Faculty Bibliography

BACKGROUND:The rise in opioid-related mortality and opioid-related emergency department (ED) visits has stimulated research on whether broader economic declines, such as the Great Recession, affect opioid-related morbidity. We examine in New York City whether one measure of morbidity-opioid-related ED visits-responded acutely to the large negative "shock" of the Great Recession. METHODS:Data comprise outpatient "treat and release" opioid-related ED visits in New York City for the 72 months spanning January 2006 to December 2011, taken from the Statewide Emergency Department Database (n = 150,246). We modeled the monthly incidence of opioid-related ED visits using Autoregressive, Integrated, Moving Average (ARIMA) time-series methods to control for patterning in ED visits before examining its potential association with the economic shock of the Great Recession. RESULTS:New York City shows a mean of 1761 outpatient ED visits per month for opioid dependence and abuse. Unexpectedly large drops in employment coincide with fewer than expected opioid dependence and abuse ED visits in that same month. The result (coefficient = 0.046, 95% Confidence Interval [CI]: 0.002, 0.090) represents a 0.8% drop in overall incidence of opioid dependence and abuse ED visits during the Great Recession. We, however, observe no association between the Great Recession and ED visits for prescription opioid overdose or heroin overdose, or with inpatient ED visits for opioid dependence and abuse. CONCLUSIONS:Findings, if replicated, indicate distinct short-term reductions in opioid-related morbidity following the Great Recession. This result diverges from previous findings of increased opioid use following extended economic downturns.

OBJECTIVE:People experiencing homelessness (PEH) have high rates of substance use, and homelessness may be an important driver of health disparities in the opioid overdose epidemic. However, few studies focus on homelessness among the opioid use disorder (OUD) treatment population. We examine national-level trends in substance use treatment admissions among PEH with OUD. METHODS:This study used data from first-time treatment admissions in the United States from the Treatment Episode Data Set: Admissions (TEDS-A) to examine characteristics and trends of adults experiencing homelessness who entered state-licensed substance use treatment programs for OUD from 2013 to 2017. We used chi-squared analyses to examine changes in characteristics of this population over time and logistic regression to assess characteristics associated with receipt of medications for opioid use disorder (MOUD) among PEH. RESULTS:Among all adults with OUD entering specialty treatment from 2013 to 2017, 12.5% reported experiencing homelessness. Compared to individuals not experiencing homelessness, PEH were more likely to be male, inject opioids, use cocaine or methamphetamine, and enter into residential detoxification treatment. PEH were less likely to enter outpatient treatment or receive MOUD. From 2013 to 2017, significant increases occurred in the proportion of PEH who had co-occurring psychiatric problems and used methamphetamines. Over time, treatment type shifted significantly from residential detoxification to outpatient treatment. Receipt of MOUD increased among PEH over time (13.7% to 25.2%), but lagged behind increases among individuals not experiencing homelessness. Among PEH, being older was associated with receiving MOUD, while concurrent methamphetamine use [adjusted odds ratio (AOR) 0.63; 95% CI 0.58, 0.69] and living in the southern United States (AOR 0.27; 95% CI 0.25, 0.30) were associated with not receiving MOUD. DISCUSSION/CONCLUSIONS:The proportion of PEH with OUD who receive medications as part of treatment increased over time, but three quarters of PEH entering treatment still do not receive this highest standard in evidence-based care. The sharp increase observed in concomitant methamphetamine use in this population is concerning and has implications for treatment.

Introduction: Although a substantial body of research documents a relationship between traumatic stress in childhood and the initiation of substance use later in the life course, only limited research has examined potential linkages between adverse childhood experiences (ACEs) and the initiation of non-medical prescription opioid use and other opioid use behaviors. The present study contributes to this growing body of work by investigating the association of childhood trauma with early initiation of a series of opioid use behaviors. Methods: New York City young adults (n = 539) ages 18-29 who reported non-medical use of prescription opioids or heroin use in the past 30 days were recruited using Respondent-Driven Sampling in 2014-16. Ten ACEs were assessed via self-report with the ACE Questionnaire. Associations between number of ACEs and self-reported ages of initiating seven opioid use behaviors (e.g., non-medical prescription opioid use, heroin use, heroin injection) were estimated with multivariable logistic regression. Results: Eighty nine percent of participants reported at least one ACE, and 46% reported four or more ACEs, a well-supported threshold indicating elevated risk for negative health consequences. Every increase of one trauma was associated with a 12-23% increase in odds of early initiation across the seven opioid use behaviors. Findings also document that the mean age at initiation increased with increasing risk severity across the behaviors, contributing to evidence of a trajectory from opioid pill misuse to opioid injection. Discussion: Increasing number of childhood traumas was associated with increased odds of earlier initiation of multiple opioid misuse behaviors. In light of prior research linking earlier initiation of substance use with increased substance use severity, present findings suggest the importance of ACEs as individual-level determinants of increased opioid use severity. Efforts to prevent onset and escalation of opioid use among at-risk youth may benefit from trauma prevention programs and trauma-focused screening and treatment, as well as increased attention to ameliorating upstream socio-structural drivers of childhood trauma.

