ArticleSpatial and spatio-temporal epidemiology2022
Correlates of cancer prevalence across census tracts in the United States: A Bayesian machine learning approach.
Article in Spatial and spatio-temporal epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Ranking factors across multiple domains in predicting adolescent mental health: a Bayesian machine learning approach.Child and adolescent psychiatry and mental health · 2025Article
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4 authors.
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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 houses built before 1960, and the proportion of population living below the poverty line. The identified predictors of neighborhood-level cancer prevalence may inform public health practitioners and policymakers to prioritize the improvement of environmental and neighborhood factors in reducing the cancer burden.
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