Correlates of cancer prevalence across census tracts in the United States: A Bayesian machine learning approach.

ABSTRACT: Preventive measures, health behaviors, environmental exposures, and sociodemographic characteristics affect individual-level cancer risks. It is unclear how they influence neighborhood-level cancer risks. We developed a large-scale neighborhood health dataset for 72,337 census tracts in the United States by combining data from three publicly available sources. We used Bayesian additive regression trees to identify the most important predictors of tract-level cancer prevalence among adults (age ≥18 years), and examined their impact on cancer prevalence using partial dependence plots. The five most important census tract-level correlates of cancer prevalence were the proportion of population who were aged 65 years and older, had routine checkup and were non-Hispanic White, the proportion of

SUBMITTER: Niu L 

PROVIDER: S-EPMC12906668 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature