Despite recently reported associations between air pollution and acute psychiatric outcomes, the association with depression onset has not, to our knowledge, been previously examined. We conducted a prospective cohort study among 41,844 women in the Nurses’ Health Study, in the United States. The women had an average age of 66.6 (standard deviation, 7.6) years, were depression-free in 1996, and were followed through 2008. May–September ozone exposures were predicted by interpolating concentrations from the 5 nearest monitors. One-, 2-, and 5-year average concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM_2.5) were predicted at each participant's residence using a spatiotemporal model. We defined depression as report of doctor's diagnosis or use of antidepressant medication. We estimated adjusted hazard ratios with time-varying Cox models. Hazard ratios for both pollutants were elevated (per 10-parts-per-billion increase in ozone, hazard ratio (HR) = 1.06; 95% confidence interval (CI): 1.00, 1.12; per 10-μg/m³ increase in 1-year PM_2.5, HR = 1.08; 95% CI: 0.97, 1.20). Associations were stronger when only antidepressant use was used to define cases (for ozone, HR = 1.08; 95% CI: 1.02, 1.14; for PM_2.5, HR = 1.12; 95% CI: 1.00, 1.25). To our knowledge, these results represent the first identification of a possible association between both long-term ozone and PM_2.5 exposure and depression onset. Although the stronger association specifically with antidepressant use may reflect that this endpoint better captures the onset time and milder cases, our findings should be interpreted with caution.

Numerous methods exist to analyze complex environmental mixtures in health studies. As an illustration of the different uses of mixture methods, we employed methods geared toward distinct research questions concerning persistent organic chemicals (POPs) as a mixture and leukocyte telomere length (LTL) as an outcome. With information on 18 POPs and LTL among 1,003 U.S. adults (NHANES, 2001–2002), we used unsupervised methods including clustering to identify profiles of similarly exposed participants, and Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) to identify common exposure patterns. We also employed supervised learning techniques, including penalized, weighted quantile sum (WQS), and Bayesian kernel machine (BKMR) regressions, to identify potentially toxic agents, and characterize nonlinear associations, interactions, and the overall mixture effect. Clustering separated participants into high, medium, and low POP exposure groups; longer log-LTL was found among those with high exposure. The first PCA component represented overall POP exposure and was positively associated with log-LTL. Two EFA factors, one representing furans and the other PCBs 126 and 118, were positively associated with log-LTL. Penalized regression methods selected three congeners in common (PCB 126, PCB 118, and furan 2,3,4,7,8-pncdf) as potentially toxic agents. WQS found a positive overall effect of the POP mixture and identified six POPs as potentially toxic agents (furans 1,2,3,4,6,7,8-hxcdf, 2,3,4,7,8-pncdf, and 1,2,3,6,7,8-hxcdf, and PCBs 99, 126, 169). BKMR found a positive linear association with furan 2,3,4,7,8-pncdf, suggestive evidence of linear associations with PCBs 126 and 169, and a positive overall effect of the mixture, but no interactions among congeners. Using different methods, we identified patterns of POP exposure, potentially toxic agents, the absence of interaction, and estimated the overall mixture effect. These applications and results may serve as a guide for mixture method selection based on specific research questions.

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM _2.5 ) concentrations differ from relatively smoothly varying total PM _2.5 . Thus, we introduced a conceptual model for computing long-term wildfire PM _2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM _2.5 exposure based on intermittent and extreme daily wildfire PM _2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM _2.5 > 5 μg/m ³ ; 2) days with non-zero wildfire PM _2.5 ; 3) mean wildfire PM _2.5 during peak exposure week; 4) smoke waves (≥2 consecutive days with >15 μg/m ³ wildfire PM _2.5 ); and 5) mean annual wildfire PM _2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM _2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM _2.5 exposure for several racial/ethnic groups.