Faculty Bibliography

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.

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.

INTRODUCTION/BACKGROUND:Methadone and buprenorphine are effective treatment for opioid use disorder (OUD), yet they are vastly under-utilized across US hospitals. To inform a national trial assessing the effectiveness of implementation strategies to increase adoption of an inpatient hospital-based opioid treatment (HBOT) model (NCT04921787), we explored barriers and facilitators to expanding medication for opioid use disorder (MOUD) within community hospitals across the United States. METHODS:From November 2021 to March 2022, we used purposeful and snowball sampling to identify and interview participants involved in inpatient care of patients with OUD from twelve community hospitals. We conducted semi-structured interviews on providers' experiences and perspectives on current treatment approaches as well as potential influences on MOUD expansion in their hospitals. We used thematic analysis to identify key barriers and facilitators that could impact implementation of an HBOT model, and organized these findings based on the Consolidated Framework for Implementation Research (CFIR). RESULTS:From qualitative interviews with 57 participants (30 physicians, 7 pharmacists, 6 nurses, and 14 professionals involved in the care of patients with OUD), we identified key barriers and facilitators mapped to CFIR's internal and outer settings. The most salient inner setting domains included tension for change and relative priority, compatibility, available resources, organizational culture, access to knowledge and information, relational connections and communications, and information technology infrastructure. Outer setting domains included policies and laws, financing, and partnerships and connections. CONCLUSIONS:Identifying potential barriers and facilitators can inform hospital-specific strategies to support implementation of HBOT. Implementation strategies that address barriers such as staff availability, knowledge, and attitudes may support increased HBOT adoption. On a broader scale, national policy changes such as increased financing and public reporting of quality metrics would address other barriers we identified and may also encourage hospitals to adopt HBOT models.

We examined a natural history of opioid overdose deaths from 1999-2021 in the United States to describe state-level spatio-temporal heterogeneity in the waves of the epidemic. We obtained overdose death counts by state from 1999-2021, categorized as involving prescription opioids, heroin, synthetic opioids, or unspecified drugs. We developed a Bayesian multivariate multiple change point model to flexibly estimate the timing and magnitude of state-specific changes in death rates involving each drug type. We found substantial variability around the timing and severity of each wave across states. The first wave of prescription-involved deaths started between 1999 and 2005, the second wave of heroin-involved deaths started between 2010 and 2014, and the third wave of synthetic opioid-involved deaths started between 2014 and 2021. The severity of the second and third waves was greater in states in the eastern half of the country. Our study highlights state-level variation in the timing and severity of the waves of the opioid epidemic by presenting a 23-year natural history of opioid overdose mortality in the United States. While reinforcing the general notion of three waves, we find that states did not uniformly experience the impacts of each wave.

OBJECTIVES/OBJECTIVE:The United States faces an ongoing drug overdose crisis, but accurate information on the prevalence of opioid use disorder (OUD) remains limited. A recent analysis by Keyes et al used a multiplier approach with drug poisoning mortality data to estimate OUD prevalence. Although insightful, this approach made stringent and partly inconsistent assumptions in interpreting mortality data, particularly synthetic opioid (SO)-involved and non-opioid-involved mortality. We revise that approach and resulting estimates to resolve inconsistencies and examine several alternative assumptions. METHODS:We examine 4 adjustments to Keyes and colleagues' estimation approach: (A) revising how the equations account for SO effects on mortality, (B) incorporating fentanyl prevalence data to inform estimates of SO lethality, (C) using opioid-involved drug poisoning data to estimate a plausible range for OUD prevalence, and (D) adjusting mortality data to account for underreporting of opioid involvement. RESULTS:Revising the estimation equation and SO lethality effect (adj. A and B) while using Keyes and colleagues' original assumption that people with OUD account for all fatal drug poisonings yields slightly higher estimates, with OUD population reaching 9.3 million in 2016 before declining to 7.6 million by 2019. Using only opioid-involved drug poisoning data (adj. C and D) provides a lower range, peaking at 6.4 million in 2014-2015 and declining to 3.8 million in 2019. CONCLUSIONS:The revised estimation equation presented is feasible and addresses limitations of the earlier method and hence should be used in future estimations. Alternative assumptions around drug poisoning data can also provide a plausible range of estimates for OUD population.

HIV-related stigma is a well-documented barrier to HIV testing in South Africa, and may be particularly likely to create reluctance to test among South African men, who have reported feeling blamed for HIV by their partners and communities. The present study presents a novel expanded social network recruitment to HIV testing (E-SNRHT) intervention explicitly designed to reduce stigma as a barrier to testing by asking people to recruit anyone they know to testing, thus allowing them to avoid the potential for increased stigma and/or blame associated with direct risk partner recruitment, and helping to normalize openly discussing HIV among social networks. We examined baseline and 6-10-week follow-up data from a 2022-2023 randomized trial in KwaZulu-Natal, South Africa that recruited 110 individuals who had been newly diagnosed with HIV and randomly assigned them to recruit people to HIV testing either via the E-SNRHT intervention or via risk network recruitment. Participants in the E-SNRHT intervention reported significant decreases in anticipated and enacted HIV-related stigma between baseline and follow-up; and the E-SNRHT intervention was more effective at decreasing enacted HIV-related stigma than was risk network recruitment. Individuals newly diagnosed with HIV by the E-SNRHT intervention reported significant increases in social support between intervention enrollment and follow-up, and all of these individuals reported participating in positive conversations about HIV services with peers in the 6-10 weeks after intervention enrollment. These findings suggest that E-SNRHT is a potentially important strategy to reduce HIV-related stigma as a barrier to HIV testing among peer networks in KwaZulu-Natal.

BACKGROUND:New diagnoses of HIV-1 infection among people who inject drugs (PWID) in Athens, Greece, saw a significant increase in 2011 and a subsequent decline after 2013. Despite this, ongoing HIV-1 transmission persisted from 2014 to 2020 within this population. Our objective was to estimate the time of infection for PWID in Athens following the HIV-1 outbreak, explore the patterns of HIV-1 dispersal over time, and determine the duration from infection to diagnosis. METHODS:Time from HIV-1 infection to diagnosis was estimated for 844 individuals infected within 4 PWID-specific clusters and for 8 PWID infected with sub-subtype A6 diagnosed during 2010-2019. Phylogeny reconstruction was performed using the maximum-likelihood method. HIV-1 infection dates were based on molecular clock calculations. RESULTS:In total 86 of 92 (93.5%) sequences from PWID diagnosed during 2016-2019 were either related to the previously identified PWID-specific clusters (n = 81) or belonged to a new A6 cluster (n = 5). The median time between infection and diagnosis was 0.42 years during the outbreak period and 0.70 years during 2016-2019 (p < 0.001). The proportion of clustered sequences from PWID was very low at 5.3% during the pre-outbreak period (1998-2009), saw an increase to 41.7% one year before the outbreak in 2010, and consistently remained high during the whole period after 2011, spanning the post-outbreak period (2016-2019) with a range from 92.9% to 100%. CONCLUSIONS:The substantial proportion of clustered infections (93.5%) during 2016-2019 implies a persistent 'slow burn' HIV outbreak among PWID in Athens, suggesting that the outbreak was not successfully eliminated. The consistently high proportion of clustered sequences since the onset of the outbreak suggests the persistence of ongoing HIV-1 transmission attributed to injection practices. Our findings underscore the importance of targeted interventions among PWID, considering the ongoing transmission rate and prolonged time from infection to diagnosis.