Medication Utilization Patterns and Outcomes Among Medicare Part D Enrollees with Common Chronic Conditions

Chartbook Medication Utilization Patterns and Outcomes Among Medicare Part D Enrollees with Common Chronic Conditions Bruce C. Stuart, F. Ellen Loh, Jing Xu, Pamela Roberto, J. Samantha Dougherty

Table of Contents 5 Executive Summary 7 Chapter 1 Introduction 9 Chapter 2 Characteristics of Medicare Beneficiaries with Diabetes, Heart Failure, and COPD Enrolled in Part D Plans 24 Chapter 3 Use and Adherence with Evidence-Based Medications in the Treatment of Diabetes 46 Chapter 4 Use and Adherence with Evidence-Based Medications in the Treatment of Heart Failure 69 Chapter 5 Use and Adherence with Evidence-Based Medications in the Treatment of COPD 90 Chapter 6 Outcomes Associated with Adherence to Evidence-based Medications in Diabetes, Heart Failure, and COPD 115 Appendix 117 Detailed Tables for Chapter 6 135 List of Authors

EXECUTIVE SUMMARY Overview This Chartbook provides a visual presentation of drug utilization patterns of Medicare beneficiaries with common chronic diseases diabetes, heart failure, and chronic obstructive pulmonary disease (COPD) enrolled in stand-alone Part D prescription drug plans (PDPs). Our goal is to demonstrate the importance of evidence-based drug therapy in the treatment of these diseases by focusing on factors that influence drug utilization behavior and outcomes of care during the first 3 years of the Medicare drug benefit. Cross-Cutting Highlights Half of all Medicare beneficiaries enrolled in PDP plans were diagnosed with diabetes, heart failure, and/ or COPD in 2006 2008, with the vast majority suffering multiple chronic diseases. Because of high disease burdens most beneficiaries were candidates for multiple evidence-based medications intended for long-term therapy. However, utilization of these recommended therapies was suboptimal. Between 55% and 65% of beneficiaries with diabetes and heart failure took the recommended drugs in any year but only 27% to 39% of COPD patients did so. Among those taking evidence-based medications, annual adherence rates were also suboptimal and declined over time. For patients with diabetes and heart failure, drug adherence rates varied between 65% and 75%. For COPD patients adherence rates were between 25% and 50%. Executive Summary 5

Higher illness burdens were associated with greater use of evidence-based therapies by beneficiaries with all three conditions. However, adherence among users tended to decline modestly with increased illness burden. The most consistent predictor of taking evidence-based medications was the number of chronic medications in each patient s drug regimen. For example, only 60% of heart failure patients taking 3 or fewer chronic drugs took angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) compared to 82% of those taking 10 or more chronic drugs. Similar patterns were observed for other drugs recommended in treating heart failure, diabetes, and COPD. More drugs in a patient s regimen were also associated with higher adherence rates to heart failure and diabetes medications. On average, beneficiaries taking 10 or more chronic drugs had adherence rates of 80% or better with beta-blockers and diuretics (heart failure) and oral antidiabetic drugs (OADs) and statins (diabetes), compared to adherence rates between 56% and 67% for patients taking 3 or fewer chronic drugs. The opposite pattern was observed for COPD patients, where more chronic drugs were associated with poorer adherence. Better adherence to evidence-based medications led to better outcomes including reduced rates of preventable hospitalization. For example, diabetes patients with adherence rates of 80% or better to OADs and ACE-I/ARBs were between 25% to 40% less likely to be hospitalized for short- and long-term diabetes complications or uncontrolled diabetes compared to those with adherence rates below 80%. Heart failure patients with high adherence to beta-blockers, ACE-I/ARBs, and cardiac glycosides were between 12% and 33% less likely to be admitted for a heart failure-related preventable hospitalization compared to those with adherence below 80%. Better drug adherence to evidence-based medications was also associated with significantly lower Medicare spending on hospital and medical services by diabetes and heart failure patients. Savings ranged from 11% (statins) to 24% (ACE-I/ARBs) for high adhering diabetes patients and from 15% (diuretics) to 24% (ACE-I/ARBs) for high-adhering heart failure patients. For COPD patients there was no significant association between adherence to COPD maintenance and rescue medications and Medicare spending. In sum, there is room for considerable improvement in utilization of evidence-based medications among Medicare Part D enrollees with common chronic conditions. Increasing adherence to these medications should be a major policy objective as improved adherence is associated with reduced rates of preventable hospitalization and lower Medicare costs for traditional Part A and Part B services. 6

Chapter 1 Introduction Objectives This Chartbook provides a visual presentation of drug utilization patterns of Medicare beneficiaries with 3 common chronic diseases diabetes, heart failure, and chronic obstructive pulmonary disease (COPD) enrolled in standalone Part D prescription drug plans (PDPs). We designed the book with 4 objectives in mind. First, we wanted to focus on treatment of conditions with well established guidelines for pharmaceutical care. Treatment guidelines for diabetes, heart failure, and COPD all emphasize the importance of evidence-based medication therapy in maintaining health and reducing risk of future complications. Profiling use and adherence with these medications will help policymakers benchmark the performance of Part D plans in achieving well-established medication quality criteria. Second, because these medications are typically prescribed for long-term use, we considered it important to track patients over several years to provide a reasonable assessment of the stability and persistence of drug utilization over time. Most published studies of the Part D program describe drug utilization patterns over a single year; we chose to track enrollees for 3 years. Our third objective was to identify key factors likely to influence Part D medication utilization rates. From a large list of potential determinants identified based on prior research, we selected age, low-income subsidy (LIS) status, illness burden, and medication regimen complexity as focal points for the Chartbook. By identifying the relationship of drug use to these factors, we hope to help policymakers better target interventions designed to improve guidelines-based prescribing and medication adherence. Our final and arguably most important objective was to examine outcomes associated with adherence to each guidelines-recommended medication for treating the 3 diseases. Diabetes, heart failure, and COPD are all Chapter 1 7 1 Introduction

considered ambulatory care sensitive conditions and patients with these conditions are at elevated risk for preventable hospitalizations. We wished to determine the extent to which good drug adherence reduced admission rates for hospital episodes identified by the Agency for Healthcare Research and Quality as potentially preventable. We also wished to determine whether good adherence had a broader impact in reduced spending on traditional Medicare hospital and medical services. In both cases, tracking patients over 3 years raises the likelihood of observing substantive impacts. treating diabetes (oral antidiabetic drugs, insulin, ACEinhibitors and ARBs, and statins). Chapter 4 focuses on drugs used in treating heart failure (ACE-inhibitors and ARBs, beta-blockers, diuretics, and cardiac glycosides). COPD medications are covered in Chapter 5 including therapies designed for maintenance use and those intended to relieve symptoms of acute exacerbations of the disease. The final chapter presents data on 3-year outcome rates for preventable hospitalization and Medicare spending for each disease/drug combination. Methods We selected our study population from a random 5% sample of Medicare beneficiaries enrolled in stand-alone Part D prescription drug plans (PDPs) between January 1, 2006 and December 31, 2008 (N=394,318). The cohorts included 122,825 beneficiaries with diabetes, 91,612 with heart failure, and 76,446 with COPD. Details on sample inclusion and exclusion criteria are presented in the appendix which also describes all measures used in the Chartbook. Layout of the Chartbook The Chartbook includes 5 chapters in addition to this one. Chapter 2 presents characteristics of the 3 study cohorts in side-by-side comparisons. Chapter 3 covers drug use and adherence with evidence-based medications used in 8

Chapter 2 Characteristics of Medicare Beneficiaries with Diabetes, Heart Failure, and COPD Enrolled in Part D Plans This chapter presents a series of charts comparing Medicare beneficiaries with diabetes, heart failure, and COPD on characteristics likely to influence medication utilization patterns and outcomes. Chapter 2 9 2 Beneficiary Characteristics

Chart 2.1 Disease Prevalence, 2006-2008 COPD only 7.3% COPD + Diabetes 3.1% Diabetes + Heart failure + Heart failure + COPD COPD 4.2% 4.8% Diabetes only 16.4% Diabetes + Heart failure 6.8% Heart failure only 7.4% N=394,318 10

Chart 2.1 Disease Prevalence, 2006-2008 This chart shows the proportions of the Part D enrollee sample (N=394,318) with diabetes, heart failure, and COPD both alone and in combination. Altogether, 31.1% had diabetes, 23.2% had heart failure, and 19.4% had COPD. There was substantial overlap among the 3 diseases. More than twothirds of all heart failure patients (68%) also had diabetes and/or COPD. A slightly smaller fraction of COPD patients (62%) had diabetes and/or heart failure. Chapter 2 11

Chart 2.2 Distribution of Diseases by Age in 2006 45.0 42.8 40.5 % 36.0 31.5 27.0 22.5 18.0 13.5 26.8 14.6 16.9 33.0 19.3 18.4 34.3 31.3 29.0 22.4 22.3 9.0 4.5 0.0 <65-SSDI 65-74 75-84 85+ Diabetes Heart Failure COPD 12

Chart 2.2 Distribution of Diseases by Age in 2006 The age distributions for diabetes, heart failure, and COPD patients are quite different. Heart failure is a disease of the oldest old, with more than half (52.7%) aged 75 or older. By contrast 37.7% of diabetics and 41.3% of COPD patients were 75 or older. At the other end of the age scale, we see that more than 25% of diabetics and COPD patients were under age 65 and received Medicare through Social Security Disability Insurance (SSDI). Fewer than 19% of heart failure patients qualified through SSDI. Chapter 2 13

Chart 2.3 Distribution of Diseases by Race/Ethnicity, 2006-2008 50 45 40 40.0 43.3 37.6 35 % 30 28.7 28.2 27.5 25 20 15 22.3 20.0 16.4 20.3 22.4 17.0 10 5 0 White Black Hispanic Other Diabetes Heart Failure COPD 14

Chart 2.3 Distribution of Diseases by Race/Ethnicity, 2006-2008 Most Medicare beneficiaries with diabetes, heart failure, and COPD were white non-hispanics. However, prevalence of these diseases was much higher among non-white beneficiaries. Hispanic Medicare beneficiaries had the highest high rate of diabetes (43.3%), while blacks had the highest prevalence of heart failure (28.2%). The prevalence of COPD was highest among Hispanics (20.3%). Chapter 2 15

Chart 2.4 Distribution of Diseases by LIS Status, 2006-2008 1 40 36 34.5 % 32 28 24 20 16 12 8 4 26.0 18.6 14.9 26.1 22.2 0 Continuous non-lis Continous LIS Enrollment Diabetes Heart Failure COPD 16

