Capitation adjustment for pediatric populations

Published: July 1, 1996
Category: Bibliography > Papers
Authors: Anderson GF, Fowler EJ
Countries: United States
Language: null
Types: Care Management, Population Health
Settings: Academic, Health Plan

Pediatrics 98:10-17.

Institute for Research and Education, HealthSystem Minnesota, Minneapolis, MN, USA

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.

Comment in Pediatrics. 1997 Apr;99(4):651.

PMID: 8668377

High Risk,Age,High-Impact Chronic Conditions,Capitation,United States,Adolescent,Ambulatory Care/classifications,Child,Preschool,Chronic Disease/economics,Demography,Diagnosis-Related Groups/statistics & numerical data,Insurance Selection Bias,Managed Care Programs/economics,Managed Care Programs/standards,Maryland,Medicaid/economics,Medicaid/standards,Minnesota

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