Cargando…

Fair regression for health care spending

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. Unfortuna...

Descripción completa

Detalles Bibliográficos
Autores principales: Zink, Anna, Rose, Sherri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540596/
https://www.ncbi.nlm.nih.gov/pubmed/31860120
http://dx.doi.org/10.1111/biom.13206
_version_ 1783591245138886656
author Zink, Anna
Rose, Sherri
author_facet Zink, Anna
Rose, Sherri
author_sort Zink, Anna
collection PubMed
description 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%).
format Online
Article
Text
id pubmed-7540596
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-75405962020-10-15 Fair regression for health care spending Zink, Anna Rose, Sherri Biometrics Biometric Practice 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%). John Wiley and Sons Inc. 2020-01-06 2020-09 /pmc/articles/PMC7540596/ /pubmed/31860120 http://dx.doi.org/10.1111/biom.13206 Text en © 2019 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Biometric Practice
Zink, Anna
Rose, Sherri
Fair regression for health care spending
title Fair regression for health care spending
title_full Fair regression for health care spending
title_fullStr Fair regression for health care spending
title_full_unstemmed Fair regression for health care spending
title_short Fair regression for health care spending
title_sort fair regression for health care spending
topic Biometric Practice
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7540596/
https://www.ncbi.nlm.nih.gov/pubmed/31860120
http://dx.doi.org/10.1111/biom.13206
work_keys_str_mv AT zinkanna fairregressionforhealthcarespending
AT rosesherri fairregressionforhealthcarespending