Chart 2.4 Distribution of Diseases by LIS Status, 2006-2008 1 The Medicare Part D program offers low-income subsidies (LIS) to enrollees who meet stringent income and asset tests. LIS status is an important factor in understanding drug utilization patterns among Part D enrollees. Approximately 85% of LIS recipients are Medicare-Medicaid dual eligibles. They were automatically enrolled in Part D on January 1, 2006. The remainder applied for LIS status. Dual eligibles pay no Part D premiums and receive drugs for nominal copays of approximately $1 (generics) and $2.50 (brands). Depending on income and asset holdings, applicant LIS recipients may be responsible for modest premium payments and slightly higher copays. Approximately two-thirds of our study population was enrolled in LIS, and LIS recipients were nearly a third more likely to have each of the 3 diseases compared to non-lis beneficiaries. 1 The percentage enrollment figures exclude between 4% and 5% of Part D enrollees who had LIS for only part of the study period. Chapter 2 17

Chart 2.5 Illness Burden of Study Cohorts in 2006 2 70 60 50 65.0 60.3 60.1 % 40 30 24.9 31.0 28.8 20 10 10.0 8.7 11.1 0 Diabetes Heart Failure COPD Low Burden Medium Burden High Burden 18

Chart 2.5 Illness Burden of Study Cohorts in 2006 2 In addition to the fact that diabetes, heart disease, and COPD frequently occur together (Chart 2.1), Part D enrollees also suffer significant comorbidy from other diseases. Our measure of illness burden is based on counts of conditions listed in the prescription drug hierarchical coexisting condition (RxHCC) program that Medicare uses to risk adjust capitation payments to Part D plans. Because the RxHCC program captures medication intensive conditions, it provides a useful metric for assessing medication needs of the Part D population (see appendix) for further details. Heart failure patients have the highest illness burden with 31% of patients recording 10 or more RxHCC conditions in 2006 and less than 9% recording 3 or fewer comorbidities. Diabetes patients had the least comorbidity, but still, 25% had 10 or more RxHCC conditions in 2006 2 Comorbidity counts include diabetes, heart failure, and COPD. Low illness burden is defined as 3 or fewer RxHCC conditions; medium burden is represented as 4 to 9 conditions, and high burden is 10 or more. Chapter 2 19

Chart 2.6 Mean Number of Chronic Drugs Used by Study Cohorts in 2006 3 70 % 60 50 40 39.8 54.7 36.5 57.3 43.5 50.8 30 20 10 5.5 6.2 5.7 0 Diabetes Heart Failure COPD Low Use Medium Use High Use 20

Chart 2.6 Mean Number of Chronic Drugs Used by Study Cohorts in 2006 3 Given the high rates of comorbidity evident in the study cohorts it is not surprising to find that they are heavy users of prescription drugs. Our measure of chronic care medications is defined as unique drugs with label indications for long-term use. Heart failure patients are the heaviest users of chronic care medications with 57.3% taking between 4 and 9 different drugs in 2006 and 6.2% taking 10 or more. On the other hand, significant fractions of each cohort took three or fewer chronic care medications in 2006, ranging from 36.5% of heart failure patients to 43.5% of COPD patients. 3 Drug counts include all chronic medications including the evidence-based medications used to treat diabetes, heart failure, and COPD. Low use is defined as 3 or fewer chronic medications; medium use includes 4 to 9 medications, and high use represents 10 or more medications. Chapter 2 21

Chart 2.7 Annual Part D Fills for Study Cohorts, 2006-2008 75 70 69 71 67 71 73 68 70 65 64 63 60 55 50 Diabetes Heart Failure COPD 2006 2007 2008 22

Chart 2.7 Annual Part D Fills for Study Cohorts, 2006-2008 Part D enrollees with these chronic conditions rely heavily on prescription medications to control their conditions, filling between 63 and 73 prescriptions per year over the study period. Heart failure patients filled the most scripts followed by individuals with diabetes and COPD. Drug use rose for beneficiaries in each cohort by an average of 5% per year between 2006 and 2008 reflecting increasing age and greater disease burden. Chapter 2 23 3Use & Adherence Diabetes

Chapter 3 Use and Adherence with Evidence-Based Medications in the Treatment of Diabetes Drug Therapy in Diabetes Diabetes management rests on 3 key factors intended to maintain normal functioning and prevent future cardiovascular, kidney, and neuropathic complications. The first factor is control of blood glucose levels through exercise and diet or, if these measures are ineffective, antidiabetic medications. Individuals who produce no insulin require insulin replacement therapy. Most type 2 diabetics require medications designed to stimulate insulin release (sulfonylureas, meglitinides, incretin mimetics), decrease insulin resistance (metformin, thiazolidinediones or TZDs), reduce digestion of carbohydrates (alpha glucosidase inhibitors) or inhibit glucagon secretion (amylin analogues). With the exception of incretin memetics and amylin analogues the medications in these pharmacologic classes are all taken orally and thus the entire class is commonly referred to as oral antidiabetic drugs or OADs. Many type 2 diabetics require 2 or more OADs to control their blood glucose levels. Individuals with the most severe cases of diabetes may require both insulin and OAD treatment. The second key to diabetes management is hypertension control with the goal of maintaining blood pressure readings below 130/80. A very high proportion of Type 2 diabetics exhibit elevated BP and thus are candidates for antihypertensive therapy. Diabetes guidelines recommend angiotensin-converting enzyme inhibitors (ACEIs) as first-line antihypertensives. Guidelines recommend angiotensin receptor blockers (ARBs) for patients who have a contraindication to or suffer adverse effects from ACEIs. Some diabetics may require additional antihypertensive therapy including diuretics, beta-blockers or calcium channel blockers. 24

The third key is control of low-density lipoprotein (LDL) cholesterol. Guidelines recommend that LDL levels be maintained below 100 mmhg for all diabetics and below 70 mmhg for those with heart disease. Statins are the drug of choice for cholesterol control. Notes on the Charts in This Chapter The charts in this chapter present statistics on use and adherence to antidiabetic agents, ACEIs/ARBs, and statins among a cohort of Medicare Part D enrollees with diabetes tracked between January 1, 2006 and December 31, 2008. We highlight differences among enrollees with and without low-income subsidies (LIS), and by age, comorbidity burden, and total number of chronic medications taken. Adherence is measured using percentage of days covered (PDC). The PDC is calculated annually to show trends and then by threeyear mean levels to show differences by drug and population characteristics. In each case, PDCs represent the percentage of days observed with supply of all drugs in each class based on data in Part D drug claims. For example, a person taking a single OAD with annual days supply of 300 would have a PDC of 300/365 x 100 = 82.2% for the year. We exclude days in each month spent in hospital and post-acute skilled nursing facility (SNF) stays because these facilities are responsible for providing all medications to inpatients. We do not compute adherence with insulin as changing dosing regimens make it difficult to reliably measure insulin adherence. Comorbidity burden is measured using the prescription drug hierarchical coexisting condition (RxHCC) model that CMS employs to risk adjust Part D capitation payments (see appendix). Our application counts the number of medication-intensive conditions listed in the RxHCC model that we observed in Medicare claims for each individual over the study period. We then classified comorbidity burden as low (3 or fewer RxHCCs), medium (4 to 9) or high (10 or more). Chronic medication counts are derived from Part D claims using a Redbook algorithm that identifies unique chronic care or maintenance medications with label indications for longterm use. As with comorbidity burden, we classified chronic medication counts as low (3 or fewer), medium (4 to 9) or high (10 or more). Chapter 3 25

Chart 3.1 Trends in Use of Evidence-Based Medications in the Treatment of Diabetes, 2006-2008 70 65 60 55 63 65 65 58 59 56 56 57 56 % 50 45 40 35 30 25 20 2006 21 2007 23 2008 25 ACEI/ARBs Oral Antidiabetics Statins Insulin 26

Chart 3.1 Trends in Use of Evidence-Based Medications in the Treatment of Diabetes, 2006-2008 A majority of Part D enrollees with diabetes used OADs, ACEIs/ARBs, and statins over the 3-year study period, but user rates were much lower than would be expected had all beneficiaries followed diabetes treatment guidelines. Relatively few Part D enrollees with diabetes used insulin, but the proportion rose from 21% in 2006 to 25% in 2008. It is important to note that use of insulin and OADs is not mutually exclusive. About 60% of insulin users also took OADs each year (data not shown). Chapter 3 27

Chart 3.2 Trends in Adherence to Evidence-Based Medications in the Treatment of Diabetes, 2006-2008 80 78 76 77 76 74 73 73 73 % 72 70 69 70 70 68 67 66 64 62 60 2006 2007 2008 ACEI/ARBs Oral Antidiabetics Statins 28

Chart 3.2 Trends in Adherence to Evidence-Based Medications in the Treatment of Diabetes, 2006-2008 A commonly accepted threshold for good medication adherence is having a percentage of days covered (PDC) of 80% or more. By that standard, Medicare beneficiaries with diabetes had suboptimal adherence with ACEIs/ARBs, OADs, and statins every year of the study. The highest rates of adherence were for oral antidiabetic drugs peaking at 77% in 2006, followed by ACEIs/ARBs at 73% in 2006 and 2007, and statins at 70% in 2007. Previous research has shown that elderly patients with chronic disease tend to become less adherent with their medications over time. Here we see that adherence was generally stable between 2006 and 2007, which may reflect the effects of users beginning therapy under the newly initiated Part D program. However, adherence declined by three percentage points in 2008 for users in every drug class. Chapter 3 29

Chart 3.3 Differences in Use of Evidence-Based Medications in the Treatment of Diabetes by Low-Income Subsidy (LIS) Status, 2006-2008 90 80 70 75 73 63 62 70 65 60 % 50 40 30 20 10 20 32 0 ACEI/ARBs Oral Antidiabetics Statins Insulin Non-LIS Enrollees LIS Enrollees 30

Chart 3.3 Differences in Use of Evidence-Based Medications in the Treatment of Diabetes by Low-Income Subsidy (LIS) Status, 2006-2008 As discussed in Chapter 2, enrollment in the low-income subsidy program in Part D means that recipients pay only nominal drug copays. Generally, low cost sharing has the effect of increasing use. However, LIS recipients also have low socioeconomic status, which tends to be associated with underuse. Except for insulin, exposure to evidence-based diabetes medications was slightly lower among LIS recipients. The much higher utilization rates for insulin by LIS recipients (32% versus 20%) suggests they had more severe disease with poorer glycemic control. Chapter 3 31

Chart 3.4 Differences in Adherence to Evidence-Based Medications in the Treatment of Diabetes by LIS Status, 2006-2008 90 80 70 73 70 76 73 68 69 60 % 50 40 30 20 10 0 ACEI/ARBs Oral Antidiabetics Statins Non-LIS Enrollees LIS Enrollees 32

