title: Performance of statistical models to predict mental health and substance abuse cost.
abstract: BACKGROUND
  Providers use risk-adjustment systems to help manage healthcare costs. Typically, ordinary least squares (OLS) models on either untransformed or log-transformed cost are used. We examine the predictive ability of several statistical models, demonstrate how model choice depends on the goal for the predictive model, and examine whether building models on samples of the data affects model choice.
  
  
  METHODS
  Our sample consisted of 525,620 Veterans Health Administration patients with mental health (MH) or substance abuse (SA) diagnoses who incurred costs during fiscal year 1999. We tested two models on a transformation of cost: a Log Normal model and a Square-root Normal model, and three generalized linear models on untransformed cost, defined by distributional assumption and link function: Normal with identity link (OLS); Gamma with log link; and Gamma with square-root link. Risk-adjusters included age, sex, and 12 MH/SA categories. To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE), mean absolute prediction error (MAPE), and predictive ratios of predicted to observed cost (PR) among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals. To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample.
  
  
  RESULTS
  The Square-root Normal model had the lowest estimates of the RMSE and MAPE, with bootstrap confidence intervals that were always lower than those for the other models. The Gamma with square-root link was best as measured by the PRs. The choice of best model could vary if smaller samples were used and the Gamma with square-root link model had convergence problems with small samples.
  
  
  CONCLUSIONS
  Models with square-root transformation or link fit the data best. This function (whether used as transformation or as a link) seems to help deal with the high comorbidity of this population by introducing a form of interaction. The Gamma distribution helps with the long tail of the distribution. However, the Normal distribution is suitable if the correct transformation of the outcome is used.
authors.affiliation: Boston University School of Public Health, Department of Biostatistics, Boston, Massachusetts, USA. montez@bu.edu
authors.forename: Maria
authors.initials: M
authors.lastname: Montez-Rath
authors.forename: Cindy L
authors.initials: CL
authors.lastname: Christiansen
authors.forename: Susan L
authors.initials: SL
authors.lastname: Ettner
authors.forename: Susan
authors.initials: S
authors.lastname: Loveland
authors.forename: Amy K
authors.initials: AK
authors.lastname: Rosen
mesh_terms: D000368:Aged; D016001:Confidence Intervals; D017281:Cost of Illness; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006302:Health Services Research; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000191:economics* / Q000453:epidemiology / Q000628:therapy; D008605:Mental Health Services / Q000191:economics*; D008875:Middle Aged; D017059:Models, Econometric; D016011:Normal Distribution; D020379:Risk Adjustment; D019966:Substance-Related Disorders / Q000191:economics* / Q000453:epidemiology / Q000628:therapy; D014481:United States / Q000453:epidemiology; D014493:United States Department of Veterans Affairs