ARE MEDICATION ADHERENCE QUALITY INDICATORS ASSOCIATED WITH CLINICAL OUTCOMES? A DISSERTATION SUBMITTED ON THE TENTH DAY OF AUGUST 2012

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1 Are medication adherence quality indicators associated with clinical outcomes? ARE MEDICATION ADHERENCE QUALITY INDICATORS ASSOCIATED WITH CLINICAL OUTCOMES? A DISSERTATION SUBMITTED ON THE TENTH DAY OF AUGUST 2012 TO THE DEPARTMENT OF GLOBAL HEALTH SYSTEMS AND DEVELOPMENT IN PARTIAL FULFILLMENT 9F THE REQUIREMENTS OF THE SCHOOL OF PUBLIC HEALTH AND TROPICAL MEDICINE OF TULANE UNIVERSITY FOR THE DEGREE OF DOCTOR OF SCIENCE BY

2 Axe medication adherence quality indicators associated with clinical outcomes? ACKNOWLEDGEMENT I would like to express my deep gratitude to the following: Doctoral Dissertation Committee: Claudia Campbell, PhD (Chair), Professor, Global Health Systems and Development, Tulane University School of Public Health & Tropical Medicine Marie (Tonette) A. Krousel-Wood, MD, MSPH, Professor of Clinical Epidemiology, Tulane School ofpublic Health and Tropical Medicine; Professor of Clinical Medicine, Tulane School of Medicine; Associate Provost, Tulane University Lizheng Shi, PhD, Associate Professor, Global Health Systems and Development, Tulane University School of Public Health & Tropical Medicine Josh Benner, SeD, PharmD, Chief Executive Officer, Rxante, Inc; Former Managing Director at the Engelberg Center for Health Care Reform, Brookings Institution Expert Guidance & Review: David Nau, PhD, RPh, CPHQ, Senior Director for Research & Performance Measurement at PQA (a phannacy quality alliance); Associate professor, University of Kentucky College of Pharmacy Department of Phannacy Practice and Science Thomas Wolfe, PharmD, Director, Medical Outcomes Specialists, Pfizer, Inc. Tim Smith, MS, Director, Offering Management, IMS Health, Inc. Data Source: IMS Health, Inc.

3 Are medication adherence quality indicators associated with clinical outcomes? To my father Tang Kar Hung and mother Mariette, who helped me understand and appreciate the meaning of quality.

4 Are medication adherence quality indicators associated with clinical outcomes? TABLE OF CONTENT Abstract... List of Tables and Figures... Chapter 1: Background and Significance... Chapter 2: Literature Review... Chapter 3: Hypotheses and Research Questions... Chapter 4: Methods-... Chapter 5: Results... Chapter 6: Discussion... Chapter 7: Conclusion and Recommendation References Appendix I Appendix II Appendix III..._ Appendix IV Appendix V

5 ABSTRACT Background: Poor adherence to chronic medications is a major public health problem. Studies have shown that poor adherence among patients with chronic illnesses such as diabetes, hypertension, and dyslipidemia, can lead to increased risk ofhospitalization (Sokol et al., 2005; Ho et al., 2006). These studies led to the development and endorsement of medication adherence standard measures in August, 2009 by organizations such as the Pharmacy Quality Alliance (PQA) and the National Quality Forum (NQF). However, no study has validated these measures for statin and anti-diabetic agents. Objective: This study evaluated the endorsed adherence measures related to statin and anti -diabetic agents and their association with reduced hospitalization. Method: A retrospective cohort study was conducted on an insured patient population (18+ years) from a large national multi-health plan database. Two cohorts of statin or oral anti-diabetic agent users were identified and their adherence patterns according to the pharmacy claim-based NQF/PQA criteria were analyzed. Logistic regression analyses were used to understand the relationship between non-adherence to statins and anti-diabetic (DM) agents and risk ofhospitalizations. Various NQF/PQA adherence thresholds and their predictability of hospitalizations were also examined. Results: After risk adjustment, NQF statin adherence measure at Proportion Days Covered (PDC) of 80%+ (PST) did not significantly predict lower hospitalizations. NQF DM adherence measure at PDC 80%+ had a protective effect on all-cause hospitalization (OR=0.805; p=0.03). Moreover, achieving statin adherence with PDC at 95%+ revealed a protective effect on hospitalization. Implications: In this large insured population, PDM had strong validation against hospitalizations. PDC at 80%+ cutoff may not work for all populations. Higher adherence cutoff may be needed for a healthier statin population. These analyses provided 5

6 important understanding for payers, employers and providers regarding the validity of these new adherence measures in predicting hospitalizations, and the design of interventions to improve measure performance. Keywords: adherence, compliance, quality, statin, HMG-CoA Reductase Inhibitor, cholesterol, diabetes, oral hypoglycemic agent, biguanide, sulfonylurea, DPP-IV Inhibitor, thiazolidinedione 6

7 LIST OF TABLES & FIGURES Table la: Baseline Characteristics (Statin cohort) Table lb: Baseline Characteristics (DM cohort) Table 2a: NQF/PQA Performance Scores on% Adherent to Statins with PDC 2::80% (PST), and respective average PDC Adherence Rates by various patient populations among Statin cohort Table 2b: NQF/PQA Performance Scores on% Adherent to Oral Anti-diabetic Agents with PDC 2::80% (PDM), and respective average PDC Adherence Rates by various patient populations among DM cohort Table 3a: %Hospitalization among Adherent vs. Non-Adherent by various patient populations (Statin cohort) Table 3b:% Hospitalization among Adherent vs. Non-Adherent by various patient populations (DM cohort) Table 4a: Logistic Regression Outputs- Statin cohort: factors associated with hospitalization (All cause & CV) Table 4b: Logistic Regression Outputs- DM cohort: factors associated with hospitalization (All cause, CV, DM) Table 5: 2x2 Frequency Tables on Adherence Thresholds (PDC 2:: 100%, 95%, 90%, 85%, 80%, 75%, 70%) vs. All-Cause Hospitalization (Statin cohort) Table 6: 2x2 Frequency Tables on Adherence Thresholds (PDC 2:: 100%, 95%, 90%, 85%, 80%, 75%, 70%) vs. All-Cause Hospitalization (DM cohort) Figure 1: ROC Curve of PST adherence in predicting all-cause hospitalizations Figure 2: ROC Curve ofpdm adherence in predicting all-cause hospitalizations 7

