Faculty Bibliography

IMPORTANCE:People who use drugs (PWUD) continue to be at risk of HIV infection, but the frequency and distribution of transmission-associated behaviors within various rural communities is poorly understood. OBJECTIVE:To examine the association of characteristics of rural PWUD with HIV transmission behaviors. DESIGN, SETTING, AND PARTICIPANTS:In this cross-sectional study, surveys of PWUD in rural communities in 10 states (Illinois, Kentucky, New Hampshire, Massachusetts, North Carolina, Ohio, Oregon, Vermont, West Virginia, and Wisconsin) were collected January 2018 through March 2020 and analyzed August through December 2022. A chain-referral sampling strategy identified convenience sample seeds who referred others who used drugs. Rural PWUD who reported any past 30-day injection drug use or noninjection opioid use "to get high" were included. EXPOSURES:Individual characteristics, including age, race, gender identity, sexual orientation, partnership status, drug of choice, and location, were collected. MAIN OUTCOMES AND MEASURES:Past 30-day frequency of behaviors associated with HIV transmission, including drug injection, syringe sharing, opposite- and same-gender partners, transactional sex, and condomless sex, was assessed. RESULTS:Of 3048 rural PWUD (mean [SD] age, 36.1 [10.3] years; 225 American Indian [7.4%], 96 Black [3.2%], and 2576 White [84.5%] among 3045 with responses; and 1737 men [57.0%] among 3046 with responses), most participants were heterosexual (1771 individuals [86.8%] among 2040 with responses) and single (1974 individuals [68.6%] among 2879 with responses). Opioids and stimulants were reported as drug of choice by 1636 individuals (53.9%) and 1258 individuals (41.5%), respectively, among 3033 individuals with responses. Most participants reported recent injection (2587 of 3046 individuals [84.9%] with responses) and condomless sex (1406 of 1757 individuals [80.0%] with responses), among whom 904 of 1391 individuals (65.0%) with responses indicated that it occurred with people who inject drugs. Syringe sharing (1016 of 2433 individuals [41.8%] with responses) and transactional sex (230 of 1799 individuals [12.8%] with responses) were reported less frequently. All characteristics and behaviors, except the number of men partners reported by women, varied significantly across locations (eg, mean [SD] age ranged from 34.5 [10.0] years in Wisconsin to 39.7 [11.0] years in Illinois; P < .001). In multivariable modeling, younger age (adjusted odds ratio [aOR] for ages 15-33 vs ≥34 years, 1.36; 95% CI, 1.08-1.72) and being single (aOR, 1.37; 95% CI, 1.08-1.74) were associated with recent injection; younger age (aOR, 1.49; 95% CI, 1.20-1.85) and bisexual orientation (aOR vs heterosexual orientation, 2.27; 95% CI, 1.60-3.23) with syringe sharing; gender identity as a woman (aOR vs gender identity as a man, 1.46; 95% CI, 1.01-2.12), bisexual orientation (aOR vs heterosexual orientation, 2.59; 95% CI, 1.67-4.03), and being single (aOR, 1.71; 95% CI, 1.15-2.55) with transactional sex; and bisexual orientation (aOR vs heterosexual orientation, 1.60; 95% CI, 1.04-2.46) and stimulants as the drug of choice (aOR vs opioids, 1.45; 95 CI, 1.09-1.93) with condomless sex with someone who injects drugs. CONCLUSIONS AND RELEVANCE:This study found that behaviors associated with HIV transmission were common and varied across communities. These findings suggest that interventions to reduce HIV risk among rural PWUD may need to be tailored to locally relevant factors.

