This decision analytical model uses the SOURCE model to elucidate factors underlying the recent decline in US overdose deaths.

Prescription depressants, particularly benzodiazepines, gabapentinoids, and Z-drugs, pose overdose risk. Understanding their prevalence in overdose fatalities and co-involved substances is critical. To identify latent substance classes in prescription depressant-involved overdose deaths. Retrospective cohort study All individuals whose fatal overdoses involved prescription depressants from 2000–2023 in Massachusetts, US (n = 8,665). Data were obtained from the Massachusetts Registry of Vital Records and Statistics. Substances were identified using ICD-10 codes. Literal text entries were available from 2015–2023. We conducted a latent class analysis to derive substance classes and a multinomial logistic regression to examine associated factors. We assessed the proportion of deaths these classes comprised over time. Five latent classes emerged and were characterized based on the substances with highest conditional probabilities within and across classes: 1) antidepressants (21.2

BACKGROUND AND AIMS:Buprenorphine-naloxone reduces overdose deaths in people with opioid use disorder (OUD). Treatment retention increases with higher daily doses. No national studies exist on retention's association with 24, 32 and 40 mg. This study aimed to: (1) estimate the effect on treatment retention of buprenorphine-naloxone doses between 4 and 40 mg compared with 16; and (2) compare the effect on treatment retention of 24, 32 and 40 mg doses. DESIGN:Observational cohort study in a national, multi-payer sample of prescription claims (IQVIA) of episodes involving buprenorphine-naloxone for OUD. Incident episodes started between 1 January 2014 and 31 March 2020, with a washout of 180 days. New episodes started with a 14+ day gap between prescriptions. SETTING:United States of America. PARTICIPANTS:The sample involved 620 229 episodes across 498 879 patients [42.3% female; mean age 37.9 (standard deviaion: 11.9)] who were dispensed prescriptions of buprenorphine-naloxone for OUD. MEASUREMENTS:The exposure was the maximum daily dose of buprenorphine-naloxone reached in the first 30 days of an episode, ranging from 4 to 40 mg. The outcome, treatment retention, was defined as having an active prescription at 1, 3, 6, 12, or 18 months. Covariates were age, sex, race and ethnicity, primary payer, and year of episode initiation. FINDINGS:Daily doses of 24, 32 and 40 mg increased retention compared with 16 mg at 1-18 months [adjusted odds ratio (aOR) range = 1.17; 95% confidence interval (CI) = 1.14, 1.20 at 18 months to 1.52 (CI = 1.49, 1.54) at 1 month, both for 24 mg]. In pairwise comparisons, 32 mg was favorable to 24 mg at 6, 12 and 18 months [aOR = 1.06 (95% CI = 1.02, 1.10) at 6 months; aOR = 1.09 (95% CI = 1.04, 1.14) at 12 months; aOR = 1.12 (95% CI = 1.06, 1.19) at 18 months], and 40 mg was favorable to 24 mg at 12 and 18 months [aOR = 1.10 (95% CI = 1.01, 1.21) at 12 months; aOR = 1.18 (95% CI = 1.06, 1.30) at 18 months]. CONCLUSIONS:Daily buprenorphine-naloxone doses of 24 mg appear to be associated with increased treatment retention compared with 16 mg and, for 6+ month episodes, 32 and 40 mg appear to be associated with increased retention compared with 24 mg.