8 Table 7: Table of relationship between PST & PDM Adherence at various cut-offs and likelihood of having all-cause hospitalization among Statin cohort & DM cohort Table 8a: Sensitivity Analyses: Impact of PST on Hospitalizations (2 yr. vs. 1 yr.) Table 8b: Sensitivity Analyses: Impact ofpdm on Hospitalizations (2 yr. vs. 1 yr.) 8

9 CHAPTER L BACKGROUND AND SIGNIFICANCE Poor adherence to chronic medications, especially for treating cardiovascular and metabolic diseases, is a well-recognized public health problem (WHO, 2003). Its impact on health care resource utilization, clinical outcomes, and health care costs has also become increasingly known through recent research (Bramley et al., 2006; Parris et al., 2005; Wei et al., 2002; Sokol et al., 2005; Goldman et al., 2006; Ho et al., 2006; Dragomir et al., 2010). However, despite the abundance of research, differences in research methods and adherence measurements resulted in inconsistent results (Caetano et al., 2006). This inconsistency makes 'it difficult to have standardized metrics and approaches to drive quality improvement initiatives, and ultimately to determine how treatment intensity and duration contribute to overall drug effectiveness. Standardization of adherence measures may help providers track and manage the effectiveness of medications they prescribe and for payers and purchasers to evaluate the quality of care being delivered in a more consistent manner. The National Quality Forum (NQF), a non-profit organization with representation from policy, consumer, academia, managed care, and industry sectors aiming to improve the quality of American health care, has recently made efforts to prioritize adherence as a quality measurement. Specifically, it endorsed a series of adherence and persistence measurements in 2009 [See APPENDIX I], hoping to facilitate and standardize their use. Unlike previous adherence measures used in research studies, these NQF adherence quality measures will have implications for greater real-world application because they were endorsed by diverse industry representatives including non-research-based organizations, and were also selected for ease of implementation in managed care and clinical settings. Additional assessment and validation of 9

10 these standardized measures as they are used in real-world settings and populations following NQF endorsement are particularly important. This research paper seeks to provide a real-world clinical validation of the patient adherencerelated quality measures endorsed by the NQF in The significance of this study is the following: Help clinicians, payers, employers and quality improvement professionals understand the clinical implications associated with the recently endorsed NQF/PQA (Pharmacy Quality Alliance) adherence measures specific to statin and diabetic medications in an insured setting using a nationally representative population Provide purchasers and sponsors important evidence with standardized measures to support their decisions on designing and sponsoring pay for performance, patient centered medical home (PCMH), and other related quality programs Findings of this study will provide important insights into understanding the degree of association between "intermediate outcomes," as measured by standardized adherence performance scores and surrogate clinical outcomes such as hospitalization. This is helpful for payers, employers and providers to more effectively leverage adherence as a way to improve the standard of care delivery, and manage health care resource utilization to reduce increasing overall health care costs. 10

11 CHAPTER 2: LITERATURE REVIEW Cardiovascular disease (CVD) is a national health and economic crisis. According to the World Health Organization (WHO) and American Heart Association (AHA), it remains the leading cause of death, taking an estimated 23.6 million lives annually by 2030 (World Health Organization, 2009), costing the United States more than $444 billion a year in 2010, and was further projected to cost over $1 trillion by 2030 (in 2008 dollars) (Heidemeich et al., 2011). Hypertension (high blood pressure), dyslipidemia (high blood cholesterol), and diabetes mellitus (or elevated blood sugar/glucose) are leading modifiable risk factors for cardiovascular disease. They are highly prevalent in the United States: constituting 31%, 34%, and 8% respectively (Gillespie et al., 2011; Kuklina et al., 2011), and are associated with increased mortality (Neaton & Wentworth, 1992). Treatment rates for hypertension, dyslipidemia, and diabetes are at historic high levels at 70%, 48%, and 84% respectively, however, control rates among treated are as low as 46%, 32%, and 22% respectively (Gillespie et al., 2011; Kuklina et al., 2011; Ali et al., 2012). Despite the efforts ofhealth care professionals and the availability of proven medications, many diagnosed and treated patients with these chronic diseases are not well-controlled. Prevalence and Burden of Poor Adherence These poor control rates can be explained by real-world barriers such as poor adherence to medications. According to WHO (2003), "Adherence is an important modifier of health system effectiveness... The population health outcomes predicted by treatment efficacy data cannot be achieved unless adherence rates are used to inform planning and project evaluation." 11

