Faculty Bibliography
Searched for:
person:cerdam01 or freids01 or hamill07 or krawcn01
Total Results:
1310
Barriers and facilitators to use of buprenorphine in state-licensed specialty substance use treatment programs: A survey of program leadership
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.
PMID: 38499248
ISSN: 2949-8759
CID: 5640212
Simulating the simultaneous impact of medication for opioid use disorder and naloxone on opioid overdose death in eight New York counties
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.
PMID: 38372618
ISSN: 1531-5487
CID: 5634012
Overall, Direct, Spillover, and Composite Effects of Components of a Peer-Driven Intervention Package on Injection Risk Behavior Among People Who Inject Drugs in the HPTN 037 Study
We sought to disentangle effects of the components of a peer-education intervention on self-reported injection risk behaviors among people who inject drugs (n = 560) in Philadelphia, US. We examined 226 egocentric groups/networks randomized to receive (or not) the intervention. Peer-education training consisted of two components delivered to the intervention network index individual only: (1) an initial training and (2) "booster" training sessions during 6- and 12-month follow up visits. In this secondary data analysis, using inverse-probability-weighted log-binomial mixed effects models, we estimated the effects of the components of the network-level peer-education intervention upon subsequent risk behaviors. This included contrasting outcome rates if a participant is a network member [non-index] under the network exposure versus under the network control condition (i.e., spillover effects). We found that compared to control networks, among intervention networks, the overall rates of injection risk behaviors were lower in both those recently exposed (i.e., at the prior visit) to a booster (rate ratio [95% confidence interval]: 0.61 [0.46-0.82]) and those not recently exposed to it (0.81 [0.67-0.98]). Only the boosters had statistically significant spillover effects (e.g., 0.59 [0.41-0.86] for recent exposure). Thus, both intervention components reduced injection risk behaviors with evidence of spillover effects for the boosters. Spillover should be assessed for an intervention that has an observable behavioral measure. Efforts to fully understand the impact of peer education should include routine evaluation of spillover effects. To maximize impact, boosters can be provided along with strategies to recruit especially committed peer educators and to increase attendance at trainings. Clinical Trials Registration Clinicaltrials.gov NCT00038688 June 5, 2002.
PMID: 37932493
ISSN: 1573-3254
CID: 5624312
Characterizing opioid overdose hotspots for place-based overdose prevention and treatment interventions: A geo-spatial analysis of Rhode Island, USA
OBJECTIVE:Examine differences in neighborhood characteristics and services between overdose hotspot and non-hotspot neighborhoods and identify neighborhood-level population factors associated with increased overdose incidence. METHODS:We conducted a population-based retrospective analysis of Rhode Island, USA residents who had a fatal or non-fatal overdose from 2016 to 2020 using an environmental scan and data from Rhode Island emergency medical services, State Unintentional Drug Overdose Reporting System, and the American Community Survey. We conducted a spatial scan via SaTScan to identify non-fatal and fatal overdose hotspots and compared the characteristics of hotspot and non-hotspot neighborhoods. We identified associations between census block group-level characteristics using a Besag-York-Mollié model specification with a conditional autoregressive spatial random effect. RESULTS:We identified 7 non-fatal and 3 fatal overdose hotspots in Rhode Island during the study period. Hotspot neighborhoods had higher proportions of Black and Latino/a residents, renter-occupied housing, vacant housing, unemployment, and cost-burdened households. A higher proportion of hotspot neighborhoods had a religious organization, a health center, or a police station. Non-fatal overdose risk increased in a dose responsive manner with increasing proportions of residents living in poverty. There was increased relative risk of non-fatal and fatal overdoses in neighborhoods with crowded housing above the mean (RR 1.19 [95 % CI 1.05, 1.34]; RR 1.21 [95 % CI 1.18, 1.38], respectively). CONCLUSION/CONCLUSIONS:Neighborhoods with increased prevalence of housing instability and poverty are at highest risk of overdose. The high availability of social services in overdose hotspots presents an opportunity to work with established organizations to prevent overdose deaths.
PMID: 38245914
ISSN: 1873-4758
CID: 5624482
PROVIDENT: Development and validation of a machine learning model to predict neighborhood-level overdose risk in Rhode Island
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.
PMID: 38180881
ISSN: 1531-5487
CID: 5623742
Initiatives to Support the Transition of Patients With Substance Use Disorders From Acute Care to Community-based Services Among a National Sample of Nonprofit Hospitals
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.
PMID: 38015653
ISSN: 1935-3227
CID: 5617392
Drug overdose risk with benzodiazepine treatment in young adults: Comparative analysis in privately and publicly insured individuals
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.
PMID: 37816665
ISSN: 1360-0443
CID: 5605012
Correction to: Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science
PMID: 37395869
ISSN: 1573-6695
CID: 5524552
Scaling Interventions to Manage Chronic Disease: Innovative Methods at the Intersection of Health Policy Research and Implementation Science
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.
PMID: 36048400
ISSN: 1573-6695
CID: 5337802
Limits to Gauge Coupling in the Dark Sector Set by the Nonobservation of Instanton-Induced Decay of Super-Heavy Dark Matter in the Pierre Auger Observatory Data
Instantons, which are nonperturbative solutions to Yang-Mills equations, provide a signal for the occurrence of quantum tunneling between distinct classes of vacua. They can give rise to decays of particles otherwise forbidden. Using data collected at the Pierre Auger Observatory, we search for signatures of such instanton-induced processes that would be suggestive of super-heavy particles decaying in the Galactic halo. These particles could have been produced during the post-inflationary epoch and match the relic abundance of dark matter inferred today. The nonobservation of the signatures searched for allows us to derive a bound on the reduced coupling constant of gauge interactions in the dark sector: α_{X}≲0.09, for 10^{9}≲M_{X}/GeV<10^{19}. Conversely, we obtain that, for instance, a reduced coupling constant α_{X}=0.09 excludes masses M_{X}≳3×10^{13} GeV. In the context of dark matter production from gravitational interactions alone, we illustrate how these bounds are complementary to those obtained on the Hubble rate at the end of inflation from the nonobservation of tensor modes in the cosmological microwave background.
PMID: 36827568
ISSN: 1079-7114
CID: 5911432
