Preprints

Environmental epidemiology has traditionally examined single exposure one at a time. Advances in exposure assessment and statistical methods now enable studies of multiple exposures and their combined health impacts. Bayesian Kernel Machine Regression (BKMR) is a widely used approach to flexibly estimates joint, nonlinear effects of multiple exposures. But BMKR is computationally intensive for large datasets, as repeated kernel inversion in Markov chain Monte Carlo (MCMC) can be time-consuming and often infeasible in practice. To address this issue, we propose using supervised random Fourier basis functions to replace the Gaussian process random effects. This re-frames the kernel machine regression into a linear mixed-effect model that facilitates computationally efficient estimation and prediction. Bayesian inference is conducted using MCMC with Hamiltonian Monte Carlo algorithms. Simulation studies demonstrate that our method yields results comparable to BKMR while significantly reduces the computation time. Our approach outperforms BKMR when the exposure-response surface has stronger dependency and when using predictive process as an alternative approximation method. Finally, we applied this approach to analyze over 270,000 birth records, examining associations between multiple ambient air pollutants and birthweight in Georgia.

Exposure to high ambient temperature is responsible for more than 11,000 deaths and over 230,000 disability-adjusted life-years in the United States each year. However, which individuals and populations are most at risk, and why, is still not well understood. In 2015, a subset of “Z” diagnosis codes (or “Z-codes”) were introduced as a standardized option for healthcare providers to document the social needs and conditions of their patients. To assess heat-related risk across social determinants of health (SDoH), we leverage these codes using a dataset of patient-level emergency department (ED) visits from seven US states. Using a bi-directional, time-stratified, case-crossover design and conditional logistic regression, we compared hospital encounters for seven different health outcomes with SDoH Z-codes at discharge to a reference group matched on age, sex, race, ethnicity, year and hospital. We investigated the following Z-code domains: inadequate housing (Z59.0, Z59.1), poverty-related (Z56.0, Z59.5-Z59.7), living alone (Z60.2), institutional living (Z65.1, Z59.3), and other problems with the social environment (other Z60 sub-codes). We calculated cumulative odds ratios (ORs) for a 3-day lag change in temperature from the 95th to 50th percentile, using ZIP code-specific temperature percentiles. Among 60,557,958 ED visits with available demographic and meteorological data, 461,468 (0.8%) included a SDoH Z-code. Across temperature metrics and outcomes, patients with SDoH Z-codes consistently showed higher associations with heat than the matched reference group without SDoH Z-codes. The largest difference was for acute kidney injury, with a ratio of ORs of 1.21 (1.10,1.33) for daily mean temperature. Notable subgroup findings included elevated kidney-related risks in patients with inadequate housing or poverty-related SDoH, increased mental health risks among those living alone, and elevated cardiovascular risks in people with other problems related to the social environment.

High temperatures are associated with adverse respiratory health outcomes and increases in ambient air pollution. Limited research has quantified air pollution's mediating role in the relationship between temperature and respiratory morbidity, such as emergency department (ED) visits. In this study, we conducted a causal mediation analysis to decompose the total effect of daily temperature on respiratory ED visits in Los Angeles from 2005 to 2016. We focused on ambient ozone as a mediator because its precursors and formation are directly driven by sunlight and temperature. We estimated natural direct, indirect, and total effects on the relative risk scale across deciles of temperature exposure compared to the median. We utilized Bayesian additive regression trees (BART) to flexibly characterize the nonlinear relationship between temperature and ozone and quantified uncertainty via posterior prediction and the Bayesian bootstrap. Our results showed that ozone partially mediated the association between high temperatures and respiratory ED visits, particularly at moderately high temperatures. We also validated our modeling approach through simulation studies. This study extends the existing literature by considering acute respiratory morbidity and employing a flexible modeling approach, offering new insights into the mechanisms underlying temperature-related health risks.

With advances in high-resolution mass spectrometry technologies, metabolomics data are increasingly used to investigate biological mechanisms underlying associations between exposures and health outcomes in clinical and epidemiological studies. Mediation analysis is a powerful framework for investigating a hypothesized causal chain and when applied to metabolomics data, a large number of correlated metabolites belonging to interconnected metabolic pathways need to be considered as mediators. To identify metabolic pathways as active mediators, existing approaches typically focus on first identifying individual metabolites as active mediators, followed by post-hoc metabolic pathway determination. These multi-stage procedures make statistical inference challenging. We propose a Bayesian biological pathway-guided mediation analysis that aims to jointly analyze all metabolites together, identify metabolic pathways directly, and estimate metabolic pathway-specific indirect effects. This is accomplished by incorporating existing biological knowledge of metabolic pathways to account for correlations among mediators, along with variable selection and dimension reduction techniques. Advantages of the proposed method is demonstrated in extensive simulation studies with real-word metabolic pathway structure. We apply the proposed method to two studies examining the role of metabolism in mediating (1) the effect of Roux-en-Y gastric bypass on glycemic control, and (2) the effect of prenatal exposure to per- and polyfluoroalkyl substances (PFAS) on gestational age at birth. Our analyses confirm metabolic pathways previously identified and provide additional uncertainty quantification for the mediation effects.