Receipt of Opioid Agonist Treatment in provincial correctional facilities in British Columbia is associated with a reduced hazard of nonfatal overdose in the month following release.

BACKGROUND: In many jurisdictions, policies restrict access to Opioid Agonist Treatment (OAT) in correctional facilities. Receipt of OAT during incarceration is associated with reduced risk of fatal overdose after release but little is known about the effect on nonfatal overdose. This study aimed to examine the association between OAT use during incarceration and nonfatal overdose in the 30 days following release. METHODS AND FINDINGS: Using linked administrative healthcare and corrections data for a random sample of 20% of residents of British Columbia, Canada we examined releases from provincial correctional facilities between January 1, 2015 -December 1, 2018, among adults (aged 18 or older at the time of release) with Opioid Use Disorder. We fit Andersen-Gill models to examine the association between receipt of OAT in custody and the hazard of nonfatal following release. We conducted secondary analyses to examine the association among people continuing treatment initiated prior to their arrest and people who initiated a new episode of OAT in custody separately. We also conducted sex-based subgroup analyses. In this study there were 4,738 releases of 1,535 people with Opioid Use Disorder. In adjusted analysis, receipt of OAT in custody was associated with a reduced hazard of nonfatal overdose (aHR 0.55, 95% CI 0.41, 0.74). This was found for prescriptions continued from community (aHR 0.49, 95%CI 0.36, 0.67) and for episodes of OAT initiated in custody (aHR 0.58, 95%CI 0.41, 0.82). The effect was greater among women than men. CONCLUSIONS: OAT receipt during incarceration is associated with a reduced hazard of nonfatal overdose after release. Policies to expand access to OAT in correctional facilities, including initiating treatment, may help reduce harms related to nonfatal overdose in the weeks following release. Differences in the effect seen among women and men indicate a need for gender-responsive policies and programming.

Exploration of weekly variation in naloxone possession and carriage among people who use opioids in New York City before, during, and after the COVID-19 pandemic.

BACKGROUND: Naloxone is critical for reversing opioid-related overdoses. However, there is a dearth of research examining how naloxone possession and carriage are impacted by time-varying individual and social determinants, and if this differed during the height of the COVID-related mitigation measures (e.g., shutdowns). METHODS: We utilized weekly ecological momentary assessments (EMA) to measure factors associated with naloxone possession and carriage among 40 people who use illicit opioids in New York City, for 24 months. Descriptive statistics were used to explore the frequency of weeks with consistent naloxone possession and carriage. Mixed effects binary and multivariable logistic regression was used to test for the impact of time-varying EMA- and baseline-level factors on each outcome. RESULTS: Approximately 70% of weekly EMAs were associated with consistent naloxone possession or carriage. In multivariable models, compared to during the height of the COVID-related shutdowns (March 12, 2020-May 19, 2021), the time before was associated with lower odds of consistent possession (Odds Ratio (OR) = 0.05, 95% Confidence Interval (CI) = 0.01-0.15) and consistent carriage (OR = 0.06, CI = 0.01-0.25). Additionally, being female (OR = 11.15, CI = 2.85-43.42), being White versus being Black or Hispanic/Latinx (OR = 8.05, CI = 1.96-33.06), and lifetime overdose (OR = 1.96, CI = 1.16-19.80) were associated with higher odds of consistent possession. Recent opioid injection (OR = 3.66, CI = 1.34-9.94), being female (OR = 7.91, CI = 3.91-8.23), and being White (OR = 5.77, CI = 1.35-24.55) were associated with higher odds of consistent carriage. Not wanting to be perceived as a drug user was reported in nearly one third (29.0%; 190/656) of EMAs where inconsistent possession was reported. CONCLUSIONS: Our findings paint a relatively positive picture of possession and carriage during COVID-related shutdowns, particularly among white and female participants, and highlight the importance of capturing time-varying factors to understand naloxone-related behavior. To curb growing disparities, outreach to equip Black and Hispanic/Latinx people with naloxone is needed as well as interventions to reduce stigma as a barrier to naloxone engagement.

Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011-2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors.

BACKGROUND: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. METHODS: We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. RESULTS: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. CONCLUSION: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Associations among drug acquisition and use behaviors, psychosocial attributes, and opioid-involved overdoses.

