Faculty Bibliography
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274
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
Longitudinal trajectories of substance use disorder treatment use: A latent class growth analysis using a national cohort in Chile
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.
PMID: 38192124
ISSN: 1360-0443
CID: 5722952
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
State-Level Firearm Laws and Firearm Homicide in US Cities: Heterogenous Associations by City Characteristics
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.
PMID: 38536598
ISSN: 1468-2869
CID: 5644932
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
Impact of 30-day prescribed opioid dose trajectory on fatal overdose risk: A population-based, statewide cohort study
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.
PMCID:10897080
PMID: 37794260
ISSN: 1525-1497
CID: 5707792
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
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
Demonstrating Agreement between Radio and Fluorescence Measurements of the Depth of Maximum of Extensive Air Showers at the Pierre Auger Observatory
We show, for the first time, radio measurements of the depth of shower maximum (X_{max}) of air showers induced by cosmic rays that are compared to measurements of the established fluorescence method at the same location. Using measurements at the Pierre Auger Observatory we show full compatibility between our radio and the previously published fluorescence dataset, and between a subset of air showers observed simultaneously with both radio and fluorescence techniques, a measurement setup unique to the Pierre Auger Observatory. Furthermore, we show radio X_{max} resolution as a function of energy and demonstrate the ability to make competitive high-resolution X_{max} measurements with even a sparse radio array. With this, we show that the radio technique is capable of cosmic-ray mass composition studies, both at Auger and at other experiments.
PMID: 38277596
ISSN: 1079-7114
CID: 5911672
Are you thinking what I'm thinking? Defining what we mean by "polysubstance use."
The rise in drug overdoses and harms associated with the use of more than one substance has led to increased use of the term "polysubstance use" among researchers, clinicians, and public health officials. However, the term retains no consistent definition across contexts. The current authors convened from disciplines including sociology, epidemiology, neuroscience, and addiction psychiatry to propose a recommended definition of polysubstance use. An iterative process considered authors' formal and informal conversations, insights from relevant symposia, talks, and conferences, as well as their own research and clinical experiences to propose the current definition. Three key concepts were identified as necessary to define polysubstance use: (1) substances involved, (2) timing, and (3) intent. Substances involved include clarifying either (1) the number and type of substances used, (2) presence of more than one substance use disorder, or (3) primary and secondary substance use. The concept of timing is recommended to use clear terms such as simultaneous, sequential, and same-day polysubstance use to describe short-term behaviors (e.g., 30-day windows). Finally, the concept of intent refers to clarifying unintentional use or exposure when possible, and greater attention to motivations of polysubstance use. These three components should be clearly defined in research on polysubstance use to improve consistency across disciplines. Consistent definitions of polysubstance use can aid in the synthesis of evidence to better address an overdose crisis that increasingly involves multiple substances.
PMCID:10939915
PMID: 37734160
ISSN: 1097-9891
CID: 5645542
