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

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

doi: 10.1097/MLR.0b013e3181844e2e
Original Article

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.

From the Center for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, Rockville, Maryland.

The views expressed in this article are those of the authors and no official endorsement by the Department of Health and Human Services or the Agency for Healthcare Research and Quality is intended or should be inferred.

Reprints: Steven B. Cohen, PhD, Center for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, 540 Gaither Rd, Rockville, MD 20850. E-mail:

© 2009 Lippincott Williams & Wilkins, Inc.