AIMS: This study sought to develop and assess an exploratory model of how demographic and psychosocial attributes, and drug use or acquisition behaviors interact to affect opioid-involved overdoses. DESIGN: We conducted exploratory and confirmatory factor analysis (EFA/CFA) to identify a factor structure for ten drug acquisition and use behaviors. We then evaluated alternative structural equation models incorporating the identified factors, adding demographic and psychosocial attributes as predictors of past-year opioid overdose. SETTING AND PARTICIPANTS: We used interview data collected for two studies recruiting opioid-misusing participants receiving services from a community-based syringe services program. The first investigated current attitudes toward drug-checking (N = 150). The second was an RCT assessing a telehealth versus in-person medical appointment for opioid use disorder treatment referral (N = 270). MEASUREMENTS: Demographics included gender, age, race/ethnicity, education, and socioeconomic status. Psychosocial measures were homelessness, psychological distress, and trauma. Self-reported drug-related risk behaviors included using alone, having a new supplier, using opioids with benzodiazepines/alcohol, and preferring fentanyl. Past-year opioid-involved overdoses were dichotomized into experiencing none or any. FINDINGS: The EFA/CFA revealed a two-factor structure with one factor reflecting drug acquisition and the second drug use behaviors. The selected model (CFI = .984, TLI = .981, RMSEA = .024) accounted for 13.1% of overdose probability variance. A latent variable representing psychosocial attributes was indirectly associated with an increase in past-year overdose probability (ß = .234, p = .001), as mediated by the EFA/CFA identified latent variables: drug acquisition (ß = .683, p < .001) and drug use (ß = .567, p = .001). Drug use behaviors (ß = .287, p = .04) but not drug acquisition (ß = .105, p = .461) also had a significant, positive direct effect on past-year overdose. No demographic attributes were significant direct or indirect overdose predictors. CONCLUSIONS: Psychosocial attributes, particularly homelessness, increase the probability of an overdose through associations with risky drug acquisition and drug-using behaviors. Further research is needed to replicate these findings with populations at high-risk of an opioid-related overdose to assess generalizability and refine the metrics used to assess psychosocial characteristics.

A Framework for Developing and Assessing Custom Case Definitions: A Demonstration Applied to Opioid Overdose in Maryland.

CONTEXT: Public health epidemiologists monitor data sources for disease outbreaks and other events of public health concern, but manual review of records to identify cases of interest is slow and labor-intensive and may not reflect evolving data practices. To automatically identify cases from electronic data sources, epidemiologists must use "case definitions" or formal logic that captures the criteria used to identify a record as a case of interest. OBJECTIVE: To establish a methodology for development and evaluation of case definitions. A logical evaluation framework to approach case definitions will allow jurisdictions the flexibility to implement a case definition tailored to their goals and available data. DESIGN: Case definition development is explained as a process with multiple logical components combining free-text and categorical data fields. The process is illustrated with the development of a case definition to identify emergency medical services (EMS) call records related to opioid overdoses in Maryland. SETTING: The Maryland Department of Health (MDH) installation of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE), which began capturing EMS call records in ESSENCE in 2019 to improve statewide coverage of all-hazards health issues. RESULTS: We describe a case definition evaluation framework and demonstrate its application through development of an opioid overdose case definition to be used in MDH ESSENCE. We show the iterative process of development, from defining how a case can be identified conceptually to examining each component of the conceptual definition and then exploring how to capture that component using available data. CONCLUSION: We present a framework for developing and qualitatively assessing case definitions and demonstrate an application of the framework to identifying opioid overdose incidents from MDH EMS data. We discuss guidelines to support jurisdictions in applying this framework to their own data and public health challenges to improve local surveillance capability.

Using quality improvement approaches to increase emergency department provider engagement in research participant enrollment during COVID-19 and opioid overdose public health emergencies.

