  Opioid misuse is a national epidemic and a significant drug related threat to
the United States. While the scale of the problem is undeniable, estimates of
the local prevalence of opioid misuse are lacking, despite their importance to
policy-making and resource allocation. This is due, in part, to the challenge
of directly measuring opioid misuse at a local level. In this paper, we develop
a Bayesian hierarchical spatio-temporal abundance model that integrates
indirect county-level data on opioid-related outcomes with state-level survey
estimates on prevalence of opioid misuse to estimate the latent county-level
prevalence and counts of people who misuse opioids. A simulation study shows
that our integrated model accurately recovers the latent counts and prevalence.
We apply our model to county-level surveillance data on opioid overdose deaths
and treatment admissions from the state of Ohio. Our proposed framework can be
applied to other applications of small area estimation for hard to reach
populations, which is a common occurrence with many health conditions such as
those related to illicit behaviors.