12 Medication adherence is one of the key factors that enable to assess true effectiveness of treatments in the real-world. Well-designed randomized or non-randomized clinical trials provide important understanding of treatment efficacy. Often times, the tightly monitored trial process and informed consent results in a Hawthorne effect (French, 1950) and selection of highly motivated patients, leading to idealistic patient adherence and outcomes. The average rates of adherence in clinical trials can be high, due to the attention study patients receive and to selection of the patients, yet even clinical trials report average adherence rates of only 43 to 78 percent among patients receiving treatment for chronic conditions (Osterberg & Blaschke, 2005). Therefore, more realistic medication adherence rates have been measured through nonexperimental observational studies, especially through analyses of administrative databases retrospectively. Monane et al. (1997) found that one out of five elderly hypertensive patients are refilling their antihypertensive medications at an acceptable level of medication possession ratio (MPR) of 0.8 or above in a Medicaid population. In a broader insured population, only 36% of patients remain adherent to concomitant antihypertensive and statin therapies after 1 year (Chapman et al., 2005). Adherence generally remains poor over time as another longer-term study finds that only a quarter of elderly patients remain adherent to statins after 5 years (Benner et al., 2002). Problems were seen in patients taking anti-diabetic medications. Approximately 10-30% ofpersons affected by type 2 diabetes mellitus withdraw from their prescribed regimen within one year of diagnosis and, among the remaining patients, nearly 20% take insufficient medication to facilitate an adequate reduction in blood glucose (Skaer, Sclar, Markowski, Won, 1993). Poor adherence to cardiovascular (CV) & metabolic medications has serious health and societal consequences as it has been shown to be linked with adverse clinical outcomes and 12

13 increased overall health care costs (Bramley et al., 2006; Parris et al., 2005; Wei et al., 2002; Sokol et al., 2005; Goldman et al., 2006; Ho et al., 2006; Dragomir et al., 2010, McCombs et al., 1994). Specifically, studies have associated poor adherence with worse blood pressure control (Bramley et al., 2006; Lee et al., 2006), worse LDL-C control (Parris et al. 2005; Lee et al. 2006), increased risk of hospitalization (Sokol et al., 2005; Goldman et al., 2006; Ho et al., 2006; Dragomir et al., 2010; Lau & Nau, 2004), more recurrent myocardial infarctions (MI) among patients with prior MI (Wei et al., 2002), arid even increased mortality (Ho et al., 2006; Wu et al., 2006). Economically, despite slight decrease in pharmacy costs, poor adherence results in higher overall health care costs (Sokol et al., 2005; McCombs et al., 1994; Salas et al., 2009). Additionally, Balkrishnan et al. (2003) found that a 10% increase in MPR for an anti-diabetic medication was associated with an 8.6% reduction in total annual health care costs. Barriers to Adherence Patient adherence to CV medications is a complex and multi-factorial problem. Risk factors include socio-demographic characteristics such as being African-American (vs. Caucasian) (Bosworth et al., 2006), female (vs. male) (Schultz et al., 2005), younger (vs. older) (Schultz et al., 2005); health care behavioral characteristics such as infrequent physician visits (Monane et al., 1997; Benner et al., 2004), few lipid testing (vs. frequent lipid testing) (Bem1er et al., 2004), use of multiple phannacies (Monane et al., 1007); forgetfulness (Cheng, Kalis & Feifer, 2001); cost of medications (Safran et al., 2005); specific comorbidities such as depression, dementia (Benner et al., 2002) and free of CVD health status (vs. established coronary artery disease) (Chapman et al., 2005; Jackevicius, Mamdani & Tu, 2002); and 13

14 treatment regimen complexity such as pill burden, dosing, combination medication use, timing of using concomitant medications, out-of-pocket costs have all been found to predict poor adherence to CV medications (Chapman et al., 2005; Agarwal et al., 2006, May; Paes et al., 1997; Benner et al., 2006, May; Goldman et al., 2004; Dezii., 2000; Hussein et al., 2010; Vanderpoel et al., 2004; Patel et al., 2008, May; Bangalore et al., 2007). While most of the predictors of adherence are related to the patients, the role of health care providers and the degree of care they provide that would influence patient adherence to medications are less known. In fact, sub-optimal physician adherence (also known as clinical inertia) to quality and appropriate cardiovascular care is emerging as an important quality indicator (Redondi et al., 2006; Phillips et al., 2001; Chassin & Galvin, 1998; Kerr et al., 2001; Grant et al., 2005). Specifically, therapy modification (adding, titrating, changing medication for a patient who has uncontrolled CV risk factors) has been studied as a form of physician adherence (Redondi et al., 2006, Grant et al., 2005; Weiner et al., 2006, May; Turner et al., 2006, May). All of these health system-related, patient-related, and provider-related factors make understanding and improving poor adherence an extremely challenging task. However, it also makes adherence improvement a targeted and clinically relevant area for improving cardiovascular quality and outcomes in real-world population. Definition of Adherence, Compliance, Persistence These studies provided important clinical rationale for the development of consensus measures (measures that are endorsed via a consensus development process by multiple stakeholders) of medication adherence, as a measure of health system performance. Developing consensus methods for defining and measuring medication and persistence has 14

15 been historically challenging. In order to demystify adherence, leading authorities such as WHO provided clear guidelines on adherence in According to the WHO (2003), "Adherence" is defined as behavior that follows the agreed recommendations of a health care provider (World Health Organization, 2003), which is used synonymously as "compliance" in many publications. WHO favors the term adherence over compliance because it better reflects the patient's involvement in his or her own health care. "Persistence" meanwhile "adds the dimension oftime to the analysis and usually represents the time over which a patient continues to fill a prescription, or the time from the initial filling of the prescription until the patient discontinues (or have extended specified gap period) refilling a prescription" (Peterson et al., 2006). Medication adherence therefore requires an understanding of whether a patient follows a physician's order of taking medication over a specified period of time, in other words, assesses treatment intensity whereas medication persistence focuses on treatment duration. Despite the establishment of these conceptual definitions by expert authorities, understanding and interpreting adherence, compliance, and persistence remains largely inconsistent. Measuring Adherence, Compliance & Persistence Monitoring the medication-taking behavior of patients is difficult in the real-world especially in large populations. In the absence of widely used medicine-taking monitoring devices and programs, administrative data sets such as phannacy claims databases provide a non-invasive, objective, and lower cost alternative to gain proxy understanding of medication adherence. Claims data do not provide medication consumption information, but rather provide assessment of medication possession, which assumes that patients consume the drug starting 15

