Exploiting Incomplete Information in Risk Adjustment Using Constrained Regression

IF 3.1 2区 经济学 Q1 ECONOMICS American Journal of Health Economics Pub Date : 2020-09-01 DOI:10.1086/710526
R. V. van Kleef, F. Eijkenaar, R. van Vliet, M. Nielen
{"title":"Exploiting Incomplete Information in Risk Adjustment Using Constrained Regression","authors":"R. V. van Kleef, F. Eijkenaar, R. van Vliet, M. Nielen","doi":"10.1086/710526","DOIUrl":null,"url":null,"abstract":"Health insurance markets with regulated premiums typically include risk adjustment (RA) to mitigate selection incentives. Even the most sophisticated RA models, however, tend to undercompensate (overcompensate) insurers for people in poor (good) health. One reason RA models are imperfect is that some predictors cannot serve as risk adjustor because they are not available for the entire population. This paper applies an indirect method to exploit such predictive information: constrained regression. Our focus is on the Netherlands where morbidity data from general practitioners (GPs) are available for only around 10 percent of the population. We combine this incomplete sample with complete data (N=16.7 million) on spending and risk adjustors. In a first step, we find that GP morbidity data are predictive net of the Dutch RA model. In a second step, we use the GP morbidity data to impose constraints on the coefficients of the RA model. This results in more RA funds being sent to undercompensated groups. Using a split-sample approach, we simulate two constrained regression models and compare the outcomes to those of an unconstrained model. Our findings indicate that constrained regression can be a useful tool to exploit predictive information that is available for only a sample of the population.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":"6 1","pages":"477 - 497"},"PeriodicalIF":3.1000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/710526","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Health Economics","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1086/710526","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 4

Abstract

Health insurance markets with regulated premiums typically include risk adjustment (RA) to mitigate selection incentives. Even the most sophisticated RA models, however, tend to undercompensate (overcompensate) insurers for people in poor (good) health. One reason RA models are imperfect is that some predictors cannot serve as risk adjustor because they are not available for the entire population. This paper applies an indirect method to exploit such predictive information: constrained regression. Our focus is on the Netherlands where morbidity data from general practitioners (GPs) are available for only around 10 percent of the population. We combine this incomplete sample with complete data (N=16.7 million) on spending and risk adjustors. In a first step, we find that GP morbidity data are predictive net of the Dutch RA model. In a second step, we use the GP morbidity data to impose constraints on the coefficients of the RA model. This results in more RA funds being sent to undercompensated groups. Using a split-sample approach, we simulate two constrained regression models and compare the outcomes to those of an unconstrained model. Our findings indicate that constrained regression can be a useful tool to exploit predictive information that is available for only a sample of the population.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用约束回归挖掘风险调整中的不完全信息
保费受监管的健康保险市场通常包括风险调整(RA),以减轻选择激励。然而,即使是最复杂的RA模型,也往往会对健康状况不佳(良好)的人的保险公司进行过度补偿。RA模型不完善的一个原因是,一些预测因子不能作为风险调节器,因为它们不适用于整个人群。本文应用了一种间接方法来利用这种预测信息:约束回归。我们的重点是荷兰,那里只有大约10%的人口可以获得全科医生的发病率数据。我们将这个不完整的样本与支出和风险调整者的完整数据(N=1670万)相结合。首先,我们发现全科医生的发病率数据是荷兰RA模型的预测网络。在第二步中,我们使用GP发病率数据对RA模型的系数施加约束。这导致更多的RA资金被送往补偿不足的群体。使用分裂样本方法,我们模拟了两个约束回归模型,并将结果与无约束模型的结果进行了比较。我们的研究结果表明,约束回归可以成为一种有用的工具,可以利用仅适用于人群样本的预测信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.30
自引率
2.70%
发文量
34
期刊介绍: The American Journal of Health Economics (AJHE) provides a forum for the in-depth analysis of health care markets and individual health behaviors. The articles appearing in AJHE are authored by scholars from universities, private research organizations, government, and industry. Subjects of interest include competition among private insurers, hospitals, and physicians; impacts of public insurance programs, including the Affordable Care Act; pharmaceutical innovation and regulation; medical device supply; the rise of obesity and its consequences; the influence and growth of aging populations; and much more.
期刊最新文献
The Effects of Post-Acute Care Payment Reform on Caregiving The Long-Term Impacts of Cigarette Taxes on Smoking. From Lawn Care to Home Care: Undocumented Immigration and Aging in Place Heterogeneous Effects of the Affordable Care Act on Emergency Department Visits and Payer Composition among Older Adults by Race and Ethnicity Increased Schooling Reduces Hospitalization Later in Life: New Evidence with Optimal Instruments from the United States
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1