An essential foundation for policy development and quality improvement is improved understanding of the ways in which prescription drugs are used in routine clinical care, which often differs widely from populations and diagnoses in premarketing trials. Despite practical challenges such as time lags in data availability, Medicaid data are particularly well-suited to monitoring rates, predictors, and other aspects of use, including disparities, dosages, durations and associated diagnoses. For example, across a range of chronic health conditions, claims-based studies have frequently documented patterns of inconsistent use or early discontinuation of therapies that represent major missed public health opportunities.7,16,31,32,39,40
Similarly, such studies have documented racial/ethnic disparities in care, even when financial coverage is comparable, providing insight into actionable intervening variables that could reduce disparities,41,42
and have received substantial attention in policy documents such as the National Healthcare Disparities Report.43
With respect to treatment outcomes and drug safety, many methodological challenges present themselves. Nevertheless, Medicaid data offer an extremely important resource for such studies. Although RCTs remain the gold standard, such data are often unavailable for a host of questions that are critically important to better-informed therapeutic decision making and improved safety and treatment outcomes. Even if there is a sharp increase in national investment in needed RCTs, such studies will never be able to answer every important question about outcomes for every beneficiary subgroup, condition, and drug or drug combination, given the many feasibility, economic, ethical, duration, and other constraints on RCT implementation. Absence of needed RCT data is particularly marked for low-income, minority, elderly and disabled populations, often under-represented in RCTs. Medicaid data provide the power to examine use and outcomes in these and other important subpopulations.
To make more effective use of the considerable potential for research on pharmaceutical use and outcomes among Medicaid beneficiaries, including dual-eligibles, several public policy initiatives merit consideration. Many studies to date have been conducted on an ad hoc basis, with datasets assembled to address a single research topic. However, effective use of these powerful but large and complex datasets is labor-intensive, requiring long-term cumulative experience, considerable investment in database development and management, linkage across multiple data sources, documentation, sensitivity analyses, and procedures for data security and rigorous protection of confidentiality, among other steps.
Optimally, costs of such investments would be amortized over multiple studies rather than on a single-study basis. These analyses require multiple types of expertise including knowledge of the conditions, populations and drugs at issue, detailed understanding of the programs and data systems, and advanced statistical and modeling methods; thus, infrastructure is needed to support and sustain multidisciplinary teams. Similarly, there is need for an array of validation studies, such as comparisons of the performance of claims-based algorithms for identification of populations with particular conditions to external criteria based on expert clinical assessment. Such studies have typically been conducted as byproducts of substantive investigations, but they deserve encouragement and funding in their own right. Thus, a more systematic program of support is needed for development and linking of administrative databases, validation studies, and utilization of these data resources to address use and outcomes of prescription drug therapy, particularly for low-income, disabled, and elderly individuals reliant on publicly-funded coverage. Such a program could usefully incorporate systematic initiatives for linkages of claims data to other sources of data on beneficiaries’ health status and outcomes, such as disease registries, surveys, electronic medical record data, and birth and death records. The National Cancer Institute has linked its Surveillance, Epidemiology and End Results (SEER) cancer registry data with Medicare claims, creating a merged database that has been a productive research tool. This represents a promising model that could be further strengthened by linkage to data on prescription drugs beyond those administered in physicians’ offices, such as Part D data. More systematic development of research datasets that link Medicaid data to other data files providing clinical and outcome data for beneficiaries will expand the potential of research with these datasets and the ability to address problems of confounding; this is a key strategy for reducing missing-variable bias and improving measurement of outcomes. For example, the potential of data linkages between Medicaid claims and vital-statistics files (eg, birth certificates) has been illustrated in important research by Cooper et al11
on medication effects on birth-defect rates, which prompted a policy response in the form of an FDA safety alert.44
A related concern for the future is the need for systematic data merging, warehousing, and access initiatives to address fragmentation of data for beneficiaries of public coverage that has been a by-product of initiatives aimed at reform. These changes include expansion of capitated Medicaid managed care programs, managed care carve-outs for specific services, such as behavioral health, and the advent of Medicare Part D. Under the Medicare Modernization Act (MMA; Pub. L. 108–173), on January 1, 2006, responsibility for prescription drug benefits for individuals dually eligible for Medicare and Medicaid was shifted from state Medicaid programs to privately-administered Medicare Prescription Drug Plans (PDPs). These individuals include elderly beneficiaries receiving Medicaid, as well as many persons with disabilities. Thus, future studies of prescription drug use and outcomes for elderly and disabled individuals reliant on publicly funded health coverage will need to access prescription drug claims data from the PDPs and merge them with other sources of data, such as Medicare Part A and B claims, and Medicaid claims for non-Medicare-covered services. Such studies will need to reintegrate now-fragmented data from multiple databases on the same individuals, while carefully protecting beneficiary confidentiality. Under MMA, PDPs provide claims-level data to the Centers for Medicare and Medicaid Services (CMS) that are used for tracking out-of-pocket costs and other purposes. These data have the potential to serve as the foundation for a powerful national research database on prescription drug use and outcomes by the elderly and disabled. However, by mid-2007, procedures for research access to Part D data were still a work in progress, and it may be a long and complex road to achieve their great research potential.
Although much more needs to be done, the existing body of work using Medicaid data to examine prescription drug use, although modest in volume, has already done much to demonstrate their power to address critical questions of prescription drug use and safety. For example, the Presidential New Freedom Commission on Mental Health made extensive use of Medicaid-based studies.45
Given its growing financial costs ($315 billion in state and federal expenditures in 2005) and public health importance as the payer for almost 1 in 5 Americans, Medicaid is a chronic subject of intense policy debate,46
and it is important that issues of utilization, quality and outcomes of care in Medicaid be adequately addressed with available data as a basis for rational discussion of policy choices. Medicaid data resources represent an important opportunity for needed studies in coming years. Further development of merged research datasets that link claims with other sources of clinical and outcome information; systematic efforts to support and disseminate validation studies; thoughtful exploitation of natural experiments; careful selection of research questions to draw on the data’s strengths while avoiding threats to validity; and further development of statistical and analytic methods to address potential sources of bias are among the tools that will help to realize this potential.