Leveraging Data Science to Understand Outcomes for Mothers and Children Affected by Opioids

项目总结/摘要-项目2 在过去的二十年里，怀孕期间患有阿片类药物使用障碍（OUD）的妇女人数以及 出生时受类阿片影响的婴儿人数大幅增加。然而，仍然有关键的知识 我们对新生儿和产后阿片类药物使用治疗如何影响孕产妇的理解存在差距， 婴儿二分体目前关于暴露于阿片类药物的婴儿的大部分研究都集中在那些被诊断为 新生儿阿片类药物戒断综合征（NOWS），可能未充分识别阿片类药物暴露的婴儿， 发展成综合症，但有不良后果的风险。患有OUD的产后妇女有以下风险： 对母亲和婴儿都有害的结果，包括过量用药、严重的孕产妇发病率，以及 死亡虽然我们知道治疗OUD的药物，包括丁丙诺啡和美沙酮， 妇女的结果，其对产妇结果和分娩后的二元稳定性的影响的证据是 有限公司此外，产妇和婴儿的福祉是交织在一起的，但我们对二元结果的理解 (e.g.,保留监护权）是有限的，研究主要集中在分开的母亲和婴儿的福祉。 对二元结局的更好理解有可能为定制治疗和精确度提供信息 治疗受阿片类药物影响的双胞胎的药物。为了解决这些关键的知识差距，我们将利用数据链接， 新的数据科学方法，1）开发和验证基于电子健康记录的算法， 识别暴露于阿片类药物的婴儿及其母亲，识别二分体健康的关键协变量，并与州建立联系 数据系统; 2）检验与产前母体治疗OUD相比， 未经治疗的母亲OUD的出生结局和产后服务利用率得到改善， 产后前6个月的服务;和3）测试的假设， 治疗OUD相比，与未经治疗的母体OUD的二联体在一个- 产后1年以及婴儿治疗是否会改变这些结果。该提案将填补关键 知识差距，并创建持久的，可扩展的数据科学方法，以促进创新，数据共享， 研究集中在受阿片类药物危机影响的母婴二人组。

PROJECT SUMMARY / ABSTRACT- PROJECT 2 Over the past two decades, the number of women with opioid use disorder (OUD) during pregnancy and the number of infants born affected by opioids has increased substantially. However, there remain key knowledge gaps in our understanding of how neonatal and postpartum treatment for opioid use impacts the maternal- infant dyad. Much of the current research around infants exposed to opioids focuses on those diagnosed with Neonatal Opioid Withdrawal Syndrome (NOWS), potentially under-identifying opioid-exposed infants that don’t develop the syndrome but are at risk for adverse outcomes. Postpartum women with OUD are at risk for outcomes that are detrimental to both mother and infant including overdose, severe maternal morbidity, and death. While we know medications for OUD, including buprenorphine and methadone, can improve short-term outcomes for women, evidence of their impact on maternal outcomes and dyadic stability after delivery is limited. Furthermore, maternal and infant wellbeing is intertwined, yet our understanding of dyadic outcomes (e.g., retaining custody) is limited and research has primarily focused on separate mother and infant wellbeing. Improved understanding of dyadic outcomes has the potential to inform tailored therapeutics and precision medicine for opioid-affected dyads. To address these key knowledge gaps we will utilize data linkages and novel data science methodologies to 1) develop and validate electronic health record-based algorithms to identify opioid-exposed infants and their mothers, identify key covariates for dyad wellbeing, and link to state data systems; 2) test the hypothesis that dyads with prenatal maternal treatment for OUD compared to dyads with untreated maternal OUD have improved birth outcomes and post-natal service utilization of recommended services in the first six months postpartum; and 3) test the hypothesis that dyads with prenatal maternal treatment for OUD compared to dyads with untreated maternal OUD have improved dyadic stability at one- year postpartum and whether infant treatment modifies those outcomes. This proposal will fill critical knowledge gaps and create enduring, scalable data science methods that will foster innovation, data sharing, and research focused on maternal-infant dyads affected by the opioid crisis.