title: Capitation adjustment for pediatric populations.
abstract: OBJECTIVE
  The objective of this study is to assess the predictive performance of current claims-based capitation adjustment methods for pediatric populations. Medicaid programs and other insurers may increasingly use these systems for capitation rate setting, physician profiling, and other purposes.
  
  
  METHODS
  Five leading models, a demographic model, ambulatory care groups, ambulatory diagnostic groups, diagnostic cost groups, and payment amounts for capitated systems, were tested by using use and expenditure data for children enrolled in the Maryland Medicaid program and a private nonprofit health maintenance organization in Minnesota. The models were tested at the individual level by using multiple regression methods and at the group level by using split-half validation to create both random and nonrandom groups. One of the nonrandom groups was created to represent children with chronic conditions.
  
  
  RESULTS
  The findings indicate that although each of the alternative methods offers an improvement over a demographic model, significant underpayment remained for high-risk children, regardless of the capitation adjustment method used.
  
  
  CONCLUSIONS
  It is concluded that children with chronic conditions would probably remain at risk for discrimination in a competitive health care market under all models tested. Limitations associated with current alternatives suggest the need for further research in the area of pediatric capitation adjustment methods.
authors.affiliation: Institute for Research and Education, HealthSystem Minnesota, Minneapolis 55416, USA.
authors.forename: E J
authors.initials: EJ
authors.lastname: Fowler
authors.forename: G F
authors.initials: GF
authors.lastname: Anderson
mesh_terms: D000293:Adolescent; D000553:Ambulatory Care / Q000145:classification; D002204:Capitation Fee / Q000592:standards*; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease / Q000191:economics; D003710:Demography; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data; D006801:Humans; D015273:Insurance Selection Bias; D008329:Managed Care Programs / Q000191:economics / Q000592:standards; D008396:Maryland; D008484:Medicaid / Q000191:economics / Q000592:standards; D008910:Minnesota; D018803:Models, Economic; D010372:Pediatrics / Q000191:economics*; D014481:United States