Statin Medication Adherence and Associated Outcomes in Type 2 Diabetes. Medicaid Enrollees with Comorbid Hyperlipidemia. Dissertation. Presented in ...
Statin Medication Adherence and Associated Outcomes in Type 2 Diabetes Medicaid Enrollees with Comorbid Hyperlipidemia
Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By Jun Wu, Ph.D.
Graduate Program in Pharmacy
The Ohio State University 2010
Dissertation Committee: Rajesh Balkrishnan, PhD, Co-Advisor Milap C. Nahata, MS, Pharm D, Co-Advisor Veronique A. Lacombe, PhD Eric Seiber, PhD
Copyright by Jun Wu 2010
2
ABSTRACT
This is the first study to evaluate statin medication adherence and associated outcomes in a diabetic population based on a national Medicaid database. A retrospective claims-based study was conducted in patients who were continuously enrolled in Medicaid from January 2004 to December 2006. Patients (≥ 18 years) with diagnosis of hyperlipidemia and type 2 diabetes were identified by international classification of disease 9th revision (ICD-9) codes based on medical claims. Prescriptions of statin or oral antidiabetic medications were identified by national drug code (NDC) based on drug claims. Demographic, clinical and medication related information was extracted from the MarketScan® Medicaid database. The eligible patients starting the use of statin medications in 2005 were followed for one year to measure medication use, hospitalization, outpatient visits, emergency room (ER) visits, and healthcare costs. A conceptual model based on modified health belief model and health services use behavior model was proposed in this study. Propensity scores were used to remove the imbalance of baseline characteristics between patients with diabetes and without diabetes. Regression models were used to compare mediation adherence and healthcare costs. Multiple linear regression analysis was implemented to assess associations between healthcare costs and statin medication possession ratio (MPR) , while logistic regression
ii
analysis was conducted to evaluate associations between medical utilization (hospitalization and ER visit) and likelihood of statin medication adherence (MPR ≥ 0.8). A total of 7184 eligible patients with hyperlipidemia were included in the study (5479 for non-diabetic group, 1705 for diabetic group). Overall patients with diabetes presented worse health conditions than those without diabetes based on total number of medications used, frequency of outpatient visits, occurrence of hospitalization and ER visit, and severity of comorbidity. Both groups showed poor statin medication adherence rate calculated by MPR that were 0.50 for non-diabetic patients and 0.61 for diabetic patients. Diabetic patients were more likely to be adherent to statin medications (OR = 1.60) and achieved lower hyperlipidemia-related healthcare costs than non-diabetic patients after adjusting for demographic, medical utilization, clinical history, and medication related variables. In diabetic group, age, race, ER visit, and total number of outpatient visits in one year prior to index date were significant predictors to estimate statin medication adherence behavior. Diabetic patients with adherence to statins presented lower risks for hospitalization (OR = 0.80, 95% CI: 0.636, 0.966) and ER visit (OR = 0.71, 95% CI: 0.519, 0.812) and lower medical care costs within one year period. Statin medication adherence plays an important role to control cholesterol level and reduce medical utilization and costs for diabetic patients enrolled in Medicaid. Early interventions are needed in lipid-lowering treatment. Effective patient education and social service might be considered in medication adherence improvement program in Medicaid enrolled patients with diabetes and comorbid hyperlipidemia.
iii
ACKNOWLEDGEMENTS
I would never have been able to finish my dissertation without the guidance of my advisors and committee members, help from colleagues, and support from my wife and my family. I would like to express my deepest gratitude to my advisor, Dr. Rajesh Balkrishnan, the Director of Center for Medication Use, Policy and Economic at University of Michigan. He not only served as my supervisor but also encouraged and challenged me throughout my academic program. Dr. Balkrishnan continually and convincingly conveyed a spirit of adventure in regard to research and scholarship, and an excitement in regard to teaching. Without his guidance and persistent help this dissertation would not have been possible. I would like to express the deepest appreciate my committee chair and co-advisor, Dr. Milap Nahata for his excellent guidance, caring, patience, and providing me with a good atmosphere for doing research. The program was one of the most important and formative experiences in my life. I benefited a lot from his comments and suggestions on the dissertation. I would like to thank my committee members Dr. Veronique Lacombe and Dr. Eric Seiber for guiding my research. Dr. Lacombe introduced me to the pathophysiology
iv
of diabetes and hyperlipidemia and the relationship between these two diseases, which was helpful to discuss the study results from clinical perspectives. Dr. Seiber expanded my health economic knowledge and health policy background, which helped me interpret the results from social and economic perspectives. Special thanks go to Dr. Gregory Reardon and Dr. Prakash Navaratnam in Informagenics, who improve my skills to manipulate and analyze claims-based data. I would like to thank Mary Lynn Davis-Ajami, Sudeep Karve, Meg Kong, and Palak Patel, who are my good friends and colleagues and were always willing to help and give their best suggestions. I really enjoyed discussing research with them. It would have been a lonely lab without them. I would also like to thank my parents. They were always supporting me and encouraging me with their best wishes. Finally I would like to thank my wife, Lianjie Xiong. She was always there cheering me up and stood by me through the good times and bad.
v
VITA
1995 …………….B.S. Pharmaceutical Sciences, Xi’an Jiaotong University, China 2000 …………….M.S. Pharmaceutics, Chongqing Medical University, China 2003 …………….Ph.D. Pharmaceutics, China Pharmaceutical University, China
PUBLICATIONS 1. Camacho FT, Wu J, Wei W, Kimmick G, Anderson RT, Balkrishnan R. Cost impact of capecitabine therapy introduction in breast cancer patients. J Med Econ, 2009, 12: 238-245.
2. Alikhan A, Daly M, Wu J, Balkrishnan R, Feldman S. Cost effectiveness of a hydroquinone / tretinoin / fluocinolone acetonide cream combination in treating melasma in the United States. J Dermatolog Treat. 2010 Jan (Epub ahead of print).
3. Zhai G, Wu J, Xiang G, Mao W, Yu B, Li H, Piao L, Lee J, and Lee RJ. Preparation, characterization and pharmacokinetics of folate receptor-targeted liposomes for docetaxel delivery. J Nanosci Nanotechnol. 2009, 9: 2155-2161.
4. Zhu W, Yu A, Wang W, Dong R, Wu J, Zhai G. Formulation design of microemulsion for dermal delivery of penciclovir. Int J Pharm, 2008, 360: 184-190.
5. Zhai G, Wu J, Zhao X, Yu B, Li H, Lu Y, Ye W, Lin YC, Lee RJ. A liposomal delivery vehicle for the anticancer agent gossypol. Anticancer Res. 2008, 28:28012805.
6. Xiang G, Wu J, Lu Y, Liu Z, Lee RJ. Synthesis and evaluation of a novel ligand for folate-mediated targeting liposomes. Int J Pharm. 2008, 356: 29-36.
vi
7. Wu J, Zhao X, Lee RJ. Lipid based nanoparticulate drug delivery system in: Nanoparticluate Drug Delivery Systems. Thassu D, Deleers M, Pathak Y, editors. New York: Informa Healthcare; 2007: 89-98.
8. Lu Y, Wu J, Wu J, Gonit M, Yang X, Lee A, Xiang G, Li H, Liu S, Marcucci G, Ratnam M, Lee RJ. Role of formulation composition in folate receptor-targeted liposomal doxorubicin delivery to acute myelogenous leukemia cells. Mol Pharm, 2007, 4:707-712.
9. Wu J, Lu Y, Lee A, Pan X, Zhao X, Lee RJ, Reversal of multidrug resistance by transferrin-conjugated liposomes co-encapsulating doxorubicin and verapamil. J Pharm Pharmaceut Sci, 2007, 10: 350-357.
10. Wu J, Lee A, Lu Y, Lee RJ. Vascular targeting of doxorubicin using cationic liposomes. Int J Pharm, 2007, 337: 329-335.
11. Wu J, Liu Q, Lee RJ. A folate receptor-targeted liposome formulation for paclitaxel. Int J Pharm, 2006, 316: 148-153.
12. Wu J, Zhu J. Determination of entrapment efficiency of acyclovir liposomes. Chinese Journal of Pharmaceutical Analysis, 2003, 23: 213-215.
13. Wu J, Zhu J. Preparation of acyclovir liposomes and studies on its stability. Acta Pharmaceutica Sinica., 2003, 38: 552-554.
FIELDS OF STUDY Major Field: Pharmacy
vii
TABLE OF CONTENTS
ABSTRACT........................................................................................................................ ii ACKNOWLEDGEMENTS............................................................................................... iv VITA .................................................................................................................................. vi TABLE OF CONTENTS................................................................................................. viii LIST OF TABLES............................................................................................................. xi LIST OF FIGURES ......................................................................................................... xiv LIST OF ABBREVIATIONS........................................................................................... xv CHAPTER 1: INTRODUCTION ....................................................................................... 1 1.1 Diabetes .................................................................................................................... 1 1.2 Hyperlipidemia in diabetes ....................................................................................... 1 1.3 Statin treatment in diabetes....................................................................................... 2 1.4 Statin medication adherence ..................................................................................... 3 1.5 Rationale and significance of the study .................................................................... 5 1.6 Specific aims and hypotheses ................................................................................... 6 CHAPTER 2: LITERATURE REVIEW ............................................................................ 8 2.1 Diabetes and Treatments........................................................................................... 8 2.2 Hyperlipidemia and statin treatment....................................................................... 12 2.3 Diabetic hyperlipidemia and statin treatment ......................................................... 16
viii
2.4 Cost-effectiveness of treating hyperlipidemia by statins ........................................ 20 2.5 Medication adherence ............................................................................................. 21 2.6 The role of comorbidity in medication adherence .................................................. 25 2.7 Measurements of medication adherence................................................................. 26 2.8 Medication adherence to diabetes treatment........................................................... 27 2.9 Medication adherence to statin treatment ............................................................... 29 2.10 Medication use behavior in Medicaid population with chronic diseases ............. 31 CHAPTER 3: CONCEPTUAL MODEL AND STUDY DESIGN.................................. 33 3.1 Conceptual model ................................................................................................... 33 3.2 MarketScan® Research Databases .......................................................................... 38 3.3 Study design............................................................................................................ 38 3.4 Data collection ........................................................................................................ 40 3.5 Outcome measures .................................................................................................. 42 3.6 Data analysis ........................................................................................................... 44 3.7 Linear regression model diagnostics....................................................................... 49 CHAPTER 4: RESULTS.................................................................................................. 51 4.1 Study population ..................................................................................................... 51 4.2 Descriptive study population characteristics and outcomes measures ................... 52 4.3 Comparison of statin medication adherence between diabetic and non-diabetic groups...................................................................................................................... 58 4.4 Comparisons of healthcare costs between diabetic and non-diabetic groups ......... 60 4.5 Subgroup analysis for patients with hyperlipidemia but without diabetes (nondiabetic group) ........................................................................................................ 62
ix
4.6 Subgroup analysis for patients with hyperlipidemia and diabetes (diabetic group)73 4.7 Linear regression model diagnostics....................................................................... 83 CHAPTER 5: DISCUSSION........................................................................................... 86 5.1 Characteristics of study population......................................................................... 87 5.2 Comparisons of medication adherent behavior between non-diabetic and diabetic groups...................................................................................................................... 88 5.3 Subgroup analysis of medication adherence for patients in non-diabetes and diabetic groups ........................................................................................................ 91 5.4 Relationship between medical utilization and statin medication adherence........... 92 5.5 Relationship between healthcare costs and statin medication adherence ............... 93 5.6 Test of the conceptual model .................................................................................. 94 5.7 Limitations of study ................................................................................................ 95 5.8 Health policy implications ...................................................................................... 97 5.9 Conclusions............................................................................................................. 99 5.10 Future study .......................................................................................................... 99 REFERENCES ............................................................................................................... 101
APPENDIX A: Approval for data access………………………………………………118 APPENDIX B: Services and License Agreement……………………………………...119 APPENDIX C: Diagnostic indicators and medications used for patient identification and data analysis…………………………………………………………..122
x
LIST OF TABLES Table 1 Oral antidiabetic medications .............................................................................. 10 Table 2 Oral combination therapy for type 2 diabetes...................................................... 11 Table 3 Statin Medications ............................................................................................... 15 Table 4 Reduction in CHD Risk with Statins in Diabetes Patient Subgroup Analysis .... 20 Table 5 Charlson’s comorbidity index and Deyo et al. modification for use with ICD-9 diagnosis .............................................................................................................. 42 Table 6 Descriptive characteristics of two study populations enrolled in Medicaid (diabetes vs. non-diabetes) (N = 7184) ................................................................ 54 Table 7 Outcomes measures of two study populations enrolled in Medicaid (diabetes vs. non-diabetes) (N = 7184) ..................................................................................... 55 Table 8 Comparison of baseline characteristics between diabetic and non-diabetic patients before and after propensity score (N = 7184)......................................... 57 Table 9 Comparison of statin medication adherence between non-diabetic (N = 5479) and diabetic (N = 1705) groups .................................................................................. 58 Table 10 Comparison of statin medication MPR between non-diabetes (N = 5479) and diabetes (N = 1705) groups enrolled in Medicaid .............................................. 59 Table 11 Comparison of annual total all-cause healthcare costs between non-diabetic (N = 5479) and diabetic (N = 1705) groups enrolled in Medicaid .......................... 61
xi
Table 12 Comparison of annual total hyperlipidemia-realted healthcare costs between non-diabetic (N = 5479) and diabetic (N = 1705) groups .................................. 62 Table 13 Predictors of statin medication adherence in non-diabetic population enrolled in Medicaid (N = 5479) .......................................................................................... 64 Table 14 Predictors of MPR in non-diabetic population enrolled in Medicaid (N = 5479) ........................................................................................................................................... 65 Table 15 Association between risk for hospitalization and statin medication adherence in non-diabetic population enrolled in Medicaid (N = 5479)................................. 66 Table 16 Association between ER visit and statin medication adherence in non-diabetic patients enrolled in Medicaid (N = 5479)........................................................... 67 Table 17 Comparison of all-cause annual healthcare costs in non-diabetic patients by statin medication adherence (N = 5479)............................................................. 68 Table 18 Comparison of hyperlipidemia-related annual healthcare costs in non-diabetic patients by statin medication adherence (N = 5479) .......................................... 68 Table 19 Associations between statin medication adherence and annual medical costs in non-diabetic patients (N = 5479)........................................................................ 70 Table 20 Associations between statin medication adherence and annual drug costs in nondiabetic patients (N = 5479) ............................................................................... 71 Table 21 Associations between total annual healthcare costs and statin medication adherence in non-diabetic patients (N = 5479)................................................... 72 Table 22 Predictors of statin medication adherence in diabetic patients enrolled in Medicaid (N = 1705) .......................................................................................... 74 Table 23 Predictors of statin MPR in diabetic patients enrolled in Medicaid (N = 1705) 75
xii
Table 24 Association between risk for hospitalization and statin medication adherence in diabetic patients enrolled in Medicaid (N = 1705)............................................. 76 Table 25 Association between risk for ER visit and statin medication adherence in diabetic patients enrolled in Medicaid (N = 1705)............................................. 77 Table 26 Comparison of annual all-cause healthcare costs in diabetic patients by statin medication adherence (N = 1705) ...................................................................... 78 Table 27 Comparison of annual hyperlipidemia-related healthcare costs in diabetic patients by statin medication adherence (N = 1705) .......................................... 78 Table 28 Associations between statin medication adherence and annual medical costs in diabetic patients by statin medication adherence (N = 1705)............................. 80 Table 29 Associations between statin medication adherence and annual drug costs in diabetic patients enrolled in Medicaid (N = 1705)............................................. 81 Table 30 Associations between statin medication adherence and total annual healthcare costs in diabetic patients enrolled in Medicaid (N = 1705)................................ 82 Table 31 Testing of multicollinearity ............................................................................... 84
xiii
LIST OF FIGURES
Figure 1 Modification of Health Belief Model by Becker and Maiman........................... 35 Figure 2 Bahavioral model of health service use (Aday and Andersen) .......................... 36 Figure 3 Conceptual model for the study based on modified health belief model (Becker and Maiman) and behavior model of health services use (Aday and Andersen)37 Figure 4 Study population with hyperlipidemia and diabetes (diabetic group)................ 51 Figure 5 Study population with hyperlipidemia but no diabetes (non-diabetic group) .... 52 Figure 6 Histogram of distribution of residuals................................................................ 85 Figure 7 QQ plot of residual distribution.......................................................................... 85
xiv
LIST OF ABBREVIATIONS
4S: Scandinavian Simvastatin Survival Study ADA: American Diabetes Association AFCAPS/TexCAPS: Air Force/Texas Coronary Atherosclerosis Prevention Study AGE: advanced glycation end products AIDS: acquired immune deficiency syndrome ASCOT: Anglo-Scandinavian cardiac outcomes trial CAD: coronary artery disease CARDS: Collabrative Atorvastatin Diabetes Study CARE: Cholesterol and Recurrent Event trial CI: confidence interval CVD: cardio vascular disease DPP-4: dipeptidyl peptidase-4 ER: emergency room FDA: Food and Drug Administration GREACE: GREek Atorvastatin and Coronary Heart Disease Evaluation Study HDL: high-density lipoprotein HIV: human immunodeficiency virus HMG-CoA: 3-hydroxy-3-methylglutaryl-coenzyme A
xv
HPS: heart protection study ICD-9: international classification of diseases 9th revision codes ICER: incremental cost-effectiveness ratio ISPOR: International Society of Pharmacoeconomics and Outcomes Research LDL: low-density lipoprotein LIPID: Long-term Intervention with Pravastatin in Ischemic Disease trial LIPS: Lescol Intervention Prevention Study MPR: medication possession ratio NCPE: national cholesterol education program NDC: national drug codes OR: odds ratio PDC: proportion of days covered PROSPER: Prospective study of pravastatin in the elderly at risk Q-Q:quantile-quantile SD: standard deviation TZD: thiazolidinedione US: The United States VIF: variance inflation factor VLDL: very low density lipoproteins WHO: World Health Organization YOLS: year of life saved
xvi
CHAPTER 1: INTRODUCTION
1.1 Diabetes Diabetes is a disease in which the pancreas does not produce insulin or the body does not properly use insulin. Because of its rising incidence worldwide and its increased risk of the developing of cardiovascular complications, diabetes represents a major public health problem. Currently diabetes is one of the leading causes of morbidity and mortality in the United States.1 According to a report by American Diabetes Association (ADA)2 in 2007, there were 23.6 million children and adults in the United States, or 7.8% of the population, who have diabetes. Although an estimated 17.9 million people have been diagnosed with diabetes, unfortunately 5.7 million people are unaware that they have the diabetes. The total annual economic cost of diabetes in 2007 was estimated to be $174 billion, comprising $116 billion of direct medical expenditures and $58 billion of indirect costs.3 1.2 Hyperlipidemia in diabetes It has been known that diabetes increases the risk of developing atherosclerosis and coronary artery disease (CAD).4,5 However, usual risk factors for coronary artery disease account for only 25-50% of increased atherosclerotic risk in diabetes.4 Hyperglycemia and dyslipidemia are two other important risk factors in diabetes to develop atherosclerosis and CAD. Hyperglycemia occurs in a late stage in the sequence
1
of events from insulin resistance to the development of diabetes,6 whereas dyslipidemia or lipoprotein abnormalities are manifested during the largely asymptomatic diabetes prodrome and contribute substantially to the increased risk of cardiovascular disease to some extent.7 In western societies, most dyslipidemias are hyperlipidemias, presenting elevated or abnormal levels of lipids and/or lipoproteins in the blood. The characteristics of hyperlipidemia include elevated triglyceride levels, low high-density lipoprotein (HDL) cholesterol levels, and high low-density lipoprotein (LDL) cholesterol levels.8 The national cholesterol education program (NCEP) guidelines indicate that patients with diabetes have same high risk of experiencing a coronary heart disease (CHD) event as patients with established history of CHD.9 Consequently the NCEP guidelines recommend an optimal LDL cholesterol goal of < 2.6 mmol/L (100mg/mL), which is consistent with the LDL cholesterol level recommended ADA.10 Once LDL cholesterol levels have been lowered to target, the next aim of lipid management is to reduce the levels of triglycerides, and to raise the level of HDL cholesterol.10 Over 95% of patients with type 2 diabetes may have more than two coronary artery disease risk factors, which suggests that most of the patients need LDL-cholesterol-lowering therapy.11 1.3 Statin treatment in diabetes Statins (or HMG-CoA reductase inhibitors) form a class of hypolipidemic agents and are now the drugs of first choice in patients with type 2 diabetes mellitus to control cholesterol levels. They are well tolerated and are as effective in patients with diabetes as in those without diabetes. The primary and secondary trials to prevent CHD by administering statins have demonstrated similar risk reductions in CHD events in both diabetes and general population.12 Overall statins decreased LDL cholesterol levels by
2
25-45% on average, varying in the stain selected and the dose regimens. In addition, many clinical trials, such as Scandinavian Simvastatin Survival Study (4S),13 Greek Atorvastatin and Coronary Heart Disease Evaluation Study14and Lescol Intervention Prevention Study15 also provided positive evidence that diabetic patients with CHD history could get similar benefits from statin therapy compared with non-diabetics. In terms of treatment costs, treating hyperlipidemia by statin medications to reduce the risk of cardiovascular disease is cost-effective for both diabetic and non-diabetic patients with a wide range from $4000 to $40000 per year of life saved due to different initial lipid levels.16 1.4 Statin medication adherence Medication adherence refers to the level of participation achieved in a medication regimen once an individual has agreed to the regimen.17 Medication nonadherence in chronic conditions is a recognized, but understudied, public health problem. Overall the average estimated adherence rate is 50% with a broad range from 15% to 93%.18 Many factors may influence patients’ adherence to the medication intervention, such as demographics, medical conditions, medication use, patient behavior, and economic status.19 Especially for the elderly, hospitalizations, re-hospitalizations, and nursing home admissions are recognized as direct costs due to medication nonadherence. In U.S. national medical expenditures attributable to cardiometabolic risk factor clusters (diabetes, hypertension, hyperlipidemia, and overweight/obesity) attained $ 80 billion in 2005, of which $ 27 billion was spent on prescription drugs.20 Previous studies suggested that healthcare costs among patients might differ due to high or low medication adherence rate.21,22 The associations between increased medication adherence and
3
improved health outcomes/lower health care costs have been established. For oral antidiabetic medications, every 10% increase in adherence rate is associated with 8.6% decrease in total healthcare cost in diabetic patients.21 The associations among antidiabetic medication adherence, healthcare costs and various related risk factors (race, types of treatment, payment systems, and dose regimens) in Medicaid population have been studied by Balkrishnan et al.21,23,24,25 In general, all of the above studies present the same conclusion that a higher adherence rate was associated with lower diabetes-related and total health care costs in the patients, although there might be some other factors affecting the outcomes.In addition, increased drug utilization can provide a net economic return if patient present good medication use behavior. Sokol et al26 presented the finding that for some chronic conditions, such as diabetes and hypercholesterolemia, a high level of medication adherence was associated with lower hyperlipidemia-related medical costs. Furthermore higher medication costs were more than offset by medical cost reductions, producing a net reduction in overall healthcare costs. The use of cardioprotective medications in individuals with type 2 diabetes increases gradually each year. However, the annual prevalence (from 2001 to 2003) of the use of statins ranges from 44% to 59%.27 The result suggests that a considerable proportion of patients lack adequate care to control their LDL cholesterol levels and reduce to CHD risk. Among those receiving lipid-lowering therapy, studies have shown variable but disappointingly high rates of discontinuation of statin therapy, poor adherence to drug regimens, and failure to reach targets for cholesterol reduction.28,29,30 It was indicated that the greatest drop occurred in the first 6 months of the treatment, followed by a more gradual decline. In the United Stated it is estimated that only about
4
50% of patients continued at six months, and 30-40% at one year.31 Similar rates have been found in Australia32 and United Kingdom.33 The National Cholesterol Education Program Adult Treatment Panel III recommendations expanded the treatment-eligible elderly population from 4.2 million to 9.7 million individuals and emphasized that adherence to lipid-lowering therapy is critical to achieve the full population effectiveness of primary and secondary prevention.9, 34 Although the rate of statin use among the elderly has risen in recent years, elderly patients failed to receive indicated lipid-lowering medications as often as 80% of the time and ultimately fewer achieved expected LDL cholesterol goals.35,36 For Medicaid patients with diabetes who are eligible to receive prescription drugs at no costs or for moderate copayments, they might have statin medication adherence problems due to nonfinancial factors and nonmedical cost burdens, such as patients’ beliefs regarding the importance to taking medication, ability to obtain and fill prescriptions by transportations, and additional costs to maintain healthy life styles.37 Thus, interventions are needed to improve medication use behavior early in lipid-lowering treatment, among high-risk groups, and those who experience coronary heart disease events after initiating treatment.27 1.5 Rationale and significance of the study Comorbid severity and related treatments are two important factors controlling diabetes conditions. Based on a large number of previous studies on the evaluations of various risk factors and health outcomes in the diabetes population, our study shifted the focus from diabetes disease to comorbidity. Hyperlipidemia, raising the cardiovascular risk, is commonly observed in patients with type 2 diabetes and statin medications are the first choice of lipid-lowering therapy. The use of statin medications can reduce the risk
5
for CHD events by decreasing the LDL cholesterol concentrations in bloodstream effectively. Further, adherence to statin medication is critical to achieve desired lipidlowering effectiveness of primary and secondary prevention. Thus, it is necessary to conduct further studies on the statin medication use behavior in diabetic patients. Socioeconomic status is another factor influencing medication use behavior. Medicaid enrollees represent characteristics of low income individuals and families to some extent. They are poor, more likely to be members to a racial/ethnic minority, and present high risk of chronic conditions and less favorable overall health status. They may be less likely to have beliefs regarding the importance to taking medication due to their low socioeconomic status. Low incomes, high rates of health problems and their beliefs may make them less likely receive appropriate medication treatment for long term.38,39 Therefore, medication use behavior in the Medicaid population is worthy of study. This may also provide important health policy implications for this disadvantaged population to improve related health and economic outcomes. Also, most of the previous studies focused on the association between statin therapy and coronary heart disease in general hyperlipidemia patients. There has been little research assessing the health outcomes on statin therapy in diabetes population. Therefore, in our study we examined the prevalence of statin medications adherence among type 2 diabetic patients with comorbid hyperlipidemia and then investigate the associations among statin medication adherence, healthcare costs and utilization. 1.6 Specific aims and hypotheses Based on literature review and behavioral models of health services, three main aims and hypotheses were put forward concerning the expected effects of statin
6
adherence on health and economic outcomes. Aim 1: To measure statin medication adherence and evaluate predictors of statin medication adherence in diabetic and non-diabetic patients. Aim 2: To assess associations between statin medication adherence and medical utilization (risks for hospitalization and emergency room visit) in diabetic and nondiabetic patients. Aim 3: To assess associations between statin medication adherence and healthcare costs (all-cause and hyperlipidemia-related total costs, drug costs, and medical care costs) in diabetic and non-diabetic patients. Hypothesis 1: There is a significant difference in statin adherence between diabetic and non-diabetic populations. Hypothesis 3: There is a significant difference in healthcare costs between statin adherent and non-adherent groups in diabetic or non-diabetic populations. Hypothesis 2: There is a significant difference in medical utilization (hospitalization and ER visit) between statin adherent and non-adherent groups in diabetic or non-diabetic populations.
