Faculty Bibliography

BACKGROUND:The COVID-19 pandemic has transformed how people seeking to reduce opioid use access treatment services and navigate efforts to abstain from using opioids. Social distancing policies have drastically reduced access to many forms of social support, but they may have also upended some perceived barriers to reducing or abstaining from opioid use. OBJECTIVE:This qualitative study aimed to identify informal coping strategies for reducing and abstaining from opioid use among Reddit users who have posted in opioid-related subreddits at the beginning of the COVID-19 pandemic. METHODS:We extracted data from two major opioid-related subreddits. Thematic data analysis was used to evaluate subreddit posts dated from March 5, 2020 to May 13, 2020 that referenced COVID-19 and opioid use, resulting in a final sample of 300 posts that were coded and analyzed. RESULTS:Of the 300 subreddit posts, 100 discussed at least one type of informal coping strategy. Those strategies included: psychological and behavioral coping skills, adopting healthy habits, and using substances to manage withdrawal symptoms. Twelve subreddit posts explicitly mentioned using social distancing as an opportunity for cessation or reduction of opioid use. CONCLUSIONS:Reddit discussion forums provided a community for people to share strategies for reducing opioid use and support others during the COVID-19 pandemic. Future research needs to assess the impact of COVID-19 on opioid use behaviors, especially during periods of limited treatment access and isolation, as these can inform future efforts in curbing the opioid epidemic and other substance related harms.

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.

Adolescent internalizing symptoms have increased since 2010, while adequate sleep has declined for several decades. It remains unclear how self-reported sleep attainment has impacted internalizing symptoms trends. Using 1991-2019 MTF data (N~390,000), we estimate age-period-cohort effects in adolescent internalizing symptoms (loneliness, self-esteem, self-derogation, depressive affect) and the association with yearly prevalence of a survey-assessed, self-reported measure of ≥ 7 hours sleep attainment. We focus our main analysis on loneliness and use median odds ratios (MORs), measures of variance in loneliness associated with period differences. We observed limited signals for cohort effects and modeled only period effects. Loneliness increased by 0.83% per year; adolescents in 2019 had 0.68 (95% CI: 0.49, 0.87) increased log-odds of loneliness compared with the mean, consistent by race/ethnicity and parental education. Girls experienced steeper increases than boys (p<0.0001). The period effect MOR for loneliness was 1.16 (variance=0.09; 95% CI: 0.06, 0.17) before adjusting for self-reported frequency of ≥ 7 hours sleep vs. 1.07 (variance=0.02; 95% CI: 0.01, 0.03) after adjusting. Adolescents across cohorts are experiencing worsening internalizing symptoms. Self-reported frequency of <7 hours sleep partially explains increases in loneliness, indicating the need for feasibility trials to study the effect of increasing sleep attainment on internalizing symptoms.

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.

BACKGROUND AND AIMS/OBJECTIVE:In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning-based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level. DESIGN/METHODS:Randomized, population-based, community intervention trial. SETTING/METHODS:Rhode Island, USA. PARTICIPANTS/METHODS:All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. INTERVENTION/METHODS:Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web-based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community-based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions. MEASUREMENTS/METHODS:The primary outcome is the municipal-level rate of fatal and non-fatal drug overdoses. Fatal overdoses will be defined as unintentional drug-related death; non-fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non-fatal overdose rates in treatment vs. control municipalities. COMMENTS/CONCLUSIONS:The findings will inform the utility of predictive modelling as a tool to improve public health decision-making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services.

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.

Pre-exposure prophylaxis (PrEP) is a medication that prevents HIV acquisition, yet PrEP uptake has been low among people who inject drugs. Stigma has been identified as a fundamental driver of population health and may be a significant barrier to PrEP care engagement among PWID. However, there has been limited research on how stigma operates in rural and urban settings in relation to PrEP. Using in-depth semi-structured qualitative interviews (n = 57) we explore PrEP continuum engagement among people actively injecting drugs in rural and urban settings. Urban participants had more awareness and knowledge. Willingness to use PrEP was similar in both settings. However, no participant was currently using PrEP. Stigmas against drug use, HIV, and sexualities were identified as barriers to PrEP uptake, particularly in the rural setting. Syringe service programs in the urban setting were highlighted as a welcoming space where PWID could socialize and therefore mitigate stigma and foster information sharing.

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.

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.