Deriving risk adjustment payment weights to maximize efficiency of health insurance markets

J Health Econ. 2018 Sep;61:93-110. doi: 10.1016/j.jhealeco.2018.07.001. Epub 2018 Jul 23.

Abstract

Risk-adjustment is critical to the functioning of regulated health insurance markets. To date, estimation and evaluation of a risk-adjustment model has been based on statistical rather than economic objective functions. We develop a framework where the objective of risk-adjustment is to minimize the efficiency loss from service-level distortions due to adverse selection, and we use the framework to develop a welfare-grounded method for estimating risk-adjustment weights. We show that when the number of risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated via a constrained least-squares regression. When the number of plan service-level allocation decisions exceeds the number of risk-adjusters, the optimal weights can be found by an OLS regression on a straightforward transformation of the data. We illustrate this method with the data used to estimate risk-adjustment payment weights in the Netherlands (N = 16.5 million).

Keywords: Adverse selection; Health insurance; Risk adjustment.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Efficiency, Organizational / economics
  • Humans
  • Insurance, Health / economics
  • Insurance, Health / organization & administration*
  • Models, Economic
  • Risk Adjustment / economics
  • Risk Adjustment / organization & administration*