Project 3: Cumulative Risk and Geospatial Health Disparities Related to Chemical and Non-Chemical Stressor Exposures

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PROJECT SUMMARY Environmental health disparities are based on differential exposures to multiple chemical and non-chemical stressors among vulnerable populations, many related to individual sociodemographic factors, others related to the housing and neighborhoods in which they live. To determine the nature and degree of environmental health disparities at a population level, and to identify meaningful intervention strategies, we need information about multi-stressor exposure patterns and analytical techniques to characterize risk and evaluate disparities across entire geographical regions. Data limitations include lack of spatial resolution in available individual sociodemographic and housing data, or lack of multivariable attributes in spatially resolved data. In addition, although many investigators have discussed environmental health disparities directly or indirectly, few have applied appropriate analytical techniques to determine quantitatively whether disparities exist (and for whom). In Project 3 we propose to build cumulative risk models and characterize the nature and extent of environmental health disparities related to health outcomes across the life span, including infant anthropometric measures (e.g. birth weight), childhood growth trajectories, and adult cardiovascular mortality. In Aim 1 we will evaluate exposure disparities across Massachusetts (MA) using the first extensive geographical database relevant to environmental health disparities, integrating numerous public databases and applying novel tools like web scraping to obtain new data streams. In Aim 2 we will develop novel exposure constructs from our geospatial disparities database related to housing, neighborhood, material hardship, and sociodemographic stressors, evaluating them using information from Projects 1 and 2. Exposure constructs will be used to describe environmental health disparity patterns and will also be shared with Project 1 to incorporate into epidemiological analyses. To characterize disparities in health outcomes, in Aim 3 we will use novel techniques to construct simulated populations with individual-level stressor exposures and attributes in Chelsea and Dorchester, MA, applying methods that can be extrapolated to any US community. We will conduct a cumulative risk assessment by combining chemical and non-chemical stressor exposure models with the simulated synthetic population and the multi-stressor epidemiological models developed in Project 1. Finally we will develop and apply quantitative measures of environmental health disparities and determine the extent to which hypothetical interventions change these measures. In summary, Project 3 introduces a unique combination of cutting-edge geospatial data and analytical methods, approaches to simulate individual-level sociodemographics and exposures at high geographic resolution, and quantitative methods to explain and interpret environmental health disparities. These techniques will provide novel insight about environmental health disparities in MA, with methods that can be extrapolated to any geographical region.