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 在新生儿健康方面，美国（美国）几乎落后于所有高收入国家，利率 表现出的不良后果，包括围产期死亡率，早产和低出生体重 近年来的最小进展。大量分布是此类结果的基础，在 种族和族裔少数民族以及社会经济地位低下的个人。而这样的人口经常 在获得足够护理方面的面部障碍，其他研究表明某些低风险人口经验 孕产妇护理的过度证明。例如，产前护理和成像的趋势上升。 有限的严格证据表明利用率增加的结果有意义地改善了结果。这个现实 需要采取新的产妇护理方法，包括：1）生成基于证据的政策和 指南，尤其是有利于弱势群体的准则； 2）更好地评估干预措施 改善护理目标的模棱两可的好处。政策和临床相关性的两个特定领域包括 获得产后避孕和胎儿胎儿监测的访问，具有潜力的策略 改善新出生的结果，但现有证据是有限的。根据这个总体目标 提案是在以下方面提供严格的证据：1）激励立即提供的医疗补助政策激励 产后长效可逆避孕（IPP-LARC）； 2） 胎儿胎儿监测。 AIM 1利用来自三个州的状态住院排放数据和一个合成 控制设计以估计医疗补助提供商付款对短期出生时提供IPP LARC的影响 间隔，早产和低出生体重。 AIM 2从一个大， 综合卫生系统和基于机器学习的倾向得分设计，以评估范围和 抗胎儿监测对围产期死亡率和其他相关新生儿结局的影响。 拟议的研究与国家儿童健康与人类发展研究所紧密保持一致 （NICHD）促进复制健康的研究优先事项，更好地了解 避孕，赋予健康怀孕能力并解决健康分布。整体奖学金培训 计划将得到多学科心态团队和协作培训环境的支持 致力于研究，专业和临床技能开发。通过机器中的桥接方法 学习和准实验评估很少用于评估母校护理 政策和准则，该建议将有助于围绕数据驱动的临床实践告知 立即进行产后避孕和胎儿胎儿监测。这样的证据将有助于 在美国提高公平的新生儿结果