Background: Structural properties of sociometric networks have been associated with behaviors related to HIV transmission. Very few studies, however, have explored the correlation between sociometric network factors and drug injection-related norms. Methods: This exploratory work: (i) describes basic structural qualities of a sociometric risk network of participants in the Transmission Reduction Intervention Project (TRIP) in Athens, Greece, in the context of a large HIV outbreak among people who inject drugs (PWID); (ii) measures HIV prevalence within specific structures within the sociometric risk network of PWID in TRIP; and (iii) explores the association of structural properties of the sociometric risk network in TRIP with drug injection-related norms. Results: The sociometric risk network in TRIP consisted of a large component (n = 241, 67.8%), a few small components (n = 36, 10.1%) with 2-10 individuals each, and some isolates (n = 79, 22.2%). HIV prevalence was significantly higher in the large component (55.6%), the 2-core (59.1%) and 3-core (66.3%) of the large component, and the 3-cliques of the cores. Drug injection-related norms were significantly associated with structural characteristics of the sociometric risk network. A safe behavioral pattern (use of unclean cooker/filter/rinse water was never encouraged) was significantly (p = 0.03) less normative among people who TRIP participants of the 2-core injected with (40.5%) than among network contacts of TRIP participants outside the 2-core (55.6%). On the contrary, at drug-using venues, 2-core members reported that safer behaviors were normative compared to what was reported by those without 2-core membership. Conclusions: Sociometric network data can give useful insights into HIV transmission dynamics and inform prevention strategies.Supplemental data for this article is available online at https://doi.org/10.1080/10826084.2021.1914103 .

Opioid-related mortality increased by nearly 400% between 2000 and 2018. In response, federal, state, and local governments have enacted a heterogeneous collection of opioid-related policies in an effort to reverse the opioid crisis, producing a policy landscape that is both complex and dynamic. Correspondingly, there has been a rise in opioid-policy related evaluation studies, as policymakers and other stakeholders seek to understand which policies are most effective. In this paper, we provide an overview of methodological challenges facing opioid policy researchers when evaluating the effects of opioid policies using observational data, as well as some potential solutions to those challenges. In particular, we discuss the following key challenges: (1) Obtaining high-quality opioid policy data; (2) Appropriately operationalizing and specifying opioid policies; (3) Obtaining high-quality opioid outcome data; (4) Addressing confounding due to systematic differences between policy and non-policy states; (5) Identifying heterogeneous policy effects across states, population subgroups, and time; (6) Disentangling effects of concurrent policies; and (7) Overcoming limited statistical power to detect policy effects afforded by commonly-used methods. We discuss each of these challenges and propose some ways forward to address them. Increasing the methodological rigor of opioid evaluation studies is imperative to identifying and implementing opioid policies that are most effective at reducing opioid-related harms.

Pre-exposure prophylaxis (PrEP) for HIV prevention may not only benefit the individual who uses it, but also their uninfected sexual risk contacts. We developed an agent-based model using a novel trial emulation approach to quantify disseminated effects of PrEP use among men who have sex with men in Atlanta, USA from 2015 to 2017. Components (subsets of agents connected through partnerships in a sexual network, but not sharing partnerships with any other agents) were first randomized to an intervention coverage level or control, then within intervention components, eligible agents were randomized to PrEP. We estimated direct and disseminated (indirect) effects using randomization-based estimators and reported corresponding 95% simulation intervals across scenarios ranging from 10% to 90% coverage in the intervention components. A population of 11,245 agents was simulated with an average of 1,551 components identified. Comparing agents randomized to PrEP in 70% coverage components to control agents, there was a 15% disseminated risk reduction in HIV incidence (95% simulation intervals = 0.65, 1.05). Individuals not on PrEP may receive a protective benefit by being in a sexual network with higher PrEP coverage. Agent-based models are useful to evaluate possible direct and disseminated effects of HIV prevention modalities in sexual networks.

