Community-engaged modeling of exposures to chemical and non-chemical stressors in a low-income community near a Superfund site

摘要

Cumulative risk assessment requires models of exposures to chemical and non-chemical stressors at sufficient geographic and demographic resolution to accurately identify high-risk subpopulations. Many non-chemical stressors are of considerable interest to community stakeholders, above and beyond their connections to chemical-oriented risk assessments, but relevant exposure models have not been leveraged for these purposes. In this study, we developed a methodology to provide high resolution exposure estimates, constructing detailed synthetic demographic microdata, and using these data as predictors in regression models for multiple stressors in New Bedford, Massachusetts, a low income community located near a Superfund site. Chemical exposure regression models utilized biomarker measurements from a cohort study conducted in the New Bedford area, and non-chemical stressor models leveraged data from the Behavioral Risk Factor Surveillance System (BRFSS). Through a series of meetings with community partners, behavior and health questions of interest from the BRFSS were identified, including fruit and vegetable consumption, obesity, and diabetes. Paralleling the structure of our chemical stressor models, we constructed multivariable regression models of the probability of eating fruits and vegetables, body mass index (BMI), and diabetes prevalence. Regression models were applied to the synthetic microdata and results mapped across the community to identify census tracts at high risk for these behaviors and outcomes in adults. Comparisons of geographic patterns of these stressors of interest to community partners with geographic patterns of chemical stressors identified areas of common emphasis. The maps and modeled demographic patterns will be used by community partners for city planning and policy activities such as parent support programs for people living with chronic diseases, locating new farmers markets, expansion of the fresh food voucher program, and prioritizing selection of existing brownfields to be converted to community gardens. Our study emphasizes the value of multi-stressor exposure modeling in the context of cumulative risk assessment, the insights provided by community engagement, and the opportunity for innovative exposure modeling approaches to connect with broader community concerns.