intensity statin users were compared at baseline (Table 5.12). Moderate and high intensity statin users were comparable across all covariates except for age, gender and the number of users who are taking the statin for primary prevention. High intensity statin users were older in age (51.2 vs. 49.1, p=0.049) more likely to be males (70.9 vs. 47.6, p<0.001) and were more likely to use a statin for secondary CVD prevention (11.6 vs. 4.3, p=0.02).

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Table 5.12 Characteristics of Moderate Intensity and High Intensity Statin Users

Moderate Intensity High Intensity n=731 n=86 p-value Time Using Statins 579.47 367.7 541.86 374.0 0.371 Age (mean, sd) 49.1 9.52 51.2 9.44 0.049 Gender (n, (%)) <0.001 Female 383 (52.4) 25 (29.1) Male 348 (47.6) 61 (70.9) Ethnicity (n, (%)) 0.211 White 442 (82.9) 57 (90.5) Black 65 (12.2) 3 (4.7) Other 27 (4.9) 3 (4.7) Education Level (n, (%)) 0.350 HS Graduate or Less 43 (11.7) 9 (20.0) Some College 101 (27.5) 9 (20.0) College Graduate 159 (43.3) 18 (40.0) Post Graduate 64 (17.4) 9 (20.0) First Waist Circumference (mean, sd)^ 39.0 6.01 39.9 5.27 0.308 First BMI (mean, sd)^ 31.2 6.50 31.2 6.2 0.959 First LDL-C (mean, sd)^ 174.45 110.94 190.11 166.35 0.354 First Triglyceride Level (mean, sd) 175.87 113.92 188.96 168.22 0.453 High Risk Waist Circumference (n, (%)) 0.929 Not High Risk (< 35 W, <40 M) 187 (43.8) 22 (43.1) High Risk (>=35 W, >=40 M) 240 (56.2) 29 (56.9) Type of Prevention (n, (%)) Primary Prevention 692 (94.7) 76 (88.4) 0.020 Secondary Prevention 39 (4.3) 10 (11.6) Number of Office Visits ξ (mean, sd) 7.16 8.99 5.93 8.28 0.226 ^ First LDL, First Triglyceride Level, first waist circumference, and first BMI refers to the LDL, Triglyceride, waist circumference or BMI measurement prior to statin exposure. For statin nonusers the first recorded measurement was used. ξ Variable was measured in the baseline year (2011).

The same comparison was done across statin mechanism of action (lipophilic or hydrophilic statin users). It was observed that lipophilic statin users were similar to hydrophilic statin users for all variables except for gender. Lipophilic statin users were more likely to be males (53.2 vs. 40.7, p=0.002) (Table 5.13).

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Table 5.13 Characteristics of Lipophilic and Hydrophilic Statin Users

Hydrophilic Statin Lipophilic Statin n=204 n=613 p-value Time Using Statins 604.7 369.3 565.8 367.8 0.191 Age (mean, sd) 49.7 9.11 49.2 9.66 0.534 Gender (n, (%)) 0.002 Female 121 (59.3) 287 (47.8) Male 83 (40.7) 326 (53.2) Ethnicity (n, (%)) 0.454 White 133 (84.2) 366 (83.6) Black 20 (12.7) 48 (11.0) Other 5 (3.2) 24 (5.5) Education Level (n, (%)) 0.401 HS Graduate or Less 12 (10.4) 40 (13.5) Some College 30 (26.1) 80 (26.9) College Graduate 47 (40.9) 130 (43.8) Post Graduate 26 (22.6) 47 (15.8) First Waist Circumference (mean, sd)^ 38.48 5.60 39.33 6.05 0.168 First BMI (mean, sd)^ 31.0 6.75 31.3 6.37 0.667 First LDL-C (mean, sd)^ 173.77 118.84 176.8 117.3 0.795 First Triglyceride Level (mean, sd) 172.9 119.53 178.67 120.74 0.630 High Risk Waist Circumference (n, (%)) 0.066 Not High Risk (< 35 W, <40 M) 53 (42.7) 156 (44.1) High Risk (>=35 W, >=40 M) 71 (57.3) 198 (55.9) Type of Prevention (n, (%)) Primary Prevention 195 (95.6) 573 (93.5) 0.271 Secondary Prevention 9 (4.4) 40 (6.5) Number of Office Visits ξ (mean, sd) 6.8 8.41 7.11 9.09 0.662 ^ First LDL, First Triglyceride Level, first waist circumference, and first BMI refers to the LDL, Triglyceride, waist circumference or BMI measurement prior to statin exposure. For statin nonusers the first recorded measurement was used. ξ Variable was measured in the baseline year (2011).

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Figure 5.13. Nelson -Aalen Cumulative Hazard Estimates for Time to T2DM Diagnoses Among Incident Statin Users and Statin Nonusers. Statin users (n=4,427) had a higher rate of T2DM compared to statin nonusers (n=305). Additionally, the proportional hazards assumption does not appear to be graphically violated. The graph has been cut off at time 1,470 days.

Figure 5.13 shows the estimated rate of T2DM among statin users and statin nonusers. The incidence rate was higher among statin users.

Figure 5.14 shows the association between time to T2DM among statin user groups. First, the statin users were compared to statin nonusers, and an increased hazard was observed (HR = 2.80, 95% CI: 1.53, 5.09). This hazard was attenuated to 2.59 (95% CI: 1.40, 4.81) after adjustment for baseline characteristics of the two groups. There was an increased risk observed for moderate dose statin use compared to high dose statin use (2.39, 95% CI: 0.32, 17.73), but these associations were not statistically significant. Similar findings

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were observed for hydrophilic statin users compared to lipophilic stain users (AHR: 1.44 (95% CI: 0.606, 3.41)).

Figure 5.14. Association Between T2DM Risk and Statin Use Among Incident Statin Users a. Model compared statin users to statin nonusers. n= 1,734. The HRs were estimated from Cox PH models, adjusting for age, number of office visits in 2011, triglyceride level, BMI, LDL-Cholesterol level, high risk waist circumference, gender, primary or secondary prevention, education level and ethnicity. b. Model compared moderate dose statin users to high dose statin users. n= 809. The HRs were estimated from Cox PH models, adjusting for age, gender, first BMI and risk level of waist circumference. c. Model compared hydrophilic statin users to lipophilic statin users. n= 809. The HRs were estimated from Cox PH models, adjusting for age, gender, first BMI and risk level of waist circumference.