7
CHAPTER 2: LITERATURE REVIEW
2.1 Diabetes and Treatments In 2000, The World Health Organization (WHO) reported that 171 million people have diabetes (2.8% of the world population) and it is estimated that the number would attain 366 million by 2030 due to rapidly increased incidence.40 In the U.S., there are 23.6 million children and adults (8% of the U.S. population) having diabetes. However, almost 25% of the diabetic patients (5.7 million) are unaware that they have the disease.2 From 2005 to 2007, the total prevalence of diabetes in U.S. increased by 13.5%.2 The study on economic costs of diabetes in U.S.41 indicated that the actual national burden of diabetes might be more than estimated costs of $174 billion due to the intangible costs, such as pain, suffering from the disease and interventions, care offered by caregivers without payment, and expenditures associated with diabetes related diseases. The obvious economic effects of diabetes on the society would be the higher insurance expenditures, reduced income because of productivity loss, and lowered quality of life for both patients and their families. There are four major types of diabetes, which are type 1 diabetes, type 2 diabetes, gestational diabetes, and pre-diabetes. Type 1 diabetes is characterized by the body’s failure to produce insulin. It is estimated that 5-10% of Americans diagnosed with diabetes have type 1 diabetes.2 Type 1 diabetes usually represents a majority of the
8
diabetes cases in children and young adults. Insulin injection is the principal treatment of type 1 diabetes. Type 2 diabetes is the most common form of diabetes resulting from insulin resistance or reduced insulin sensitivity combined with reduced insulin secretion. There are various factors that may increase the onset of type 2 diabetes, such as biological elements, environmental exposure, and life style.2 Obesity has been proven one of the most important risk factors accounting for 55% of diagnosis of type 2 diabetes.42 Mokada et al43 reported that the risk for diagnosed diabetes in adults with a BMI of 40 or higher were 7.37 times the risk for diabetes in adults with normal weight. Aging is the other important factor. In the U.S., 23.1% (12.2 million) of all people aged 60 years or old have diabetes.2 Those with family history of diabetes also have higher risk to develop type 2 diabetes. In 2007, 1.6 million new cases of diabetes occurred in people aged 20 years or older. The increased prevalence of childhood obesity may contribute to the increased incidence in the young age group.2 Usually the first step to treat type 2 diabetes is to change a life style by increasing physical activity, meal planning with low carbohydrate intake, and losing weight. The next step is oral antidiabetic medication treatments. Currently there are six classes of drugs to control the blood glucose levels with different mechanisms, including sulfonylureas, meglitinides, biguanides, thiazolidinediones, DPP-4 inhibitors, alpha-glucosidase inhibitors (Table 1).44 Due to the different ways of the antidiabetic drugs to adjust the glucose levels, oral combination therapy may be used to improve the effectiveness (Table 2).45 Additionally two new injectable drugs have been approved by the FDA. One is pramlintide (Symlin®), a relatively new adjunct treatment for type 1 and type 2 diabetes.46 The other one is exenatide (Byetta®), a new class of medications (incretin mimetics) approved for the
9
treatment of type 2 diabetes.47 If oral medication eventually failed, an insulin therapy is necessary to control the glucose levels.
Table 1 Oral antidiabetic medications Class of oral antidiabetic medications
Mechanism to control glucose levels
Generic Name
Brand name
Chlorpropamide Diabinese® Glyburide Sulfonylureas
Improve insulin production Glimepiride
Repaglinide
Glucotrol® Glucotrol® XL Prandin®
Nateglinide
Starlix®
Metformin
Glucophage®
Rosiglitazone
Avandia®
Pioglitazone
Actos®
Sitagliptin
Januvia®
Acarbose
Precose®
Meglitol
Glyset®
Glipizide
Meglitinides
Improve insulin production
Biguanides
Reduce hepatic glucose output and increase uptake of glucose by the periphery, including skeletal muscle
Substantially attenuate Thiazolidinediones insulin resistance
DPP-4 Inhibitors
Alpha-glucosidase inhibitors
Inhibition of the DPP4 enzyme prolongs and enhances the activity of incretins that play an important role in insulin secretion and blood glucose control regulation Slow the digestion of starch in the small intestine
10
Micronase® Glynase® Diabeta® Glycron® Amaryl®
Table 2 Oral combination therapy for type 2 diabetes Generic Name
Brand name
Metformin with glyburide
Glucovance
Glipizide with metformin
Metaglip®
Pioglitazone with metformin
Actoplus Met®
Rosiglitazone with metformin
Avandamet
Pioglitazone with glimepiride
Duetact®
Sitagliptin with metformin
Janumet®
Repaglinide with metformin
Prandimet
Rosiglitazone with glimepiride
Avandaryl®
®
®
®
Gestational diabetes occurs immediately after pregnancy and affects about 4% of all pregnant women in the U.S. It is estimated that there are 135,000 new cases of gestational diabetes in the U.S. each year.2 Main treatments for gestational diabetes comprise low carbohydrate intake meal, regular exercising and losing weight. It is worth noting that 5% to 10% of women with gestational diabetes could develop type 2 diabetes years later.2 Therefore, keeping a healthy lifestyle for long term would be helpful to prevent diabetes after gestational diabetes. Tuomilehto et al.48 studied prevention of type 2 diabetes by changes in life style among subjects with impaired glucose tolerance. After 4 year follow-up study period, the risk of diabetes was reduced by 58% in the intervention group. The changes in lifestyle included reducing weight, controlling intake of fat, and increasing intake of fiber and physical activity. Pre-diabetes means that the blood glucose levels are higher than normal but not high enough to be diagnosed with type 2 diabetes. ADA reported that 57 million Americans have pre-diabetes.2 Research has found that development of type 2 diabetes 11
could be prevented or delayed if patients with pre-diabetes take action to manage the blood glucose, such as healthy food choice, 30 minutes a day of moderate physical activity, and losing about 5-10% body weight.43, 49 Various complications may occur in diabetic patients due to uncontrolled blood glucose levels. The acute complications include diabetes ketoacidosis, hyperglycemia hyperosmolar sate, hypoglycemia, and respiratory infections. Chronic complications include cardiovascular disease, retinopathy, neuropathy, and nephropathy.50 CHD is the leading cause of death in patients with type 2 diabetes. Hu et al51assessed the impact of diabetes on mortality from all causes and coronary heart disease in women with 20 year follow-up period. The study demonstrated that compared with control group (no diabetes or CHD history), age-adjusted relative risk ratios were 3.39 for women with diabetes but no CHD, 3.00 for women with CHD but no diabetes, and 6.84 for women with both diabetes and CHD history. The fatal CHD event occurrence in women with both conditions was 25.1 times the occurrence in control group. The study also suggested that diabetes was associated with dramatically increased risks of death from fatal CHD. Most diabetic patients have risk factors, such as hypertension and hyperlipidemia that increase the risk to develop heart disease and stroke causing more than 65% of people with diabetes to die of cardiovascular diseases. However, diabetic patients can reduce the risk by active treatments of diabetes, hypertension, and hyperlipidemia. 2.2 Hyperlipidemia and statin treatment Hyperlipidemia is a disease with presence of elevated or abnormal levels of lipids in the bloodstream, including cholesterol, cholesterol esters, phospholipids and triglycerides.52 They're transported in the blood as part of large molecules (containing
12
both lipid and proteins) called lipoproteins. There are three major classes of lipoproteins found in the serum:53 low density lipoproteins (LDL), high density lipoproteins (HDL), and very low density lipoproteins (VLDL, triglyceride-rich lipoproteins). Additionally, intermediate density lipoprotein (IDL) is another lipoprotein family included in the LDL measurement in clinical test. Chylomicrons are also triglyceride-rich lipopropteins formed in the intestine from dietary fat and appear in the blood after a meal containing fat. Usually hyperlipidemia is characterized by elevated triglyceride levels, low HDL cholesterol levels, and high LDL cholesterol levels. Epidemiological evidence has shown that a low level of LDL cholesterol is an independent risk factor associated with increased CHD morbidity and mortality.9 LDL cholesterol (60-70% of the total serum cholesterol) is a major atherogenic lipoprotein to develop atherosclerosis and a major cause of CHD. The Framinghan Heart Study54 and other epidemiological studies55, 56 indicated that there is a direct relationship between high LDL cholesterol levels and the incidence of CHD. Therefore, LDL cholesterol control is the primary target of cholesterol-lowering therapy in the patients with hyperlipidemia to reduce the risk for CHD. HDL cholesterol (20-30% of the total serum cholesterol) has been proven to prevent the development of atherosclerosis.9 Cinical studies suggested that low HDL cholesterol is a potential target of hyperlipidemia therapy.57 The VLDL (10-15% of the total serum cholesterol) are triglyceride-rich lipoproteins. VLDL remnants with partially degraded VLDL are relatively enriched in cholesterol ester, which is also a risk factor similar to LDL for development of atherosclerosis.9 Elevated serum triglyceride-rich lipoproteins positively correlate with incidence of CHD. Studies suggested that VLDL cholesterol, as a marker for remnant
13
lipoproteins is a potential target of lipid-lowering therapy.58 Overall LDL management is the first step to treatment hyperlipidemia. However, clinical evidence demonstrated that VLDL and HDL also play key roles in atherosclerosis.59 Thus, consideration of reducing VLDL and raising HDL cholesterol would be the next step after LDL cholesterol levels have been lowered to the target. According to a report from American Heart Association,60 the age-adjusted prevalence of high LDL cholesterol in US adults was 26.6% in 1988-1994 and 25.3% in 1999-2004. The awareness of high LDL cholesterol increased from 39.2% to 63% between the periods of 1988-1994 and 1999-2004. The medication use of lipid-lowering therapy increased from 11.7% to 40.8%. Among those with high LDL cholesterol, the treatment on LDL cholesterol control increased from 4.0% to 25.1%. Statins (or HMG-CoA reductase inhibitors) are a family of antihyperlipidemic drugs to lower LDL cholesterol levels in those patients at risk of cardiovascular disease. Statins inhibit the activity of the enzyme HMG-CoA reductase, which is the ratecontrolling enzyme of the mevalonate pathway of cholesterol production. The elimination of LDL cholesterol from bloodstream is enhanced due to the stimulation of LDL receptors resulted from the competitive inhibition of the HMG-CoA reductase.61 The statins consist of two groups based on derivation: fermentation-derived and synthetic. Table 3 shows the various statin agents. The LDL-lowering potency varies among the statin medications.62
14
Table 3 Statin Medications Statin
Brandname
Atorvastatin
Lipitor®
Fluvastatin
Lescol®, Lescol® XL
Lovastatin
Mevacor®, Altocor®, Altoprev®
Pitavastatin
Livalo®
Pravastatin
Pravachol®
Rosuvastatin
Crestor®
Simvastatin
Zocor®
Simvastatin+Ezetimibe
Vytorin®
Lovastatin+Niacin extended-release
Advicor®
Atorvastatin+Amlodipine Besylate
Caduet®
Simvastatin+Niacin extended-release
Simcor®
Many clinical trials confirmed that high serum concentrations of LDL cholesterol are an important risk factor for CHD and that the primary target is to lower LDL cholesterol levels. The use of statin medications can reduce the risk for CHD events by decreasing the LDL cholesterol concentrations in bloodstream effectively. The heart protection study (HPS) was conducted in patients (aged 40 to 80 years) with coronary disease, other occlusive arterial disease, or diabetes, which means those patients were at risk for a cardiovascular disease (CVD) event.63 Patients administering simvastatin (40mg/day) showed that all-cause mortality was significantly reduced by 13%. Major vascular events and coronary death rate were reduced by 24% and 18%. The study also suggests that simvastatin may achieve similar reductions in relative risk for CHD regardless of the baseline levels of LDL cholesterol (from less than 100mg/dL to higher than 135 mg/dL). The prospective study of pravastatin in the elderly at risk (PROSPER)
15
evaluated the efficacy of pravastatin in older patients aged 70 to 82 years with a history of vascular disease or at high risk of CVD and stroke.64 The study showed that pravastatin reduced LDL cholesterol levels by 34%. Nonfatal myocardial infarction and CHD death were decreased by 19% in the treatment group. CHD mortality was reduced by 24%. Anglo-Scandinavian cardiac outcomes trial (ASCOT) assessed the atorvastatin treatment in hypertensive patients.65 The LDL cholesterol levels were reduced by 29% on average. The risk to experience the events of nonfatal myocardial infarction and fatal CHD were decreased by 36%. Total cardiovascular events and coronary events were reduced by 21% and 29%. Overall the clinical trials suggest that statin therapy can decrease the level of LDL cholesterol and reduce the risk for CHD in patients with hyperlipidemia. However, healthy lifestyle must be an indispensible part of the risk reduction therapy. 2.3 Diabetic hyperlipidemia and statin treatment Lipid metabolism in type 2 diabetes is associated with a series of factors. Hyperglycemia and insulin resistance play important roles in the onset of hyperlipidemia. Diabetic hyperlipidemia consists of three components: an elevation of small, dense LDL particles, increased remnant triglyceride-rich lipoprotein particles, especially VLDL, and low plasma levels of HDL. These three components are strong atherogenic factors to promote the lipid accumulation in the arterial wall and atherosclerotic plaque formation. Hyperglycemia greatly accelerates the rate of advanced glycation end products (AGE) formation that enhances deposition of cholesterol.66 AGEs are formed due to nonenzymatic reactions between intracellular glucose-derived dicarbonyl precursors (glyoxal, methylglyoxal, and 3deoxyglucosone) with the amino groups of both intracellular and
16
extracellular proteins. AGEs can directly cross-link extracellular matrix proteins decreasing protein removal while enhancing protein deposition. Additionally AGEmodified matrix components also trap nonglycated plasma or interstitial protein. For example, in large vessels, trapping of LDL retards its efflux from arterial wall and increases the deposition of cholesterol in the intima, thus accelerating atherogenesis.66 Insulin resistance is the fundamental pathophysiologic mechanisms of diabetic hyperlipidemia, highly correlated with hypertriglyceridemia, especially high concentration of VLDL, and postprandial lipemia.67 The insulin resistant-state impairs the normal suppression of fatty-acid release from adipose tissue in the postprandial state. Thus, the flux of free fatty acids to the liver increases and overproduction of VLDL from these substrates occurs when hyperinsulinaemia is present. Dysfunction of insulin in type 2 diabetic patients fails to regulate the balance between intestinally derived and liverderived triglyceride-rich lipoproteins. Consequently inappropriate production of VLDL by the liver impairs the balance and leads to hypertriglyceridaemia.68 Hypertriglyceridaemia also contributes to low concentration of HDL. Due to decreased lipoprotein lipase activity and impaired lipolyis in type 2 diabetics, fewer surface remnants (redundant phospholipids and apolipoproteins form lipolysis of trygeceride-rich lipoproteins) are available to be transferred to HDL particles, which reduce the HDL concentration.68 In additional, the large amount of triglyceride-rich lipoproteins and their prolonged residence time in the blood increase the exchange of esterified cholesterol from HDL to triglyceride-rich lipoproteins and of triglyceride to HDL particles, which results in enrichment of HDL particle core with triglyceride
17
presenting a faster catabolic rate than normal HDL and leading to lower number of HDL particles.68 Consequently diabetic patients with hyperlipidemia generally have high risk for CHD. The national cholesterol education program (NCEP) guidelines69 recognize that patients with diabetes have same high risk of experiencing a coronary heart disease (CHD) event as patients with established CHD and recommend intensive risk-factor management. The primary target of lipid management is to reduce LDL cholesterol levels. Once the LDL cholesterol levels have been lowered to target, the next aim is to reduce the triglyceride levels and elevate HDL cholesterol levels. Hyperlipidemia is commonly observed in patients with type 2 diabetes, so most of the patients are recommended to receive lipid-lowering therapy to achieve the risk reduction in CHD. Carmena and Betteridge70 emphasized that cardiovascular disease prevention was more important in risk-reduction therapies for type 2 diabetic patients than only glucose or lipid control. The lipid management in type 2 diabetes should consider that insulin resistance, increased lipolysis, and overproduction of LDL particles are the base of diabetes hyperlipidemia. Currently statin medications are the first choice to in patients with type 2 diabetes to control cholesterol level. Clinical trials have confirmed that risk reduction in CHD events were similar in diabetes and non-diabetic patients. Overall statins decreased LDL cholesterol levels by 25-45% on average, depending on the stain selected and the dose regimens (Table 4). HPS study investigated the 5963 diabetic patients aged 40 to 80 years receiving simvastain (40 mg/day).61,75 The results showed that simvastatin significantly reduce the first-event rates for CHD, strokes and revascularizations by 22%, which is similar to those for nondiabetic patients (25%). In the extended analysis of Scandinavian
18
Simvastatin Survival Study (4S) study including 483 diabetic patients (251 on simvastatin) and 678 patients with impaired fasting glucose (343 on simvastatin), risk reduction of CHD was 42%, compared to the corresponding risk reduction of 32% in overall population.78 The Greek Atorvastatin and Coronary Heart Disease Evaluation Study (GREACE) also demonstrated a 58% risk reduction in diabetic group (313 patients).74 In The Lescol Intervention Prevention Study, fluvastatin therapy (80mg per day) was associated a 51% risk reduction in diabetic group (120 on fluvastatin; 80 on placebo).77 Some studies, such as AFCAPS/TexCAPS study (n = 155), may have limitation of statistical power due to relatively small sample size in diabetes population.70 Overall these findings support the conclusion that diabetic patients with CHD may obtain similar benefits of statin therapy to non-diabetic patients. Based on the presence of low LDL levels (less than 130mg/mL), therapy increasing HDL cholesterol with fibrates could be more beneficial than statin therapy alone.66
19
Table 4 Reduction in CHD Risk with Statins in Diabetes Patient Subgroup Analysis Number of
Drug and daily
Study
Reduction in CHD risk (%)
diabetic
dose(mg)
patients
Diabetes
Overall
155
43
37***
Atorvastatin 10
2532
16
36***
CARDS
Atorvastatin 10
5963
37***
-
CARE74
Pravastatin 40
586
25*
24**
GREACE75
Atorvastatin 10-80
313
58***
-
HPS61,76
Simvastatin 40
5963
22***
25***
LIPID77
Pravastatin 40
782
19
24***
Fluvastatin 80
202
51**
50*
Simvastatin 20-40
483
42***
32***
AFCAPS/TexCAPS71 Lovastatin 20-40 ASCOT-LLA
72
73
LIPS
78
4S extended study79
AFCAPS/TexCAPS: Air Force/Texas Coronary Atherosclerosis Prevention Study, ASCOT-LLA: Anglo Scandinavian Cardiac Outcomes Trial-Lipid Lowering Arm, CARDS: Collabrative Atorvastatin Diabetes Study, CARE: Cholesterol and Recurrent Event trial, GREACE: GREek Atorvastatin and Coronary Heart Disease Evaluation Study, HPS: Heart Protection Study, LIPID: Long-term Intervention with Pravastatin in Ischemic Disease trial, LIPS: Lescol Intervention Prevention Study, 4S: Scandinavian Simvastatin Survival Study * P < 0.05, ** p < 0.01, *** p < 0.001
2.4 Cost-effectiveness of treating hyperlipidemia by statins Cost effectiveness analysis is used to estimate the cost of achieving a given health care objective (usually a life-year saved) and provide quantitative evidence to health policy makers. Usually if the cost a medical treatment is less than $50,000 per additional year of life saved, it is generally viewed favorably80. The cost-effectiveness of statins depends on the baseline level of risk when treatment starts and type of statin used. In the US, treatment with simvastatin for cardiovascular CVD patients without diabetes showed
20
that incremental cost-effectiveness ratio (ICER) ranged from $8,799 to $21,628 per year of life saved (YOLS). For diabetic patients without CVD, the ICER was estimated from $5,063 to $23,792.81 Grover et al82 also drew the similar cost-effective conclusion on the simvastatin treatment based on the gender and baseline cholesterol levels. The study indicated that ICERs for nondiabetes men with CVD disease ranged from $ 5,000 per YOLS (aged 60 years, baseline cholesterol: 211mg/dL) to $14,000 per YOLS (aged 40 years, baseline cholesterol: 149mg/dL). Nondiabetes women with CVD disease showed the similar cost-effective results. For diabetes men without known CVD (mean cholesterol level: 135 mg/dL), the ICER ranged from $7,000 to $15,000 per YOLS. However, the ICERs for diabetes women ranged from $24,000 to $40,000 per YOLS.
