Calculating Disease-Based Medical Care Expenditure Indexes For Medicare Beneficiaries: A Comparison of Method and Data Choices

Disease-based medical care expenditure indexes are currently of interest to measurement economists and have been the subject of several recent papers. These papers, however, produced widely different results for medical care inflation and also varied in the datasets and methods used, making it difficult to compare them. In this paper, using two data sources and two different methods for calculating expenditure indexes for the Medicare population, we compare the indexes produced and establish some results that will help guide policymakers in choosing indexes for this population. First, we find that the two methods we examine (primary diagnosis and a regression-based method) produce the same results for the aggregate index and have a moderate level of agreement in which diseases contribute the most to growth in per capita health-care spending. Since the primary diagnosis method is preferable because of its greater transparency, this result implies that we may use the regression-based method when the data is not suitable for the primary diagnosis method without too great a loss of accuracy. Second, we find that the two data sources, the Medicare Current Beneficiary Survey and the Medical Expenditure Panel Survey, produce very similar results in the aggregate but there is some evidence that they treat chronic illnesses differently. As the MCBS has a larger sample and more comprehensive coverage of Medicare beneficiaries than the MEPS, it seems that a regression-based expenditure index based on the MCBS is overall preferable for fee-for-service Medicare beneficiaries. We discuss further avenues for research, such as comparing our results with indexes created with commercial groupers, and what data to use for Medicare private plan enrollees.