Faculty Bibliography

Amid the ongoing overdose crisis, U.S. lawmakers are considering policy reforms that could significantly change availability and accessibility of methadone treatment (MT) for opioid use disorder (OUD). However, uncertainty remains about which potential changes will lead to the greatest health benefits while minimizing unintended harms. In this protocol, we describe a planned NIH-funded study (R21DA061660) to simulate alternative MT delivery scenarios currently being considered in U.S. policy discussions, and estimate their impact on population-level rates of treatment initiation and retention and opioid overdose across different sociodemographic groups. We will use an agent-based model focused on 16 counties in NY State to simulate two alternative policy scenarios compared to the current status quo of opioid-treatment program (OTP) delivered MT: 1) office-based prescribing by addiction-certified providers with pharmacy and OTP dispensing; and 2) office-based prescribing by general practitioners with pharmacy and OTP dispensing. Agents will represent individuals with OUD and we will simulate access to MT based on alternative policy scenarios (e.g., locations of existing OTPs vs. provider offices and pharmacies). Probabilities of treatment initiation, retention, and opioid overdose will be informed by estimates from the scientific literature and administrative datasets from NY State. Multiple implementation scenarios will be considered to account for potential variation in adoption of office-based methadone by patients, providers, and pharmacies. To ensure relevance to directly impacted communities and policy makers, the study involves a collaboration between academic researchers and NY State government partners and relies on input from an Expert Advisory Board of people with lived and living experience with methadone, addiction medicine, and policy experts. Findings will be disseminated via a public dashboard. This study will inform ongoing policy discussions and shed light on the potential of researcher-policy partnerships to promote evidence-based policies that can reduce overdose and improve population health.

BACKGROUND:Firearm violence remains a leading cause of death and injury in the United States. Prior research supports that alcohol exposures, including individual-level alcohol use and alcohol control policies, are modifiable risk factors for firearm violence, yet additional research is needed to support prevention efforts. OBJECTIVES/OBJECTIVE:This scoping review aims to update a prior 2016 systematic review on the links between alcohol exposure and firearm violence to examine whether current studies indicate causal links between alcohol use, alcohol interventions, and firearm violence-related outcomes. ELIGIBILITY CRITERIA/METHODS:Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a comprehensive search of published studies was conducted, replicating the search strategy of the prior review but focusing on studies published since 2015. The review included published studies of humans, conducted in general populations of any age, gender, or racial/ethnic group, that examined the relationship between an alcohol-related exposure and an outcome involving firearm violence or risks for firearm violence. Excluded were small studies restricted to special populations, forensic or other technical studies, non-original research articles such as reviews, and studies that relied solely on descriptive statistics or did not adjust for confounders. SOURCES OF EVIDENCE/METHODS:The review included published studies indexed in PubMed, Web of Science, and Scopus. Eligible articles were published on or after January 1, 2015. The latest search was conducted on December 15, 2023. CHARTING METHODS/METHODS:Using a structured data collection instrument, data were extracted on the characteristics of each study, including the dimension of alcohol exposure, the dimension of firearm violence, study population, study design, statistical analysis, source of funding, main findings, and whether effect measure modification was assessed and, if so, along what dimensions. Two authors independently conducted title/abstract screening, full-text screening, and data extraction until achieving 95% agreement, with discrepancies resolved through discussion. RESULTS:The search yielded 797 studies. Of these, 754 were excluded and 43 met the final inclusion criteria. Studies addressed a range of alcohol exposures and firearm violence-related outcomes, primarily with cross-sectional study designs; 40% considered effect measure modification by any population characteristic. Findings from the 21 studies examining the relationship of individual-level alcohol use or alcohol use disorder (AUD) with firearm ownership, access, unsafe storage, or carrying indicated a strong and consistent positive association. Seven studies examined associations of individual-level alcohol use or AUD with firearm injury or death; these also indicated a pattern of positive associations, but the magnitude and precision of the estimates varied. Eight studies examined the impact of neighborhood proximity or density of alcohol outlets and found mixed results that were context- and study design-dependent. Two studies linked prior alcohol-related offenses to increased risk of firearm suicide and perpetration of violent firearm crimes among a large cohort of people who purchased handguns, and two studies linked policies prohibiting firearm access among individuals with a history of alcohol-related offenses to reductions in firearm homicide and suicide. Finally, four studies examined alcohol control policies and found that greater restrictiveness was generally associated with reductions in firearm homicide or firearm suicide. CONCLUSIONS:Findings from this scoping review continue to support a causal relationship between alcohol exposures and firearm violence that extends beyond acute alcohol use to include AUD and alcohol-related policies. Policies controlling the availability of alcohol and prohibiting firearm access among individuals with alcohol-related offense histories show promise for the prevention of firearm violence. Additional research examining differential impacts by population subgroup, alcohol use among perpetrators of firearm violence, policies restricting alcohol outlet density, and randomized or quasi-experimental study designs with longitudinal follow-up would further support inferences to inform prevention efforts.

