Need for comprehensive and timely data to address the opioid overdose epidemic without a blindfold

ODMAP: Stakeholder Perspectives on a Novel Public Health and Public Safety Overdose Surveillance System

This pilot study explores the utilization of the Overdose Detection Mapping Application Program (ODMAP) as a tool for enhancing collaboration between the public health and public safety sectors to address the overdose epidemic in the United States. Through qualitative interviews with ODMAP users, key themes emerged, including the role of data sharing in facilitating collaboration, challenges posed by divergent data privacy standards, and the need for clearer guidance on cross-sector data sharing. Findings highlight ODMAP's potential to integrate data for targeted interventions at individual and population levels. Future research directions include overcoming data sharing barriers, strategically utilizing data across sectors, and rigorously evaluating the impact of cross-sector partnerships on overdose morbidity and mortality. Overall, this study underscores the importance of ODMAP in fostering coordinated responses to the overdose crisis and provides valuable insights for improving overdose surveillance and intervention efforts.

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

PURPOSE

Standard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo.

METHODS

We describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach.

RESULTS

Using two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption.

CONCLUSIONS

MDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.

Effect of the Communities that HEAL intervention on receipt of behavioral therapies for opioid use disorder: A cluster randomized wait-list controlled trial

BACKGROUND

The U.S. opioid overdose crisis persists. Outpatient behavioral health services (BHS) are essential components of a comprehensive response to opioid use disorder and overdose fatalities. The Helping to End Addiction Long-Term® (HEALing) Communities Study developed the Communities That HEAL (CTH) intervention to reduce opioid overdose deaths in 67 communities in Kentucky, Ohio, New York, and Massachusetts through the implementation of evidence-based practices (EBPs), including BHS. This paper compares the rate of individuals receiving outpatient BHS in Wave 1 intervention communities (n = 34) to waitlisted Wave 2 communities (n = 33).

METHODS

Medicaid data included individuals ≥18 years of age receiving any of five BHS categories: intensive outpatient, outpatient, case management, peer support, and case management or peer support. Negative binomial regression models estimated the rate of receiving each BHS for Wave 1 and Wave 2. Effect modification analyses evaluated changes in the effect of the CTH intervention between Wave 1 and Wave 2 by research site, rurality, age, sex, and race/ethnicity.

RESULTS

No significant differences were detected between intervention and waitlisted communities in the rate of individuals receiving any of the five BHS categories. None of the interaction effects used to test the effect modification were significant.

CONCLUSIONS

Several factors should be considered when interpreting results-no significant intervention effects were observed through Medicaid claims data, the best available data source but limited in terms of capturing individuals reached by the intervention. Also, the 12-month evaluation window may have been too brief to see improved outcomes considering the time required to stand-up BHS.

TRIAL REGISTRATION

Clinical Trials.gov http://www.

CLINICALTRIALS

gov: Identifier: NCT04111939.

An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records

The goal of this study is to develop and validate a lightweight, interpretable machine learning (ML) classifier to identify opioid overdoses in emergency medical services (EMS) records. We conducted a comparative assessment of three feature engineering approaches designed for use with unstructured narrative data. Opioid overdose annotations were provided by two harm reduction paramedics and two supporting annotators trained to reliably match expert annotations. Candidate feature engineering techniques included term frequency-inverse document frequency (TF-IDF), a highly performant approach to concept vectorization, and a custom approach based on the count of empirically-identified keywords. Each feature set was trained using four model architectures: generalized linear model (GLM), Naïve Bayes, neural network, and Extreme Gradient Boost (XGBoost). Ensembles of trained models were also evaluated. The custom feature models were also assessed for variable importance to aid interpretation. Models trained using TF-IDF feature engineering ranged from AUROC = 0.59 (95% CI: 0.53-0.66) for the Naïve Bayes to AUROC = 0.76 (95% CI: 0.71-0.81) for the neural network. Models trained using concept vectorization features ranged from AUROC = 0.83 (95% 0.78-0.88)for the Naïve Bayes to AUROC = 0.89 (95% CI: 0.85-0.94) for the ensemble. Models trained using custom features were the most performant, with benchmarks ranging from AUROC = 0.92 (95% CI: 0.88-0.95) with the GLM to 0.93 (95% CI: 0.90-0.96) for the ensemble. The custom features model achieved positive predictive values (PPV) ranging for 80 to 100%, which represent substantial improvements over previously published EMS encounter opioid overdose classifiers. The application of this approach to county EMS data can productively inform local and targeted harm reduction initiatives.

