Faculty Bibliography

BACKGROUND/UNASSIGNED:Rates of cannabis use disorder (CUD) have increased disproportionately among Veterans Administration (VA) patients with psychiatric disorders compared to patients with no disorder. However, VA patient samples are not representative of all U.S. adults, so results on disproportionate increases in CUD prevalence could have been biased. To address this concern, we investigated whether disproportionate increases in the prevalence of cannabis outcomes among those with psychiatric disorders would replicate in nationally representative samples of U.S. adults. METHODS/UNASSIGNED: = 36,309). Outcomes were any past-year non-medical cannabis use, frequent non-medical use (≥3 times weekly), and DSM-IV CUD. Psychiatric disorders included mood, anxiety and antisocial personality disorders. Logistic regression was used to generate predicted prevalences of the outcomes, prevalence differences calculated and additive interactions compared differences between those with and without psychiatric disorders. RESULTS/UNASSIGNED:Cannabis outcomes increased more among those with psychiatric disorders. The difference in prevalence differences included any past-year non-medical cannabis use, 2.45% (95%CI = 1.29-3.62); frequent non-medical cannabis use, 1.58% (95%CI = 0.83-2.33); CUD, 1.40% (95%CI = 0.58-2.21). For most specific disorders, prevalences increased more among those with the disorder. CONCLUSIONS/UNASSIGNED:In the U.S. general population, rates of cannabis use and CUD increased more among adults with psychiatric disorders than other adults, similar to findings from VA patient samples. Results suggest that although VA patients are not representative of all U.S. adults, findings from this important patient group can be informative. Greater clinical and policy attention to CUD is warranted for adults with psychiatric disorders.

IMPORTANCE/UNASSIGNED:Opioid-related overdose accounts for almost 80 000 deaths annually across the US. People who use drugs leaving jails are at particularly high risk for opioid-related overdose and may benefit from take-home naloxone (THN) distribution. OBJECTIVE/UNASSIGNED:To estimate the population impact of THN distribution at jail release to reverse opioid-related overdose among people with opioid use disorders. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:This study developed the agent-based Justice-Community Circulation Model (JCCM) to model a synthetic population of individuals with and without a history of opioid use. Epidemiological data from 2014 to 2020 for Cook County, Illinois, were used to identify parameters pertinent to the synthetic population. Twenty-seven experimental scenarios were examined to capture diverse strategies of THN distribution and use. Sensitivity analysis was performed to identify critical mediating and moderating variables associated with population impact and a proxy metric for cost-effectiveness (ie, the direct costs of THN kits distributed per death averted). Data were analyzed between February 2022 and March 2024. INTERVENTION/UNASSIGNED:Modeled interventions included 3 THN distribution channels: community facilities and practitioners; jail, at release; and social network or peers of persons released from jail. MAIN OUTCOMES AND MEASURES/UNASSIGNED:The primary outcome was the percentage of opioid-related overdose deaths averted with THN in the modeled population relative to a baseline scenario with no intervention. RESULTS/UNASSIGNED:Take-home naloxone distribution at jail release had the highest median (IQR) percentage of averted deaths at 11.70% (6.57%-15.75%). The probability of bystander presence at an opioid overdose showed the greatest proportional contribution (27.15%) to the variance in deaths averted in persons released from jail. The estimated costs of distributed THN kits were less than $15 000 per averted death in all 27 scenarios. CONCLUSIONS AND RELEVANCE/UNASSIGNED:This study found that THN distribution at jail release is an economical and feasible approach to substantially reducing opioid-related overdose mortality. Training and preparation of proficient and willing bystanders are central factors in reaching the full potential of this intervention.

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.

OBJECTIVE:To ascertain whether a novel expanded social network recruitment to HIV testing (E-SNRHT) intervention recruits men and individuals with previously-undiagnosed HIV at higher rates than risk network recruitment. DESIGN/METHODS:Initial "seed" participants were prospectively randomly assigned to the E-SNRHT intervention or to risk network recruitment. Their network members were included in the study arm of their recruiter. SETTING/METHODS:Three Department of Health clinics and two drug treatment centers (DTCs) in the Msunduzi municipality of KwaZulu-Natal, South Africa. PARTICIPANTS/METHODS:Clinics and DTCs referred 110 newly-HIV-diagnosed adult "seeds" to the study from June 2022-February 2023. E-SNRHT seeds were asked to recruit network members as described below; risk network recruitment arm seeds were asked to recruit recent sex and/or injection partners. Presenting a recruitment coupon (from clinic/DTC staff or another participant) was required for eligibility. INTERVENTION/METHODS:E-SNRHT seeds were shown educational material about HIV transmission risks and then asked to recruit anyone they know (e.g., friends, family) whom they thought could benefit from HIV testing. MAIN OUTCOME MEASURES/METHODS:Rates of recruiting men to HIV testing and locating individuals with previously-undiagnosed HIV. RESULTS:E-SNRHT recruited significantly higher proportions of men to HIV testing (70.3% vs. 40.4%; χ2 = 16.33; p < .0005) and located significantly more previously-undiagnosed cases of HIV per seed than risk network recruitment (rate ratio = 9.40; p < .0001). E-SNRHT also recruited significantly higher proportions of women with previously-undiagnosed HIV (29.0% vs. 10.7%; χ2 = 3.87; p = .049). CONCLUSIONS:E-SNRHT is an important strategy to expand the reach of HIV testing among men and undiagnosed cases of HIV in KwaZulu-Natal.

