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Modeling Age-Adjusted Rates from Spatio-Temporal Data Sets with Excess Zero Counts (304176)

Ali Arab, Georgetown University 
Mary Charlton, University of Iowa 
*Melissa Jay, University of Iowa 
Jacob Oleson, University of Iowa 

Keywords: Excess zeros, hurdle model, cancer, disease mapping

Age-adjusted rates are frequently used by epidemiologists to compare disease incidence and mortality across populations. In small geographic regions, age-adjusted rates computed directly from the data are subject to considerable variability making them unreliable. Therefore, we desire an approach that accounts for the excessive number of zero counts in disease mapping datasets, which are naturally present for low-prevalence diseases and are further innated when stratifying the dataset by age group. Bayesian modeling approaches are naturally suited to employ spatial and temporal smoothing to produce more stable estimates of age-adjusted rates for small areas. We propose a Bayesian hierarchical spatio-temporal hurdle model for counts and demonstrate how age-adjusted rates can be estimated from the hurdle model. We illustrate our modeling approach on county-level liver cancer and colorectal cancer datasets and compare it to the traditional Poisson model commonly employed in disease mapping applications.