Using latent class modeling to jointly characterize economic stress and multi-pollutant exposure.

Background:Work is needed to better understand how joint exposure to environmental and economic factors influence cancer. We hypothesize that environmental exposures vary with socioeconomic status (SES) and urban/rural locations, and that areas with minority populations coincide with high economic disadvantage and pollution. Methods:To model joint exposure to pollution and SES, we develop a latent class mixture model (LCMM) with three latent variables (SES-Advantage, SES-Disadvantage and Air Pollution) and we compare the LCMM fit to K-means clustering. We ran an ANOVA to test for high exposure levels in non-Hispanic black populations. The analysis is at the census tract level for the entire state of North Carolina (NC). Results:The LCMM was a better and more nuanced fit to the data than K-means clustering. Our LCMM had two sub-levels (low, high) within each latent class. The worst levels of exposure (high SES disadvantage, low SES advantage, high pollution) are found in 22% of the census tracts, while the best levels (low SES disadvantage, high SES advantage, low pollution) are found in 5.7%. Overall, 34.1% of the census tracts exhibit high disadvantage, 66.3% have low advantage and 59.2% have high mixtures of toxic pollutants. Areas with higher SES disadvantage had significantly higher non-Hispanic black population density (NHBPD; p<0.001) and NHBPDwas higher in areas with higher pollution (p<0.001). Conclusions:Joint exposure to air toxins and SES varies with rural/urban location and coincides with minority populations. Impact:Our model can be extended to provide a holistic modeling framework for estimating disparities in cancer survival.