title: Pharmacy-and diagnosis-based risk adjustment for children with Medicaid.
abstract: BACKGROUND
  Risk adjustment is useful for adjusting health care payments based on patients' health status.
  
  
  OBJECTIVE
  This work seeks to examine how well pharmacy- and diagnosis-based risk adjusters predict child health expenditures in Medicaid populations.
  
  
  METHODS
  We used 1994-1995 Medicaid claims files for all children ages 0-18 years who were not covered by managed care in 3 states: Georgia, New Jersey, and Wisconsin. We examined separately 6 risk adjustment methods, 2 pharmacy-based and 4 diagnosis-based. We compared predictive accuracy of the methods for the whole sample and stratified by state and Medicaid enrollment category.
  
  
  RESULTS
  Models with risk adjustment (either diagnosis- or pharmacy-based) had better predictive accuracy than demographic models. The pharmacy and diagnosis-based models had similar predictive accuracy. Risk adjuster performance differed by Medicaid enrollment category and state. Risk-adjusted models generally underpredict expenditures in populations with worse health status (eg, those in the Supplemental Security Income program [SSI]). The pharmacy-based models performed well for children in SSI relative to children in foster care.
  
  
  CONCLUSIONS
  Both pharmacy- and diagnosis-based risk adjustment improved the prediction of health expenditures compared models without risk adjustment. No single risk adjuster performed best in all situations, suggesting that optimal choices of risk adjusters may differ by purpose and context.
authors.affiliation: Center for Child and Adolescent Health Policy, MassGeneral Hospital for Children, Department of Pediatrics, Harvard Medical School, Boston, MA 02114, USA. kkuhlthau@partners.org
authors.forename: Karen
authors.initials: K
authors.lastname: Kuhlthau
authors.forename: Timothy G
authors.initials: TG
authors.lastname: Ferris
authors.forename: Roger B
authors.initials: RB
authors.lastname: Davis
authors.forename: James M
authors.initials: JM
authors.lastname: Perrin
authors.forename: Lisa I
authors.initials: LI
authors.lastname: Iezzoni
mesh_terms: D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D016303:Clinical Pharmacy Information Systems; D003953:Diagnosis-Related Groups / Q000191:economics*; D011307:Drug Prescriptions / Q000191:economics*; D005260:Female; D005544:Forecasting; D005581:Foster Home Care; D005102:Health Expenditures; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008484:Medicaid; D017059:Models, Econometric; D020379:Risk Adjustment / Q000379:methods*; D012943:Social Security; D014481:United States