Background and Aims: Early alcohol initiation is linked to the later development of problem drinking and other negative health outcomes. While a growing body of research categorizes early drinking behaviors into experimentation (onset of sipping) and initiation (first full drink), the factors associated with the transition between these stages remain underexplored. This study aimed to evaluate the influence of individual, interpersonal, and environmental factors on the time from alcohol experimentation to initiation among preadolescent youth. Design: We used data from the Adolescent Brain Cognitive Development (ABCD) Study (2016-2021). Participants who reported no baseline alcohol use and sipped before consuming a full drink during the study were included to ensure a clear temporal sequence from experimentation to initiation. Setting: The ABCD Study is conducted across 21 research sites in the United States. Participants: We included 1,213 youths in the final sample, whose ages ranged from 8 to 11 at baseline and 12 to 14 at the end of the study period. Measurements: Experimentation was defined as the first instance of alcohol sipping, while initiation was defined as consuming at least one full drink during the study period. An extended Cox model was used to examine the effects of sociodemographic characteristics, alcohol expectancies, family and peer dynamics, and neighborhood-level factors on the likelihood of alcohol initiation following experimentation. Findings: Among 1,213 youths, 87 (7.2%) participants had their first sip and later first drink by the 45th month after baseline. Older age at onset of sipping was associated with the highest likelihood of alcohol initiation (adjusted hazard ratio [HR]=6.40, 95% confidence interval [CI]: 3.72-11.02, p<0.001), followed by peer alcohol use (adjusted HR=4.11, 95% CI: 2.55-6.65, p<0.001), household rules allowing alcohol consumption (adjusted HR=2.14, 95% CI: 1.17-3.91, p=0.014), family conflict (adjusted HR=1.14, 95% CI: 1.04-1.26, p=0.007), and positive alcohol expectancies (adjusted HR=1.10, 95% CI: 1.01-1.21, p=0.032). Older age was associated with a decreased likelihood of alcohol initiation (adjusted HR=0.30, 95% CI: 0.18-0.51, p<0.001), after accounting for age at onset of sipping. No significant differences in the likelihood of alcohol initiation were observed based on sex, race, ethnicity, household income, parental drinking problems, alcohol availability, and neighborhood safety. Conclusions: Social and familial influences, age at onset of sipping, and positive alcohol expectancies emerged as robust factors influencing early alcohol experimentation preceding initiation. These findings suggest that early interventions engaging youths's social networks should be considered for the prevention of alcohol use progression among preadolescent youth. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used only data obtained by request from the National Institute of Mental Health (NIMH) Data Archive. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available from the ABCD Study via the NIMH Data Archive.

The overdose epidemic in the United States is evolving, with a rise in stimulant (cocaine and/or methamphetamine)-only and opioid and stimulant-involved overdose deaths for reasons that remain unclear. We conducted interviews and group model building workshops in Massachusetts and South Dakota. Building on these data and extant research, we identified six dynamic hypotheses, explaining changes in stimulant-involved overdose trends, visualized using causal loop diagrams. For stimulant- and opioid-involved overdose deaths, three dynamic hypotheses emerged: (1) accidental exposure to fentanyl from stimulants; (2) primary stimulant users increasingly using opioids, often with resignation; (3) primary opioid (especially fentanyl) users increasingly using stimulants to balance the sedating effect of fentanyl. For stimulant-only overdose deaths, three additional dynamic hypotheses emerged: (1) disbelief that death could occur from stimulants alone, and doubt in testing capabilities to detect fentanyl; (2) the stimulant supply has changed, leading to higher unpredictability and thus higher overdose risk; and (3) long-term stimulant use contributing to deteriorating health and increasing overdose risk. These hypotheses likely each explain a portion of the recent trends in stimulant- involved overdoses. However, confusion and uncertainty around the drug supply emerged as a central theme, underscoring the chaotic and unpredictable nature of the stimulant market. Our findings indicate the need for research to develop targeted public health interventions, including analyzing the extent of the effect of contamination on overdoses, reducing confusion about the stimulant supply, and examining historical stimulant use trends.

Background Fentanyl and its analogs contribute substantially to drug overdose deaths in the United States. There is concern that people using drugs are being unknowingly exposed to fentanyl, increasing their risk of overdose death. This study examines temporal trends and spatial variations in the co-occurrence of fentanyl with other seized drugs. Methods We identified fentanyl co-occurrence (the proportion of samples of non-fentanyl substances that also contain fentanyl) among 9 substances or substance classes of interest: methamphetamine, cannabis, cocaine, heroin, club drugs, hallucinogens, and prescription opioids, stimulants, and benzodiazepines. We used serial cross-sectional data on drug reports across 50 states and the District of Columbia from the National Forensic Laboratory Information System, the largest available database on the U.S. illicit drug supply, from January 2013 to December 2023. Findings We analyzed data from 11,940,207 samples. Fentanyl co-occurrence with all examined substances increased monotonically over time (Mann-Kendall p < 0.0001). Nationally, fentanyl co-occurrence was highest among heroin samples (approx. 50%), but relatively low among methamphetamine (<= 1%), <= 1%), cocaine (<= 4%), <= 4%), and other drug samples. However, co-occurrence rates have grown to over 10% for cocaine and methamphetamine in several Northeast states in 2017-2023. - 2023. Interpretation Fentanyl co-occurs most commonly with heroin, but its presence in stimulant supplies is increasing in some areas, where it may pose a disproportionately high risk of overdose. Funding This work was partly supported by FDA grant U01FD00745501. This article reflects the views of the authors and does not represent the views or policies of the FDA or US Department of Health and Human Services. Copyright (c) 2024 The Author(s). Published by Elsevier Ltd.

