Faculty Bibliography

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.

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.

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.

BACKGROUND:Non-fatal overdose is a leading predictor of subsequent fatal overdose. For individuals who are incarcerated, the risk of experiencing an overdose is highest when transitioning from a correctional setting to the community. We assessed if enrollment in jail-based medications for opioid use disorder (MOUD) is associated with lower risk of non-fatal opioid overdoses after jail release among individuals with opioid use disorder (OUD). METHODS:This was a retrospective, observational cohort study of adults with OUD who were incarcerated in New York City jails and received MOUD or did not receive any MOUD (out-of-treatment) within the last three days before release to the community in 2011-2017. The outcome was the first non-fatal opioid overdose emergency department (ED) visit within 1 year of jail release during 2011-2017. Covariates included demographic, clinical, incarceration-related, and other characteristics. We performed multivariable cause-specific Cox proportional hazards regression analysis to compare the risk of non-fatal opioid overdose ED visits within 1 year after jail release between groups. RESULTS:MOUD group included 8660 individuals with 17,119 incarcerations; out-of-treatment group included 10,163 individuals with 14,263 incarcerations. Controlling for covariates and accounting for competing risks, in-jail MOUD was associated with lower non-fatal opioid overdose risk within 14 days after jail release (adjusted HR=0.49, 95% confidence interval=0.33-0.74). We found no significant differences 15-28, 29-56, or 57-365 days post-release. CONCLUSION/CONCLUSIONS:MOUD group had lower risk of non-fatal opioid overdose immediately after jail release. Wider implementation of MOUD in US jails could potentially reduce post-release overdoses, ED utilization, and associated healthcare costs.

People who inject drugs (PWID) have an increased risk of HIV infection partly due to injection behaviors often related to opioid use. Medications for opioid use disorder (MOUD) have been shown to reduce HIV infection risk, possibly by reducing injection risk behaviors. MOUD may benefit individuals who do not receive it themselves but are connected through social, sexual, or drug use networks with individuals who are treated. This is known as spillover. Valid estimation of spillover in network studies requires considering the network's community structure. Communities are groups of densely connected individuals with sparse connections to other groups. We analyzed a network of 277 PWID and their contacts from the Transmission Reduction Intervention Project. We assessed the effect of MOUD on reductions in injection risk behaviors and the possible benefit for network contacts of participants treated with MOUD. We identified communities using modularity-based methods and employed inverse probability weighting with community-level propensity scores to adjust for measured confounding. We found that MOUD may have beneficial spillover effects on reducing injection risk behaviors. The magnitudes of estimated effects were sensitive to the community detection method. Careful consideration should be paid to the significance of community structure in network studies evaluating spillover.

The high overdose mortality rates in the United States poses several questions: Why have they been increasing exponentially since 1979? Why are they so high? And how can they be greatly reduced? Building on past research, the causes of the increase seem to be deeply rooted in US social and economic structures and processes, rather than due only to opioid prescription patterns or the advent of synthetic opioids. Given this, we consider what changes might be needed to reverse the exponentially-increasing overdose mortality. We use a path dependency argument to argue that the United States political, economic, and public health systems have helped create this crisis and, unfortunately, continue to heighten it. These same systems suggest that proposals to expand harm reduction and drug treatment capacity, to decriminalize or legalize drugs, or to re-industrialize the country sufficiently to reduce "communities of despair" will not be enacted at a scale sufficient to end the overdose crisis. We thus suggest that in the United States serious improvements in overdose rates and related policies and structures require massive social movements with a broad social change agenda.

OBJECTIVE/UNASSIGNED:Policy ramifications of the COVID-19 pandemic shape the concurrent housing and overdose crises in the USA. Housing insecurity is a known risk factor for overdose, yet how residential eviction may influence fatal overdose risk is understudied. We sought to evaluate the spatiotemporal relationship between neighbourhood-level residential eviction rates and overdose mortality in Rhode Island (RI) before and during a statewide eviction moratorium in response to COVID-19. METHODS/UNASSIGNED:We conducted an ecological study at the census tract level in RI (N=240) by modelling the association between quintiles of eviction rates and fatal overdose rates from 2016 to 2021. We applied a Bayesian spatiotemporal approach using an integrated nested Laplace approximation and adjusted for an a priori determined set of time-varying demographic and policy covariates. RESULTS/UNASSIGNED:Descriptively, we observed a direct, dose-response relationship between quintiles of eviction incidence rates over the full study period and fatal overdose. Prior to the implementation of a statewide eviction moratorium, census tracts in the highest eviction quintile had increased rates of overdose mortality, relative to those in the lowest quintile (posterior mean relative rate = 1.49, 95% credible interval: 1.05 to 2.13). Associations during the periods of eviction moratorium were non-significant. CONCLUSION/UNASSIGNED:This work highlights the neighbourhood-level relationship between residential eviction and fatal overdose risk in the absence of an eviction moratorium. Enhanced investment in eviction prevention policies, such as rent relief and limitations to the circumstances under which landlords can file for eviction, may complement harm reduction efforts to reduce neighbourhood-level overdose inequalities.

BACKGROUND:The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, SiCLOPS (Simulation of Community-Level Overdose Prevention Strategy), we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. METHODS:Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and non-fatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. RESULTS:Counties required unique combinations of modeled interventions to achieve 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1,000% increases in naloxone, depending on the county. CONCLUSIONS:Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.

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.

Policy implementation is a key component of scaling effective chronic disease prevention and management interventions. Policy can support scale-up by mandating or incentivizing intervention adoption, but enacting a policy is only the first step. Fully implementing a policy designed to facilitate implementation of health interventions often requires a range of accompanying implementation structures, like health IT systems, and implementation strategies, like training. Decision makers need to know what policies can support intervention adoption and how to implement those policies, but to date research on policy implementation is limited and innovative methodological approaches are needed. In December 2021, the Johns Hopkins ALACRITY Center for Health and Longevity in Mental Illness and the Johns Hopkins Center for Mental Health and Addiction Policy convened a forum of research experts to discuss approaches for studying policy implementation. In this report, we summarize the ideas that came out of the forum. First, we describe a motivating example focused on an Affordable Care Act Medicaid health home waiver policy used by some US states to support scale-up of an evidence-based integrated care model shown in clinical trials to improve cardiovascular care for people with serious mental illness. Second, we define key policy implementation components including structures, strategies, and outcomes. Third, we provide an overview of descriptive, predictive and associational, and causal approaches that can be used to study policy implementation. We conclude with discussion of priorities for methodological innovations in policy implementation research, with three key areas identified by forum experts: effect modification methods for making causal inferences about how policies' effects on outcomes vary based on implementation structures/strategies; causal mediation approaches for studying policy implementation mechanisms; and characterizing uncertainty in systems science models. We conclude with discussion of overarching methods considerations for studying policy implementation, including measurement of policy implementation, strategies for studying the role of context in policy implementation, and the importance of considering when establishing causality is the goal of policy implementation research.