2.5 Medication adherence Two essential conditions are needed for patients to achieve improved health outcomes.83 One is the accurate medical advice and the other one is to comply with the good advice. If patients follow the ill-founded advice, adverse events and new health problems could occur to worsen the health condition. However, good advice may not work if patient do not have compliant behavior. Both conditions would be essential for patients to improve the health conditions. Medication persistence and medication compliance are two indices to evaluate patient medication use behavior. According to the terminology and definition guidance published by International Society of Pharmacoeconomics and Outcomes Research (ISPOR),84 medication persistence refers to the act of continuing the treatment for the prescribed duration. It may be defined as “the duration of time from initiation to discontinuation of therapy.” Medication compliance and medication persistence are two
21
different constructs. Medication compliance refers to the act of conforming to the recommendations made by the provider with respect to timing, dosage, and frequency of medication taking. Medication adherence was selected as a synonym of medication compliance. The terminology medication adherence is used through this study because agreement between patients and health professionals is emphasized. Therefore medication adherence is defined as the level of participation achieved in a medication regimen once an individual has agreed to the regimen.85 Overall the average estimated adherence rate is 50% with a broad range from 15% to 93%.86 In the elderly aged 60 years or above, the medication adherence rate ranges from 26% to 59%. It was estimated that approximate 50% of the patients take the correct dose of medications.87 Hospitalizations, re-hospitalizations, and nursing home admissions were the direct causes to raise the health care costs in elderly patients due to medication nonadherence. Previous studies showed that 10% of hospital admissions and 23% of nursing home admission were associated with poor medication adherence, which may result in a heavy economic burden on patients and society.88 Why do some people present adherence to treatment whereas others not? Several theoretical models have been developed to predict and explain the medication use behavior, including behavioral theory, self-efficacy theory, theories of reasoned action and planned behavior, and transtheoretical model. The behavioral model emphasizes immediate reinforcement to any response that makes a subject adherence to medical advice.89 The model uses cues (written reminders, telephone calls), rewards (money and compliments) and contracts (written agreements between practitioner and patient) to reinforce adherence behaviors. Self-efficacy theory assumes that humans have to control
22
over their life but have limited power to use cognitive process for self-regulation.90 Selfefficacy is an important element in the concept of reciprocal determinism that defines human function as a product of the interaction of behavior, environment, and person variables (self-efficacy and other cognitive process). Self-efficacy reflects people’s confidence that can accomplish desired outcomes by conducting necessary behavior. This theory was used to predict adherence to a healthy food choice in diabetic patients. The study suggested that increasing self-efficacy was associated with adherence to appropriate diet (beta = 0.54) and was a potentially effective strategy to improve dietary self-care.91 Theories of reasoned action and planned behavior focus on behavioral intentions that are attitude and subjective norm and intentions to act that are perceived behavioral control.92 These two theories have been used to predict adherence to various health related behavior. Hausenblas et al.93 assessed the association these two theories and adherence to an exercise program. The results suggested strong association between intention and exercise behavior, attitude and intention, attitude and exercise behavior, perceived behavioral control and intention, and perceived behavioral control and exercise behavior. However, the association between subjective norm and intention was moderate. The transtheoretical model consists of five stages in making changes in behavior, including precontemplation, contemplation, preparation, action and maintenance).94 A meta-analysis by Rosen found that use of this model varies by stage and the sequencing of processes is not consistent for different health problems.95 For example, in smoking cession, cognitive processes were used in earlier stages than behavioral process. In diet change, use of behavioral and cognitive processes increased together. In general, studies
23
on the above models suggested that self-perception and intention to comply with medical advice are two important factors to the compliant behavior. Balkrishnan summarized seven categories of factors to predict patient medication use behavior, including demographic (age, race, sex, and socioeconomic status), medical (type and severity of disease, number and severity of comorbidities, health related quality of life), medication (type and number of medications, regimen and side effects), economic (income level, insurance coverage, health care costs), physician-patient interaction (patient satisfaction), and patients’ health-related knowledge and beliefs.19 In terms of age, some studies suggested that among elderly patients (>= 65 years old), age may be positively correlated with adherence rate. Patients aged 85 years or above were 2.12 times more likely to show adherence to medication than those aged between 65 and 74 years old.96 Usually patients with severe disease would be assumed to be motivated to demonstrate compliant behavior. However, studies on the association between adherence and severity of disease did not present consistent evidence. In HIV disease patients, Catz et al97 reported that frequency of missed doses was strongly associated with detectable HIV viral loads. Among those who missed doses weekly, 77% of the patients had detectable viral load (>=400 copies/mL). Among those who never missed in past 3 months, 62% of the patients had undetectable viral load (< 400 copies/mL). A Metaanalysis by DiMatteo et al98 suggested that the objective severity of disease and patients’ perception of the severity are significant predictors for adherence behavior. The study showed that patients who are most severely ill with serious disease may be at greatest risk for nonadherence to treatment. Gao et al99 evaluated the relationship of severity of
24
HIV/AIDS, health beliefs and medication adherence. The results indicated that paitents who have experienced more complications had motivation to be adherent to medication regimens to alleviate severe health conditions. Basically the simpler the regimen, and the shorter duration, the greater is compliance. Although no significant difference in adherence rates were observed between once daily (73%) and twice daily (75%) regimens, as the frequency of administration increased, the adherence rate diminished significantly with three times daily (52%) and four times daily (42%) administration.100 Some patients reported that they forgot to take medications unintentionally. However, some study showed that 71% of nonadherence was intentional mainly due to patient’s self-perception that medication was not needed or side effects.101 Besides the above predictors, the following personal factors are also associated with medication adherence behavior: social support, emotional support, personal belief, communication between practitioner and patient. Overall, medication adherence is a recognized public health problem but needs further study. 2.6 The role of comorbidity in medication adherence Comorbidity may precede, be concurrent with, or occur after the principal diagnosis. Having a comorbid condition may increase (or decrease) likelihood of having positive or negative outcome for the principal illness. Previous studies demonstrated that number and severity of comorbidities were associated with medication adherence. Medication nonadherence was substantially more common among diabetic patients with multiple comorbid chronic illnesses. Piette et al102 reported that diabetic patients with more than 3 comorbidities showed higher risk of cost-related medication underuse. Many
25
studies also present a consistent conclusion that patients presenting higher comorbid score (more severe comorbidity) are more likely to have medication nonadherence problem. Balkrishnan et al21 assessed predictors of medication adherence in an older population with type 2 diabetes. The results showed that an increase in the Charlson index (severity of comorbidity) was significantly associated with a 0.0062-point decrease in medication adherence rate. Yang et al103 identified predictors of medication nonadhernce among patients with diabetes in Medicare Part D programs. Medication adherence was calculated as the proportion of days covered (PDC; number of days with medication on hand/number of days in the specified time interval). Higher comorbidity scores were more likely to be nonadherent to antidiabetic agents, antihypertensive drugs, and statin medications. Depression is one of common comorbidities in diabetic patients.104 Katon et al105 evaluated association between comorbid depression and medication adherence in patients with diabetes. It was found that patients with depression were more likely to present poor adherence to antidiabetic medications (OR = 1.98), antihypertensives (OR = 2.06), and LDL control medications (OR = 2.43). 2.7 Measurements of medication adherence Currently there are different ways to measure medication use behaviors, including blood drug concentration test, electronic compliance monitoring, using pharmacy prescription refills, refill pattern regularity, pill counts and weights, patient self report, and patient logs.106 However, every method has some limitations and assumptions. The direct method of measuring adherence rate is to determine the drug concentrations in blood or urine.107,108,109 This method is expensive and inconvenient for patients. Further, the drug concentration in blood depends on the drug behavior in body. Some of drugs
26
cannot be detected in a few hours due to the short half life, which could mislead the results. Most of the other methods focus on the indirect methods to evaluate the adherence rate. Electronic medication event monitors and utilize microprocessors to measure and record date and time of medication use. The limitation of electronic monitoring is that patients would be assumed to take medications as long as the pack is open. However, this assumption cannot reflect accurate information. Electronic devices are very expensive and not practical for use in daily life.100 Patient interview or selfreport is a good tool for qualitative analysis. On the other hand, it may have observation and selection biases and the accuracy of the responses depend on the patient honesty and cognitive ability. Some patients may refuse to answer some questions and provide false information resulting in misclassifications.110 For retrospective and observational study, using prescription record is an efficient and economical strategy.111 Method based on prescription refill records has the same assumption as electronic monitoring that patients would be assumed to take medications if they refill the prescriptions. Additionally this method cannot capture the unusual refill behavior and multiple refills across different pharmacy systems. Since no perfect method has been established to measure the medication adherence, discussions on the limitations of the measurements are very important. 2.8 Medication adherence to diabetes treatment Long term medication use is necessary for type 2 diabetic patients to control blood glucose levels. Medication adherence is positively correlated with blood glucose control. The associations among antidiabetic medication adherence, healthcare costs and various related risk factors, such as race, antidiabetic therapeutic class, regimen, and
27
payment system in Medicaid population were studied by Balkrishnan and his group. One of the explorations examined the relationship among self-reported health status data, subsequent antidiabetic medication adherence, and health care service utilization in older adults with type 2 diabetes mellitus in a managed care setting.21 The study found that increased comorbidity severity and emergency room visit were strongly associated with decreased antidiabetic medication therapy and rising antidiabetic medication adherence remained the strongest predictor of descending total annual health care costs. The other study by Shenolikar and Balkrishnan23 determined the association between race and medication adherence among type 2 diabetic patients. The results showed that the adherence rate of oral antidiabetic medication was significantly higher in whites than that in African Americans. However, there is no significant difference in medication adherence between African Americans and all other races after introduction of pioglitazone treatment.24 To investigate the impact of antidiabetic medication therapy on the medication adherence, Balkrishnan et al25 compared the outcomes associated with thiazolidinedione (TZD) therapy and other oral antidiabetic agents. The study demonstrated that the use of TZDs in a low-income population is associated with better adherence than other oral antidiabetic treatments. Study on Medicaid payment systems indicated that patients enrolled in capitated health plans were more likely to receive better quality of care than those in fee-for-service plans. However, patients with capitated health plan showed lower medication adherence rate and presented higher frequencies of hospitalizations and emergency room visit than those with fee-for-service plans.112 In terms of dose regimens, patients with fixed dose combination therapy presented significantly lower adherence rate than those with monotherapy and dual therapy.
28
Patients receiving fixed dose combination therapy also indicated lower health care costs than the other two groups.113 Overall all of the above studies present the similar conclusion that a higher adherence rate was associated with lower diabetes-related and total health care costs in the patients, although there might be some other factors affecting the outcomes. 2.9 Medication adherence to statin treatment Lipid-lowering therapy requires long term medication administration so that patients can obtain benefits to reduce the risk for CVD events. However, studies suggest that medication adherence is poor and disappointing in patients receiving statin treatment. In US and Canada, patients failed to fill prescriptions for lipid-lowering drugs for about 40% of one year period. After 5 year follow-up in US population, only 52% of those patients kept using lipid-lowering drugs.30 Jackevicius et al28 evaluated the adherence with statin therapy in elderly patients aged 66 years or older with and without acute ACS. Two-year medication adherence rates were 40% for ACS, 36% for chronic CAD, and 25% for those without coronary disease (primary prevention). Compared to the ACS patients, chronic CAD patients are 1.14 times more likely to present nonadherent behavior. The odds of medication nonadherence in patients for primary prevention increased 92% compared to those in ACS patients. Due to early discontinuation, many patients may not receive sufficient benefit from statin treatment. Benner et al30 describe the long-term persistence in use of statin in elderly patients aged 65 years and older. The mean proportion of days covered (PDC) by a statin was 79% in the first 3 months, 56% after 6 months, and 42% after 1 year. Only 25% of the patients presented a PDC (>=80%) after 5 years. The study suggested that greatest drop occurred in first 6 months of lipid-
29
lowering therapy. Schneeweiss et al114 assessed the effects of drug cost sharing on adherence to statin treatment in patients (aged 66 years and older) with and without acute myocardial infarction. The study found that fixed patient copayment and coinsurance negatively associated with medication adherence but not on their initial therapy after myocardial infarction. Compared to patients with full coverage, adherence to new statin therapy was reduced by 5.4% (from 55.8% to 50.5%) under a copayment and subsequent coinsurance policy. McGinnis et al115 presented higher adherence rate of statins in patients enrolled in a secondary prevention program. The overall PDC was 75.4% over 3 years. The risk for all-cause mortality was reduced by 56% in those with PDC > 80%. However, the study concluded that statin adherence still needs to be improved in this population due to high mortality. Larsen et al116 also showed a high persistence of statin therapy in a Danidh population. More than 85% of the patients presented PDC >= 80% during the 5 year study period. However, the study also suggested that larger proportion of patients under 45 years quit treatment before they obtain benefit from statin therapy to reduce CHD morbidity and mortality. Thus early interventions are necessary to improve statin medication adherence among high risk groups. Hyperlipidemia is commonly observed in patients with type 2 diabetes raising the risk of CHD, the leading cause of death in patients with type 2 diabetes. However, there are very few studies investigate statin medication adherence in the diabetes subgroup. Further study is needed to explore statin medication use behavior in diabetes population and investigate effects of diabetes on statin medication use, and associations between statin adherence and healthcare costs and medical utilization.
30
2.10 Medication use behavior in Medicaid population with chronic diseases Three elements are indispensable for patients’ access to prescription drugs, which are (1) adequate insurance coverage or sufficient financial capability to purchase drugs, (2) appropriate physical capability to obtain and fill prescriptions by various transportations, and (3) good medication adherence behavior.117 For Medicaid enrollees, due to cost sharing for Medicaid prescription drug benefit in some states, nominal or moderate copayment could be a financial barrier if Medicaid patients with low incomes have to fill several prescriptions monthly. Nelson et al118 reported that utilization rates and expenditures after copayment implementation were found declined. In terms of differential impact of copayment on various therapeutic groups, expenditures for cardiovascular, cholinergic, diuretic, and psychotherapeutic agents presented a significant change in the long-term trend.119 Based on a report on American’s access to prescription drugs,120 almost 40% of low-income people with chronic conditions and eligible for Medicaid were unable to fill at lease one prescription due to cost concerns. In 2003, more than 14 million American people aged 18 or above with chronic conditions (greater than 50% with low incomes), could not pay all of their prescriptions. Nearly 20% of those adults had public insurance such as Medicaid. Previous research has also shown that prior authorization and use of formularies might affect access to prescription drugs.121 Other studies have suggested that prescription nonfinancial factors and nonmedical cost burdens play important roles in access to medication treatment.122 Shinogle and Wiener evaluated medication use behavior among Medicaid users.123 Among nonfinancial barriers, transportation plays a key role in medication use behavior. People without transportation are more likely to need assistance in or have difficulty
31
obtaining and filling prescriptions, which implies importance of social services. Mental health is another factor influencing Medicaid enrollees to take medications. The study has shown that poorer mental health status is correlated with poorer medication use behavior. Additional reasons for not receiving needed medications include patient being sick, frail, disable, having no assistance in obtain drugs, and inadequate authorization to use mail service pharmacies. In summary, it is important to study medication use behavior in Medicaid population with chronic disease beyond financial issues as state governments implement cost containment in Medicaid prescription drug benefit. Studies would provide health policy implication to governments to assist eligible low income individuals and families, especially older people and younger disabilities in accessing medication therapy and improving health outcomes. Nearly 95% of diabetic patients need statin treatment to control LDL level and reduce CHD risk. However, very few studies were conducted to examine statin use in this specific population. Based on our knowledge, no studies assessed the statin adherence associated outcomes, such as medical utilization and healthcare costs in diabetic population. Our study would be the first one to explore statin medication adherence and evaluate adherence associated outcomes in type 2 diabetes patients based on large national Medicaid database. Predictors of statin adherence would be identified in Medicaid diabetic population, which might be used by health providers to enhance patient consultation. Associations between statin adherence and healthcare costs would be evaluated to provide implications for health policy makers to improve medication use and control medical utilization and healthcare costs in Medicaid population.
32
CHAPTER 3: CONCEPTUAL MODEL AND STUDY DESIGN
3.1 Conceptual model The conceptual model proposed for this study is a combination model based on two health behavior models, the modification of health belief model124 and the AdayAndersen model.125 The model consists of three components, including structure, process, and outcomes. Originally the health belief model including three major elements (individual perceptions, modifying factors, and likelihood of action) was used to predict preventive health behavior.124 Individual perceptions determine patients’ perceived susceptibility to disease and perceived severity of disease, which reflects patient readiness to take action to disease. Modifying factors consist of demographic (age, sex, race, etc.), sociopsychological (personality, social class, peer and reference group pressure, etc.), and cues to action (advice from others, reminder from health care providers, mass media campaigns, and illness of family members). The outcome is the likelihood of taking recommended preventive health action. Based on the health belief model, Becker and Maiman hypothesized a modified model for predicting and explaining compliance behavior. There are three components in the modified health belief model, comprising readiness to undertake recommended compliance behavior, modified and enabling factors
33
and compliant behaviors. Motivations, value of illness threat reduction and probability that compliant behavior could reduce the threat are three elements for readiness to undertake a compliant behavior. These three elements indicate the extent to which patients concern about their health conditions and seek effective strategies to improve health outcomes. Modifying and enabling factors consist of demographic, structural, attitudes, interaction and enabling elements. These factors that can be used to examine and predict the likelihood of compliant behaviors illustrate the characteristics of patients, such as socioeconomic status, clinical and medication related factors, and humanistic factors. The Aday-Andersen behavior model of health services use was initially created in 1960s to evaluate access to medical care.125 This model has four components, including predisposing characteristics, enabling resources, need and use of health services. By adding measures of access, concepts of mutability, currently the model has been developed to phase 4 – an emerging model, consisting of environment, population characteristics, health behavior and outcomes (Figure 2). The environment component provides health care system information (policy, resources, and organization) and other external environment situations. Population characteristics are similar to the modifying and enabling factors in health belief model. Health behavior includes information of personal health practices and health service utilizations. The last one is outcomes reflecting patient health conditions and satisfaction. This model provides a profile of multiple influences on health services use and subsequently health status. It also presents feedback loops indicating that health outcomes may in turn affects predisposing factors, perceived need for health services and health behavior.