Chart 3.4 Differences in Adherence to Evidence-Based Medications in the Treatment of Diabetes by LIS Status, 2006-2008 Adherence among users of evidence-based diabetes medication was broadly comparable for those with and without the LIS slightly higher for non-lis users of ACEIs/ARBs and OADs and slightly lower for statins. Chapter 3 33

Chart 3.5 Differences in Use of Evidence-Based Medications in the Treatment of Diabetes by Age, 2006-2008 90 80 70 60 67 78 75 67 68 62 63 59 73 67 % 50 48 47 40 36 30 27 24 22 20 10 0 ACEI/ARBs Oral Antidiabetics Statins Insulin <65 65-74 75-84 85+ 34

Chart 3.5 Differences in Use of Evidence-Based Medications in the Treatment of Diabetes by Age, 2006-2008 This chart shows rates of drug exposure at any time over the 3-year study period by age. Age is an important predictor of drug utilization by Medicare beneficiaries with diabetes, particularly for insulin. Thirty-six percent of disabled beneficiaries under age 65 used insulin compared to just 22% among beneficiaries aged 85+. The high rate of insulin use among disabled beneficiaries also explains why LIS recipients had high insulin usage rates (Chart 3.3). LIS enrollment is concentrated in this eligibility group and LIS enrollees generally exhibit poorer health status compared to enrollees who are not subsidized. Exposure to the other drug classes all peaked in the 65 74 age group with usage rates between 68% (OADs) and 78% (ACEIs/ARBs) and then declined markedly with increasing age. Drug use among the oldest beneficiaries (85+) was more than 20 percentage points lower for OADs and statins, and 11 percentage points lower for ACEIs/ARBs. Chapter 3 35

Chart 3.6 Differences in Adherence to Evidence-Based Medications in the Treatment of Diabetes by Age, 2006-2008 90 80 70 67 72 72 72 70 75 75 75 66 68 69 70 60 % 50 40 30 20 10 0 ACEI/ARBs Oral Antidiabetics Statins <65 65-74 75-84 85+ 36

Chart 3.6 Differences in Adherence to Evidence-Based Medications in the Treatment of Diabetes by Age, 2006-2008 Age is not as important a predictor of adherence among medication users. As with drug exposure, the lowest adherence rates were observed among disabled beneficiaries under age 65, but there were no age-related differences in adherence to ACEIs/ARBs and OADs above age 65. Statin adherence actually rose slightly with age. No age group met the standard of good adherence (PDC 80%) for any of the 3 drug classes. Chapter 3 37

Chart 3.7 Differences in Use of Evidence-Based Medications in the Treatment of Diabetes by Illness Burden, 2006-2008 90 % 80 70 60 50 61 74 75 57 64 58 50 68 70 40 38 30 20 10 18 26 0 ACEI/ARBs Oral Antidiabetics Statins Insulin Low Burden Medium Burden High Burden 38

Chart 3.7 Differences in Use of Evidence-Based Medications in the Treatment of Diabetes by Illness Burden, 2006-2008 Comorbidity can complicate drug regimens, but the results are not always in the expected direction. Diabetics with low illness burden ( 3 RxHCC conditions) had the lowest exposure to diabetes medications in every drug class. Increasing comorbidity was associated with small increases in exposure to ACEIs/ARBS and statins. Insulin use was also positively associated with greater morbidity, likely a result of higher rates of diabetes complications among those with poor glycemic control. Insulin use also appears to substitute for some OADs among those with a high illness burden. Chapter 3 39

Chart 3.8 Differences in Adherence to Evidence-Based Medications in the Treatment of Diabetes by Illness Burden, 2006-2008 90 80 70 72 73 67 74 75 71 67 68 67 60 % 50 40 30 20 10 0 ACEI/ARBs Oral Antidiabetics Statins Low Burden Medium Burden High Burden 40

Chart 3.8 Differences in Adherence to Evidence-Based Medications in the Treatment of Diabetes by Illness Burden, 2006-2008 Illness burden has a smaller impact on drug adherence than on drug exposure. Drug users with a medium burden (4-9 conditions) had the highest adherence rates with drugs in all 3 classes. Increasing condition counts beyond that point had a small depressive effect on adherence. This is most evident with OADs and least evident with statins. Chapter 3 41

Chart 3.9 Differences in Use of Evidence-Based Medications in the Treatment of Diabetes by Chronic Drug Use, 2006-2008 % 90 80 70 60 50 64 79 84 53 68 77 57 72 81 56 40 30 20 10 18 33 0 ACEI/ARBs Oral Antidiabetics Statins Insulin Low Use Medium Use High Use 42

Chart 3.9 Differences in Use of Evidence-Based Medications in the Treatment of Diabetes by Chronic Drug Use, 2006-2008 Chart 3.9 shows that more drugs in the regimen increases the likelihood that diabetics will use appropriate medication regimens. In fact, we see a consistent, monotonic relationship between more drug use and total chronic care medications in all 4 drug classes. This association can be partly attributed to the positive relationship between comorbidity and drug use (Chart 3.7), but there is a much steeper gradient with respect to drug counts. For those with high chronic care use (10+ drugs), between 77% and 84% were exposed to OADs, ACEIs/ARBs, and statins. Often, patients with diabetes have high cholesterol and hypertension requiring the use of multiple medications to adequately control these complications. Diabetes complications can also lead to reduced kidney function, which is treated by ACEIs/ARBs Chapter 3 43

Chart 3.10 Differences in Adherence to Evidence-Based Medications in the Treatment of Diabetes by Chronic Drug Use, 2006-2008 90 80 70 60 64 74 78 67 77 81 80 59 71 % 50 40 30 20 10 0 ACEI/ARBs Oral Antidiabetics Statins Low Use Medium Use High Use 44

Chart 3.10 Differences in Adherence to Evidence-Based Medications in the Treatment of Diabetes by Chronic Drug Use, 2006-2008 There is controversy in the literature about whether more drugs in the regimen increases or reduces drug adherence. This chart provides strong evidence that taking more chronic care drugs is associated with better adherence to diabetes therapy. The least adherent individuals in our study population were those with low levels of chronic drug use (3 or fewer medications). The most adherent were high drug users taking 10 or more chronic drugs. Moreover, for high users, mean adherence with OADs and statins reached the threshold for good adherence (PDC 80%). Chapter 2 45 4 Use & Adherence Heart Failure

Chapter 4 Use and Adherence with Evidence-Based Medications in the Treatment of Heart Failure Drug Therapy in Heart Failure Heart failure patients are typically treated with a combination of prescription medications designed to reduce blood pressure, reduce exertion on the heart, reduce fluid buildup, and improve heart function. Guidelines developed by American College of Cardiology and the American Heart Association (ACC/AHA) recommend ACE-inhibitors/ARBs and beta-blockers as drugs shown to reduce mortality in clinical trials based upon Level 1 evidence. ACEIs and ARBs are antihypertensive agents, but are recommended in heart failure patients regardless of blood pressure readings because they reduce the workload on the heart and thus slow cardiac deterioration. Beta-blockers decrease the heart rate and cardiac output which also reduces the workload of the heart. Certain beta-blockers (metroprolol, bisoprolol, carvedilol) are approved specifically for heart failure treatment. The ACC/AHA guidelines recommend diuretics for relief of congestive symptoms associated with fluid retention. The most commonly prescribed are loop diuretics (e.g., furosemide) for moderate heart failure. Thiazide and thiazide-like diuretics are also prescribed either alone for mild cases of heart failure or together with loop diuretics for severe cases. Potassium-sparing diuretics are used in patients with low potassium concentrations in the blood (hypokalemia). Cardiac glycosides (e.g., digoxin) were once first line treatments for heart failure but have been largely supplanted by ACEIs/ARBs, beta-blockers, and diuretics. They are now typically reserved for heart failure patients with atrial fibrillation to control ventricular rhythm. 46

Various other medications are used in heart failure treatment including aldosterone receptor antagonists (e.g., spironolactone and eplenerone) for patients with severe cardiomyopathy, nitrates (e.g., isosorbide dinitrate), recombinant neuroendocrine hormones, and vasopressin receptor agonists. Notes on the Charts in This Chapter We present 2006 2008 data showing use and adherence with the 4 main drug classes used in heart failure treatment: ACEIs/ARBs, beta-blockers, diuretics, and cardiac glycosides. As in the previous chapter we measure adherence using percentage of days covered (PDC). Comorbidity burden is measured using counts of RxHCC conditions and counts of chronic medications are measured using a Redbook indicator for drugs with label indications for long-term use. Chapter 4 47

Chart 4.1 Use of Evidence-Based Medications in the Treatment of Heart Failure, 2006-2008 70 60 60 55 64 50 % 40 30 20 10 13 0 2006-2008 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside 48

Chart 4.1 Use of Evidence-Based Medications in the Treatment of Heart Failure, 2006-2008 Between 55% and 64% of Medicare beneficiaries with heart failure used ACEIs/ARBs, beta-blockers, and or diuretics between 2006 and 2008. Just 13% used cardiac glycosides. Utilization rates were very stable over this time period. Although heart failure guidelines recommend that patients take both ACEI/ARBs and beta-blockers concurrently unless contraindicated, we found that only (37%) met that standard (data not shown). We also found that many diuretic users were not taking either ACEI/ARBs (44%) or beta-blockers (37%). Chapter 4 49

Chart 4.2 Trends in Adherence to Evidence-Based Medications in the Treatment of Heart Failure, 2006-2008 80 78 % 76 74 72 70 68 66 64 75 74 72 69 74 72 71 68 72 68 65 65 62 60 2006 2007 2008 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside 50

Chart 4.2 Trends in Adherence to Evidence-Based Medications in the Treatment of Heart Failure, 2006-2008 Annual adherence rates were suboptimal among users of medications in all 4 drug classes. Adherence was highest for beta-blockers (between 72% and 74% each year) followed by ACEIs/ARBs (between 68% and 72%). However, PDCs for both classes fell slightly over the 3-year period. The relatively low average PDC rates for diuretics and the steeply falling rates for cardiac glycosides may be explained by the fact that drugs in these 2 classes are taken to improve heart failure symptoms, and physicians will direct patients to stop taking the drugs when symptoms subside. Chapter 4 51

Chart 4.3 Differences in Use of Evidence-Based Medications in the Treatment of Heart Failure by LIS Status, 2006-2008 90 80 70 71 69 76 73 60 58 % 50 49 40 30 20 10 20 14 0 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside Non-LIS Enrollees LIS Enrollees 52