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Figure 5.15 Sensitivity Analysis of the Impact of the Incident Statin Use Definition on Adjusted and Unadjusted Hazard Ratios for T2DM Development Among Statin Users Compared to Nonusers Models compared statin users to statin nonusers. The Adjusted HRs were estimated from Cox PH models, adjusting for age, number of office visits in 2011, triglyceride level, BMI, LDL-cholesterol level, high risk waist circumference, gender, primary or secondary prevention, education level and ethnicity.

The reason underlying the elevated T2DM risk among statin users was investigated but there were no associations after subgroup analyses by class and intensity of dose. To examine these differences across incident statin users, the statin exposure variable was replaced in the Cox proportional hazards model with a variable containing information about the duration of statin use. Statin users were divided by duration of statin use as less than one year, between one and less than two years of use, and greater than (or equal to) two years of use. The results from the full Cox PH regression are shown for the adjusted and unadjusted models in Figure 5.16. Statin users who used statins for two or more years had an increased hazard for T2DM.

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Figure 5.16. Time to T2DM by Statin Duration Categories Among 1,734 Participants Statin use for 2 years or longer was associated with increased risk of T2DM development. This association was not observed for other durations of statin use (<1 year or for 1-2 years). Additionally, there is no visual trend observed of increasing risk across statin durations.

Descriptive analyses across these duration groups identified that individuals using statins for 2 or more years had a higher likelihood to progress to T2DM based on their characteristics at baseline compared to the other duration groups (Table 5.14). This may be because the members of this group were older in age (50.2 vs. 49.1 vs. 38.2) compared to <1 year duration users and 1-2 year duration users. Additionally, statin users in this group were more likely to be female (54.0 vs. 50.7 vs. 46.6) and had a higher proportion with a high -risk waist circumference (59.1 vs. 56.0 vs. 54.9), however this difference in waist circumference was not statistically significant.

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Table 5.14 Selected Differences in the Characteristics of Incident Statin Users Across Duration Categories

< 1 year 1 – 2 years > 2 years of of statin use of statin use statin use n=142 n=384 n=291 p-value Age (mean, sd) 38.2 10.1 49.1 9.8 50.2 8.8 <0.001 Gender (n, (%))

Female 72 (50.7) 179 (46.6) 157 (54.0) <0.001 Male 70 (49.3) 205 (53.4) 134 (46)

High Risk Waist Circumference No 44 (44.0) 111 (45.1) 54 (40.9) 0.830 Yes 56 (56.0) 135 (54.9) 78 (59.1) First Waist Circumference 39.1 5.9 38.8 5.5 39.6 6.7 0.03 (mean, sd)^ First BMI (mean, sd)^ 30.9 5.9 30.9 5.9 32.06 7.6 0.15 First LDL-C (mean, sd)^ 176.7 103.7 183.0 131.9 163.5 98.8 <0.001 First Triglyceride Level (mean, 183.2 178.9 111.9 132.1 165.3 103.0 0.527 sd) 2 Lipophilic Statin Use Yes 108 (76.1) 295 (76.8) 210 (72.2) 0.365 No 34 (23.9) 89 (23.2) 81 (27.8) Statin Type Low Dose 124 (87.3) 362 (94.3) 267 (91.8) 0.03 High Dose 18 (12.7) 22 (5.7) 24 (8.3) ^ First LDL, First Triglyceride Level, first waist circumference, and first BMI refers to the LDL, Triglyceride, waist circumference or BMI measurement prior to statin exposure. For statin nonusers the first recorded measurement was used. A one-way ANOVA was used to compare differences in means across multiple levels of duration.

DISCUSSION

In summary, the analysis of elevated glycosylated hemoglobin among nondiabetic incident statin users found borderline significant findings, that differed in statistical significance across years. Specifically, when HbA1c was measured in 2013, the probability of elevated HbA1c was 5.1% higher for statin users compared to statin nonusers, but this association was not statistically significant (p=0.140). In 2014 the 153

probability of elevated HbA1c was 6.0% higher for statin users (p=0.041) compared to statin nonusers. In comparisons of hydrophilic and lipophilic statin use, hydrophilic statin users had a 2.5% higher probability of elevated HbA1c in 2013 compared to lipophilic statin users and a 7.3% higher probability of elevated HbA1c compared to lipophilic statin users in 2014, but these findings were not statistically significant. Similarly moderate intensity statin use was associated with a higher probability of elevated HbA1c, but again findings were inconsistent in terms of statistical significance. In 2013, moderate intensity statin users had a 5% higher probability of elevated HbA1c (PD = 0.051, p=0.516), while in 2014 moderate intensity statin users had a 12% higher probability of elevated HbA1c compared to high intensity statin users (PD= 0.121, p= 0.051). These findings should be interpreted with caution as there were a smaller number of patients in the groups when evaluating statin users by type, which may explain the low precision in the estimates.

Glycemic Control for Any Statin Users

Two randomized controlled trials have reviewed the effects of statins on glycemic control. A study by Liew et al. focused on a population of hypertensive patients in Malaysia and compared statin nonusers to statin users. Their data used retrospective medical review at a primary care clinic, and the majority of statin users took simvastatin. They found that HbA1c was elevated among the entire group of statin users (AOR = 1.29, (95% CI 1.01, 1.65)) after adjusting for diabetes, diabetic medication use and fasting blood glucose [111]. When the study investigators stratified this association by diabetes status the association remained among diabetic patients after adjustment for age and use of diabetic medications (AOR 1.21, p = 0.037, 95% CI 1.01, 1.44), but not for nondiabetic patients [111]. The authors state that they may have been underpowered to compare nondiabetic patients (statin users n=72; statin nonusers n=33) with measured HbA1c data as a limitation.

The removal of diabetic patients in the current study due to the complexity of patient lifestyle patterns, knowledge of a diabetic diagnosis and lack of information about

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compliance with diabetic medications prevented a direct comparison to Liew et al’s population. The current study found that among (2,207 patients (in 2013) and 2,302 patients (in 2014)) that there was an increased prevalence of elevated HbA1c, however this association was inconsistent across years. It is possible that the exclusion of diabetics from this analysis removed those who were at increased risk, and because the test results used to diagnose these patients were not available in the data, nor was a consistent increased risk for elevated HbA1c observed as previous studies have found. Additionally, the magnitude of the association for the current patients was much higher than other studies. This pattern mirrors the association seen for time to T2DM, as statin users with elevated HbA1c measured in 2014 were at higher risk than statin users with HbA1c measured in 2013. These were the same patients who were developing T2DM and using statins for two years or longer.

