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.
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.
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.
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.
Mots-clés Pascal : Soin santé primaire, Coût, Economie santé, Programme diagnostic, Valeur prédictive, Pédiatrie, Etude cohorte, Enfant, Homme
Mots-clés Pascal anglais : Primary health care, Costs, Health economy, Diagnostic program, Predictive value, Pediatrics, Cohort study, Child, Human
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 96-0351287
Code Inist : 002B30A01A1. Création : 10/04/1997.