Abstract
BACKGROUND: Mapping cancer incidence is crucial for analyzing and visualizing patterns across geographic areas. While many studies map cancer incidence at subnational levels (e.g., state, county), publicly available county-level data, especially for less common cancers, is limited. METHODS: Using data from the NAACCR CiNA research database (2005 to 2019), we developed spatio-temporal hierarchical models to smooth/predict annual age-group-specific case counts for all U.S. counties. We compared Poisson and zero-truncated Poisson likelihoods and various priors. Model performance was assessed using Deviance Information Criterion (DIC), Weighted Akaike Information Criterion (WAIC), and average absolute relative deviation (AARD). Modeled age-adjusted rates were mapped to visualize spatial and temporal patterns. RESULTS: Modeled age-adjusted rates were produced for 16 selected sex-specific cancer sites across 3,109 counties from 2005 to 2019. AARD values varied by site and context, being lowest for common cancers and populous counties and highest for rare cancers and sparsely populated areas. Compared with maps of observed rates, modeled maps were smoother and more coherent, filling gaps and reducing extreme values driven by small case counts, and preserving large-scale geographic gradients and temporal trends. CONCLUSIONS: The standard Poisson hierarchical mixed-effects model showed superior accuracy and computational efficiency and was selected for final estimation. As expected, the most accurate predictions are for more common cancer sites in more populous areas, and the least accurate predictions are for rarer cancers in areas with lower population. IMPACT: The resulting estimates and maps could support surveillance, trend analysis, disparity identification, targeted interventions, and broader research efforts.