It is possible that the lack of HbA1c measurement prior to statin use may have created uncertainty around prediabetes status prior to statin use. If there were more prediabetics taking statins in the analysis of the 2014 HbA1c compared to the 2013 HbA1c this could explain the observed association using 2014 HbA1c values that did not hold when using the 2013 data.

Glycemic Control Among Hydrophilic and Lipophilic Statin Users

Hydrophilic and lipophilic statins may have different effects on HbA1c levels due to differences in their mechanisms of action and absorption into tissues. Research has supported that lipophilic and hydrophilic statins may have differential effects on glycemic parameters [112].This mechanism of drug metabolism is believed to contribute to the findings of impaired insulin secretion and insulin resistance that have been observed in human studies.

The current study found that there was no difference in glycemic control between incident hydrophilic and lipohilic statin users. In this study, lipophilic statin users most

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frequently took simvastatin (58.8%) or atorvastatin (38.9%) and the majority of hydrophilic statin users took pravastatin (63.8%).

Previous studies of lipophilic and hydrophilic statins and elevated HbA1c have had inconsistent findings. They also have had more homogeneity in the specific statins used among study participants. The Pravastatin or Atorvastatin Evaluation and Infection Therapy- Thrombolysis in Myocardial Infarction 22 (Prove It TIMI 22) trial found that high dose atorvastatin (80mg) (a lipophilic statin) significantly increased risk of elevated HbA1c >6 compared to pravastatin (40mg) (a hydrophilic statin) (RR 1.84 (95% CI: 1.52–2.22), p < 0.0001)) [113]. Both of these statins also slightly increased average HbA1c, atorvastatin increased HbA1c levels by 0.37% and pravastatin increased levels by 0.18% [113]. Ishikawa et al’s study on nondiabetic patients with hyperlipidemia [112] conversely found that atorvastatin (lipophilic) and not pravastatin (hydrophilic) use was associated with an increase in HbA1c levels after an average period of 9.7 months of use (p<0.0001). The lack of an association could be explained by the heterogenieity in type of statin among the lipophilic statin user group.

Glycemic Control Among High Intensity and Moderate Intensity Statin Users

The use of specific statins across statin intensities in this study were heterogenous; high intensity statin users were almost exclusively using atorvastatin (94.5%), and moderate intensity statin users most commonly used atorvastatin (37.9%) or simvastatin (31.7%), followed by pravastatin (18.2%) or rosuvastatin(10.3%). As was the case for the current analysis of types of statins, the exclusion of diabetics prevents study comparability to populations of diabetics.

Few studies have reported on glycemic control and statin dose. One clinical trial, the Prove It TIMI 22 trial found that high dose atorvastatin (80mg) significantly increased risk of elevated HbA1c >6 compared to pravastatin (40mg) (RR 1.84 (95% CI: 1.52– 2.22), p < 0.0001)) [113]. The current study found no difference in elevated HbA1c across moderate and high intensity statin doses, one plausible explanation for this is the 156

heterogeneity in statins used (both lipophilic and hydrophilic) across the high and moderate dose users.

New Onset T2DM

The Justification for the use of Statins in Primary Prevention: An Intervention Trial Evaluating Rosuvastatin (JUPITER) reported an increase in physician diagnosed diabetes (RR 1.25, 95% CI 1.05–1.49) [114]. Among patients treated with rosuvastatin, there was a 27% increase in investigator-reported diabetes mellitus compared to placebo treated patients [115]. Several meta-analyses of randomized controlled trials have been published in the area of incident T2DM among statin users. Sattar et al’s meta-analysis of 91,140 participants across 13 statin randomized controlled trials found a 9% increase in odds for incident diabetes among statin users. (OR= 1.09, 95% CI: 1.02 – 1.17), with little heterogeneity between trials [55]. A meta-analysis by Rajpathak et al. which included 6 statin trials with 57,593 participants reported a small increase in diabetes risk (RR= 1.13, 95% CI: 1.03-1.23), however, this association became insignificant when the WOSCOPS trial was included (RR= 1.06, 95% CI: 0.93–1.25) [114]. The West of Scotland Coronary Preention Study (WOSCOPS) found that statins were protective against the development of new onset diabetes (RR= 0.70, (95% CI: 0.50–0.99) .

A recent study by Culvier et al. using data from the Women’s Health Initiative (WHI), reported that statin use conveys an increased risk of new-onset diabetes in postmenopausal women (RR= 1.48, 95% CI: 1.38, 1.59), and noted that the effect appears to be a medication class effect (lipophilic statins were higher risk than hydrophilic statins), unrelated to potency or individual statin [57]. The current study did not observe a medication class effect, but this was likely because there was more heterogeneity in the population relative to the WHI. Some of the limitations of the WHI study included the measurement of statins via survey and no opportunity to measure changes in statin type, and the statins were unequally represented in this study. This

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observation of unequal statin representation would be expected in an observational study design, and is something that was also observed in the current data.

These findings should be interpreted with caution because the calculated measure of effect for incident T2DM after statin use was considerably larger than what previous studies have found HR: 2.59 (95% CI: 1.40, 4.81), even after adjustment for differences at baseline in our Cox proportional hazards model. The evaluation of duration of statin treatment found that a higher risk was observed among the group of statin users (with more than 2 years of statin duration). The progression to T2DM among this group was likely a reflection of confounding factors between statin users and nonusers and not a result of risk conferred to patients from statin use. The covariates that were more represented in this group of individuals included higher age, female gender, higher BMI (not statistically significant), higher waist circumference (not statistically significant). All of these factors predispose an individual to be more likely to develop T2DM, and although these variables were included in this analysis, patient behaviors may explain this pattern. It is possible that individuals who have been taking statins for the longest period of time tolerate the treatment better than other statin users who have one or two side effects early in treatment and then discontinue use. Thus, individuals who switch off of statin therapy are prevented from having the side effect of T2DM. If there was a dose response relationship among users of statins, and across durations of use, the observed increased risk would have been more aligned with Bradford Hill’s Criteria for Causation [48], and suggestive of a T2DM as a causal effect of statin exposure.

