Faculty Bibliography

Black men who have sex with men (MSM) have been consistently reported to have the highest estimated HIV incidence and prevalence among MSM. Despite broad theoretical understanding that discrimination is a major social and structural determinant that contributes to disparate HIV outcomes among Black MSM, relatively little extant research has empirically examined structural discrimination against sexual minorities as a predictor of HIV outcomes among this population. The present study therefore examines whether variation in policies that explicitly discriminate against lesbian, gay, and bisexual (LGB) people and variation in policies that explicitly protect LGB people differentially predict metropolitan statistical-area-level variation in late HIV diagnoses among Black MSM over time, from 2008 to 2014. HIV surveillance data on late HIV diagnoses among Black MSM in each of the 95 largest metropolitan statistical areas in the United States, from 2008 to 2014, were used along with data on time-varying state-level policies pertaining to the rights of LGB people. Results from multilevel models found a negative relationship between protective/supportive laws and late HIV diagnoses among Black MSM, and a positive relationship between discriminative laws and late HIV diagnoses among Black MSM. These findings illuminate the potential epidemiological importance of policies pertaining to LGB populations as structural determinants of HIV outcomes among Black MSM. They suggest a need for scrutiny and elimination of discriminatory policies, where such policies are currently in place, and for advocacy for policies that explicitly protect the rights of LGB people where they do not currently exist.

BACKGROUND AND AIMS:Longitudinal studies have revealed that substance use treatment use is often recurrent among patients; the longitudinal patterns and characteristics of those treatment trajectories have received less attention, particularly in the global south. This study aimed to disentangle heterogeneity in treatment use among adult patients in Chile by identifying distinct treatment trajectory groups and factors associated with them. DESIGN:National-level registry-based retrospective cohort. SETTING AND PARTICIPANTS:Adults admitted to publicly funded substance use disorder treatment programs in Chile from November 2009 to November 2010 and followed for 9 years (n = 6266). MEASUREMENTS:Monthly treatment use; type of treatment; ownership of the treatment center; discharge status; primary substance used; sociodemographic. FINDINGS:A seven-class treatment trajectory solution was chosen using latent class growth analysis. We identified three trajectory groups that did not recur and had different treatment lengths: Early discontinuation (32%), Less than a year in treatment (19.7%) and Year-long episode, without recurrence (12.3%). We also identified a mixed trajectory group that had a long first treatment or two treatment episodes with a brief time between treatments: Long first treatment, or immediate recurrence (6.3%), and three recurrent treatment trajectory groups: Recurrent and decreasing (14.2%), Early discontinuation with recurrence (9.9%) and Recurrent after long between treatments period (5.7%). Inpatient or outpatient high intensity (vs. outpatient low intensity) at first entry increased the odds of being in the longer one-episode groups compared with the Early discontinuation group. Women had increased odds of belonging to all the recurrent groups. Using cocaine paste (vs. alcohol) as a primary substance decreased the odds of belonging to long one-episode groups. CONCLUSIONS:In Chile, people in publicly funded treatment for substance use disorder show seven distinct care trajectories: three groups with different treatment lengths and no recurring episodes, a mixed group with a long first treatment or two treatment episodes with a short between-treatment-episodes period and three recurrent treatment groups.

BACKGROUND:Both increases and decreases in patients' prescribed daily opioid dose have been linked to increased overdose risk, but associations between 30-day dose trajectories and subsequent overdose risk have not been systematically examined. OBJECTIVE:To examine the associations between 30-day prescribed opioid dose trajectories and fatal opioid overdose risk during the subsequent 15 days. DESIGN/METHODS:Statewide cohort study using linked prescription drug monitoring program and death certificate data. We constructed a multivariable Cox proportional hazards model that accounted for time-varying prescription-, prescriber-, and pharmacy-level factors. PARTICIPANTS/METHODS:All patients prescribed an opioid analgesic in California from March to December, 2013 (5,326,392 patients). MAIN MEASURES/METHODS:Dependent variable: fatal drug overdose involving opioids. Primary independent variable: a 16-level variable denoting all possible opioid dose trajectories using the following categories for current and 30-day previously prescribed daily dose: 0-29, 30-59, 60-89, or ≥90 milligram morphine equivalents (MME). KEY RESULTS/RESULTS:Relative to patients prescribed a stable daily dose of 0-29 MME, large (≥2 categories) dose increases and having a previous or current dose ≥60 MME per day were associated with significantly greater 15-day overdose risk. Patients whose dose decreased from ≥90 to 0-29 MME per day had significantly greater overdose risk compared to both patients prescribed a stable daily dose of ≥90 MME (aHR 3.56, 95%CI 2.24-5.67) and to patients prescribed a stable daily dose of 0-29 MME (aHR 7.87, 95%CI 5.49-11.28). Patients prescribed benzodiazepines also had significantly greater overdose risk; being prescribed Z-drugs, carisoprodol, or psychostimulants was not associated with overdose risk. CONCLUSIONS:Large (≥2 categories) 30-day dose increases and decreases were both associated with increased risk of fatal opioid overdose, particularly for patients taking ≥90 MME whose opioids were abruptly stopped. Results align with 2022 CDC guidelines that urge caution when reducing opioid doses for patients taking long-term opioid for chronic pain.

