Birth Outcomes of Second Children after Community-based Home Visiting

项目摘要 剖宫产相关结局，特别是剖宫产率、胎龄和出生体重，表明产妇 和儿童健康，并对儿童的健康产生终身影响。家访（HV）是一种既定的 改善儿童和家庭健康的方法、养育子女的做法以及与社会和健康的联系 服务大多数HV项目针对的是第一次怀孕的母亲，部分原因是假设以后 儿童也将受益。第二次孕前（可能是产前）的HV目标和活动 有可能改善第二个孩子的出生结果。然而，有稀疏的数据， 生育结果;让母亲在怀孕期间参与的方案侧重于第一个孩子和其他方案 在第一个孩子出生时让母亲参与，不关注任何生育结果。我们提出 使用来自全州HV计划（养育家庭网络[NFN]）和出生证明的数据来检查 第二次分娩的家庭访问的母亲。大多数母亲在第一个孩子出生后不久就参加了全国新生力量， 采用以证据为基础的家长作为教师（PAT）课程。这些NFN数据的可用性提供了一个 有机会审查一个大型的社区为基础的计划，而无需昂贵的数据收集。出生证明数据 将用于创建本观察性研究的比较组。新生力量第二代的出生结果 将儿童（n= 3，000名儿童）与：（a）其年长的兄弟姐妹（其出生导致入组的儿童 在NFN中; n= 3，000），（B）被筛选为符合NFN条件但未提供该计划的家庭的儿童 由于家访和其他后勤原因（n=650），以及（c）倾向评分匹配组 非NFN儿童（根据孕产妇健康、个人 人口统计学和邻里特征; n= 6，000）。我们会检查出生间隔，产前检查 护理，剖宫产率，孕龄，出生体重的第二个孩子的NFN家庭，相比， 上面描述的群体。我们将按种族和年龄（<20岁与≥20岁）进行亚组分析， 弱势群体中母亲的差异。我们还将检查计划参与度（辍学和缺课 访问），以及计划参与和出生结果之间的关联。这 该项目将产生关于全州范围内的第二个孩子的潜在长期利益的证据， 家访的母亲这一证据对于支持这些项目的持续资助和 确定晚生孩子将获得好处的假设是否证明关注第一次生育的母亲是合理的。 了解计划参与对后期成果的影响将有助于计划优先考虑保留 努力该项目将加强耶鲁大学护理学院的研究环境， 学生有机会参与二级数据分析，并通过在学校建立专业知识， 利用大型行政数据集和先进的统计方法进行研究。

Project Summary Birth-related outcomes, specifically cesarean section rates, gestational age, and birth weight, indicate maternal and child health, and have life-long impacts on the health of children. Home visiting (HV) is an established approach to improve the health of children and families, parenting practices, and linkage to social and health services. The majority of HV programs target first-time mothers, in part due to the assumption that later children will also benefit. HV goals and activities in the second preconception, and possibly prenatal, period have the potential to improve second child birth outcomes. However, there are sparse data on subsequent birth outcomes; programs that engage mothers during pregnancy focus on the first child and other programs engage mothers around the time of the first child's birth and do not focus on any birth outcomes. We propose to use data from a statewide HV program (Nurturing Families Network [NFN]) and birth certificates to examine second births to home visited mothers. NFN enrolls most mothers shortly after the birth of their first child and uses the evidence-based Parent as Teacher (PAT) curriculum. The availability of these NFN data provides an opportunity to examine a large community-based program without costly data collection. Birth certificate data will be used to create comparison groups for this observational study. Birth outcomes for the NFN second children (n=3,000 children) will be compared to: (a) their older siblings (the child whose birth led to enrollment in NFN; n=3,000), (b) children of families who were screened as eligible for NFN, but not offered the program due to home visitor availability and other logistical reasons (n=650), and (c) a propensity-score-matched group of non-NFN children (created using the likelihood to enroll in the program based on maternal health, individual demographics, and neighborhood characteristics; n=6,000). We will examine birth spacing, receipt of prenatal care, cesarean section rate, gestational age, and birth weight for second children of NFN families, compared to the groups described above. We will conduct sub-analyses by race and by age (<20 vs. ≥20 years) to examine differences for mothers in vulnerable groups. We will also examine program engagement (dropout and missed visits) for those enrolled in NFN and associations between program engagement and birth outcomes. This project will generate evidence on the potential long-term benefits of a statewide program to the second child of home-visited mothers. This evidence is important to support continued funding of these programs and to determine if the assumption that later children will gain benefits justifies the focus on first-time mothers. Understanding the impact of program engagement on later outcomes will help the program prioritize retention efforts. This project will strengthen the research environment of the Yale School of Nursing by providing students an opportunity to engage in secondary data analysis and by building expertise in the school for conducting research using large administrative datasets and utilizing advanced statistical methods.