Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.

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.

Developing an Environmental Health Sciences COVID-19 Research Agenda: Results from the NIEHS Disaster Research Response (DR2) Work Group's Modified Delphi Method.

Leveraging the community of practice recently established through the U.S. National Institute of Environmental Health Sciences (NIEHS) Disaster Research Response (DR2) working group, we used a modified Delphi method to identify and prioritize environmental health sciences Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and associated Coronavirus Disease 2019 (COVID-19) research questions. Twenty-six individuals with broad expertise across a variety of environmental health sciences subdisciplines were selected to participate among 45 self-nominees. In Round 1, panelists submitted research questions and brief justifications. In Round 2, panelists rated the priority of each question on a nine-point Likert scale. Responses were trichotomized into priority categories (low priority; medium priority; and high priority). A research question was determined to meet consensus if at least 69.2% of panelists rated it within the same priority category. Research needs that did not meet consensus in round 2 were redistributed for re-rating. Fourteen questions met consensus as high priority in round 2, and an additional 14 questions met consensus as high priority in round 3. We discuss the impact and limitations of using this approach to identify and prioritize research questions in the context of a disaster response.