16 the day of dispensing, use the drug as prescribed, and consume all medications obtained (Hess et al., 2006). Medication possession does not necessarily reflect actual medication consumption or consumption exactly according to physician's order. Therefore this measure may overestimate true medication adherence. Nonetheless, Hess et al. (2006) states that administrative data are convenient, noninvasive, objective and relatively inexpensive to obtain (Hess et al., 2006). More importantly, adherence estimates derived from administrative databases with ties to electronic records and laboratory files have been associated with clinical outcomes such as blood pressure control, cholesterol control, recurrent myocardial infarction, and hospitalizations (Bramley et al., 2006; Parris et al., 2005; Wei et al., 2002; Sokol et al., 2005; Goldman et al., 2006; Ho et al., 2006; Dragomir et al., 2010). As administrative databases of pharmacy records become more commonly used for health services research purposes and managed care decision making, measures of adherence have also grown in sophistication. Peterson et al. (2007), on behalf of the International Society of Pharmacoeconomics and Outcomes Research (ISPOR) Medication Compliance and Persistence Special Interest Group (SIG), and Hess and colleagues (2006) provide detailed explanations of the variety of available measures of adherence. Particularly, Medication Possession Ratios (MPR), and Proportion Days Covered (PDC) have become commonly used measures to assess the degree of medication adherence. Below is a brief description of the few most commonly used measures: Illustration 1: MPR, PDC, MPRm Calculation Fonnula Number of days of medication supplied 16

17 MPR= Last claim date - first claim date. Number of days of medication supplied MPRm= X 100% (Last claim date- first claim date+ last days' supply) Number of days of medication supplied, PDC*= X 100% Total of days within a pre-specified evaluated study time period *Note: PDC is always capped at 100% (Sometimes presented as a number ::;1} Its denominator is typically a clinically meaningful time period that is the same for all studied patients (e.g. 90 days, 180 days, 360 days, etc.) The key limitation of the MPR measures is the potential of overestimation of adherence. The denominator ofmpr represents days within a studied refill interval, indicating a time period between first refill and last refill. This means that MPR denominator for different individuals may differ greatly [See Illustration 2] (Hess et al., 2006). In response to the limitation of the simple MPRs, researchers introduced a modified MPR (MPRm), which uses total days supplied divided by the difference between last claim date and first claim date plus last days' (refill) supply. This approach reduces overestimation of possession rates, but still does not address the standardization of the denominator. Nevertheless, another issue is that due to assumption of each participant being 100% adherent during the last dispensation period, MPRm consistently produced an adherence value higher than those of other measures (Hess et al., 2006). Moreover, Hess et al. (2006) reported that 4 different published measures have 17

18 been called an "MPR", creating much confusion among researchers and decision-makers. Accordingly there was no consensus on measurement endorsement until the 2009 endorsement by NQF of several measures of medication adherence as indicators of the quality of drug therapy management Illustration 2: MPR: Subject #1: I[ 30_] 20 [ 30_j 20 j[_30_] MPR = = 0.6 Subject #2: I[ 30_] 7 [ 30 J 60 1[_30 _] MPR= 60/127 =0.49 Subject #3: I[ 30 ] 71[_30_j MPR= 30/37 = 0.81 Average MPR-based Adherence= [ ]/3 = 0.63 Note: The numbers for each subject are days of medication supplied. Each bracket represents a filllrefill The bolded box represents the adherence measure assessment period. Illustration 3: MPRm (factoring the supply of the last refill): Subject #1: I[ 30 ] 20 [ 30_j 20 [ 30 Jl MPR = 90/130 = 0.69 Subject #2: I[ 30 ] 7 [ 30 ] 60 [ 30 Jl MPR= 90/157 =0.57 Subject #3: I[ 30_] 7 [ 30 j MPR = 60/67 =

19 Average MPR-based Adherence= [ ]/3 = 0.72 Note: The numbers for each subject are days of medication supplied. Each bracket represents a fill/refill. The bolded box represents the adherence measure assessment period. On the other hand, PDC, which was first introduced by Benner et al. (2002) and is a better measure of adherence, has been used with increasing frequency (Peterson) in important adherence-related studies tested for association with outcomes (Ho et al., 2006; Goldman et al., 2006; Dragomir et al., 2010). The advantage is three fold: first the PDC standardizes the denominator, providing further accuracy of assessing adherence among a studied population without overestimation [See Illustrations 2, 3 vs. 4]; second, the PDC allows for greater understanding of adherence to multiple-drug/class regimen over a period of time [See Illustration 5], providing important public health insight to policy decision-makers; and last, by using a constant denominator for all patients, PDC is sensitive to both refill intensity and persistence of utilization [See Illustration #5 & #6]. PDC can be analyzed as a continuous measure or divided into categories for use as an ordinal or dichotomous variable (Peterson et al., 2007) [See Illustration 4]. Illustration 4: PDC over evaluation period of 180 days: Subject#1: I[ 30 ] 20 [ 30~ 20 [ 30 ] lend of 180 PDC = 90/180*100% =50%; Adherent (PDC~80%) =No Subject #2: I[ 30 ] 7 [ 30 J 60 [ 30 lend of 180 PDC = 90/180*100% =50%; Adherent (PDC~80%) =No 19