34
READINESS TO UNDERTAKE RECOMMENDED COMPLIANCE BEHAVIOR
MODIFYING AND ENABLING FACTORS
COMPLIANT BEHAVIOR
Motivations Concerns about health matters in general Willingness to seek and accept medical directions Intention to comply Positive health activities Value of illness threat reduction Subjective estimate of: Susceptibility Vulnerability to illness Extent of possible bodily harm Extent of possible interference with social roles Presence of symptoms Probability that compliant behavior will reduce the threat Subjective estimate of: Proposed regimen’s safety Proposed regimen’s efficacy to prevent, delay or cure
Demographic (very young or old) Structural (cost, duration, complexity, side effects, accessibility of regimen, need for new patterns of behavior) Likelihood of: Attitudes (satisfaction with physician, clinical procedures and facilities) Interaction (length, depth, continuity, mutuality of expectation, quality and type of doctor-patient relationship, physician agreement with patient, feedback to patient)
Compliance with preventive health recommendations and prescribed regimens
Enabling (prior experience with action, illness or regimen, source of advice and referral)
Figure 1 Modification of Health Belief Model by Becker and Maiman
35
ENVIRONMENT
POPULATION CHARACTERISTICS
Health care system
Predisposing characteristics
External environme
Enabling resources
HEALTH
OUTCOMES
Personal health practices
Perceived health status
Use of health services
Need
Evaluated health status Consumer satisfaction
Figure 2 Bahavioral model of health service use (Aday and Andersen)
The model used in this study combines the modified health belief model and behavioral model of health services use (Figure 3). The model has three components, structure, process, and outcomes, based on the assumption of the readiness of patients to undertake compliant behavior, including motivations, value of illness threat reduction and probability that compliant behavior will reduce the threats to the disease or condition. The structure provided patient characteristics, such as demographic (age, sex, and race), clinical (diagnosis of diabetes and hyperlipidemia, severity of comorbidity, history of CHD events), medication related (antidiabetic therapeutic class, statin treatment, concomitant insulin use) and health insurance (Medicaid). Process was medication use adherence behavior measured by medication possession ratio. Outcomes were associated with medication adherence behavior reflected by health care utilizations and costs and health condition control. 36
SELF-READINESS TO UNDERTAKE HEALTH BEHAVIOR (An assumption, not measured in this
STRUCTURE
PROCESS
OUTCOMES
study) Concern about health conditions Willingness to seek and accept medical advice Intention to take positive health activities and comply Subjective estimates of value of illness threat reduction Subjective estimates of probability that compliant behavior could reduce the threat, such as safety and efficacy of treatment
Demographic variables: Age, gender, race Clinical variables: Diagnosis of disease, Number and severity of comorbidity Medication related variables: Type of therapy for disease and comorbid conditions, Medications taken for disease and comorbid conditions
Medication adherence behavior (Measured by medication possession ratio)
Health care utilizations Health care costs Health condition control
Figure 3 Conceptual model for the study based on modified health belief model (Becker and Maiman) and behavior model of health services use (Aday and Andersen)
37
®
3.2 MarketScan Research Databases MarketScan® Data warehouse was created to provide high quality data, including individual level healthcare claims, lab test results, and hospital discharge information from large employers, managed care organizations, hospitals, Medicare, and Medicaid programs.126 In this study, The MarketScan® Medicaid Database was used to evaluate the statin medication adherence. This database represents eight states with various sizes across the United State. It contains outpatient and inpatient services, prescription drug claims, long-term care and enrollment data. The MarketScan® Medicaid Dataset also provides demographic related variables, such as age, gender, ethnicity, federal aid category, and Medicare eligibility. 3.3 Study design 3.3.1 Study population Patients were continuously enrolled in Medicaid health plan with diagnosis of type 2 diabetes and hyperlipidemia. All of the patients took oral antidiabetic and statin medications. 3.3.2 Study perspective This study took a payer perspective, a third party payer (Medicaid). The study may help Medicaid health plan achieve improved health outcomes and lower healthcare costs. It would also be beneficial in making coverage to employees, influencing the health policy decisions and enhancing interventions to treat diseases and comorbidities. 3.3.3 Patient selection (Diabetes cohort with hyperlipidemia) (1) Inclusion criteria:
38
•
Patients aged >=18 years old at the time of first prescription of statin medication
•
Diagnosis of type 2 diabetes based on ICD-9 codes (250.x0 or 250.x2)
•
Diagnosis of hyperlipidemia based on ICD-9 codes (272.x)
•
At least one prescription record of an oral antidiabetic medication based on NDC codes between January 1, 2005 and December 31, 2005.
•
At least one prescription record of a statin medication based on NDC codes between January 1, 2005 and December 31, 2005.
•
At least one year pre-index and at least one year post-index continuous enrollment in Medicaid. The index date is defined as the date of first statin medication prescription in the diabetes population during the year of 2005.
•
Patients who had no statin prescription fill for one year prior to index date.
•
Complete follow-up period was 12 months after the index event.
•
The actual study period was from January 1, 2004 to December 31, 2006.
(2) Exclusion criteria: •
Patients who were eligible for both Medicaid and Medicare were excluded.
3.3.4 Patient selection (non-diabetes cohort with hyperlipidemia) (1) Inclusion criteria: •
Patients aged >=18 years old at the time of first prescription of statin medication
•
Diagnosis of hyperlipidemia based on ICD-9 codes (272.x)
•
At least one prescription record of a statin medication based on national drug codes (NDC) between January 1, 2005 and December 31, 2005.
39
•
At least one year pre-index and at least one year post-index continuous enrollment in Medicaid. The index event is defined as the date of first statin medication prescription in the non-diabetes population during the year of 2005.
•
Patients who had no statin prescription fill for one year prior to index event.
•
Complete follow-up period was 12 months after the index event.
•
The actual study period was from January 1, 2004 to December 31, 2006
(2) Exclusion criteria: •
Patients who were eligible for both Medicare and Medicaid were excluded.
3.4 Data collection (1) Demographics Data on age, gender, and race were drawn from the MarketScan® Medicaid dataset. (2) Disease diagnosis Diabetes and hyperlipidemia diagnosis was identified based on ICD-9 codes as defined in the patient selection. CVD history was identified based on a broad set of cardiovascular codes, including myocardial infarction, stable or unstable angina, congestive heart failure, and stroke. (3) Medication use Statin medications and oral antidiabetic medications were identified during 12month study period for the eligible patients based on NDC codes as defined in the patient selection. The total number of medications used 1 year prior to index date was counted for each patient as baseline medication use. (4) Hospitalization and ER visits
40
Hospitalization and ER visits were identified based on the service codes. A hospitalization or ER claim showing a primary diagnosis of any condition was considered a visit due to an all-cause visit. Percentages of patients with at least one hospitalization and ER visit 1 year prior to index date were calculated respectively as baseline health services utilization. (5) Outpatient visits Outpatient visits were identified based on outpatient service codes (physician office and hospital outpatient). An outpatient claim showing a primary diagnosis of any conditions was considered an all cause-visit. The total number of outpatient visits 1 year prior to index date was counted for each patient as baseline health services utilization. (6) Severity of diabetes Severity of diabetes was measured indirectly based on complications occurrence 1 year prior to index date. ICD-9 codes were used to identify diabetic complications, including ketoacidosis, nephropathy, retinopathy, neuropathy, peripheral circulatory disorders, and other complications. (6) Comorbidity Charlson comorbidity index based on ICD-9 codes was used to assess the severity of comorbidity.127 The index assigned weights for a number of major conditions (ranging from 1 to 6). The index severity score was calculated for each patient by totaling the assigned weight for each of patient’s comorbidities 1 year prior to index date.
41
Table 5 Charlson’s comorbidity index and Deyo et al. modification for use with ICD-9 diagnosis Weights
1
2
3 6
Condition
ICD-9 code
Myocardial infarction
410, 412
Congestive heart failure
398, 402, 428
Peripheral vascular disease
440-447
Cerebrovascular disease
430-433, 435
Dementia
290, 291, 294
Rheumatologic disease
710, 714, 725
Ulcer disease
531-534
Mild liver disease
571, 573
Hemiplegia
342, 434, 436, 437
Moderate or severe renal disease
403, 404, 580-586
Diabetes
250
Any tumor
140-195
Leukemia
204-208
Lymphoma
200, 202, 203
Moderate or severe liver disease
070, 570, 572
Metastatic solid tumor
196-199
AIDS
042-044
3.5 Outcome measures (1) Medication adherence Medication adherence was defined as the level of participation achieved in a medication regimen once an individual has agreed to the regimen. The medication adherence was calculated based on the drug claims under an assumption that a prescription filled is a prescription taken. Adherence was measured by using medication
42
possession ratio (MPR).128 MPR is the ratio of the sum of days supplied by each prescription fill divided by the days needing the medication (365 days minus the number of days of hospitalization). The 365-day period was the number of days within the 1 year post-index period. MPR =
Total number of days of drug dispensed 365 − hospitalization days
(2) Prevalence of medication adherence
Adherence prevalence =
Number of people adherent to statins Total number of people receiving statins during the study period
Prevalence of medication adherence was evaluated by using MPR score as a proxy for medication adherence. The MPR of 0.8 is an appropriate threshold suggesting fairly continuous medication use. Those with a MPR score of higher than or equal to 0.8 were considered as adherence to statins. (3) Medical utilization and Health care costs Risks for hospitalization and ER visit (at least one hospitalization or ER visit during study period) were measured as medical utilization. This was defined as the probability of 1 or more hospitalizations or ER visits during 1 year period. Patients were followed up for complete healthcare service utilization (hospitalization, ER visits, outpatient physician and hospital visits, and medication use) for one-year period. Reimbursement rates made by Medicaid were used to compute all-
43
cause and hyperlipidemia-related healthcare costs, including prescription drugs, medical care, and total costs. All-cause costs were drug and medical costs associated with any condition during one year period. Hyperlipidemia-related costs were statin medications and hyperlipidemia-related medical costs indentified by primary ICD-9 codes in medical claims based on a set of cardiovascular codes such as myocardial infarction, stable or unstable angina, congestive heart failure, and stroke. They were a subset of all-cause costs. Medical costs were defined as the sum of outpatient costs (including ER) and inpatient costs. Total healthcare costs were defined as the sum of drug costs and medical costs.
3.6 Data analysis 3.6.1 Propensity Score In observational studies, investigators are not able to assign treatment in patients randomly. The treated and non-treated groups may present large differences on their baseline characteristics. Propensity score is a conditional probability of a patient receiving a particular treatment based on observed baseline covariates. This strategy can balance observed covariates in two study groups and reduce bias in comparing outcomes129. Due to the large sample size in study population (n= 7184) and a big difference in sample size between non-diabetic (n = 5479) and diabetic (n = 1705) groups, imbalanced baseline of characteristics of two groups might be expected. Propensity score was used to remove differences in descriptive analysis between non-diabetes and diabetic groups so that baseline characteristics would be equivalent to compare outcome measures. A
44
logistic regression was conducted to estimate probability of each subject belonging to non-diabetic or diabetic group. Variables included in the logistic model were age, sex, race and medical services used and clinical history in the baseline year, including total number of outpatient visits, total number of medications used, whether or not hospitalization or ER visit, and CVD history. Stratification was used with quintile propensity scores to compare baseline characteristics between non-diabetes and diabetic groups. Finally propensity score was included in the regression as one variable to compare outcomes between two groups.130
3.6.2 Statistics and hypothesis testing Data for this study were made available as SAS files. Statistical analysis and hypothesis testing were conducted by using SAS 9.1. One main dataset (including diabetic and non-diabetic patients) was used to test objectives 1 – 3. The dataset for subgroup were used to test objectives 4 – 6.
Objectives 1: To compare descriptive characteristics between diabetic and nondiabetic patients.
Objective 2: To compare statin medication possession ratios (MPR) and prevalence of statin adherence between diabetic and non-diabetic groups Prevalence of medication adherence was evaluated by using MPR score as a proxy for medication adherence. The MPR of 0.8 is an appropriate threshold suggesting fairly continuous medication use.131 Those with a MPR score of higher than or equal to 0.8 were considered as adherent to a medication. A multiple linear regression was used to evaluate MPRs (response variable) between diabetic and non-diabetic patients. Primary independent variable was group
45
(with diabetes or without diabetes). Other independent variables included in the model were demographics (age, gender, and race), baseline clinical factors (severity of diabetes, comorbidity, and history of CVD), baseline medication related factors (total number of medications used in one year) and baseline medical care utilization (hospitalization, ER, and outpatient visits).
MPR = β0 +β1 (with or without diabetes) + β2(demographic factors) + β3(clinical factors) + β4 (medication related factors) + β5 (medical utilization related factors) + ε A logistic regression was used to evaluate statin adherence behavior (MPR ≥ 0.8, response variable) between diabetic and non-diabetic patients. Primary independent variable was group (with diabetes or without diabetes). Other independent variables included in the model were demographics (age, gender, and race), baseline clinical factors (severity of diabetes, comorbidity, and history of CVD), baseline medication related factors (total number of medications used in one year) and baseline medical care utilizations (hospitalization, ER and outpatient visits).
Ln (odds of adherence) = β0 +β1 (with or without diabetes) + β2(demographic factors) + β3(clinical factors) + β4 (medication related factors) + β5 (medical utilization related factors) + ε Objective 3: To compare all-cause healthcare costs and hyperlipidemia-related healthcare costs between diabetic and non-diabetic groups A multiple linear regression was used with the natural logarithm of all-cause or hyperlipidemia-related healthcare costs as a dependent variable. Primary independent variable was group (with or without diabetes). Other independent variables included in the model were demographics (age, gender, and race), baseline clinical factors (severity
46
of diabetes, comorbidity, and history of CVD), baseline medication related factors (total number of medications used in one year) and baseline medical care utilizations (hospitalization, ER and outpatient visits).
Ln (all-cause costs) = β0 +β1 (with or without diabetes) + β2(demographic factors) + β3(clinical factors) + β4 (medication related factors) + β5 (medical utilization related factors) + ε Ln (hyperlipidemia-related costs) = β0 +β1 (with or without diabetes) + β2(demographic factors) + β3(clinical factors) + β4 (medication related factors) + β5 (medical utilization related factors) + ε Objective 4: To identify predictors of statin MPR and statin adherence behavior in non-diabetic and diabetic patients respectively. A multiple linear regression was used to evaluate MPR (response variable) in diabetes or non-diabetes subgroups. Independent variables included in the model were demographics (age, gender, and race), baseline clinical factors (severity of diabetes, comorbidity, and history of CVD), baseline medication related factors (total number of medications used in one year) and baseline medical care utilizations (hospitalization, ER and outpatient visits).
MPR = β0 + β1(demographic factors) + β2(clinical factors) + β3 (medication related factors) + β4 (medical utilization related factors) + ε A logistic regression was used to evaluate statin adherence behavior (MPR ≥ 0.8, response variable) in diabetes or non-diabetes subgroups. Independent variables included in the model were demographics (age, gender, and race), baseline clinical factors (severity of diabetes, comorbidity, and history of CVD), baseline medication related
47
factors (total number of medications used in one year) and baseline medical care utilizations (hospitalization, ER and outpatient visits).
Ln (odds of adherence) = β0 + β1(demographic factors) + β2(clinical factors) + β3 (medication related factors) + β4 (medical utilization related factors) + ε Objective 5: To examine association between medical utilization (risks for hospitalization or ER visit) and statin adherence in diabetic or non-diabetic subgroup. A logistic regression was used to evaluate risk for hospitalization or ER visit between adherent and non-adherent groups in diabetic or non-diabetic populations. Primary independent variable was statin adherence. Other independent variables included in the model were demographics (age, gender, and race), baseline clinical factors (severity of diabetes, comorbidity, and history of CVD), baseline medication related factors (total number of medications used in one year) and baseline medical care utilizations (hospitalization, ER and outpatient visits).
Ln (odds of hospitalization or ER visit) = β0 + β1(adherence to statin) + β2(demographic factors) + β3(clinical factors) + β4(medication related factors) + β5(medical utilization related factors) + ε Objective 6: To assess associations between statin medication adherence and various healthcare costs in diabetic or non-diabetic subgroup. A multiple linear regression was used with the natural logarithm of various healthcare costs (all-cause or hyperlipidemia-related total healthcare, all-cause or hyperlipidemia-related medical costs, or all-cause or statin medication costs) as a dependent variable. The medical costs were sum of outpatient (including ER) and inpatient costs. Primary independent variable was statin adherence (yes/no). Other
48
independent variables included in the model were demographics (age, gender, and race), baseline clinical factors (severity of diabetes, comorbidity, and history of CVD), baseline medication related factors (total number of medications used in one year) and baseline medical care utilizations (hospitalization, ER and outpatient visits).
Ln (various healthcare costs) = β0 +β1 (statin adherence) + β2(demographic factors) + β3(clinical factors) + β4 (medication related factors) + β5 (medical utilization related factors) + ε 3.7 Linear regression model diagnostics In linear regression model, it has been assumed that a dependent variable changes linearly with each continuous predictor and that the errors (residuals) present a normal distribution with mean zero and constant variance for every value of the predictors (homoscedasticity). Additionally all the independent variables are not substantially correlated with each other.132 Violation of these assumptions may introduce biases in estimation of regression coefficients and result in inaccurate statistical significance in hypothesis tests.133 In this study, the assumptions of normality of residuals and homoscedasticity and multicollinearity of predictors were evaluated.
3.7.1 Normality of residuals and homoscedasticity Normality of residuals is necessary for tests of statistical significance. Various graphic methods have been used to evaluate the normality of residuals (ε). In this study, a histogram plot and normal quantile-quantile (Q-Q) plot were used to evaluate the normality of residuals.132 The histogram displays the frequency of data points falling into various ranges as a bar chart. The normal Q-Q plot is useful for comparing the frequency distribution of the data to a normal distribution by distinguishing lines that are straight
49
from ones that are not. The shape and direction of the curvature can be used to diagnose the deviation from normality. The assumption of homoscedasticity means that the variance of the linear regression is constant across observations. When the assumption is violated, the accuracy of statistical significance can be affected, such as p-value and confidence intervals. A SPEC test and Durbin-Watson statistic were used to assess independence of data and identical distribution of residuals (homoscedasticity).134,135
3.7.2 Multicollinearity Multicollinearity occurs when some or all of the independent variables are substantially correlated with each other or when one or more of the independent variables are almost linear combinations of the other independent variables. Violation of this assumption would make it difficult to determine the importance of predictors because the effects of the predictors are confounded due to the correlations among the independent variables.115 The standard errors of partial regression coefficients may increase as the correlations among the independent variables increase. Variance inflation factor (VIF) was used to assess the multicollinearity.133 As VIF values increase, the variance of the partial regression coefficient. High values for VIF indicate that a particular independent variable is a linear combination of the other independent variables. If a VIF value is greater than 10, there is a reason for concern. The other method is to check the matrix of bivariate correlations (intercorrelations among independent variables). If the value of intercorrelation is less than 0.3, there could be a slight concern of multicollinearity.
50
CHAPTER 4: RESULTS
4.1 Study population
A total of 7184 patients satisfied inclusion criteria in final analysis of which 5479 patients belonged to non-diabetic group and 1705 patients belonged to diabetic group. Figures 4 and 5 described patient selection.
MarketScan eligible Medicaid population with hyperlipidemia and diabetes in 2005 (N=5343)
Use both statins and oral antidiabetic medication in 2005 (N=2996)
Continuous enrollment one year pre-index date and one year post-index date (2004-2006) (N = 1705)
Statin medication adherence (MPR ≥ 0.8) (N = 624)
Statin medication nonadherence (MPR < 0.8) (N = 1081)
Figure 4 Study population with hyperlipidemia and diabetes (diabetic group)
51
MarketScan eligible Medicaid population with hyperlipidemia but without diabetes in 2005 (N= 15923)
Start statin medication use in 2005 (N= 10476)
Continuous enrollment one year pre-index date and one year post-index date (2004-2006) (N = 5479)
Statin medication adherence (MPR ≥ 0.8) (N = 1401)
Statin medication nonadherence (MPR < 0.8) (N = 4078)
Figure 5 Study population with hyperlipidemia but no diabetes (non-diabetic group)
4.2 Descriptive study population characteristics and outcomes measures Table 6 presented descriptive characteristics of subjects in two study groups, nondiabetic and diabetic groups. There were 5479 eligible patients included in non-diabetic group and 1705 patients included in diabetic group in the study. Overall baseline characteristics of the two groups were not equivalent due to significant differences in all variables (p < 0.05). On average the non-diabetic group was 2 years younger than the diabetic group. Patients aged 55 years and above accounted for 34% of non-diabetic population and 43% of diabetic population. In terms of gender, females were more than two thirds of all patients in both groups. White patients accounted for the highest portion in both non-diabetic (63%) and diabetic (54%) groups. Black patients in diabetic group were about 8% more than those in non-diabetic group. Regarding the baseline of health
52
services used in one year prior to index date, nearly half of patients in both groups had at least one ER visit and about 25% of patients had hospitalization experience. Within previous one year period, number of medications used by patients with diabetes was almost 1.5 times the number of medications used by patients without diabetes. In terms of clinical baseline in previous one year, more than 30% of patients in both groups presented CVD diagnosis. Severity of comorbidity was also significantly different between the two groups. Only 1.5% of patients in non-diabetic group demonstrated medium or high comorbidity scores. However, more than 50% of patients in diabetic group presented comorbidity scores greater than 2.
53
Table 6 Descriptive characteristics of two study populations enrolled in Medicaid (diabetes vs. non-diabetes) (N = 7184)
Variables Age (years) 18-44 (%) 45-54 (%) 55-64 (%) >=65 (%) Sex Male (%) Female (%) Race White (%) Black (%) Hispanic (%) Other (%)
Non-diabetes (N = 5479) Mean (SD) 48.8 (11.09) 33.6 32.6 30.4 3.4
Diabetes (N=1705) Mean (SD)
p-value (t-test or χ2 test)
51.0 (10.33) 24.8 32.6 38.0 4.6
< 0.0001
0.0005 32.8 67.2
28.3 71.7
63.4 24.5 1.4 10.7
54.0 32.4 2.0 11.6
< 0.0001
Health services used and clinical history in 1 year prior to index date CVD history (%) 34.9 Total number of Medications 11.4 (7.51) Total number of outpatient 15.9 (7.99) visits ER visit (%) 49.0 Hospitalization (%) 23.9 Diabetic complications (%) Ketoacidosis Nephropathy Retinopathy Neuropathy Peripheral circulatory disorder Other 1 0.19 (0.726) Comorbidity Score 0-2 (%) 98.6 3-4 (%) 0.8 >=5 (%) 0.6
30.6 17.0 (9.27)
0.0022 < 0.0001
19.2 (8.87)
< 0.0001
52.4 27.6 32.4 3.1 3.7 5.7 11.6 4.3 12.6 3.33 (2.358) 54.2 27.8 18.0
0.0138 0.019 < 0.0001
CVD: cardiovascular disease, ER: emergency room, SD: standard deviation 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities.
54
Table 7 Outcomes measures of two study populations enrolled in Medicaid (diabetes vs. non-diabetes) (N = 7184) Non-diabetes(N=5479) Mean (median or SD)1 0.50 (0.325) 25.6 -
Outcomes MPR (statins) Adherence to statins2 (%) MPR (antidiabetics)
Diabetes(N=1705) Mean (median or SD)1 0.61 (0.323) 36.7 0.71 (0.295)
p-Value (t-test) < 0.0001 < 0.0001
Health services used and clinical history during 1 year study period Total number of Medications Total number of outpatient visits ER visit (%) Hospitalization (%) Diabetic complications (%) Total costs ($)
Drug costs ($)
Outpatient costs5($)
Inpatient costs ($)
9.9 (6.62) 14.2 (7.64)
18.1 (8.93) 17.2 (7.78)
< 0.0001 < 0.0001
48.3 20.9 -
52.3 29.0 29.4
0.014 < 0.0001
8598 (4163)
16225 (8729)
< 0.0001
1916 (753)
2611 (1017)
0.0136
Diabetes4
-
1599 (846)
All-cause
2437 (1645)
5785 (4580)
< 0.0001
Statin4
594 (488)
729 (672)
0.0002
-
740 (429)
3017 (1616)
3652 (2175)
< 0.0001
419 (132)
301 (192)
< 0.0001
-
480 (126)
3144 (0)
6788 (0)
< 0.0001
903 (0)
1580 (0)
0.0167
-
378 (0)
All-cause3 Hyperlipidemia
Diabetes
4
4
All-cause Hyperlipidemia
4
Diabetes4 All-cause Hyperlipidemia Diabetes
4
4
ER: emergency room, MPR: medication possession ratio, SD: standard deviation 1. MPR = Mean (SD), Costs = Mean (median) 2. Adherence: patients with MPR ≥ 0.8 3. Total all-cause costs = all-cause drug + all cause outpatient (ER included) + all-cause inpatient 4. Hyperlipidemia-related and diabetes-related costs were identified by primary ICD-9 codes in medical claims data and NDC drug claims data. For hyperlipidemia, disease-related medical costs were identified by a set of cardiovascular codes. Statin medications were used for hyperlipidemiarelated drug costs. For diabetes, oral antidiabetic medications were used for diabetes-related drug costs. Total disease-related costs = disease-related-drug costs + disease-related outpatient (ER included) costs + disease-related inpatient costs 5. Outpatient costs included physician office visits, hospital outpatient visits and ER visits.
