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

Alexandra R Drake1, Eric W Christensen1, Augusto C Ochoa2

  • 1Harvey L. Neiman Health Policy Institute, Reston, Virginia.

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Summary
This summary is machine-generated.

Community factors significantly impact cancer screening, prevalence, and mortality rates across US counties. Understanding these diverse influences, like smoking rates and uninsured status, is key for targeted public health interventions and policy changes.

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Area of Science:

  • Public Health
  • Geospatial Analysis
  • Cancer Epidemiology

Background:

  • Cancer screening, prevalence, and mortality rates exhibit significant geographic variation across US counties.
  • The specific community factors driving these disparities are not fully understood.

Purpose of the Study:

  • To determine the relative importance of various community measures in explaining county-level variations in cancer screening, prevalence, and mortality.
  • Focus on breast, colorectal, lung, and prostate cancers.

Main Methods:

  • Geospatial cross-sectional analysis utilizing random forest algorithms.
  • Examined 24 community measures (health behaviors, socioeconomic, environmental factors).
  • Data sourced from Medicare beneficiaries (2020) for screening/prevalence and National Cancer Institute for mortality (2016-2020).

Main Results:

  • Mortality: Smoking rates (lung, colorectal) and non-Hispanic Black population share (breast, prostate) were key factors.
  • Prevalence: Uninsured rate (breast), unemployment (colorectal), limited healthy food access (lung), and poor physical health (prostate) were significant.
  • Screening: Hispanic population (breast), poverty (colorectal), air pollution (lung), and Air Toxics Cancer Risk (prostate) ranked highest. Environmental factors like the Environmental Justice Index also showed associations.

Conclusions:

  • The influence of community factors on cancer outcomes varies significantly by cancer type and outcome (screening, prevalence, mortality).
  • Identifying high-ranking factors across cancer types, such as uninsured rates for prevalence, offers targets for policy and intervention.
  • Results can inform tailored population health strategies to reduce cancer disparities.