BackgroundAlthough medications for opioid use disorder (MOUD) are effective for treating opioid use disorder (OUD), persistent barriers still prevent patients from accessing this life-saving care. Policies to increase MOUD access have produced suboptimal results. This study presents a qualitative system dynamics model that elucidates the complexities of accessing and staying in MOUD treatment.MethodsWe utilized a community-based system dynamics approach to modeling the MOUD treatment system. We engaged a cohort of system experts/stakeholders, including individuals who had received MOUD, treatment providers, and policymakers, in interviews and group model building to develop and refine a simulation model. We then created a qualitative causal loop diagram based on insights gained while developing the simulation model and a review of interview transcripts.ResultsThe causal loop diagram captures four key factors affecting treatment initiation, retention, and leaving: (1) fraught interactions between patients and healthcare providers; (2) stigma-driven regulation of MOUD creating a culture of fear and defensive medicine; (3) a punitive culture in clinics and opioid treatment programs offering MOUD; and (4) the internalization of the abstinence narrative contributing to premature termination of treatment.ConclusionsOur analysis highlights how interdependent and non-linear feedback processes diminish or counteract the effectiveness and sustainability of MOUD policy interventions. Due to system memory and cultural resistance to change, even rolling back reactionary policies may do little to curb established behavioral patterns. In addition, conflicting and competing strategies among various actors within the system contribute to goal misalignment and a lack of standardization of care.

This cohort study examines racial and ethnic differences in the duration of buprenorphine treatment for opioid use disorder in the US from 2006 to 2020.

In the midst of the opioid crisis in the US, efforts to mitigate overdose risks have become paramount, leading some states to introduce mandates for coprescribing the life-saving overdose reversal drug naloxone. These mandates were designed to specifically address people receiving opioid analgesics who had an elevated risk for overdose. This included people receiving high opioid dosages, those concurrently using benzodiazepines, or those with a history of substance use disorder or overdose. Using a nationally representative, multipayer cohort of patients receiving prescription opioids, we investigated how naloxone codispensing rates changed at the state level from 2016 to 2021 among patients with an elevated risk for overdose. Then we used controlled interrupted time series analyses to assess mandates' longitudinal impact on naloxone codispensing in ten states that implemented mandates. We observed an immediate and significant increase in the naloxone codispensing rates in eight states after the implementation of mandates. Nevertheless, in five of these states, the codispensing rates exhibited a subsequent downward trend after the initial increase. State mandates show potential for improving naloxone codispensing; however, mandates alone might not be adequate for sustained change. Further research is needed to identify strategies complementing and enhancing the impact of mandates in combating the overdose crisis.

ObjectivesThe United States faces an ongoing drug overdose crisis, but accurate information on the prevalence of opioid use disorder (OUD) remains limited. A recent analysis by Keyes et al used a multiplier approach with drug poisoning mortality data to estimate OUD prevalence. Although insightful, this approach made stringent and partly inconsistent assumptions in interpreting mortality data, particularly synthetic opioid (SO)-involved and non-opioid-involved mortality. We revise that approach and resulting estimates to resolve inconsistencies and examine several alternative assumptions.MethodsWe examine 4 adjustments to Keyes and colleagues' estimation approach: (A) revising how the equations account for SO effects on mortality, (B) incorporating fentanyl prevalence data to inform estimates of SO lethality, (C) using opioid-involved drug poisoning data to estimate a plausible range for OUD prevalence, and (D) adjusting mortality data to account for underreporting of opioid involvement.ResultsRevising the estimation equation and SO lethality effect (adj. A and B) while using Keyes and colleagues' original assumption that people with OUD account for all fatal drug poisonings yields slightly higher estimates, with OUD population reaching 9.3 million in 2016 before declining to 7.6 million by 2019. Using only opioid-involved drug poisoning data (adj. C and D) provides a lower range, peaking at 6.4 million in 2014-2015 and declining to 3.8 million in 2019.ConclusionsThe revised estimation equation presented is feasible and addresses limitations of the earlier method and hence should be used in future estimations. Alternative assumptions around drug poisoning data can also provide a plausible range of estimates for OUD population.