20 Subject#3: I[ 30 ]7[ 30 j lend of 180 PDC = 60/180*100% = 33%; Adherent (PDC_::::80%) =No Average adherence over 180 days= Sum of all med covered days [90, 90, 60] divided by sum of total days in study evaluation period [180, 180, 180] * 100% = [ ]1[ ]*100% = [240/540]*100% = 44%. Average: A dichotomous variable of adherence (Adherent? YIN) is derived if PDC_::::80%. The numbers for each subject are days of medication supplied. Each bracket represents a fill/refill. The bolded box represents the adherence measure assessment period Illustration 5: PDC of statin class over evaluation period of 180 days: Subject#1: I[ A30 ] 20 [ A30 j 20 [ A30 j lend of 180 PDC = 90/180* 100% = 50% Subject #2: I[ S30 j 7 [ S30 _] 60 [ A30 j lend of 180 PDC = 90/180*100% =50% Subject #3: I[ S30 j 7 [ S30 j lend of 180 PDC = 60/180*100% = 33% Average adherence to the class of statins over 180 days= Sum of all covered days of statins [90, 90, 60] divided by Sum of total days in study evaluation period [180, 180, 180]*100% = [ ]/[ ]*100% = [240/540]*100% = 44%. Note: A=Atorvastatin, S=Simvastatin. The numbers for each subject are days of medication supplied. Each bracket represents a fill/refill. The bolded box represents the adherence measure assessment period 20

21 Illustration 6: Persistence of statin class over evaluation period of 180 days (Discontinuation or Censorship criteria set at gap interval greater than 30 days): Subject #1: I[ A30_] 20 [ A30_] 20 [ A30_] lend of 180 Days to discontinuation= 130 Days; Persistent within 180 Days? Answer= No Subject#2: I[ S30_] 7 [ S30_] 60 [ A30_] lend of 180 Days to discontinuation= 67 Days; Persistent within 180 Days? Answer= No Subject #3: I[ S30_] 7 [ S30_] lend of 180 Days to discontinuation= 30 Days; Persistent within 180 Days? Answer= No Average days to discontinuation of class of statins over 180 days= 76 Days. Proportion of patients being persistent within 180 days? Answer= 0% Note: A=Atorvastatin, S=Simvastatin. The numbers for each subject are days of medication supplied. Each bracket represents a fill/refill. The balded box represents the adherence measure assessment period The public health implications of medication non-adherence and growing familiarity with standardized adherence/persistence metrics have led to the development and recent endorsement of standard medication adherence measures. In August 2009, NQF endorsed six new sets ofi.nedication adherence measures in therapy areas that utilize the following medications: Beta-Blockers (BB), Angiotensin-Converting Enzyme Inhibitor/Angiotensin- Receptor Blocker (ACEI/ARB), Calcium-Channel Blockers (CCB), diabetes medications, Statins, and schizophrenia medications based on PDC and the MPR/MPRm metrics. PDCsbased metrics are provided by NCQA (The National Committee for Quality Assurance)/PQA, 21

22 and MPR/MPRm metrics are provided by Center of Medicare & Medicaid Services (CMS). The PDC-based measures have broader applications because operationally they only require use of pharmacy claims data and also they can be used in broader insured population of 18+ years as opposed to CMS-based focus on Medicare seniors of 65+ years of age or Medicare Part D beneficiaries [See APPENDIX I]. These measurements play an important role in contributing to standardized quality metrics related to drug therapy management. Since they were provisionally endorsed, further validation is needed to test their real world application by assessing the association between these specific measures and clinical outcomes such as hospitalizations. Furthermore, unlike most existing studies that look at adherence and hospitalizations during the same time period, a multi-stage cohort study can better assess the impact of adherence and covariates on subsequent or future hospitalizations in a chronologically sequential manner. However, large sample sizes and robust longitudinal study designs are typically needed to test these relationships. Lastly, for many years, an 80% medication adherence/compliance rate defined by refill proportions was used as a threshold to determine acceptable adherence. MPRs at 80% or greater were used as a measure of high adherence. No studies have leveraged a large nationally representative patient population to evaluate associations between changes in various adherence thresholds and hospitalization. 22

23 CHAPTER 3: HYPOTHESES AND RESEARCH QUESTIONS The theoretical framework of this study is based on two key constructs ofnqf-based adherence and hospitalization. The central purpose of this study is to test the construct validity of this NQF-based adherence measure by assessing its predictability ofhospitalization in a nationally representative insured patient population in the United States. Theoretically and based on historical similar studies, poor or low adherence (vs. optimal/high adherence) to medications leads to higher rates of hospitalization. The proposed study seeks to answer two questions: 1) Are NQF/PQA-endorsed PDC adherence measures for key chronic medication categories such as statins or oral anti-diabetic agents sufficient and valid for predicting outcomes? 2) Is the typical cut-off of 80% most appropriate for measuring adherence to statins or oral anti-diabetic agents and predicting outcomes? As discussed in the earlier section, because NQF/PQA adherence measures have broader applicability and less stringent data requirements, the hypotheses that will be tested in this study will use only the subset of adherence measures among the newly endorsed NQF quality indicators (See APPENDIX I: Measure #0541 ). Furthermore, this study will only be focusing on validating the subset of measures related to statins and oral anti-diabetic agents because these medications have more straightforward clinical indications (high-cholesterol, and diabetes mellitus) than the antihypertensive agents, which can be used to treat hypertension or various cardiovascular diseases. Such a focus will reduce issues related to confounding by indication in the analyses. The following NQF/PQA adherence measures are selected for validation in this study: 23