Chart 4.3 Differences in Use of Evidence-Based Medications in the Treatment of Heart Failure by LIS Status, 2006-2008 LIS recipients with heart failure were less likely to use medication in all 4 drug classes between 2006 and 2008. The difference was particularly pronounced for beta-blockers and cardiac glycosides. Fewer than half of all LIS recipients (49%) used any beta-blocker over this period compared to 58% for non- LIS Part D enrollees. Just 14% of LIS enrollees used cardiac glycosides compared to 20% of non-lis enrollees. There were smaller differences in utilization rates for ACEIs/ARBs and diuretics between LIS and non- LIS enrollees. Chapter 4 53

Chart 4.4 Differences in Adherence to Evidence-Based Medications in the Treatment of Heart Failure by LIS Status, 2006-2008 90 80 70 71 69 74 71 66 66 70 69 60 % 50 40 30 20 10 0 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside Non-LIS Enrollees LIS Enrollees 54

Chart 4.4 Differences in Adherence to Evidence-Based Medications in the Treatment of Heart Failure by LIS Status, 2006-2008 Mean adherence rates for non-lis and LIS enrollees with heart failure were broadly comparable over the 2006-2008 period with the largest difference being just 3 percentage points for beta-blockers (74% for non-lis enrollees compared to 71% for LIS recipients). Chapter 4 55

Chart 4.5 Differences in Use of Evidence-Based Medications in the Treatment of Heart Failure by Age, 2006-2008 90 % 80 70 60 50 65 75 71 63 47 55 54 49 65 74 76 77 40 30 20 10 11 15 18 20 0 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside <65 65-74 75-84 85+ 56

Chart 4.5 Differences in Use of Evidence-Based Medications in the Treatment of Heart Failure by Age, 2006-2008 There are several noteworthy age-related differences in use of evidence-based heart failure medications. The lowest exposure rates were for disabled beneficiaries under age 65. Heart failure patients in this eligibility group were between 8 and 10 percentage points less likely to use ACEIs/ARBs, beta-blockers, and diuretics compared to those aged 65 74. Use of diuretics and cardiac glycosides increased steadily with advancing age indicating greater need for symptom relief among older cohorts of heart failure patients. On the other hand, use of both ACEIs/ARBs and beta-blockers peaked in the 65 74 age group and then declined steadily with increasing age. Heart failure patients aged 85 or older were 12 percentage points less likely to use ACEIs/ARBs than those aged 65 74. Chapter 4 57

Chart 4.6 Differences in Adherence to Evidence-Based Medications in the Treatment of Heart Failure by Age, 2006-2008 90 80 70 60 65 70 71 70 67 72 73 75 62 66 67 70 69 70 70 67 % 50 40 30 20 10 0 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside <65 65-74 75-84 85+ 58

Chart4.6 Differences in Adherence to Evidence-Based Medications in the Treatment of Heart Failure by Age, 2006-2008 Increasing age is positively associated with adherence for all 4 heart failure medication classes. The difference in PDC between the youngest age group (<65) and the oldest (85+) was 3 percentage points for cardiac glycosides and 8 percentage points for beta-blockers and diuretics. Chapter 4 59

Chart 4.7 Differences in Use of Evidence-Based Medications in the Treatment of Heart Failure by Illness Burden, 2006-2008 90 80 70 60 56 70 73 60 69 60 73 78 % 50 40 46 30 20 10 13 16 18 0 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside Low Burden Medium Burden High Burden 60

Chart 4.7 Differences in Use of Evidence-Based Medications in the Treatment of Heart Failure by Illness Burden, 2006-2008 Heart failure patients with the lowest illness burden ( 3 RxHCC conditions) were least likely to use medications in all 4 classes. Those with the greatest illness burden (10+ RxHCC conditions) were the most likely to use medications in each class. The increases in drug use across the spectrum of illness burden are quite remarkable: 23 percentage points for beta-blockers, 18 percentage points for diuretics, 17 percentage points for ACEIs/ARBs, and 5 percentage points for cardiac glycosides. Chapter 4 61

Chart 4.8 Differences in Adherence to Evidence-Based Medications in the Treatment of Heart Failure by Illness Burden, 2006-2008 90 80 70 71 71 72 66 73 71 66 67 71 71 65 65 60 % 50 40 30 20 10 0 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside Low Burden Medium Burden High Burden 62

Chart 4.8 Differences in Adherence to Evidence-Based Medications in the Treatment of Heart Failure by Illness Burden, 2006-2008 Chart 4.8 shows that illness burden had a relatively small effect on adherence with all 4 classes of heart failure medications. The highest adherers were patients with low to medium illness burden, while those with the highest burden exhibited the lowest adherence rates. Chapter 4 63

Chart 4.9 Differences in Use of Evidence-Based Medications in the Treatment of Heart Failure by Chronic Drug Use, 2006-2008 % 100 90 80 70 60 50 60 75 82 52 67 74 60 80 91 40 30 20 10 11 19 25 0 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside Low Use Medium Use High Use 64

Chart 4.9 Differences in Use of Evidence-Based Medications in the Treatment of Heart Failure by Chronic Drug Use, 2006-2008 Medicare beneficiaries with heart failure are much more likely to use evidence-based drug therapy as the number of chronic care drugs in their regimens rises. As with comorbidity counts, the gradient was quite remarkable: a 31 percentage point higher diuretic user rate for beneficiaries taking 10+ chronic drugs compared to those taking 3 medications, 22 percentage point higher rates for beta-blockers and ACEIs/ARBs, and a 14 percentage point higher cardiac glycoside user rate. Chapter 4 65

Chart 4.10 Differences in Adherence to Evidence-Based Medications in the Treatment of Heart Failure by Chronic Drug Use, 2006-2008 90 80 70 60 62 72 77 64 74 81 56 69 79 63 71 75 % 50 40 30 20 10 0 ACEI/ARBs Beta Blockers Diuretics Cardiac Glycoside Low Use Medium Use High Use 66

Chart 4.10 Differences in Adherence to Evidence-Based Medications in the Treatment of Heart Failure by Chronic Drug Use, 2006-2008 Not only do heart failure patients with increasing complex medication regimes use more evidence-based drugs to treat their disease, adherence to these medications also increases. Adherence with heart failure medications rises steadily with more drugs in the regimen. Compared to diuretic users taking 3 chronic drugs, those with 10+ drugs had a PDC rate 23 percentage points higher. For beta-blocker users the difference was 17 percentage points. For ACEI/ARB users it was 15 percentage points. And for users of cardiac glycosides, the difference was 12 percentage points. Chapter 4 67

68

Chapter 5 Use and Adherence with Evidence-Based Medications in the Treatment of COPD Drug Therapy in COPD The goals of COPD therapy are to reduce respiratory symptoms including the frequency and severity of acute exacerbations of the disease, to prevent disease progression, to increase tolerance for exercise, and to improve quality of life. It is particularly important to reduce or, ideally, prevent exacerbations of the disease as these episodes accelerate deterioration of lung function. Therapy guidelines developed by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) emphasize long-term management of the disease with maintenance medications and short-term management of COPD exacerbation episodes with rescue medications. COPD maintenance medications include long-acting anticholinergics (LAAC) such as tiotropium and longacting beta-adrenergics (LABA) including salmeterol or a combination of the two. Randomized clinical studies have demonstrated that these agents reduce COPD-related hospitalization in patients with moderate to severe disease. COPD controller medications include short-acting bronchodilator therapy with beta-agonists and anticholinergics (if not already used in conjunction with maintenance therapy). Glucocorticoids (e.g., prednisone) are also effective in calming acute exacerbations of the disease, but long-term use of these agents is not recommended. Notes on the Charts in this Chapter We present 2006 2008 data showing use and adherence with drug products classified as COPD maintenance or rescue preparations. As in previous chapters drug adherence is measured using percentage of days covered (PDC). Comorbidity burden is measured using counts of RxHCC conditions and number of different chronic medications is measured using a Redbook indicator for drugs with label indications for long-term use. Chapter 5 69 5Use & Adherence COPD

Chart 5.1 Use of Evidence-Based Medications in the Treatment of COPD, 2006-2008 50 40 39 % 30 27 20 10 0 2006-2008 COPD Maintenance Drugs COPD Rescue Drugs 70

Chart 5.1 Use of Evidence-Based Medications in the Treatment of COPD, 2006-2008 An average of just 39% of Medicare beneficiaries with COPD in our sample used maintenance medications over the study period compared to only 27% using rescue medications. There was relatively little overlap (18%) in use of the 2 types of medications (data not shown) indicating that most patients with COPD either used maintenance or rescue drugs or used none at all. Chapter 5 71

Chart 5.2 Trends in Adherence to Evidence-Based Medications in the Treatment of COPD, 2006-2008 60 51 50 45 46 40 34 % 30 25 25 20 10 0 2006 2007 2008 COPD Maintenance Drugs COPD Rescue Drugs 72

Chart 5.2 Trends in Adherence to Evidence-Based Medications in the Treatment of COPD, 2006-2008 Adherence was poor among users of both types of COPD medications in 2006 and it declined further in 2007. Low adherence with rescue medications might indicate lack of need for symptomatic relief from acute exacerbations of the disease, but that would not explain low adherence with maintenance medications which represent the best available treatments to prevent such exacerbations. Chapter 5 73

Chart 5.3 Differences in Use of Evidence-Based Medications in the Treatment of COPD by LIS Status, 2006-2008 60 50 46 53 46 % 40 30 32 20 10 0 COPD Maintenance Drugs COPD Rescue Drugs Non-LIS Enrollees LIS Enrollees 74

Chart 5.3 Differences in Use of Evidence-Based Medications in the Treatment of COPD by LIS Status, 2006-2008 Non-LIS Part D enrollees were much less likely than LIS recipients to use either type of COPD medications between 2006 2008. The difference was 7 percentage points for maintenance medications and 14 percentage points for rescue medications. These large differences in exposure by LIS status may reflect more severe disease among LIS recipients. Chapter 5 75

Chart 5.4 Differences in Adherence to Evidence-Based Medications in the Treatment of COPD by LIS Status, 2006-2008 60 50 48 46 40 % 30 24 28 20 10 0 COPD Maintenance Drugs COPD Rescue Drugs Non-LIS Enrollees LIS Enrollees 76

Chart 5.4 Differences in Adherence to Evidence-Based Medications in the Treatment of COPD by LIS Status, 2006-2008 We found relatively little difference in adherence to COPD maintenance medications by LIS status during the study period. Adherence to rescue medications was four percentage points higher among LIS recipients, which would be expected if these beneficiaries had more severe COPD disease than non-lis recipients. Chapter 5 77

Chart 5.5 Differences in Use of Evidence-Based Medications in the Treatment of COPD by Age, 2006-2008 70 % 60 50 40 55 54 47 41 52 42 35 33 30 20 10 0 COPD Maintenance Drugs COPD Rescue Drugs <65 65-74 75-84 85+ 78

