Regression-Based Sampling for Persons with High Health Expenditures

Background. Given the high concentration of health care expenditures among a relatively small percentage of the population, the 1997 Medical Expenditure Panel Survey was designed to learn more about these high expenditure individuals by oversampling them. Objective. Oversampling high expenditure individuals enables more precise estimation of what the nation's health care dollar buys and who pays it. It also enhances the ability to discern the causes of high health care expenses and the characteristics of the individuals who incur them. Method. Using the 1987 National Medical Expenditure Survey, a probabilistic model was developed to select households from the 1996 National Health Interview Survey likely to contain individuals incurring high levels of medical expenditures in the 1997 MEPS. The accuracy of the selection model, and the degree to which the high expenditure population was oversampled, are assessed with the 1997 MEPS data. Results. Over half of the persons selected by the regression model were expected to have high health expenditures. Of the 456 persons selected by the model for oversampling, 257 individuals or 56.4% did, in fact, have high expenditures. Regression-based sampling increased the proportion of MEPS individuals with high expenditures from 14.3% without oversampling to 17.2% of the total cohort with oversampling (or from 938–1,126 persons). Conclusion. This paper demonstrates that a model-based approach to oversampling a high expenditure population, or any population with dynamic characteristics, can be highly successful in terms of sampling yield and accuracy

Bootstrapping (finance) · Econometrics · Economics · Environmental health · Health care · Health insurance · Medical Expenditure Panel Survey · National Health Interview Survey · Oversampling · Population · Sampling (signal processing) · Statistics · Computer Science · demographic modeling and climate adaptation · Global Health Care Issues · Health disparities and outcomes · Healthcare Policy and Management · Mathematics · Medicine

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