Faculty Bibliography

Despite well-studied associations of state firearm laws with lower state- and county-level firearm homicide, there is a shortage of studies investigating differences in the effects of distinct state firearm law categories on various cities within the same state using identical methods. We examined associations of 5 categories of state firearm laws-pertaining to buyers, dealers, domestic violence, gun type/trafficking, and possession-with city-level firearm homicide, and then tested differential associations by city characteristics. City-level panel data on firearm homicide cases of 78 major cities from 2010 to 2020 was assessed from the Centers for Disease Control and Prevention's National Vital Statistics System. We modeled log-transformed firearm homicide rates as a function of firearm law scores, city, state, and year fixed effects, along with time-varying city-level confounders. We considered effect measure modification by poverty, unemployment, vacant housing, and income inequality. A one z-score increase in state gun type/trafficking, possession, and dealer law scores was associated with 25% (95% confidence interval [CI]:-0.37,-0.1), 19% (95% CI:-0.29,-0.07), and 17% (95% CI:-0.28, -0.4) lower firearm homicide rates, respectively. Protective associations were less pronounced in cities with high unemployment and high housing vacancy, but more pronounced in cities with high income inequality. In large US cities, state-level gun type/trafficking, possession, and dealer laws were associated with lower firearm homicide rates, but buyers and domestic violence laws were not. State firearm laws may have differential effects on firearm homicides based on city characteristics, and city-wide policies to enhance socioeconomic drivers may add benefits of firearm laws.

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.

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.

INTRODUCTION/BACKGROUND:Medications for opioid use disorder (MOUD), including buprenorphine, reduce overdose risk and improve outcomes for individuals with opioid use disorder (OUD). However, historically, most non-opioid treatment program (non-OTP) specialty substance use treatment programs have not offered buprenorphine. Understanding barriers to offering buprenorphine in specialty substance use treatment settings is critical for expanding access to buprenorphine. This study aims to examine program-level attitudinal, financial, and regulatory factors that influence clients' access to buprenorphine in state-licensed non-OTP specialty substance use treatment programs. METHODS:We surveyed leadership from state-licensed non-OTP specialty substance use treatment programs in New Jersey about organizational characteristics, including medications provided on- and off-site and percentage of OUD clients receiving any type of MOUD, and perceived attitudinal, financial, and regulatory barriers and facilitators to buprenorphine. The study estimated prevalence of barriers and compared high MOUD reach (n = 36, 35 %) and low MOUD reach (n = 66, 65 %) programs. RESULTS:Most responding organizations offered at least one type of MOUD either on- or off-site (n = 80, 78 %). However, 71 % of organizations stated that fewer than a quarter of their clients with OUD use any type of MOUD. Endorsement of attitudinal, financial, and institutional barriers to buprenorphine were similar among high and low MOUD reach programs. The most frequently endorsed government actions suggested to increase use of buprenorphine were facilitating access to long-acting buprenorphine (n = 95, 96 %), education and stigma reduction for clients and families (n = 95, 95 %), and financial assistance to clients to pay for medications (n = 90, 90 %). CONCLUSIONS:Although non-OTP specialty substance use programs often offer clients access to MOUD, including buprenorphine, most OUD clients do not actually receive MOUD. Buprenorphine uptake in these settings may require increased financial support for programs and clients, more robust education and training for providers, and efforts to reduce the stigma associated with medication among clients and their families.

BACKGROUND:Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI). METHODS:We used statewide data from RI from 2016-2020 to develop an ensemble machine learning model predicting neighborhood-level fatal overdose risk. Our ensemble model integrated gradient boosting machine and Super Learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch. RESULTS:Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods. CONCLUSIONS:We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources.

BACKGROUND:Hospitals are a key touchpoint to reach patients with substance use disorders (SUDs) and link them with ongoing community-based services. Although there are many acute care interventions to initiate SUD treatment in hospital settings, less is known about what services are offered to transition patients to ongoing care after discharge. In this study, we explore what SUD care transition strategies are offered across nonprofit US hospitals. METHODS:We analyzed administrative documents from a national sample of US hospitals that indicated SUD as a top 5 significant community need in their Community Health Needs Assessment reports (2019-2021). Data were coded and categorized based on the nature of described services. We used data on hospitals and characteristics of surrounding counties to identify factors associated with hospitals' endorsement of transition interventions for SUD. RESULTS:Of 613 included hospitals, 313 prioritized SUD as a significant community need. Fifty-three of these hospitals (17%) offered acute care interventions to support patients' transition to community-based SUD services. Most (68%) of the 53 hospitals described transition strategies without further detail, 23% described scheduling appointments before discharge, and 11% described discussing treatment options before discharge. No hospital characteristics were associated with offering transition interventions, but such hospitals were more likely to be in the Northeast, in counties with higher median income, and states that expanded Medicaid. CONCLUSIONS:Despite high need, most US hospitals are not offering interventions to link patients with SUD from acute to community care. Efforts to increase acute care interventions for SUD should identify and implement best practices to support care continuity.

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.

BACKGROUND AND AIMS/OBJECTIVE:Benzodiazepines (BZDs) carry a risk for drug overdose and are prescribed alone or simultaneously with selective-serotonin reuptake inhibitors (SSRIs) for the treatment of anxiety and depression in young adults. We aimed to measure risks of drug overdose following BZD treatment initiation, and simultaneous BZD and SSRI initiation, compared with SSRI treatment alone in young adults with depression or anxiety. DESIGN, SETTING, PARTICIPANTS/METHODS:The cohort study used administrative databases covering privately (MarketScan, 1/1/2009-12/31/2018) and publicly (Medicaid, 1/1/2015-12/31/2016) insured young adults (18-29 years) in the United States. Those with depression or anxiety diagnoses newly initiating BZD or SSRI treatment (without BZD or SSRI prescriptions in prior year) were included. Simultaneous "BZD + SSRI" initiation was defined as starting BZD and SSRI treatment on the same day. The cohorts included 604 664 privately insured young adults (BZD = 22%, BZD + SSRI = 10%, SSRI = 68%) and 110 493 publicly insured young adults (BZD = 23%, BZD + SSRI = 5%, SSRI = 72%). MEASUREMENTS/METHODS:Incident medically treated drug overdose events were identified from emergency department and inpatient encounters (ICD poisoning codes) within 6 months of treatment initiation. Crude and propensity-score adjusted cumulative incidence and hazard ratios (HR) were estimated. Sub-analyses evaluated drug overdose intent. FINDINGS/RESULTS:Adjusted HRs of drug overdose for BZD vs. SSRI treatment was 1.36 (95% confidence interval [CI]:1.23-1.51) in privately and 1.59 (95%CI:1.37-1.83) in publicly insured young adults. The adjusted HRs of drug overdose for BZD + SSRI treatment vs. SSRI treatment were 1.99 (95%CI:1.77-2.25) in privately and 1.98 (95%CI:1.47-2.68) in publicly insured young adults. CONCLUSIONS:Among young adults in the United States, initiating benzodiazepine treatment for anxiety and depression, alone or simultaneously with selective-serotonin reuptake inhibitors (SSRI), appears to have an increased risk of medically treated drug overdose compared with SSRI treatment alone. These associations were observed in publicly and privately insured individuals.