Relation of Swine Industrial Livestock Operation Air Emissions Exposures to Sleep Duration and Time Outdoors in Residential Host Communities

Residents of communities hosting swine industrial livestock operations (ILOs) in North Carolina are exposed to mixtures of air pollutants originating from animal confinements, waste lagoons, and waste spray-field systems. To add to the understanding of swine ILO impacts on nearby community residents, I estimated the impact of swine ILO air emissions on sleep and time outdoors. These outcomes have not been formally assessed using epidemiologic methods, but are important components of quality-of-life, have implications for health and disease, and have been raised as concerns by community members. Acute exposure effects on sleep and time outdoors were estimated by applying discrete-time hazard models to data collected in the Community Health Effects of Industrial Hog Operations (CHEIHO) study. CHEIHO was a community-based, participatory research study that coupled continuous monitoring of pollutant plume markers with twice-daily odor and activity diaries. Dynamic Bayesian network models were used to estimate the total chronic effect of exposures accounting for potential feedback between subsequent exposures and outcomes. Detectible swine ILO pollutants at night was associated with an average sleep deficit of approximately 15 minutes. Exposure to outdoor odors was associated with decreased odds of being outdoors during the following hour (OR 0.62, 95% interval 0.44 to 0.89). Dynamic models estimated that the total effects of exposures exceeded the expected total effect calculated by summing individual acute effects, suggesting the importance of a feedback mechanism. The results demonstrate measurable and important impacts of ILO air emissions on sleep and time outdoors among those living nearby. The modeling approaches used were robust to bias from factors that remained constant for each participant over the course of the study and to factors that varied with the time-of-day or the weather, suggesting a causal effect. Policy interventions to reduce community exposures to swine ILO emissions from lagoon-and-sprayfield systems could have positive impacts on public health in rural North Carolina communities.Doctor of Philosoph

Implementing machine learning methods with complex survey data: Lessons learned on the impacts of accounting sampling weights in gradient boosting.

Despite the prominent use of complex survey data and the growing popularity of machine learning methods in epidemiologic research, few machine learning software implementations offer options for handling complex samples. A major challenge impeding the broader incorporation of machine learning into epidemiologic research is incomplete guidance for analyzing complex survey data, including the importance of sampling weights for valid prediction in target populations. Using data from 15, 820 participants in the 1988-1994 National Health and Nutrition Examination Survey cohort, we determined whether ignoring weights in gradient boosting models of all-cause mortality affected prediction, as measured by the F1 score and corresponding 95% confidence intervals. In simulations, we additionally assessed the impact of sample size, weight variability, predictor strength, and model dimensionality. In the National Health and Nutrition Examination Survey data, unweighted model performance was inflated compared to the weighted model (F1 score 81.9% [95% confidence interval: 81.2%, 82.7%] vs 77.4% [95% confidence interval: 76.1%, 78.6%]). However, the error was mitigated if the F1 score was subsequently recalculated with observed outcomes from the weighted dataset (F1: 77.0%; 95% confidence interval: 75.7%, 78.4%). In simulations, this finding held in the largest sample size (N = 10,000) under all analytic conditions assessed. For sample sizes <5,000, sampling weights had little impact in simulations that more closely resembled a simple random sample (low weight variability) or in models with strong predictors, but findings were inconsistent under other analytic scenarios. Failing to account for sampling weights in gradient boosting models may limit generalizability for data from complex surveys, dependent on sample size and other analytic properties. In the absence of software for configuring weighted algorithms, post-hoc re-calculations of unweighted model performance using weighted observed outcomes may more accurately reflect model prediction in target populations than ignoring weights entirely

Association of distance to swine concentrated animal feeding operations with immune-mediated diseases: An exploratory gene-environment study

Background: Concentrated animal feeding operations (CAFOs) are a source of environmental pollution and have been associated with a variety of health outcomes. Immune-mediated diseases (IMD) are characterized by dysregulation of the normal immune response and, while they may be affected by gene and environmental factors, their association with living in proximity to a CAFO is unknown. Objectives: We explored gene, environment, and gene-environment (GxE) relationships between IMD, CAFOs, and single nucleotide polymorphisms (SNPs) of prototypical xenobiotic response genes AHR, ARNT, and AHRR and prototypical immune response gene PTPN22. Methods: The exposure analysis cohort consisted of 6,464 participants who completed the Personalized Environment and Genes Study Health and Exposure Survey and a subset of 1,541 participants who were genotyped. We assessed the association between participants’ residential proximity to a CAFO in gene, environment, and GxE models. We recombined individual associations in a transethnic model using METAL meta-analysis. Results: In White participants, ARNT SNP rs11204735 was associated with autoimmune diseases and rheumatoid arthritis (RA), and ARNT SNP rs1889740 was associated with RA. In a transethnic genetic analysis, ARNT SNPs rs11204735 and rs1889740 and PTPN22 SNP rs2476601 were associated with autoimmune diseases and RA. In participants living closer than one mile to a CAFO, the log-distance to a CAFO was associated with autoimmune diseases and RA. In a GxE interaction model, White participants with ARNT SNPs rs11204735 and rs1889740 living closer than eight miles to a CAFO had increased odds of RA and autoimmune diseases, respectively. The transethnic model revealed similar GxE interactions. Conclusions: Our results suggest increased risk of autoimmune diseases and RA in those living in proximity to a CAFO and a potential role of the AHR-ARNT pathway in conferring risk. We also report the first association of ARNT SNPs rs11204735 and rs1889740 with RA. Our findings, if confirmed, could allow for novel genetically-targeted or other preventive approaches for certain IMD