Leveraging Causal Inference and Machine Learning Methods to Advance Evidence-Based Maternal Care and Improve Newborn Health Outcomes

利用因果推理和机器学习方法推进循证孕产妇护理并改善新生儿健康结果

• 批准号: 10604856
• 负责人: Rajet Vatsa
• 金额: $ 3.99万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10604856
• 关键词: Address; Adoption; Affect; Area; Birth; Birth Intervals; Birth Rate; Caring; Cesarean section; Characteristics; Clinical; Clinical Skills; Contraceptive methods; Data; Dedications; Discipline of obstetrics; Disparity; Early Diagnosis; Electronic Health Record; Environment; Equity; Ethnic Origin; Evaluation; Face; Fees; Fellowship; Fetal Death; Fetal Distress; Generations; Goals; Guidelines; Health; Health Care Costs; Health Disparities Research; Health Services Accessibility; High Risk Woman; Hospitals; Image; Incentives; Income; Individual; Inequity; Inpatients; Integrated Health Care Systems; Intervention; Left; Light; Live Birth; Location; Low Birth Weight Infant; Low income; Machine Learning; Masks; Massachusetts; Medicaid; Mentorship; Methods; Modality; National Institute of Child Health and Human Development; Newborn Infant; Outcome; Patients; Perinatal mortality demographics; Policies; Policy Analysis; Population; Postpartum Period; Pregnancy; Pregnancy Interval; Premature Birth; Prenatal care; Professional Organizations; Provider; Quasi-experiment; Race; Reproductive Health; Research; Research Priority; Risk; Small for Gestational Age Infant; Subgroup; System; Training; United States; Variant; Veins; Vulnerable Populations; Woman; adverse outcome; antenatal; clinical practice; clinically relevant; design; empowerment; ethnic minority; evidence base; experience; fetal; geographic difference; health data; health disparity; healthy pregnancy; improved; improved outcome; low socioeconomic status; machine learning method; mortality; multidisciplinary; neonatal health; payment; postpartum contraception; programs; racial minority; skill acquisition; stillbirth; tool; trend

PROJECT SUMMARY/ABSTRACT The United States (U.S.) trails behind almost all high-income nations with respect to newborn health, with rates of adverse outcomes, including perinatal mortality, preterm birth, and low birthweight, having demonstrated minimal progress in recent years. Significant disparities underlie such outcomes, with higher rates among racial and ethnic minorities and individuals of low socioeconomic status. While such populations frequently face barriers in accessing adequate care, other studies suggest that certain low-risk populations experience over-provision of maternal care. Rising trends in prenatal care and imaging, for example, are tempered by limited rigorous evidence that increases in utilization have meaningfully improved outcomes. This reality necessitates a renewed approach to maternity care, including: 1) generation of evidence-based policies and guidelines, particularly ones that benefit vulnerable populations; and 2) better assessment of interventions with ambiguous benefit to improve targeting of care. Two specific areas of policy and clinical relevance include access to immediate postpartum contraception and antepartum fetal surveillance, strategies with potential to improve newborn outcomes but for which existing evidence is limited. Accordingly, the overall goal of this proposal is to produce rigorous evidence on: 1) a Medicaid policy incentivizing provision of immediate postpartum long-acting reversible contraception (IPP-LARC); and 2) the scope and effect of antepartum fetal surveillance. Aim 1 utilizes state inpatient discharge data from three states and a synthetic control design to estimate the impact of Medicaid provider payments for provision of IPP-LARC on short birth intervals, preterm birth, and low birthweight. Aim 2 leverages electronic health record data from a large, integrated health system and a machine-learning-based propensity score design to assess the scope and effect of antepartum fetal surveillance on perinatal mortality and other related newborn outcomes. The proposed research aligns closely with the National Institute of Child Health and Human Development’s (NICHD) research priorities to promote reproductive health, better understand the health impacts of contraception, empower healthy pregnancies, and address health disparities. The overall fellowship training plan will be supported by a multidisciplinary mentorship team and a collaborative training environment dedicated to research, professional, and clinical skills development. Through bridging methods in machine learning and quasi-experimental evaluation that have rarely been used in the evaluation of maternal care policies and guidelines, this proposal will be instrumental in informing data-driven clinical practice around immediate postpartum contraception and antepartum fetal surveillance. Together, such evidence will help advance equitable newborn outcomes in the U.S.

项目摘要/摘要 美国（美国）在新生儿健康方面，中国落后于几乎所有高收入国家， 的不良后果，包括围产期死亡率，早产，低出生体重，已经证明， 近年来进展甚微。这些结果背后存在着重大差异， 少数民族和社会经济地位低下的个人。虽然这些人经常 面临障碍，在获得适当的照顾，其他研究表明，某些低风险人群的经验 过度提供产妇护理。例如，产前护理和成像的上升趋势受到以下因素的影响： 有限的严格证据表明，利用率的提高有意义地改善了结果。这一现实 需要对产妇护理采取新的办法，包括：1）制定以证据为基础的政策， 准则，特别是有利于弱势群体的准则; 2）更好地评估干预措施， 模糊的好处，以提高护理的针对性。政策和临床相关性的两个具体领域包括 获得产后立即避孕和产前胎儿监测， 改善新生儿结局，但现有证据有限。因此，这一总体目标 该提案旨在提供以下方面的严格证据：1）医疗补助政策鼓励提供即时医疗服务， 产后长效可逆避孕（IPP-LARC）; 2）范围和效果 产前胎儿监护目的1利用三个州的州住院病人出院数据和一个综合的 对照设计，以估计医疗补助提供者支付IPP-LARC对短产的影响 间隔，早产和低出生体重。Aim 2利用来自大型， 综合卫生系统和基于机器学习的倾向评分设计，以评估范围和 产前胎儿监护对围产儿死亡率及其他相关新生儿结局影响的 拟议的研究与国家儿童健康和人类发展研究所的 （NICHD）研究优先事项，以促进生殖健康，更好地了解 避孕，增强健康怀孕的能力，并解决健康差距。整体研究金培训 该计划将得到多学科指导小组和协作培训环境的支持 致力于研究，专业和临床技能的发展。机器中的直通桥接方法 学习和准实验评价，很少用于评价孕产妇保健 政策和指南，该提案将有助于为数据驱动的临床实践提供信息， 产后立即避孕和产前胎儿监护。这些证据将有助于 在美国促进公平的新生儿结果。