Risk adjusting capitation: Applications in employed and disabled populations

Risk adjustment may be a sensible strategy to reduce selection bias because it links managed care payment directly to the costs of providing services. In this paper we compare risk adjustment models in two populations (public employees and their dependents, and publicily-insured low income individuals with disabilities) in Washington State using two statistical approaches and three health status measures. We conclude that a two-part logistic/GLM (generalized linear model) statistical model performs better in populations with large numbers of individuals who do not use health services. This model was successfully implemented in the employed population, but the managed care program for the publicly insured population was terminated before risk adjustment could be applied. The choice of the most appropriate health status measure depends on purchasers' principles and desired outcomes.