55
Table 7 compared MPRs and various healthcare costs between non-diabetes and diabetic groups. Overall healthcare costs of diabetic group were significantly higher than those of non-diabetic group (p < 0.05), including total all-cause costs, total hyperlipidemia-related costs, total all-cause drug costs, statin medication costs, all-cause outpatient costs, hyperlipidemia-related outpatient costs, all-cause inpatient costs and hyperlipidemia-related inpatient costs. Total all-cause healthcare costs in diabetic group were nearly $7700 more than the costs in non-diabetic group within one year period. In terms of costs in hyperlipidemia and related treatments, patients with diabetes spent $700 less than those with diabetes within one year. Costs of all medications for diabetic group were more than 2 times the costs for diabetic group. Diabetic patients also spent $135 more than non-diabetic patients on statin medications. In terms of outpatient costs, diabetic patients presented $ 635 higher in all-cause costs but $ 118 lower in hyperlipidemia-related costs than non-diabetic group. Patients with diabetes also spent $ 3644 more on all-cause inpatient costs and $ 677 more on hyperlipidemia-related inpatient costs than those without diabetes. Both groups showed low MPR values of statin medications that were 0.5 for non-diabetic group and 0.6 for diabetic group. Statin medication adherence was poor in the two study populations. Only 25% of patients in non-diabetic group and 35% of patients in diabetic group demonstrated MPR greater than 0.8. Regarding the healthcare utilization, diabetic group appeared to use more medications and utilize more healthcare services, which implied poorer overall healthcare conditions in this group during the study period. Table 8 compared characteristics for patients between non-diabetic group and diabetic group before and after propensity score stratification. It is obvious that before propensity score conducted all of the characteristics were significantly different (p < 56
0.05), which implied that baseline of the two groups was not on balance for outcomes comparison. After propensity score stratification with 5 quintiles, it removed imbalance of 7 out of 9 variables. Total number of medications and comorbidity score presented significant differences between two groups (p < 0.001) after propensity scores, so these variables were included into regression models for adjustment analysis.
Table 8 Comparison of baseline characteristics between diabetic and non-diabetic patients before and after propensity score (N = 7184) Non-diabetes (N = 5479)
Diabetes (N=1705)
p-value before propensity
p-value after propensity
Mean (SD)
Mean (SD)
score
score
Age (years)
48.8 (11.09)
51.0 (10.33)
< 0.0001
0.2746
Sex Male (%)
32.8
28.3
0.0005
0.8927
67.2
71.7 < 0.0001
0.5683
Variables
Female (%) Race White (%) Black (%) Hispanic (%)
63.4 24.5 1.4
54.0 32.4 2.0
Other (%)
10.7
11.6
Clinical and medical utilization 1 year prior to index date CVD history (%)
34.9
30.6
0.0022
0.7331
Total number of medications
11.4 (7.51)
17.0 (9.27)
< 0.0001
< 0.0001
Total number of outpatient visits
15.9 (7.99)
19.3 (8.87)
< 0.0001
0.0851
52.4 27.6
0.0138 0.019
0.5933 0.2857
3.33 (2.358)
< 0.0001
< 0.0001
49.0 ER visit (%) Hospitalization (%) 23.9 Comorbidity Score
1
0.19 (0.726)
CVD: cardiovascular disease, ER: emergency room, SD: standard deviation 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities.
57
4.3 Comparison of statin medication adherence between diabetic and non-diabetic groups Table 9 Comparison of statin medication adherence between non-diabetic (N = 5479) and diabetic (N = 1705) groups Independent variable
Dependent variable: Statin medication adherence Odds ratio (95% CI)
Group(diabetes vs. non-diabetes)
1.60 (1.289, 2.236)***
Age (years)
1.03 (1.022, 1.032)***
Sex (Female vs. male)
0.82 (0.728, 0.917)**
Race Black vs. white
0.65 (0.565, 0.737)***
Hispanic vs. white
1.04 (0.673, 1.507)
Other vs. white
0.76 (0.633, 0.900)**
Total number of medications1
1.01 (0.999, 1.016)
Comorbidity score2
0.98 (0.930, 1.029)
Propensity score
1.16 (1.021, 1.796)*
CI: confidence interval, *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Total number of medications was counted in one year prior to index date. 2. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date. .
Table 9 compared likelihood of statin medication adherence behavior between non-diabetic group and diabetic group by using a logistic regression. Patients with diabetes were 1.60 times more likely to be adherent to statin medications. Additionally, age, sex, and race were associated with statin adherence. Generally with one year older in age and one outpatient visit increased, the odds of adherence behavior were increased by 3% and 2%, respectively. Female patients reduced odds of adherence by 18% compared
58
with male patients. Black patients presented 35% lower adherence likelihood than white patients.
Table 10 Comparison of statin medication MPR between non-diabetes (N = 5479) and diabetes (N = 1705) groups enrolled in Medicaid Independent variable
Dependent variable: MPR Estimated coefficient (SE)
Group (diabetes vs. non-diabetes)
0.090 (0.0214)***
Age (years)
0.0044 (0.000351)***
Sex (Female vs. Male)
-0.042 (0.00805)***
Race Black vs. white Hispanic vs. white Other vs. white
-0.049 (0.0134) -0.024 (0.0152) -0.041 (0.0122)***
Total number of medications1
0.0033 (0.000624)**
Comorbidity score2
0.0036 (0.00366)
Propensity score
0.133 (0.0903)**
Intercept
0.17 (0.0335)***
R2 = 0.2657, MPR: medication possession ratio, SE: standard error, *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Total number of medications was counted in one year prior to index date. 2. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date. .
Table 10 compared MPR of statin medications between non-diabetes and diabetic groups. Overall 27% of variation in MPR can be explained by this linear regression model. Patients with diabetes resulted in 0.09 unit increase in MPR of statins. Additionally total numbers of medications, age, and sex were also significant predictors
59
in this model. With 10 years older in age, MPR were raised 0.044 unit. Every one more medication used was associated with 0.0033 unit increase in MPR.
4.4 Comparisons of healthcare costs between diabetic and non-diabetic groups Table 11 compared total healthcare costs between non-diabetes and diabetic groups. Due to highly skewed variable of total costs, a natural log transformation was conducted for the dependent variable in a linear regression model. The value of R2 showed that 35% of variation in natural log of total all-cause healthcare costs can be explained by this regression model. Although diabetic patients appeared to reduce total healthcare costs, there was no statistical significance shown in this model. Age, sex, total number of medications used, and comorbidity score were significant predictors in the model. For all patients, with 10 years older in age, total healthcare costs were increased by 3.5%. Female patients spent on health care 13% less than male patients did within one year. Patients using one more medication and showing one unit increase in comorbidity score elevated total healthcare costs by 4% and 4.2% respectively. Table 12 compared total hyperlipidemia-related healthcare costs between nondiabetes and diabetic groups. Due to highly skewed variable of total hyperlipidemiarelated costs, a natural log transformation was conducted for the dependent variable in a linear regression model. All independent variables accounted for 26% of variation in hyperlipidemia-related costs in this linear regression model. Total healthcare costs related to hyperlipidemia treatments for patients with diabetes were 16% less than the costs for patients without diabetes. Age, sex, total number of medications used, and comorbidity score were also significant predictors in this regression model. For all patients, with 10 years older in age, the hyperlipidemia-related costs were increased by 10%. Female
60
patients spent on hyperlipidemia-related treatments 15% less than male patients. Patients who used one more medication and presented one unit increase in comorbidity score elevated hyperlipidemia-related healthcare costs by 1.2% and 8% respectively.
Table 11 Comparison of annual total all-cause healthcare costs between non-diabetic (N = 5479) and diabetic (N = 1705) groups enrolled in Medicaid Dependent variable: Independent variable
Ln (total all-cause healthcare costs1) Estimated coefficient (SE)
Group (diabetes vs. non-diabetes)
-0.059 (0.0429)
Age (years)
0.0035 (0.00078)***
Sex (Female vs. Male)
-0.13 (0.0178)***
Race Black vs. white Hispanic vs. white Other vs. white
-0.028 (0.0292) -0.10 (0.0419) -0.031 (0.0270)
Total number of medications2
0.040 (0.0014)***
Comorbidity score3
0.041 (0.0081)***
Propensity score
0.18 (0.0668)*
Intercept
7.75 (0.0741)***
R2 = 0.3499, SE: standard error, *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Total all-cause healthcare costs = all-cause drug costs + all-cause outpatient costs (ER included) + all-cause inpatient costs 2. Total number of medications was counted in one year prior to index date. 3. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities one year prior to index date.
61
Table 12 Comparison of annual total hyperlipidemia-realted healthcare costs between non-diabetic (N = 5479) and diabetic (N = 1705) groups Dependent variable: Independent variable
Ln (total hyperlipidemia-related healthcare costs1) Estimated coefficient (SE)
Group (diabetes vs. non-diabetes)
-0.17 (0.0578)**
Age (years)
0.0098 (0.0010)***
Sex (Female vs. Male)
-0.14 (0.0238)***
Race Black vs. white Hispanic vs. white Other vs. white
-0.074 (0.0373) -0.040 (0.0641) 0.031 (0.0361)
Total number of medications2 3
0.012 (0.00173)***
Comorbidity score
0.075 (0.0108)***
Propensity score
0.12 (0.0893)*
Intercept
6.01 (0.0963)***
R2 = 0.2597, SE: standard error *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Hyperlipidemia-related costs were identified by primary ICD-9 codes in medical claims data based on a set of cardiovascular codes and NDC in drug claims data. Statin medications were used for hyperlipidemia-related drug costs. Total hyperlipidemia-related costs = statin costs + hyperlipidemia-related outpatient (ER included) costs + hyperlipidemia-related inpatient costs.
2. Total number of medications was counted in one year prior to index date. 3. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
4.5 Subgroup analysis for patients with hyperlipidemia but without diabetes (nondiabetic group) Table 13 examined predictors of statin medication adherence behavior in nondiabetic patients by using a logistic regression. Age, sex, race, ER visit and numbers of
62
outpatient visits and medications used in previous year were associated with statin medication adherence (p < 0.05). The odds of adherent behavior increased 3% with every one year increase in age. Female patients were 0.84 time less likely to be adherent to satin medications. In terms of race, the adherence behavior in white patients was 40% higher than in black patients. The odds of adherence behavior for patients with ER visit were reduced by 42% compared to those without ER visit within previous one year period. With one outpatient visit increase and one more medication used in previous one year, statin medication adherence also rose by 2% and 3%. Table 14 examined the predictors of statin MPR in patients without diabetes. Overall age, sex, race, ER visit, total number of medications used, number of outpatient visits were significant predictors to estimate MPR in this group. More than 35% of variation in MPR of statins can be explained by this linear regression model. With every 10 years increase in age, MPR value rose by 0.042. In terms of sex, female patients presented 0.04 lower in MPR than male. ER visit experience reduced MPR by 0.08. Total numbers of medications used and outpatient visits were positively correlated with MPR. Every one medication and one outpatient visit increased resulted in 0.0045 and 0.002 increase in MPR.
63
Table 13 Predictors of statin medication adherence in non-diabetic population enrolled in Medicaid (N = 5479)
Independent variable
Dependent variable: statin adherence1 Odds ratio (95% CI)
Age (years)
1.03 (1.020, 1.032)***
Sex (Female vs. Male)
0.84 (0.734, 0.959)**
Race Black vs. white
0.61 (0.520, 0.719)***
Hispanic vs. white
0.88 (0.529, 1.464)
Other vs. white
0.73 (0.594, 0.903)**
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
1.14 (0.989, 1.322)
ER visit (yes vs. no)
0.58 (0.505, 0.663)***
Hospitalization (yes vs. no)
1.01 (0.856, 1.182)
Total number of medications
1.03 (1.015, 1.034)***
Total number of outpatient visits
1.02 (1.011, 1.019)***
Comorbidity score2
1.07 (0.984, 1.165)
CI: confidence interval, CVD: cardiovascular diseases, ER: emergency room *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Adherence: patients with medication possession ratio (MPR) ≥ 0.8 2. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date. .
64
Table 14 Predictors of MPR in non-diabetic population enrolled in Medicaid (N = 5479) Independent variable
Dependent variable: MPR Estimated coefficient (SE)
Age (years)
0.0042 (0.00039)***
Sex (Female vs. Male)
-0.040 (0.0091)***
Race Black vs. white Hispanic vs. white Other vs. white
-0.056 (0.0151)** -0.031 (0.0117) -0.039 (0.0141)**
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
0.018 (0.0102)
ER visit (yes vs. no)
-0.079 (0.0092)***
Hospitalization (yes vs. no)
-0.014 (0.0112)
Total number of medications
0.0045 (0.00073)***
Total number of outpatient visits
0.0020 (0.00030)***
1
Comorbidity score
0.012 (0.00619)
Intercept
0.31 (0.0273)***
R2 = 0.3519, CVD: cardiovascular diseases, ER: emergency room, MPR: medication possession ratio, SE: standard error, *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
Association between risk for hospitalization and statin medication adherence in group without diabetes were evaluated in Table 15. Besides the primary predictor statin adherence, previous one year health service utilization and clinical related predictors such as CVD history, ER visits, hospitalization, number of medications used, and comorbidity score were significantly associated with risk hospitalization (p < 0.05). The risk for hospitalization was reduced by one third in patients presenting adherent behavior. With
65
every one year increase in age, the risk for hospitalization was raised by 2%. The risk for hospitalization in patients with previous CVD history was 1.92 times the risk in patients without CVD history. Patients with previous ER visits and hospitalization were 1.76 times and 2.46 times more likely to be hospitalized than those without ER visits and hospitalization in one previous year. One more medication used and one unit increase in comorbidty score raised the risk for hospitalization by 4% and 28%, respectively.
Table 15 Association between risk for hospitalization and statin medication adherence in non-diabetic population enrolled in Medicaid (N = 5479) Independent variable
Dependent variable: likelihood of hospitalization Odds ratio (95% CI)
Statin adherence (yes vs. no)
0.68 (0.575, 0.811)***
Age
1.02 (1.008, 1.022)***
Sex (Female vs. Male)
0.96 (0.808, 1.119)
Race Black vs. white
0.95 (0.790, 1.141)
Hispanic vs. white
0.59 (0.284, 1.229)
Other vs. white
0.90 (0.703, 1.158)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
1.92 (1.640, 2.247)***
ER visit (yes vs. no)
1.76 (1.512, 2.059)***
Hospitalization (yes vs. no)
2.46 (2.100, 2.884)***
Total number of medications
1.04 (1.032, 1.053)***
Total number of outpatient visits
1.00 (0.999, 1.008)
1
Comorbidity score
1.28 (1.174, 1.391)***
CVD: cardiovascular disease, ER: emergency room, MPR: medication possession ratio, CI: confidence interval, *: p < 0.05, **: p < 0.01, ***: p < 0.0011. 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
66
Table 16 Association between ER visit and statin medication adherence in non-diabetic patients enrolled in Medicaid (N = 5479) Independent variable
Dependent variable: likelihood of ER visit Odds ratio (95% CI)
Statin adherence( yes vs. no)
0.71 (0.619, 0.817)***
Age
0.98 (0.974, 0.985)***
Sex (Female vs. Male)
1.32 (1.157, 1.496)***
Race Black vs. white Hispanic vs. white Other vs. white
1.00 (0.864, 1.147) 1.39 (0.849, 2.269) 0.88 (0.724, 1.074)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
1.40 (1.220, 1.612)***
Hospitalization (yes vs. no)
1.31 (1.124, 1.525)***
ER visit previous year (yes vs. no)
3.33 (2.947, 3.755)***
Total number of medications2
1.05 (1.039, 1.060)***
Total number of outpatient visits
1.00 (0.995, 1.003)
Comorbidity score
1
1.17 (1.069, 1.272)***
CI: confidence interval, CVD: cardiovascular disease, ER: emergency room visit *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
Table 16 presented an association between ER visit and statin adherent behavior in non-diabetic patients. Primary variable statin adherence was negatively associated with occurrence of ER visit in patients without diabetes. Patients with adherence to statins reduced risk for ER visit by 29%. Additionally, previous CVD history and hospitalization raised risk for ER visit by 40% and 31% respectively. If patients presented an ER visit in previous one year, the risk for ER visit in the study period was also increase 2.33 times.
67
One more medication used and one unit increase in comorbidity score increased risk for ER visit by 5% and 17% respectively.
Table 17 Comparison of all-cause annual healthcare costs in non-diabetic patients by statin medication adherence (N = 5479) All-cause costs
Adherence (N = 1401)
Non-adherence (N = 4078)
p-value
($)
Mean (median)
Mean (median)
(t-test)
Total
10156 (5428)
8078 (3739)
0.0043
Drug
3680 (2612)
2014 (1309)
< 0.0001
Outpatient
3222 (1666)
2951 (1593)
0.0526
Inpatient
3254 (0)
3113 (0)
0.8373
Table 18 Comparison of hyperlipidemia-related annual healthcare costs in non-diabetic patients by statin medication adherence (N = 5479) Hyperlipidemia-
Adherence (N = 1401)
Non-adherence (N = 4078)
p-value
related costs ($)
Mean (median)
Mean (median)
(t-test)
Total
2323 (1356)
1791 (580)
0.0064
Drug
1101 (1061)
421 (340)
< 0.0001
Outpatient
475 (144)
401 (129)
0.1026
Inpatient
746 (0)
958 (0)
0.2536
Tables 17 and 18 compared all-cause and hyperlipidemia-related healthcare costs in non-diabetic patients without adjustments. Overall adherent patients demonstrated significantly higher all-cause and hyperlipidemia-related healthcare costs and higher drug costs. No significant differences were shown in outpatient and inpatient costs. The results
68
suggested that an increase in drug costs may contribute to the elevated all-cause and hyperlipidemia-related costs. Table 19 evaluated the association between medical costs (inpatient and outpatient costs) and statin medication adherence behavior in non-diabetic group after adjusting for demographic, medical utilization, clinical history, and medication related variables. Overall adherent behavior decreased all-cause costs but no significant difference was demonstrated in hyperlipidemia-related costs due to different statin adherence. Patients with statin adherence showed 11% lower all-cause medical costs than those without adherence behavior, which was consistent with decreased likelihood of hospitalization and ER visit. Age, previous CVD history, ER visit, hospitalization and total number of medications used were also positively associated with total costs and hyperlipidemia-related costs. Table 20 assessed the relationship between statin medication adherence and drug costs in non-diabetic patients after adjusting for demographic, medical utilization, clinical history, and medication related variables. Overall statin adherence behavior was associated with higher all-cause and hyperlipidemia-related drug costs. Adherent patients presented 103% higher all-cause drug costs and 225% higher statin medication costs than non-adherent patients. The degrees of increased drug costs were greater than those of decreased medical costs, which may resulted in higher total all-cause-healthcare costs and hyperlipidemia-related costs due to medication adherence behavior. In addition, age, CVD history, ER visit, and comorbidity score were also significant predictors to estimate all-cause drug costs and statin medication costs.
69
Table 19 Associations between statin medication adherence and annual medical costs in non-diabetic patients (N = 5479) Independent variable
Dependent variable: ln (allcause medical costs1) Estimated coefficient (SE)
Statin adherence (yes vs. no)
-0.10 (0.0414)**
Dependent variable: ln (hyperlipidemia-related medical costs2) Estimated coefficient (SE) -0.042 (0.0444)
Age (years)
0.0035 (0.00171)*
0.013 (0.00182)***
Sex (Female vs. Male)
0.067 (0.0389)
0.21 (0.0427)***
Race Black vs. white Hispanic vs. white Other vs. white
-0.18 (0.0657) 0.0085 (0.158) 0.053 (0.0598)
0.046 (0.0707) 0.073 (0.169) 0.18 (0.0655)**
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
0.53 (0.0423)***
1.14 (0.0468)***
ER visit (yes vs. no)
0.44 (0.0384)***
0.16 (0.0421)***
Hospitalization (yes vs. no)
0.34 (0.0466)***
0.29 (0.0511)***
Total number of medications
0.038 (0.00244)***
0.0069 (0.00306)*
Total number of outpatient
0.020 (0.00126)***
-0.0018 (0.00139)
Comorbidity score3
0.21 (0.0260)***
0.20 (0.0280)***
Intercept
8.10 (0.111)***
5.67 (0.149)***
R2
0.3404
0.2438
visits
CVD: cardiovascular disease, ER: emergency room, SE: standard error *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. All-cause medical costs = all-cause outpatient costs (ER included) + all-cause inpatient costs 2. Hyperlipidemia-related medical costs were identified by primary ICD-9 codes in medical claims data based on a set of cardiovascular codes and NDC in drug claims data. Statin medications were used for hyperlipidemia-related drug costs. Total hyperlipidemia-related medical costs = hyperlipidemia-related outpatient (ER included) costs + hyperlipidemia-related inpatient costs. 3. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
70
Table 20 Associations between statin medication adherence and annual drug costs in non-diabetic patients (N = 5479) Independent variable
Statin adherence (yes vs. no)
Dependent variable: ln (allcause drug costs) Estimated coefficient (SE) 0.71 (0.0293)***
Dependent variable: ln (statin medication costs1) Estimated coefficient (SE) 1.18 (0.0260)***
Age (years)
0.0033 (0.00121)**
0.0038 (0.00109)***
Sex (Female vs. Male)
-0.72 (0.0275)**
-0.044 (0.0246)
Race Black vs. white Hispanic vs. white Other vs. white
-0.18 (0.0464)** -0.25 (0.112)** 0.021 (0.035)
0.034 (0.0415) -0.05 (0.0996) 0.072 (0.0376)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
-0.071 (0.0299)*
0.053 (0.0269)*
ER visit (yes vs. no)
-0.064 (0.0350)*
-0.052 (0.0243)*
Hospitalization (yes vs. no)
-0.056 (0.0329)
-0.047 (0.0296)**
Total number of medications
0.034 (0.00197)***
0.00045 (0.00177)
Total number of outpatient
0.0061 (0.000891)***
-0.00017 (0.000766)
Comorbidity score2
0.13 (0.0180)***
0.047 (0.0162)**
Intercept
6.58 (0.0813)***
6.43 (0.0874)***
R2
0.4714
0.3046
visits
CVD: cardiovascular disease, ER: emergency room, SE: standard error *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Statin medications were identified by NDC in drug claims data. 2. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index year.
