Faculty Bibliography

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

ABSTRACT/UNASSIGNED:Chronic pain is associated with mental and physical health difficulties and is prevalent among veterans. Cannabis has been put forth as a treatment for chronic pain, and changes in laws, attitudes, and use patterns have occurred over the last two decades. Differences in prevalence of non-medical cannabis use and cannabis use disorder (CUD) were examined across two groups: veterans/non-veterans and those reporting/not reporting recent pain. Data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (2012-2013; n=36,309) were analyzed using logistic regression. Prevalence Differences (PD) for three cannabis outcomes: (1) past-year non-medical cannabis use, (2) frequent (≥3 times a week) non-medical use, and (3) DSM-5 CUD were estimated for those reporting recent moderate-severe pain (veterans/non-veterans), and veterans reporting/not reporting recent pain. Difference in differences were calculated to investigate prevalence differences on outcomes associated with residence in a state with medical cannabis laws (MCLs). Associations between physical and mental health and cannabis variables were tested. Results indicated that the prevalence of recent pain was greater among veterans (PD=7.25%, 95% CI [4.90, 9.60]). Among veterans, the prevalence of frequent cannabis use was greater among those with pain (PD=1.92%, 98% CI [0.21, 3.63]), and, among veterans residing in a state with MCLs, the prevalence of CUD was greater among those reporting recent pain (PD=3.88%, 98% CI [0.36, 7.39]). Findings failed to support the hypothesis that cannabis use improves mental or physical health for veterans with pain. Providers treating veterans with pain in MCL states should monitor such patients closely for CUD.

Over the past decade, US adolescents' depressive symptoms have increased, and changing religious beliefs and service attendance may be contributing factors. We examined the contribution of religious factors to depressive symptoms among 417,540 US adolescents (grades: 8, 10, 12), years:1991-2019, in survey-weighted logistic regressions. Among adolescents who felt religion was personally important, those who never attended services had 2.23 times higher odds of reporting depressive symptoms compared to peers attending weekly. Among adolescents who did not feel that religion was important, the pattern was reversed. Among adolescents, concordance between importance of religion and religious service attendance may lower risk of depressive symptoms. Overall, we estimate that depressive symptom trends would be 28.2% lower if religious factors had remained at 1991 levels.

Zoonotic epidemics and pandemics have become frequent. From HIV/AIDS through COVID-19, they demonstrate that pandemics are social processes as well as health occurrences. The roots of these pandemics lie in changes in the socioeconomic interface between humanity and non-human host species that facilitate interspecies transmission. The degree to which zoonoses spread has been increased by the greater speed and extent of modern transportation and trade. Pre-existing sociopolitical and economic structures and conflicts in societies also affect pathogen propagation. As an epidemic develops, it can itself become a social and political factor, and change and interact with pre-existing sociobehavioral norms and institutional structures. This paper uses a "Big Events" approach to frame these processes. Based on this framework, we discuss how social readiness surveys implemented both before and during an outbreak might help public health predict how overall systems might react to an epidemic and/or to disease control measures, and thus might inform interventions to mitigate potential adverse outcomes or possibly preventing outbreaks from developing into epidemics. We conclude by considering what "pathways measures", in addition to those we and others have already developed, might usefully be developed and validated to assist outbreak and epidemic disease responses.

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models offer a tool to help us understand and address this complex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings. Further, we created a database of model parameters used for model calibration, and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and Agent-Based models (16%). Over a third evaluated intervention cost-effectiveness (40%), and another third (39%) focused on treatment and harm reduction services for people with opioid use disorder (OUD). More than half (61%) discussed calibrating their models to empirical data, and 31% discussed validation approaches used in their modeling process. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation/relapse, emergency medical services, and mortality parameters. This database offers a tool that future modelers can use to identify potential model inputs and evaluate comparability of their models to prior work. Future applications of simulation models to this field should actively tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.

Agent-based modeling and G-computation can both be used to estimate impacts of intervening on complex systems. We explored each modeling approach within an applied example: interventions to reduce posttraumatic stress disorder (PTSD). We used data from a cohort of 2,282 adults representative of the adult population of the New York City metropolitan area from 2002-2006, of whom 16.3% developed PTSD over their lifetimes. We built four models: G-computation, an agent-based model with no between-agent interactions, an agent-based model with violent interaction dynamics, and an agent-based model with neighborhood dynamics. Three interventions were tested: reducing violent victimization by 1) 37.2% (real-world reduction), 2) 100%, and 3) supplementing the income of 20% of lower-income participants. The G-computation model estimated population-level PTSD risk reductions of 0.12% (95% CI: -0.16, 0.29), 0.28% (95% CI: -0.30, 0.70), and 1.55% (95% CI: 0.40, 2.12), respectively. The agent-based model with no interactions replicated the findings from G-computation. Introduction of interaction dynamics modestly decreased estimated intervention effects (income supplement risk reduction dropped to 1.47%), whereas introduction of neighborhood dynamics modestly increased effectiveness (income supplement risk reduction increased to 1.58%). As compared with G-computation, agent-based modeling permitted deeper exploration of complex systems dynamics at the cost of further assumptions.

