BMC Public Health (Oct 2021)

Spatial analysis of colorectal cancer outcomes and socioeconomic factors in Virginia

  • Esther J. Thatcher,
  • Fabian Camacho,
  • Roger T. Anderson,
  • Li Li,
  • Wendy F. Cohn,
  • Pamela B. DeGuzman,
  • Kathleen J. Porter,
  • Jamie M. Zoellner

DOI
https://doi.org/10.1186/s12889-021-11875-6
Journal volume & issue
Vol. 21, no. 1
pp. 1 – 10

Abstract

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Abstract Background Colorectal cancer (CRC) disparities vary by country and population group, but often have spatial features. This study of the United States state of Virginia assessed CRC outcomes, and identified demographic, socioeconomic and healthcare access contributors to CRC disparities. Methods County- and city-level cross-sectional data for 2011–2015 CRC incidence, mortality, and mortality-incidence ratio (MIR) were analyzed for geographically determined clusters (hotspots and cold spots) and their correlates. Spatial regression examined predictors including proportion of African American (AA) residents, rural-urban status, socioeconomic (SES) index, CRC screening rate, and densities of primary care providers (PCP) and gastroenterologists. Stationarity, which assesses spatial equality, was examined with geographically weighted regression. Results For incidence, one CRC hotspot and two cold spots were identified, including one large hotspot for MIR in southwest Virginia. In the spatial distribution of mortality, no clusters were found. Rurality and AA population were most associated with incidence. SES index, rurality, and PCP density were associated with spatial distribution of mortality. SES index and rurality were associated with MIR. Local coefficients indicated stronger associations of predictor variables in the southwestern region. Conclusions Rurality, low SES, and racial distribution were important predictors of CRC incidence, mortality, and MIR. Regions with concentrations of one or more factors of disparities face additional hurdles to improving CRC outcomes. A large cluster of high MIR in southwest Virginia region requires further investigation to improve early cancer detection and support survivorship. Spatial analysis can identify high-disparity populations and be used to inform targeted cancer control programming.

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