Analysis of Wastewater Samples to Explore Community Substance Use in the United States: Pilot Correlative and Machine Learning Study

BACKGROUND

Substance use disorder and associated deaths have increased in the United States, but methods for detecting and monitoring substance use using rapid and unbiased techniques are lacking. Wastewater-based surveillance is a cost-effective method for monitoring community drug use. However, the examination of the results often focuses on descriptive analysis.

OBJECTIVE

The objective of this study was to explore community substance use in the United States by analyzing wastewater samples. Geographic differences and commonalities of substance use were explored.

METHODS

Wastewater was sampled across the United States (n=12). Selected drugs with misuse potential, prescriptions, and over-the-counter drugs and their metabolites were tested across geographic locations for 7 days. Methods used included wastewater assessment of substances and metabolites paired with machine learning, specifically discriminant analysis and cluster analysis, to explore similarities and differences in wastewater measures.

RESULTS

Geographic variations in the wastewater drug or metabolite levels were found. Results revealed a higher use of methamphetamine (z=-2.27, P=.02) and opioids-to-methadone ratios (oxycodone-to-methadone: z=-1.95, P=.05; hydrocodone-to-methadone: z=-1.95, P=.05) in states west of the Mississippi River compared to the east. Discriminant analysis suggested temazepam and methadone were significant predictors of geographical locations. Precision, sensitivity, specificity, and F1-scores were 0.88, 1, 0.80, and 0.93, respectively. Finally, cluster analysis revealed similarities in substance use among communities.

CONCLUSIONS

These findings suggest that wastewater-based surveillance has the potential to become an effective form of surveillance for substance use. Further, advanced analytical techniques may help uncover geographical patterns and detect communities with similar needs for resources to address substance use disorders. Using automated analytics, these advanced surveillance techniques may help communities develop timely, tailored treatment and prevention efforts.

Hippocampal volume loss in individuals with a history of non-fatal opioid overdose

Incidence of opioid-related overdoses in the United States has increased dramatically over the past two decades. Despite public emphasis on overdose fatalities, most overdose cases are not fatal. Although there are case reports of amnestic syndromes and acute injury to the hippocampus following non-fatal opioid overdose, the effects of such overdoses on brain structure are poorly understood. Here, we investigated the neuroanatomical correlates of non-fatal opioid overdoses by comparing hippocampal volume in opioid use disorder (OUD) patients who had experienced an opioid overdose (OD; N = 17) with those who had not (NOD; N = 32). Voxel-based morphometry showed lower hippocampal volume in the OD group than in the NOD group, which on post hoc analysis was evident in the left but not the right hippocampus. These findings strengthen the evidence that hippocampal injury is associated with non-fatal opioid overdose, which is hypothesized to underlie overdose-related amnestic syndrome.

Effects of sex and estrous cycle on intravenous oxycodone self-administration and the reinstatement of oxycodone-seeking behavior in rats

Introduction

The increasing misuse of both prescription and illicit opioids has culminated in a national healthcare crisis in the United States. Oxycodone is among the most widely prescribed and misused opioid pain relievers and has been associated with a high risk for transition to compulsive opioid use. Here, we sought to examine potential sex differences and estrous cycle-dependent effects on the reinforcing efficacy of oxycodone, as well as on stress-induced or cue-induced oxycodone-seeking behavior, using intravenous (IV) oxycodone self-administration and reinstatement procedures.

Methods

In experiment 1, adult male and female Long-Evans rats were trained to self-administer 0.03 mg/kg/inf oxycodone according to a fixed-ratio 1 schedule of reinforcement in daily 2-h sessions, and a dose-response function was subsequently determined (0.003-0.03 mg/kg/inf). In experiment 2, a separate group of adult male and female Long-Evans rats were trained to self-administer 0.03 mg/kg/inf oxycodone for 8 sessions, followed by 0.01 mg/kg/inf oxycodone for 10 sessions. Responding was then extinguished, followed by sequential footshock-induced and cue-induced reinstatement tests.

