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The American Journal of Managed Care July 2011
Systolic Blood Pressure Control After Participation in a Hypertension Intervention Study
Lesley K. Welch, PharmD; Kari L. Olson, PharmD; Karen E. Snow, Lauren Pointer, MS; Anne Lambert-Kerzner, MSPH; Edward P. Havranek, MD; David J. Magid, MD, MPH; and P. Michael Ho, MD, PhD
Colorectal Cancer Screening Use Among Insured Primary Care Patients
Deirdre A. Shires, MPH, MSW; George Divine, PhD; Michael Schum, PhD; Margaret J. Gunter, PhD; Dorothy L. Baumer, MS; Danuta Kasprzyk, PhD; Daniel E. Montano, PhD; Judith Lee Smith, PhD; and Jennifer Elston-Lafata, PhD
Cost-Effectiveness of 21-Gene Assay in Node-Positive, Early-Stage Breast Cancer
Burton F. Vanderlaan, MD; Michael S. Broder, MD; Eunice Y. Chang, PhD; Ruth Oratz, MD, FACP; and Tanya G. K. Bentley, PhD
Impact of Celecoxib Restrictions in Medicare Beneficiaries With Arthritis
Anthony M. Louder, PhD, RPh; Ashish V. Joshi, PhD; Amy T. Ball, PharmD; Joseph C. Cappelleri, PhD; Michael C. Deminski, MS, RPh; and Robert J. Sanchez, PhD
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Using Medicare Data for Comparative Effectiveness Research: Opportunities and Challenges
Vicki Fung, PhD; Richard J. Brand, PhD; Joseph P. Newhouse, PhD; and John Hsu, MD, MBA, MSCE
Obtaining Patient Feedback at Point of Service Using Electronic Kiosks
Danae N. DiRocco, MPH; and Susan C. Day, MD, MPH
Addressing Healthcare Inequities in Israel by Eliminating Prescription Drug Copayments
Asher Elhayany, MD, MPA; and Shlomo Vinker, MD
Adherence to Medication Under Mandatory and Voluntary Mail Benefit Designs
Joshua N. Liberman, PhD; David S. Hutchins, MHSA, MBA; Will H. Shrank, MD; Julie Slezak, MS; and Troyen A. Brennan, JD, MD
Effects of Standardized Outreach for Patients Refusing Preventive Services: A Quasiexperimental Quality Improvement Study
Stephen D. Persell, MD, MPH; Elisha M. Friesema, BA; Nancy C. Dolan, MD; Jason A. Thompson, BA; Darren Kaiser, MS; and David W. Baker, MD, MPH

Using Medicare Data for Comparative Effectiveness Research: Opportunities and Challenges

Vicki Fung, PhD; Richard J. Brand, PhD; Joseph P. Newhouse, PhD; and John Hsu, MD, MBA, MSCE
This review article explores the Medicare data available for researchers and approaches that could be used to enhance the data%u2019s value for comparative effectiveness research.
Part D data can also be linked to traditional Medicare data on beneficiaries’ other medical claims (eg, hospital, skilled nursing facility, hospice, physician). For example, the Inpatient Standard Analytic File includes claims for inpatient stays, including diagnosis and procedure codes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM]), Diagnosis Related Groups (a classification system used for prospective Medicare payments), date, facility, and cost information. The Carrier File contains claims for noninstitutional providers, largely physicians. It also contains diagnosis and procedure codes (ICD-9-CM and CMS Healthcare Common Procedure Coding System codes), reimbursement amounts, and provider identifiers.

Medicare Advantage (Part C) Data

Information at the beneficiary level on inpatient and outpatient service use and diagnoses within the Medicare Advantage program is not currently available to researchers.

Monthly enrollment information is available in the Beneficiary Summary File so beneficiaries’ transitions between traditional fee-for-service Medicare and Medicare Advantage can be tracked. Prior to the introduction of Part D, beneficiaries could switch between traditional Medicare and Medicare Advantage plans monthly. In 2006, beneficiaries were restricted to switching only during the first 6 months of the year, and from 2007 onward, the first 3 months of the year.

Additional Linkable Data Sets

Medicare claims data also can be linked with Medicaid data for dual-eligible beneficiaries, as well as data sets focused on specific subpopulations or collected via surveys, such as the Surveillance Epidemiology and End Results Cancer Registry, the Long Term Care Minimum Data Set, the Home Health Outcome and Assessment Information Set, the Medicare Current Beneficiary Survey, and the Health and Retirement Survey. While the available sample size is more limited when linking to these data sources, they can provide a richer set of sociodemographic, health, and clinical characteristics than is available in the claims data alone.

RESEARCH DESIGN AND ANALYTIC STRATEGIES

A substantial body of literature describes the limitations of using observational data for CER, as well as potential strategies for addressing these limitations.13-17 Data and measurement quality, the formation and stability of comparison groups, and methods to deal with crossover effects or switching between therapies are particularly relevant for observational data studies and have been described elsewhere.18-20

Despite the range of information available on Medicare beneficiaries’ drug treatments and diagnoses, simple analyses using currently available Medicare data are likely to yield biased results (Table 2). Because patients are not randomly assigned to treatments, there may be important differences between the comparison groups with respect to the factors that contributed to patients receiving different treatments or therapies, such as disease severity, risk factors, and comorbidities (eg, confounding by indication).21,22 The lack of randomization creates analytic challenges because it is not possible using administrative data to capture all factors that contribute to physicians’ prescribing decisions.