BACKGROUND:Identifying community characteristics associated with firearm assault could facilitate prevention. We investigated the effect of community firearm dealer and alcohol outlet densities on individual risk of firearm assault injury. METHODS:In this density-sampled case-control study of Californians, January 2005-September 2015, cases comprised all residents with a fatal or nonfatal firearm assault injury. For each month, we sampled controls from the state population in a 4:1 ratio with cases. Exposures were monthly densities of county-level pawn and nonpawn firearm dealers and ZIP code-level off-premises alcohol outlets and bars and pubs ("bars/pubs"). We used case-control-weighted G-computation to estimate risk differences (RD) statewide and among younger Black men, comparing observed exposure densities to hypothetical interventions setting these densities to low. We estimated additive interactions between firearm and alcohol retailer density. Secondary analyses examined interventions targeted to high exposure density or outcome burden areas. RESULTS:There were 67,850 cases and 268,122 controls. Observed (vs. low) densities of pawn firearm dealers and off-premises alcohol outlets were individually associated with elevated monthly risk of firearm assault per 100,000 people (RD pawn dealers : 0.06, 95% CI: 0.05, 0.08; RD off-premises outlets : 0.01, 95% CI: 0.01, 0.03), but nonpawn firearm dealer and bar/pub density were not; models targeting only areas with the highest outcome burden were similar. Among younger Black men, estimates were larger. There was no interaction between firearm and alcohol retailer density. CONCLUSIONS:Our results are consistent with the hypothesis that limiting pawn firearm dealers and off-premises alcohol outlet densities can reduce interpersonal firearm violence.

OBJECTIVE:The COVID-19 pandemic has significantly affected people's ability to buy, sell, and obtain items that they use in their daily lives. It may have had a particularly negative effect on the ability of people who use illicit opioids to obtain them because the networks they relied on are illicit and not part of the formal economy. Our objective in this research was to examine if, and how, disruptions related to COVID-19 of illicit opioid markets have affected people who use illicit opioids. METHOD:We collected 300 posts--including replies to posts--related to the intersection of COVID-19 and opioid use from Reddit.com, a forum that has several discussion threads (i.e., subreddits) dedicated to opioids. We then coded posts from the two most popular opioid subreddits during the early pandemic period (March 5, 2020-May 13, 2020) using an inductive/deductive approach. RESULTS:We found two themes related to active opioid use during the early pandemic: (a) changes in drug supply and difficulty obtaining opioids, and (b) buying less-trustworthy drugs from lesser-known sources. CONCLUSIONS:Our findings suggest that COVID-19 has created market conditions that place people who use opioids at risk of adverse outcomes, such as fatal overdose.

BACKGROUND:Due to practical challenges associated with genetic sequencing in low-resource environments, the burden of hepatitis C virus (HCV) in forcibly displaced people is understudied. We examined the use of field applicable HCV sequencing methods and phylogenetic analysis to determine HCV transmission dynamics in internally displaced people who inject drugs (IDPWID) in Ukraine. METHODS:In this cross-sectional study, we used modified respondent-driven sampling to recruit IDPWID who were displaced to Odesa, Ukraine, before 2020. We generated partial and near full length genome (NFLG) HCV sequences using Oxford Nanopore Technology (ONT) MinION in a simulated field environment. Maximum likelihood and Bayesian methods were used to establish phylodynamic relationships. RESULTS:Between June and September 2020, we collected epidemiological data and whole blood samples from 164 IDPWID (PNAS Nexus.2023;2(3):pgad008). Rapid testing (Wondfo® One Step HCV; Wondfo® One Step HIV1/2) identified an anti-HCV seroprevalence of 67.7%, and 31.1% of participants tested positive for both anti-HCV and HIV. We generated 57 partial or NFLG HCV sequences and identified eight transmission clusters, of which at least two originated within a year and a half post-displacement. CONCLUSIONS:Locally generated genomic data and phylogenetic analysis in rapidly changing low-resource environments, such as those faced by forcibly displaced people, can help inform effective public health strategies. For example, evidence of HCV transmission clusters originating soon after displacement highlights the importance of implementing urgent preventive interventions in ongoing situations of forced displacement.

