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Quantifying Associations between Environmental and Social Stressors
Citation:
Huang, H., R. Tornero-Velez, AND T. Barzyk. Quantifying Associations between Environmental and Social Stressors. 2016 International Society of Environmental Epidemiology, Rome, September 01 - 04, 2016.
Impact/Purpose:
Presented at the 2016 International Society of Environmental Epidemiology in Rome, Italy.
Description:
Introduction: Association rule mining (ARM) has been widely used to identify associations between various entities in many fields. Although some studies have utilized it to analyze the relationship between chemicals and human effects, fewer have used this technique to identify and quantify associations among environmental and social stressors. Methods: We created socio-demographic variables based on U. S. Census tract-level income, race/ethnicity population percentage, education attainment level, and age information from 2010-2014, 5-year summary files in the American Community Survey database, and generated chemical variables by utilizing the 2011 National-Scale Air Toxics Assessment (NATA) tract-level air pollutant exposure concentration data. We then applied ARM to quantify and visualize associations between these variables. Boostrapping, random sampling with replacement, was used to estimate the 95% confidence interval for certain statistical measures of the resultant rules. Results: Tracts with an average population age of 40 to 50 years old, a low percentage of racial/ethnic minorities (0-10%), and moderate income levels (20 ~ 40% of the residents have income levels lower than poverty line) were more likely to have lower chemical exposure concentrations (in the 1st quartile). Neighborhoods with a high percentage of racial/ethnic minorities (70 ~100% of the tract) and populations (70 ~ 100% of the tract) with incomes lower than the predefined poverty line tended to have higher chemical exposure concentrations (in the 4th quartile), especially for diesel PM, 1, 3-butadiene, and toluene. Interestingly, tracts with low-education population percentages (10 ~ 30% of the residents have an associate degree or above) were inclined to have low chemical exposure concentration (in the 1st quartile). Conclusions: Unsupervised data mining methods such as ARM can be used to address environmental inequalities and social disparities. Co-occurrence of multiple environmental and social stressors can be informative for public health decision making.