Chart 5.5 Differences in Use of Evidence-Based Medications in the Treatment of COPD by Age, 2006-2008 Exposure to both COPD maintenance and rescue medications falls sharply with age. The highest rates of use for both medication classes are seen among under age 65 recipients of Social Security Disability Insurance, the lowest among beneficiaries aged 85+ Age-related exposure to COPD maintenance medications was 14 percentage points lower among beneficiaries aged 85+ compared to those aged <65. For rescue medications the difference was 19 percentage points. Chapter 5 79

Chart 5.6 Differences in Adherence to Evidence-Based Medications in the Treatment of COPD by Age, 2006-2008 60 50 40 45 49 46 43 % 30 20 30 28 24 20 10 0 COPD Maintenance Drugs COPD Rescue Drugs <65 65-74 75-84 85+ 80

Chart 5.6 Differences in Adherence to Evidence-Based Medications in the Treatment of COPD by Age, 2006-2008 Adherence among users of COPD maintenance medication was highest among beneficiaries aged 65 74. The lowest adherence rates were observed among beneficiaries aged <65 and 85+. Adherence to rescue medications fell sharply with increasing age. Beneficiaries aged 85+ had PDC values 10 percentage points lower than those aged <65. Chapter 5 81

Chart 5.7 Differences in Use of Evidence-Based Medications in the Treatment of COPD by Illness Burden, 2006-2008 70 % 60 50 40 42 50 55 34 41 46 30 20 10 0 COPD Maintenance Drugs COPD Rescue Drugs Low Burden Medium Burden High Burden 82

Chart 5.7 Differences in Use of Evidence-Based Medications in the Treatment of COPD by Illness Burden, 2006-2008 We found that exposure to COPD maintenance and rescue drugs rises sharply with increasing illness burden. Beneficiaries with low burden ( 3 RxHCC conditions) were 13 percentage points less likely to receive maintenance medications compared to those with high burden (10+ RxHCC conditions), and were 12 percentage points less likely to receive rescue drugs. Chapter 5 83

Chart 5.8 Differences in Adherence to Evidence-Based Medications in the Treatment of COPD by Illness Burden, 2006-2008 70 % 60 50 40 30 20 53 47 43 35 28 23 10 0 COPD Maintenance Drugs COPD Rescue Drugs Low Burden Medium Burden High Burden 84

Chart 5.8 Differences in Adherence to Evidence-Based Medications in the Treatment of COPD by Illness Burden, 2006-2008 Adherence to COPD maintenance and rescue medications was highest among beneficiaries with the lowest illness burden. Adherence to maintenance medications was 10 percentage points lower among beneficiaries with high burden compared to those with low burden. For rescue drug users, adherence was 12 percentage points lower among those with the greatest comorbidity burden. Chapter 5 85

Chart 5.9 Differences in Use of Evidence-Based Medications in the Treatment of COPD by Chronic Drug Use, 2006-2008 80 70 60 56 72 60 % 50 40 42 34 46 30 20 10 0 COPD Maintenance Drugs COPD Rescue Drugs Low Use Medium Use High Use 86

Chart 5.9 Differences in Use of Evidence-Based Medications in the Treatment of COPD by Chronic Drug Use, 2006-2008 The probability of using COPD maintenance and rescue drugs was substantially higher among beneficiaries taking the largest number of chronic care medications. Those with the high use (10+ different medications over the study period) were 30 percentage points more likely to take maintenance medications than those taking 3 or fewer chronic care drugs. The difference was 26 percentage points for rescue medications. Chapter 5 87

Chart 5.10 Differences in Adherence to Evidence-Based Medications in the Treatment of COPD by Chronic Drug Use, 2006-2008 70 60 59 50 48 % 40 30 20 39 23 28 33 10 0 COPD Maintenance Drugs COPD Rescue Drugs Low Use Medium Use High Use 88

Chart 5.10 Differences in Adherence to Evidence-Based Medications in the Treatment of COPD by Chronic Drug Use, 2006-2008 Adherence among users of COPD maintenance and rescue medications was also positively related to medication counts. Maintenance drug users with high drug use had PDC rates 20 percentage points above those with low use. The difference was smaller (10 percentage points) among users of COPD rescue medications. Chapter 6 89 6 Outcomes

Chapter 6 Outcomes Associated with Adherence to Evidence-based Medications in Diabetes, Heart Failure, and COPD In this chapter we examine clinical and economic outcomes associated with adherence to evidence-based pharmacotherapy in treating diabetes, heart failure, and COPD among Medicare beneficiaries enrolled in stand-alone Part D plans in 2006-2008. Prevention Quality Indicators Our first set of outcome measures are Prevention Quality Indicators (PQIs) developed by the Agency for Healthcare Research and Quality (AHRQ). Altogether there are 16 PQIs covering various ambulatory care-sensitive conditions defined as conditions for which good ambulatory care can potentially prevent the need for hospitalization, or for which early intervention can prevent complications or more severe disease. The PQIs relating to potentially avoidable hospitalization among diabetics are: (PQI#1) diabetes short-term complications admission rate, (PQI#3) diabetes long-term complications admission rate, (PQI#14) uncontrolled diabetes admission rate, and (PQI#16) rate of lower-extremity amputation among patients with diabetes. There is a large scientific literature supporting the contention that well managed treatment with evidence-based drug therapy can reduce complications of diabetes for the first 3 of these PQIs. 7 We used diagnostic codes in Medicare Part A and B claims files between 2006 and 2008 to implement PQI#1, PQI#3, and PQI#14. For each measure we identified the proportion of diabetics in our study sample that had one or more episodes of that hospitalization type over the 3-year study period. Hospitalizations with primary diagnostic codes for heart failure (PQI#8) and COPD (PQI#5) are both considered potentially avoidable. We coded these events in the same way as for the diabetes PQIs and report 3-year inpatient admission rates for each disease. 90

Medicare Cost Savings Our second outcome measure assesses the relationship between drug adherence and concomitant spending on traditional Medicare Part A and B services. There is a growing literature demonstrating that good chronic care medication management can avert future hospital and medical services thus resulting in system savings, but much of this research is based on small samples followed over short periods of time. Here we compare mean Medicare spending over 3 years for each disease cohort by adherence status. Notes on Causal Inference Two common confounding factors are frequently encountered in drug outcomes studies. Confounding by indication arises when individuals who use the drug are more likely to suffer negative outcomes (higher rates of hospitalization say) independent of the effectiveness of the medication. The term confounding by indication comes from the fact that physicians are frequently more aggressive in recommending therapy to high-risk patients. In such cases it may appear that the drug actually has negative effects in simple bivariate comparisons. Multivariate studies that take account of clinical indications are often necessary to isolate the true impact of the medication, and even that may not be sufficient to entirely remove the effects of confounding. A second common bias, called the healthy adherer effect, works in the opposite direction. High adherers with one medication are more likely to practice other healthy behaviors compared to poor adherers, and they thus are more likely to have better outcomes irrespective of the impact of a particular medication. Here too, multivariate studies are necessary to remove the bias. To address these two common confounding factors, we present adjusted estimates of the impact of medication adherence on preventable hospitalizations and Medicare costs. These models control for age, gender, race, geographic region, low-income subsidy status, disease measures, medication counts, and adherence with other drugs in patients regimens. Details are available in the appendix. 7 The evidence base for the impact of drug therapy in reducing diabetes-related amputations is less strong due both to the relative rarity of amputations among type-2 diabetics and the long time interval over which these serious complications arise. For this reason, we have not implemented PQI#16. Chapter 6 91

Chart 6.1 Preventable Hospitalizations and Mean Monthly Medicare Spending among Diabetics, 2006-2008 16 14 12 10 $1,100 $1,600 $1,400 $1,200 $1,000 % 8 6 $800 $600 4 2 0.4% 2.0% 0.4% $400 $200 0 Short-Term Complication Long-Term Complication Uncontrolled Diabetes Monthly Medicare Spending $0 Preventable Hospitalization Rate Monthly Medicare Spending 92

Chart 6.1 Preventable Hospitalizations and Mean Monthly Medicare Spending among Diabetics, 2006-2008 Chart 6.1 shows 3-year prevalence rates for diabetes PQIs (hospitalizations for short- and long-term complications of diabetes and uncontrolled diabetes) together with mean monthly Medicare spending. All 3 diabetes PQIs are rare events with hospital admissions for short-term complications and uncontrolled diabetes affecting less than 1% of Part D enrollees with the disease over 3 years. Hospitalization for long-term complications of diabetes affect 5 times as many individuals, but the 3-year admission rate is still low (2.0%). Diabetes patients in our study population spent an average of $1,100 per month on Part A and B Medicare services between 2006 and 2008. Chapter 6 93

Chart 6.2 Preventable Hospitalizations due to Short-Term Diabetes Complication among Evidence-Based Drug Users with Diabetes, 2006-2008 0.5 0.4 0.44 0.40 0.36 % 0.3 0.26 0.25 0.2 0.19 0.1 0.0 ACEI/ARBs* Oral Antidiabetics* Statins* PDC<80% PDC 80% * Values of PDC<80% and PDC>=80% groups significantly different at p=0.05 94

Chart 6.2 Preventable Hospitalizations due to Short-Term Diabetes Complication among Evidence-Based Drug Users with Diabetes, 2006-2008 Chart 6.2 compares hospitalization rates for short-term diabetes complications among users of ACEI/ ARBs, OADs, and statins who did or did not achieve the conventional criterion for good adherence (PDC 80%) over the 3-year study period. Overall, very few diabetes patients were hospitalized with short-term complications (less than half a percent for each drug user group), but the rates were significantly lower among high adherers. The biggest difference was for OADs where high adherers were half as likely to be hospitalized (0.19%) compared to those with poorer adherence (0.40%). Chapter 6 95

Chart 6.3 Preventable Hospitalizations due to Long-Term Complications of Diabetes among Evidence-Based Drug Users with Diabetes, 2006-2008 3.0 2.78 2.5 2.0 2.52 1.95 1.89 2.10 1.99 % 1.5 1.0 0.5 0.0 ACEI/ARBs* Oral Antidiabetics* Statins* PDC<80% PDC 80% * Values of PDC<80% and PDC>=80% groups significantly different at p=0.05 96

Chart 6.3 Preventable Hospitalizations due to Long-Term Complications of Diabetes among Evidence-Based Drug Users with Diabetes, 2006-2008 This chart compares hospitalizations for long-term diabetes complications for users of ACEIs/ARBs, OADs, and statins by level of adherence. Good medication adherers were systematically less likely to be hospitalized for long-term diabetes complications compared to those with poorer adherence. The largest difference was observed among OAD users, where the relative risk of hospitalization was one-third lower for high adherers. Chapter 6 97

