title: Using the SF-12 health status measure to improve predictions of medical expenditures.
abstract: BACKGROUND
  Relatively few studies have used self-reported health status in models to predict medical expenditures, and many of these have used the SF-36.
  
  
  OBJECTIVE
  We sought to examine the ability of the briefer SF-12 measure of health status to predict medical expenditures in a nationally representative sample.
  
  
  METHODS
  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.
  
  
  RESULTS
  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 R 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 R 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 R, a generalized linear model (GLM) with a Poisson variance function was consistently superior to a GLM with a gamma variance function.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, Rockville, Maryland 20850, USA. jfleishm@ahrq.gov
authors.forename: John A
authors.initials: JA
authors.lastname: Fleishman
authors.forename: Joel W
authors.initials: JW
authors.lastname: Cohen
authors.forename: Willard G
authors.initials: WG
authors.lastname: Manning
authors.forename: Mark
authors.initials: M
authors.lastname: Kosinski
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002908:Chronic Disease / Q000453:epidemiology* / Q000628:therapy; D003710:Demography; D005260:Female; D005544:Forecasting; D019538:Health Care Surveys; D005102:Health Expenditures / Q000706:statistics & numerical data / Q000639:trends*; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D008403:Mass Screening / Q000191:economics / Q000706:statistics & numerical data; D008875:Middle Aged; D020380:Needs Assessment / Q000706:statistics & numerical data*; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D011795:Surveys and Questionnaires; D014481:United States / Q000453:epidemiology