Results

In the dose-response experiment, oxycodone produced a typical inverted U-shape function with 0.01 mg/kg/inf representing the maximally effective dose in both sexes. No sex differences were detected in the reinforcing efficacy of oxycodone. In the second experiment, the reinforcing effects of 0.01-0.03 mg//kg/inf oxycodone were significantly attenuated in females during proestrus/estrus as compared to metestrus/diestrus phases of the estrous cycle. Neither males nor females displayed significant footshock-induced reinstatement of oxycodone seeking, but both sexes exhibited significant cue-induced reinstatement of oxycodone seeking at magnitudes that did not differ either by sex or by estrous cycle phase.

Discussion

These results confirm and extend previous work suggesting that sex does not robustly influence the primary reinforcing effects of oxycodone nor the reinstatement of oxycodone-seeking behavior. However, our findings reveal for the first time that the reinforcing efficacy of IV oxycodone varies across the estrous cycle in female rats.

Effects of sex and estrous cycle on intravenous oxycodone self-administration and the reinstatement of oxycodone-seeking behavior in rats

The increasing misuse of both prescription and illicit opioids has culminated in a national healthcare crisis in the United States. Oxycodone is among the most widely prescribed and misused opioid pain relievers and has been associated with a high risk for transition to compulsive opioid use. Here, we sought to examine potential sex differences and estrous cycle-dependent effects on the reinforcing efficacy of oxycodone, as well as on stress-induced or cue-induced oxycodone-seeking behavior, using intravenous (IV) oxycodone self-administration and reinstatement procedures. In experiment 1, adult male and female Long-Evans rats were trained to self-administer 0.03 mg/kg/inf oxycodone according to a fixed-ratio 1 schedule of reinforcement in daily 2-hr sessions, and a dose-response function was subsequently determined (0.003-0.03 mg/kg/inf). In experiment 2, a separate group of adult male and female Long-Evans rats were trained to self-administer 0.03 mg/kg/inf oxycodone for 8 sessions, followed by 0.01 mg/kg/inf oxycodone for 10 sessions. Responding was then extinguished, followed by sequential footshock-induced and cue-induced reinstatement tests. In the dose-response experiment, oxycodone produced a typical inverted U-shape function with 0.01 mg/kg/inf representing the maximally effective dose in both sexes. No sex differences were detected in the reinforcing efficacy of oxycodone. In the second experiment, the reinforcing effects of 0.01-0.03 mg//kg/inf oxycodone were significantly attenuated in females during proestrus/estrus as compared to metestrus/diestrus phases of the estrous cycle. Neither males nor females displayed significant footshock-induced reinstatement of oxycodone seeking, but both sexes exhibited significant cue-induced reinstatement of oxycodone seeking at magnitudes that did not differ either by sex or by estrous cycle phase. These results confirm and extend previous work suggesting that sex does not robustly influence the primary reinforcing effects of oxycodone nor the reinstatement of oxycodone-seeking behavior. However, our findings reveal for the first time that the reinforcing efficacy of IV oxycodone varies across the estrous cycle in female rats.

Rapid opioid overdose response system technologies

PURPOSE OF REVIEW

Opioid overdose events are a time sensitive medical emergency, which is often reversible with naloxone administration if detected in time. Many countries are facing rising opioid overdose deaths and have been implementing rapid opioid overdose response Systems (ROORS). We describe how technology is increasingly being used in ROORS design, implementation and delivery.

RECENT FINDINGS

Technology can contribute in significant ways to ROORS design, implementation, and delivery. Artificial intelligence-based modelling and simulations alongside wastewater-based epidemiology can be used to inform policy decisions around naloxone access laws and effective naloxone distribution strategies. Data linkage and machine learning projects can support service delivery organizations to mobilize and distribute community resources in support of ROORS. Digital phenotyping is an advancement in data linkage and machine learning projects, potentially leading to precision overdose responses. At the coalface, opioid overdose detection devices through fixed location or wearable sensors, improved connectivity, smartphone applications and drone-based emergency naloxone delivery all have a role in improving outcomes from opioid overdose. Data driven technologies also have an important role in empowering community responses to opioid overdose.

