An important challenge to addressing the opioid overdose crisis is the lack of information on the size of the population of people who misuse opioids (PWMO) in local areas. This estimate is needed for better resource allocation, estimation of treatment and overdose outcome rates using appropriate denominators (i.e., the population at risk), and proper evaluation of intervention effects. In this study, we used a Bayesian hierarchical spatio-temporal integrated abundance model that integrates multiple types of county-level surveillance outcome data, state-level information on opioid misuse, and covariates to estimate the latent (hidden) counts and prevalence of PWMO across New York State counties (2007-2018). The model assumes that each opioid-related outcome reflects a partial count of the number of PWMO, and leverages these multiple sources of data to circumvent limitations of parameter estimation associated with other types of abundance models. Model estimates showed a reduction in the prevalence of PWMO during the study period, with important spatial and temporal variability. The model also provided county-level estimates of rates of treatment and opioid overdoses using the PWMO as denominators. This modeling approach can identify the size of hidden populations to guide public health efforts to confront the opioid overdose crisis across local areas.
Estimation of opioid misuse prevalence in New York State counties, 2007-2018. A Bayesian spatio-temporal abundance model approach
American Journal of Epidemiology [Epub 2024 Mar 6]. doi: 10.1093/aje/kwae018.