24 Proportion Days Covered (PDC): 5 rates by therapeutic category (Note: Only 2 rates- DM meds & Statins, will be analyzed in this study): %of 18yrs+ who met PDC of 80% or higher for diabetes mellitus (DM) medications (oral anti-diabetic or hypoglycemic agents only) and statins during the measurement year (subset of#0541) (sponsored by NCQA/PQA) o The following variables will be used to represent the PDC measure: PDC Performance Rate for statin adherence = PST PDC Performance Rate for DM med adherence = PDM The research questions of this study are as follow: What are the associations between these selected NQF/PQA-endorsed adherence perfonnance measures and hospitalizations? Do these measures predict hospitalizations adjusting for other covariates? Is typical "adherence" as defined by PDC 80%+ (with these selected NQF-adherence measures) still most appropriate for measuring adherence to statins or oral antidiabetic agents and predicting outcomes? The main research hypothesis of this study is that non-adherence to statins (or HMG-CoA reductase inhibitors, a class of cholesterol lowering therapy) and oral anti-diabetic agents (or oral hypoglycemic medications only) as technically specified in the recently endorsed NQF adherence measures (PST, PDM) are not associated with an increased likelihood of all-cause, cardiovascular, and diabetes-related (DM) hospitalizations. A secondary hypothesis is that the 24

25 80% threshold in existing adherence quality measures will yield the strongest predictive association with clinical outcomes: Adherence Measures and Hospitalizations: Null hypothesis: NQF/PQA adherence measures do not predict hospitalizations. Alternate hypothesis: NQF/PQA adherence measures predict hospitalizations Is 80%+ Optimal? Null hypothesis: PST or PDM at 80% does not yield highest statistical significance, indicating optimality. Alternate hypothesis: PST or PDM at 80% yields highest statistical significance, indicating optimality. 25

26 CHAPTER 4: METHODS Study Design: This study uses a retrospective cohort design on a nationally representative, large, patient database called IMS Lifelink Integrated Claims Database (formerly called the Pharmetrics Patient-Centric Database), which contains integrated pharmaceutical and medical claims of more than 55 million covered lives representing over 90 health plans across the United States. The database includes more than 2 billion health care transactions including de-identified information on enrollment, prescriptions, office visits, diagnostic tests, and hospital stays. Patients in the database are representative of the national insured patient population in the United States. Inpatient and outpatient diagnoses are in International Classification of Diseases, 9th Revision, Clinical Modification [ICD-9-CM] fonnat, and procedures are in Current Procedural Terminology, 4th Edition [CPT-4] format. Community and mail order pharmacy claim records are identified through Generic Product Identifier (GPI) codes (IMS, 2005). Technical specifications for the cohorts and measures were obtained from PQA [See APPENDIX II & III]. Data Extraction & Inclusion/Exclusion Requirement: The following criteria were used for data extraction and exclusion froin the final data set used in the analysis. Data were extracted for patients who were 18 years or above with a prescription claim (at least 1 fill) of an oral anti-diabetic agent or a statin between 7/1/07 and 12/31/07 [See APPENDIX II for detailed definition of medications], who met continuous enrollment eligibility, and for whom data were available for a total of at least 2.5 years including a 6 month period pre-index date at least 1 year of adherence 26

27 measurement, and 1 year of follow up on outcomes. This data set was extracted from 1% random sample of 55 million covered lives, integrating claims containing demographic, enrollment, diagnoses, pharmaceutical, outpatient and inpatient resource utilization information. Study Design & Measurement Period (defined by NCQA/PQA-Nau method [See APPENDIX III]: Baseline characteristics of patients were for a 6 month pre-index period. The index prescription date was anytime on or after 7/1/07 and adherence was assessed between the index Rx date through the calendar year of Dec Illustration 7 below illustrates the measurement timeline for each patient included in the data set. Illustration 7: Study Design and Timeline e.g. Patient A with initial Rx I I e.g. 6mth pre- 1 Index Baseline : Characteristics : ( ~ e.g. End of Patient A adherence measurement period 1 I : Patient A Follow up: 2 yr : hospitalization measurement I Hospitalization fo~low-up (2 years) NQF/PQA PST/PDM Adherence Measuremen perio~ (thru end of calendar yr) (At least 1 year I 1/1/07 7/1/07 1/1/08 7/1/08 1/1/09 12/31/09 3 yr. continuous enrollment 27

28 Key Variables: The main dependent variables used to assess outcomes associated with non-adherence to medications are different measures ofhospitalizations that occurred during a 1-year follow up period post the measurement of adherence. Specifically, hospitalization measures included 1) having any kind of hospitalization ("All-cause Hospitalization") (YIN); 2) any kind of cardiovascular-related hospitalization ("CV Hospitalization") (YIN); and 3) any kind of diabetes-related hospitalization ("DM Hospitalization") (YIN) (to be used for oral antidiabetic Agent cohort (DM Cohort)). All outcomes are dichotomous variables. CV hospitalization is defined as any hospitalization for a diagnoses of myocardial infarction (MI) or angina (ICD-9 41 O.xx- 414.xx), a medical procedure (coronary artery bypass grafting, angiography, angioplasty, stent); or cerebrovascular disease including intracerebral hemorrhage ( 431.xx), other and unspecified intracerebral hemorrhage ( 432.xx), occlusion and stenosis ofprecerebral arteries (433), occlusion cerebral arteries (434), acute but ill-defined cerebrovascular disease (436), and other and ill-defined cerebrovascular disease (437) or related medical procedure; or peripheral vascular disease ( ) or related medical procedure; or chronic heart failure (398.91, , , , 428.0, 428.1, and 428.9); or arrhythmia ( ), a medical procedure involving a pacemaker; and valvular heart disease ( ) (Dragomir et al., 2010). The CV outcome definition included events with either a primary discharge diagnosis of interest or a procedure of interest listed above using only inpatient claims; outpatient claims were not considered. The main independent variable is adherence to statins & anti-diabetic medications based on the NCQA/PQA-PDC over a 12 to 18-month period through the next calendar year [See APPENDIX III]. Adherence is represented by the following dichotomous (YIN) variables: 28

