Relatively few studies have used self-reported health status in models to predict medical expenditures, and many of these have used the SF-36.
We sought to examine the ability of the briefer SF-12 measure of health status to predict medical expenditures in a nationally representative sample.
We used data from the 2000–2001 panel of the Medical Expenditure Panel Study. Respondents (n = 5542) completed the SF-12 in a questionnaire. Interviews obtained data on demographics and selected chronic conditions. Data on expenditures incurred subsequent to the interview were obtained in part from provider records. We examined different regression model specifications and compared different statistical estimation techniques.
Adding the SF-12 to a regression model improved the prediction of subsequent medical expenditures. In a model with only age and gender, adding the SF-12 increased R2 from 0.06 to 0.13. The coefficients for the Physical Component Summary (PCS) and the Mental Component Summary (MCS) of the SF-12 for this model were −0.045 (P < 0.01) and −0.012 (P < 0.01), respectively. In a model including demographic characteristics, chronic conditions, and previous expenditures, adding the SF-12 increased the R2 from 0.26 to 0.29. The coefficients for the PCS and the MCS for this model were −0.025 (P < 0.001) and −0.005 (P = 0.15), respectively. A single general health status question performed almost as well as the full SF-12. Models estimated using ordinary least squares had undesirable properties. In terms of R2, a generalized linear model (GLM) with a Poisson variance function was consistently superior to a GLM with a gamma variance function.
Information on self-reported health status is useful in predicting medical expenditures. The extent to which the SF-12 adds predictive power over a comprehensive array of diagnostic data remains to be examined.
From the *Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, Rockville, Maryland; †the Harris School of Public Policy Studies, the University of Chicago, Illinois; and ‡QualityMetric, Inc., Lincoln, Rhode Island.
Supported by the Agency for Healthcare Research and Quality, Rockville, MD.
The views expressed in this article are those of the authors, and no official endorsement by the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services is intended or should be inferred.
Reprints: John A. Fleishman, PhD, Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, 540 Gaither Road, Rockville, MD 20850. E-mail: firstname.lastname@example.org.