Integration of psychosocial and chemical stressors in risk assessment

Cumulative risk assessment for combinations of environmental and psychosocial stressors: A systematic review

With the growing awareness of stressors, cumulative risk assessment (CRA) has been proposed as a potential method to evaluate possible additive and synergistic effects of multiple stressors on human health, thus informing environmental regulation and protecting public health. However, CRA is still in its exploratory stage due to the lack of generally accepted quantitative approaches. It is an ideal time to summarize the existing progress to guide future research. To this end, a systematic review of the literature on CRA issues dealing with combinations of environmental and psychosocial stressors was conducted in this study. Using typology and bibliometric analysis, the body of knowledge, hot topics, and research gaps in this field were characterized. It was found that research topics and objectives mainly focus on qualitative analysis and community settings; more attention should be paid to the development of quantitative approaches and the inclusion of occupational settings. Further, the roles of air pollution and vulnerability factors in CRA have attracted the most attention. This study concludes with views on future prospects to promote theoretical and practical development in this field; specifically, CRA is a multifaceted topic that requires substantial collaborations with various stakeholders and substantial knowledge from multidisciplinary fields. This study presents an overall review as well as research directions worth investigating in this field, which provides a historical reference for future study. Integr Environ Assess Manag 2024;20:602-615. © 2023 SETAC.

Generation of the Chemical and Social Stressors Integration Technique (CASS-IT) to identify areas of holistic public health concern: An application to North Carolina

Due to structural racism and income inequality, exposure to environmental chemicals is tightly linked to socioeconomic factors. In addition, exposure to psychosocial stressors, such as racial discrimination, as well as having limited resources, can increase susceptibility to environmentally induced disease. Yet, studies are often conducted separately in fields of social science and environmental science, reducing the potential for holistic risk estimates. To tackle this gap, we developed the Chemical and Social Stressors Integration Technique (CASS-IT) to integrate environmental chemical and social stressor datasets. The CASS-IT provides a framework to identify distinct geographic areas based on combinations of environmental chemical exposure, social vulnerability, and access to resources. It incorporates two data dimension reduction tools: k-means clustering and latent profile analysis. Here, the CASS-IT was applied to North Carolina (NC) as a case study. Environmental chemical data included toxic metals - arsenic, manganese, and lead - in private drinking well water. Social stressor data were captured by the CDC's social vulnerability index's four domains: socioeconomic status, household composition and disability, minority status and language, and housing type and transportation. Data on resources were derived from Federal Emergency Management Agency (FEMA's) Resilience and Analysis Planning Tool, which generated measures of health resources, social resources, and information resources. The results highlighted 31 NC counties where exposure to both toxic metals and social stressors are elevated, and health resources are minimal; these are counties in which environmental justice is of utmost concern. A census-tract level analysis was also conducted to demonstrate the utility of CASS-IT at different geographical scales. The tract-level analysis highlighted specific tracts within counties of concern that are particularly high priority. In future research, the CASS-IT can be used to analyze United States-wide environmental datasets providing guidance for targeted public health interventions and reducing environmental disparities.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.