  Monitoring small-area geographical population trends in opioid mortality has
large scale implications to informing preventative resource allocation. A
common approach to obtain small area estimates of opioid mortality is to use a
standard disease mapping approach in which population-at-risk estimates are
treated as fixed and known. Assuming fixed populations ignores the uncertainty
surrounding small area population estimates, which may bias risk estimates and
under-estimate their associated uncertainties. We present a Bayesian Spatial
Berkson Error (BSBE) model to incorporate population-at-risk uncertainty within
a disease mapping model. We compare the BSBE approach to the naive (treating
denominators as fixed) using simulation studies to illustrate potential bias
resulting from this assumption. We show the application of the BSBE model to
obtain 2020 opioid mortality risk estimates for 159 counties in GA accounting
for population-at-risk uncertainty. Utilizing our proposed approach will help
to inform interventions in opioid related public health responses, policies,
and resource allocation. Additionally, we provide a general framework to
improve in the estimation and mapping of health indicators.