71
Table 21 Associations between total annual healthcare costs and statin medication adherence in non-diabetic patients (N = 5479)
Independent variable
Dependent variable: ln (total allcause healthcare costs1) Estimated coefficient (SE)
Statin adherence (yes vs. no)
0.30 (0.029)***
Dependent variable: ln (total hyperlipidemiarelated healthcare costs2) Estimated coefficient (SE) 0.78 (0.0291)***
Age (years)
0.0042 (0.00196)***
0.0074 (0.00196)***
Sex (Female vs. Male)
-0.0537 (0.0272)*
-0.15 (0.0273)***
Black vs. white
-0.011 (0.0461)
0.012 (0.0461)
Hispanic vs. white
-0.106 (0.111)*
-0.15 (0.111)*
Other vs. white
-0.011 (0.0419)
0.083 (0.0409)*
Race
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
0.30 (0.0297)***
0.56 (0.030)***
ER visit (yes vs. no)
0.20 (0.0267)***
0.030 (0.0269)
Hospitalization (yes vs. no)
0.26 (0.0326)***
0.16 (0.0327)***
Total number of medications
0.036 (0.00195)***
0.016 (0.0021)***
Total number of outpatient
0.012 (0.000884)***
0.0011 (0.000886)
Comorbidity score3
0.20 (0.0179)***
0.16 (0.0174)***
Intercept
8.59 (0.0754)***
7.30 (0.0952)***
0.3521
0.2915
visits
2
R
CVD: cardiovascular disease, ER: emergency room, SE: standard error *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. All-cause total costs = all-cause outpatient costs (ER included) + all-cause drug costs + allcause inpatient costs 2. Hyperlipidemia-related costs were identified by primary ICD-9 codes in medical claims data based on a set of cardiovascular codes and NDC in drug claims data. Statin medications were used for hyperlipidemia-related drug costs. Total hyperlipidemia-related costs = statin costs + hyperlipidemia-related outpatient (ER included) costs + hyperlipidemia-related inpatient costs. 3. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
72
Table 21 presented associations between total healthcare costs and statin medication adherence in non-diabetic patients after adjusting demographic, medical utilization, clinical history, and medication related variables. Overall 35% of variance in natural log transformed total healthcare costs and 29% of variance in natural log transformed hyperlipidemia-related costs can be explained by the linear regression models in Table 21. Adherence to statins was positively associated with these two healthcare costs. Patients with adherent behavior resulted in an increase in all-cause healthcare costs by 35% and an increase in hyperlipidemia-related healthcare costs by 118%. Age, race, sex, previous CVD history and hospitalization, number of medications used in one previous year, and comorbidy were also significant in these two regression models.
4.6 Subgroup analysis for patients with hyperlipidemia and diabetes (diabetic group) Table 22 identified predictors of statin medication adherence behavior in patients with hyperlipidemia and diabetes. Age, race, ER visit and total number of outpatient visits were significant predictors to estimate statin medication adherence likelihood in this group. The odds of adherence to statin medications were increased by 3% with one year older in age. In terms of race, black patients were 0.69 times less likely to be adherent to statins than white patients. ER visit experience also reduced odds of adherence behavior by 27%. With one total number of outpatient visits increased, the odds of adherence to statins were increased by 1%.
73
Table 22 Predictors of statin medication adherence in diabetic patients enrolled in Medicaid (N = 1705)
Independent variable
Dependent variable: statin adherence Odds ratio (95% CI)
Age (years)
1.03 (1.019, 1.041)***
Sex (Female vs. Male)
0.81 (0.645, 1.022)
Race Black vs. white Hispanic vs. white Other vs. white
0.69 (0.545, 0.881)** 1.26 (0.627, 2.529) 0.80 (0.574, 1.113)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
0.91 (0.713, 1.158)
ER visit (yes vs. no)
0.73 (0.587, 0.911)**
Hospitalization (yes vs. no)
1.13 (0.875, 1.450)
Diabetic complications (yes vs. no)
0.91 (0.712, 1.137)
Total number of medications
0.99 (0.977, 1.004)
Total number of outpatient visits
1.01 (1.003, 1.017)**
1
Comorbidity score
0.97 (0.924, 1.019)
CI: confidence interval, CVD: cardiovascular disease, ER: emergency room *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities.
Predictors of statin MPR in diabetic group were identified in Table 23 by using a multiple linear regression. Among all the predictors, age, race, ER visit, total numbers of medications and outpatient visits were significant predictors to estimate statin MPR in this group (p < 0.05). With 10 years older in age, MPR increased by 0.043. Compared to white patients, black patients reduced MPR by 0.06. ER visit and total number of medications used in previous one year were negatively related to MPR. Patients with 74
previous ER visit decreased MPR by 0.052. Every one more medication used in previous year was associated with 0.0028 decreased in MPR.
Table 23 Predictors of statin MPR in diabetic patients enrolled in Medicaid (N = 1705)
Independent variable
Dependent variable: MPR Estimated coefficient (SE)
Age
0.0043 (0.00075)***
Sex (Female vs. Male)
-0.025 (0.0172)
Race Black vs. white Hispanic vs. white Other vs. white
-0.059 (0.0313)* 0.018 (0.0465) -0.051 (0.0253)*
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
0.0059 (0.0184)
ER visit (yes vs. no)
-0.052 (0.0162)**
Hospitalization (yes vs. no)
-0.022 (0.0186)
Diabetic complications (yes vs. no)
0.011 (0.0177)
Total number of medications
-0.0028 (0.00103)**
Total number of outpatient visits
0.0018 (0.000528)***
Comorbidity score1
-0.0030 (0.00335)
Intercept
0.23 (0.038)***
R2 = 0.2519, CVD: cardiovascular diseases, ER: emergency room, MPR: medication possession ratio, SE: standard error, *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
75
Table 24 Association between risk for hospitalization and statin medication adherence in diabetic patients enrolled in Medicaid (N = 1705)
Independent variable
Dependent variable: likelihood of hospitalization Odds ratio (95% CI)
Statin adherence (yes vs. no)
0.80 (0.636, 0.966)*
Age
0.99 (0.983, 1.008)
Sex (Female vs. Male)
1.25 (0.953, 1.646)
Race Black vs. white Hispanic vs. white Other vs. white
0.84 (0.642, 1.103) 1.05 (0.438, 2.502) 0.84 (0.565, 1.238)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
1.38 (1.059, 1.797)*
ER visit (yes vs. no)
1.61 (1.252, 2.076)***
Hospitalization (yes vs. no)
2.30 (1.772, 2.992)***
Diabetic complications (yes vs. no)
1.54 (1.204, 1.980)***
Total number of medications
1.08 (1.060, 1.095)***
Total number of outpatient visits
1.00 (0.996, 1.012)
1
Comorbidity score
1.31 (1.235, 1.394)***
CI: confidence interval, CVD: cardiovascular disease, ER: emergency room *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities one year prior to index date.
Table 24 evaluated an association between risk for hospitalization and statin medication adherence in diabetic group. Overall statin medication adherence was negatively associated with 20% decreased likelihood of hospitalization. Healthcare service utilization and clinical conditions in one previous year were also significant in the
76
model, such as CVD history, ER visit, diabetic complications, previous hospitalization, total number of medication used, and comorbidity score.
Table 25 Association between risk for ER visit and statin medication adherence in diabetic patients enrolled in Medicaid (N = 1705)
Independent variable
Dependent variable: likelihood of ER visit Odds ratio (95% CI)
Statin adherence (yes vs. no)
0.71 (0.519, 0.812)**
Age
0.97 (0.956, 0.977)***
Sex (Female vs. Male)
1.32 (1.033, 1.682)*
Race Black vs. white Hispanic vs. white Other vs. white
1.09 (0.852, 1.393) 1.12 (0.538, 2.316) 0.87 (0.618, 1.244)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
1.36 (1.053, 1.764)*
Hospitalization (yes vs. no)
1.09 (0.831, 1.415)
ER visit (yes vs. no)
3.22 (2.578, 4.011)***
Diabetic complications (yes vs. no)
1.08 (0.848, 1.375)
Total number of medications
1.04 (1.024, 1.056)***
Total number of outpatient visits
1.01 (0.992, 1.012)
Comorbidity score1
1.07 (1.015, 1.124)*
CI: confidence interval, CVD: cardiovascular diseases, ER: emergency room visit *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
Table 25 demonstrated an association between likelihood of ER visit and statin adherence behavior in diabetic patients. Statin adherence reduced odds of ER visit by 77
nearly 30%. Age, sex, previous ER visit, CVD history, comorbidity and total number of medication used were also significant in the regression model.
Table 26 Comparison of annual all-cause healthcare costs in diabetic patients by statin medication adherence (N = 1705) All-cause costs
Adherence (N = 624)
Non-adherence (N = 1081)
($)
Mean (median) ($)
Mean (median) ($)
Total
14308 (9728)
17210 (7930)
0.0521
Drug
7201 (5885)
4917 (3730)
< 0.0001
Outpatient
3337 (2023)
3813 (2325)
0.0411
Inpatient
3770 (0)
8480 (0)
0.0007
p-value (t-test)
Table 27 Comparison of annual hyperlipidemia-related healthcare costs in diabetic patients by statin medication adherence (N = 1705) Hyperlipidemia- Adherence (N = 624) Non-adherence (N = 1081)
p-value (t-test)
related costs ($)
Mean (median) ($)
Mean (median) ($)
Total
2275 (1405)
2784 (771)
0.2638
Drug
1135 (1076)
488 (427)
< 0.0001
Outpatient
276 (189)
314 (192)
0.0406
Inpatient
864 (0)
1982 (0)
0.0137
Tables 26 and 27 compared all-cause healthcare costs and hyperlipidemia-related healthcare costs in diabetic patients without adjustments. Patients with statin adherence behavior tended to show lower means of all-cause and hyperlipidemia-related healthcare costs than those with non-adherence. However, the differences in these total costs were
78
not statistically significant. Adherent patients presented significant higher all-cause and hyperlipidemia-related drug costs but lower all-cause and hyperlipidemia-related medical costs including outpatient and inpatient costs. Table 28 evaluated associations between statin medication adherence behavior and medical care costs (all-cause and hyperlipidemia-related costs) in diabetic group after adjusting demographic, medical utilization, clinical history, and medication related variables. Adherent patients decreased all-cause medical costs by 15% and hyperlipidemia-related medical costs by 12%. Among independent variables, sex, healthcare services utilized in one previous year (ER visit, hospitalization, total numbers of medications used and outpatient visits) and clinical conditions in one previous year (CVD history, diabetic complication, and comorbidity) were significant for association between all-cause healthcare and adherence. Previous conditions such as CVD history, diabetic complications, and comorbidity were significant in the hyperlipidemia-related costs model. Table 29 assessed associations between statin medication adherence behavior and drug costs (all-cause and statin drug costs) in diabetic patients after adjusting for demographic, medical utilization, clinical history, and medication related variables. Patients with statin adherence behavior increased all-cause drug costs by 60% and statin drug costs by 180%. The huge increases in both drug costs may result in higher total healthcare costs and hyperlipidemia-related costs.
79
Table 28 Associations between statin medication adherence and annual medical costs in diabetic patients by statin medication adherence (N = 1705) Independent variable
Dependent variable: ln (allcause medical costs1) Estimated coefficient (SE)
Statin Adherence (yes vs. no)
-0.14 (0.0638)*
Dependent variable: ln (hyperlipidemia-related medical costs2) Estimated coefficient (SE) -0.11 (0.0700)*
Age (years)
-0.0047 (0.00309)
0.0016 (0.00338)
Sex (Female vs. Male)
0.19 (0.0684)***
-0.059 (0.0758)
Race Black vs. white Hispanic vs. white Other vs. white
0.064 (0.104) -0.0031 (0.228) -0.012 (0.0992)
0.053 (0.114) 0.23 (0.251) 0.16 (0.109)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
0.16 (0.0721)**
0.24 (0.0829)**
ER visit (yes vs. no)
0.34 (0.0638)***
0.041 (0.0727)
Hospitalization (yes vs. no)
0.46 (0.075)***
0.18 (0.0818)*
Diabetic complications
0.36 (0.0684)***
0.68 (0.0736)***
Total number of medications
0.018 (0.00408)***
0.0059 (0.00459)
Total number of outpatient visits
0.014 (0.00112)***
0.00061 (0.00237)
Comorbidity score3
0.200 (0.013)***
0.15 (0.0152)***
Intercept
8.22 (0.167)***
5.52 (0.255)***
0.4422
0.2192
(yes vs. no)
2
R
CVD history: cardiovascular disease, ER: emergency room, SE: standard error *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. All-cause medical costs = all-cause outpatient costs (ER included) + all-cause inpatient costs 2. Hyperlipidemia-related medical costs were identified by primary ICD-9 codes in medical claims data based on a set of cardiovascular codes and NDC in drug claims data. Statin medications were used for hyperlipidemia-related drug costs. Total hyperlipidemia-related medical costs = hyperlipidemia-related outpatient (ER included) costs + hyperlipidemia-related inpatient costs. 3. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
80
Table 29 Associations between statin medication adherence and annual drug costs in diabetic patients enrolled in Medicaid (N = 1705) Independent variable
Statin adherence (yes vs. no)
Dependent variable: ln (allcause drug costs) Estimated coefficient (SE) 0.47 (0.0310)***
Dependent variable: ln (statin medication costs1) Estimated coefficient (SE) 1.03 (0.0377)***
Age (years)
0.0010 (0.00150)
0.00050 (0.00182)
Sex (Female vs. Male)
-0.047 (0.0336)
0.0039 (0.0409)
Race Black vs. white Hispanic vs. white Other vs. white
-0.13 (0.0506)* -0.17 (0.110)** -0.11 (0.0480)*
-0.015 (0.0615) -0.16 (0.134) -0.049 (0.0583)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
0.031 (0.0478)
0.079 (0.0424)
ER visit (yes vs. no)
-0.090 (0.0322)**
-0.012 (0.0392)
Hospitalization (yes vs. no)
0.062 (0.0362)
0.014 (0.0440)
Diabetic complications (yes vs.
0.13 (0.0332)***
0.086 (0.0404)*
Total number of medications
0.034 (0.00203)***
0.0059 (0.00237)*
Total number of outpatient visits
0.0020 (0.00104)
0.0013 (0.00121)
Comorbidity score2
0.028 (0.00671)***
0.0016 (0.00877)
Intercept
7.57 (0.113)***
6.74 (0.137)***
0.3898
0.3207
no)
2
R
CVD: cardiovascular disease, ER: emergency room, SE: standard error *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. Statin medications were identified by NDC in drug claims data. 2. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
81
Table 30 Associations between statin medication adherence and total annual healthcare costs in diabetic patients enrolled in Medicaid (N = 1705) Independent variable
Dependent variable: ln (total all-cause healthcare costs1) Estimated coefficient (SE)
Statin adherence (yes vs. no)
0.23 (0.0319)***
Dependent variable: ln (total hyperlipidemiarelated healthcare costs2) Estimated coefficient (SE) 0.60 (0.166)***
Age
0.0040 (0.00153)*
0.0022 (0.00220)*
Sex (Female vs. Male)
0.028 (0.0345)
-0.064 (0.0495)
Race Black vs. white Hispanic vs. white Other vs. white
-0.030 (0.0513) -0.069 (0.0745) -0.080 (0.0493)
0.037 (0.0721) -0.060 (0.0132) -0.098 (0.0707)
Health services used and clinical history in 1 year prior to index date CVD history (yes vs. no)
0.073 (0.0481)
0.16 (0.0534)**
ER visit3 (yes vs. no)
0.099 (0.0330)**
-0.027 (0.047)
Hospitalization (yes vs. no)
0.82 (0.0372)***
0.083 (0.0533)
Diabetic complications (yes vs.
0.25 (0.0341)**
0.31 (0.0493)***
Total number of medications
0.025 (0.00256)***
0.00030 (0.0030)
Total number of outpatient visits
0.0059 (0.000773)***
0.0013 (0.00111)
Comorbidity score3
0.13 (0.00690)***
0.095 (0.0099)***
Intercept
8.91 (0.116)***
7.19 (0.166)***
0.3932
0.2416
no)
2
R
CVD history: cardiovascular disease, ER: emergency room, SE: standard error *: p < 0.05, **: p < 0.01, ***: p < 0.001 1. All-cause total costs = all-cause outpatient costs (ER included) + all-cause drug costs + allcause inpatient costs 2. Hyperlipidemia-related costs were identified by primary ICD-9 codes in medical claims data based on a set of cardiovascular codes and NDC in drug claims data. Statin medications were used for hyperlipidemia-related drug costs. Total hyperlipidemia-related costs = statin costs + hyperlipidemia-related outpatient (ER included) costs + hyperlipidemia-related inpatient costs. 3. Weights were assigned for a number of major conditions (range, 1-6) based on Charlson index. Comorbidity score was calculated for each patient by totaling assigned weight for each of patient’s comorbidities in one year prior to index date.
82
Table 30 presented associations between total healthcare costs and statin medication adherence in diabetic patients after adjusting for demographic, medical utilization, clinical history, and medication related variables. Adherence to statins was associated with 25% increased in all-cause costs and 82% increased in hyperlipidemiarelated costs. Besides primary predictor statin adherence, age, healthcare service utilized in one previous year such as ER visit, hospitalization, diabetic complications, total numbers of medications and outpatient visits, and comorbidity score were significant variables in the model for total all-cause costs. Age, previous CVD history, diabetic complications, and comorbidity were significant in the hyperlipidemia-related costs model.
4.7 Linear regression model diagnostics Assumptions of linear regression model were tested by various statistical methods described in methods section. Overall all linear regression models used in the study did not violate assumptions, including homoscedasticity, data independence, and normal distribution of residuals. The following regression model diagnostic results were from association between total health costs and statin medication adherence in non-diabetic group. Durbin-Watson (D-W) statistic showed 2.014 (close to 2.0) indicating independence of data. SPEC test results showed p-value 0.1535 > 0.05 indicating error term (residuals) were independent and identically distributed. Therefore, these two tests demonstrated that linear regression model in our study satisfied assumptions of homoscedasticity and data independence. Table 31 presented multicollinearity of variables in linear regression model. VIF values of all variables were less than 10, which
83
meant that there were no correlations among independent variables. Figures 6 and 7 indicated that residuals in the linear regression were normal distribution.
Table 31 Testing of multicollinearity Variables
Variance Inflation (VIF)
Age
1.10
Sex
1.04
Race
1.03
Total number of medications
1.80
Total number of outpatient visits
1.34
CVD history
1.13
ER visit
1.55
Hospitalization
1.34
Comobid score
3.49
CVD: cardiovascular disease, ER: emergency room, VIF: Variance inflation factor
84
Figure 6 Histogram of distribution of residuals
Figure 7 QQ plot of residual distribution
85
CHAPTER 5: DISCUSSION
Previous studies mainly focused on the association between statin adherence and reduced risk for CHD based on clinical populations. Based on our knowledge, this would be the first observational study to evaluate statin medication adherence and associated outcomes in a diabetic population using a large national Medicaid database. We found both diabetic and non-diabetic patients presented poor statin adherence. Only 35% of diabetics and 25% of non-diabetics presented MPR ≥ 0.8. Thus, improving statin adherence plays an important role to help patients obtain benefits from lipid-lowering therapy. However, it is worth noting that diabetic patients were 1.60 times more likely to be adherent to statins than non-diabetic patients. Disparities in baseline characteristics might be attributed to different statin use behavior in diabetic and non-diabetic groups. Although we get the consistent conclusion with previous studies that medication adherence leads to lower medical utilization and medical costs, unlike those studies, we found total all-cause and hyperlipidemia-related costs rose due to increased use of statins. Because of unavailable generics for statins during the study period and fast growing drug prices, drug costs accounted for nearly 50% of total healthcare cost and cannot be offset by decreased medical costs.
86
5.1 Characteristics of study population All eligible subjects were from MarketScan® Medicaid database representing eight states with various sizes across the United State. All patients had complete healthcare utilization data during one year study period, including outpatient and inpatient services, prescription drug claims, and enrollment data. Therefore, there were not many missing values in the dataset. Overall more than 95% of people in non-diabetes and diabetic groups were younger than 65 years because Medicare beneficiaries were excluded by the study. For those dual eligible enrollees, their medical and drug claims were filed to Medicare. Medicaid paid their Part B and D premiums and Medicare cost sharing. The characteristic of gender in this study was different from other observational studies. Females were two times males in Medicaid enrollees associated with components of coverage groups. Medicaid essentially provides coverage for four groups of low income people: pregnant women and adults in families with children, children, the elderly, and individuals with disability. Nearly one third people in these two groups presented CVD history, which implied that hyperlipidemia may be correlated with many forms of cardiovascular diseases, such as atherosclerosis, myocardial infarction, congestive heart failure, and stroke. This is also a reason that hyperlipidemia – related costs were identified by a broad set of cardiovascular codes. In many settings, some acute conditions are more likely to be used for primary diagnostic coding, especially in cases of hospitalization or ER visits. Medical utilization and costs might be underestimated if diagnostic codes were restricted to hyperlipidemia only. Diabetic patients presented greater total numbers of medications and outpatient visits, and higher comorbidity scores in one year prior to index date than non-diabetic 87
patients. These significant differences reflected worse health conditions in diabetic patients and demonstrated unequivalent baselines of the two groups. Therefore, propensity score was used to remove imbalance in two groups to compare their outcome measures.
