Department of Evidence and Intelligence for Action, Information Systems for Health Unit, Pan American Health Organization, Washington, DC, 20037, USA.
Department of Environmental Health, Risk Science Center, University of Cincinnati, Cincinnati, OH, 45267, USA.
J Urban Health. 2019 Mar;96(Suppl 1):57-71. doi: 10.1007/s11524-018-00338-w.
We report integration of the United States Environmental Protection Agency's (USEPA) United States Environmental Justice Screen (EJSCREEN) database with our Public Health Exposome dataset to interrogate 9232 census blocks to model the complexity of relationships among environmental and socio-demographic variables toward estimating adverse pregnancy outcomes [low birth weight (LBW) and pre-term birth (PTB)] in all Ohio counties. Using a hill-climbing algorithm in R software, we derived a Bayesian network that mapped all controlled associations among all variables available by applying a mapping algorithm. The results revealed 17 environmental and socio-demographic variables that were represented by nodes containing 69 links accounting for a network with 32.85% density and average degree of 9.2 showing the most connected nodes in the center of the model. The model predicts that the socio-economic variables low income, minority, and under age five populations are correlated and associated with the environmental variables; particulate matter (PM) level in air, proximity to risk management facilities, and proximity to direct discharges in water are linked to PTB and LBW in 88 Ohio counties. The methodology used to derive significant associations of chemical and non-chemical stressors linked to PTB and LBW from indices of geo-coded environmental neighborhood deprivation serves as a proxy for design of an African-American women's cohort to be recruited in Ohio counties from federally qualified community health centers within the 9232 census blocks. The results have implications for the development of severity scores for endo-phenotypes of resilience based on associations and linkages for different chemical and non-chemical stressors that have been shown to moderate cardio-metabolic disease within a population health context.
我们报告了将美国环境保护署（USEPA）的美国环境正义筛选器（EJSCREEN）数据库与我们的公共卫生暴露组数据集进行整合，以调查 9232 个普查区块，从而建立模型来研究环境和社会人口变量之间的复杂关系，以估计俄亥俄州所有县的不良妊娠结局（低出生体重（LBW）和早产（PTB））。我们使用 R 软件中的爬山算法，得出了一个贝叶斯网络，该网络通过应用映射算法，映射了所有可用变量之间的所有受控关联。结果揭示了 17 个环境和社会人口变量，这些变量由包含 69 个链接的节点表示，该网络的密度为 32.85%，平均度数为 9.2，表明模型中心的节点连接最紧密。该模型预测，社会经济变量（低收入、少数族裔和五岁以下人口）与环境变量（空气中的颗粒物（PM）水平、接近风险管理设施以及接近水中的直接排放物）相关联，与俄亥俄州 88 个县的 PTB 和 LBW 相关。从地理编码环境邻里剥夺指数中得出与 PTB 和 LBW 相关的化学和非化学应激因素的显著关联的方法，可作为在俄亥俄州从符合条件的社区健康中心招募非裔美国妇女队列的设计代理，这些中心位于 9232 个普查区块内。研究结果对基于与不同化学和非化学应激因素的关联和联系，为基于内表型的弹性严重程度评分的发展提供了启示，这些因素已在人群健康背景下显示可调节心血管代谢疾病。
