Linking mixtures of air pollution exposures and preterm birth with a self-organizing map.

backgroundExposure to nitrogen dioxide (NO

objectivesTo implement a novel epidemiologic machine learning framework for linking high-dimensional mixture exposures and health outcomes, with an application in preterm birth and residential exposure to PM

methodsData comes from a retrospective cohort of 44,874 individuals living in Utah who gave birth for the first time between 2013 and 2016. Fine-scale air pollution estimates were used to create a high-dimensional self-organizing map (a type of unsupervised neural network) of exposure mixtures. We used cluster analysis to group similar weekly exposure mixtures. Pregnancies were linked to the clusters, and Bayesian mixed-effects logistic regression was used to estimate odds ratios at each gestational week with false discovery rate (FDR) multiple comparison adjustments.

resultsExposure to certain mixtures at critical windows was associated with a higher risk of preterm birth. In particular, maternal exposure to Cluster 10 (a high O SIGNIFICANCE: Using a novel machine learning approach, we identified several patterns of exposure to mixtures, primarily composed of O3 and PM2.5, which may be associated with preterm birth at critical windows. The proposed framework reduces complexity while preserving time-varying exposures. IMPACT: Exposure to air pollution mixtures at critical times during pregnancy may have synergistic effects on the risk of preterm birth, and few studies have examined these relationships using neural networks. We propose an epidemiologic machine learning framework for dimension reduction, which assigns exposure types to individuals over time and allows for estimation of odds ratios of the outcome of interest. Using this method, we describe patterns of exposure to mixtures of NO