# Fair Regression for Health Care Spending

arXiv: https://arxiv.org/abs/1901.10566 · HTML: https://mlsanity.com/paper/1901.10566/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 10 of 10)

- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Understanding and Mitigating Accuracy Disparity in Regression](https://mlsanity.com/paper/2102.12013/) (2021)
- [Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare](https://mlsanity.com/paper/2202.01906/) (2022)
- [A comparison of approaches to improve worst-case predictive model performance over patient subpopulations](https://mlsanity.com/paper/2108.12250/) (2021)
- [Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling](https://mlsanity.com/paper/2501.13219/) (2025)
- [Generative AI in Medicine](https://mlsanity.com/paper/2412.10337/) (2024)
- [A Huber loss-based super learner with applications to healthcare expenditures](https://mlsanity.com/paper/2205.06870/) (2022)
- [Liouville PDE-based sliced-Wasserstein flow](https://mlsanity.com/paper/2505.17204/) (2025)
- [Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease](https://mlsanity.com/paper/2512.17340/) (2025)
- [A Statistical Framework for Data-Driven Discovery of Differential Performance in Clinical Risk Prediction Models](https://mlsanity.com/paper/2608.01333/) (2026)

## Abstract (arXiv)

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%).