Algorithmic fairness in predictive models to eliminate disparities in adverse infant outcomes: A case for race

预测模型中的算法公平性可消除不良婴儿结局的差异：种族案例

基本信息

批准号：
10571289
负责人：
Clare Brown
金额：
$ 12.5万
依托单位：
UNIV OF ARKANSAS FOR MED SCIS
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-26 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10571289
关键词：
Address Adult Algorithms Arkansas Attitude Awareness Behavioral Big Data Birth Birth Certificates Black Populations Black race Caring Classification Clinical Collection Communities Competence Data Databases Demographic Factors Discrimination Ethnic Origin Exclusion Failure Focus Groups Fright Geography Goals Guidelines Health Healthcare Hispanic Infant Infant Health Infant Mortality Infrastructure Institute of Medicine (U.S.)Insurance Carriers Insurance Claim Review Intervention Lead Life Cycle Stages Link Logistic Regressions Low Birth Weight Infant Measures Medicaid Medical Methodology Methods Minority Minority Women Modeling Native Hawaiian or Other Pacific Islander Neurodevelopmental Disorder Not Hispanic or Latino Outcome Patient Self-Report Perinatal Predictive Analytics Prejudice Prenatal care Privatization Provider Publishing Qualitative Research Race Recommendation Reduce health disparities Reporting Research Research Methodology Research Personnel Resource Allocation Resources Respiration Disorders Risk Seminal Techniques Testing Third-Party Payer Training Woman advanced analytics adverse birth outcomes adverse outcome algorithm development algorithmic bias at-risk pregnancies base beneficiary career community based participatory research community engagement disparity elimination disparity reduction econometrics evidence base evidence based guidelines experience health care delivery health disparity health management health plan improved infancy infant outcome insurance claims lens maternal outcome perinatal health perinatal outcomes population health prediction algorithm predictive modeling premature prevent programs public-private partnership racial and ethnic racial and ethnic disparities random forest regression algorithm skills social factors

项目摘要

PROJECT SUMMARY Non-Hispanic Black infants have twice the rates of low birthweight births as non-Hispanic White infants. As disparities in adverse birth outcomes drive disparities in infant mortality and adverse outcomes across the life course, improving inequities in birth outcomes is a national priority. Despite this longstanding inequity, many public and private payers are unable to address disparities in adverse infant outcomes because of a lack of race/ethnicity data. This K01 fills a critical need for evidence-based recommendations for collection and use of racial/ethnic data among payers to enable population health management programs to develop predictive algorithms that could be used to reduce adverse birth outcomes. Failure to include race/ethnicity in predictive models used for resource allocation may ultimately lead to biased algorithms that exacerbate health disparities. Aim 1 of this study will use an algorithmic fairness framework to test multiple algorithms for developing predictive models for low birthweight birth. In addition to testing model accuracy, predictive models will be tested for seven measures of algorithmic fairness to assess whether having race/ethnicity improves algorithmic fairness (e.g., equal [or better] predictive accuracy for non-White relative to White women) after applying four fairness-enhancing approaches. This project will utilize medical claims, birth certificates, and beneficiary information from the Arkansas All Payer Claims Database. Linkage to the birth certificates uniquely allows this study to have race/ethnicity, which are absent in the commercial claims given lack of collection by most payers. The seminal Institute of Medicine Report Unequal Treatment recommended collection of race/ethnicity to mitigate disparities in health and healthcare delivery; however, it is well known that payers fear accusations of redlining and rarely collect race/ethnicity in most states. Research on payer and provider views regarding collection of race/ethnicity has been conducted, but similar research on the views of minority beneficiaries are severely lacking. Aim 2 of this study will conduct racially-homogenous focus groups among Black, Hispanic, and Marshallese women in Arkansas regarding attitudes on acceptability of collecting and using race/ethnicity data as well as administrative aspects (e.g., when to collect the data), with an emphasis on perinatal programs. These aims will provide an evidence-base and serve as a national model for collecting and using racial/ethnic data with community input. Large third-party payers have the infrastructure to improve health disparities, but lack a community-engaged approach to inform collection and use of these data to guide development of algorithms using an equitable framework. The K01 will allow the investigator to build on her expertise in insurance claims analysis to acquire skillsets in predictive modeling, community engagement, and qualitative methodologies. These important skillsets will allow the researcher to achieve her long-term goals of becoming a productive and independent researcher with a focus on identifying and mitigating factors that serve as drivers of racial/ethnic disparities in adverse infant and maternal outcomes.

项目概要非西班牙裔黑人婴儿的低出生体重出生率是非西班牙裔白人婴儿的两倍。作为不良出生结果的差异导致婴儿死亡率和一生不良结果的差异当然，改善出生结果的不平等是国家的优先事项。尽管不平等现象长期存在，但许多人由于缺乏足够的资金支持，公共和私人付款人无法解决婴儿不良结局的差异。种族/民族数据。该 K01 满足了对收集和使用基于证据的建议的迫切需求付款人之间的种族/民族数据，使人口健康管理计划能够制定预测可用于减少不良出生结果的算法。未能将种族/民族纳入预测范围用于资源分配的模型最终可能会导致算法出现偏差，从而加剧健康差距。本研究的目标 1 将使用算法公平框架来测试用于开发的多种算法低出生体重出生的预测模型。除了测试模型的准确性外，预测模型还将测试了七种算法公平性指标，以评估种族/民族是否可以改善算法应用四项后的公平性（例如，非白人相对于白人女性的预测准确性相同[或更好]）增强公平性的方法。该项目将利用医疗索赔、出生证明和受益人来自阿肯色州所有付款人索赔数据库的信息。与出生证明的链接独特地允许这样做研究种族/民族，由于大多数付款人缺乏收款，商业索赔中没有种族/民族。开创性的医学研究所报告《不平等待遇》建议收集种族/族裔信息，以缩小健康和医疗保健服务方面的差距；然而，众所周知，付款人担心被指控在大多数州，红线并很少收集种族/民族信息。关于付款人和提供者观点的研究已经进行了种族/民族的收集，但对少数群体受益人的观点进行了类似的研究严重缺乏。本研究的目标 2 将在黑人、西班牙裔、和阿肯色州的马绍尔妇女对收集和使用种族/族裔的可接受性的态度数据以及管理方面（例如，何时收集数据），重点是围产期计划。这些目标将提供证据基础并作为收集和使用的国家模型具有社区意见的种族/民族数据。大型第三方支付者拥有改善健康的基础设施差异，但缺乏社区参与的方法来告知这些数据的收集和使用，以指导使用公平框架开发算法。 K01 将允许调查员在她的基础上进行研究保险索赔分析方面的专业知识，以获得预测建模、社区参与和定性方法。这些重要的技能将使研究人员能够实现她的长期目标成为一名富有成效的独立研究人员，重点关注识别和减轻影响因素作为婴儿和孕产妇不良后果的种族/民族差异的驱动因素。