Classifying High-Risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach.

BACKGROUND: Persistent opioid use is a common occurrence after surgery and prolonged exposure to opioids may result in escalation and dependence. The objective of this study was to develop machine-learning-based predictive models for persistent opioid use after major spine surgery. METHODS: Five classification models were evaluated to predict persistent opioid use: logistic regression, random forest, neural network, balanced random forest, and balanced bagging. Synthetic Minority Oversampling Technique was used to improve class balance. The primary outcome was persistent opioid use, defined as patient reporting to use opioids after 3 months postoperatively. The data were split into a training and test set. Performance metrics were evaluated on the test set and included the F1 score and the area under the receiver operating characteristics curve (AUC). Feature importance was ranked based on SHapley Additive exPlanations (SHAP). RESULTS: After exclusion (patients with missing follow-up data), 2611 patients were included in the analysis, of which 1209 (46.3%) continued to use opioids 3 months after surgery. The balanced random forest classifiers had the highest AUC (0.877, 95% confidence interval [CI], 0.834-0.894) compared to neural networks (0.729, 95% CI, 0.672-0.787), logistic regression (0.709, 95% CI, 0.652-0.767), balanced bagging classifier (0.859, 95% CI, 0.814-0.905), and random forest classifier (0.855, 95% CI, 0.813-0.897). The balanced random forest classifier had the highest F1 (0.758, 95% CI, 0.677-0.839). Furthermore, the specificity, sensitivity, precision, and accuracy were 0.883, 0.700, 0.836, and 0.780, respectively. The features based on SHAP analysis with the highest impact on model performance were age, preoperative opioid use, preoperative pain scores, and body mass index. CONCLUSIONS: The balanced random forest classifier was found to be the most effective model for identifying persistent opioid use after spine surgery.

Perioperative Regional Anesthesia on Persistent Opioid Use and Chronic Pain after Noncardiac Surgery: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Whether regional anesthesia impacts the development of chronic postsurgical pain is currently debateable, and few studies have evaluated an effect on prolonged opioid use. We sought to systematically review the effect of regional anesthesia for adults undergoing noncardiac elective surgery on these outcomes. METHODS: A systematic search was conducted in MEDLINE, EMBASE, CENTRAL, and CINHAL for randomized controlled trials (from inception to April 2022) of adult patients undergoing elective noncardiac surgeries that evaluated any regional technique and included one of our primary outcomes: (1) prolonged opioid use after surgery (continued opioid use ≥2 months postsurgery) and (2) chronic postsurgical pain (pain ≥3 months postsurgery). We conducted a random-effects meta-analysis on the specified outcomes and used the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach to rate the quality of evidence. RESULTS: Thirty-seven studies were included in the review. Pooled estimates indicated that regional anesthesia had a significant effect on reducing prolonged opioid use (relative risk [RR] 0.48, 95% CI, 0.24-0.96, P = .04, I 2 0%, 5 trials, n = 348 patients, GRADE low quality). Pooled estimates for chronic pain also indicated a significant effect favoring regional anesthesia at 3 (RR, 0.74, 95% CI, 0.59-0.93, P = .01, I 2 77%, 15 trials, n = 1489 patients, GRADE moderate quality) and 6 months (RR, 0.72, 95% CI, 0.61-0.85, P < .001, I 2 54%, 19 trials, n = 3457 patients, GRADE moderate quality) after surgery. No effect was found in the pooled analysis at 12 months postsurgery (RR, 0.44, 95% CI, 0.16-1.17, P = .10). CONCLUSIONS: The results of this study suggest that regional anesthesia potentially reduces chronic postsurgical pain up to 6 months after surgery. Our findings also suggest a potential decrease in the development of persistent opioid use.

Disparities in Medication Use for Criminal Justice System-Referred Opioid Use Disorder Treatment.

