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Incorporating self-reported health measures in risk equalization through constrained regression.

A A Withagen-Koster1, R C van Kleef2, F Eijkenaar2

  • 1Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands. koster@eshpm.eur.nl.

The European Journal of Health Economics : HEPAC : Health Economics in Prevention and Care
|January 10, 2020
PubMed
Summary

Constrained regression (CR) offers a novel method to improve health insurance risk equalization by better accounting for self-reported health. This approach can reduce under/overcompensation for certain groups, potentially leading to fairer outcomes in insurance markets.

Keywords:
Constrained regressionHealth insuranceRisk equalizationRisk selectionSurvey data

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Area of Science:

  • Health economics
  • Insurance market regulation
  • Statistical modeling

Background:

  • Health insurance markets use risk equalization to manage spending variations.
  • Existing systems often undercompensate individuals with poor self-reported health, creating risk selection incentives.
  • Self-reported health is typically excluded from risk adjustment due to feasibility concerns.

Purpose of the Study:

  • To investigate constrained regression (CR) as an alternative method for incorporating self-reported health into risk equalization models.
  • To assess the impact of CR on under/overcompensation for different population groups compared to traditional methods.
  • To evaluate the effectiveness of CR in mitigating risk selection incentives in health insurance.

Main Methods:

  • Utilized large-scale administrative (N=17 million) and health survey (N=380,000) data from the Netherlands.
  • Estimated five constrained regression (CR) models with varying degrees of coefficient restriction.
  • Compared CR models against the 2016 Dutch ordinary least squares (OLS) risk equalization model.

Main Results:

  • Constrained regression improved outcomes for groups not explicitly adjusted for but worsened outcomes for explicitly adjusted groups.
  • Lighter constraints in CR models demonstrated superior performance over OLS based on a novel metric summarizing under/overcompensation.
  • The study quantified the reduction in under/overcompensation for self-reported general health groups across different CR constraint levels (20% to 100%).

Conclusions:

  • Constrained regression presents a viable strategy for integrating self-reported health into risk equalization, addressing limitations of current models.
  • The findings suggest that carefully calibrated constraints can enhance the fairness of risk equalization systems.
  • CR offers a promising avenue for reducing insurer incentives for risk selection by better accounting for unobserved health status variations.