Abstract
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.
Highlights
All humans are exposed to a diverse array of chemicals across their lifetimes
We describe the selected PRIME methods that fall into the following five research question categories: (1) Overall effect estimation: What is the overall effect of the mixture and what is the magnitude of association? (2) Toxic agent identification: Which exposures are associated with the outcome? What exposures are most important? (3) Pattern identification: Are there specific exposure patterns in the data? (4) A priori defined groups: What are the associations between an outcome and a priori defined groups of exposures? (5) Interactions and non-linearities: Are there interactions among exposures, and if so, what patterns of effect modification are identified? Is the exposure-response surface non-linear? Since most methods address more than one research question, the categorization below should not be considered exclusive
Research questions following Gibson et al, 2019 review: (1) Overall effect estimation: What is the overall effect of the mixture and what is the magnitude of association? (2) Toxic agent identification: Which congeners or chemicals are associated with the outcome? What congeners/chemicals are most important? (3) Pattern identification: Are there specific exposure patterns in the data? These can be managed with clustering and dimension reduction methods
Summary
All humans are exposed to a diverse array of chemicals across their lifetimes. Cumulatively, and at a given point in time, a person’s individual exposures constitute a mixture of compounds. Determining how mixtures of exposures impact human health is a longstanding priority of the National Institute of Environmental Health Sciences (NIEHS) and the environmental health research community. NIEHS supported workshops in 2011 and 2015, which provided insights into the limitations and opportunities for mixtures research and brought to light statistical methods considerations for data analyses [1,2]. Mixtures data used for analyses includes biomarkers of chemical exposures measured in biological and environmental samples as well as questionnaire-based information, air pollution monitoring, and geospatial data. It can be challenging to accurately analyze this data because variables are often highly correlated, include many missing observations (due to limits of detection for common chemical assays) or violate important assumptions of common statistical modeling strategies. Markov model can be used and what the ‘background’ levels of pollutants are measured across an urban area.
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