Importance: Individuals with opioid use disorder (OUD) and criminal justice system involvement experience high rates of overdose death. Historical data point to limited use of medications for opioid use disorder (MOUD) in criminal justice system-referred treatment for OUD as playing a role. However, how MOUD use among those referred to treatment by the criminal justice system has changed relative to other referral sources over time is still unclear, as well as how it varies across states. Objective: To examine disparities in the use of MOUD between individuals referred to treatment by the criminal justice system compared to other referral sources over time. Design, Setting, and Participants: This cross-sectional study included admissions to specialty substance use treatment facilities for OUD in the national Treatment Episodes Dataset-Admissions from 2014 to 2021. Logistic regression models were used to examine trends in the probability of MOUD use among individuals with and without criminal justice referrals for OUD treatment, as well as any differential trends by state. The data were analyzed from September 2023 to August 2024. Main Outcome and Measure: The main outcome was the probability that treatment for individuals with OUD included MOUD. Results: A total of 3 235 445 admissions were analyzed in the study data. Among individuals referred to OUD treatment by the criminal justice system, the probability that treatment included MOUD increased by 3.42 percentage points (pp) (95% CI, 3.37 pp to 3.47 pp) annually from 2014 to 2021. This was faster than the increase in the probability of MOUD use for noncriminal justice-referred admissions (2.49 pp [95% CI, 2.46 pp to 2.51 pp) and reduced, but did not eliminate, disparities in MOUD use between individuals with and without criminal justice system-referred treatment. In 2021, only 33.6% of individuals in criminal justice system-referred treatment received MOUD, 15.6 pp lower than for individuals referred to treatment by other sources. Trends in the probability of MOUD use varied substantially for individuals in criminal justice system-referred treatment across states, but very few experienced enough growth to eliminate this disparity. Conclusions and Relevance: The results of this cross-sectional study suggest that targeted efforts to address persistent disparities in MOUD use among those with OUD and criminal justice system involvement are needed to address the poor health outcomes experienced by this population.

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.

BACKGROUND: The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. OBJECTIVE: We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. METHODS: We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. RESULTS: In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. CONCLUSIONS: This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

Correlates of fentanyl preference among people who use drugs in Rhode Island.

BACKGROUND: Fentanyl is increasingly pervasive in the unregulated drug supply and is a driver of drug overdose deaths in the United States. The aims of this study were to characterize and identify correlates of fentanyl preference among people who use drugs (PWUD) in Rhode Island (RI). METHODS: Using bivariate analysis, we examined associations between fentanyl preference and sociodemographic and psychosocial characteristics at baseline among participants enrolled in the RI Prescription Drug and Illicit Drug Study from August 2020-February 2023. Fentanyl preference was operationalized based on responses to a five-point Likert scale: "I prefer using fentanyl or drugs that have fentanyl in them." Participants who responded that they "strongly disagree," "disagree," or were "neutral" with respect to this statement were classified as not preferring fentanyl, whereas participants who responded that they "agree" or "strongly agree" were classified as preferring fentanyl. RESULTS: Among 506 PWUD eligible for inclusion in this analysis, 15% expressed a preference for fentanyl or drugs containing fentanyl as their drug of choice. In bivariate analyses, preference for fentanyl was positively associated with younger age, white race, lifetime history of overdose, history of injection drug use, past month enrollment in a substance use treatment program, past month treatment with medications for opioid use disorder, and preferences for heroin and crystal methamphetamine (all p < 0.05). Descriptive data yielded further insight into reasons for fentanyl preference, the predominant having to do with perceived effects of the drug and desire to avoid withdrawal symptoms. CONCLUSIONS: Only a relatively small subset of study participants preferred drugs containing fentanyl. Given the increased prevalence of fentanyl contamination across substances within the unregulated drug market, the result for PWUD is increasingly less agency with respect to choice of drug; for example, people may be forced to use fentanyl due to restricted supply and the need to mitigate withdrawal symptoms, or may be using fentanyl without intending to do so. Novel and more effective interventions for PWUD, including increased access to age-appropriate harm reduction programs such as fentanyl test strips and overdose prevention centers, are needed to mitigate fentanyl-related harms.

ADHD: prevalence and effect on opioid use disorder treatment outcome in a French sample of patients receiving medication for opioid use disorder-the influence of impulsivity as a mediating factor.

BACKGROUND: Opioid use disorder (OUD) poses a global health challenge, and despite medications for opioid use disorder (MOUD) and psychosocial interventions, relapse remains a significant concern. Comorbid psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), are one of the major factors associated with poor OUD treatment outcome. We aimed to estimate the frequency of probable ADHD (in childhood and in adulthood) in patients with OUD; to assess the factors associated with this comorbidity; and to explore the factors that mediate the relationship between ADHD and OUD treatment outcome. METHODS: We conducted an observational study using a sample of 229 patients aged 18 years and older who were diagnosed with OUD and had received MOUD for at least six months. Participants were assessed through a structured interview and self-report questionnaires. Multivariate logistic regressions and a mediation analysis were performed. RESULTS: Almost half of the participants reported probable ADHD in childhood, and ADHD persisted into adulthood among two-thirds of the patients. The factors associated with poor OUD treatment outcome included earlier onset of OUD, lower education, and greater impulsivity. There was no direct effect of probable ADHD in childhood on OUD treatment outcome, but there was an indirect effect through negative urgency, the tendency to respond impulsively to negatively connoted emotional experiences. CONCLUSIONS: The findings suggest that ADHD symptoms, particularly impulsivity, may contribute to vulnerability in opioid use and play a crucial role in treatment outcomes for this population. TRIAL REGISTRATION: ClinicalTrials identifier NCT01847729.