IMPORTANCE/UNASSIGNED:Given the personal and social burdens of opioid use disorder (OUD), understanding time trends in OUD prevalence in large patient populations is key to planning prevention and treatment services. OBJECTIVE/UNASSIGNED:To examine trends in the prevalence of OUD from 2005 to 2022 overall and by age, sex, and race and ethnicity. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This serial cross-sectional study included national Veterans Health Administration (VHA) electronic medical record data from the VHA Corporate Data Warehouse. Adult patients (age ≥18 years) with a current OUD diagnosis (using International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] and International Statistical Classification of Diseases, Tenth Revision, Clinical Modification [ICD-10-CM] codes) who received outpatient care at VHA facilities from January 1, 2005, to December 31, 2022, were eligible for inclusion in the analysis. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The main outcome was OUD diagnoses. To test for changes in prevalence of OUD over time, multivariable logistic regression models were run that included categorical study year and were adjusted for sex, race and ethnicity, and categorical age. RESULTS/UNASSIGNED:The final sample size ranged from 4 332 165 to 5 962 564 per year; most were men (89.3%-95.0%). Overall, the annual percentage of VHA patients diagnosed with OUD almost doubled from 2005 to 2017 (0.60% [95% CI, 0.60%-0.61%] to 1.16% [95% CI, 1.15%-1.17%]; adjusted difference, 0.55 [95% CI, 0.54-0.57] percentage points) and declined thereafter (2022: 0.97% [95% CI, 0.97%-0.98%]; adjusted difference from 2017 to 2022, -0.18 [95% CI, -0.19 to -0.17] percentage points). This trend was similar among men (0.64% [95% CI, 0.63%-0.64%] in 2005 vs 1.22% [95% CI, 1.21%-1.23%] in 2017 vs 1.03% [95% CI, 1.02%-1.04%] in 2022), women (0.34% [95% CI, 0.32%-0.36%] in 2005 vs 0.68% [95% CI, 0.66%-0.69%] in 2017 vs 0.53% [95% CI, 0.52%-0.55%] in 2022), those younger than 35 years (0.62% [95% CI, 0.59%-0.66%] in 2005 vs 2.22% [95% CI, 2.18%-2.26%] in 2017 vs 1.00% [95% CI, 0.97%-1.03%] in 2022), those aged 35 to 64 years (1.21% [95% CI, 1.19%-1.22%] in 2005 vs 1.80% [95% CI, 1.78%-1.82%] in 2017 vs 1.41% [95% CI, 1.39%-1.42%] in 2022), and non-Hispanic White patients (0.44% [95% CI, 0.43%-0.45%] in 2005 vs 1.28% [95% CI, 1.27%-1.29%] in 2017 vs 1.13% [95% CI, 1.11%-1.14%] in 2022). Among VHA patients aged 65 years or older, OUD diagnoses increased from 2005 to 2022 (0.06% [95% CI, 0.06%-0.06%] to 0.61% [95% CI, 0.60%-0.62%]), whereas among Hispanic or Latino and non-Hispanic Black patients, OUD diagnoses decreased from 2005 (0.93% [95% CI, 0.88%-0.97%] and 1.26% [95% CI, 1.23%-1.28%], respectively) to 2022 (0.61% [95% CI, 0.59%-0.63%] and 0.82% [95% CI, 0.80%-0.83%], respectively). CONCLUSIONS AND RELEVANCE/UNASSIGNED:This serial cross-sectional study of national VHA electronic health record data found that the prevalence of OUD diagnoses increased from 2005 to 2017, peaked in 2017, and declined thereafter, a trend primarily attributable to changes among non-Hispanic White patients and those younger than 65 years. Continued public health efforts aimed at recognizing, treating, and preventing OUD are warranted.

