Academic journal article Journal of Economics and Economic Education Research

Standards, Practices, and Methods for the Use of Administrative Claims Data

Academic journal article Journal of Economics and Economic Education Research

Standards, Practices, and Methods for the Use of Administrative Claims Data

Article excerpt


In a recent paper, Safran et al., (2007) discuss the increasing secondary use of health data for research and other purposes. The authors note that the "lack of coherent policies and standard good practices for secondary use of health data impedes efforts to transform the U.S. health care system" (p. 1). This paper seeks to contribute to this important discussion in two ways. First, a set of standards and practices for researchers to follow is proposed for the acquisition and proper use of administrative data. Second, the literature is reviewed that relates to specific shortcomings with administrative databases and methods to address the problems. The paper is geared towards students with an interest in health economics, but may also be useful to other students and established researchers given the increasing use of administrative data (both health-related and otherwise). The goal is to help researchers use administrative data correctly so that policy makers can have greater confidence in findings, and consequently research can have a greater effect on public policy.

Public health care programs in the U.S. such as Medicare and Medicaid finance health care for millions of people. The information collected as a result of health care delivery, enrolling members, and reimbursing for services is referred to as administrative data (Iezzoni, 1997). Despite widespread use for research purposes, there exist limited standards and practices for researchers to adhere to in using administrative data (Retchin & Ballard, 1998; Safran, et al., 2007). In addition, while undergraduate and graduate students in economics (and other social sciences) encounter a wide array of courses during their education, few academic programs teach students how to acquire and properly use data.

This paper focuses on data from the two largest government-funded health care programs, Medicare and Medicaid, but the issues discussed in this paper apply to all types of administrative records. The focus was chosen because of the sensitive nature and yet widespread use of such data, the increased vulnerability of the subjects, and the evolving U.S. federal regulatory landscape for healthcare information in general. Examples are discussed based on experiences during the lead author's five years at the Centers for Medicare & Medicaid Services (CMS), the government agency that oversees the programs.


First, let's review a few of the advantages and shortcomings of using administrative data for research. There are a number ofadvantages to administrative data (Iezzoni, 2002; Pandiani & Banks, 2003; Roos, Menec, & Currie, 2004; Roos et al., 2008). It is conceivable to study (almost) all individuals age 65 and above with Medicare enrollment and claims data. The use of population based data enables questions to be considered that could not be addressed with a sample. However, due to cost considerations and the sheer size of the databases, almost all studies use a sample. For example, as discussed in more detail later in the paper, much research uses a 5% sample of Medicare beneficiaries which is approximately 800,000 people. Despite being a small proportion of beneficiaries, the sample size remains substantial and limits concerns about the generalizability of results found in small sample studies. In addition, the large size also allows for adequate numbers of minorities for statistical analysis.

The records are not limited to specific types of setting (e.g., hospitals). Information can be longitudinal covering individuals and institutions across many years. Confidentiality can be maintained due to the large sample sizes. The data exist, and thus are relatively inexpensive to acquire compared to primary data collection, plus the low cost also allows for easy replication of previous studies. Survey attrition due to a loss of contact or refusal to participate is also minimized. …

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