New Onset T2DM Among High Intensity and Moderate Intensity Statin Users

Incident statin users taking high intensity statins had no difference in T2DM risk compared to moderate intensity statin users. A meta- analysis of 32,752 patients from five large clinical trials investigated the impact of intensity of statin therapy on incident T2DM [56]. Users of high intensity statins had a 12% greater risk of developing incident T2DM compared to those treated with moderate intensity statins (RR 1.12, 95% CI: 1.04,

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1.22) [56]. In these five trials, (PROVE IT TIMI-22, A to Z, TNT, IDEAL and SEARCH) the high intensity statin users took either atorvastatin or simvastatin and moderate intensity statin users used pravastatin, simvastatin or atorvastatin. It is interesting to note that several (n=4) of these large trials found no overall association, but once the meta-analysis compared the effect estimates, the pooled estimate for T2DM among high intensity statin users compared to moderate intensity statin users was statistically significant. It is possible that the heterogeneity between statins used in these different trials would lead to no overall dose effect due to the underlying differences across mechanism of actions for the specific statin treatments used.

Previous studies have observed similar patterns of increased risk among patients with one, or more characteristics of metabolic syndrome. It is possible that some of the newly diagnosed diabetes cases were actually prevalent diabetics because there was not a relatively large study period to search for previous diabetes diagnoses. If it was expected for the number of prevalent diabetics to be differential between groups it would influence the measure of effect, but if it were balanced, it would have no effect on the findings. At baseline both groups were comparable in healthcare utilization (number of doctors visits), therefore the rate of misclassified new diabetics that are really prevalent diabetics may be comparable across the two groups. The applicability of this project to patient care and medication use patterns creates a scenario where one should be careful about accuracy when reporting study findings. A retrospective chart review for all diabetes cases is planned to validate the date of diabetes diagnosis from claims data (which was used for this study) prior to the formal publication of these findings in a peer-reviewed publication.

Limitations and Strengths

Some of the limitations of this study include a lack of information about mail away prescriptions or medications that were filled and were being taken by a patient prior to starting on the insurance plan. A sensitivity analysis at several different cutoffs was

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performed to see how results were influenced if incident users starting a medication prior to 2011 were excluded and considered to be prevalent users. This alteration in definitions did not change the results of the analyses.

A second limitation is that there were not variables for all the risk factors that can influence development of T2DM. Some potentially important covariates that were not included were family history of T2DM, HbA1c prior to statin use, and exercise or dietary habits at baseline. Statin users are more likely to have elevated LDL-cholesterol and triglyceride levels. These risk factors for cardiovascular disease are strongly tied to lifestyle (diet and physical activity). The ability to measure HbA1c would have allowed a better understanding in differences in risk across statin users at baseline. For example, knowing how many statin users were prediabetic could have allowed us to better estimate baseline risk. The use of the health assessment survey data allowed inclusion of survey data that was more descriptive than what has been done in prior studies using observational data and medical claims.

There were no results from laboratory tests that would be collected during a primary care visit. The absence of these values prevented a physician-diagnosed test of T2DM. The availability of biometric screening values for all study participants regardless of disease status allowed the prevention of detection bias or preferential data collection on individuals who were sick and seeking medical care. Initially it was sought to examine differences in lipophilic statin use and doses of statins among incident statin users, but the smaller cell sizes precluded these analyses. Additionally, the distribution of statins that were used was uneven, and those statins that were used at higher doses were all lipophilic in class.

A more complex model that incorporated propensity scores for the analyses of time to T2DM was unable to be used because time varying exposures were used to fairly account for study time among participants who started statin exposure after baseline. The group of statin users and nonusers were systematically different at baseline. Statistical adjustment

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for clinically meaningful confounders was implemented to address these differences, but it is possible that there were differences in groups that were not addressed thus resulting in our large magnitude of association. Future studies focusing on a more homogenous population of patients with metabolic syndrome will address the questions that resulted from our heterogeneous population. The current study examined relatively short term use of statins (3.7 years or less) compared to those who would be using the medication for a lifetime, therefore these findings will not be representative of long term exposure to statins. Statin users in the current study started to use a statin and then continued its use until the end of the study period. As more health systems convert data to EMRs observational studies using these sources of data will be more reflective of more applicable exposure levels to different medications.

The analysis was limited to those who had visited the doctor at least once during the study period. This is because the diagnosis of diabetes was dependent on a patient visiting their physician, therefore there may be patients who did not see their physicians thus we did not observe the outcome. This criterion for study inclusion limits the generalizability of this study to groups who do not visit their healthcare provider. The use of pharmacy data to measure exposure to medication use assumes that patients who fill their prescriptions are continuously taking their medications. To address this it was required that a patient to fill their medication more than 2 times during the study period. 85% of incident statin users were classified as taking their medication for half a year or longer, which may be considered a surrogate marker for taking the medication.

It is expected that physicians were aware of the FDA’s recommendations regarding drug use and associated warnings. A formal consumer advisory report was released by the FDA in January of 2014 to caution patients and physicians about the dysglycemic effects of statins [116]. The timing of this was in the middle of the current study, so this historical event may have influenced physician prescribing by motivating physicians to re-consider statin prescribing practices and monitor patients taking status on a regular basis.

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Study strengths included a large patient population (approximately 5,000 patients) with the availability of data to describe all of a patient’s healthcare use during the study period, including exposure to pharmaceuticals. The study was set up as a retrospective cohort, and the presence of exposure prior to the outcome allows us to make stronger causal associations than in a different study design. The use of an observational study reflects “real world” physician care patterns and patient risk factors. The biometric measurements provided the ability to adjust for pre- statin use values in the models and the presence of survey data allowed incorporation of clinically meaningful demographic factors.

As the US is faced with a rise in obesity prevalence and metabolic syndrome across the population of patients being treated with statins, this raises questions about the re- evaluation of statin use among patients that have a high burden of metabolic syndrome and risk to progress to T2DM. Other studies have identified that the benefit of taking a statin for cardiovascular event prevention far outweigh the risk of transitioning to T2DM among statin users[117]. It is hoped that as lifestyle programs like the Diabetes Prevention Program are promoted in primary care settings, physicians will have greater comfort in counseling patients to put emphasis on health promotion by adopting a healthy lifestyle thus reducing risk for both T2DM and CVD.

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CHAPTER 6

Discussion of Findings: Conclusions and Implications For Future Research

OVERVIEW

The overall findings from this study add to the body of literature investigating T2DM. This project explored two distinct risk factors and mediators of T2DM: behavior and pharmaceutical use. Patients were identified who were interested in the Diabetes Prevention Program that was delivered to small group classes at the company worksite were systematically different from those who did not express interest. Biomarker trajectories to predict enrollment in the DPP did not discriminate between these two groups. The nondiabetic patients who were using statins for cardiovascular disease did not have elevated HbA1c levels and the analysis of time to T2DM identified that the increased risk of T2DM among statin users was limited to patients with comorbid conditions. Similarly, the nondiabetic patients who were taking antidepressants for listed indications did not have elevated HbA1c levels and our analysis of time to T2DM identified no increased risk of T2DM among antidepressant users.