SUMMARY

This review highlights the importance of technology applied to every aspect of ROORS. Key areas of development include the need to protect marginalized groups from algorithmic bias, a better understanding of individual overdose trajectories and new reversal agents and improved drug delivery methods.

Antecedents of fatal overdose in an adult cohort identified through administrative record linkage in Indiana, 2015-2022

BACKGROUND

The United States continues to experience unprecedented rates of overdose mortality and need to identify effective policies or practices that can be implemented. This study aims to measure the prevalence, frequency, timing, and rate of touchpoints that occurred prior to a fatal overdose where communities might intervene.

METHODS

In collaboration with Indiana state government, we conducted record-linkage of statewide administrative datasets to vital records (January 1, 2015, through August 26, 2022) to identify touchpoints (jail booking, prison release, prescription medication dispensation, emergency department visits, and emergency medical services). We examined touchpoints within 12-months prior to a fatal overdose among an adult cohort and explored variation over time and by demographic characteristics.

RESULTS

Over the 92-month study period there were 13,882 overdose deaths (89.3% accidental poisonings, X40-X44) in our adult cohort that were record-linked to multiple administrative datasets and revealed nearly two-thirds (64.7%; n=8980) experienced an emergency department visit, the most prevalent touchpoint followed by prescription medication dispensation, emergency medical services responses, jail booking, and prison release. However, with approximately 1 out of every 100 returning citizens dying from drug overdose within 12-months of release, prison release had the highest touchpoint rate followed by emergency medical services responses, jail booking, emergency department visits, and prescription medication dispensation.

CONCLUSION

Record-linking administrative data from routine practice to vital records from overdose mortality is a viable means of identifying where resources should be situated to reduce fatal overdose, with potential to evaluate the effectiveness of overdose prevention efforts.

Prescription drugs with potential for misuse: protocol for a multi-indicator analysis of supply, detection and the associated health burden in Ireland between 2010 and 2020

INTRODUCTION

There is an increasing concern about the misuse of prescription drugs. Misuse refers to the intentional repurposing of prescribed drugs and/or the use of illicitly sourced prescription drugs, which may be counterfeit or contaminated. Drugs with the greatest potential for misuse are prescription opioids, gabapentinoids, benzodiazepines, Z-drugs and stimulants.

OBJECTIVE

The aim of this study is to provide a comprehensive analysis of the supply, patterns of use and health burden associated with prescription drugs with potential for misuse (PDPM) in Ireland between 2010 and 2020. Three inter-related studies will be carried out. The first study will describe trends in supply of PDPM using law enforcement drug seizures data and national prescription records from national community and prison settings. The second study aims to estimate trends in the detection of PDPM across multiple early warning systems using national forensic toxicology data. The third study aims to quantify the health burden associated with PDPM nationally, using epidemiological indicators of drug-poisoning deaths, non-fatal intentional drug overdose presentations to hospitals and drug treatment demand.

METHODS AND ANALYSIS

A retrospective observational study design, with repeated cross-sectional analyses, using negative binomial regression models or, where appropriate, joinpoint regression.

ETHICS AND DISSEMINATION

The study has received approval from the RCSI Ethics Committee (REC202202020). Results will be disseminated in peer-reviewed journals, scientific and drug policy meetings and with key stakeholders via research briefs.

Drug Overdose Deaths Among Non-Hispanic Black Men in the U.S.: Age-Specific Projections Through 2025

Introduction

Fatal drug overdoses have risen sharply in the U.S. since 2015, reaching their highest levels during the pandemic. Non-Hispanic Black men have been disproportionately harmed by this latest surge; overdose mortality per 100,000 has increased fourfold since 2015. Whether the mortality rate will continue to climb is unknown. In this study, we addressed the narrower question of which age groups are likely to experience a significant increase or decrease in the burden of drug overdose deaths through 2025, based on foreseeable changes in the age structure of the Black male population.

