Abstract A007: Using latent class modeling to characterize exposome impacts on health disparities

Abstract

Abstract Recent work suggests that air pollutants, toxic chemicals, and other environmental exposures negatively impact health in a synergistic way. We hypothesize that these environmental exposures will vary with socioeconomic status (SES), and urban versus rural location, and contribute to health disparities. In order to measure and understand this phenomenon, we propose a latent class mixture model of multi-pollutant exposures and SES. This builds on our previous development of an SES mixture model, which includes 13 indicators of socioeconomic advantage (eg profession, education) and disadvantage (eg unemployment, single-parent households, etc). Our model identifies levels of joint exposure to three classes of toxic pollutants: volatile organic compounds (VOC), particulate matter (PM) and heavy metals (HM). We use publicly available data from the American Community Survey and the EPA, including the National Air Toxics Assessment (NATA), for the 2,174 census tracts in North Carolina (NC). Model results indicate that mixtures of 2 levels of pollutants, 2 levels of socioeconomic advantage and 2 levels of socioeconomic disadvantage best fit data for NC. We present estimated class-membership for the state of NC, where 34.1% of the census tracts exhibit high disadvantage, 66.3% have low advantage, and 59.2% of census tracts have high mixtures of toxic pollutants. While frequently observed in urban or regional city/suburban areas, our model shows that rural areas are detected at the highest levels of pollution as well. Areas of high advantage are focused on urban/suburban and coastal areas, while disadvantage dominates the eastern half of the state as well as many urban areas. Areas with higher SES disadvantage had significantly higher black population density (p<0.001). Similarly, black population density was higher in areas with higher pollution (p<0.001). Our next focus is to incorporate spatial correlations into our models to better characterize the interactive nature of multi-pollutant and economic exposures, and apply to specific cohorts with cancer outcomes. Taken together, these extensions will be incorporated into a holistic, exposome modelling framework for estimating disparities in cancer survival. Citation Format: Alexandra Larsen, Viktoria Kolpacoff, Victoria Seewaldt, Terry Hyslop. Using latent class modeling to characterize exposome impacts on health disparities [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A007.