Fair Regression for Health Care Spending

Abstract

The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk adjustment formulas are known to underpredict spending for specific groups of enrollees leading to undercompensated payments to health insurers. This incentivizes insurers to design their plans such that individuals in undercompensated groups will be less likely to enroll, impacting access to health care for these groups. To improve risk adjustment formulas for undercompensated groups, we expand on concepts from the statistics, computer science, and health economics literature to develop new fair regression methods for continuous outcomes by building fairness considerations directly into the objective function. We additionally propose a novel measure of fairness while asserting that a suite of metrics is necessary in order to evaluate risk adjustment formulas more fully. Our data application using the IBM MarketScan Research Databases and simulation studies demonstrate that these new fair regression methods may lead to massive improvements in group fairness (e.g., 98%) with only small reductions in overall fit (e.g., 4%).

arXiv comment30 pages, 3 figures

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Cited by in corpus (8)

• Ethical Machine Learning in Health Care
• An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
• What Do Compressed Deep Neural Networks Forget?
• Fair Regression for Health Care Spending
• Fairness Violations and Mitigation under Covariate Shift
• Reproducibility in Machine Learning for Health
• Identifying Undercompensated Groups Defined By Multiple Attributes in Risk Adjustment
• Counterfactual Reasoning for Fair Clinical Risk Prediction