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

项目摘要/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)的妇女数量和 受阿片类药物影响出生的婴儿数量大幅增加。然而，仍有一些关键知识 我们对新生儿和产后使用阿片类药物的治疗如何影响母亲-理解上的差距- 婴儿二人组。目前关于接触阿片类药物的婴儿的大部分研究集中在那些被诊断为患有阿片类药物的婴儿。 新生儿阿片类药物戒断综合征(NOWS)，可能低估了没有 发展成综合症，但有不良后果的风险。产后患有先天性巨结肠症的妇女有患上 对母亲和婴儿都有害的后果，包括服药过量、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，可以改善短期 对于妇女的结局，其对产妇结局和产后稳定性的影响的证据是 有限的。此外，母亲和婴儿的福祉是相互交织的，但我们对二元结果的理解 (例如，保留监护权)是有限的，研究主要集中在母婴分开的福祉上。 对二元结局的更好的理解有可能为量身定做的治疗和精确度提供信息 治疗阿片类药物影响的二联体的药物。为了解决这些关键的知识差距，我们将利用数据链接和 用于1)开发和验证基于电子健康记录的算法的新数据科学方法 确定接触阿片类药物的婴儿和他们的母亲，确定影响二元幸福感的关键协变量，并将其与状态联系起来 数据系统；2)检验双联体与双联体的产前孕产妇治疗相比的假说 未经治疗的产妇OUD可改善分娩结局和产后服务利用率 产后前六个月的服务；以及3)检验与产前产妇有二元性的假设 与未经治疗的母体OUD相比，治疗OUD的二倍体稳定性提高了一倍. 产后一年，以及婴儿治疗是否改变这些结果。这项建议将填补关键的 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、 研究重点是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.