Chart 6.4 Preventable Hospitalizations due to Uncontrolled Diabetes among Evidence-Based Drug Users with Diabetes, 2006-2008 0.7 0.6 0.61 % 0.5 0.4 0.3 0.46 0.29 0.31 0.41 0.33 0.2 0.1 0.0 ACEI/ARBs* Oral Antidiabetics* Statins* PDC<80% PDC 80% * Values of PDC<80% and PDC>=80% groups significantly different at p=0.05 98

Chart 6.4 Preventable Hospitalizations due to Uncontrolled Diabetes among Evidence-Based Drug Users with Diabetes, 2006-2008 Chart 6.4 shows that hospital admission rates for uncontrolled diabetes were lower among those with good adherence to ACEIs/ARBs, OADs, and statins. As with the other diabetes PQIs, good adherence to oral antidiabetic drugs offers the biggest benefit in reducing the risk of hospitalization. However, statistically significant reductions in hospitalization risk were also associated with good adherence to ACEIs/ARBs and statins. Chapter 6 99

Chart 6.5 Mean Monthly Medicare Spending among Evidence-Based Drug Users with Diabetes, 2006-2008 $1,800 $1,600 $1,400 $1,200 $1,000 $1,292 $982 $1,156 $919 $1,158 $1,038 $800 $600 $400 $200 $0 ACEI/ARBs* Oral Antidiabetics* Statins* PDC<80% PDC 80% * Values of PDC<80% and PDC>=80% groups significantly different at p=0.05 100

Chart 6.5 Mean Monthly Medicare Spending among Evidence-Based Drug Users with Diabetes, 2006-2008 Chart 6.5 shows differences in mean monthly spending on Medicare Part A and B services as a function of adherence to ACEIs/ARBs, OADs, and statins. As with avoidable hospitalizations, good adherence to evidence-based medications used in diabetes treatment was associated with lower spending on traditional Medicare services. Adherence effects were biggest for users of ACEIs/ARBs where spending by good adherers was 24% lower compared to poorer adherers. The difference was 21% for OAD users and 10% for statin users. All differences were statistically significant. Chapter 6 101

Chart 6.6 Preventable Hospitalizations and Mean Monthly Medicare Spending among Heart Failure Patients, 2006-2008 25 $2,000 20 $1,367 $1,800 $1,600 $1,400 % 15 10 $1,200 $1,000 $800 5 0 5.9% $600 $400 $200 $0 Preventable Hospitalization Rate Monthly Medicare Spending 102

Chart 6.6 Preventable Hospitalizations and Mean Monthly Medicare Spending among Heart Failure Patients, 2006-2008 Chart 6.5 shows preventable hospitalization rates and Medicare spending for heart failure patients enrolled in Part D plans in 2006 2008. Nearly 6% of these beneficiaries were hospitalized with a primary diagnosis of heart failure over the 3-year study period. Heart failure patients cost the Medicare program an average of $1,367 per month between 2006 and 2008. Chapter 6 103

Chart 6.7 Preventable Hospitalizations among Evidence- Based Drug Users with Heart Failure, 2006-2008 14 13.1 12 10 8.5 8.7 8.5 9.1 % 8 6 7.5 5.8 7.5 4 2 0 Beta Blockers* ACEI/ARBs* Diuretics* Cardiac Glycoside* PDC<80% PDC 80% * Values of PDC<80% and PDC>=80% groups significantly different at p=0.05 104

Chart 6.7 Preventable Hospitalizations among Evidence- Based Drug Users with Heart Failure, 2006-2008 This chart compares potentially preventable heart failure-related hospitalization rates by adherence with beta-blockers, ACEIs/ARBs, diuretics, and cardiac glycosides. Heart failure-related hospitalization represents a significant risk for these patients, ranging between 5.8% and 13.1%. For users of beta-blockers, ACEI/ARBs, and cardiac glycosides good adherence (PDC 80%) was associated with significantly lower risks of avoidable hospitalization compared to those with adherence below PDC=0.80. The differences were particularly noteworthy for ACEIs/ARBs and cardiac glycosides where the relative risk of hospitalization was 30% to 33% lower among good adherers. On the other hand, avoidable hospitalization rates for good adherers with diuretics were actually higher than for poor adherers (8.5% versus 7.5%), possibly reflecting the fact that diuretics are taken to reduce fluid retention which can lead to hospitalization. Chapter 6 105

Chart 6.8 Mean Monthly Medicare Spending among Evidence- Based Drug Users with Heart Failure, 2006-2008 $1,800 $1,600 $1,400 $1,200 $1,000 $800 $600 $400 $200 $1,650 $1,590 $1,392 $1,210 $1,469 $1,634 $1,253 $1,258 $0 Beta Blockers* ACEI/ARBs* Diuretics* Cardiac Glycoside* PDC<80% PDC 80% * Values of PDC<80% and PDC>=80% groups significantly different at p=0.05 106

Chart 6.8 Mean Monthly Medicare Spending among Evidence- Based Drug Users with Heart Failure, 2006-2008 Chart 6.8 shows that good adherence (PDC 80%) to all 4 evidence-based drug classes used in treating heart failure was associated with significantly lower Medicare spending on Part A and B services. The differences ranged from 15% lower for diuretics to 24% lower for ACEI/ARBs. Chapter 6 107

Chart 6.9 Preventable Hospitalizations and Mean Monthly Medicare Spending among COPD Patients, 2006-2008 40 35 30 25 $1,244 $1,600 $1,400 $1,200 $1,000 % 20 $800 15 10 5 0 7.7% $600 $400 $200 $0 Preventable Hospitalization Rate Monthly Medicare Spending 108

Chart 6.9 Preventable Hospitalizations and Mean Monthly Medicare Spending among COPD Patients, 2006-2008 The next 3 charts focus on COPD outcomes. Chart 6.9 shows that 7.7% of the COPD patients in our sample were hospitalized with a COPD-related admission during the 3-year study period. Mean monthly Medicare costs for the sample were $1,244. Chapter 6 109

Chart 6.10 Preventable Hospitalizations due to COPD among Evidence-Based Drug Users with COPD, 2006-2008 20 18 17.9% 19.0% 16 % 14 12 10 12.6% 13.4% 8 6 4 2 0 COPD Maintenance Drugs* COPD Rescue Drugs* PDC<80% PDC 80% * Values of PDC<80% and PDC>=80% groups significantly different at p=0.05 110

Chart 6.10 Preventable Hospitalizations due to COPD among Evidence-Based Drug Users with COPD, 2006-2008 Chart 6.10 compares COPD-related hospitalizations for good adherers (PDC 80%) and poorer adherers with COPD maintenance and rescue medications. Good adherence with evidence-based treatments for COPD would appear to offer little protection from avoidable hospitalization as the risk of hospitalization was 42% higher among good adherers of both types of medications compared to individuals with poorer adherence. Since clinical trials have demonstrated that these drugs actually lower hospitalization risk, the observed effect must be due to other confounding factors that we could not control for. Chapter 6 111

Chart 6.11 Mean Monthly Medicare Spending among Evidence-Based Drug Users with COPD, 2006-2008 $1,800 $1,600 $1,400 $1,340 $1,338 $1,380 $1,426 $1,200 $1,000 $800 $600 $400 $200 $0 COPD Maintenance Drugs* COPD Rescue Drugs* PDC<80% PDC 80% * Values of PDC<80% and PDC>=80% groups significantly different at p=0.05 112

Chart 6.11 Mean Monthly Medicare Spending among Evidence-Based Drug Users with COPD, 2006-2008 Chart 6.11 shows that good adherence to COPD maintenance and rescue medications has virtually no association with Medicare spending. Mean monthly Medicare costs differed by just $2 between good and poor adherers with COPD maintenance drugs and by $46 for rescue medications. Neither difference was statistically significant. Chapter 6 113

114

APPENDIX Data and Study Samples The study sample was selected from a random 5% sample of Medicare beneficiaries in 2006 and followed for 3 years. Inclusion criteria included a diagnosis of diabetes (ICD-9 codes: 250.xx, 357.2, 362.01, 362.02 or 366.41) or COPD (ICD-9 codes: 491.xx, 492.0, 492.8, 494.0, 494.1 or 496) on at least 1 inpatient, skilled nursing facility or home health agent claim or 2 hospital outpatient or physician claims, or heart failure (ICD-9 codes: 389.91, 402.01, 402.11, 402.91, 404.xx or 428.xx) on at least 1 inpatient, hospital outpatient or physician claims between January 1999 and June 2006. We restricted the study subjects to beneficiaries with continuous Part A, B and stand-alone fee-for-service Part D coverage throughout the entire study period. We excluded Medicare Advantage prescription drug (MAPD) plans because they lacked Part A and B claims. We also restricted the study subjects to beneficiaries survived throughout the entire study period. The final sample sizes are 122,825 for diabetics, 91,612 for heart failure patients and 76,446 for COPD patients. Measures Study measures included: (1) any exposure and adherence with medications used in the treatment of diabetes, heart failure, and COPD; (2) outcomes associated with better adherence among drug users; and (3) selected characteristics of patients that may affect drug adherence. The first set of measures were derived from the Part D prescription drug event (PDE) files. We used national drug codes (NDC) to identify all PDEs in all drug classes of interest. Fill date and days supply reported on the PDEs were used to measure the numerators for our adherence measure the monthly percentage of days covered (PDC) over the 3-year study period. The denominator for this ratio was 1096 days minus any inpatient hospital or SNF days. Inpatient days identified from Part A claims were subtracted from the PDC denominator because facilities are responsible for providing all medications for inpatient use. Covariates used in the APPENDIX 115 Appendix

statistical models to control for confounding by indication and healthy adherer bias are described in the following section. Statistical Analysis For Chapter 6 charts, we used logistic regression models to estimate adjusted PQI hospitalization rates and generalized linear models (GLMs) with a gamma distribution and log link to estimate adjusted monthly Medicare spending. The main independent variables in these models were adherence to evidence-based medication to each specific disease (ACEI/ARBs, oral anti-diabetics and statins for diabetes; beta blockers, ACEI/ARBs, diuretics and cardiac glycoside for heart failure; and maintenance drugs and rescue drugs for COPD) measured by proportion of days covered (PDC) and dichotomized into PDC>=80% and PDC<80%. Other covariates included age, gender, race, geographic region, lowincome subsidy status, diabetes (for heart failure and COPD cohorts), heart failure (for diabetes and COPD cohorts), COPD (for diabetes and heart failure cohorts), prescription drug hierarchical coexisting condition counts (RxHCC), and chronic medication counts. To control for healthy adherer bias, we also included use of and adherence to other recommended drugs besides the drugs of interest for each condition. For example, in the equations testing whether adherence to ACEIs/ARBs among diabetic patients reduced PQIs and Medicare spending, we added dummy variables for any use of OADs and statins together with continuous PDC variables for these two drug classes. The rationale for this specification is that any latent healthy adherer effect common to all drugs in the equation will be covaried out, thus purging the adherence variable of interest (ACEIs/ARBs in this example) from bias associated with the common behavior. We used similar specifications for all of the other drug/ disease equations. From these regressions we calculated predicted values for preventable hospitalization and monthly Medicare spending. All analyses were conducted using Stata 11.2. The full results from these regression models are presented in the next section. The study was approved by the institutional review board at the University of Maryland Baltimore. 116