5.2 Comparisons of medication adherent behavior between non-diabetic and diabetic groups The medication adherence results demonstrated that patients in both groups presented poor MPRs that were 0.50 for non-diabetes and 0.61 for diabetes respectively. Only 25% of non-diabetic patients and 35% diabetic patients were adherent to statin medications for one year. The low prevalence of statin adherence within one year for both groups is consistent with the NCEP report in which only 30 - 40% of hyperlipidemia patients continue taking statins at one year.31 It is worth noting that adherence behavior in non-diabetic group is even worse. Therefore, for all hyperlipidemia patients statin medication adherence is still a serious problem to be addressed. Previous studies suggested that as people become older they are more likely to be concerned with their health and comply with medical advice.136 In our study, mean ages for both groups were about 50 years suggesting that the study population were relatively younger than Medicare population. Some people may not become concerned with their health conditions seriously. In addition, hyperlipidemia is a lifestyle disease, so elevated cholesterol does not lead to any specific symptoms unless it has been longstanding. Many Medicaid patients due to their low educational level and socioeconomic status are not able to realize importance of taking statin medication to prevent occurrence of CHD. Community health programs could be implemented to deliver basic cardiovascular
88
disease knowledge to people and help them understand necessity to take statins for primary or secondary prevention. Serious side effects of statins, such as muscle problem and liver or kidney failure, and drug interactions may also make patients stop taking statins for a certain period.28 Especially for diabetic patients, total number of medications used within one year was 17 so that drug interactions have to be considered. Medicaid offers full coverage of prescription drugs but cost sharing for Medicaid copayment policy on prescription drugs implemented in some states may influence patient medication utilization. Although federal Medicaid law restricts copayment between $0.50 and $3, this nominal or moderate cost sharing could represent a financial barrier for low-income population, especially for those patients with chronic diseases requiring long term medication use. Hartung et al137 found that after copay implementation utilization of prescription drugs decline significantly by 17.2%. Among diabetic patients, change in diabetes-related drugs was not significant, but non-diabetes drugs presented a significant 11.6% reduction. Thus, to some extent even nominal cost sharing could be a financial factor influencing medication utilization in Medicaid patients. On average diabetic patients use 17 medications within previous one year reflecting a high refill frequency. In addition, keeping a healthy lifestyle including low fat and high fiber diets and increased physical activity is still needed for statin medication users to control cholesterol level and reduce CVD events. Therefore, indirect medical costs on transportation and lifestyle changes could result in nonmedical financial burdens making patients fail to achieve expected effectiveness. Inconvenience to refill prescriptions might make patients stop using non-diabetes drugs and just focus on several necessary medications. Overall full coverage of prescription drugs could still make
89
Medicaid patient reduce medication utilization due to copay policy implementation and nonmedical costs. It is worth noting that diabetes did not worsen statin medication adherence. Results showed that diabetic patients were 1.60 times more likely to be adherent to statins and increased MPR by 0.09 adjusting for demographic, medical utilization, clinical history, and medication related variables. The mean age for diabetic group was 2 years older than non-diabetic group. Percentage of people older than 55 years in diabetic group was 10% higher than that of people in non-diabetic group. The difference in age distribution suggested that people in diabetic group are more likely to consider their health status and be willing to seek treatment and accept medical advice. Additionally diabetic patients have to take oral antidiabetic medication every day for long term, which help them be used to take medications on regular basis. Higher number of outpatient visits may help establish a good relationship between patients and their physicians. Under this circumstance, patients are more likely to comply with medical advice. Physicians also have more opportunities to offer patient education and remind them to take medications. All-cause healthcare costs and hyperlipidemia-related healthcare costs presented different relationships with patients in the two groups. Diabetic group presented lower hyperlipidemia-related healthcare costs that may result from relatively better statin adherence in diabetic patients. However, due to higher medical utilization and poorer overall health conditions in diabetic patients, all-cause healthcare costs did not decreased significantly.
90
5.3 Subgroup analysis of medication adherence for patients in non-diabetes and diabetic groups Non-diabetic and diabetic groups shared some common predictors to estimate MPR and likelihood of adherence, including age, race, previous ER visit, total number of outpatients in one year prior to index date were significant predictors. Older people are more likely to be adherent to statins in both groups. This has been confirmed by previous studies that people are more likely to be concerned with their health conditions as they become older.96 Our study results suggested racial disparity in statin adherence for both non-diabetes and diabetic patients. African Americans presented lower MPR and odds of adherence than whites. Shenolikar’s study on racial differences in oral antidiabetic medication adherence also demonstrated similar outcomes that being an African American was associated with decreased medication adherence24. Different cultures and health beliefs could be attributed to racial difference in statin use in Medicaid population. Patients with ER visit in one previous year are less likely to be adherent to statins, which implied that patients’ perceived cardiovascular risk did not influence their medication taking behavior in the study population. Outpatient visits were positively associated with adherence. Communication between patients and physicians was enhanced due to frequent visits, which helps patients receive more education and consultation and comply with medical advice. However, non-diabetic and diabetic groups presented different associations between total number of medications used in previous one year and MPR. Total number of medications used in previous one year was negatively associated with MPR in diabetics. However, increased number medications in one year prior to index event was
91
associated with increased MPR in non-diabetic group. Non-diabetic patients presented better health conditions and younger mean age and only use 10 medications in one year. Thus, they are more likely to be able to manage medication use. Diabetic patients used 17 medications in one previous year and 18 medications during study period. Some serious drug side effects and drug interactions may make them stop a treatment or switch to other drugs. These significant predictors might be helpful for health professionals to recognize risk patients to conduct a pharmaceutical intervention to improve their medication use. Benner30 and Donnelly’s138 studies presented that previous CVD history can increase statin adherence. However, our study showed that CVD history was not a significant predictor. Subjects in our study were relatively young due to exclusion criteria so that CVD risk was not able to make these young patients realized the threats from the diseases. How to establish a good intention and attitude to health could be considered for people younger than 65 years.
5.4 Relationship between medical utilization and statin medication adherence Occurrence of ER visit and hospitalization during study period was both negatively associated with statin adherence rate in two groups. Especially, hospitalization and ER visit are direct results from medication non-adherence in diabetic patients. For example, acute complications due to uncontrolled blood glucose level might be direct causes leading to hospitalization.10 CVD events and related acute conditions might result from uncontrolled and longstanding cholesterol.26 Sokol et al26 also concluded that for diabetes and hyperlipidemia conditions, higher hospitalization risk was associated with lower adherence levels in patients covered by private insurance. Hospitalizations, rehospitalizations, and nursing home admissions are recognized as direct costs of
92
medication nonadherence in the elderly people. Disease progression due to medication nonadherence may also influence health related quality of life and result in indirect costs. Thus, improving medication adherence would be an effective strategy for hyperlipidemia and diabetic patients to control health conditions and medical utilization.
5.5 Relationship between healthcare costs and statin medication adherence Previous studies provided evidence that medication adherence behavior is associated with both lower total healthcare costs and hyperlipidemia-related healthcare costs.24 Additionally increased costs in drugs may be offset by decreased costs in medical utilization.26 Our study showed that statin medication adherence decreased medical care costs, such as inpatient and outpatient costs. However, total all-cause and hyperlipidemiarelated healthcare costs were positively associated with statin medication adherence in diabetic group. Patients with diabetes presented higher total healthcare costs if they are adherent to statins. Skyrocketing drug prices since 1995 might account for the elevated total healthcare costs. Kaiser Family Foundation reported that prescription drug costs have outpaced other categories of health care spending, rising rapidly from 1995 to early 2000s.139 Between 1997 and 2002, increases in spending on prescription drugs accounted for 19% of increases in the national health spending, which has been one of the fastest growing components compared to hospital and physician service.140 Table 17 showed that among non-diabetic patients drug costs accounted for 36% and 25% of total all-cause healthcare costs for adherent patients and non-adherent patients respectively. Table 26 presented higher proportions of drug costs in diabetic group. Drug costs of diabetic patients accounted for 50% and 28% of all-cause healthcare costs for adherent patients
93
and nonadherent patients. Although medical care utilization was significantly reduced due to statin medication adherence, high drug costs cannot be offset by decreased medical care costs. Drug Trend Report showed that lipid-lowering drugs was the largest single category of Medicare Part D plan spending accounting for 12% of plan costs and over 12% of spending growth in 2006.141 Before 2006, generics were not available for this therapeutic class, which could be another reason for high drug costs in adherent patients in our study. First-time generics were launched in 2006, statin use started shifting to new generics resulting in a brandname utilization decline and lower drug costs. Therefore, lower total healthcare costs associated with higher statin medications adherence could be demonstrated as statin drug costs decreased with more and more available generics. One year study period may not be long enough to detect occurrence of hopisalization and ER visit in newly diagnosed hyperlipidemia patients because healthcare costs results showed that 50% patients did not experience hospitalization (median of inpatient costs = 0). The decreased medical utilization and medical costs only reflected the benefit of statin adherence for advanced hyperlipidemia patients. Thus, a longitudinal study is suggested in the future to investigate association between healthcare costs and statin adherence for long term lipid-lowering drug therapy, such as 5 or 10 years.
5.6 Test of the conceptual model The conceptual model used in the study combined the modified health belief model and behavioral model of health services use (Figure 3), comprised of structure, process, and outcomes. Based on the assumption of the readiness of patients to undertake
94
compliant behavior, the component of structure indicates potential predictors to estimate statin adherent behavior in Medicaid population with diabetes and covariates for the regression analysis to assess adherence associated outcomes. Among those potential predictors, demographics (age and race), medical care related factors (ER visit history and frequency of outpatient visits), and medication related factor (total number of medication used) were identified as significant predictors of statin adherence rate. After adjusting for measured covariates based on the structure component, the conceptual model also reflected associations between process (statin adherence) and outcomes (medical utilization and healthcare costs) with statistical significance, in which improved adherence to statins (primary independent variable) was associated with reduced medical utilization and lower medical costs. Overall the conceptual model fit the suggested study hypotheses appropriately and might be used to estimated statin medication use behavior and evaluate adherence associated outcomes.
5.7 Limitations of study All of the subjects were from MarketScan® Medicaid database so that the conclusion of the study cannot be generalized to the other populations, such as people covered by Medicare or private health insurance. The characteristics of Medicaid enrollees presented mean age of 50 years due to inclusion criteria and a large proportion of females due to eligibility of Medicaid. People with employer-sponsored health insurance and Medicare plan have economic factors to influence their medication use behavior, such as copayment, coinsurance, or donut hole for Medicare patients. Although nominal or moderate cost sharing could affect medication utilization in Medicaid
95
population, indirect costs and other nonfinancial factors due to their low socioeconomic status would play key roles in medication use. Medication possession ratio was used to measure medication adherence. The adherence measure assumed that a prescription filled is a prescription taken. Actually we cannot obtain the real information on the medication use behavior. Sometimes patients may forget to take medication unintentionally or refuse to take medication due to side effects or personal reasons. In addition some unusual refill patterns may not be measured by MPR. Therefore, the measured medication possession ratio cannot reflect the medication use behavior comprehensively. This is a retrospective observational study based on medical and drug claims. The causal relationship among various risk factors and medication use behavior and other examined outcomes cannot be established. All results estimated likelihood of adherence behavior based on predictors and evaluated strength of association between medication adherence and healthcare costs. Causal relationship should be established by conducting an experimental study with a randomization assignment. Self-readiness to undertake health behavior was assumed in conceptual model and was not measured in the study. Patients’ beliefs about statin treatment and hyperlipidemia were not considered, which may bias the results. Beliefs about disease and health could differ due to various factors, such as cultures, races, socioeconomic status, and education levels. Different health beliefs may lead to various attitudes, intentions, and behavior. Social support is another important factor to affect medication adherence. The level of social support one receives from family and friends is one of strong predictors of adherence.143 The study could not
96
obtain patients’ marriage and family information so that social support as a predictor was not measured to assess medication adherence.
5.8 Health policy implications Clinical trials demonstrated that patients achieve benefit of using statins only after 1 to 2 years of continuous treatment.69 Thus, for both primary and secondary prevention, statins are intended to be used for long term to achieve expected effectiveness. Further, withdrawal of statins in patients with acute coronary syndromes may increase CVD event rates.30 Our study showed that hyperlipidemia patients with or without diabetes both presented low statin adherence and that adherent behavior reduced medical utilization and resulted in lower medical care costs. On average only 35% of diabetic patients continued statin therapy for one year in our study. Therefore, statin medication adherence is a serious problem in hyperlipidemia patients, especially for those with diabetes because hyperlipidemia is one of important risk factors leading to atherosclerosis and coronary hear disease in diabetic patients. Over 95% of patients with type 2 diabetes may have more than two coronary artery disease risk factors, which suggests that most of the patients need LDL-cholesterol-lowering therapy.11 How to improve statin medication adherence to reduce CVD events and medical utilization would be important for health policy makers. Medicaid offers full prescription drug coverage for eligible patients to diminish economic factors for low income population and improve their medication use. However, indirect costs including transportation and lifestyle change, patient education, and social support would be main reasons for discontinuing medication therapy in Medicaid patients with chronic diseases. Patients have to spend time and money on transportation to refill
97
prescription and visit their doctors frequently. In addition, keeping healthy lifestyle is still necessary to achieve full benefit even if patients continue using statin medications to lower cholesterol levels. Thus, extra money and time spent on transportation to refill prescriptions, healthy diets, and physical activity increase indirect costs on lipid-lowering therapy and become nonmedical financial burdens for low-income population. Health policy maker are suggested to consider these indirect financial issues to assist Medicaid patients in improving medication use. Elevated cholesterol does not lead to specific symptoms unless it has been longstanding, so realizing importance of lipid-lowering therapy should be enhanced through patient education. Community health programs could be established to deliver basic knowledge of hyperlipidemia and diabetes by inviting health professionals to give presentations, dispensing reading materials to families, or provide consulting services for low-income patients. Effective patient education may change health beliefs, modify attitudes and intentions to health and finally form compliant behavior. Greisinger and Balkrishnan reported that conducting diabetes education was associated with reduced hospitalization risk.142 Social support is also important for such a disadvantaged population due to their socioeconomic status. Social support could be tangible and intangible help a person receives from family members, friends, community, and health professionals. Tanner and Feldman143 conducted an experimental study to explore rates of adherence to appointment in chronic ill patients with low-income level. Results showed that social support counseling led to significant better adherence to appointment keeping than an interview, a telephone call or a postcard reminder. Strong social support is also positively
98
related to the likelihood that hypertensive patients will present adherence to medical advice. Providing appropriate social support from family, community and healthcare providers could be another feasible strategy to improve medication adherence. Overall medication adherence issues for Medicaid enrollees differ from those for people covered by Medicare or private health insurance plans. Non-financial factors and indirect costs could be more important to be considered due to characteristics of this disadvantage population.
5.9 Conclusions This is the first study to assess statin medication adherence and associated outcomes in type 2 diabetes patients based on national Medicaid population. Compared to non-diabetic patients, patients with diabetes and hyperlipidemia are more likely to be adherent to statins. However, both groups presented poor statin adherence rate. In diabetic patients, age, race, total number of outpatient visits and ER visit in one year prior to index date were significant predictors to estimate statin medication adherence behavior. Diabetic patients with adherence to statin presented lower risks of hospitalization and ER visit and lower medical care costs within one year period.
5.10 Future study This is a preliminary study trying to explore statin medication use behavior in hyperlipidemia patients with or without diabetes. The conclusion of the study can only represent medication use and medical utilization in an eligible Medicaid population. In our study, patients eligible for both Medicare and Medicaid were excluded, so assessing statin medication use behavior in elderly patients would be conduct in the future as Medicare Part D has been implemented since 2006. Effects of cost sharing on statin
99
adherence may also be evaluated in this population. Elderly people account for a large proportion of healthcare costs. How to improve elderly patients statin medication adherence, reduce medical care utilization and control total healthcare costs would be significant for health policy maker and third party payers. Since patients have to use statin medications for a long term to get full benefit and reduce CVD events, a longitudinal study is needed to evaluate impacts of statin medication adherence on healthcare costs, medical utilization, and health-related quality of life. For example, it is necessary to investigate how long it takes newly diagnosed hyperlipidemia patients to obtain benefits from statin adherence after they start lipidlowering therapy.
100
REFERENCES
1.
Kleinman JC, Donahue RP, Harris MI, Finucane FF, Madans JH, Brock DB. Mortality among diabetics in a national sample. Am J Epidemiol. 1988; 128: 389-401.
2.
American Diabetes Association. [Internet]. Alexandria (VA): [Cited 2009 Sept 17]. All about diabetes. Available from: http: // www. diabetes. org / about diabetes.jsp
3.
American Diabetes Association. [Internet]. Alexandria (VA): [Cited 2009 Sept 17]. Direct and Indirect Costs of Diabetes in the United States. Available from http://www.diabetes.org/diabetes-statistics/cost-of-diabetes-in-us.jsp
4.
Bierman EL. George Lyman Duff Memorial Lecture. Atherogenesis in diabetes. Arterioscler Thromb. 1992; 12:647-656.
5.
Raman M, Nesto RW. Heart disease in diabetes mellitus. Endocrinol Metab Clin North Am. 1996;25:425-438.
6.
Ledru F, Ducimetière P, Battaglia S, Courbon D, Beverelli F, Guize L, Guermonprez JL, Diébold B. New diagnostic criteria for diabetes and coronary artery disease: insights from an angiographic study. J Am Coll Cardiol. 2001; 37:1543-1550.
7.
Betteridge J. Benefits of lipid-lowering therapy in patient with type 2 diabetes mellitus. Am J Med. 2005; 118:10S-15S.
8.
Taskinen MR. Diabetes dyslipidaemia: from basic research to clinical practice. Diabetologia. 2003; 46: 733-749.
101
9.
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001; 285: 2486-2497.
10.
American Diabetes Association Management of dyslipidemia in adults with diabetes (Position Statement). Diabetes Care. 2002; 25: S74-S77.
11.
Harris MI. Hypercholesterolemia in diabetes and glucose intolerance in the U.S. population. Diabetes Care. 1991;14: 366-374.
12.
Shepherd J. Dyslipidaemia in diabetic patients: time for a rethink. Diabetes, Obesity and Metabolism. 2007; 9: 609-616.
13.
Pyŏrälä K, Pedersen TR, Kjekshus J, Faergeman O, Olsson AG, Thorgeirsson G. Cholesterol lowering with simvastatin improves prognosis of diabetic patients with coronary heart disease. A subgroup analysis of the Scandinavian Simvastatin Survival Study (4S). Diabetes Care. 1997; 20:614-620.
14.
Athyros VG, Papageorgiou AA, Symeonidis AN, Didangelos TP, Pehlivanidis AN, Bouloukos VI, Mikhailidis DP; GREACE Study Collaborative Group. Early benefit from structured care with atorvastatin in patients with coronary heart disease and diabetes mellitus. Angiology. 2003; 54:679-690.
15.
Arampatzis CA, Goedhart D, Serruys PW, Saia F, Lemos PA, de Feyter P; LIPS Investigators. Fluvastatin reduces the impact of diabetes on long-term outcome after coronary intervention--a Lescol Intervention Prevention Study (LIPS) substudy. Am Heart J. 2005;149:329-335.
16.
Grover SA, Coupal L, Zowall H, Dorais M. Cost-effectiveness of treating hyperlipidemia in the presence of diabetes : who should be treated? Circulation. 2000;102:722-727.
17.
Mihalko SL, Brenes GA, Farmer DF, Katula JA, Balkrishnan R, Bowen DJ. Challenges and innovations in enhancing adherence. Control Clinical Trials. 2004;25: 447-457.
102
18.
Singh N, Squier C, Sivek C, Wagener M, Nguyen MH, Yu VL. Determinants of compliance with antiretroviral therapy in patients with human immunodeficiency virus: prospective assessment with implications for enhancing compliance. AIDS Care. 1996; 8(3):261-9.
19.
Balkrishnan R. Predictors of medication adherence in the eldrly. Clin Ther, 1998; 20: 764-771.
20.
Sullivan PW, Ghushchyan V, Wyatt HR, Hill JO. The medical cost of cardiometabolic risk factor clusters in the United States. Obesity. 2007; 15: 3150-5158.
21.
Balkrishnan R, Rajagopalan R, Camacho FT, Huston SA, Murray FT, Anderson R. Predictors of medication adherence and associated health care costs in an older population with type 2 diabetes mellitus: a longitudinal cohort study. Clin Ther. 2003; 25, 2958-2971.
22.
Aday LA, Begley CE, Lairson DR, Balkrishnan R. Evaluating the healthcare system: effectiveness, efficiency, and equity. Chicago, IL: Health Administration Press; 2004.
23.
Shenolikar RA, Balkrishnan R, Camacho FT, Whitmire JT, Anderson RT. Race and medication adherence in Medicaid enrollees with type-2 diabetes. J Natl Med Assoc. 2006; 98:1071-1077.
24.
Shenolikar RA, Balkrishnan R, Camacho FT, Whitmire JT, Anderson RT. Comparison of medication adherence and associated health care costs after introduction of pioglitazone treatment in African Americans versus all other races in patients with type 2 diabetes mellitus: a retrospective data analysis. Clin Ther. 2006; 28:1199-1207.
25.
Balkrishnan R, Rajagopalan R, Shenolikar RA, Camacho FT, Anderson RT. Outcomes associated with introduction of thiazolidinedione therapy in Medicaid enrolled patients with type 2 diabetes: an updated and expanded retrospective analysis. Curr Med Res Opin. 2006; 22:551-559.
103
26
Sokol MC; McGuigan KA; Verbrugge RR; Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. Med Care. 2005; 43: 521530.
27.
Kreyenbuhl J, Medoff DR, Seliger SL, Dixon LB. Use of medications to reduce cardiovascular risk among individuals with psychotic disorders and type 2 diabetes. Schizophr Res. 2008;101: 256-265 .
28.
Jackevicius CA, Mamdani M and Tu JV. Adherence with statin therapy in elderly patients with and without acute coronary syndromes, JAMA. 2002;288: 462-467.
29.
Avorn J, Monette J, Lacour A, Bohn RL, Monane M, Mogun H, LeLorier J. Persistence of use of lipid-lowering medications. A cross-national study. JAMA. 1998; 279: 1458-1462.
30.
Benner JS, Glynn RJ, Mogun H, Neumann PJ, Weinstein MC, Avorn J. Longterm persistence in use of statin therapy in elderly patients. JAMA. 2002; 288: 455-461.
31.
Third report on National Cholesterol Education Program expert panel on detection, evaluation and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. IX adherence. Circulation. 2002; 106: 33593366.
32.
Simons LA, McCallum J, Simons J, Friedlander Y. Relationship of peak expiratory flow rate with mortality and ischaemic heart disease in elderly Australians. Med J Aust. 1997; 166:526-529.
33.
Carter S, Taylor D. A question of choice: Compliance in medicine taking. National Prescribing Center; 2005 [cited 2008 Jan 29]. Available from: http://www.npc.co.uk/med_partnership/resource/major-reviews/a question-of choice.html
34.
Fedder DO, Koro CE, L'Italien GJ. New National Cholesterol Education Program III guidelines for primary prevention lipid-lowering drug therapy:
104
projected impact on the size, sex, and age distribution of the treatment-eligible population. Circulation. 2002;105: 152-156.
35.
McCormick D, Gurwitz JH, Lessard D, Yarzebski J, Gore JM, Goldberg RJ. Use of aspirin, beta-blockers, and lipid-lowering medications before recurrent acute myocardial infarction: missed opportunities for prevention? Arch Intern Med. 1999;159: 561-567.
36.
Lemaitre RN, Furberg CD, Newman AB, Hulley SB, Gordon DJ, Gottdiener JS, McDonald RH, Psaty BM. Time trends in the use of cholesterol-lowering agents in older adults. Arch Intern Med. 1998;158:1761-1768.