Objectives: Yearly rolling aggregate trends or rates are commonly used to analyze trends in overdose deaths, but focusing on long-term trends can obscure short-term fluctuations (eg, daily spikes). We analyzed data on spikes in daily fatal overdoses and how various spike detection thresholds influence the identification of spikes. Materials and Methods: We used a spike detection algorithm to identify spikes among 16 660 drug-related overdose deaths (from any drug) reported in Massachusetts’ vital statistics from 2017 through 2023. We adjusted the parameters of the algorithm to define spikes in 3 distinct scenarios: deaths exceeding 2 adjusted moving SDs above the 7-, 30-, and 90-day adjusted moving average. Results: Our results confirmed the on-the-ground observation that there are days when many more people die of overdoses than would be expected based on fluctuations due to differences among people alone. We identified spikes on 5.8% to 20.6% of the days across the 3 scenarios, annually, constituting 11.1% to 31.6% of all overdose deaths. The absolute difference in percentage points of days identified as spikes varied from 5.2 to 11.5 between 7- and 30-day lags and from 0 to 4.6 between 30- and 90-day lags across years. When compared with the adjusted moving average across the 3 scenarios, in 2017 an average of 3.9 to 5.5 additional deaths occurred on spike days, while in 2023 the range was 3.7 to 6.0. Practice Implications: A substantial percentage of deaths occurred annually on spike days, highlighting the need for effectively monitoring short-term overdose trends. Moreover, our study serves as a foundational analysis for future research into exogenous events that may contribute to spikes in overdose deaths, aiming to prevent future deaths.

Importance:Buprenorphine is an effective and cost-effective medication to treat opioid use disorder (OUD), but is not readily available to many people with OUD in the US. The current cost-effectiveness literature does not consider interventions that concurrently increase buprenorphine initiation, duration, and capacity. Objective:To conduct a cost-effectiveness analysis and compare interventions associated with increased buprenorphine treatment initiation, duration, and capacity. Design and Setting:This study modeled the effects of 5 interventions individually and in combination using SOURCE, a recent system dynamics model of prescription opioid and illicit opioid use, treatment, and remission, calibrated to US data from 1999 to 2020. The analysis was run during a 12-year time horizon from 2021 to 2032, with lifetime follow-up. A probabilistic sensitivity analysis on intervention effectiveness and costs was conducted. Analyses were performed from April 2021 through March 2023. Modeled participants included people with opioid misuse and OUD in the US. Interventions:Interventions included emergency department buprenorphine initiation, contingency management, psychotherapy, telehealth, and expansion of hub-and-spoke narcotic treatment programs, individually and in combination. Main Outcomes and Measures:Total national opioid overdose deaths, quality-adjusted life years (QALYs) gained, and costs from the societal and health care perspective. Results:Projections showed that contingency management expansion would avert 3530 opioid overdose deaths over 12 years, more than any other single-intervention strategy. Interventions that increased buprenorphine treatment duration initially were associated with an increased number of opioid overdose deaths in the absence of expanded treatment capacity. With an incremental cost- effectiveness ratio of $19 381 per QALY gained (2021 USD), the strategy that expanded contingency management, hub-and-spoke training, emergency department initiation, and telehealth was the preferred strategy for any willingness-to-pay threshold from $20 000 to $200 000/QALY gained, as it was associated with increased treatment duration and capacity simultaneously. Conclusion and Relevance:This modeling analysis simulated the effects of implementing several intervention strategies across the buprenorphine cascade of care and found that strategies that were concurrently associated with increased buprenorphine treatment initiation, duration, and capacity were cost-effective.

Objectives Because buprenorphine treatment of opioid use disorder reduces opioid overdose deaths (OODs), expanding access to care is an important policy and clinical care goal. Policymakers must choose within capacity limitations whether to expand the number of people with opioid use disorder who are treated or extend duration for existing patients. This inherent tradeoff could be made less acute with expanded buprenorphine treatment capacity. Methods To inform such decisions, we used a validated simulation model to project the effects of increasing buprenorphine treatment-seeking, average episode duration, and capacity (patients per provider) on OODs in the United States from 2023 to 2033, varying the start time to assess the effects of implementation delays. Results Results show that increasing treatment duration alone could cost lives in the short term by reducing capacity for new admissions yet save more lives in the long term than accomplished by only increasing treatment seeking. Increasing provider capacity had negligible effects. The most effective 2-policy combination was increasing capacity and duration simultaneously, which would reduce OODs up to 18.6% over a decade. By 2033, the greatest reduction in OODs (≥20%) was achieved when capacity was doubled and average duration reached 2 years, but only if the policy changes started in 2023. Delaying even a year diminishes the benefits. Treatment-seeking increases were equally beneficial whether they began in 2023 or 2025 but of only marginal benefit beyond what capacity and duration achieved. Conclusions If policymakers only target 2 policies to reduce OODs, they should be to increase capacity and duration, enacted quickly and aggressively.