INTRODUCTION/BACKGROUND:People with chronic pain are at increased risk of opioid misuse. Less is known about the unique risk conferred by each pain management treatment, as treatments are typically implemented together, confounding their independent effects. This study estimated the extent to which pain management treatments were associated with risk of opioid use disorder (OUD) for those with chronic pain, controlling for baseline demographic and clinical confounding variables and holding other pain management treatments at their observed levels. METHODS:Data were analyzed in 2024 from 2 chronic pain subgroups within a cohort of non-pregnant Medicaid patients aged 35-64 years, 2016-2019, from 25 states: those with (1) chronic pain and physical disability (CPPD) (N=6,133) or (2) chronic pain without disability (CP) (N=67,438). Nine pain management treatments were considered: prescription opioid (1) dose and (2) duration; (3) number of opioid prescribers; opioid co-prescription with (4) benzo- diazepines, (5) muscle relaxants, and (6) gabapentinoids; (7) nonopioid pain prescription, (8) physical therapy, and (9) other pain treatment modality. The outcome was OUD risk. RESULTS:Having opioids co-prescribed with gabapentin or benzodiazepine was statistically significantly associated with a 37-45% increased OUD risk for the CP subgroup. Opioid dose and duration also were significantly associated with increased OUD risk in this subgroup. Physical therapy was significantly associated with an 18% decreased risk of OUD in the CP subgroup. DISCUSSION/CONCLUSIONS:Coprescription of opioids with either gabapentin or benzodiazepines may substantially increase OUD risk. More positively, physical therapy may be a relatively accessible and safe pain management strategy.

OBJECTIVES/OBJECTIVE:The United States faces an ongoing drug overdose crisis, but accurate information on the prevalence of opioid use disorder (OUD) remains limited. A recent analysis by Keyes et al used a multiplier approach with drug poisoning mortality data to estimate OUD prevalence. Although insightful, this approach made stringent and partly inconsistent assumptions in interpreting mortality data, particularly synthetic opioid (SO)-involved and non-opioid-involved mortality. We revise that approach and resulting estimates to resolve inconsistencies and examine several alternative assumptions. METHODS:We examine 4 adjustments to Keyes and colleagues' estimation approach: (A) revising how the equations account for SO effects on mortality, (B) incorporating fentanyl prevalence data to inform estimates of SO lethality, (C) using opioid-involved drug poisoning data to estimate a plausible range for OUD prevalence, and (D) adjusting mortality data to account for underreporting of opioid involvement. RESULTS:Revising the estimation equation and SO lethality effect (adj. A and B) while using Keyes and colleagues' original assumption that people with OUD account for all fatal drug poisonings yields slightly higher estimates, with OUD population reaching 9.3 million in 2016 before declining to 7.6 million by 2019. Using only opioid-involved drug poisoning data (adj. C and D) provides a lower range, peaking at 6.4 million in 2014-2015 and declining to 3.8 million in 2019. CONCLUSIONS:The revised estimation equation presented is feasible and addresses limitations of the earlier method and hence should be used in future estimations. Alternative assumptions around drug poisoning data can also provide a plausible range of estimates for OUD population.

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.