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Article Abstract

Online ISSN: 1099-176X    Print ISSN: 1091-4358
The Journal of Mental Health Policy and Economics
Volume 3, Issue 3, 2000. Pages: 129-137

Published Online: 30 Apr 2001

Copyright © 2000 John Wiley & Sons, Ltd.

 Research Article
Risk adjustment for high utilizers of public mental health care
Kanika Kapur. 1 *, Alexander S Young 2 3, Dennis Murata 4
1RAND Corporation, 1700 Main Street, Santa Monica, CA 90401, USA
2West Los Angeles Veterans HealthCare Center, MIRECC, Building 210A, Los Angeles, CA 90073, USA
3UCLA Neuropsychiatric Institute, 10920 Wilshire Boulevard, Suite 300, Los Angeles, CA 90024, USA
4Los Angeles County Department of Mental Health, 550 South Vermont Avenue, 12th Floor, Los Angeles, CA 90020, USA

*Correspondence to Kanika Kapur., RAND Corporation, 1700 Main Street, Santa Monica, CA 90401, USA

Funded by:
 VISN 22 Mental Illness, Research, Education and Clinical Center of the Department of Veterans Affairs.
 NIMH UCLA-RAND Research Center on Managed Care for Psychiatric Disorders; Grant Number: P50 MH-54623
 Ernst Van Loben Sels Charitable Foundation
 Zellerbach Family Fund

Background: Publicly funded mental health systems are increasingly implementing managed care systems, such as capitation, to control costs. Capitated contracts may increase the risk for disenrollment or adverse outcomes among high cost clients with severe mental illness. Risk-adjusted payments to providers are likely to reduce providers' incentives to avoid or under-treat these people. However, most research has focused on Medicare and private populations, and risk adjustment for individuals who are publicly funded and severely mentally ill has received far less attention.
Aims of the Study: Risk adjustment models for this population can be used to improve contracting for mental health care. Our objective is to develop risk adjustment models for individuals with severe mental illness and assess their performance in predicting future costs. We apply the risk adjustment model to predict costs for the first year of a pilot capitation program for the severely mentally ill that was not risk adjusted. We assess whether risk adjustment could have reduced disenrollment from this program.
Methods: This analysis uses longitudinal administrative data from the County of Los Angeles Department of Mental Health for the fiscal years 1991 to 1994. The sample consists of 1956 clients who have high costs and are severely mentally ill. We estimate several modified two part models of 1993 cost that use 1992 client-based variables such as demographics, living conditions, diagnoses and mental health costs (for 1992 and 1991) to explain the variation in mental health and substance abuse costs.
Results: We find that the model that incorporates demographic characteristics, diagnostic information and cost data from two previous years explains about 16 percent of the in-sample variation and 10 percent of the out-of-sample variation in costs. A model that excludes prior cost covariates explains only 5 percent of the variation in costs. Despite the relatively low predictive power, we find some evidence that the disenrollment from the pilot capitation initiative input have been reduced if risk adjustment had been used to set capitation rates.
Discussion: The evidence suggests that even though risk adjustment techniques have room to improve, they are still likely to be useful for reducing risk selection in capitation programs. Blended payment schemes that combine risk adjustment with risk corridors or partial fee-for-service payments should be explored.
Implications for Health Care Provision, Use, and Policy: Our results suggest that risk adjustment methods, as developed to data, do not have the requisite predictive power to be used as the sole approach to adjusting capitation rates. Risk adjustment is informative and useful; however, payments to providers should not be fully capitated, and may need to involve some degree of risk sharing between providers and public mental health agencies. A blended contract design may further reduce incentives for risk selection by incorporating a partly risk-adjusted capitation payment, without relying completely on the accuracy of risk adjustment models.
Implications for Further Research: Risk adjustment models estimated using data sets containing better predictors of rehospitalization and more precise clinical information are likely to have higher predictive power. Further research should also focus on the effect of combination contract designs. Copyright © 2000 John Wiley & Sons, Ltd.

Received: 20 March 2000; Accepted: 4 September 2000