Abstract
BackgroundEvaluating environmental health risks in communities requires models characterizing geographic and demographic patterns of exposure to multiple stressors. These exposure models can be constructed from multivariable regression analyses using individual-level predictors (microdata), but these microdata are not typically available with sufficient geographic resolution for community risk analyses given privacy concerns.MethodsWe developed synthetic geographically-resolved microdata for a low-income community (New Bedford, Massachusetts) facing multiple environmental stressors. We first applied probabilistic reweighting using simulated annealing to data from the 2006–2010 American Community Survey, combining 9,135 microdata samples from the New Bedford area with census tract-level constraints for individual and household characteristics. We then evaluated the synthetic microdata using goodness-of-fit tests and by examining spatial patterns of microdata fields not used as constraints. As a demonstration, we developed a multivariable regression model predicting smoking behavior as a function of individual-level microdata fields using New Bedford-specific data from the 2006–2010 Behavioral Risk Factor Surveillance System, linking this model with the synthetic microdata to predict demographic and geographic smoking patterns in New Bedford.ResultsOur simulation produced microdata representing all 94,944 individuals living in New Bedford in 2006–2010. Variables in the synthetic population matched the constraints well at the census tract level (e.g., ancestry, gender, age, education, household income) and reproduced the census-derived spatial patterns of non-constraint microdata. Smoking in New Bedford was significantly associated with numerous demographic variables found in the microdata, with estimated tract-level smoking rates varying from 20% (95% CI: 17%, 22%) to 37% (95% CI: 30%, 45%).ConclusionsWe used simulation methods to create geographically-resolved individual-level microdata that can be used in community-wide exposure and risk assessment studies. This approach provides insights regarding community-scale exposure and vulnerability patterns, valuable in settings where policy can be informed by characterization of multi-stressor exposures and health risks at high resolution.
Highlights
Evaluation of environmental health risks in communities has increasingly focused on the combined risks to health from multiple agents or stressors, defined by the United States Environmental Protection Agency (US EPA) as ‘‘cumulative risk assessment’’ [1]
We develop synthetic microdata for a low-income community living near a Superfund site (New Bedford, Massachusetts), where these synthetic microdata include multivariable individual-level attributes and spatially resolved geographic assignment
We focused on the census tract as the unit of analysis, because more candidate constraints were available at the census tract level than at the block group level
Summary
Evaluation of environmental health risks in communities has increasingly focused on the combined risks to health from multiple agents or stressors, defined by the United States Environmental Protection Agency (US EPA) as ‘‘cumulative risk assessment’’ [1] This is in part because of the growing recognition that background exposures and susceptibility characteristics need to be considered in developing appropriate dose-response models, and relates to environmental justice concerns and general decision relevance [2,3]. This cumulative risk framework theoretically considers multiple chemical exposures, and the effects of social stressors and other factors of the built and social environment that operate at either individual or community levels [4]. These exposure models can be constructed from multivariable regression analyses using individual-level predictors (microdata), but these microdata are not typically available with sufficient geographic resolution for community risk analyses given privacy concerns
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