The information provided in these chapters will be useful to program planners, health educators, patients and healthcare providers. These findings have implications beyond the specific proposed conclusions, as each study revealed topics needing further exploration. This chapter highlights the key findings of the project, discusses the implications of these findings and suggests topics for future research.

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AIM 1

Summary

The analysis of 217 prediabetics interested in the DPP compared to 1,941 prediabetics who were not interested in the DPP revealed that these two groups were significantly different in demographic factors and health behavior patterns in unadjusted analyses. Prediabetic patient characteristics that were associated with being more engaged in the DPP included being female, black, older in age, having a higher HbA1c level, being free of hypertension, visiting the doctor more frequently in the past year and having lower self-efficacy to make healthy lifestyle changes.

Interpretation The findings suggest that prediabetics expressing interest in the DPP are not reflective of the greater prediabetic population. The majority of other studies evaluating reach have found differences between those who do and do not elect to enroll in health promotion programs, and this was found to be the case for patients expressing interest in the DPP.

Public Health Significance This study is one of the first to evaluate reach for a worksite implementation of the DPP. This data can be used to inform future recruitment strategies for patient engagement and recruitment to recruit a population of prediabetics into the DPP who is more reflective of the greater DPP population at this worksite.

Future Research Directions When a larger sample size of prediabetics expressing interest in the DPP is available it will be valuable to evaluate differences in engagement between genders because it is known that males take a less active role in their health and are less proactive when it comes to engaging in health promoting behaviors [118].

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Additionally, describing the characteristics of individuals who do and do not participate describes the reach of the DPP, but it does not address the barriers to participation or reasons why prediabetics do not elect to enroll in the program. Subsequent work will evaluate how organizational factors and perceptions of the Diabetes Prevention Program influence participation so that future strategies can be used to increase participation and engagement in health promotion programs. Barriers will be evaluated to engagement including other methods of program delivery (online, offsite, and virtual delivery of class sessions). Part of this evaluation will include determining why only 50% of those expressing interest in the program have enrolled using the data from this analysis. Strategies to engage groups who are currently under-represented will include offering male-only classes, and performing subgroup analyses of the effectiveness of the DPP for weight loss in these groups for targeted communication and marketing to under- represented groups. This includes a subgroup analysis of those who have hypertension, to show that weight loss was effective in reducing hypertension in prediabetics, a subgroup analysis of those who had high self-efficacy and did not enroll in the DPP to see if they had been effective in weight loss during the year until the next biometric screening. Just because someone did not enroll in the DPP does not mean that they will be unable to take action on their own and improve their health behaviors through alternative means.

AIM 2

Summary

The analysis of 989 incident antidepressant users and antidepressant nonusers with mental health diagnoses or associated antidepressant indications focused on a two- pronged approach of investigating if antidepressants raise an individuals’ risk for T2DM development. First, differences were compared in elevated hemoglobin A1c among nondiabetics among incident antidepressant users and nonusers and then by type of antidepressant (other antidepressant or SSRI). No differences were observed in the prevalence of elevated HbA1c than between antidepressant users vs. nonusers or other

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antidepressant users vs. SSRI users. Second, time was compared to T2DM among all incident antidepressant users and nonusers and no elevated risk was observed for T2DM among incident antidepressant users. Further investigation by type of antidepressant found no association between medication use and subsequent T2DM development.

Interpretation

These findings suggest that the antidepressant users in the study population did not have a higher risk for T2DM than antidepressant nonusers, and no differences were observed in subgroup analyses by antidepressant type. This observation is consistent with other studies and has been previously reported in the literature.

Public Health Significance

These studies add to the existing body of research of T2DM development following antidepressant use. While none of the associations in this study were significant, the comparisons between these findings and the existing body of literature on this topic identify meaningful future areas of exploration.

Future Research Directions

Physician under-coding of mental health diagnoses will limit the generalizability of observational studies using EMRs. To address this methodological concern, future studies will investigate other methods for pharmacoepidemiology analyses accounting for missing data in EMRs. The inclusion of type of physician in this analysis and others could be a meaningful piece of information to incorporate in future studies.

Data was not available on exercise, which is an important confounder of the association between depression and T2DM. Patients who exercise more are less likely to develop T2DM, but patients with severe depression are less likely to exercise. The availability of

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pedometer data through employee worksite wellness programs is another avenue to explore to incorporate physical activity into an analysis to predict subsequent T2DM.

The lack of data available for TCA users implies that either TCA users are under-coded for mental health diagnoses and only a small number were identified using this analysis plan, or TCA use has declined in the US in recent years due to the stigma around TCAs. To address this research question, examining patterns of TCA prescribing using this population and a larger commercial insurance population that is representative of the greater US will be useful. Additionally, obtaining a larger reference database will allow a better examination of the association of T2DM among TCA users while incorporating clinically meaningful variables to address weight gain, weight changes and baseline differences between groups.

AIM 3

Summary

The analysis of 4,867 incident statin users and statin nonusers with cardiovascular disease focused on a two-pronged approach of investigating if statins raise an individuals’ risk for T2DM development. First, differences in elevated hemoglobin A1c were compared among nondiabetic incident statin users and nonusers, and then by class of statin (lipophilic or hydrophilic) and intensity of treatment (moderate dose and high intensity dose). Statin users had slightly higher prevalence of elevated HbA1c than statin nonusers, but this finding was borderline significant, and no statistically significant differences were observed by class of statin and intensity of treatment. Second, time to T2DM was compared among all incident statin users and nonusers and there was an elevated risk for T2DM among incident statin users. Further investigation identified that this effect was isolated to a subgroup of statin users who had been using statins for 2 or more years, who were more female, older, and slightly higher in waist circumference than other statin users.

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Interpretation

These findings suggest that the statin users who had an increased risk of T2DM were at higher risk for T2DM than other statin users in this population. This observation is consistent with other studies and has been previously reported in the literature.

Public Health Significance

The first sub study identified that physicians are doing a relatively good job of detecting new diabetics in this population because when they were removed from the study no statistically significant increased glycemic effect of statin use was observed. The second sub study identified that more work is needed to understand the risk of T2DM among a higher risk group of patients with multiple risk factors for T2DM so that better approaches can be identified for disease management in this population.

Future Research Directions

When more time has passed statin use can be better described over a larger time period that is more reflective of statin use during an adult’s lifetime. As more EMR data is available, ways will be identified ways to expand these analyses to incorporate a more diverse populations with higher risk. A larger dataset over a longer timeframe will also allow the incorporation of more complex methodology to address selection bias and time- varying confounding factors.