BACKGROUND AND AIMS/OBJECTIVE:Extended-release naltrexone (XR-NTX) and sublingual buprenorphine (SL-BUP) are both approved for opioid use disorder (OUD) treatment in any medical setting. We aimed to compare the real-world effectiveness of XR-NTX and SL-BUP. DESIGN AND SETTING/METHODS:This was an observational active comparator, new user cohort study of Medicaid claims records for patients in New Jersey and California, USA, 2016-19. PARTICIPANTS/CASES/METHODS:The participants were adult Medicaid patients aged 18-64 years who initiated XR-NTX or SL-BUP for maintenance treatment of OUD and did not use medications for OUD in the 90 days before initiation. Our cohort included 1755 XR-NTX and 9886 SL-BUP patients. MEASUREMENTS/METHODS:We examined two outcomes up to 180 days after medication initiation: (1) composite of medication discontinuation and death and (2) composite of overdose and death. FINDINGS/RESULTS:In adjusted analyses, treatment with XR-NTX was more likely to result in discontinuation or death by the end of follow-up than treatment with SL-BUP: cumulative risk 75.9% [95% confidence interval (CI) = 73.9%, 77.9%] versus 62.2% (95% CI = 61.2%, 63.2%), respectively (risk difference = 13.7 percentage points, 95% CI = 11.4, 16.0). There was minimal difference in the cumulative risk of overdose or death by the end of follow-up: XR-NTX 3.9% (95% CI = 3.0%, 4.8%) versus SL-BUP 3.3% (95% CI = 2.9%, 3.7%); risk difference = 0.5 percentage points, 95% CI = -0.4, 1.5. Results were consistent across sensitivity analyses. CONCLUSIONS:Medicaid patients in California and New Jersey, USA, receiving treatment for opioid use disorder stayed in treatment longer on sublingual buprenorphine than on extended-release naltrexone, but the risk of overdose was similar. Most patients in this study discontinued medication within 6 months, regardless of which medication was initiated.

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.

People living with HIV (PLWH) and people who use drugs are vulnerable populations who may face barriers to accessing health services and may have irregularities in immune function. People with undiagnosed HIV infection may be particularly likely to have compromised immune function. However, research about whether/how HIV status is related to COVID-19-related health outcomes has been equivocal, and research on the predictors of COVID-19-related health service access/uptake has been limited in Sub-Saharan African settings. Among 470 participants of a peer-recruitment-based HIV-testing intervention in KwaZulu-Natal, we examined whether HIV status and/or hard drug use were associated with uptake of COVID-19 testing and vaccination, and whether they moderated the relationship between COVID-19 vaccination status and COVID-19 IgG antibody status. Women were significantly more likely than men to report testing for COVID-19 (OR = 1.84; p = 0.002) and being vaccinated (OR = 1.79; p = 0.002). Neither HIV status nor drug use was associated with likelihood of getting tested or vaccinated. Vaccinated participants (90% of whom obtained vaccines more than 6 months before the study) were significantly more likely to test positive for COVID-19 IgG antibodies (OR = 6.86; p < 0.0005). This relationship held true for subgroups of PLWH and participants with previously undiagnosed/uncontrolled HIV infection, and was not moderated by HIV status or hard drug use. These findings may suggest that both people who use drugs and PLWH were served as well as other people by KwaZulu-Natal's COVID-19 response. However, gender-based disparities in COVID-19 service uptake suggest that special care should be taken during future COVID-19 outbreaks or other new epidemics to improve access to related healthcare services among men in this region.

BACKGROUND:Most patients in specialty drug treatment programs that are not federally licensed Opioid Treatment Programs (OTPs) programs do not receive medications for opioid use disorder (MOUD). METHODS:We linked results from a survey of non-OTP treatment program directors in New Jersey (n = 81) to statewide administrative records of admissions for opioid use to those programs between July 2021-June 2022. Using multi-level regression, we examined the association of three types of factors with planned MOUD use: program survey responses, client-level factors, and program-level client characteristic mix. RESULTS:Of 9583 opioid treatment admissions in non-OTP settings, 41 % included treatment plans involving MOUD. Programs where directors reported staff concerns about buprenorphine's efficacy or diversion had a lower proportion of clients with planned MOUD, as did programs reporting too little physical space to prescribe. Being self-referred to treatment, unemployed and not looking for work, aged 30-49, heroin use (vs. prescription opioid use), and stimulant use in addition to opioids, were positively associated with planned MOUD; while non-Medicaid insurance, and Black and Hispanic race/ethnicity, were negatively associated with planned MOUD. Clients were more likely to have planned MOUD if their programs had a higher proportion of clients aged 30 or older, heroin as primary "drug of abuse," stimulant use, and not working but actively looking for work. CONCLUSION/CONCLUSIONS:Findings suggest addressing program staff attitudes toward buprenorphine could help increase planned MOUD. There is also a need to improve access for clients with non-Medicaid insurance, address within-program race and ethnic disparities, and address employment-related barriers to medication.