Lessons for future pandemics: Temporal evolution and rural-urban variations in the impacts of the COVID-19 on opioid use treatment.

The COVID-19 pandemic introduced imminent and lasting impacts on the opioid crisis in the U.S., including a significant increase in opioid overdose and deaths and in use of telehealth in treatment. What lessons can we learn from the treatment transition during the pandemic that could help tackle the opioid crisis when future pandemics strike? In this paper, we conducted a phone survey with opioid treatment facilities in Pennsylvania to examine the COVID-19's impacts on treatment facilities and individuals with opioid use disorder during the first year of the pandemic. We separated the lockdown period (Mid-March through Mid-May, 2020) from the reopening period that followed, and urban areas from rural areas, to explore temporal evolution and rural-urban variations in the COVID-19's impacts. We found rural-urban heterogeneity in facilities' adoption of telehealth in treatment and in challenges and risk factors faced by their clients during the lockdown period. During the reopening, telehealth was adopted by most facilities, and telehealth-related challenges became less salient; however, both rural and urban facilities reported higher relapse risks faced by their clients, citing factors more likely to be at clients' end and related to socioeconomic stressors and mental health. Our results highlight the vitality of addressing socioeconomic and mental health challenges faced by individuals with OUD, via government policies and community interventions, when future pandemics strike. The findings also indicate the importance of maintaining facilities' financial well-being to provide treatment services.

Federal opioid agonist therapy policy: interrupted time series analysis of the impact of the methadone exemption removal across eight provinces in Canada.

BACKGROUND: Federal deregulation of opioid agonist therapies are an attractive policy option to improve access to opioid use disorder care and achieve widespread beneficial impacts on growing opioid-related harms. There have been few evaluations of such policy interventions and understanding effects can help policy planning across jurisdictions. METHODS: Using health administrative data from eight of ten Canadian provinces, this study evaluated the impacts of Health Canada's decision in May 2018 to rescind the requirement for Canadian health professionals to obtain an exemption from the Canadian Drugs and Substance Act to prescribe methadone for opioid use disorder. Over the study period of June 2017 to May 2019, we used descriptive statistics to capture overall trends in the number of agonist therapy prescribers across provinces and we used interrupted time series analysis to determine the effect of this decision on the trajectories of the agonist therapy prescribing workforces. RESULTS: There were important baseline differences in the numbers of agonist therapy prescribers. The province with the highest concentration of prescribers had 7.5 more prescribers per 100,000 residents compared to the province with the lowest. All provinces showed encouraging growth in the number of prescribers through the study period, though the fastest growing province grew 4.5 times more than the slowest. Interrupted time series analyses demonstrated a range of effects of the federal policy intervention on the provinces, from clearly positive changes to possibly negative effects. CONCLUSIONS: Federal drug regulation policy change interacted in complex ways with provincial health professional regulation and healthcare delivery, kaleidoscoping the effects of federal policy intervention. For Canada and other health systems such as the US, federal policy must account for significant subnational variation in OUD epidemiology and drug regulation to maximize intended beneficial effects and mitigate the risks of negative effects.

Tailoring opioid use prevention content for juvenile diversion programs with adolescents and their caregivers.