29 PST (YIN) (Dichotomous): YES if achieved statin PDC 80%+ PDM (YIN) (Dichotomous): YES if achieved oral anti-diabetic agent PDC 80%+ These dichotomous variables were based on NQF/PQA's definition of PDC: 5 rates by therapeutic category (Note: Only 2 rates- oral anti-diabetic agents & statins, will be analyzed in this study): % of 18yrs+ who met 80%+ for oral anti-diabetic agents, Statins during the measurement year (selected from #541) (Sponsored by NCQA/PQA) [See APPENDIX III]. The adherence measurement period in this study was time between index date and through the next calendar year until Dec 31, Mean PDC rates will also be reported as continuous variables The following covariates will be used for assessing baseline characteristics of the 1 percent sample of the population studied, stratifying descriptive analyses, statistical adjustment for multivariate analyses. Age (Continuous, Categorical) - Gender (Categorical) - Geographic region (Categorical) - Presence ofcv comorbidities (MI, CHD, Stroke, CHF, Hypertension, Diabetes (for Statin Cohort), Dyslipidemia (for DM Cohort)) (Dichotomous- YIN) - Presence of non-cv comorbidities (e.g. Depression, Renal Disease) (Dichotomous -YIN) - Charlson Comorbidity Index (CCI) (Categorical) - Baseline number of unique medications (Continuous, Categorical) - Baseline mean patient co-pay (Continuous, Categorical) 29

30 Payer type (commercial, Medicaid, Medicare Risk..etc.) (Categorical) (IMS, 2010): Commercial: Commercial plans are primarily employer-based. The health plan assumes the risk of insuring the enrolled members. Medicaid: Medicaid is a state and federal health insurance program for qualifying low income individuals, contracts in some cases with private health insurers to manage the health care for Medicaid enrollees. The health plan assumes the financial risk of insuring the enrollees and typically manages the plan like an HMO. Medicare Risk: Medicare a federal health plan for senior individuals and individuals with selected disabilities, contracts in some cases with private health insurers to manage the health care for Medicare enrollees. The health plan assumes the financial risk of insuring the enrollees and typically manages the plan like an HMO. Medicare Risk plans typically cover more services, including drugs, than traditional Medicare insurance, although the choice of providers and access to providers is more limited than traditional Medicare insurance. Medigap or Medicare Supplemental plans are included in the Unknown and Others category. Self-insured: Self-insured plans are a subset of Commercial plans where the employer assumes the risk of insuring the population. Large employers typically self-inure while small employers typically do not. With the exception of who is at risk (i.e. the employer), self-insured plans are typically run like HMO, PPO, or POS plans. New User (no prior use of index drug during 6-month pre-index baseline period) (Dichotomous - YIN) 30

31 - Having a prior hospitalization during baseline/index year (2007) (Dichotomous - YIN) Database characteristics: HIP AA Protection: Data for this analysis were obtained from a multi-health plan patient-level longitudinal database called IMS Lifelink Integrated Claims Database. This database does not contain "individually identifiable health information" defined by HHS (the Department of Health and Human Services).This follows the privacy and confidentiality principles according to the Final Privacy Rule of HIP AA (the Health Insurance Portability and Accountability Act of 1996). Specifically, the database adopts the following methodology for data extraction (IMS, 2005): Limitation of the types of fields requested or accepted for a given dataset: o Dis-allowance of obvious identifiers in any dataset (e.g., name, street address, Social Security Number) o Request age or year (not date) ofbirth o Reclassification of patients age 89 or older as "Age 99" o Aggregation of data by region rather than state. Using five key variables (Gender, Age Group, ICD-9 diagnostic code, CPT-4 procedure code, Geographic Region), analyze each dataset prior to release into the production (de-identified) database: o Sum the count of the instances of only one respondent at the intersection of each of the five key variables. These are the number of possibly identifiable sample-unique patients in the dataset. 31

32 o Divide the sum (above) into the total number of insured patients (from the U.S. Government Area Resource Files). o Dataset is accepted for inclusion into the IMS Health database only if resulting value is less than 0.1 percent. Random sample (1%) (IMS, 2005): A 1% random sample of the overall database was extracted for use for the research. IMS Health, Inc. uses a "Population Builder" tool to produce the sample databases (1, 5, 10, 50 or whatever% sample). The population builder scans the database choosing patients based on a random uniform distribution. It uses the uniform function in SAS to accomplish this. From SAS, "UNIFORM(SEED) - generates values from a random uniform distribution between 0 and 1." The seed is the time of day. The code produces a value between 0 and 1 for each patient. The code then multiplies this value by 100. If the value is less than or equal to the% sample desired then the patient is selected for the sample. Once a list of patients is accumulated, this list is used as a feeder to the claims inclusion/exclusion criteria. Data Storage (IMS, 2005): Any data provided by the data provider hereunder were stored by the investigator in a personal computer, all reasonable precautions were taken against error in gathering, storing, and analyzing infonnation and data in accordance with established data processing principles. Measurement & Analyses: 32

33 Baseline demographic and clinical characteristics were described in the cohorts. All pharmacy and medical claims rendered during the adherence measurement and outcome follow-up periods were used to calculate all the post-index measures and outcomes of interest: 1. Describe performance scores based on NCQA/PQA criteria (Statin & DM cohorts): a. PST & PDM:% Adherent (2::80% PDC) (at least 1 year) b. Additional analyses were performed on average PDC rates 2. Hospitalizations ( ) among various cohorts 3. Factors associated with hospitalization: a. Multivariable Logistic Regression models were used to understand how demographic, clinical, and adherence-based independent variables predict hospitalizations (Having All-cause, CV or DM related hospitalization) (YIN) Sample Size determination Power analysis for database cohort studies are not common since most of these national administrative datasets are quite large in sample size to begin with compared with clinical trials. Moreover, effect size in adherence is also not well-known compared with other clinical parameters. Dezii (2000) demonstrated a significant difference of 20% in patients being persistent with fixed-dose antihypertensive therapy (AHT) vs. separate pill regimens of AHT. In addition, Ho et al (2006) indicated that 25% increase in medication adherence with CV medications is associated with reduction in HbA1c, BP and LDL levels. Therefore, in a x2 test comparison of2 groups (adherent vs. non-adherent), assuming 25% effect size and alpha= 0.5, achieving power (1-P) of 80% would require a sample size of at least 206. Achieving power of 95% would require sample size of at least 317 (Analyses calculated by G*Power 33

