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

• 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
• 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

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.

项目摘要 非西班牙裔黑人婴儿的低出生体重率是非西班牙裔白色婴儿的两倍。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，改善生育结果的不平等是国家的优先事项。尽管这种长期的不平等，许多 公共和私人支付者无法解决婴儿不良结局的差异，因为缺乏 种族/民族数据。本K 01满足了收集和使用基于证据的建议的迫切需求， 支付者之间的种族/民族数据，使人口健康管理计划能够预测 可以用来减少不良出生结果的算法。未将人种/种族纳入预测 用于资源分配的模型可能最终导致有偏见的算法，从而加剧健康差距。 本研究的目标1将使用算法公平性框架来测试多个算法， 低出生体重儿的预测模型。除了测试模型的准确性，预测模型将 测试了算法公平性的七项指标，以评估种族/民族是否能改善算法公平性。 公平性（例如，非白色女性相对于白色女性的预测准确性相等[或更好] 加强公平的方法。该项目将利用医疗索赔，出生证明和受益人 阿肯色州所有付款人索赔数据库的信息。与出生证明的联系使这一点成为可能。 由于大多数付款人没有收集，因此商业索赔中不存在种族/民族。 开创性的医学研究所报告不平等待遇建议收集种族/民族， 减少健康和医疗保健提供方面的差距;然而，众所周知，支付者担心被指控 在大多数州，种族/民族被划上红线，很少收集。研究付款人和供应商的意见， 已经收集了种族/民族的意见，但对少数群体受益人的意见进行了类似的研究， 严重缺乏。本研究的目标2将在黑人、西班牙裔、 阿肯色州的马绍尔妇女对收集和使用种族/族裔信息的可接受性的态度 数据以及管理方面（例如，何时收集数据），重点是围产期方案。 这些目标将提供一个证据基础，并作为收集和使用 种族/民族数据与社区投入。大型第三方支付者拥有改善健康的基础设施 差异，但缺乏一个社区参与的方法，为收集和使用这些数据提供信息， 使用公平框架开发算法。K 01可以让调查员在她的基础上 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实现她的长期目标， 成为一名富有成效的独立研究人员，专注于识别和减轻服务于 作为不利的婴儿和孕产妇结果的种族/民族差异的驱动因素。