INTRODUCTION: Opioid use (OU) skyrockets as youth transition into young adulthood, indicating adolescence is a critical time for prevention. The juvenile legal system (JLS) presents an ideal setting for OU prevention, as it is the single largest referral source for youth outpatient OU treatment, after self-referral. However, no OU prevention programs have been developed for youth in JLS diversion programs or their families. The current formative study established specific OU prevention needs and preferences of families in JLS programs, to guide future tailored prevention efforts. METHODS: We interviewed 21 adolescents with substance use and 20 of their caregivers referred by a JLS diversion program in the northeastern United States to explore their OU-related knowledge, personal experiences, motivations, and behavioral skills. We used a deductive qualitative analysis approach wherein data were analyzed using an a priori coding framework based on the Information Motivation Behavioral Skills model. RESULTS: Caregivers knew more about OU than youth, several of whom misidentified both opiates and non-opiates. Few participants reported a history of personal OU, though many knew of others' OU. Participants perceived several potential motivations for OU: mental health problems, relationships, life stressors, difficulty accessing preferred substances, and experimentation. Though often unfamiliar with the symptoms, participants were eager to learn skills to identify and manage a suspected overdose. CONCLUSIONS: Youth involved in a JLS diversion program and their caregivers were open to and eager for tailored opioid use prevention content to help them reduce risks. We discuss implications for OU prevention and intervention program development.

Dissemination of health content through social networks: YouTube and opioid use disorders.

INTRODUCTION: Most Americans now access social media platforms, including YouTube, to obtain health information. However, few studies have evaluated the quality of YouTube content related to opioid use disorder (OUD), including medications for OUD (MOUD; buprenorphine) and harm reduction resources (e.g., naloxone). The purpose of this cross-sectional analysis was to assess the quality, accuracy, and reliability of MOUD and harm reduction-related video content available on YouTube. METHODS: The study team conducted a YouTube search between June 2022 and July 2022 using key words related to MOUD and harm reduction content (e.g., "suboxone," "methadone," "Narcan"). The 5 most viewed videos from each search term were analyzed for quality (i.e., Global Quality Scale; GQS), accuracy (i.e., JAMA Benchmark Criteria), and reliability (i.e., DISCERN). Videos that were non-English, duplicate, or that did not directly mention OUD, MOUD, or harm reduction were excluded from the review (N = 6). RESULTS: YouTube videos (N = 70) were mostly produced by medical professionals (27.1 %), independent nonmedical users (21.4 %; e.g., vloggers, individuals documenting their experiences), medical organizations (17.1 %; e.g., hospitals, treatment programs), and/or media (14.3 %; e.g., news agencies). The target audience was primarily the general public (65.7 %), people who use opioids (20.0 %), and healthcare providers (10.0 %). Videos containing MOUD content (N = 64, 61.4 %) mostly focused on suboxone (25.0 %), methadone (23.4 %), Sublocade (14.1 %), and subutex/buprenorphine (14.1 %). The median quality score was 2 based on the GQS with 3 videos receiving the highest quality rating (5). Two videos were highly rated for accuracy per all three JAMA Benchmark criteria. Videos produced by nonmedical educational channels had the highest overall reliability scores on the DISCERN criteria (median 4), followed by medical professionals (median 3), and medical organizations (median 2.5). CONCLUSION: The overall quality, accuracy, and reliability of MOUD and harm reduction related content posted on YouTube is poor. The lack of evidence-based content posted on YouTube reinforces the need for public health expert involvement in disseminating guideline-based content on social media.

The opioid epidemic and accessibility to free Wi-Fi: internet access is a human rights issue.

The opioid epidemic has taken the lives of thousands of people across North America and Europe. Moreover, lack of housing, inflation, and a rapidly changing economy have affected millions of people, and many have become homeless. Many governments, researchers, health agencies, and not-for-profits have offered innovative ways to tackle this crisis, including many harm-reduction technologies that rely on Internet. In the age of the first artificial intelligence (AI) revolution, where reliance and accessibility to Internet have become a necessity for finding jobs, housing, affordable food, social services, social connection, and staying alive, the creation of free Wi-Fi zones around inner city neighborhood by towns and municipalities is not only a cost-effective way to reduce death, social costs, but a human rights issue during the initial stage of first A.I. revolution.

An individual-based dynamic model to assess interventions to mitigate opioid overdose risk.

