Identifying Outcomes and Implementation Strategies to Optimize Prenatal Care Coordination

项目总结和摘要 很少有干预措施成功解决孕产妇健康结果中的种族差异。社会条件-或 健康的社会和结构决定因素是美国持续存在种族差异的主要原因。 提供家访和护理协调的综合护理模式可支持孕产妇保健， 有意义地减少差距的潜力。产前护理协调（PNCC）是一项按服务收费的医疗补助计划。 福利，在包括威斯康星州在内的几个州都有。其目标是降低不良出生结果的发生率 包括高危母亲和婴儿的低出生体重和早产。PNCC服务包括 健康教育、护理协调、社会支持以及便利获得医疗护理和社会服务 在怀孕期间和分娩后60天内为享受医疗补助的母亲提供服务。这一长期目标 研究的目的是改善高危母亲的健康状况，减少种族和民族之间的差异。 通过加强PNCC福利的实施和影响，具体而言，本研究旨在： 1：描述接受PNCC服务与五种不同的孕产妇健康结果之间的关系， 2011年至2019年期间在威斯康星州分娩的母亲。利用一个大型的多数据库数据集， 从2011年至2019年的威斯康星州出生记录到医疗补助索赔和其他行政数据来源，我们将 为五个目标结果中的每一个建立回归模型：1）充分的产前护理，2）产后6周 访问，3）利用所需的行为健康服务，4）发生严重的孕产妇发病率，和5） 急诊科的就诊次数，考虑到母亲的种族和地理位置。 2：评估使用结构化护理模式指导PNCC实现5个目标结局的治疗效果 在2011年至2019年期间接受PNCC服务的母亲中，（a）考虑到与PNCC的互动 提供者类型和地点类型，以及（B）另外考虑母亲的种族和地理位置。数据将 使用PNCC站点类型（公共卫生部门，临床 环境、社区机构）和提供者类型（护士、社区保健工作者、社会工作者） 作为协变量，并且个体嵌套在水平1的这些站点内。 3：质量标准描述了不同PNCC提供商对使用结构化 指导PNCC服务的模式、影响PNCC的执行过程以及PNCC的目标成果。 使用演绎定性方法，我们将在五点进行半结构化访谈和焦点小组讨论。 全国各地的PNCC网站，不同的提供商类型和设置，谁为客户提供服务，从种族和 种族多样的社区，包括黑人和美洲原住民母亲。 分析将根据实施科学综合框架（CFIR）进行，该框架描述了 这些因素可以影响执行，并且足够灵活，可以在多种情况下应用。

PROJECT SUMMARY AND ABSTRACT Few interventions successfully address racial disparities in maternal health outcomes. Social conditions—or social and structural determinants of health—are the major causes of persistent racial disparities in the US. Integrated models of care that offer home visiting and care coordination can support maternal health and have the potential to meaningfully reduce disparities. Prenatal care coordination (PNCC) is a fee-for-service Medicaid benefit, available in several states including Wisconsin. Its goal is to reduce rates of adverse birth outcomes including low birthweight and premature birth among mothers and infants at high risk. PNCC services include health education, care coordination, social support, and facilitating access to medical care and social services during pregnancy and up to 60 days following delivery for Medicaid-covered mothers. The long-term aim of this research is to improve the health outcomes of high-risk mothers and reduce disparities between racial and ethnic groups by strengthening the PNCC benefit’s implementation and impact. Specifically, this study aims to: 1: Describe the association of receiving PNCC services with five distinct maternal health outcomes among mothers who delivered in WI between 2011 and 2019. Leveraging a large, multisource data set, which links all Wisconsin birth records from 2011-2019 to Medicaid claims and other administrative data sources, we will develop regression models for each of five target outcomes: 1) adequate prenatal care, 2) a 6-week postpartum visit, 3) utilization of needed behavioral health services, 4) occurrence of severe maternal morbidity, and 5) emergency department visits, accounting for mother’s race and geographic location. 2: Evaluate the treatment effect of using a structured model of care to guide PNCC on the 5 target outcomes among mothers who received PNCC services between 2011 and 2019, (a) accounting for interactions with PNCC provider type and site type, and (b) additionally accounting for mother’s race and geographic location. Data will be analyzed using two-level logistic regression models with PNCC site type (public health department, clinical setting, community-based agency) at level 2 and provider type (nurse, community health worker, social worker) as a covariate, and individuals nested within those sites at level 1. 3: Qualitatively describe diverse PNCC providers’ perspectives on the relative advantage of using a structured model to guide PNCC services, implementation processes that impact PNCC, and target outcomes of PNCC. Using a deductive qualitative approach, we will conduct semi-structured interviews and focus groups at five PNCC sites across the state that vary by provider type and setting, and who serve clients from racially and ethnically diverse communities including Black and Native American mothers. Analyses will be framed by the Consolidated Framework for Implementation Science (CFIR), which describes factors that can influence implementation and is flexible enough to apply in multiple contexts.