This article traces the evolution of the Big Events framework since it began as an attempt to understand why sociopolitical transitions in the Former Soviet Union, South Africa, and Indonesia were followed by HIV outbreaks. Big Events frameworks have evolved over time, but all versions try to concretize how macrosocial changes lead to social, personal and environmental changes that shape risk environments and drug use or other behavioral patterns in ways that may lead to epidemics. Important stages in the evolution of the Big Events framework included understanding that the sequelae of Big Events were contingent rather than deterministic, and the development of new survey measures to understand pathways through which Big Events affect social and epidemiologic outcomes. On a broader level, the Big Events framework is a useful crystallization and application of more abstract sociological, social epidemiologic and Marxist frameworks about upstream/downstream relationships and how major social changes are related to epidemics. As such, they raise issues of how to conduct research on dialectical interaction processes. On another level, this article traces the Big Events "style of thought" as Mannheim (Mannheim, 1971) termed it, within the historical context of changes in public health and social science theory, particularly during and after the 1960s.

An important challenge to addressing the opioid overdose crisis is the lack of information on the size of the population of people who misuse opioids (PWMO) in local areas. This estimate is needed for better resource allocation, estimation of treatment and overdose outcome rates using appropriate denominators (ie, the population at risk), and proper evaluation of intervention effects. In this study, we used a bayesian hierarchical spatiotemporal integrated abundance model that integrates multiple types of county-level surveillance outcome data, state-level information on opioid misuse, and covariates to estimate the latent (hidden) numbers of PWMO and latent prevalence of opioid misuse across New York State counties (2007-2018). The model assumes that each opioid-related outcome reflects a partial count of the number of PWMO, and it leverages these multiple sources of data to circumvent limitations of parameter estimation associated with other types of abundance models. Model estimates showed a reduction in the prevalence of PWMO during the study period, with important spatial and temporal variability. The model also provided county-level estimates of rates of treatment and opioid overdose using the numbers of PWMO as denominators. This modeling approach can identify the sizes of hidden populations to guide public health efforts in confronting the opioid overdose crisis across local areas. This article is part of a Special Collection on Mental Health.

OBJECTIVE:To ascertain whether a novel expanded social network recruitment to HIV testing (E-SNRHT) intervention recruits men and individuals with previously-undiagnosed HIV at higher rates than risk network recruitment. DESIGN/METHODS:Initial "seed" participants were prospectively randomly assigned to the E-SNRHT intervention or to risk network recruitment. Their network members were included in the study arm of their recruiter. SETTING/METHODS:Three Department of Health clinics and two drug treatment centers (DTCs) in the Msunduzi municipality of KwaZulu-Natal, South Africa. PARTICIPANTS/METHODS:Clinics and DTCs referred 110 newly-HIV-diagnosed adult "seeds" to the study from June 2022-February 2023. E-SNRHT seeds were asked to recruit network members as described below; risk network recruitment arm seeds were asked to recruit recent sex and/or injection partners. Presenting a recruitment coupon (from clinic/DTC staff or another participant) was required for eligibility. INTERVENTION/METHODS:E-SNRHT seeds were shown educational material about HIV transmission risks and then asked to recruit anyone they know (e.g., friends, family) whom they thought could benefit from HIV testing. MAIN OUTCOME MEASURES/METHODS:Rates of recruiting men to HIV testing and locating individuals with previously-undiagnosed HIV. RESULTS:E-SNRHT recruited significantly higher proportions of men to HIV testing (70.3% vs. 40.4%; χ2 = 16.33; p < .0005) and located significantly more previously-undiagnosed cases of HIV per seed than risk network recruitment (rate ratio = 9.40; p < .0001). E-SNRHT also recruited significantly higher proportions of women with previously-undiagnosed HIV (29.0% vs. 10.7%; χ2 = 3.87; p = .049). CONCLUSIONS:E-SNRHT is an important strategy to expand the reach of HIV testing among men and undiagnosed cases of HIV in KwaZulu-Natal.

BACKGROUND:Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery. METHODS:We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using marginal structural models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors. RESULTS:There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87). CONCLUSIONS:These methods will be useful for evaluating intervention packages in studies with network features.

INTRODUCTION/BACKGROUND:People with chronic pain are at increased risk of opioid misuse. Less is known about the unique risk conferred by each pain management treatment, as treatments are typically implemented together, confounding their independent effects. This study estimated the extent to which pain management treatments were associated with risk of opioid use disorder (OUD) for those with chronic pain, controlling for baseline demographic and clinical confounding variables and holding other pain management treatments at their observed levels. METHODS:Data were analyzed in 2024 from 2 chronic pain subgroups within a cohort of non-pregnant Medicaid patients aged 35-64 years, 2016-2019, from 25 states: those with (1) chronic pain and physical disability (CPPD) (N=6,133) or (2) chronic pain without disability (CP) (N=67,438). Nine pain management treatments were considered: prescription opioid (1) dose and (2) duration; (3) number of opioid prescribers; opioid co-prescription with (4) benzo- diazepines, (5) muscle relaxants, and (6) gabapentinoids; (7) nonopioid pain prescription, (8) physical therapy, and (9) other pain treatment modality. The outcome was OUD risk. RESULTS:Having opioids co-prescribed with gabapentin or benzodiazepine was statistically significantly associated with a 37-45% increased OUD risk for the CP subgroup. Opioid dose and duration also were significantly associated with increased OUD risk in this subgroup. Physical therapy was significantly associated with an 18% decreased risk of OUD in the CP subgroup. DISCUSSION/CONCLUSIONS:Coprescription of opioids with either gabapentin or benzodiazepines may substantially increase OUD risk. More positively, physical therapy may be a relatively accessible and safe pain management strategy.