BACKGROUND: Illicit opioid overdose continues to rise in North America and is a leading cause of death. Mathematical modeling is a valuable tool to investigate the epidemiology of this public health issue, as it can characterize key features of population outcomes and quantify the broader effect of structural and interventional changes on overdose mortality. The aim of this study is to quantify and predict the impact of key harm reduction strategies at differing levels of scale-up on fatal and nonfatal overdose among a population of people engaging in unregulated opioid use in Toronto. METHODS: An individual-based model for opioid overdose was built featuring demographic and behavioural variation among members of the population. Key individual attributes known to scale the risk of fatal and nonfatal overdose were identified and incorporated into a dynamic modeling framework, wherein every member of the simulated population encompasses a set of distinct characteristics that govern demographics, intervention usage, and overdose incidence. The model was parametrized to fatal and nonfatal overdose events reported in Toronto in 2019. The interventions considered were opioid agonist therapy (OAT), supervised consumption sites (SCS), take-home naloxone (THN), drug-checking, and reducing fentanyl in the drug supply. Harm reduction scenarios were explored relative to a baseline model to examine the impact of each intervention being scaled from 0% use to 100% use on overdose events. RESULTS: Model simulations resulted in 3690.6 nonfatal and 295.4 fatal overdoses, coinciding with 2019 data from Toronto. From this baseline, at full scale-up, 290 deaths were averted by THN, 248 from eliminating fentanyl from the drug supply, 124 from SCS use, 173 from OAT, and 100 by drug-checking services. Drug-checking and reducing fentanyl in the drug supply were the only harm reduction strategies that reduced the number of nonfatal overdoses. CONCLUSIONS: Within a multi-faceted harm reduction approach, scaling up take-home naloxone, and reducing fentanyl in the drug supply led to the largest reduction in opioid overdose fatality in Toronto. Detailed model simulation studies provide an additional tool to assess and inform public health policy on harm reduction.

Opioid Use and the Risk of Ventricular Arrhythmias: A Systematic Review and Meta-Analysis.

BACKGROUND: The association between opioid use and the risk of ventricular arrhythmias (VA) is poorly understood. AIMS: The objective of this study was to synthesize the evidence on the risk of VA associated with opioid use. MATERIALS & METHODS: We systematically searched the Cochrane Library, Embase, MEDLINE, and CINAHL databases in July 2022. Risk of bias was assessed using the Cochrane risk for bias tool for randomized controlled trials (RCTs) and ROBINS-I for observational studies. Certainty of evidence was assessed using GRADE. RESULTS: We included 15 studies (12 observational, 2 post hoc analyses of RCTs, 1 RCT). Most studies focused on opioid use for maintenance therapy (n = 9), comparing methadone to buprenorphine (n = 13), and reported QTc prolongation (n = 13). Six observational studies had a critical risk of bias, and one RCT was at high risk of bias. Two studies could not be included in the meta-analysis as they reported a different outcome and studied an opioid antagonist. Meta-analysis of 13 studies indicated that the use of methadone was associated with an increased risk of VA compared to the use of buprenorphine, morphine, placebo, or levacetylmethadol (risk ratio [RR], 2.39; 95% CI, 1.31-4.35; I2 = 60%). The pooled estimate varied greatly between observational studies (RR, 2.12; 95% CI, 1.15-3.91; I2 = 62%) and RCTs (RR, 14.09; 95% CI, 1.52-130.61; I2 = 0%), but both indicated an increased risk. CONCLUSION: In this systematic review and meta-analysis, we found that methadone use is associated with more than twice the risk of VA compared to comparators. However, our findings should be interpreted cautiously given the limited quality of the available evidence.

Development of a 5-Step Electronic Medical Record-Based Algorithm to Identify Patients with Opioid Use Disorder in Pregnancy.

Objectives: This study aimed to develop and validate an algorithm for the identification of opioid use disorder (OUD) in pregnant patients using electronic medical record (EMR) data. Materials and Methods: A cohort of pregnant patients from a single institution was used to develop and validate the algorithm. Five algorithm components were used, and chart reviews were conducted to confirm OUD diagnoses based on established criteria. Positive predictive values (PPV) of each of the algorithm's components were assessed. Results: Of the 334 charts identified by the algorithm, 256 true cases were confirmed. The overall PPV of the algorithm was 76.6%, with 100% accuracy for outpatient medication lists, and high PPVs ranging from 81.3% to 93.4% across other algorithm components. Discussion and Conclusion: The study highlights the significance of a multifaceted approach in identifying OUD among pregnant patients, aiming to improve patient care and target interventions for patients at risk.

Decrease in injection and rise in smoking and snorting of heroin and synthetic opioids, 2000-2021.