PURPOSE: We utilized quality improvement (QI) approaches to increase emergency department (ED) provider engagement with research participant enrollment during the opioid crisis and coronavirus disease (COVID-19) pandemic. The context of this work is the Evaluating Microdosing in the Emergency Department (EMED) study, a randomized trial offering buprenorphine/naloxone to ED patients through randomization to standard or microdosing induction. Engaging providers is crucial for participant recruitment to our study. Anticipating challenges sustaining long-term engagement after a 63% decline in provider referrals four months into enrollments, we applied Plan-Do-Study-Act (PDSA) cycles to develop and implement an engagement strategy to increase and sustain provider engagement by 50% from baseline within 9 months. METHODS: Our engagement strategy was centered on Coffee Carts rounds: 5-min study-related educational presentations for providers on shift; and a secondary initiative, a Suboxone Champions program, to engage interested providers as study-related peer educators. We used provider referrals to our team as a proxy for study engagement and report the percent change in mean weekly referrals across two PDSA cycles relative to our established referral baseline. RESULTS: A QI approach afforded real-time review of interventions based on research and provider priorities, increasing engagement via mean weekly provider referrals by 14.5% and 49% across two PDSA cycles relative to baseline, respectively. CONCLUSIONS: Our Coffee Carts and Suboxone Champions program are efficient, low-barrier, educational initiatives to convey study-related information to providers. This work supported our efforts to maximally engage providers, minimize burden, and provide life-saving buprenorphine/naloxone to patients at risk of fatal overdose.

RéSUMé: BUT: Nous avons utilisé des approches d'amélioration de la qualité (AQ) pour accroître l'engagement des fournisseurs des services d'urgence (SU) avec l'inscription des participants à la recherche pendant la crise des opioïdes et la pandémie de maladie à coronavirus (COVID-19). Le contexte de ce travail est l'étude Evaluating Microdosing in the Emergency Department (EMED), un essai randomisé offrant de la buprénorphine/naloxone aux patients aux urgences par randomisation à l'induction standard ou au microdosage. L'engagement des fournisseurs est crucial pour le recrutement des participants à notre étude. En anticipant les difficultés à maintenir un engagement à long terme après une baisse de 63 % des recommandations de fournisseurs quatre mois après les inscriptions, nous avons appliqué le Plan-Do-Study-Act (PDSA) cycles d'élaboration et de mise en œuvre d'une stratégie d'engagement visant à accroître et à maintenir l'engagement des fournisseurs de 50 % par rapport au niveau de référence dans les neuf mois. MéTHODES: Notre stratégie de mobilisation était axée sur les tournées de Coffee Carts : des présentations éducatives de cinq minutes sur l'étude pour les fournisseurs sur le quart de travail; et une initiative secondaire, un programme Suboxone Champions, pour mobiliser les fournisseurs intéressés en tant que pairs éducateurs liés à l'étude. Nous avons utilisé les recommandations des fournisseurs à notre équipe comme indicateur de la participation à l'étude et nous avons signalé le pourcentage de changement dans les recommandations hebdomadaires moyennes pour deux cycles PDSA par rapport à notre base de référence établie. RéSULTATS: Une approche d'AQ a permis d'examiner en temps réel les interventions en fonction des priorités de la recherche et des fournisseurs, ce qui a augmenté l'engagement par l'intermédiaire des recommandations hebdomadaires moyennes des fournisseurs de 14,5 % et de 49 % au cours de deux cycles de PDSA par rapport au niveau de référence, respectivement. CONCLUSION: Notre programme Coffee Carts and Suboxone Champions est une initiative éducative efficace et peu contraignante qui permet de transmettre aux fournisseurs des renseignements sur les études. Ce travail a appuyé nos efforts visant à mobiliser au maximum les fournisseurs, à réduire au minimum le fardeau et à fournir de la buprénorphine/naloxone vitale aux patients à risque de surdose mortelle.

United States marijuana legalization and opioid mortality trends before and during the first year of the COVID-19 pandemic.