37
Piette JD, Wagner TH, Potter MB, Schillinger D. Health insurance status, costrelated medication underuse, and outcomes among diabetic patients in three systems of care. Med Care. 2004;42:102-109.
38.
Evans JM, Newton RW, Ruta DA, MacDonald TM, Morris AD. Socioeconomic status, obesity and prevalence of type 1 and type 2 diabetes mellitus. Diabet Med. 2000; 17: 478 – 480.
39
Cunningham, P.: Medicaid Cost Containment and Access to Prescription Drugs. Health Affairs. 2005; 24:780-789.
40.
World Health Organization. [Internet]. Prevalence of diabetes. [Cited 2009 Oct 17]. Available from http: // www.who.int/ diabetes/ facts/ world_figures/ en/
41
Hogan P, Dall T, Nikolov P; American Diabetes Association. Economic costs of diabetes in the US in 2002. Diabetes Care. 2003;26:917-932.
42
Sherwin RS, Anderson RM, Buse JB, Chin MH, Eddy D, Fradkin J, Ganiats TG, Ginsberg HN, Kahn R, Nwankwo R, Rewers M, Schlessinger L, Stern M, Vinicor F, Zinman B;American Diabetes Association; National Institute of Diabetes and Digestive and Kidney Diseases. Prevention or delay of type 2 diabetes. Diabetes Care. 2004 Jan;27 Suppl 1:S47-54.
105
43
Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, Marks JS. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003;289:76-79.
44
American Diabetes Association. [Internet]. Alexandria (VA): [Cited 2009 Sept 17]. Other diabetes medications. Available from: http: // www. diabetes. org / about - diabetes.jsp
45
Charpentier G. Oral combination therapy for type 2 diabetes. Diabetes Metab Res Rev. 2002;18 Suppl 3:S70-S76.
46
Ryan GJ, Jobe LJ, Martin R. Pramlintide in the treatment of type 1 and type 2 diabetes mellitus. Clin Ther. 2005; 27: 1500–1512.
47
Norris SL, Lee N, Thakurta S, Chan BK. Exenatide efficacy and safety: a systematic review. Diabet Med. 2009;26:837-846.
48
Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, IlanneParikka P, Keinänen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M; Finnish Diabetes Prevention Study Group. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344:1343-1350.
49
Norris SL, Zhang X, Avenell A, Gregg E, Bowman B, Schmid CH, Lau J. Longterm effectiveness of weight-loss interventions in adults with pre-diabetes: a review. Am J Prev Med. 2005;28:126-139.
50
Nathan DM, Cleary PA, Backlund JY, Genuth SM, Lachin JM, Orchard TJ, Raskin P, Zinman B; Diabetes Control and Complications Trial / Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005;353:2643-2653.
51
Hu FB, Stampfer MJ, Solomon CG, Liu S, Willett WC, Speizer FE, Nathan DM, Manson JE. The impact of diabetes mellitus on mortality from all causes and coronary heart disease in women: 20 years of follow-up. Arch Intern Med. 2001;161:1717-1723.
106
52
Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Final Report. Circulation. 2002; 106: 3163.
53
American Heart Association. [Internet]. Cholesterol [cited 2009 Oct 4]. Available from: http: // www. americanheart.org /presenter. jhtml? identifier= 3046103
54
Wilson PWF, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837-1847.
55
Stamler J, Wentworth D, Neaton JD, for the MRFIT Research Group. Is relationship between serum cholesterol and risk of premature death from coronary heart disease continuous and graded? Findings in 356222 primary screenees of the Multiple Risk Factor Intervention Trial (MRFIT). JAMA 1986;256:2823-2828.
56
Lipid Research Clinics Program. The Lipid Research Clinics Coronary Primary Prevention Trial results II: The relationship of reduction in incidence of coronary heart disease to cholesterol lowering.JAMA 1984;251:365-374.
57
Gordon DJ, Probstfield JL, Garrison RJ, Neaton JD, Castelli WP, Knoke JD, Jacobs DR Jr, Bangdiwala S, Tyroler HA. High-density lipoprotein cholesterol and cardiovascular disease: four prospective American studies. Circulation 1989;79:8-15.
58
Bjorkegren J, Boquist S, Samnegard A, Lundman P, Tornvall P, Ericsson C-G, Hamsten A. Accumulation of apolipoprotein C-I-rich and cholesterol-rich VLDL remnants during exaggerated postprandial triglyceridemia in normolipidemic patients with coronary artery disease. Circulation 2000;101:227-230.
59
Grundy SM, Cleeman JI, Merz CN, Brewer HB Jr, Clark LT, Hunninghake DB, Pasternak RC, Smith SC Jr, Stone NJ; National Heart, Lung, and Blood Institute; American College of Cardiology. Implications of recent clinical trials
107
for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation. 2004;110:227-239.
60
Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB, Flegal K, Ford E, Furie K, Go A, Greenlund K, Haase N, Hailpern S, Ho M, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott M, Meigs J, Mozaffarian D, Nichol G, O'Donnell C, Roger V, Rosamond W, Sacco R, Sorlie P, Stafford R, Steinberger J, Thom T, Wasserthiel-Smoller S, Wong N, WylieRosett J, Hong Y; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2009;119: e21-e181.
61
Endo A. The discovery and development of HMG-CoA reductase inhibitors. J Lipid Res. 1992;33:1569-1582.
62
Shepherd J, Hunninghake DB, Barter P, McKenney JM, Hutchinson HG. Guidelines for lowering lipids to reduce coronary artery disease risk: a comparison of rosuvastatin with atorvastatin, pravastatin, and simvastatin for achieving lipid-lowering goals. 2003. Atheroscler Suppl. 2004;5:115-123.
63
Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled trial. Lancet. 2002;360:7-22.
64
Shepherd J, Blauw GJ, Murphy MB, Bollen EL, Buckley BM, Cobbe SM, Ford I, Gaw A, Hyland M, Jukema JW, Kamper AM, Macfarlane PW, Meinders AE, Norrie J, Packard CJ, Perry IJ, Stott DJ, Sweeney BJ,Twomey C, Westendorp RG; PROSPER study group. PROspective Study of Pravastatin in the Elderly at Risk. Pravastatin in elderly individuals at risk of vascular disease (PROSPER): a randomised controlled trial. Lancet. 2002;360:1623-1630.
65
Sever PS, Dahlöf B, Poulter NR, Wedel H, Beevers G, Caulfield M, Collins R, Kjeldsen SE, Kristinsson A, McInnes GT, Mehlsen J, Nieminen M, O'Brien E, Ostergren J; ASCOT investigators. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-thanaverage cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial--Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial. Lancet. 2003;361:1149-1158.
108
66
Kumar V, Abbas AK, Fausto N, Aster JC. Robbin and Cotran pathologic basis of disease. 8th ed. Philadelphia, PA: Saunders an imprint of Elsevier Inc.; 2010.
67
Kreisberg RA. Diabetes dyslipidemia. Am J Cardiol. 1998; 82: 67U-73U.
68
Syvänne M, Taskinen MR. Lipids and lipoproteins as coronary risk factors in non-insulin-dependent diabetes mellitus. Lancet. 1997; 350 (Suppl 1):SI20-23.
69
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001, 285: 2486-2497.
70
Carmena R, Betteridge DJ. Statins and diabetes. Semin Vasc Med. 2004;4:321332.
71
Downs JR, Clearfield M, Weis S, Whitney E, Shapiro DR, Beere PA, Langendorfer A, Stein EA, Kruyer W, Gotto AM Jr. Primary prevention of acute coronary events with lovastatin in men and women with average cholesterol levels: results of AFCAPS/TexCAPS. Air Force/Texas Coronary Atherosclerosis Prevention Study. JAMA. 1998; 279:1615-1622.
72
Sever PS, Dahlöf B, Poulter NR, Wedel H, Beevers G, Caulfield M, Collins R, Kjeldsen SE, Kristinsson A, McInnes GT, Mehlsen J, Nieminen M, O'Brien E, Ostergren J; ASCOT investigators. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-thanaverage cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial--Lipid Lowering Arm (ASCOT-LLA): a multicentre randomised controlled trial. Lancet. 2003; 361:1149-1158.
73
Thomason MJ, Colhoun HM, Livingstone SJ, Mackness MI, Betteridge DJ, Durrington PN, Hitman GA, Neil HA, Fuller JH; CARDS Investigators. Baseline characteristics in the Collaborative AtoRvastatin Diabetes Study (CARDS) in patients with Type 2 diabetes. Diabet Med. 2004; 21:901-905.
74
Goldberg RB, Mellies MJ, Sacks FM, Moyé LA, Howard BV, Howard WJ, Davis BR, Cole TG, Pfeffer MA, Braunwald E. Cardiovascular events and their 109
reduction with pravastatin in diabetes and glucose-intolerant myocardial infarction survivors with average cholesterol levels: subgroup analyses in the cholesterol and recurrent events (CARE) trial. The Care Investigators. Circulation, 1998; 98: 2513-2519.
75
Athyros VG, Papageorgiou AA, Mercouris BR, Athyrou VV, Symeonidis AN, Basayannis EO, Demitriadis DS, Kontopoulos AG. Treatment with atorvastatin to the National Cholesterol Educational Program goal versus 'usual' care in secondary coronary heart disease prevention. The GREek Atorvastatin and Coronary-heart-disease Evaluation (GREACE) study. Curr Med Res Opin. 2002; 18:220-228.
76
Collins R, Armitage J, Parish S, Sleigh P, Peto R; Heart Protection Study Collaborative Group. MRC/BHF Heart Protection Study of cholesterol-lowering with simvastatin in 5963 people with diabetes: a randomised placebo-controlled trial. Lancet. 2003; 36: 2005-2016.
77
The Long-Term Intervention with Pravastatin in Ischaemic Disease(LIPID) Study Group. Prevention of cardiovascular events and death with pravastatin in patients with coronary heart disease and a broad range odf initial cholesterol levels. N Engl J Med. 1998; 339: 1339-1357.
78
Arampatzis CA, Goedhart D, Serruys PW, Saia F, Lemos PA, de Feyter P; LIPS Investigators. Fluvastatin reduces the impact of diabetes on long-term outcome after coronary intervention--a Lescol Intervention Prevention Study (LIPS) substudy. Am Heart J. 2005;149:329-335.
79
Pyŏrälä K, Pedersen TR, Kjekshus J, Faergeman O, Olsson AG, Thorgeirsson G. Cholesterol lowering with simvastatin improves prognosis of diabetic patients with coronary heart disease. A subgroup analysis of the Scandinavian Simvastatin Survival Study (4S). Diabetes Care. 1997; 20:614-620.
80
Santerre RE, Neun SP. Health economics: theories, insights, and industry studies. 4th ed. Mason: South-Western; 2007.
81
Grover SA, Coupal L, Zowall H, Alexander CM, Weiss TW, Gomes DR. How cost-effective is the treatment of dyslipidemia in patients with diabetes but without cardiovascular disease? Diabetes Care. 2001;24:45-50.
110
82
Grover SA, Coupal L, Zowall H, Dorais M. Cost-effectiveness of treating hyperlipidemia in the presence of diabetes: who should be treated? Circulation. 2000;102:722-727.
83
Brannon L, Feist J. Health psychology: an introduction to behavior and health. 5th ed. Belmont: Thomson; 2004.
84
International Society of Pharmacoeconomics and Outcomes Research. [Internet]. Lawrenceville (NJ): [Cited 2009 Dec 11]. Medication Compliance and Persistence: Terminology and Definitions. Available from: http://www.ispor.org/sigs/medcompliance/MedicationComplianceandPersistence. asp
85
Mihalko SL, Brenes GA, Farmer DF, Katula JA, Balkrishnan R, Bowen DJ. Challenges and innovations in enhancing adherence. Control Clinical Trials. 2004;25: 447-457.
86
Singh N, Squier C, Sivek C, Wagener M, Nguyen MH, Yu VL. Determinants of compliance with antiretroviral therapy in patients with human immunodeficiency virus: prospective assessment with implications for enhancing compliance. AIDS Care. 1996; 8:261-269.
87
Stewart RB, Caranasos GJ. Medication compliance in the elderly. Clin Pharmacol Ther. 1989; 73: 1551-1563.
88
Col N, Fanale JE, Kornhom P. The role of medication noncompliance and adverse drug reactions in hospitalizations in the elderly. Arch Intern Med. 1990; 150: 841-845.
89
British Association of Behavioural and Cognitive Psychotherapies: What are Cognitive and/or Behavioural Psychotherapies? [Cited 2009 Nov 3] Available from: http: / www.babcp.com / silo / files / what-is-cbt.pdf.
90
Bandura A. Social congnitive theory: An agentic perspective. Annu Rev Psychol. 2001; 52: 1-26.
111
91
Senécal C, Nouwen A, White D. Motivation and dietary self-care in adults with diabetes: are self-efficacy and autonomous self-regulation complementary or competing constructs? Health Psychol. 2000;19:452-457.
92
Schifter DE, Ajzen I. Intention, perceived control, and weight loss: an application of the theory of planned behavior. J Pers Soc Psychol. 1985;49:843851.
93
Hausenblas, Heather A.; Carron, Albert V.; Mack, Diane E. Application of the theories of reasoned action and planned behavior to exercise behavior: A metaanalysis. Journal of Sport & Exercise Psychology. 1997; 19: 36-51.
94
Prochaska JO, DiClemente CC, Norcross JC. In search of how people change. Applications to addictive behaviors. Am Psychol. 1992;47:1102-1114.
95
Rosen CS. Is the sequencing of change processes by stage consistent across health problems? A meta-analysis. Health Psychol. 2000;19:593-604.
96
Monane M, Bohn RL, Gurwitz JH, Glynn RJ, Levin R, Avorn J. Compliance with antihypertensive therapy among elderly Medicaid enrollees: the roles of age, gender, and race. Am J Public Health. 1996;86:1805-1808.
97
Catz SL, Kelly JA, Bogart LM, Benotsch EG, McAuliffe TL. Patterns, correlates, and barriers to medication adherence among persons prescribed new treatments for HIV disease. Health Psychol. 2000;19:124-133.
98
DiMatteo MR, Haskard KB, Williams SL. Health beliefs, disease severity, and patient adherence: a meta-analysis. Med Care. 2007;45:521-528.
99
Gao X, Nau DP, Rosenbluth SA, Scott V, Woodward C. The relationship of disease severity, health beliefs and medication adherence among HIV patients. AIDS Care. 2000;12:387-398.
100
Greenberg RN. Overview of patient compliance with medication dosing: a literature review. Clin Ther. 1984, 6: 593-599.
112
101
Cooper JK, Love DW, Raffoul PR. Intentional prescription nonadherence (noncompliance) by the elderly. J Am Geriatr Soc. 1982; 30: 329-333.
102
Piette JD, Wagner TH, Potter MB, Schillinger D. Health insurance status, costrelated medication underuse, and outcomes among diabetic patients in three systems of care. Med Care. 2004;42:102-109.
103
Yang Y, Thumula V, Pace PF, Banahan BF 3rd, Wilkin NE, Lobb WB. Predictors of medication nonadherence among patients with diabetes in Medicare Part D programs: a retrospective cohort study. Clin Ther. 2009;31:2178-2188; discussion 2150-2151.
104
Kilbourne AM, Reynolds CF 3rd, Good CB, Sereika SM, Justice AC, Fine MJ. How does depression influence diabetes medication adherence in older patients? Am J Geriatr Psychiatry. 2005;13:202-210.
105
Katon W, Russo J, Lin EH, Heckbert SR, Ciechanowski P, Ludman EJ, Korff MV. Depression and diabetes: factors associated with major depression at fiveyear follow-up. Psychosomatics. 2009;50:570-579.
106
Balkrishnan R. The importance of medication adherence in improving chronicdisease related outcomes: what we know and what we need to further know. Med Care. 2005;43:517-520.
107
Choo PW, Rand CS, Inui TS, Lee MT, Canning C, Platt R. Derivation of adherence metrics from electronic dosing records. J Clin Epidemiol. 2001;54:619-626.
108
Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X. Interventions for enhancing medication adherence. Cochrane Database Syst Rev. 2008;16(2): CD000011.
109
Farmer KC. Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice. Clin Ther. 1999;21:1074-1090.
113
110
Aschengrau A, Seage GR. Essentials of epidemiology in public health. 3rd ed. Sudbury: Jones and Bartlett; 2003.
111
Steiner JF, Koepsell TD, Fihn SD, Inui TS. A general method of compliance assessment using centralized pharmacy records. Med Care 1988;26:814–823.
112
Pawaskar MD. Medicaid payment systems: impact on quality of care, medication adherence and healthcare service utilizations in type 2 diabetes Medicaid enrollees [dissertation]. Columbus, (OH): The Ohio State University; 2008.
113
Jayawant SS. Effect of dosing regimens on medication use, healthcare resource utilixation, and costs in Medicaid enrolled type 2 diabetes mellitus patients [dissertation]. Columbus, (OH): The Ohio State University; 2008.
114
Schneeweiss S, Patrick AR, Maclure M, Dormuth CR, Glynn RJ. Adherence to statin therapy under drug cost sharing in patients with and without acute myocardial infarction: a population-based natural experiment. Circulation. 2007;115:2128-2135.
115
McGinnis BD, Olson KL, Delate TM, Stolcpart RS. Statin adherence and mortality in patients enrolled in a secondary prevention program. Am J Manag Care. 2009;15:689-695.
116
Larsen J, Andersen M, Kragstrup J, Gram LF. High persistence of statin use in a Danish population: compliance study 1993-1998. Br J Clin Pharmacol. 2002; 53:375-378.
117
Shinogle J, Wiener JM. Medication use among Medicaid users of home and community-based services. Health Care Financ Rev. 2006 Fall; 28:103-116.
118
Nelson AA, Reeder CE, Dickson MW. The Effect of a Medicaid Drug Copayment Program on the Utilization and Cost of Prescription Services. Medical Care 1984;22:724-735.
119
Reeder, C.E. and Nelson, A.A.: The Differential Impact of Copayment on Drug Use in a Medicaid Population. Inquiry 1985 Winter; 22:396-403. 114
120
Reed M. An update on Americans' access to prescription drugs. Issue Brief Cent Stud Health Syst Change. 2005 May;(95):1-4.
121
Cunningham P. Medicaid Cost Containment and Access to Prescription Drugs. Health Affairs. 2005; 24:780-789.
122
Piette JD, Wagner TH, Potter MB, Schillinger D. Health insurance status, costrelated medication underuse, and outcomes among diabetic patients in three systems of care. Med Care. 2004;42:102-109.
123
Shinogle J, Wiener JM. Medication use among Medicaid users of home and community-based services. Health Care Financ Rev. 2006 Fall; 28:103-116.
124
Becker MH, Maiman LA. Sociobehavioral determinants of compliance with health and medical care recommendations. Med care. 1975;13:10-24.
125
Andersen RM. Revisting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36:1-10.
126
Adamson DM, Chang S, Hansen L. White paper: health research data for the real world: the MarketScan databases. Ann Arbor (MI): Thomson; 2008.
127
Charlson ME, Pompei P, Ales KL, McKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40:373-383.
128
Steiner JK, Koepsell TD, Fihn SD, Inui TS. A general method of compliance assessment using centralized pharmacy records. Description and validation. Med Care. 1988;26:814-823.
129
Risenbaum PR. Conditional permutation tests and propensity score in observational studies. J Am Statist Assoc. 1983; 79: 565-574.
115
130
D’Agostino RB. Tutorial in biostatistics: propensity score methods for biad reduction in the comparison of a treatment to a non-randomized control group. Statist Med. 1998; 17: 2265-2281.
131
Silla R, Xia F, Aubert RE. Estimating medication persistency using administrative claims data. Am J Manag Care. 2005;11:449-457.
132
Vittinghoff E, Shiboski S, Glidden DV, McCulloch CE. Regression methods in biostatistics: linear, logistic, survival, and repeated measures models. New York: Springer; 2004.
133
Draper NR, Smith H. Applied regression analysis. New York: John Wiley & Sons; 1998.
134
Pagano M, Gauvreau K. Principle of biostatistics. 2nd ed. Pacific Grove (CA): Duxbury; 2000.
135
Knter MH, Nachtscheim CJ, Neter J. Applied linear statistical models. 4th ed. Columbus: Mcgram-Hill; 2004.
136
Lynch DJ, Birk TJ, Weaver MT, Gohara AF, Leighton RF, Repka FJ, Walsh ME. Adherence to exercise interventions in the treatment of hypercholesterolemia. J Behav Med. 1992;15:365-77.
137
Hartung DM, Carlson MJ, Kraemer DF, Haxby DG, Ketchum KL, Greenlick MR. Impact of a Medicaid copayment policy on prescription drug and health services utilization in a fee-for-service Medicaid population. Med Care. 2008 Jun;46(6):565-72.
138
Donnelly LA, Doney AS, Morris AD, Palmer CN, Donnan PT. Long-term adherence to statin treatment in diabetes. Diabet Med. 2008;25:850-855.
139
Kaiser Family Foundation. [Internet]. Prescription drug costs. [Cited 2010 Feb 19]. Available from: http://www.kaiseredu.org/topics_im.asp?id=352&parentID=68&imID=1
116
140
Kaiser Family Foundation. [Internet]. Your resource for health policy information, research, and analysis: prescription drug trends. [Cited 2010 Feb 19]. Available from: http://www.kff.org/rxdrugs/upload/3057_07.pdf
141
Drug Trend Report 2007. [Internet]. [Cited 2010 Feb 19]. Available from: http://medco.mediaroom.com/index.php?s=64&cat=5
142
Greisinger AJ, Balkrishnan R, Shenolikar RA, Wehmanen OA, Muhammad S, Champion PK. Diabetes care management participation in a primary care setting and subsequent hospitalization risk. Dis Manag. 2004 Winter;7:325-332.
143
Tanner EK, Feldman RH. Strategies for enhancing appointment keeping in lowincome chronically ill clients. Nurs Res. 1997;46:342-344.
117
APPENDIX A: Approval for data access
118
APPENDIX B: Services and License Agreement
119
120
121
APPENDIX C: Diagnostic indicators and medications used for patient identification and data analysis Conditions
Diabetes
Patient
Analysis of disease-related
identification1
medical costs1
250.x0, 250.x2
250.x0, 250.x2
Drug classes2
Oral antidiabetic drugs
Hyperlipidemia
272.xx
272.x, 410.xx-417.xx, 425.x,
Statins
428.xx, 429.0-429.3, 433.xx438.xx, 440.x, 444.xx 1. Patients and disease-related medical costs were identified by primary international classification of diseases 9th revision (ICD-9) codes in medical claims data. 2. Drugs were identified by national drug codes (NDC) in drug claims data.
122