INTRODUCTION:Rates of illicit opioid use are particularly high among young adults, yet research on overdose experience and factors associated with overdose in this population remains limited. This study examines the experiences and correlates of non-fatal overdose among young adults using illicit opioids in New York City (NYC). METHODS:539 participants were recruited via Respondent-Driven Sampling in 2014-2016. Eligibility criteria included: aged 18-29 years old; current residence in NYC; and nonmedical prescription opioid (PO) use and/or heroin use in the past 30 days. Participants completed structured interviews to assess their socio-demographics, drug use trajectories, current substance use and lifetime and most recent overdose experiences, and were tested on-site for hepatitis C virus (HCV) antibodies. RESULTS:43.9% of participants reported lifetime overdose experience; of these, 58.8% had experienced two or more overdose events. The majority of participants' most recent overdoses (63.5%) were due to polysubstance use. In bivariable analyses, after RDS adjustment, having ever overdosed was correlated with: household income of >$100,00 growing up (vs. $51,000-100,000); lifetime homelessness; HCV antibody-positive status; lifetime engagement in regular nonmedical benzodiazepine use, regular heroin injection and regular PO injection; and using a non-sterile syringe in the past 12 months. Multivariable logistic regression identified childhood household income >$100,00 (AOR=1.88), HCV-positive status (AOR=2.64), benzodiazepine use (AOR=2.15), PO injection (AOR=1.96) and non-sterile syringe use (AOR=1.70) as significant independent correlates of lifetime overdose. A multivariable model with multiple overdoses (vs. one) found only lifetime regular heroin use and PO injection to be strong correlates. DISCUSSION:Results indicate a high prevalence of lifetime and repeated overdose among opioid-using young adults in NYC, highlighting a need for intensified overdose prevention efforts for this population. The strong associations of HCV and indices of polydrug use with overdose suggest that prevention efforts should address the complex risk environment in which overdose occurs, attending to the overlapping nature of disease-related risk behavior and overdose risk behavior among young people who inject opioids. Overdose prevention efforts tailored for this group may find it useful to adopt a syndemic conception of overdose that understands such events as resulting from multiple, and often interrelated, risk factors.

OBJECTIVES:Behavioral health diagnoses are frequently underreported in administrative health data. For a pragmatic trial of a hospital addiction consult program, we sought to determine the sensitivity of Medicaid claims data for identifying patients with opioid use disorder (OUD). METHODS:A structured review of electronic health record (EHR) data was conducted to identify patients with OUD in 6 New York City public hospitals. Cases selected for review were adults admitted to medical/surgical inpatient units who received methadone or sublingual buprenorphine in the hospital. For cases with OUD based on EHR review, we searched for the hospitalization in Medicaid claims data and examined International Classification of Diseases, Tenth Revision discharge diagnosis codes to identify opioid diagnoses (OUD, opioid poisoning, or opioid-related adverse events). Sensitivity of Medicaid claims data for capturing OUD hospitalizations was calculated using EHR review findings as the reference standard measure. RESULTS:Among 552 cases with OUD based on EHR review, 465 (84.2%) were found in the Medicaid claims data, of which 418 (89.9%) had an opioid discharge diagnosis. Opioid diagnoses were the primary diagnosis in 49 cases (11.7%), whereas in the remainder, they were secondary diagnoses. CONCLUSION:In this sample of hospitalized patients receiving OUD medications, Medicaid claims seem to have good sensitivity for capturing opioid diagnoses. Although the sensitivity of claims data may vary, it can potentially be a valuable source of information about OUD patients.

Trauma, violence, and their consequences for population health are shaped by complex, intersecting forces across the life span. We aimed to illustrate the strengths of agent-based modeling (ABM), a computational approach in which population-level patterns emerge from the behaviors and interactions of simulated individuals, for advancing trauma research; Method: We provide an overview of agent-based modeling for trauma research, including a discussion of the model development process, ABM as a complement to other causal inference and complex systems approaches in trauma research, and past ABM applications in the trauma literature; Results: We use existing ABM applications to illustrate the strengths of ABM for trauma research, including incorporating interactions between individuals, simulating processes across multiple scales, examining life-course effects, testing alternate theories, comparing intervention strategies in a virtual laboratory, and guiding decision making. We also discuss the challenges of applying ABM to trauma research and offer specific suggestions for incorporating ABM into future studies of trauma and violence; Conclusion: Agent-based modeling is a useful complement to other methodological advances in trauma research. We recommend a more widespread adoption of ABM, particularly for research into patterns and consequences of individual traumatic experiences across the life course and understanding the effects of interventions that may be influenced by social norms and social network structures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