BACKGROUND: To determine if marijuana legalization was associated with reduced opioid mortality. STUDY DESIGN: The United States (US) opioid mortality trend during the 2010-2019 decade was compared in states and District of Columbia (jurisdictions) that had implemented marijuana legalization with states that had not. Acceleration of opioid mortality during 2020, the first year of the coronavirus disease 2019 (COVID-19) pandemic, was also compared in recreational and medicinal-only legalizing jurisdictions. METHODS: Joinpoint methodology was applied to the Centers for Disease Control and Prevention WONDER data. Trends in legalizing jurisdictions were cumulative aggregates. RESULTS: The overall opioid and fentanyl death rates and the percentage of opioid deaths due to fentanyl increased more during 2010-2019 in jurisdictions that legalized marijuana than in those that did not (pairwise comparison p = 0.007, 0.05, and 0.006, respectively). By 2019, the all-opioid and fentanyl death rates were 44 and 50 percent greater in the legalizing than in the nonlegalizing jurisdictions, respectively. When the COVID-19 pandemic hit in 2020, jurisdictions that implemented recreational marijuana legalization before 2019 had significantly greater increases in both overall opioid and fentanyl death rates than jurisdictions with medicinal-only legalization. For all-opioids, the mean (95 percent confidence interval) 2019-to-2020 increases were 46.5 percent (36.6, 56.3 percent) and 29.1 percent (20.2, 37.9 percent), respectively (p = 0.02). For fentanyl, they were 115.6 percent (80.2, 151.6 percent) and 55.4 percent (31.6, 79.2 percent), respectively (p = 0.01). CONCLUSIONS: During the past decade, marijuana legalization in the US was associated at the jurisdiction level with a greater acceleration in opioid death rate. An even greater increase in opioid mortality occurred in recreational-legalizing jurisdictions with the onset of the COVID-19 pandemic. Marijuana legalization is correlated with worsening of the US opioid epidemic.

Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.

PURPOSE: Identifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention. METHODS: We leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity. We compared the performance of each model using several performance measures and identified factors that were most predictive of opioid overdose within racial/ethnic groups and across models. RESULTS: We found that out of 44,246 prison releases in Massachusetts between 2015-2020, 2237 (5.1%) resulted in opioid overdose in the 90 days following release. The performance of the two predictive models varied. The single weight model had high sensitivity (79%) and low specificity (56%) for predicting opioid overdose and was more sensitive for White non-Hispanic individuals (sensitivity = 84%) than for racial/ethnic minority individuals. CONCLUSIONS: Stratified models had better balanced performance metrics for both White non-Hispanic and racial/ethnic minority groups and identified different predictors of overdose between racial/ethnic groups. Across racial/ethnic groups and models, involuntary commitment (involuntary treatment for alcohol/substance use disorder) was an important predictor of opioid overdose.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.

BACKGROUND: This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. METHODS: In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). RESULTS: The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. CONCLUSIONS: Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Opioid Overdose Knowledge Among Adolescents and Young Adults.

This cross-sectional study examines awareness of opioid overdose, the ability to administer naloxone, and the willingness to help during an overdose on college campuses across the US.

Characteristics of Opioid Toxicity Deaths Among Adolescents and Young Adults in Ontario Prior To and During the COVID-19 Pandemic.

PURPOSE: To characterize opioid toxicity deaths among adolescents and young adults in Ontario, Canada, prior to and during the first year of the COVID-19 pandemic. METHODS: We conducted a descriptive, cross-sectional study of opioid toxicity deaths among individuals aged 15-24 in Ontario in the year prior to (March 17, 2019, to March 16, 2020) and the first year of the pandemic (March 17, 2020, to March 16, 2021) using administrative health databases. We analyzed circumstances surrounding death, substances contributing to death, and health-care encounters prior to death. RESULTS: We identified 284 deaths among Ontarians aged 15-24, including 115 in the year preceding and 169 in the first year of the pandemic. Fentanyl contributed to 84.3% of deaths in the prepandemic year, rising to 93.5% (p = .012) the following year. Stimulants contributed to approximately half of deaths in both periods (41.7% prepandemic and 49.1% during pandemic). In both periods, roughly one in 4 decedents had a health-care encounter in the week prior to death and less than 20% of those with an opioid use disorder received opioid agonist treatment in the 30 days prior to death. DISCUSSION: Among young Ontarians, the number of opioid-related deaths increased by 47% in the first year of the COVID-19 pandemic. Fentanyl contributed to the vast majority of deaths, with non-opioid substances (primarily stimulants) also contributing to approximately half of deaths. Patterns of health-care utilization prior to death suggest opportunities to better connect this population to services that address opioid use disorder needs and promote harm reduction.

Public Health Interventions and Overdose-Related Outcomes Among Persons With Opioid Use Disorder.

