ArticleBiometrics2020
Fair regression for health care spending.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Cross-sectional analysis of accuracy versus interpretability in Medicare Advantage risk adjustment.Health policy OPEN · 2026Article
- Evaluating the role of cost and fairness in machine learning models of suicide risk.Scientific reports · 2026Article
- A Machine Learning Model to Improve Risk Adjustment Accuracy in Medicare.Health services research · 2026Article
- Considerations for evaluating the practical utility of machine learning in suicide risk estimation: the role of cost and equity.Research square · 2025Article
- Selecting risk adjusters with penalized regression and expert judgment: evidence from Colombia.Health economics review · 2025Article
- Algorithms to Improve Fairness in Medicare Risk Adjustment.JAMA health forum · 2025Article
- Trust, Trustworthiness, and the Future of Medical AI: Outcomes of an Interdisciplinary Expert Workshop.Journal of medical Internet research · 2025Article
- The Challenges Ahead: Concepts, Analytics, and Ethics of Value-Based Care in Applied Behavior Analysis.Behavior analysis in practice · 2024Review
- A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas.JAMA health forum · 2024Article
- Association Between Community-Level Social Risk and Spending Among Medicare Beneficiaries: Implications for Social Risk Adjustment and Health Equity.JAMA health forum · 2023Article
- Equal accuracy for Andrew and Abubakar-detecting and mitigating bias in name-ethnicity classification algorithms.AI & society · 2023Article
- A two-stage super learner for healthcare expenditures.Health services & outcomes research methodology · 2022Article
- A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity.Science advances · 2022Article
- A comparison of approaches to improve worst-case predictive model performance over patient subpopulations.Scientific reports · 2022Article
- Revisiting performance metrics for prediction with rare outcomes.Statistical methods in medical research · 2021Article
- Identifying undercompensated groups defined by multiple attributes in risk adjustment.BMJ health & care informatics · 2021Article
- Probabilistic Machine Learning for Healthcare.Annual review of biomedical data science · 2021Review
- Ethical Machine Learning in Healthcare.Annual review of biomedical data science · 2021Article
- Responsible Artificial Intelligence (AI) for Value Formation and Market Performance in Healthcare: the Mediating Role of Patient's Cognitive Engagement.Information systems frontiers : a journal of research and innovation · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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 demonstrates that these new fair regression methods may lead to massive improvements in group fairness (eg, 98%) with only small reductions in overall fit (eg, 4%).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.