Tables of Detailed Regression Results for Chapter 6 117

Table 6.1 Estimated Marginal Effects of Drug Adherence on Preventable Hospitalizations due to Diabetes Short-Term Complication among Evidence-Based Drug Users with Diabetes, 2006-2008 ACEI/ARB Users (N=77,328) Estimated Marginal Effects in % Oral Antidiabetic Users (N=68,731) Statin Users (N=68,436) Drug Adherence* Low (ref) - - - High -0.20 (0.05) -0.22 (0.05) -0.11 (0.05) Chronic Medication Count Categories <=3 (ref) - - - 4-9 -0.06 (0.05) 0 (0.05) 0.03 (0.06) 10+ -0.16 (0.11) -0.20 (0.13) -0.05 (0.11) Low Income Subsidy 0.32 (0.08) 0.26 (0.07) 0.16 (0.07) Age (%) <65 SSDI (ref) - - - 65-74 -0.53 (0.07) -0.32 (0.06) -0.44 (0.07) 75-84 -0.46 (0.07) -0.20 (0.05) -0.42 (0.07) 85+ -0.96 (0.21) -0.55 (0.17) -0.52 (0.16) Sex (%) Female (ref) - - - Male 0.12 (0.04) 0.09 (0.05) 0.06 (0.05) 118

Race/ethnicity White (ref) - - - Black 0.04 (0.05) 0.15 (0.05) 0.10 (0.05) Hispanic -0.02 (0.10) 0.02 (0.09) 0.01 (0.11) Other -0.21 (0.14) -0.12 (0.12) -0.18 (0.15) Region Northeast (ref) - - - North Central 0.09 (0.07) -0.07 (0.07) 0.06 (0.07) South -0.06 (0.06) -0.11 (0.06) -0.03 (0.06) West -0.03 (0.08) -0.05 (0.07) -0.07 (0.08) Core chronic conditions Diabetes - - - Heart failure -0.14 (0.05) -0.11 (0.05) -0.13 (0.05) COPD -0.17 (0.06) 0.02 (0.05) -0.10 (0.06) RxHCC count 0.04 (0.01) 0.02 (0.01) 0.03 (0.01) Any use of ACEIs/ARBs 0.16 (0.08) 0.28 (0.10) Mean PDC of ACEIs/ARBs -0.20 (0.09) -0.08 (0.11) Any use of oral antidiabetics 0.24 (0.07) 0.25 (0.08) Mean PDC of oral antidiabetics -0.59 (0.11) -0.52 (0.12) Any use of statins 0.13 (0.07) 0.16 (0.07) Mean PDC of statins -0.11 (0.09) -0.18 (0.09) Note: * Low adherence is defined as PDC<80% and high adherence as PDC 80% Values significantly different from reference category at p=0.05 Appendix 119

Table 6.2 Estimated Marginal Effects of Drug Adherence on Preventable Hospitalizations due to Diabetes Long-Term Complication among Evidence-Based Drug Users with Diabetes, 2006-2008 ACEI/ARB Users (N=77,328) Estimated Marginal Effects in % Oral Antidiabetic Users (N=68,731) Statin Users (N=68,436) Drug Adherence* Low (ref) - - - High -0.57 (0.11) -0.89 (0.13) -0.11 (0.12) Chronic Medication Count Categories <=3 (ref) - - - 4-9 0.42 (0.15) 0.29 (0.16) 0.57 (0.17) 10+ -0.13 (0.24) -0.29 (0.26) 0.07 (0.25) Low Income Subsidy 0.71 (0.14) 0.91 (0.16) 0.73 (0.15) Age (%) <65 SSDI (ref) - - - 65-74 -0.97 (0.14) -0.43 (0.14) -0.90 (0.14) 75-84 -0.79 (0.15) -0.06 (0.16) -0.80 (0.16) 85+ -1.23 (0.25) 0.12 (0.25) -0.92 (0.26) Sex (%) Female (ref) - - - Male 0.51 (0.11) 0.51 (0.12) 0.36 (0.12) 120

Race/ethnicity White (ref) - - - Black 0.78 (0.13) 0.85 (0.15) 0.75 (0.14) Hispanic 0.22 (0.24) 0.15 (0.25) 0.03 (0.24) Other 0.01 (0.24) -0.08 (0.26) -0.02 (0.26) Region Northeast (ref) - - - North Central -0.06 (0.16) -0.31 (0.17) 0.02 (0.16) South -0.45 (0.14) -0.50 (0.15) -0.45 (0.15) West -0.11 (0.18) -0.17 (0.19) -0.11 (0.19) Core chronic conditions Diabetes - - - Heart failure 0.42 (0.12) 0.73 (0.13) 0.60 (0.12) COPD -0.52 (0.13) -0.49 (0.15) -0.59 (0.13) RxHCC count 0.33 (0.02) 0.32 (0.02) 0.31 (0.02) Any use of ACEIs/ARBs 0.95 (0.23) 1.06 (0.22) Mean PDC of ACEIs/ARBs -0.56 (0.25) -0.66 (0.24) Any use of oral antidiabetics 1.59 (0.21) 1.45 (0.21) Mean PDC of oral antidiabetics -0.98 (0.26) -0.88 (0.25) Any use of statins -0.30 (0.20) -0.23 (0.21) Mean PDC of statins 0.25 (0.25) -0.01 (0.26) Note: * Low adherence is defined as PDC<80% and high adherence as PDC 80% Values significantly different from reference category at p=0.05 Appendix 121

Table 6.3 Estimated Marginal Effects of Drug Adherence on Preventable Hospitalizations due to Uncontrolled Diabetes among Evidence-Based Drug Users with Diabetes, 2006-2008 ACEI/ARB Users (N=77,328) Estimated Marginal Effects in % Oral Antidiabetic Users (N=68,731) Statin Users (N=68,436) Drug Adherence* Low (ref) - - - High -0.18 (0.05) -0.31 (0.06) -0.08 (0.05) Chronic Medication Count Categories <=3 (ref) - - - 4-9 0.10 (0.06) 0.04 (0.07) 0.07 (0.07) 10+ 0.24 (0.10) 0.15 (0.11) 0.15 (0.10) Low Income Subsidy 0.40 (0.09) 0.51 (0.10) 0.37 (0.09) Age (%) <65 SSDI (ref) - - - 65-74 -0.19 (0.06) -0.19 (0.07) -0.21 (0.06) 75-84 -0.23 (0.06) -0.28 (0.07) -0.25 (0.07) 85+ -0.16 (0.11) -0.07 (0.11) -0.13 (0.11) Sex (%) Female (ref) - - - Male 0.06 (0.05) 0.13 (0.05) 0.12 (0.05) 122

Race/ethnicity White (ref) - - - Black 0.22 (0.06) 0.26 (0.07) 0.21 (0.06) Hispanic 0.33 (0.08) 0.39 (0.09) 0.22 (0.09) Other 0.08 (0.11) 0.03 (0.13) -0.07 (0.14) Region Northeast (ref) - - - North Central 0.06 (0.07) 0.06 (0.08) 0.01 (0.07) South -0.13 (0.06) -0.09 (0.07) -0.03 (0.06) West -0.22 (0.08) -0.17 (0.09) -0.09 (0.09) Core chronic conditions Diabetes - - - Heart failure 0.05 (0.05) 0.14 (0.06) 0.12 (0.05) COPD -0.02 (0.05) -0.04 (0.06) -0.04 (0.06) RxHCC count 0.03 (0.01) 0.03 (0.01) 0.02 (0.01) Any use of ACEIs/ARBs 0.14 (0.10) 0.23 (0.10) Mean PDC of ACEIs/ARBs -0.20 (0.12) -0.28 (0.11) Any use of oral antidiabetics 0.64 (0.09) 0.58 (0.09) Mean PDC of oral antidiabetics -0.38 (0.09) -0.38 (0.09) Any use of statins 0.15 (0.08) 0.11 (0.09) Mean PDC of statins -0.19 (0.10) -0.19 (0.12) Note: * Low adherence is defined as PDC<80% and high adherence as PDC 80% Values significantly different from reference category at p=0.05 Appendix 123

Table 6.4 Estimated Marginal Effects of Drug Adherence on Mean Monthly Part A and B Spending among Evidence-Based Drug Users with Diabetes, 2006-2008 ACEI/ARB Users (N=77,328) Estimated Marginal Effects in Dollars Oral Antidiabetic Users (N=68,731) Statin Users (N=68,436) Drug Adherence* Low (ref) - - - High -314 (11) -237 (10) -120 (12) Chronic Medication Count Categories <=3 (ref) - - - 4-9 286 (13) 234 (13) 267 (15) 10+ 272 (22) 231 (21) 264 (23) Low Income Subsidy 113 (13) 112 (12) 87 (13) Age (%) <65 SSDI (ref) - - - 65-74 -109 (14) -8 (14) -86 (15) 75-84 -92 (14) 49 (14) -74 (15) 85+ -77 (20) 94 (21) -24 (23) Sex (%) Female (ref) - - - Male 73 (11) 63 (11) 75 (12) 124

Race/ethnicity White (ref) - - - Black 186 (15) 103 (15) 171 (17) Hispanic 183 (23) 148 (25) 137 (22) Other -39 (24) -53 (24) -48 (26) Region Northeast (ref) - - - North Central -11 (15) -25 (15) -31 (15) South 5 (14) -30 (13) -34 (14) West 8 (17) 14 (18) -7 (18) Core chronic conditions Diabetes - - - Heart failure 359 (11) 295 (11) 356 (11) COPD 142 (11) 164 (11) 138 (12) RxHCC count 178 (2) 162 (2) 178 (2) Any use of ACEIs/ARBs 201 (21) 264 (27) Mean PDC of ACEIs/ARBs -201 (25) -230 (33) Any use of oral antidiabetics 6 (22) 17 (24) Mean PDC of oral antidiabetics -142 (28) -122 (31) Any use of statins -96 (20) -83 (19) Mean PDC of statins 43 (23) 28 (22) Note: * Low adherence is defined as PDC<80% and high adherence as PDC 80% Values significantly different from reference category at p=0.05 Appendix 125

