An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio

Estimation of the prevalence of opioid misuse in New York State counties, 2007-2018: a bayesian spatiotemporal abundance model approach

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 (ie, the population at risk), and proper evaluation of intervention effects. In this study, we used a bayesian hierarchical spatiotemporal 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) numbers of PWMO and latent prevalence of opioid misuse 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 it 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 overdose using the numbers of PWMO as denominators. This modeling approach can identify the sizes of hidden populations to guide public health efforts in confronting the opioid overdose crisis across local areas. This article is part of a Special Collection on Mental Health.

Responding to the US opioid crisis: leveraging analytics to support decision making

The US is experiencing a severe opioid epidemic with more than 80,000 opioid overdose deaths occurring in 2022. Beyond the tragic loss of life, opioid use disorder (OUD) has emerged as a major contributor to morbidity, lost productivity, mounting criminal justice system costs, and significant social disruption. This Current Opinion article highlights opportunities for analytics in supporting policy making for effective response to this crisis. We describe modeling opportunities in the following areas: understanding the opioid epidemic (e.g., the prevalence and incidence of OUD in different geographic regions, demographics of individuals with OUD, rates of overdose and overdose death, patterns of drug use and associated disease outbreaks, and access to and use of treatment for OUD); assessing policies for preventing and treating OUD, including mitigation of social conditions that increase the risk of OUD; and evaluating potential regulatory and criminal justice system reforms.

A spatio-temporal hierarchical model to account for temporal misalignment in American Community Survey explanatory variables

The American Community Survey (ACS) is one of the most vital public sources for demographic and socioeconomic characteristics of communities in the United States and is administered by the U.S. Census Bureau every year. The ACS publishes 5-year estimates of community characteristics for all geographical areas and 1-year estimates for areas with population of at least 65,000. Many epidemiological and public health studies use 5-year ACS estimates as explanatory variables in models. However, doing so ignores the uncertainty and averages over variability during the time-period which may lead to biased estimates of covariate effects of interest. In this paper, we propose a Bayesian hierarchical model that accounts for the uncertainty and disentangles the temporal misalignment in the ACS multi-year time-period estimates. We show via simulation that our proposed model more accurately recovers covariate effects compared to models that ignore the temporal misalignment. Lastly, we implement our proposed model to quantify the relationship between yearly, county-level characteristics and the prevalence of frequent mental distress for counties in North Carolina from 2014 to 2018.

A Descriptive Study on Opioid Misuse Prevalence and Office-Based Buprenorphine Access in Ohio Prior to the Removal of the Drug Addiction Treatment Act of 2000 Waiver

Background Medications for the treatment of opioid use disorder (MOUD) are effective evidence-based strategies to reduce opioid overdose deaths. Strategies to optimize MOUD availability and uptake are needed. Objective We aim to describe the spatial relationship between the estimated prevalence of opioid misuse and office-based buprenorphine access in the state of Ohio prior to the removal of the Drug Addiction Treatment Act of 2000 (DATA 2000) waiver requirement. Methods We conducted a descriptive ecological study of county-level (N=88) opioid misuse prevalence and office-based buprenorphine prescribing access in Ohio in 2018. Counties were categorized into urban (with and without a major metropolitan area) and rural. The county-level prevalence estimates of opioid misuse per 100,000 were derived from integrated abundance modeling. Utilizing data from the Ohio Department of Mental Health and Addiction Services, as well as the state's Physician Drug Monitoring Program (PDMP), buprenorphine access per 100,000 was estimated by the number of patients in each county that could be served by office-based buprenorphine (prescribing capacity) and the number of patients served by office-based buprenorphine (prescribing frequency) for opioid use disorder. The ratios of opioid misuse prevalence to both prescribing capacity and frequency were calculated by county and mapped. Results Less than half of the 1,828 waivered providers in the state of Ohio in 2018 were prescribing buprenorphine, and 25% of counties had no buprenorphine access. The median estimated opioid misuse prevalence and buprenorphine prescribing capacity per 100,000 were highest in urban counties, particularly those with a major metropolitan area. Although the median estimated opioid misuse prevalence was lower in rural counties, all counties in the highest quartile of estimated misuse prevalence were rural. In addition, the median buprenorphine prescribing frequency was highest in rural counties. While the ratio of opioid misuse prevalence to buprenorphine prescribing capacity was lowest in urban counties, the ratio of opioid misuse prevalence to buprenorphine prescribing frequency was lowest in rural counties. Opioid misuse prevalence and buprenorphine prescribing frequency demonstrated similar spatial patterns, with highest levels in the southern and eastern portions of the state, while office-based buprenorphine prescribing capacity did not. Conclusion Urban counties had higher buprenorphine capacity relative to their burden of opioid misuse; however, access was limited by buprenorphine prescribing frequency. In contrast, in rural counties, a minimal gap was evident between prescribing capacity and frequency, suggesting that buprenorphine prescribing capacity was the major factor limiting access. While the recent deregulation of buprenorphine prescribing should help improve buprenorphine access, future research should investigate whether deregulation similarly impacts buprenorphine prescribing capacity and buprenorphine prescribing frequency.