34 software Version 3, available from de/ abteilungenl aap/ gpower3/ download-and -register The following steps were used to create the analytical cohort file. Steps to Create Analytical Cohort File: A. Identify Drug of interest from CLAIMS file:: 1. From CLAIMS file, claims that included a pharmaceutical claim were selected. Sub CLAIMS called CLAIMS-P was created. 2. CLAIMS-P file was merged with a Pharmaceutical Rx Reference File based on GPI codes to create a CLAIMS-P-RX. 3. In CLAIMS-P-RX,: -claim with Anti-diabetic (oral hypoglycemic agents only)+ Anti-hyperlipidemic (statins only= HMG COA Reductase Inhibitor) was identified. 4. CLAIMS-P-RX-DM file (contains only oral anti-diabetic claims that match drugs specified in APPENDIX II excluding insulin) was generated and saved. 5. CLAIMS-P-RX-Statin file (contains only statin claims that match drugs specified in APPENDIX II) was generated and saved. B. Identify and Filter patients with specified drug claims that match studied period: 1. From CLAIMS-P-RX-DM & CLAIMS-P-RX-ST, files were sorted by patient ID and FROM_DT(From date). 2. All claims were dropped except earliest FROM _DT for each patient. File was saved. 3. Patients with earliest FROM_DTbetween July 1 5 \ 2007 and Dec 31 5 \ 2007 were kept. 34

35 4. CLAIMS-P-RX-DM & CLAIMS-P-RX-ST files were merged. All other data were dropped except 3 columns: patient ID, DM or Statin flag, FROM_DT. This created a simple file that identified patients with index drug of interest in study measurement period. This was then renamed into P A TIENTRX file. C. Apply inclusion and exclusion criteria 1. Focus on only patients of 18+ years: P ATIENTRX was merged with Enrollment file. Specifically, patients with year ofbirth (DER_YOB) less than current year-18 were selected. 2. P ATIENTRX18plus file with patients that matched study criteria was derived. D. Create files with analytic variables of interest 1. Age: Age variable was created from DER _ YOB variable from ENROLLMENT file Age was then derived by subtracting DER_YOB from 2007 Patients were then grouped into different age categories: <45, 45-54, 55-64, 65+ years. 2. Gender (Dichotomous): DER_SEXfrom ENROLLMENT file was added to CLAIMS file 3. Geographic Region (Categorical): e From ENROLLMENT File, REGION variable was added to CLAIMS file 4. Presence of specific pre-index CV comorbidities (CHD/CAD, Diabetes, CHF, Stroke, HTN, DYS) (Dichotomous- YIN): [See APPENDIX IV] 35

36 New variables for pre-index CV comorbidities were created from CLAIMS file. Pre-Index period was defined as from Index date minus 6 months, to Index date. Claims that have the ICD-9 or CPT code criteria within Pre-Index period was selected. 5. Presence of other specific non-cv comorbidities (Depression, Renal Disease) (Dichotomous - YIN): From CLAIMS file, new variables for other key comorbidities were created. Claim that has ICD-9 or CPT code criteria [See APPENDIX IV] within Pre-Index period was selected. 6. Charlson Comorbidity Index (CCI) (Categorical): CCI-specific comorbidities occurring between apatient's Index date and Index date minus 180 days were identified (Deyo, Cherkin, Ciol, 1992). CCI scores were derived from unique CCI ICD-9 codes using the reference table from Deyo, Cherkin, Ciol (1992). CCI comorbidities can be derived from any of the four levels of diagnosis codes. Unique pre-index CCI scores were summed for each patient and were grouped into CCI categories of: <1, 1, 2-3, and 4+ for Statin Cohort, and ~1, 2-3, 4+ for DM Cohort. 7. Number ofunique baseline medications (Continuous, Categorical): From CLAIMS file, a new variable was created based on counting the number ofunique GPI code (GPI-11level), representing an unique 36

37 medication for each Patient ID within Pre-Index period from Index date minus 180 days to Index date. Mean value was then assigned to the following categories: 0-2, 3-4, 5-6, Payer type (Commercial, Medicare Risk, Medicaid.. etc.) (Categorical): e From ENROLLMENT2 file, STRING_TYPEwithPAY_TYPEwere selected to create Payer type categorical variables (e.g. Commercial = C..etc.) within Pre-Index period. 9. Mean baseline patient co-pay (Continuous, Categorical) From CLAIMS file, derive estimated patient co-pay or out-of-pocket expenses by subtracting PAID amount from ALLOWED amount within Pre-Index period. A categorical variable was derived from the continuous variable by assigning patients to different copay intervals: <$5, $5-$9, $10-$19, $20-39, $ New User (YIN) (Dichotomous) (No prior use of index medication during 6-month baseline period): From CLAIMS file, a variable was created to distinguish patients who had or had not an index drug claim within the Pre-Index period. A "New User" was defined as not having an index drug claim during pre-index period. Pre-Index period was defined as from Index date minus 6 months, to Index date. 11. Prior All-cause hospitalization in 2007 (YIN) (Dichotomous): 37