Plan selection and switching within the Part D program beneficiaries also create challenges. Beneficiaries choose to enroll in plans with varying characteristics that can influence treatment decisions, such as different delivery systems (eg, Medicare Advantage vs PDP), benefit designs, and formularies. Beneficiaries are encouraged to choose plans basedon these characteristics to minimize treatment disruptions and lower out-of-pocket drug costs, so there is likely to be subst antial self-selection into Part D plans.23,24 For example, there was evidence of substantial adverse selection into plans that offered full coverage during the standard coverage gap in 2006, prompting these plans to leave the market the following year.25 In addition, the availability of employer-sponsored drug coverage for current or retired employees also influences the characteristics of beneficiaries choosing to enroll in PartD plans; an estimated 8.3 million beneficiaries received retiree drug coverage in 2010. Beneficiaries can also switch to and from plans during the open enrollment phase of each year (eg, based on changes in clinical need). Beneficiaries’ plan choice and geographic location may have important implications for their access to different treatment alternatives. 26,27 While a number of patient, provider, and plan characteristics are available in Medicare data sets, simply adjusting for these factors in a multivariate model is unlikely to sufficiently address selection differences between patient groups that contribute to treatment decisions, plan choice, or health outcomes.

Structural Aspects of the Medicare Program and Natural Experiments

Certain features of the Medicare program present opportunities to design studies that mitigate the traditional limitations of observational data analyses and improve the

relevancy of study findings. For example, ongoing changes in Medicare or Part D plan policies may provide opportunities to examine natural experiments. Policy changes that are determined at higher levels (eg, program, plan) are likely to result in changes in treatment patterns that are unrelated to underlying beneficiary characteristics. For example, within the Part D program, some plans may change their formulary requirements from year to year, while other plans’ formularies remain stable. These variations could be used to create quasiexperimental treatment and control groups if researchers can identify comparable plans with stable enrollment.28

Instrumental variables are another approach for accounting for unobserved differences between groups. Instrumental variables are related to treatment assignment, but not to other patient risk factors that are associated with the treatment choice or health outcome of interest. Numerous studies have demonstrated that the use of instrumental variables can decrease the bias associated with unmeasured confounders15-17,29; however, this approach is dependent on finding a high-quality instrument, which can be difficult. Millions of beneficiaries who receive Part D low-income subsidies are randomly assigned into qualified stand-alone drug plans. Differences in plan formularies for these beneficiaries could be associated with variations in drug use that are unrelated to underlying risk factors, given that patients were randomly assigned to plans. Thus, plan formulary coverage for these beneficiaries represents a potential high-quality instrument because it removes the association between plan choice and patient risk factors.

Importantly, all of these approaches may not provide information that clinicians want when treating patients (ie, what is the best medical strategy for an individual patient vs the average or marginal effect for a group of patients). For example, the instrumental variable approach generally provides estimates of a marginal effect (eg, the effect on outcomes if the probability of receiving a treatment went from X percent to X Y percent). However, if properly interpreted such information could still be valuable for informing policy or coverage decisions.

RECOMMENDATIONS/POLICY IMPLICATIONS

The release of Part D event data and related files was an important step; however, additional changes in the data policy could make Medicare data more useful for CER.

Release data faster. There currently is an approximate 21-month time lag between care received by beneficiaries and the availability of Part A, B, and D data to researchers. This lag hinders the ability to compare newer therapies, for which effectiveness and safety information could have the most value for patients and providers. New therapies also tend to be more expensive than older ones, and information on their use has value for policy makers making coverage decisions.30

Release all of the data elements necessary to identify the care options available to individual beneficiaries. These data including information on drug formularies, cost sharing (including drug tiers), and linkages between patients, physicians, hospitals, and plans. These factors can vary substantially across beneficiaries and influence treatment choices and adherence. These data could also be useful for developing valid instruments or identifying natural experiments. They can be released while protecting the identities of the patients, physicians, hospitals, and plans involved (eg, via coded identifiers and data use agreements). There have been promising developments in this area, particularly with respect to linkages across data sets (eg, establishment of the Chronic Condition Data Warehouse) and increases in available Part D data elements.

Release Part D plan assignment data for low-income subsidy beneficiaries. Medicare randomly assigns low-income subsidy beneficiaries to qualified plans, but a number choose other plans or remain in the same plan even if the plan no longer qualifies as a benchmark plan in the following year. The group that truly is randomly assigned to its Part D plan represents the most promising natural experiment; however, data on random assignment versus choice are not currently available to researchers.

Link data on care received for individual beneficiaries across all Medicare programs, including Medicare Advantage. About 10 million beneficiaries are now enrolled in Medicare Advantage plans, and there has been substantial growth in recent years.31 Providing researchers with comprehensive information on medical care received for both fee-forservice and Medicare Advantage beneficiaries would increase the generalizability of comparisons and provide valuable information on how delivery systems and physician/hospital networks affect care. These data are particularly relevant to questions concerning medical spending, delivery systems, and workforce needs.

Create specific Medicare data sets for top comparative effectiveness priority topics identified by the Institute of Medicine.32 The Centers for Medicare & Medicaid Services is currently conducting a pilot project to create CER public use files for a 5% sample of Medicare beneficiaries.33,34 As part of this effort, they could also consider creating supplemental data sets tailored for CER priorities. For example, one priority topic is the comparison of treatment strategies for atrial fibrillation, including surgery, catheter ablation, and prescription drugs. The creation and release of a single data set including all Medicare beneficiaries with a diagnosis of or treatment for atrial fibrillation would permit numerous researchers to more rapidly evaluate treatments both overall and for clinically important subgroups of patients, apply innovative

 
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