Methods

We used the 2020 and provisional 2021 age-specific mortality rates from the Centers for Disease Control WONDER (Wide-Ranging Online Data for Epidemiologic Research) database and the standard population balancing equation to project overdose deaths in 2025. Overdose deaths were identified by ICD-10 codes. We bracketed the projections between 2 plausible alternatives: a pessimistic forecast based on time series extrapolations and an optimistic forecast that assumes success nationally in lowering overdose deaths through prevention, treatment, and harm reduction initiatives.

Results

Among Black men aged 31-47 years, overdose deaths in 2025 are expected to increase by 440 or 11% (95% CI=8%, 14%) relative to 2020. By contrast, overdose deaths among younger Black men aged 19-30 years are expected to decline by 160 or -9% (95% CI= -15%, -5%). Among older Black men aged 48-64 years, overdose deaths are also expected to decline by 330 or -7% (95% CI= -10%, -4%). Similar results were found using 2021 provisional mortality rates.

Conclusions

Overdose deaths are predicted to increase significantly over current levels among Black men in their 30s and 40s. Local policy makers should direct harm reduction resources, such as naloxone kits, syringes, and fentanyl test strips, to places frequented by Black men in this age group. Outreach messaging should be tailored to resonate with men of middle age. Equally urgent is the scaling up of nonstigmatizing, evidence-based drug treatment and recovery support services in Black neighborhoods.

Community overdose surveillance: Fentanyl involvement in overdose deaths in rural Michigan

Purpose

Examine fatal overdose toxicology trends to contribute toward understanding the outreach and treatment needs of people who use drugs in rural areas.

Methods

We describe toxicology results from overdose deaths that occurred between January 1, 2018, and December 31, 2020, in 11 rural counties in Michigan, a state with relatively high rates of overdose mortality. One-way ANOVA with Tukey's HSD posthoc tests were used to test statistically significant differences in the frequency of substances detected between years.

Findings

Decedents (N = 107) were male (72.9%), White (96.3%), non-military (96.3%), unemployed (71.0%), married (73.9%), and had a mean age of 47 years old. The number of observed overdose deaths increased considerably from 2019 to 2020, with an increase of 72.4%. Fentanyl was the most common substance detected and had a 94% increase during the three-year period to present in 70% of all the deaths in these counties in 2020. Among the deaths we examined where cocaine was detected, 69% also contained fentanyl, and in deaths where methamphetamine was detected, 77% also contained fentanyl.

Conclusion

Findings could inform rural health and outreach initiatives aimed at reducing overdose risks by providing education on the risks of stimulant and opioid couse but also the widespread saturation of illicit drugs that contain fentanyl. Lowthreshold harm reduction interventions are discussed amid limited prevention and treatment resources in rural communities.

Why haven't we solved the addiction crisis?

The current addiction crisis has destroyed a multitude of lives, leaving millions of fatalities worldwide in its wake. At the same time, various governmental agencies dedicated to solving this seemingly never-ending dilemma have not yet succeeded or delivered on their promises. We understand that addictive behavioral seeking is a multi-faceted neurobiological and spiritually complicated phenomenon. However, although the substitution replacement approach, especially to treat Opioid Use Disorder (OUD), has importance for harm reduction in the short term, it does not bring about a harm-free recovery or prevention. Instead, we propose a promising novel approach that uses genetic risk testing with induction of dopamine homeostasis and an objective Brain Health Check during youth. Our model involves a six-hit approach known as the "Reward Dysregulation Syndrome Solution System," which can identify addiction risk and target the root cause of addiction, dopamine dysregulation. While we applaud all past sophisticated neurogenetic and neuropharmacological research, our opinion is that in the long term, addiction scientists and clinicians might characterize preaddiction using tests; for example, administering the validated RDSQuestionarre29, genetic risk assessment, a modified brain health check, or diagnostic framing of mild to moderate Substance Use Disorder (SUD). The preaddiction concept could incentivize the development of interventions to prevent addiction from developing in the first place and target and treat neurotransmitter imbalances and other early indications of addiction. WC 222.