Ambient Air Pollution, Weather, and Placental Abruption (APWA)

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ABSTRACT Placental abruption results in hemorrhage, ischemia, and fetal hypoxia, placing a tremendous health burden on both the mother and the newborn. Efforts to understand the etiology of this devastating obstetrical complication have been disap- pointing. This project will delineate environmentally-associated pathways to abruption and determine the impact of pol- lutant triggers that are implicated in acute versus chronic placental abruption. Given that one-fourth of all abruption cases have an acute etiology and 15% of abruptions may recur in future pregnancies, the role of environmental triggers is a critically important, yet unexplored, opportunity to understand the pathophysiology of this enigmatic obstetrical compli- cation. The project will capitalize on high resolution exposure and health outcome data as it aims to develop a birth linkage database that will include hospital discharges linked to both stillbirths and live births-infant deaths to resident women in California, Florida, Massachusetts, Michigan, and South Carolina (estimated 16 million births, including 155,000 abruption cases) between 2000–2016. For each pregnancy we will assign average daily ambient exposure to fine particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5), its constituents (elemental carbon and organic carbon, sulfate, nitrate, and ammonium), as well as gaseous pollutants (nitrogen dioxide and ozone), using spatiotemporally resolved models that predict exposure for each residential location. We will also assign every residence with average daily temperature, humid- ity, dew point, heat waves, and atmospheric air pressure. The project will focus on disentangling the relative contributions of ambient air pollution and weather-related conditions on acute abruption through a bi-directional, time-stratified case- crossover design, and those of abruptions with chronic underpinnings using a cohort design. We will apply distributed lag linear and non-linear models to identify critical windows of exposure, Bayesian Kernel Machine Regression to characterize associations based on multi-pollutant exposures, and causal interaction-mediation decomposition analyses through ischemic placental disease (preeclampsia, fetal growth disturbances). We will consider individual– and neighborhood–level con- founders derived from residential census tracts. All associations will be corrected for simultaneous exposure and outcome misclassification, as well as for exposure measurement error owing to maternal residential mobility through a regression calibration approach. The ubiquitous nature of air pollution and weather exposures and their potential impact on adverse perinatal outcomes, as well as the preliminary data supporting the associations, presents unprecedented opportunities to address implications of the adverse impact of air pollution and weather-related exposures on placental abruption and related obstetrical complications. This project aligns with 2 major critical areas of research–Co-exposures, and Data Sci- ence and Big Data–outlined in the 2018-2023 strategic goals of NIH-National Institute of Environmental Health Sciences.