Air Pollution and Pregnancy Complications in Complex Urban Environments: Risks, Heterogeneity, and Mechanisms

妊娠并发症(例如妊娠期糖尿病[GDM]、妊娠期高血压[GHTN]和妊娠前期 子痫和子痫[PE/E]是围产儿发病率和死亡率的主要原因。研究已经检查了 空气污染与妊娠并发症的关系，但有重大限制，包括1)依赖于 出生证明或账单/索赔数据，其中信息可能丢失或其有效性值得怀疑，以确定 结果和合并症；2)有限地考虑影响、混淆和/或其他 环境因素；3)因缺乏住址历史而导致的空气污染暴露错误分类；4) 关注单个空气污染物，而不是空气污染物混合物；5)缺乏对 暴露时间和地点、产妇状况、其他环境因素造成的空气污染风险，以及 结果的亚类；6)缺乏对将孕产妇共病与 结果。我们建议进行一项为期4年的研究，以解决这些限制并加深对以下影响的了解 空气污染物混合物对妊娠并发症的影响。我们将利用最先进的时空空气污染 建模和新的统计方法，用来检查个人和复合曝光曲线 年约400,000例单胎妊娠的纵向(怀孕前至产后)妊娠队列 2008-2018年，在怀孕20周后导致活产或胎儿死亡，并已有预期记录 Kaiser电子健康档案(EHR)中的高质量临床数据和住址 南加州在南加州8个县的永久会员。主要结果是GHTN， PE/E，GDM。我们将估计个人水平的空气污染物暴露(颗粒物及其组成 和使用复杂时空模型的交通相关污染物)、天气(气温、相对湿度 湿度、压力)和建筑环境措施(绿化、可步行、噪音、社区资源) 根据预期记录的母亲地址。我们将检查的协变量包括母体 合并症；既往妊娠史和结局；个人和相关的社会经济状况 (S)指标；孕期就业和职业分类；自我报告的体力活动和 抽烟。将使用互补的统计方法来评估暴露在混合空气中的影响 污染物，同时考虑到天气、建筑环境和SES的共同暴露。我们将检查效果 SES、母体因素和其他环境暴露的修饰，以及 母体因素对空气污染和结局之间的关系。我们将阐明怀孕的风险 暴露于空气污染的并发症、因自闭症引起的风险的异质性、母亲的状况以及其他 环境因素、潜在的潜在机制、易感亚群和时间窗口 敏感度。确定可改变的环境风险因素和高危亚群可能有助于 设计有针对性的干预措施，以减少这些风险和相关的不良孕产妇和胎儿结局。

Pregnancy complications (e.g. gestational diabetes [GDM], gestational hypertension [GHTN], and pre- eclampsia & eclampsia [PE/E] are major causes of perinatal morbidity and mortality. Studies have examined associations of air pollution with pregnancy complications, but have major limitations, including 1) reliance on birth certificates or billing/claims data where information may be missing or of questionable validity to ascertain outcomes and co-morbidities; 2) limited consideration of effect confounding and/or modification by other environmental factors; 3) air pollution exposure misclassification due to lack of residential address history; 4) focus on individual air pollutants rather than air pollutant mixtures; 5) lack of focus on the heterogeneity of the risk from air pollution by time and place of exposures, maternal conditions, other environmental factors, and sub-types of outcomes; 6) lack of understanding of the mediation pathways linking maternal co-morbidity with outcomes. We propose a 4-year study to address these limitations and advance knowledge of the impact of air pollutant mixture on pregnancy complications. We will leverage state-of-the-art spatiotemporal air pollution modeling and novel statistical methods that examine both individual and composite exposure profiles with a longitudinal (pre-conception through postpartum) pregnancy cohort of ~400,000 singleton pregnancies in 2008-2018 that result in a live birth or fetal death after 20 weeks gestation that have prospectively-recorded high quality clinical data and residential addresses from the electronic health record (EHR) of Kaiser Permanente Southern California members in 8 southern California counties. Primary outcomes are GHTN, PE/E, GDM. We will estimate individual-level air pollutant exposures (particulate matter and its composition and traffic-related pollutants using sophisticated spatiotemporal models), weather (air temperature, relative humidity, pressure), and built environment measures (greenness, walkability, noise, neighborhood resources) based on prospectively-recorded maternal addresses. Covariates we will examine include maternal comorbidities; history of previous pregnancies and outcomes; individual and contextual socioeconomic status (SES) indicators; employment during pregnancy and job classification; self-reported physical activity and smoking. Complementary statistical methods will be used to evaluate effects of exposure to a mixture of air pollutants while accounting for co-exposure to weather, built environment, and SES. We will examine effect modifications by SES, maternal factors, and other environmental exposures, and the potential mediating role of maternal factors on associations between air pollution and the outcomes. We will elucidate risk of pregnancy complications from air pollution exposure, heterogeneity of risk due to SES, maternal conditions, and other environmental factors, potential underlying mechanisms, susceptible sub-populations, and time windows of susceptibility. Identification of modifiable environmental risk factors and high-risk subpopulation may help to design targeted interventions to reduce these risks and associated adverse maternal and fetal outcomes.