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

项目总结 非西班牙裔黑人婴儿的低出生体重率是非西班牙裔白人婴儿的两倍。AS 不良出生结局的差异导致婴儿死亡率和一生不良结局的差异 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，许多人 公共和私人付款人无法解决婴儿不良结局方面的差异，因为缺乏 种族/民族数据。本K01满足了收集和使用基于证据的建议的迫切需要 支付者中的种族/民族数据，使人口健康管理计划能够制定预测性 可以用来减少不良生育后果的算法。未能将种族/民族纳入预测 用于资源分配的模型最终可能导致有偏见的算法，从而加剧健康差距。 这项研究的目标1将使用算法公平框架来测试用于开发的多个算法 预测低出生体重儿出生的模型。除了测试模型的准确性外，预测模型还将 对算法公平性的七个衡量标准进行测试，以评估种族/民族是否会改善算法 公平(例如，与白人女性相比，非白人女性的预测准确率相同[或更高]) 促进公平的方法。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。与出生证明的联系唯一地允许这一点 研究有种族/民族，这在商业索赔中没有，因为大多数付款人缺乏收集。 开创性医学研究所报告不平等待遇建议收集种族/民族 减少保健和医疗保健提供方面的差距；然而，众所周知，付款人担心 在大多数州，红线和很少收集种族/民族。关于支付者和提供者观点的研究 已经进行了种族/族裔收集，但对少数群体受益者的观点进行了类似的研究 严重缺乏。这项研究的目标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式。 有社区投入的种族/民族数据。大型第三方支付者拥有改善健康的基础设施 差距，但缺乏社区参与的方法，为收集和使用这些数据提供信息，以指导 使用公平的框架开发算法。K01将允许调查员在她的基础上 在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 定性方法论。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，专注于确定和减轻有助于 作为造成不良婴儿和产妇结局的种族/族裔差异的驱动因素。

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.