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Cancer

Community Factors and County-Level Cancer Screening, Prevalence, and Mortality

Strategic Insights

— 1 min read

Researchers at the Harvey L. Neiman Health Policy Institute have conducted a geospatial cross-sectional analysis to assess the relative importance of community measures for explaining county-level variance in cancer screening, prevalence, and mortality rates for breast, colorectal, lung, and prostate cancers. The study utilized random forest algorithms to estimate the relative importance of 24 community measures, including health behaviors and lifestyle, socioeconomic, and environmental factors. The analysis revealed significant community-factor to cancer-outcome associations, with county-level choropleth maps displaying the relationships.

Key Takeaways:

  • The top-ranking explanatory community factors for mortality were smoking rate for both lung and colorectal cancers, and the non-Hispanic Black population share for both breast and prostate cancers.
  • For prevalence and screening rates, the top factors were unique for each cancer type, with uninsured rate, unemployment, limited access to health foods, and poor physical health being the top-ranked factors for breast, colorectal, lung, and prostate cancer prevalence, respectively.
  • Hispanic population, poverty, air pollution, and Air Toxics Cancer Risk were ranked highest for screening rates, respectively, for each cancer type.
  • Two environmental factors, Environmental Justice Index and Air Toxics Cancer Risk, had the most top-5 associations for mortality and screening, respectively.
  • Uninsured rates for prevalence and poverty rates for screening were also important across cancer types.
  • Factors that ranked high across cancer types for a given outcome, such as uninsured rates for cancer prevalence, represent opportune targets for future study and broader policy change.

Statistics:

  • 87% of a nationally representative 5% of 2020 Medicare Fee-For-Service beneficiaries were aged 65 years or older.
  • The top-ranking explanatory community factors for mortality had a relative importance of 100%.
  • 24 community measures were used to estimate relative importance in the random forest algorithms.
  • 24 community measures included in the analysis were health behaviors and lifestyle, socioeconomic, and environmental factors.
  • 5-year (2016-2020) mean mortality rates were from the National Cancer Institute's State Cancer Profiles.

Sources:

  • "Community Factors and County-Level Cancer Screening, Prevalence, and Mortality." JAMA Network Open, 2025;8(10).
  • Harvey L. Neiman Health Policy Institute. "This geospatial cross-sectional analysis across all US counties used random forest algorithms to estimate the relative importance of 24 community measures..."
  • NewsRx. "New Cancer Study Findings Have Been Reported by Researchers at Harvey L. Neiman Health Policy Institute (Community Factors and County-Level Cancer Screening, Prevalence, and Mortality)." Cancer Weekly. November 4, 2025; p 30.

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