Table 6.5 Estimated Marginal Effects of Drug Adherence on Preventable Hospitalizations due to Heart Failure among Evidence-Based Drug Users with Heart Failure, 2006-2008 Beta Blocker Users (N=47,762) Estimated Marginal Effects in % ACEI/ARB Users (N=54,870) Diuretic Users (N=58,244) Cardiac Glycoside Users (N=12,273) Drug Adherence* Low (ref) - - - - High -0.96 (0.26) -2.95 (0.24) 0.94 (0.24) -4.08 (0.59) Chronic Medication Count Categories <=3 (ref) - - - - 4-9 2.19 (0.37) 1.31 (0.31) 2.10 (0.33) 2.37 (0.95) 10+ 1.10 (0.56) 0.84 (0.48) 1.53 (0.51) 0.30 (1.29) Low Income Subsidy 0.53 (0.30) 0.30 (0.27) 0.39 (0.25) 0.51 (0.66) Age (%) <65 SSDI (ref) - - - - 65-74 -0.22 (0.40) -0.42 (0.36) 0.57 (0.37) -0.80 (0.95) 75-84 0.79 (0.38) 0.57 (0.34) 1.63 (0.36) -0.02 (0.90) 85+ 2.33 (0.42) 2.53 (0.39) 3.69 (0.40) 0.48 (1.03) Sex (%) Female (ref) - - - - Male 0.27 (0.28) 0.01 (0.25) 0.12 (0.25) 1.21 (0.63) 126

Race/ethnicity White (ref) - - - - Black 2.12 (0.33) 1.97 (0.30) 1.53 (0.31) 3.22 (0.83) Hispanic 0.95 (0.62) 0.37 (0.54) -0.03 (0.60) 0.85 (1.58) Other -1.53 (0.71) -0.61 (0.61) -1.04 (0.65) 0.24 (1.69) Region Northeast (ref) - - - - North Central 0.32 (0.35) 0.36 (0.33) 0.28 (0.33) 0.22 (0.85) South 0.17 (0.34) 0.07 (0.30) 0.27 (0.30) -1.32 (0.79) West -0.70 (0.45) -0.58 (0.40) -1.02 (0.41) -1.47 (1.02) Core chronic conditions Diabetes 1.41 (0.26) 0.95 (0.24) 1.52 (0.24) 1.15 (0.60) Heart failure - - - - COPD 1.53 (0.24) 1.66 (0.23) 2.23 (0.22) 2.94 (0.59) RxHCC count 0.45 (0.03) 0.38 (0.03) 0.40 (0.03) 0.58 (0.08) Any use of beta blockers 6.50 (0.45) 5.94 (0.44) 9.04 (1.17) Mean PDC of beta blockers 0.31 (0.41) 1.32 (0.43) -0.72 (1.16) Any use of ACEIs/ARBs 7.66 (0.50) 6.60 (0.43) 8.36 (1.16) Mean PDC of ACEIs/ARBs -4.56 (0.46) -3.99 (0.42) -4.91 (1.16) Any use of diuretics 6.85 (0.66) 6.90 (0.64) 14.58 (1.98) Mean PDC of diuretics 6.28 (0.48) 5.44 (0.41) 7.91 (1.10) Any use of cardiac glycoside 8.15 (0.50) 7.18 (0.44) 8.16 (0.46) Mean PDC of cardiac glycoside -4.18 (0.69) -3.39 (0.59) -3.87 (0.63) Note: * Low adherence is defined as PDC<80% and high adherence as PDC 80% Values significantly different from reference category at p=0.05 Appendix 127

Table 6.6 Estimated Marginal Effects of Drug Adherence on Mean Monthly Part A and B Spending among Evidence-Based Drug Users with Heart Failure, 2006-2008 Beta Blocker Users (N=47,762) Estimated Marginal Effects in Dollars ACEI/ARB Users (N=54,870) Diuretic Users (N=58,244) Cardiac Glycoside Users (N=12,273) Drug Adherence* Low (ref) - - - - High -261 (16) -389 (14) -221 (13) -385 (30) Chronic Medication Count Categories <=3 (ref) - - - - 4-9 212 (20) 215 (17) 173 (18) 176 (42) 10+ 31 (32) 97 (27) 68 (27) 29 (60) Low Income Subsidy 140 (18) 162 (16) 141 (15) 126 (32) Age (%) <65 SSDI (ref) - - - - 65-74 -393 (24) -253 (21) -122 (20) -234 (50) 75-84 -467 (23) -297 (21) -171 (20) -310 (49) 85+ -419 (28) -245 (24) -119 (22) -237 (55) Sex (%) Female (ref) - - - - Male 80 (16) 61 (14) 86 (14) 101 (31) 128

Race/ethnicity White (ref) - - - - Black 471 (24) 327 (20) 218 (19) 348 (51) Hispanic 320 (37) 221 (30) 149 (31) 420 (74) Other 6 (43) -65 (36) -42 (37) -18 (80) Region Northeast (ref) - - - - North Central -63 (22) -24 (20) -32 (20) -92 (43) South -43 (21) 11 (19) 3 (18) -38 (40) West 11 (29) 26 (24) 9 (23) 109 (50) Core chronic conditions Diabetes 142 (15) 120 (14) 111 (14) 65 (28) Heart failure - - - - COPD 192 (15) 225 (13) 280 (12) 280 (27) RxHCC count 216 (30) 194 (2) 184 (2) 188 (5) Any use of beta blockers 348 (25) 237 (24) 315 (56) Mean PDC of beta blockers 14 (28) 65 (28) 22 (60) Any use of ACEIs/ARBs 308 (28) 219 (24) 223 (55) Mean PDC of ACEIs/ARBs -273 (34) -247 (28) -253 (62) Any use of diuretics -75 (28) -55 (25) 86 (53) Mean PDC of diuretics 46 (31) 33 (25) 156 (50) Any use of cardiac glycoside 354 (40) 381 (38) 359 (33) Mean PDC of cardiac glycoside -142 (57) -235 (52) -182 (44) Note: * Low adherence is defined as PDC<80% and high adherence as PDC 80% Values significantly different from reference category at p=0.05 Appendix 129

Table 6.7 Estimated Marginal Effects of Drug Adherence on Preventable Hospitalizations due to COPD among Evidence-Based Drug Users with COPD, 2006-2008 Estimated Marginal Effects in % Maintenance Drug Users (N=28,985) Rescue Drug Users (N=20,456) Drug Adherence* Low (ref) - - High 4.78 (0.50) 4.87 (0.92) Chronic Medication Count Categories <=3 (ref) - - 4-9 -0.37 (0.51) -0.42 (0.62) 10+ -4.71 (0.85) -3.85 (0.98) Low Income Subsidy 0.71 (0.52) 0.75 (0.66) Age (%) <65 SSDI (ref) - - 65-74 -0.29 (0.52) -0.35 (0.61) 75-84 -1.11 (0.57) -0.89 (0.66) 85+ -1.78 (0.86) -1.38 (1.00) Sex (%) Female (ref) - - Male -2.07 (0.44) -1.55 (0.54) 130

Race/ethnicity White (ref) - - Black 1.24 (0.65) 1.49 (0.74) Hispanic 0.58 (1.21) 1.88 (1.33) Other 0.22 (1.13) -0.40 (1.27) Region Northeast (ref) - - North Central 0.57 (0.60) -0.04 (0.70) South 1.49 (0.56) 0.78 (0.66) West -2.85 (0.74) -2.95 (0.82) Core chronic conditions Diabetes -0.39 (0.45) -0.21 (0.51) Heart failure 4.3 (0.45) 4.02 (0.55) COPD - - RxHCC count 0.43 (0.06) 0.39 (0.07) Any use of maintenance medications 9.71 (0.94) Mean PDC of maintenance medications 10.32 (0.85) Any use of rescue medications 4.19 (0.51) Mean PDC of rescue medications 9.19 (0.86) Note: * Low adherence is defined as PDC<80% and high adherence as PDC 80% Values significantly different from reference category at p=0.05 Appendix 131

Table 6.8 Estimated Marginal Effects of Drug Adherence on Mean Monthly Part A and B Spending among Evidence-Based Drug Users with COPD, 2006-2008 Estimated Marginal Effects in Dollars Maintenance Drug Users (N=28,985) Rescue Drug Users (N=20,456) Drug Adherence* Low (ref) - - High -5 (22) 41 (45) Chronic Medication Count Categories <=3 (ref) - - 4-9 150 (22) 145 (28) 10+ 10 (31) -59 (39) Low Income Subsidy 84 (22) 151 (30) Age (%) <65 SSDI (ref) - - 65-74 -61 (23) -88 (28) 75-84 -132 (25) -150 (31) 85+ -128 (34) -139 (40) Sex (%) Female (ref) - - Male 57 (19) 93 (25) 132

Race/ethnicity White (ref) - - Black 219 (30) 221 (34) Hispanic 230 (51) 226 (74) Other -106 (51) -41 (61) Region Northeast (ref) - - North Central -45 (26) -38 (33) South -10 (24) -31 (32) West 5 (32) 35 (40) Core chronic conditions Diabetes 62 (19) 66 (23) Heart failure 433 (19) 435 (25) COPD - - RxHCC count 177 (3) 186 (4) Any use of maintenance medications 152 (32) Mean PDC of maintenance medications 236 (38) Any use of rescue medications 128 (21) Mean PDC of rescue medications 214 (40) Note: * Low adherence is defined as PDC<80% and high adherence as PDC 80% Values significantly different from reference category at p=0.05 Appendix 133

134

Part D Chartbook Author List Bruce Stuart, PhD Professor and Director The Peter Lamy Center on Drug Therapy and Aging Department of Pharmaceutical Health Services Research University of Maryland Baltimore F. Ellen Loh, MBA, BSPharm PhD candidate Department of Pharmaceutical Health Services Research University of Maryland Baltimore Pamela Roberto, MPP Senior Director Pharmaceutical Research and Manufacturers of America Graduate Student Department of Pharmaceutical Health Services Research University of Maryland Baltimore J. Samantha Dougherty, PhD Director Pharmaceutical Research and Manufacturers of America Jing Xu, MA Graduate Student Gerontology PhD Program University of Maryland Baltimore University of Maryland, Baltimore County 135

Chartbook Medication Utilization Patterns and Outcomes Among Medicare Part D Enrollees with Common Chronic Conditions Bruce C. Stuart, F. Ellen Loh, Jing Xu, Pamela Roberto, J. Samantha Dougherty