BACKGROUND AND AIMS:Low outcome prevalence, often observed with opioid-related outcomes, poses an underappreciated challenge to accurate predictive modeling. Outcome class imbalance, where non-events (i.e. negative class observations) outnumber events (i.e. positive class observations) by a moderate to extreme degree, can distort measures of predictive accuracy in misleading ways, and make the overall predictive accuracy and the discriminatory ability of a predictive model appear spuriously high. We conducted a simulation study to measure the impact of outcome class imbalance on predictive performance of a simple SuperLearner ensemble model and suggest strategies for reducing that impact. DESIGN, SETTING, PARTICIPANTS:Using a Monte Carlo design with 250 repetitions, we trained and evaluated these models on four simulated data sets with 100 000 observations each: one with perfect balance between events and non-events, and three where non-events outnumbered events by an approximate factor of 10:1, 100:1, and 1000:1, respectively. MEASUREMENTS:We evaluated the performance of these models using a comprehensive suite of measures, including measures that are more appropriate for imbalanced data. FINDINGS:Increasing imbalance tended to spuriously improve overall accuracy (using a high threshold to classify events vs non-events, overall accuracy improved from 0.45 with perfect balance to 0.99 with the most severe outcome class imbalance), but diminished predictive performance was evident using other metrics (corresponding positive predictive value decreased from 0.99 to 0.14). CONCLUSION:Increasing reliance on algorithmic risk scores in consequential decision-making processes raises critical fairness and ethical concerns. This paper provides broad guidance for analytic strategies that clinical investigators can use to remedy the impacts of outcome class imbalance on risk prediction tools.

OBJECTIVES/OBJECTIVE:This study aimed to examine trends in cannabis-positive urine drug screens (UDSs) among emergency department (ED) patients from 2008 to 2019 using data from the Veterans Health Administration (VHA) health care system, and whether these trends differed by age group (18-34, 35-64, and 65-75 years), sex, and race, and ethnicity. METHOD/METHODS:VHA electronic health records from 2008 to 2019 were used to identify the percentage of unique VHA patients seen each year at an ED, received a UDS, and screened positive for cannabis. Trends in cannabis-positive UDS were examined by age, race and ethnicity, and sex within age groups. RESULTS:Of the VHA ED patients with a UDS, the annual prevalence positive for cannabis increased from 16.42% in 2008 to 27.2% in 2019. The largest increases in cannabis-positive UDS were observed in the younger age groups. Male and female ED patients tested positive for cannabis at similar levels. Although the prevalence of cannabis-positive UDS was consistently highest among non-Hispanic Black patients, cannabis-positive UDS increased in all race and ethnicity groups. DISCUSSION/CONCLUSIONS:The increasing prevalence of cannabis-positive UDS supports the validity of previously observed population-level increases in cannabis use and cannabis use disorder from survey and administrative records. Time trends via UDS results provide additional support that previously documented increases in self-reported cannabis use and disorder from surveys and claims data are not spuriously due to changes in patient willingness to report use as it becomes more legalized, or due to greater clinical attention over time.

BACKGROUND AND AIMS:Both local socio-economic conditions and prescription opioid supply are associated with drug overdose deaths, which exhibit substantial geographical heterogeneity across the United States. We measured whether the associations of prescription opioid supply with drug overdose deaths vary by local socio-economic conditions. DESIGN:Ecological county-level study, including 3109 US counties between 2006 and 2019 (n = 43 526 county-years) using annual mortality data. SETTING:United States. CASES:A total of 711 447 drug overdose deaths. MEASUREMENTS:We modeled overdose counts using Bayesian hierarchical Poisson models, estimating associations between four types of drug overdose deaths (deaths involving any drugs, any opioid, prescription opioids only and heroin), prescription opioid supply and five socio-economic indicators: unemployment, poverty rate, income inequality, Rey index (components include mean household income, % high school graduates, % blue-collar workers and unemployment rate), and American human development index (HDI; an indicator of community wellbeing). FINDINGS:Drug overdose deaths and all substance-specific overdose deaths were higher in counties with higher income inequality [adjusted odds ratios (aORs) = 1.09-1.13], Rey index (aORs = 1.15-1.21) and prescription opioid supply (aORs = 1.14-1.21), and lower in counties with higher HDI scores (aORs = 0.75-0.92). Poverty rate, income inequality and HDI scores were found to modify the effect of prescription opioid supply on heroin overdose deaths. The plot of the interactions showed that when disadvantage is high, increasing prescription opioid supply does not increase heroin overdose deaths. The less disadvantage there is, indicated by lower poverty rates, higher HDI scores and lower income inequality, the greater the effect of increasing prescription opioid supply relative to population size on heroin overdose deaths in US counties. CONCLUSIONS:In the United States, prescription opioid supply is associated with higher drug overdose deaths; associations are stronger in counties with less disadvantage and less income inequality, but only for heroin overdose deaths.