BACKGROUND: Injecting, smoking, and snorting heroin/synthetic opioids is each associated with unique health risks. It is unclear how route of administration (ROA) preferences have shifted during the opioid epidemic. METHODS: Using 2000-2021 admissions data from SAMHSA TEDS-A, we analyzed trends in heroin/synthetic opioid ROA preferences and factors associated with these preferences. RESULTS: 7,881,318 heroin/synthetic opioid admissions reported injection, smoking, or snorting preference. Nationally, injection peaked in 2014 (69.9 %) and nadired in 2021(52.2 %), snorting nadired in 2014 (24.9 %) and peaked in 2021 (36.4 %), and smoking rose steadily from 2.5 % in 2005 to a peak of 11.4 % in 2021. From 2000-2021, the number of states with ≥10 % smoking rates grew from 2 to 27 (highest: 57.0 % in Arizona in 2021). In 2021, increased adjusted prevalence ratios (APR) of non-injection versus injection use were associated with older age at first opioid use (APR 1.52 [95 % CI: 1.51, 1.54] for those 30+ relative to ≤20), and all race/ethnicities relative to non-Latino White individuals (highest: Black individuals, APR 1.77 [1.75, 1.78]). Geography strongly predicted smoking versus snorting (Mountain APR 6.91 [6.64, 7.19], Pacific APR 6.61 [6.35, 6.88], reference: New England). CONCLUSIONS: ROA preferences of heroin/synthetic opioids have changed substantially since 2000, with: 1) recent decreases in injection nationally; 2) increased smoking, particularly in the western US; and, 3) recent increased snorting in the eastern US. Smoking is now prevalent and growing. Public health implications include an increasing number of smoking-related fatal overdoses and the probable reduction of injection-specific morbidity and increase in smoking-specific morbidity.

The cascade of care for commercially-insured persons with opioid use disorder and comorbid HIV and HCV infections.

BACKGROUND: Opioid use disorder (OUD) significantly impacts individual and public health and exacerbated further by concurrent infectious diseases. A syndemic approach is needed to address the intertwined OUD, HIV, and HCV epidemics, including the expanded use of medications for opioid use disorder (MOUD). METHODS: To identify MOUD scale-up opportunities, we conducted a retrospective cohort study, representing commercially insured persons, and created the OUD care continuum, including HIV and HCV influences in adults (18-64 years) newly diagnosed with OUD in 2019 using Merative MarketSan data. RESULTS: Among 124,467,633 individuals, the prevalence of OUD was 0.4 % (95 % CI: 0.36 %-0.46 %; N = 497,871), with 327,277 (65.7 %, 95 % CI: 65.60 %-65.87 %) newly diagnosed in 2019. Among these newly diagnosed individuals (54 % men, mean age 44±0.01), 53,568 (27.0 %, 95 % CI: 26.4 %-27.5 %) were prescribed MOUD, with retention rates at 1, 3, and 6 months being 89.0 % (95 % CI: 88.2 %-89.8 %), 66.0 % (95 % CI: 64.8 %-67.2 %), and 50.3 % (95 % CI: 48.3 %-51.6 %), respectively. Buprenorphine was the most prescribed MOUD (79.6 %, 95 % CI: 78.6 %-80.7 %), followed by XR-NTX (14.9 %, 95 % CI:14.0 %-15.8 %) and methadone (5.5 %, 95 % CI: 4.9 %-6.1 %). Six-month retention was highest for methadone (73.4 %, 95 % CI: 73.0 %-73.8 %), however, followed by buprenorphine (55.7 %, 95 % CI: 55.3 %-57.1 %) and substantially lower for XR-NTX (12.6 %, 95 % CI: 10.6 %-14.6 %). Screening for HIV and HCV was low among OUD enrollees (11.1 %, 14.4 %), slightly higher for MOUD initiators (18.0 %, 21.6 %). Being prescribed MOUD was correlated with HCV infection (AOR: 2.54; 95 % CI: 2.41-2.68), HCV/HIV coinfection (AOR: 1.89; 95 % CI: 1.41-2.53), and hospitalization for OUD-related services (AOR: 1.14; 95 % CI: 1.11-1.17), yet hospitalization for OUD-related services was positively correlated with XR-NTX (AOR: 2.72; 95 % CI: 2.56-2.85) prescription and negatively with methadone (AOR: 0.19; 95 % CI: 0.16-0.23) prescription. Having HIV was negatively correlated with being prescribed methadone (AOR: 0.33; 95 % CI: 0.13-0.86). CONCLUSIONS: Substantial gaps in the OUD cascade persist, underscoring better implementation opportunities for MOUD prescription in hospital-based settings and expanding access to methadone beyond highly regulated sites given its low coverage yet high treatment retention.