Pregnancy-Associated Mortality

总结 更广泛的背景特征可能是理解孕产妇死亡率的关键。公众 健康问题的方法允许同时评估个人特征 以及在自然、建筑和社会环境暴露背景下的行为 环境.我们的概念模型解决了个体生物学， 行为，和社会特征内（和不可分割的）的背景下， 他们生活的环境。不健康环境的不公平分布 因此，暴露造成孕产妇死亡率的种族或社会经济差异， 人口水平。本补充的总体目标是开发，使用一个全系统的 计算方法，结合个人和环境水平的多层次模型 孕产妇死亡率的预测因素，无论是对美国作为一个整体， 具体来说这将建立在母项目的目标，即解决社会环境如何 增加与怀孕有关死亡风险。在父R 01县开发的数据集- 2005-2018年美国所有州的孕产妇死亡率估计值和个人水平估计值 从路易斯安那州2010-2017年-将被链接到公共卫生麻烦数据库，一个大数据 存储库（> 55，000个变量），包含自然，建筑和社会的测量 3，141个县和县等效环境，跨越15年。这 提供可在县进行分析的时空背景环境数据 或与居住地址相关联。将应用高维计算方法 多层次分析，以确定环境中最强烈的方面， 与产妇死亡率有关。通过检查父母补助金的结果，国家- 水平分析，和一个国家的个人水平分析，我们将建立一个 产妇死亡生态的全面情况。

SUMMARY Broader contextual characteristics may be key to understanding maternal mortality. The public health exposome approach allows for the simultaneous evaluation of individual characteristics and behaviors within the context of environmental exposures from the natural, built, and social environments. Our conceptual model addresses the intersection of individuals' biological, behavioral, and social characteristics within (and inseparable from) the context of the environments in which they live. The inequitable distribution of unhealthy environmental exposures therefore produces racialized or socioeconomic disparities in maternal mortality at the population level. The overall goal of this supplement is to develop, using an exposome-wide computational approach, multi-level models incorporating individual and environmental-level predictors of maternal mortality, both for the United States as a whole, and Louisiana specifically. This will build on the parent project's aim of addressing how social contexts increase risk for pregnancy-related mortality. Datasets developed in the parent R01 – county- level maternal mortality estimates from all US states 2005-2018 and individual-level estimates from Louisiana 2010-2017 - will be linked to the public health exposome database, a large data repository (>55,000 variables) containing measures of the natural, built, and social environments for 3,141 counties and county equivalents, and spanning over 15 years. This provides spatial-temporal, contextual environmental data that can be analyzed at the county level or linked to residential addresses. High-dimensional computational methods will be applied to multilevel analysis to identify the aspects of the environment that are most strongly associated with maternal mortality. By examining the results from the parent grant, the national- level analysis, and an individual-level analysis of a single state, we will establish a comprehensive picture of the ecology of maternal mortality.