Exposomic modeling approaches for social and environmental determinants of health

Studies of human health have recently expanded to focus on the exposome paradigm, encompassing allexposures humans encounter from conception onward. The central theme of this work is to develop and test novel statistical methodologies that can address the challenges of the complex relationships between environmental exposures, socioeconomic distress, and health outcomes. However, source, measurement, and volume intricacies inherent to these data have constrained progression of statistical methods for key research questions.

In this work, we explore three approaches to characterizing community health and its potential impact on several types of disease outcomes. In the first approach, we implement a latent class model to socioeconomic and comorbidities data and explore these classifications as fixed effects in an ecological spatial model of COVID-19 cases and deaths in NYC during two time periods of the pandemic. In the second, we use a non-parametric Bayesian approach to form socio-economic and pollution cluster profiles across US counties. We then use these profiles to inform a Bayesian spatial model on breast cancer mortality for data from 2014. In the final approach, we utilize a latent network model traditionally used in psychometrics research to explore structural racism. Using information from five domains (employment, education, housing, health, and criminal justice), we identify new variable complexes to illustrate the complex the manifestations of structural racism at the census tract level in Pennsylvania.