Racism as a public health issue in environmental health disparities and environmental justice: working toward solutions.

BACKGROUND: Environmental health research in the US has shown that racial and ethnic minorities and members of low-socioeconomic groups, are disproportionately burdened by harmful environmental exposures, in their homes, workplace, and neighborhood environments that impact their overall health and well-being. Systemic racism is a fundamental cause of these disproportionate exposures and associated health effects. To invigorate and inform current efforts on environmental justice and to raise awareness of environmental racism, the National Institute of Environmental Health Sciences (NIEHS) hosted a workshop where community leaders, academic researchers, and NIEHS staff shared perspectives and discussed ways to inform future work to address health disparities. OBJECTIVES: To share best practices learned and experienced in partnerships between academic researchers and communities that are addressing environmental racism across the US; and to outline critical needs and future actions for NIEHS, other federal agencies, and anyone who is interested in conducting or funding research that addresses environmental racism and advances health equity for all communities. DISCUSSION: Through this workshop with community leaders and researchers funded by NIEHS, we learned that partnerships between academics and communities hold great promise for addressing environmental racism; however, there are still profound obstacles. To overcome these barriers, translation of research into plain language and health-protective interventions is needed. Structural changes are also needed in current funding mechanisms and training programs across federal agencies. We also learned the importance of leveraging advances in technology to develop creative solutions that can protect public health.

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.