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Monitoring small-area geographical population trends in opioid mortality haslarge scale implications to informing preventative resource allocation. Acommon approach to obtain small area estimates of opioid mortality is to use astandard disease mapping approach in which population-at-risk estimates aretreated as fixed and known. Assuming fixed populations ignores the uncertaintysurrounding small area population estimates, which may bias risk estimates andunder-estimate their associated uncertainties. We present a Bayesian SpatialBerkson Error (BSBE) model to incorporate population-at-risk uncertainty withina disease mapping model. We compare the BSBE approach to the naive (treatingdenominators as fixed) using simulation studies to illustrate potential biasresulting from this assumption. We show the application of the BSBE model toobtain 2020 opioid mortality risk estimates for 159 counties in GA accountingfor population-at-risk uncertainty. Utilizing our proposed approach will helpto inform interventions in opioid related public health responses, policies,and resource allocation. Additionally, we provide a general framework toimprove in the estimation and mapping of health indicators.

A Bayesian Spatial Berkson error approach to estimate small area opioid mortality rates accounting for population-at-risk uncertainty