AIMS, DESIGN AND SETTING:We sought to describe longitudinal trends in buprenorphine receipt and buprenorphine-waivered providers in the United States from 2003 to 2021 and measure whether the relationship between the two differed after capacity-building strategies were enacted nationally in 2017. This was a retrospective study of two separate cohorts covering the years 2003-21, testing whether the association between two trends in these cohorts changed comparing 2003 to 2016 and from 2017 to 2021, among buprenorphine providers in the United States, regardless of treatment setting. Patients receiving dispensed buprenorphine at retail pharmacies. PARTICIPANTS:All providers who have obtained a waiver to prescribe buprenorphine in the United States, and an estimate of the annual number of patients who had buprenorphine for opioid use disorder (OUD) dispensed to them at a retail pharmacy. MEASUREMENTS:We synthesized and summarized data from multiple sources to assess the cumulative number of buprenorphine-waivered providers over time. We used national-level prescription data from IQVIA to estimate annual buprenorphine receipt for OUD. FINDINGS:From 2003 to 2021, the number of buprenorphine-waivered providers in the United States increased from fewer than 5000 in the first 2 years of Food and Drug Administration (FDA) approval to more than 114 000 in 2021, while patients receiving buprenorphine products for OUD increased from approximately 19 000 to more than 1.4 million. The strength of association between waivered providers and patients is significantly different before and after 2017 (P < 0.001). From 2003 to 2016, for each additional provider, there was an average increase of 32.1 [95% confidence interval (CI) = 28.7-35.6] patients, but an increase of only 4.6 (95% CI= 3.5-5.7) patients for each additional provider, beginning in 2017. CONCLUSIONS:In the United States, the relationship between the rates of growth in buprenorphine providers and patients became weaker after 2017. While efforts to increase buprenorphine-waivered providers were successful, there was less success in translating that into significant increases in buprenorphine receipt.

In 2020, the ongoing US opioid overdose crisis collided with the emerging COVID-19 pandemic. Opioid overdose deaths (OODs) rose an unprecedented 38%, due to a combination of COVID-19 disrupting services essential to people who use drugs, continued increases in fentanyls in the illicit drug supply, and other factors. How much did these factors contribute to increased OODs? We used a validated simulation model of the opioid overdose crisis, SOURCE, to estimate excess OODs in 2020 and the distribution of that excess attributable to various factors. Factors affecting OODs that could have been disrupted by COVID-19, and for which data were available, included opioid prescribing, naloxone distribution, and receipt of medications for opioid use disorder. We also accounted for fentanyls' presence in the heroin supply. We estimated a total of 18,276 potential excess OODs, including 1,792 lives saved due to increases in buprenorphine receipt and naloxone distribution and decreases in opioid prescribing. Critically, growth in fentanyls drove 43% (7,879) of the excess OODs. A further 8% is attributable to first-ever declines in methadone maintenance treatment and extended-released injectable naltrexone treatment, most likely due to COVID-19-related disruptions. In all, 49% of potential excess OODs remain unexplained, at least some of which are likely due to additional COVID-19-related disruptions. While the confluence of various COVID-19-related factors could have been responsible for more than half of excess OODs, fentanyls continued to play a singular role in excess OODs, highlighting the urgency of mitigating their effects on overdoses.

This cohort study investigates factors associated with abrupt discontinuation of long-term high-dose opioid treatment at the national level and across US states.

Background: Online communities such as Reddit can provide social support for those recovering from opioid use disorder. However, it is unclear whether and how advice-seekers differ from other users. Our research addresses this gap by identifying key characteristics of r/suboxone users that predict advice-seeking behavior. Objective: The objective of this analysis is to identify and describe advice-seekers on Reddit for buprenorphine-naloxone use using text annotation, social network analysis, and statistical modeling techniques. Methods: We collected 5258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n = 137) or not advice-seeking (n = 65). We also annotated each posting user's buprenorphine-naloxone use status (current versus formerly taking and, if currently taking, whether inducting or tapering versus other stages) and quantified their connectedness using social network analysis. To analyze the relationship between Reddit users' advice-seeking and their social connectivity and medication use status, we constructed four models which varied in their inclusion of explanatory variables for social connectedness and buprenorphine use status. Results: The stepwise model containing "total degree" (p = 0.002), "using: inducting/tapering" (p < 0.001), and "using: other" (p = 0.01) outperformed all other models. Reddit users with fewer connections and who are currently using buprenorphine-naloxone are more likely to seek advice than those who are well-connected and no longer using the medication, respectively. Importantly, advice-seeking behavior is most accurately predicted using a combination of network characteristics and medication use status, rather than either factor alone. Conclusions: Our findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.