Using Medicare Data for Comparative Effectiveness Research: Opportunities and Challenges | Page 1
Published Online: July 14, 2011
Vicki Fung, PhD; Richard J. Brand, PhD; Joseph P. Newhouse, PhD; and John Hsu, MD, MBA, MSCE
Information on the safety, effectiveness, and value of medical care requires detailed clinical data from large numbers of patients receiving care in real-world settings. With the introduction of Medicare Part D prescription drug benefits in 2006, Medicare began to collect information on the use of prescription drugs for more than 27 million beneficiaries.1 Previously, this information was not widely available for Medicare beneficiaries, although drug use data were available for dual-eligible Medicare beneficiaries via Medicaid claims. The more comprehensive collection of drug use data allows for linkage with previously available information from Parts A and B including
inpatient and outpatient diagnoses, and major clinical events for millions of beneficiaries, including many persons over the age of 65 years. These data provide a promising resource for assessing the comparative effectiveness of many types of care across a range of settings and geographic areas in the United States.
Because Medicare collects data for payment and administrative purposes and not for research, there are several limitations to using these observational data. The care beneficiaries receive will vary depending on where they live, the types of Medicare plans they choose, and their physicians and hospitals. In other words, there may be factors associated with both the care beneficiaries receive and the outcomes of care; these factors confound assessments of care effectiveness and limit the validity of simple comparisons. However, exploiting certain program aspects and ongoing natural experiments within the Medicare program can mitigate some biases associated with purely observational data.
In this review article, we discuss these strengths and limitations of using Medicare data for comparative effectiveness research (CER) and propose policy recommendations for improving the usefulness of these data for patients, providers, and policy makers.
COMPARATIVE EFFECTIVENESS RESEARCH
There is a profound need for more evidence to guide clinical and policy decisions on drug treatments, devices, interventions, care delivery, payment models, and delivery systems. For example, while there is arguably substantial trial evidence supporting the use of many prescription drugs, this evidence often provides limited guidance for actual clinical decisions.
There are 2 main approaches for developing comparative effectiveness evidence: (1) clinical trials, including randomized clinical trials (RCTs) and pragmatic trials, and (2) studies using observational data from actual practice. While doubleblinded RCTs represent the gold standard for generating clinical evidence, they have a number of practical limitations. Specifically, trials have historically compared single drugs with placebo rather than with existing alternative drugs, or examined them in combination with commonly used drug regimens. Trials are done under rigorous experimental conditions (efficacy) rather than real-world situations (effectiveness), and are not designed to evaluate costs or rare adverse events. Randomized clinical trials also tend to be expensive and examine relatively short-term effects. Moreover, trials may have limited generalizability to specific subgroups of patients (eg, the elderly, racial/ethnic minorities, those with severe diseases) because they tend to target relatively homogeneous patient groups rather than the broader mix seen in actual practice.2-4 Pragmatic trials attempt to overcome some of these limitations by focusing on more heterogeneous groups of patients and by evaluating effectiveness under routine care; however, existing evidence from pragmatic trials is in short supply and funding for these types of studies is limited.5,6
Studies using observational, longitudinal data sets could provide complementary information that addresses many of the limitations associated with RCTs. For example, Medicare collects information on millions of beneficiaries and allows for linkages across a range of claims data, including inpatient, outpatient, and prescription drug data. Having a large sample of individuals is critical for ensuring adequate statistical power for studying rare conditions or specific patient subgroups. Use of observational data (including Medicare data), however, requires consideration of numerous factors such as the types of Medicare plans for inpatient, outpatient, and drug services; coverage/cost sharing for treatments; availability of physicians and hospitals; and clustering of patients by physician. In addition, there can be variations in practice patterns across geographic areas. Examinations of drug use within Part D should consider these various levels of analysis and account for factors
that could affect drug use, adherence, and ultimately outcomes, including a range of patient, provider, and plan-level characteristics. In short, the strengths and limitations of both clinical trials and observational data analyses should be considered carefully when evaluating the value of these approaches for addressing specific questions.
The following sections describe relevant structural aspects of the Medicare Part D program, the Medicare data available for researchers, and potential approaches that could be used to create quasi-experiments and enhance the value of historical Medicare data for CER.
MEDICARE PART D PROGRAM
Medicare currently collects diagnostic and treatment information through 4 programs: Part A (inpatient), Part B (outpatient), Part C (Medicare Advantage, which includes medical information for beneficiaries enrolled in managed care organizations), and Part D (prescription drugs). Part D is administered by private plans either as stand-alone Prescription Drug Plans (PDPs) that supplement traditional Medicare or Medicare Advantage Prescription Drug (MAPD) plans that bundle Part A, B, and D benefits. Part D is a voluntary benefit; in 2009 about 27 of 45 million Medicare beneficiaries were enrolled in a Part D plan, including 9.6 million lowincome beneficiaries who received additional premium and cost-sharing subsidies from Medicare. The Centers for Medicare & Medicaid Services (CMS) randomly assigns low-income subsidy beneficiaries who have not chosen a Part D plan to qualified stand-alone drug plans.7
Beneficiaries choose their Part D plans; these plans have some autonomy in determining their benefit structures and formulary drug lists provided they meet basic Medicare requirements. For example, all plans must offer benefits at least as generous as the defined standard. Medicare also requires that plans cover at least 2 drugs within a therapeutic class. However, plans can determine coverage for specific drugs within a class, as well as tier placement and utilization management requirements.
The use of utilization management tools such as prior authorization has grown since Part D’s introduction; these tools are most often used for drugs that are newer, more expensive, or more risky, with greater potential for adverse effects or with less available evidence on the possible benefits or harms.8
MEDICARE DATA CURRENTLY AVAILABLE FOR RESEARCH
Part D Event Data
Table 1 outlines the available Part D research data files.10 The primary data source for Part D drug utilization is the Part D Event (PDE) files, which are currently available for
2006-2008. These files contain detailed information on each drug event for PDP and MAPD plan beneficiaries, and encrypted beneficiary, pharmacy, prescriber, and plan identifiers that allow linkage with other files such as inpatient and outpatient claims data, and Part D plan characteristics files. The PDE contains information on each drug dispensed including the National Drug Code, the quantity dispensed, and days of supply, allowing for the examination of therapy adherence and persistence based on dispensing data, which have been previously validated.11,12
The PDE data also capture cost information such as total drug costs and patient payments. These data allow for examination of variation in spending patterns and cost-of-care analyses. The PDE data also specify the benefit phase during which each prescription was filled (eg, deductible or initial coverage phase) based on the benefit structure implemented by each beneficiary’s plan, which affects costs for both patients and payers. This file also includes plan-specific information on the formulary coverage for each drug dispensed, including the tier and utilization management requirements. Plan-level information on formulary and benefit structures can be valuable for identifying quasi-experiments or instrumental variables for statistical analyses.
Beneficiary Summary Files
PDF is available on the last page.