T2DM diagnoses will be re-examined through medical record review to validate that the “incident diagnoses” identified through claims are the true first date of T2DM diagnosis for each patient. This study did not incorporate diet or physical activity due to a lack of data collected prior to statin use. A future direction of investigation of research will be around how physicians are discussing nutrition and physical activity with patients prior to recommending statin use. Patient perceptions of chronic disease and perceptions and barriers to adopting a healthy lifestyle (diet and physical activity) once a statin is initiated

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could result in meaningful information around how to better engage patients in their health. More than half of the working, insured population (56.2%) is affected by CVD, so future projects in this area have the potential to have a large footprint on patient health.

CONCLUSIONS

In conclusion, this work examined behavioral and pharmacoepidemiological risk factors and mediators for T2DM to identify opportunities to intervene on disease progression and development. The first aim identified that prediabetics expressing interest in the DPP are not reflective of the greater prediabetic population and future work will focus on engaging under-represented prediabetic groups in health promotion programs. The pharmacoepidemiological aims identified that nondiabetic patients taking statins or antidepressants did not have an elevated risk for disturbances in glucose homeostasis. While statin users were at higher risk for diabetes due to underlying comorbid conditions, elevated T2DM risk was not attributed to either statin or antidepressant use. Future observational studies of pharmacoepidemiology will focus on improvement of methods to obtain high quality estimates of risk factors for intervention to prevent disease.

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APPENDIX A: Health Assessment Survey Instrument

Covariate Question Survey Response Options Gender The user's gender. F or M DOB The user's date of birth. Date Ethnic Origin What is your ethnic origin? =No value reported 0=Asian 1=Black or African- American 2=Hispanic or Latino 4=American Indian or Alaska Native 5=Native Hawaiian or other Pacific Islander 6=White/Caucasian 7=Multi-ethnic 8=Other 9=Don't Know Education Level What is the highest level of education you =No value reported have completed? 1=Post-graduate or professional school 2=College graduate 3=Some college or vocational school 4=High school graduate 5=Some high school 6=Grade school or less. Average Fruits How many fruits and vegetables do you eat 0=0 1=1-2 2=3-4 And Vegetables in an average day? 3=5-6 4=7-8 5=9 or more Average Sugary How many sugary drinks to you have in an 0=0 1=1 2=2 Drinks average day? 3=3 4=4 5=5 or more Average How many foods that you think are 0=0 1=1 2=2 Unhealthy unhealthy do you eat in an average day? 3=3 4=4 5=5 or more Foods Lifestyle Cardio How often do you do the following kinds of 0-7 days per week Exercise Days exercise? (days per week) Exercise that works your heart, like jogging, cardio machines, aerobic dancing, brisk walking, swimming, or other such exercises. Lifestyle Cardio How often do you do the following kinds of 0-240 minutes per session Exercise exercise? (Minutes per session) Minutes Exercise that works your heart, like jogging, cardio machines, aerobic dancing, brisk walking, swimming, or other such exercises. Lifestyle Cardio About how much of this exercise is vigorous, 0=None 1=25% 2=50% Exercise meaning you are too out of breath to talk 3=75% 4=100% Vigorous easily? Percentage

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Appendix A Continued Lifestyle How often do you do the following kinds of 0-7 days per week Strength exercise? (Days per week) Exercise Days Strength-building exercise like weightlifting, push-ups, sit-ups, yoga, Pilates, or other such exercises. Lifestyle How often do you do the following kinds of 0-240 minutes per session Strength exercise? (Minutes per session) Exercise Strength-building exercise like weightlifting, Minutes push-ups, sit-ups, yoga, Pilates, or other such exercises. Alcohol Drinks How many alcoholic drinks do you usually 0-99 drinks per week Per Week have in a week? Alcohol Binge Have you had 5 or more alcoholic drinks in a = No reported value Per Month single sitting in the last month? 0= No 1= Yes Alcohol Reduce Do you ever feel that you should cut down = No reported value Drinking on your drinking? 0= No 1= Yes Use Of Do you smoke cigarettes? 0=Never used 1=Currently use Cigarettes 2=Previously used Current Years How many years have you smoked =No value reported Cigarettes cigarettes? 0-99 years Use Amount How many cigarettes do you typically smoke =No value reported Cigarettes per day? 0=Fewer than 10 1=10-19 2=20-39 3=40 or more Former Years How many years did you smoke cigarettes? =No value reported Cigarettes 0-99 Time Cigarette How long ago did you quit smoking =No reported value Free cigarettes? 0= < 6 months 1=6 months - < 12 months 2=12 months - < 10 yrs 3=> =10 years Former Use How many cigarettes did you typically =No value reported Amount smoke per day? 0=Fewer than 10 1=10-19 Cigarettes 2=20-39 3=40 or more Use Of Other Do you use other forms of tobacco (cigars, 0=Never used 1=Currently use Tobacco pipes, snuff, chewing tobacco)? 2=Previously used Time Other How long as it been since you last used any =No reported value Tobacco Free other form of tobacco (cigars, pipes, snuff, 0= < 6 months chewing tobacco)? 1=6 months - < 12 months 2=12 months - < 10 years 3=>=10 years Average Sleep How many hours do you typically sleep per 0-24 hours Hours night? Restful Sleep Do you generally feel well-rested after 0=Always 1=Most of the time sleeping? 2=Sometimes 3=Rarely 4=Never Lifestyle Over the past 2 weeks, have you felt down, 0=No Depression depressed, or hopeless all or most of the 1=Yes time? Lifestyle Over the past 2 weeks, have you felt little 0=No Anhedonia interest or pleasure in doing things all or 1=Yes most of the time? Continued 182