Importance: Given the high number of opioid overdose deaths in the US and the complex epidemiology of opioid use disorder (OUD), systems models can serve as a tool to identify opportunities for public health interventions. Objective: To estimate the projected 3-year association between public health interventions and opioid overdose-related outcomes among persons with OUD. Design, Setting, and Participants: This decision analytical model used a simulation model of the estimated US population aged 12 years and older with OUD that was developed and analyzed between January 2019 and December 2023. The model was parameterized and calibrated using 2019 to 2020 data and used to estimate the relative change in outcomes associated with simulated public health interventions implemented between 2021 and 2023. Main Outcomes and Measures: Projected OUD and medications for OUD (MOUD) prevalence in 2023 and number of nonfatal and fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. Results: In a baseline scenario assuming parameters calibrated using 2019 to 2020 data remained constant, the model projected more than 16 million persons with OUD not receiving MOUD treatment and nearly 1.7 million persons receiving MOUD treatment in 2023. Additionally, the model projected over 5 million nonfatal and over 145 000 fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. When simulating combinations of interventions that involved reducing overdose rates by 50%, the model projected decreases of up to 35.2% in nonfatal and 36.6% in fatal opioid-involved overdoses among persons with OUD. Interventions specific to persons with OUD not currently receiving MOUD treatment demonstrated the greatest reduction in numbers of nonfatal and fatal overdoses. Combinations of interventions that increased MOUD initiation and decreased OUD recurrence were projected to reduce OUD prevalence by up to 23.4%, increase MOUD prevalence by up to 137.1%, and reduce nonfatal and fatal opioid-involved overdoses among persons with OUD by 6.7% and 3.5%, respectively. Conclusions and Relevance: In this decision analytical model study of persons with OUD, findings suggested that expansion of evidence-based interventions that directly reduce the risk of overdose fatality among persons with OUD, such as through harm reduction efforts, could engender the highest reductions in fatal overdoses in the short-term. Interventions aimed at increasing MOUD initiation and retention of persons in treatment projected considerable improvement in MOUD and OUD prevalence but could require a longer time horizon for substantial reductions in opioid-involved overdoses.

Trends in pediatric prescription-opioid overdoses in U.S. emergency departments from 2008-2020: An epidemiologic study of pediatric opioid overdose ED visits.

BACKGROUND: Opioid overdose was declared a public health emergency in the United States, but much of the focus has been on adults. Child and adolescent exposure and access to unused prescription-opioid medications is a big concern. More research is needed on the trend of pediatric (age 0-17) prescription-opioid overdose emergency department (ED) visits in the United States, particularly during the COVID-19 pandemic year. METHODS: This retrospective epidemiological study used the 2008-2020 Nationwide Emergency Department Sample to provide a national estimate of ED visits related to prescription-opioid overdose. Inclusion criteria were 0-17-year-old patients treated at the ED due to prescription-opioid overdose. Eligible visits were identified if their medical records included any administrative billing codes for prescription-opioid overdose. National estimates were broken down by age groups, sex, geographic region, primary payer, median household income by zip code, ED disposition, and hospital location/teaching status. Incidence rate per 100,000 U.S. children was calculated for age groups, sex, and geographic region. RESULTS: Overall, the prescription-opioid overdose ED visits for patients from 0-17 years old in the United States decreased by 22% from 2008 to 2019, then increased by 12% in 2020. Most patients were discharged to home following their ED visit; however, there was a 42% increase in patients admitted from 2019 to 2020. The prescription-opioid overdose rate per 100,000 U.S. children was highest in the 0 to 1 and 12 to 17 age groups, with the 12 to 17 group increasing by 27% in 2020. ED visits in the West and Midwest saw prescription-opioid visits increase by 58% and 20%, respectively, from 2019-2020. CONCLUSIONS: Prescription-opioid overdose ED visits among U.S. children and adolescents decreased over the past decade until 2019. However, there was a substantial increase in ED visits from 2019 to 2020, suggesting the potential impact due to the then-emerging COVID-19 pandemic. Findings suggest focusing on young children and adolescents to reduce further prescription-opioid overdoses in the United States.

A Dynamic Model of Opioid Overdose Deaths in Canada during the Co-Occurring Opioid Overdose Crisis and COVID-19 Pandemic.

