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Medical Care:
doi: 10.1097/MLR.0b013e3181844e2e
Original Article

The Utility of Prediction Models to Oversample the Long-Term Uninsured

Cohen, Steven B. PhD; Yu, William W. MA

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Objectives: To evaluate the performance of prediction models in identifying the long-term uninsured and their utility for oversampling purposes in national health care surveys.

Data and Methods: Nationally representative data from the Medical Expenditure Panel Survey (MEPS) were used to examine national estimates of nonelderly adults without health insurance coverage for 2 consecutive years and to identify the factors that distinguished them from the short-term uninsured and those who are continually insured. The MEPS data were also used in the development of the prediction models to identify individuals most likely to experience long-term spells without coverage in the future. The prediction models were developed using data from the MEPS panel covering 2004–2005 and evaluated with an independent MEPS panel.

Results: Study findings revealed these prediction models to be markedly effective statistical tools in facilitating an efficient over-sample of individuals likely to be uninsured for long periods of duration in the future. Use of these models for oversampling purposes, to support a 50% increase in sample yield over a self-weighting design, permits the selection of the target sample of individuals who are continuously uninsured for 2 consecutive years in the most cost-efficient manner. This methodology allows for an overall sample size specification for nonelderly adults that is at least 25% lower than a design without access to the predictor variables from a screening interview or without application of oversampling techniques.

Conclusions: This examination of the performance of probabilistic models, to both identify and facilitate an oversample of the long-term uninsured, demonstrates the viability of these model-based sampling methodologies for adoption in national health care surveys.

© 2009 Lippincott Williams & Wilkins, Inc.


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