Faculty Bibliography

INTRODUCTION/BACKGROUND:While morbidity and mortality related to synthetic opioids such as illicitly manufactured fentanyl (IMF) are monitored in the U.S., there has been a lack of national survey data focusing on use. Survey data are important as self-report can help estimate prevalence of use among living persons. METHODS:Data were examined from the 2022 National Survey on Drug Use and Health, a nationally representative probability sample of noninstitutionalized individuals age ≥12 in the U.S. (N=59,069). Prevalence and correlates of past-year use of IMF were estimated. Data were analyzed in 2024. RESULTS:The estimated prevalence of past-year IMF use was 0.23% (95% confidence interval [CI]: 0.17-0.31). Compared to no past-year use, individuals were at increased odds for IMF use if proxy-diagnosed with use disorder involving use of cannabis (aOR=3.72, 95% CI: 1.34-10.32), cocaine (aOR=11.96, 95% CI: 4.78-29.93), methamphetamine (aOR=5.60, 95% CI: 1.65-19.02), heroin (aOR=20.56, 95% CI: 8.90-47.52), and/or prescription opioids (aOR=10.65, 95% CI: 3.54-32.03). (Mis)use without use disorder was only significant for prescription opioids (aOR=5.77, 95% CI: 2.55-13.06). Those receiving treatment for substance use in the past year were also at increased odds for use (aOR=5.79, 95% CI: 2.58-13.00). CONCLUSIONS:Prevalence of IMF use is rare in the general U.S. POPULATION/METHODS:While past-year (mis)use of other drugs (without use disorder) was not consistently associated with IMF use, cannabis, cocaine, methamphetamine, heroin, and prescription opioid use disorder was associated with higher odds of IMF use, suggesting that more "severe" use of various drugs is more of a risk factor than use.

Medications for opioid use disorder (MOUD) increase retention in care and decrease mortality during active treatment; however, information about the comparative effectiveness of different forms of MOUD is sparse. Observational comparative effectiveness studies are subject to many types of bias; a robust framework to minimize bias would improve the quality of comparative effectiveness evidence. This paper discusses the use of target trial emulation as a framework to conduct comparative effectiveness studies of MOUD with administrative data. Using examples from our planned research project comparing buprenorphine-naloxone and extended-release naltrexone with respect to the rates of MOUD discontinuation, we provide a primer on the challenges and approaches to employing target trial emulation in the study of MOUD.

INTRODUCTION/BACKGROUND:Concurrent electronic nicotine delivery system (ENDS) and cigarette (dual) use is harmful. Identifying longitudinal trajectories of ENDS and cigarette use among dual users can help to determine the public health impact of ENDS and inform tobacco control policies and interventions. OBJECTIVES/OBJECTIVE:(1) To identify independent and joint trajectories of ENDS and cigarette use among wave (W) 1 adult dual users across W1 to W5 of the Population Assessment of Tobacco and Health (PATH) Study; and (2) identify W1 predictors of ENDS and cigarette joint trajectory group membership. METHODS:We used group-based trajectory modelling to estimate independent and joint trajectories of ENDS and cigarette use from wave 1 (W1; 2013-2014) to wave 5 (W5; 2018-2019) among W1 adult established dual users of ENDS and cigarettes (n=545) from the PATH Study. We used multinomial logistic regression to identify W1 predictors of joint trajectories. RESULTS:Two ENDS (early quitters=66.0%, stable users=34.0%) and three cigarette (stable users=55.2%, gradual quitters=27.3%, early quitters=17.5%) trajectories of W1 were identified. In joint trajectory analysis, 41.6% of participants were early ENDS quitters and stable cigarette users; 14.8% early ENDS quitters and gradual cigarette quitters; 14.6% stable ENDS users and stable cigarette users; 11.2% stable ENDS users and gradual cigarette quitters; 10.3% early ENDS quitters and early cigarette quitters; and 7.4% stable ENDS users and early cigarette quitters. Cigarette and ENDS use frequency, nicotine dependence, cannabis use and other non-combusted tobacco product use predicted trajectory group membership (p values <0.05). CONCLUSIONS:Most dual users maintained long-term cigarette smoking or dual use, highlighting the need to address cessation of both products. Continued monitoring of trajectories and their predictors is needed, given ongoing changes to the ENDS marketplace.