With over 40,000 opioid-related overdose deaths between January 2016 and June 2023, the opioid-overdose crisis is a significant public health concern for Canada. The opioid crisis arose from a complex system involving prescription opioid use, the use of prescription opioids not as prescribed, and non-medical opioid use. The increasing presence of fentanyl and its analogues in the illegal drugs supply has been an important driver of the crisis. In response to the overdose crisis, governments at the municipal, provincial/territorial, and federal levels have increased actions to address opioid-related harms. At the onset of the COVID-19 pandemic, concerns emerged over how the pandemic context may impact the opioid overdose crisis. Using evidence from a number of sources, we developed a dynamic mathematical model of opioid overdose death to simulate possible trajectories of overdose deaths during the COVID-19 pandemic. This model incorporates information on prescription opioid use, opioid use not as prescribed, non-medical opioid use, the level of fentanyl in the drug supply, and a measure of the proportion deaths preventable by new interventions. The simulated scenarios provided decision makers with insight into possible trajectories of the opioid crisis in Canada during the COVID-19 pandemic, highlighting the potential of the crisis to take a turn for the worse under certain assumptions, and thus, informing planning during a period when surveillance data were not yet available. This model provides a starting point for future models, and through its development, we have identified important data and evidence gaps that need to be filled in order to inform future action.

Association of buprenorphine retention and subsequent adverse outcomes following non-fatal overdose: An analysis using statewide linked Maryland databases.

INTRODUCTION: Patients receiving buprenorphine after a non-fatal overdose have lower risk of future nonfatal or fatal overdose, but less is known about the relationship between buprenorphine retention and the risk of adverse outcomes in the post-overdose year. OBJECTIVE: To examine the relationship between the total number of months with an active buprenorphine prescription (retention) and the odds of an adverse outcome within the 12 months following an index non-fatal overdose. MATERIALS AND METHODS: We studied a cohort of people with an index non-fatal opioid overdose in Maryland between July 2016 and December 2020 and at least one filled buprenorphine prescription in the 12-month post-overdose observation period. We used individually linked Maryland prescription drug and hospital admissions data. Multivariable logistic regression models were used to examine buprenorphine retention and associated odds of experiencing a second non-fatal overdose, all-cause emergency department visits, and all-cause hospitalizations. RESULTS: Of 5439 people, 25% (n=1360) experienced a second non-fatal overdose, 78% had an (n=4225) emergency department visit, and 37% (n=2032) were hospitalized. With each additional month of buprenorphine, the odds of experiencing another non-fatal overdose decreased by 4.7%, all-cause emergency department visits by 5.3%, and all-cause hospitalization decreased by 3.9% (p<.0001, respectively). Buprenorphine retention for at least nine months was a critical threshold for reducing overdose risk versus shorter buprenorphine retention. CONCLUSIONS: Buprenorphine retention following an index non-fatal overdose event significantly decreases the risk of future overdose, emergency department use, and hospitalization even among people already on buprenorphine.

Overdose from Unintentional Fentanyl Use when Intending to Use a Non-opioid Substance: An Analysis of Medically Attended Opioid Overdose Events.

Fentanyl-mixed and substituted heroin is well-documented, but less is known about unintentional fentanyl use among people using stimulants. To determine the prevalence of and racial and ethnic disparities in unintentional fentanyl use among people experiencing a medically attended opioid overdose, we reviewed 448 suspected non-fatal overdose cases attended by a community paramedic overdose response team in San Francisco from June to September 2022. We applied a case definition for opioid overdose to paramedic records and abstracted data on intended substance use prior to overdose. Among events meeting case criteria with data on intended substance use, intentional opioid use was reported by 57.3%, 98.0% of whom intended to use fentanyl. No intentional opioid use was reported by 42.7%, with most intending to use stimulants (72.6%), including methamphetamine and cocaine. No intentional opioid use was reported by 58.5% of Black, 52.4% of Latinx, and 28.8% of White individuals (p = 0.021), and by 57.6% of women and 39.5% of men (p = 0.061). These findings suggest that unintentional fentanyl use among people without opioid tolerance may cause a significant proportion of opioid overdoses in San Francisco. While intentional fentanyl use might be underreported, the magnitude of self-reported unintentional use merits further investigation to confirm this phenomenon, explore mechanisms of use and disparities by race and ethnicity, and deploy targeted overdose prevention interventions.

LatinX harm reduction capital, medication for opioid use disorder, and nonfatal overdose: A structural equation model analysis among people who use drugs in Massachusetts.