BACKGROUND:Improving HIV diagnosis, access to care and effective antiretroviral treatment provides our global strategy to reduce HIV incidence. To reach this goal we need to increase our knowledge about local epidemics. HIV infection dates would be an important information towards this goal, but they are largely unknown. To date, methods to estimate the dates of HIV infection are based mainly on laboratory or molecular methods. Our aim was to validate molecular clock inferred infection dates that were estimated by analysing sequences from 145 people living with HIV (PLHIV) with known transmission dates (clinically estimated infection dates). METHODS:All HIV sequences were obtained by Sanger sequencing and were previously found to belong to well-established molecular transmission clusters (MTCs). RESULTS:Our analysis showed that the molecular clock inferred infection dates were correlated with the clinically estimated ones (Spearman's Correlation coefficient = 0.93, p < 0.001) and that there was an agreement between them (Lin's concordance correlation coefficient = 0.92, p < 0.001). For most cases (61.4%), the molecular clock inferred preceded the clinically estimated infection dates. The median difference between clinically and molecularly estimated dates of infection was of 0.18 (IQR: -0.21, 0.89) years. The lowest differences were identified in people who inject drugs of our study population. CONCLUSIONS:) of nodes within clusters provides a reliable approximation of HIV infections for PLHIV infected within MTCs. Next-generation sequencing data and molecular clock estimates based on heterochronous sequences provide, probably, more reliable methods for inferring infection dates. However, since these data are not available in most of the HIV clinical laboratories, our approach, under specific conditions, can provide a reliable estimation of HIV infection dates and can be used for HIV public health interventions.

Big Events are periods during which abnormal large-scale events like war, economic collapse, revolts, or pandemics disrupt daily life and expectations about the future. They can lead to rapid change in health-related norms, beliefs, social networks and behavioural practices. The world is undergoing such Big Events through the interaction of COVID-19, a large economic downturn, massive social unrest in many countries, and ever-worsening effects of global climate change. Previous research, mainly on HIV/AIDS, suggests that the health effects of Big Events can be profound, but are contingent: Sometimes Big Events led to enormous outbreaks of HIV and associated diseases and conditions such as injection drug use, sex trading, and tuberculosis, but in other circumstances, Big Events did not do so. This paper discusses and presents hypotheses about pathways through which the current Big Events might lead to better or worse short and long term outcomes for various health conditions and diseases; considers how pre-existing societal conditions and changing 'pathway' variables can influence the impact of Big Events; discusses how to measure these pathways; and suggests ways in which research and surveillance might be conducted to improve human capacity to prevent or mitigate the effects of Big Events on human health.

BACKGROUND AND AIMS/OBJECTIVE:Persons with substance use disorders (SUDs) are at elevated risk of suicide death. We identified novel risk factors and interactions that predict suicide among men and women with SUD using machine learning. DESIGN/METHODS:Case-cohort study. SETTING/METHODS:Denmark. PARTICIPANTS/METHODS:The sample was restricted to persons with their first SUD diagnosis during 1995 to 2015. Cases were persons who died by suicide in Denmark during 1995 to 2015 (n = 2774) and the comparison subcohort was a 5% random sample of individuals in Denmark on 1 January 1995 (n = 13 179). MEASUREMENTS/METHODS:Suicide death was recorded in the Danish Cause of Death Registry. Predictors included social and demographic information, mental and physical health diagnoses, surgeries, medications, and poisonings. FINDINGS/RESULTS:Persons among the highest risk for suicide, as identified by the classification trees, were men prescribed antidepressants in the 4 years before suicide and had a poisoning diagnosis in the 4 years before suicide; and women who were 30+ years old and had a poisoning diagnosis 4 years before and 12 months before suicide. Among men with SUD, the random forest identified five variables that were most important in predicting suicide; reaction to severe stress and adjustment disorders, drugs used to treat addictive disorders, age 30+ years, antidepressant use, and poisoning in the 4 prior years. Among women with SUD, the random forest found that the most important predictors of suicide were prior poisonings and reaction to severe stress and adjustment disorders. Individuals in the top 5% of predicted risk accounted for 15% of all suicide deaths among men and 24% of all suicides among women. CONCLUSIONS:In Denmark, prior poisoning and comorbid psychiatric disorders may be among the most important indicators of suicide risk among persons with substance use disorders, particularly among women.