People with substance use disorders (SUDs) are increasingly admitted to general hospitals; however, many hospital systems lack both formal structures and skilled staff to provide high-quality care for inpatients with SUDs. Inpatient addiction consult services (ACSs), which are increasingly being implemented around the country, are an evidence-based strategy to add focused care for people with SUDs into the general medical setting. In 2018, New York City Health + Hospitals (H + H) launched an ACS program called Consult for Addiction Care and Treatment in Hospitals in six hospitals, supported by a team of addiction consult experts to deliver teaching and technical assistance (TTA) for the Consult for Addiction Care and Treatment in Hospitals ACSs. This commentary describes the TTA, which included site visits, introductory educational lectures, case conferences, ad hoc support, implementation assistance, and the creation of an addiction care guide. Similar TTA services could be used in the future when hospitals or systems want to launch novel clinical programs.

OBJECTIVES/OBJECTIVE:To evaluate the impact of a mentoring program to encourage staff-delivered sleep-promoting strategies on sleep, function, depression, and anxiety among skilled nursing facility (SNF) residents. DESIGN/METHODS:Modified stepped-wedge unit-level intervention. SETTING AND PARTICIPANTS/METHODS:Seventy-two residents (mean age 75 ± 15 years; 55.9% female, 41% non-Hispanic White, 35% Black, 20% Hispanic, 3% Asian) of 2 New York City urban SNFs. METHODS:. RESULTS:. CONCLUSIONS AND IMPLICATIONS/CONCLUSIONS:SNF residents had improvements in sleep quality and depression with intervention, but improvements were not sustained at 3-month follow-up. The COVID-19 pandemic led to premature study termination, so full impacts remain unknown.

OBJECTIVES/OBJECTIVE:To describe the implementation of a mentored staff-delivered sleep program in nursing facilities. DESIGN/METHODS:Modified stepped-wedge unit-level intervention. SETTING AND PARTICIPANTS/METHODS:This program was implemented in 2 New York City nursing facilities, with partial implementation (due to COVID-19) in a third facility. METHODS:Expert mentors provided staff webinars, in-person workshops, and weekly sleep pearls via text messaging. We used the integrated Promoting Action on Research Implementation in Health Services (i-PARiHS) framework as a post hoc approach to describe key elements of the SLUMBER implementation. We measured staff participation in unit-level procedures and noted their commentary during unit workshops. RESULTS:We completed SLUMBER within 5 units across 2 facilities and held 15 leadership meetings before and during program implementation. Sessions on each unit included 3 virtual webinar presentations and 4 in-person workshops for each nursing shift, held over a period of 3 to 4 months. Staff attendance averaged >3 sessions per individual staff member. Approximately 65% of staff present on each unit participated in any given session. Text messaging was useful for engagement, educational reinforcement, and encouraging attendance. We elevated staff as experts in the care of their residents as a strategy for staff engagement and behavior change and solicited challenging cases from staff during workshops to provide strategies to address resident behavior and encourage adoption when successful. CONCLUSIONS AND IMPLICATIONS/CONCLUSIONS:Engaging staff, leadership, residents, and family of nursing facilities in implementing a multicomponent sleep quality improvement program is feasible for improving nursing facilities' sleep environment. The program required gaining trust at multiple levels through presence and empathy, and reinforcement mechanisms (primarily text messages). To improve scalability, SLUMBER could evolve from an interdisciplinary investigator-based approach to internal coaches in a train-the-trainer model to effectively and sustainably implement this program to improve sleep quality for facility residents.

BACKGROUND:Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE:This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS:We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS:Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS:ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.