BACKGROUND: We introduce the concept of harm reduction capital (HRCap) as the combination of knowledge, resources, and skills related to substance use risk reduction, which we hypothesize to predict MOUD use and opioid overdose. In this study, we explored the interrelationships between ethnicity, HRCap, nonfatal overdose, and MOUD use among PWUD. METHODS: Between 2017 and 2019, people who currently or in the past used opioids and who lived in Massachusetts completed a one-time survey on substance use history, treatment experiences, and use of harm reduction services. We fit first-order measurement constructs for positive and negative HRCap (facilitators and barriers). We used generalized structural equation models to examine the inter-relationships of the latent constructs with LatinX self-identification, past year overdose, and current use of MOUD. RESULTS: HRCap barriers were positively associated with past-year overdose (b=2.6, p<0.05), and LatinX self-identification was inversely associated with HRCap facilitators (b=-0.49, p<0.05). There was no association between overdose in the past year and the current use of MOUD. LatinX self-identification was positively associated with last year methadone treatment (b=0.89, p<0.05) but negatively associated with last year buprenorphine treatment (b=-0.68, p<0.07). Latinx PWUD reported lower positive HRCap than white non-LatinX PWUD and had differential utilization of MOUD. CONCLUSION: Our findings indicate that a recent overdose was not associated with the current use of MOUD, highlighting a severe gap in treatment utilization among individuals at the highest risk. The concept of HRCap and its use in the model highlight substance use treatment differences, opportunities for intervention, and empowerment.

Trends in non-fatal and fatal opioid overdoses during the first two years of the coronavirus disease-2019 pandemic.

PURPOSE: This study assessed opioid-involved overdose rates by age, sex, and race-ethnicity across strict pandemic mitigation phases and how this varied across data systems. METHODS: We examined opioid-involved overdoses using medical examiner and hospital data for Cook County, Illinois between 2016-2021. Multivariable segmented regression was used to assess weekly overdose rates across subgroups of age, sex and race/ethnicity and strict pandemic mitigation phases. RESULTS: The overall rate of weekly opioid-involved overdoses increased when assessing the medical examiner (ß = 0.01; 95% CI = 0.01,0.02; P ≤ .001) and emergency department visits data sources (ß = 0.15; 95% CI = 0.09,0.20; P ≤ .001) but not for the hospital admissions data source. We found differences in overdose rates across subgroups and phases of pandemic mandates. Fatal overdoses increased during lockdown-1 while admissions and emergency department (ED) visits for opioid-involved overdoses generally decreased across all phases of pandemic mitigation mandates except for the period following lockdown-1. Across pandemic mitigation phases, Hispanics and individuals under 25 years did not demonstrate any change in admissions and ED visits for overdoses. CONCLUSIONS: We underscore the importance of utilizing multiple sources of surveillance to better characterize opioid-involved overdoses and for public health planning.

Examining the association of rurality with opioid-related morbidity and mortality in Georgia: A geospatial analysis.

INTRODUCTION: The US opioid epidemic continues to escalate, with overdose deaths being the most-used metric to quantify its burden. There is significant geographic variation in opioid-related outcomes. Rural areas experience unique challenges, yet many studies oversimplify rurality characterizations. Contextual factors, such as area deprivation, are also important to consider when understanding a community's need for treatment services and prevention programming. This study aims to provide a geospatial snapshot of the opioid epidemic in Georgia using several metrics of opioid-related morbidity and mortality and explore differences by rurality across counties. METHODS: This was a spatial ecologic study. Negative binominal regression was used to model the relationship of county rurality with four opioid-related outcomes - overdose mortality, emergency department visits, inpatient hospitalizations, and overdose reversals - adjusting for county-level sex, racial/ethnic, and age distributions. Area Deprivation Index was also included. RESULTS: There was significant geographic variation across the state for all four opioid-related outcomes. Counts remained highest among the metro areas. For rates, counties in the top quartile of rates varied by outcome and were often rural areas. In the final models, rurality designation was largely unrelated to opioid outcomes, with the exception of medium metro areas (inversely related to hospitalizations and overdose reversals) and non-core areas (inversely related to hospitalizations), as compared to large central metro areas. Higher deprivation was significantly related to increased ED visits and hospitalizations, but not overdose mortality and reversals. CONCLUSIONS: When quantifying the burden of the opioid epidemic in a community, it is essential to consider multiple outcomes of morbidity and mortality. Understanding what outcomes are problematic for specific communities, in combination with their demographic and socioeconomic context, can provide insight into gaps in the treatment continuum and potential areas for intervention. Additionally, compared to demographic and socioeconomic factors, rurality may no longer be a salient predictor of the severity of the opioid epidemic in an area.