Stigma is a fundamental driver of adverse health outcomes. Although stigma is often studied at the individual level to focus on how stigma influences the mental and physical health of the stigmatized, considerable research has shown that stigma is multilevel and structural. This paper proposes a theoretical approach that synthesizes the literature on stigma with the literature on scapegoating and divide-and-rule as strategies that the wealthy and powerful use to maintain their power and wealth; the literatures on racial, gender, and other subordination; the literature on ideology and organization in sociopolitical systems; and the literature on resistance and rebellion against stigma, oppression and other forms of subordination. we develop a model of the "stigma system" as a dialectic of interacting and conflicting structures and processes. Understanding this system can help public health reorient stigma interventions to address the sources of stigma as well as the individual problems that stigma creates. On a broader level, this model can help those opposing stigma and its effects to develop alliances and strategies with which to oppose stigma and the processes that create it.

OBJECTIVE:Availability of medications for opioid use disorder (MOUD) remains sparse. To date, there has been no national, state-by-state comparison of patient MOUD utilization relative to treatment availability and burden of overdose deaths. We aimed to quantify, for each state, the number of MOUD patients relative to (1) office-based buprenorphine providers and opioid treatment programs (OTPs) and (2) overdose deaths. METHODS:We conducted a spatial analysis of patients receiving MOUD from OTPs or buprenorphine providers in March 2017 across all 50 states and Washington, DC. For each state, we calculated the number of patients receiving MOUD from OTPs and buprenorphine prescriptions, relative to available OTPs and buprenorphine providers; as well as ratios of number of patients receiving MOUD relative to overdose deaths. RESULTS:In March 2017, 942,368 patients attended an OTP (410,288) or received a buprenorphine prescription (486,318). Patient to OTP ratio was highest in West Virginia, Delaware, Washington, DC, New Jersey, New Hampshire, Connecticut and Ohio, ranging from 91 to 193 patients per OTP in the first quintile to 430 to 648 in the fifth. Patient to buprenorphine provider ratio was highest in Kentucky and West Virginia, ranging from 3 to 7 patients per provider in the first quintile to 19 to 28 in the fifth. Median MOUD patients per overdose death was 21 (IQR:14.9-28.2). Of high overdose states, Washington, DC, New Jersey, and Ohio had the smallest number of patients on MOUD relative to deaths. CONCLUSIONS:High patient volume relative to treatment availability in overdose-burdened areas may indicate strain on MOUD providers and OTPs. Promoting greater utilization while expanding MOUD providers and programs is critical.

Criminal justice involved individuals have a high rate of opioid overdose death following release. In March 2020, New York City jails released over 1000 inmates due to concern of COVID-19 outbreaks in county jails. The closure of addiction treatment clinics further complicated efforts to expand access to medications for opioid use disorder among criminal justice involved adults. The New York City Health + Hospitals Virtual Buprenorphine Clinic established in March 2020 offered low-threshold telemedicine-based opioid treatment with buprenorphine-naloxone, specifically for criminal justice involved adults post-release. We describe a case report of the novel role of tele-conferencing for the provision of buprenorphine-naloxone for jail-released adults with opioid use disorder experiencing homelessness during the COVID-19 pandemic. The patient is a 49-year-old male with severe opioid use disorder released from New York City jail as part of its early release program. He then started using diverted buprenorphine-naloxone, and 1 month later a harm-reduction specialist at his temporary housing at a hotel referred him to an affiliated buprenorphine provider and then eventually to the New York City Health + Hospitals Virtual Buprenorphine Clinic, where he was continued on buprenorphine-naloxone, and was followed biweekly thereafter until being referred to an office-based opioid treatment program. For this patient, telemedicine-based opioid treatment offered a safe and feasible approach to accessing medication for opioid use disorder during the COVID-19 pandemic and following incarceration.

OBJECTIVES/OBJECTIVE:The purpose of this study was to assess the feasibility and clinical impact of telemedicine-based opioid treatment with buprenorphine-naloxone following the Coronavirus disease 2019 pandemic. METHODS:Participants included in this retrospective analysis consisted of adult New York City residents with opioid use disorder eligible for enrollment in the NYC Health+Hospitals Virtual Buprenorphine Clinic between March and May 2020 (n = 78). Follow-up data were comprised of rates of retention in treatment at 2 months, referrals to community treatment, and induction-related events. RESULTS:During the initial 9 weeks of clinic operations, the clinic inducted 78 patients on to buprenorphine-naloxone and completed 252 visits. Patient referrals included non-NYC Health + Hospitals (n = 22, 28.2%) and NYC Health + Hospitals healthcare providers (n = 17, 21.8%), homeless shelter staff (n = 13, 16.7%), and the NYC Health + Hospitals jail reentry program in Rikers Island (n = 11, 14.1%). At 8 weeks, 42 patients remained in care (53.8%), 21 were referred to a community treatment program (26.9%), and 15 were lost to follow-up (19.2%). No patients were terminated from care due to disruptive behavior or suspicions of diversion or misuse of Buprenorphine. Adverse clinical outcomes were uncommon and included persistent withdrawal symptoms (n = 8, 4.3%) and one nonfatal opioid overdose (0.5%). CONCLUSIONS:Telemedicine-based opioid treatment and unobserved home induction on buprenorphine-naloxone offers a safe and feasible approach to expand the reach of opioid use disorder treatment, primary care, and behavioral health for a highly vulnerable urban population during an unprecedented natural disaster.