Faculty Bibliography

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.

BACKGROUND:Limiting the incidence of opioid-naïve patients who transition to long-term opioid use (i.e., continual use for > 90 days) is a key strategy for reducing opioid-related harms. OBJECTIVE:To identify variables constructed from data routinely collected by prescription drug monitoring programs that are associated with opioid-naïve patients' likelihood of transitioning to long-term use after an initial opioid prescription. DESIGN/METHODS:Statewide cohort study using prescription drug monitoring program data PARTICIPANTS: All opioid-naïve patients in California (no opioid prescriptions within the prior 2 years) age ≥ 12 years prescribed an initial oral opioid analgesic from 2010 to 2017. METHODS AND MAIN MEASURES/UNASSIGNED:Multiple logistic regression models using variables constructed from prescription drug monitoring program data through the day of each patient's initial opioid prescription, and, alternatively, data available up to 30 and 60 days after the initial prescription were constructed to identify probability of transition to long-term use. Model fit was determined by the area under the receiver operating characteristic curve (C-statistic). KEY RESULTS/RESULTS:Among 30,569,125 episodes of patients receiving new opioid prescriptions, 1,809,750 (5.9%) resulted in long-term use. Variables with the highest adjusted odds ratios included concurrent benzodiazepine use, ≥ 2 unique prescribers, and receipt of non-pill, non-liquid formulations. C-statistics for the day 0, day 30, and day 60 models were 0.81, 0.88, and 0.94, respectively. Models assessing opioid dose using the number of pills prescribed had greater discriminative capacity than those using milligram morphine equivalents. CONCLUSIONS:Data routinely collected by prescription drug monitoring programs can be used to identify patients who are likely to develop long-term use. Guidelines for new opioid prescriptions based on pill counts may be simpler and more clinically useful than guidelines based on days' supply or milligram morphine equivalents.

OBJECTIVES:The rapid increase in opioid overdose and opioid use disorder (OUD) over the past 20 years is a complex problem associated with significant economic costs for healthcare systems and society. Simulation models have been developed to capture and identify ways to manage this complexity and to evaluate the potential costs of different strategies to reduce overdoses and OUD. A review of simulation-based economic evaluations is warranted to fully characterize this set of literature. METHODS:A systematic review of simulation-based economic evaluation (SBEE) studies in opioid research was initiated by searches in PubMed, EMBASE, and EbscoHOST. Extraction of a predefined set of items and a quality assessment were performed for each study. RESULTS:The screening process resulted in 23 SBEE studies ranging by year of publication from 1999 to 2019. Methodological quality of the cost analyses was moderately high. The most frequently evaluated strategies were methadone and buprenorphine maintenance treatments; the only harm reduction strategy explored was naloxone distribution. These strategies were consistently found to be cost-effective, especially naloxone distribution and methadone maintenance. Prevention strategies were limited to abuse-deterrent opioid formulations. Less than half (39%) of analyses adopted a societal perspective in their estimation of costs and effects from an opioid-related intervention. Prevention strategies and studies' accounting for patient and physician preference, changing costs, or result stratification were largely ignored in these SBEEs. CONCLUSION:The review shows consistently favorable cost analysis findings for naloxone distribution strategies and opioid agonist treatments and identifies major gaps for future research.

BACKGROUND:Big events (i.e., unique historical disruptions) like the COVID-19 epidemic and its associated period of social distancing can transform social structures, social interactions, and social norms. Social distancing rules and the fear of infection have greatly reduced face-to-face interactions, increased loneliness, reduced ties to helping institutions, and may also have disrupted the opioid use behaviors of people who use drugs. This research used Reddit to examine the impact of COVID-19 on the social networks and social processes of people who use opioids. METHODS:Data were collected from the social media forum, Reddit.com. At the beginning of the COVID-19 pandemic in the U.S. (March 5, 2020, to May 13, 2020), 2,000 Reddit posts were collected from the two most popular opioid subreddits (r/OpiatesRecovery, r/Opiates). Posts were reviewed for relevance to COVID-19 and opioid use resulting in a final sample of 300. Thematic analysis was guided by the Big Events framework. RESULTS:The COVID-19 pandemic was found to create changes in the social networks and daily lives among persons who use opioids. Adaptions to these changes shifted social networks leading to robust social support and mutual aid on Reddit, including sharing and seeking advice on facing withdrawal, dealing with isolation, managing cravings, and accessing recovery resources. CONCLUSIONS:Reddit provided an important source of social support and mutual aid for persons who use opioids. Findings indicate online social support networks are beneficial to persons who use opioids, particularly during big events where isolation from other social support resources may occur.

HIV incidence among men who have sex with men (MSM) in Greece remains unchanged despite effective response to a recent outbreak among people who inject drugs (PWID). Network factors are increasingly understood to drive transmission in epidemics. The primary objective of the study was to characterize MSM in Greece, their sexual behaviors, and sexual network mixing patterns. We investigated the relationship between serostatus, sexual behaviors, and self-reported sex networks in a sample of MSM in Athens, Greece, generated using respondent driven sampling. We estimated mixing coefficients (r) based on survey-generated egonets. Additionally, multiple logistic regression was used to estimate adjusted odds ratios (AOR) and to assess relationships between serostatus, sexual behaviors, and sociodemographic indicators. A sample of 1,520 MSM participants included study respondents (n = 308) and their network members (n = 1,212). Mixing based on serostatus (r = 0.12, σ_r = 0.09-0.15) and condomless sex (r = 0.11, σ_r = 0.07-0.14) was random. However, mixing based on sex-drug use was highly assortative (r = 0.37, σ_r = 0.32-0.42). This study represents the first analysis of Greek MSM sexual networks. Our findings highlight protective behavior in two distinct network typologies. The first typology mixed assortatively based on serostatus and sex-drug use and was less likely to engage in condomless sex. The second typology mixed randomly based on condomless sex but was less likely to engage in sex-drug use. These findings support the potential benefit of HIV prevention program scale-up for this population including but not limited to PrEP.

Drug use involves social interactions. Therefore, norms in the proximal environment of people who inject drugs (PWID) can favor behaviors that may result in HIV transmission. This work aimed at studying drug injection-related norms and their potential association with risky behaviors among PWID in Athens, Greece, in the context of economic recession and political activism that followed the fiscal crisis and soon after a recent HIV outbreak had leveled off. The Transmission Reduction Intervention Project (TRIP) was a social network-based approach (June 2013 to July 2015) that involved two groups of PWID seeds-with recent HIV infection and with long-term HIV infection and one control group of HIV-negative PWID. Network contacts of seeds were also enrolled. TRIP participants answered a questionnaire that included items on injection-related norms and behaviors. TRIP recruited 320 PWID (HIV positive, 44.4%). TRIP participants, especially those without HIV, often recalled or perceived as normative among their partners and in their networks some behaviors that can lead to HIV transmission. TRIP participants who recalled that they were encouraged by their regular drug partners to use an unclean syringe were almost twice as likely to report that they share syringes [odds ratio (OR) = 2.03; 95% confidence interval (CI) = 1.86-2.21], or give syringes to someone else (OR = 1.70; 95% CI = 1.42-2.04) as those who did not recall such an encouragement. Associations were modified by HIV status. HIV negatives, who were reportedly encouraged to share nonsyringe injecting equipment, were almost 4.5 times as likely to share that material as HIV-negative participants who were not encouraged (OR = 4.59, 95% CI = 4.12-5.11). Further research is needed on the multiple determinants (social, economic, and political) of norms in the social environments of PWID. Since peer norms are associated with risky behaviors, interventions should be developed to encourage norms and peer pressure against the sharing of injection equipment.