Appendix A Continued Lifestyle In the past year, have you experienced any of 0=No Hopelessness the following intensely for 2 weeks or more? 1=Yes Feelings of hopelessness or guilt Lifestyle In the past year, have you experienced any of 0=No Appetite Loss the following intensely for 2 weeks or more? 1=Yes Loss of appetite, weight gain/loss Lifestyle In the past year, have you experienced any of 0=No Decreased the following intensely for 2 weeks or more? 1=Yes Energy Decreased energy/fatigue Lifestyle In the past year, have you experienced any of 0=No Sadness the following intensely for 2 weeks or more? 1=Yes Persistent sadness Lifestyle In the past year, have you experienced any of 0=No Insomnia the following intensely for 2 weeks or more? 1=Yes Insomnia/oversleeping Lifestyle In the past year, have you experienced any of 0=No Concentrating the following intensely for 2 weeks or more? 1=Yes Difficulty concentrating/making decisions Lifestyle In the past year, have you experienced any of 0=No Anxiety the following intensely for 2 weeks or more? 1=Yes Persistent or troublesome anxiety Lifestyle Job How strongly do you agree or disagree with 0=Strongly Disagree 1=Disagree Satisfaction the following statements? In general, I am 2=Neutral 3=Agree satisfied with my job. 4=Strongly Agree Lifestyle Life How strongly do you agree or disagree with 0=Strongly Disagree 1=Disagree Satisfaction the following statements? In general, I am 2=Neutral 3=Agree satisfied with my life. 4=Strongly Agree Lifestyle How strongly do you agree or disagree with 0=Strongly Disagree 1=Disagree Affected By the following statements? In the past year, 2=Neutral 3=Agree Stress stress has affected my health or well-being. 4=Strongly Agree Lifestyle Family How strongly do you agree or disagree with 0=Strongly Disagree 1=Disagree Support the following statements? I receive support 2=Neutral 3=Agree from my family or friends. 4=Strongly Agree Lost Work If you are employed, during the last month, 0-100 % Percentage what percentage of your work performance was affected by an underlying health condition? Missed Work During the last month, how many days were 0-31 days Days you absent from work or unable to do your normal activities due to illness or injury? Overall Health Overall, how would you rate your health? 0=Excellent 1=Very Good 2=Good 3=Fair 4=Poor Doctor Visits In the past year, approximately how many 0-100 times times have you: Been to the doctor or clinic? - or - In the past year (excluding any pregnancy-related appointments), approximately how many times have you: Been to the doctor or clinic?

Continued 183

Appendix A Continued Overnight In the past year, approximately how many 0-99 times Hospitalization times have you: Been hospitalized overnight? - or - In the past year (excluding any pregnancy-related appointments), approximately how many times have you: Been hospitalized overnight? Emergency In the past year, approximately how many 0-20 times Room Visits times have you: Been to the emergency room? - or - In the past year (excluding any pregnancy-related appointments), approximately how many times have you: Been to the emergency room? Presence Of Has a doctor ever diagnosed you with: Heart 0=No Congestive failure 1=Yes Heart Failure Presence Of Has a doctor ever diagnosed you with: Heart 0=No Heart Disease disease 1=Yes Presence Of Has a doctor ever diagnosed you with: Heart 0=No Heart Attack attack 1=Yes Presence Of Has a doctor ever diagnosed you with: Atrial 0=No Atrial fibrillation 1=Yes Fibrillation Presence Of Has a doctor ever diagnosed you with: High 0=No High Blood blood pressure 1=Yes Pressure Presence Of Has a doctor ever diagnosed you with: High 0=No High cholesterol 1=Yes Cholesterol Presence Of Has a doctor ever diagnosed you with: 0=No 1=Yes Stroke Stroke Presence Of Has a doctor ever diagnosed you with: 0=No 1=Yes Depression Depression Depression Do you currently have symptoms? =No reported value Symptoms 0=No 1=Yes Depression How much does your depression impact your =No reported value Impact daily life? 0=Not much 1=Moderately 2=Severely Depression How well are you managing your =No reported value Managing depression? 0=It's under control 1=It could be better 2=It's not going well Depression Are you currently being treated? =No reported value Treatment 0=No 1=Yes Depression How often do you miss a dose of medication =No reported value Missing for your depression? 0=Rarely 1=Sometimes Medication 2=Often 3=I don't take medication for this condition

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Appendix A Continued Healthy Indicate your level of commitment or interest no reported value Changes in each of the healthy changes. 0 = I have no need to Increase Cardio Get more cardiovascular exercise 1= I have no plans to Exercise 2= I plan to within the next 6 months 3=I plan to within the next month 4= I have been, < 6 months 5= I have been > 6 months Healthy Indicate your level of commitment or interest no reported value Changes in each of the healthy changes. 0 = I have no need to Increase Get more strength- building exercise 1= I have no plans to Strength 2= I plan to within the next 6 Exercise months 3=I plan to within the next month 4= I have been, < 6 months 5= I have been > 6 months Healthy Indicate your level of commitment or interest no reported value Changes in each of the healthy changes. 0 = I have no need to Improve Diet Eat better 1= I have no plans to 2= I plan to within the next 6 months 3=I plan to within the next month 4= I have been, < 6 months 5= I have been > 6 months Healthy Indicate your level of commitment or interest no reported value Changes Weight in each of the healthy changes. 0 = I have no need to Management Manage your weight better. 1= I have no plans to 2= I plan to within the next 6 months 3=I plan to within the next month 4= I have been, < 6 months 5= I have been > 6 months Self-efficacy How confident are you that you can make 0 = extremely confident healthy changes to improve your lifestyle? 1=very confident 2= confident 3= somewhat confident 4= not at all confident

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APPENDIX B: CDC Risk Assessment

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APPENDIX C: DPP Recruitment Brochure

OhioHealthy Diabetes Prevention Program (DPP)

A FAITH-BASED, NOT-FOR-PROFIT HEALTHCARE SYSTEM RIVERSIDE METHODIST HOSPITAL + GRANT MEDICAL CENTER DOCTORS HOSPITAL + GRADY MEMORIAL HOSPITAL + DUBLIN METHODIST HOSPITAL DOCTORS HOSPITAL–NELSONVILLE + HARDIN MEMORIAL HOSPITAL MARION GENERAL HOSPITAL + REHABILITATION HOSPITAL + O’BLENESS HOSPITAL MEDCENTRAL MANSFIELD HOSPITAL + MEDCENTRAL SHELBY HOSPITAL WESTERVILLE MEDICAL CAMPUS + HEALTH AND SURGERY CENTERS PRIMARY AND SPECIALTY CARE + URGENT CARE + WELLNESS + HOSPICE HOME CARE + 28,000 PHYSICIANS, ASSOCIATES & VOLUNTEERS

