LAUSR

Blood pressure is a leading risk factor for cardiovascular disease, influenced by chemical and non-chemical stressors. Exposure reduction strategies can potentially improve public health, but there are analytical challenges in developing quantitative models of health benefits, including the need for detailed multi-stressor exposure models, corresponding health evidence, and methods to simulate changes in exposure and resultant health benefits. These challenges are pronounced in low-income urban communities, where residents are often simultaneously exposed to numerous chemical and non-chemical stressors. For New Bedford (Massachusetts, USA), a low-income community near a Superfund site, we simulated geographically-resolved individual data, and applied previously published structural equation models developed from National Health and Nutrition Examination Survey (NHANES) data. These models simultaneously predict exposures to multiple chemicals (e.g., lead (Pb), cadmium (Cd), and polychlorinated biphenyls (PCBs)) and non-chemical factors (e.g., socioeconomic status), and determine their combined effects on blood pressure. We then modeled counterfactual scenarios reducing exposures and estimated the resulting changes in blood pressure distribution in the community. Results indicated small shifts in mean blood pressure and percentage of normotensive individuals with a reduction of Pb and/or PCB exposure. For example, a reduction in PCB to the lowest 10th percentile exposure in the NHANES resulted in a 2.4 mm Hg shift in systolic blood pressure (SBP), corresponding with 3% fewer individuals with SBP in the Stage 2 hypertension category [SBP ≥140]. Our model also emphasized the importance of the multi-stressor framework by simulating benefits of reductions in smoking rates, given positive associations with Pb and Cd but inverse associations with body mass index and blood pressure. This research demonstrates the ability to jointly consider chemical and non-chemical exposures and their impact on cardiovascular health, using approaches generalizable to other cumulative risk assessment applications.