W Hwang1, H T Ireys, G F Anderson. 1. Department of Health Policy and Management, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, MD, USA. whwang@jhsph.edu
Abstract
OBJECTIVE: Several capitation payment systems have been developed and implemented recently by public and private insurers as well as by individual managed care organizations. Many pediatricians have expressed concern that methods for establishing capitation rates may not adequately account for the higher expected expenditures for children with chronic health conditions. In this study, we evaluate a demographic- and 4 diagnosis-based models, paying particular attention to their performance for children with chronic health conditions. METHODS: We selected children 18 years of age and under who were enrolled in the Maryland Medicaid Program in 1995 and 1996. We defined the population of children with chronic health conditions using ICD-9 codes. Individual and group-level analyses were utilized to measure the ability of the different risk adjustment models to predict expenditures in 1996 based upon information available in 1995. RESULTS: All 4 diagnosis-based models significantly outperformed the demographic model for children overall and for children with chronic health conditions. Differences between diagnosis-based models were small, especially as the size of test populations increased. CONCLUSIONS: Risk adjustment methods that account directly for health status promise to reduce incentives to exclude children with chronic illnesses from managed care plans and to provide a foundation for more appropriate payments to pediatricians who care for these children.
OBJECTIVE: Several capitation payment systems have been developed and implemented recently by public and private insurers as well as by individual managed care organizations. Many pediatricians have expressed concern that methods for establishing capitation rates may not adequately account for the higher expected expenditures for children with chronic health conditions. In this study, we evaluate a demographic- and 4 diagnosis-based models, paying particular attention to their performance for children with chronic health conditions. METHODS: We selected children 18 years of age and under who were enrolled in the Maryland Medicaid Program in 1995 and 1996. We defined the population of children with chronic health conditions using ICD-9 codes. Individual and group-level analyses were utilized to measure the ability of the different risk adjustment models to predict expenditures in 1996 based upon information available in 1995. RESULTS: All 4 diagnosis-based models significantly outperformed the demographic model for children overall and for children with chronic health conditions. Differences between diagnosis-based models were small, especially as the size of test populations increased. CONCLUSIONS: Risk adjustment methods that account directly for health status promise to reduce incentives to exclude children with chronic illnesses from managed care plans and to provide a foundation for more appropriate payments to pediatricians who care for these children.
Authors: Alison A Galbraith; Dennis Ross-Degnan; Stephen B Soumerai; Allyson M Abrams; Kenneth Kleinman; Meredith B Rosenthal; J Frank Wharam; Alyce S Adams; Irina Miroshnik; Tracy A Lieu Journal: Am J Manag Care Date: 2010-11 Impact factor: 2.229
Authors: Paul J Chung; Craig F Garfield; Marc N Elliott; Katherine D Vestal; David J Klein; Mark A Schuster Journal: Acad Pediatr Date: 2013-03-09 Impact factor: 3.107
Authors: Mark A Schuster; Paul J Chung; Marc N Elliott; Craig F Garfield; Katherine D Vestal; David J Klein Journal: JAMA Date: 2008-09-03 Impact factor: 56.272
Authors: Alexis D Leal; Holly Van Houten; Lindsey Sangaralingham; Rachel A Freedman; Ahmedin Jemal; Heather B Neuman; Tufia C Haddad; Robert W Mutter; Theresa H M Keegan; Sarah S Mougalian; Charles L Loprinzi; Cary P Gross; Nilay Shah; Kathryn J Ruddy Journal: Clin Breast Cancer Date: 2017-09-22 Impact factor: 3.225
Authors: Alyna T Chien; Joseph P Newhouse; Lisa I Iezzoni; Carter R Petty; Sharon-Lise T Normand; Mark A Schuster Journal: Pediatrics Date: 2017-10-03 Impact factor: 7.124
Authors: Mark A Schuster; Paul J Chung; Marc N Elliott; Craig F Garfield; Katherine D Vestal; David J Klein Journal: Am J Public Health Date: 2009-01-15 Impact factor: 9.308
Authors: Alison A Galbraith; Dennis Ross-Degnan; Stephen B Soumerai; Irina Miroshnik; J Frank Wharam; Kenneth Kleinman; Tracy A Lieu Journal: Pediatrics Date: 2009-04 Impact factor: 7.124