Pregnancy-Associated Mortality

摘要 更广泛的背景特征可能是理解孕产妇死亡率的关键。公众 健康暴露方法允许同时评估个体特征 以及在自然、建筑和社会环境暴露的背景下的行为 环境。我们的概念模型解决了个体的生物学、 行为和社会特征在(和离不开)环境中 它们所处的环境。不健康环境分配不公 因此，暴露在#年的孕产妇死亡率方面产生了种族主义或社会经济差异。 人口水平。本附录的总体目标是开发，使用广泛的曝光 计算方法，包括个人和环境级别的多级别模型 产妇死亡率的预测指标，包括整个美国和路易斯安那州 具体地说。这将建立在父项目的目标之上，即解决社交背景如何 增加与妊娠相关的死亡风险。在母公司R01-县开发的数据集- 2005-2018年美国各州孕产妇死亡率水平估计和个人水平估计 从路易斯安那州2010-2017-将链接到公共卫生暴露数据库，一个大型数据 存储库(&gt；55,000个变量)，包含对自然、已建和社会环境的测量 3,141个县和相当于县的环境，跨度超过15年。这 提供可在该县进行分析的时空环境数据 级别或链接到居住地址。将应用高维计算方法 到多层次分析，以确定环境中最强烈的方面 与产妇死亡率有关。通过检查父母拨款的结果，国家- 级别分析，以及单个状态的个人级别分析，我们将建立一个 全面了解孕产妇死亡的生态状况。

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.