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You made a smart choice by How the Program Works completing a biometric screening! With a trained lifestyle coach, Centers for Disease Control and Prevention (CDC) approved curriculum, Now, you can use your results to make group support, and incentives, OhioHealthy’s DPP some healthy changes. Because your groups meet once a week for 16 weeks, then once a HgbA1c level was between 5.7 and 6.4* month for 6 months to help you maintain your healthy lifestyle changes. By meeting with others who have at your biometric screening, and you prediabetes you can celebrate each other’s successes are a member of the OhioHealthy and work together to overcome obstacles. Medical Plan, you qualify for Proven Success in Helping Others participation in the OhioHealthy With Prediabetes Diabetes Prevention Program (DPP). OhioHealthy DPP’s approach is proven to prevent or delay type 2 diabetes. The program is part of the Good News National Diabetes Prevention Program led by the CDC. Research has shown that early intervention can reverse The DPP has had documented success by: the course of diabetes for people with prediabetes. If + Reducing the progression to diabetes by 58% in 3 years you have prediabetes, a lifestyle change can cut your + risk of developing type 2 diabetes by more than half. Maintaining a 34% reduction at 10 years. OhioHealthy DPP will meet in a variety of locations How the OhioHealthy DPP Can Help You around Central Ohio, at times that are convenient for you. + The lifestyle changes you make in the OhioHealthy DPP can help you prevent or delay type 2 diabetes. Interested in Joining or Learning More? + As part of a small group, you will work with a Call or e-mail us today! trained lifestyle coach and other participants to OhioHealthy Diabetes Prevention Program Coordinator learn the skills you need to make lasting lifestyle Phone: (614) 544.4103 or changes. This personalized attention will cover E-mail: [email protected] topics including: Please provide your name, address and clock number. If • Learning to eat healthy inquiry is about a family member, please provide your • Adding physical activity to your life clock number and we will contact you regarding the program. • Managing stress • Staying motivated • Being a fat and calorie detective • Being active – A way of life • Take charge of what’s around you • Four keys to healthy eating out + There are no out of pocket costs + You can earn up to $75 in gift cards simply for participating

*If your HgbA1C level is 6.1 or higher, we recommend that you visit your physician to rule out Type 2 diabetes.

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More Facts About Prediabetes Q: Is prediabetes common? Yes. In the U.S it is estimated that: Prediabetes Can Lead to Type 2 Diabetes One out of three American adults has prediabetes, + 86 million people have prediabetes. This affects and most of them do not know it. Having prediabetes roughly 1 in 3 U.S. adults. means your blood sugar level is higher than normal, + Only 7% of those with prediabetes are aware of but not high enough to be diagnosed as diabetes. This their status. raises your risk of type 2 diabetes, heart disease, Q: Why do I need to know if I have prediabetes? and stroke. Diabetes prevention IS possible. When action is taken Without some intervention, as much as 1/3 of people early enough, diabetes can be prevented. Studies with prediabetes will develop Type 2 diabetes within have shown that simple lifestyle changes including ﬁve years. Type 2 diabetes is a serious condition that modest weight loss and increased physical activity can lead to health issues such as heart attack, stroke, can help people with prediabetes prevent or delay the blindness, kidney failure, or loss of toes, feet, or legs. development of type 2 diabetes by up to 58%. Risk factors for Type 2 diabetes include: Q: What test is being done to test for diabetes at the + 45 years of age or older OhioHealth Biometric Screening session? Finger-stick testing will be used for the blood draw to + Being overweight get on-the-spot immediate results for blood glucose as + A family history of type 2 diabetes measured by Hemoglobin A1c (HgbA1c) and cholesterol + Physically active fewer than 3 times per week screening. + Having diabetes while pregnant which disappeared Q: How can I tell if I have prediabetes? after delivering the baby (gestational diabetes) Results of a HgbA1c test that are at a level between or giving birth to a baby that weighed more than 5.7% and 6.4% shows you are at risk for prediabetes. nine pounds The lower numbers are best on this test. Frequently Asked Questions Q: What is HgbA1c? Hemoglobin A1c measures your average blood sugar Q: What is prediabetes? level for the past two to three months. If your HgbA1c Prediabetes is a condition people get before they are is 6.1 or higher, you may want to see your physician to diagnosed with diabetes. Prediabetes is characterized rule out Type 2 diabetes. Either way, you could beneﬁt by blood sugar levels that are higher than normal but from this program. not yet high enough to be diagnosed as diabetes. Q: Could I have prediabetes and not know it? Q: Who is at risk for prediabetes? Yes. It is estimated that just 7% of people with If you fall into three or more of the following prediabetes are aware of their condition. People with categories, you may be at risk for prediabetes prediabetes often don’t have or notice any symptoms. or diabetes It is important for those at risk to regularly monitor + HgbA1c level between 5.7 and 6.4 their health with their doctor and seek lifestyle + Obesity changes to maintain health. + Inactive lifestyle (exercising less than 2x/week) Q: What are the symptoms of prediabetes? + Above-normal cholesterol levels Often there are no symptoms associated with + Family history of diabetes prediabetes. This is why most people with prediabetes + History of gestational diabetes do not even know they have it. Possible symptoms of + Aged 45 years or older prediabetes include unusual thirst, a frequent desire to urinate, blurred vision, or fatigue.

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Q: What is the risk of progression to diabetes? Without lifestyle changes to improve their health, 15% to 30% of people with prediabetes will develop type 2 diabetes within 5 years. In the original DPP study, this averaged 11% per year. Q: I have a friend/spouse/family member with diabetes, does that mean I have diabetes? While a family history and other factors put you at-risk for diabetes, a blood test is the only way to see if you have prediabetes or diabetes. Q: What is the OhioHealthy DPP? The OhioHealthy DPP is a program that helps reduce the progress to diabetes by using lifestyle changes to help achieve a modest weight loss and increase physical activity. Lifestyle coaches work with you to identify emotions and situations that can sabotage you success, and the group process encourages participants to share strategies for dealing with challenging situations. Q: What does the OhioHealthy DPP include? Where is it held? The OhioHealthy DPP includes 16 weekly meetings followed by 6 monthly meetings. These one-hour long group sessions will be held in a variety of locations and timeframes to ﬁt your schedule. Q: How much does it cost to participate in OhioHealthy’s DPP? There is no cost for you to join the OhioHealthy DPP for OhioHealthy Medical Plan participants. In fact, you can earn up to $75 in gift cards by participating. Q: Can I participate in the OhioHealthy DPP? To qualify for the OhioHealthy Diabetes Prevention Program, participants must be overweight/obese (BMI 24 or higher), age 18 or older, and have a HgbA1c between 5.7 and 6.4 or have been diagnosed with prediabetes. Q: How do I join? By calling (614) 544.4103, you can reserve your spot in the next session of the OhioHealthy DPP session starting in October. You can also send a conﬁdential email to [email protected], just be sure to include your name, address and clock number. If inquiry is about a family member, please provide your clock number and we will contact you regarding the program.

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