Spatiotemporal Modeling Approach to Mapping Geographic and Temporal Variation in Cancer Incidence Rates for US...

Mapping cancer incidence is crucial for analyzing and visualizing patterns across geographic areas. Although many studies map cancer incidence at subnational levels (e.g., state, county), publicly available county-level data, especially for less common cancers, are limited.

Using data from the North American Association of Central Cancer Registries CiNA research database (2005-2019), we developed spatiotemporal hierarchical models to smooth/predict annual age group-specific case counts for all US counties. We compared Poisson and zero-truncated Poisson likelihoods and various priors. Model performance was assessed using the deviance information criterion, weighted Akaike information criterion, and average absolute relative deviation (AARD). Modeled age-adjusted rates were mapped to visualize spatial and temporal patterns.

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 while preserving large-scale geographic gradients and temporal trends.

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 populations.

The resulting estimates and maps could support surveillance, trend analysis, disparity identification, targeted interventions, and broader research efforts.