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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Mapping cancer incidence is vital for understanding geographic patterns.
  • Publicly available county-level cancer data is often limited, particularly for rare cancers.

Purpose of the Study:

  • To develop and validate spatio-temporal hierarchical models for smoothing and predicting annual age-group-specific cancer case counts at the U.S. county level.
  • To generate reliable county-level cancer incidence rates for visualization and analysis across geographic areas.

Main Methods:

  • Utilized the North American Association of Central Cancer Registries (NAACCR) CiNA database (2005-2019).
  • Developed and compared Poisson and zero-truncated Poisson spatio-temporal hierarchical models.
  • Assessed model performance using Deviance Information Criterion (DIC), Weighted Akaike Information Criterion (WAIC), and average absolute relative deviation (AARD).

Main Results:

  • Generated modeled age-adjusted cancer rates for 16 sex-specific cancer sites across 3,109 U.S. counties (2005-2019).
  • Modeled maps exhibited enhanced smoothness and coherence compared to observed rates, reducing data gaps and extreme values.
  • Prediction accuracy varied, with higher accuracy for common cancers in populous areas and lower accuracy for rare cancers in sparsely populated regions.

Conclusions:

  • The standard Poisson hierarchical mixed-effects model demonstrated superior accuracy and computational efficiency.
  • The resulting smoothed cancer incidence estimates and maps can significantly support public health surveillance, trend analysis, and targeted interventions.
  • These data products enhance the ability to identify geographic cancer disparities and inform research efforts.