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.

Longitudinal dose patterns among patients newly initiated on long-term opioid therapy in the United States, 2018 to 2019: an observational cohort study and time-series cluster analysis.

ABSTRACT: Opioid prescribing varies widely, and prescribed opioid dosages for an individual can fluctuate over time. Patterns in daily opioid dosage among patients prescribed long-term opioid therapy have not been previously examined. This study uses a novel application of time-series cluster analysis to characterize and visualize daily opioid dosage trajectories and associated demographic characteristics of patients newly initiated on long-term opioid therapy. We used 2018 to 2019 data from the IQVIA Longitudinal Prescription (LRx) all-payer pharmacy database, which covers 92% of retail pharmacy prescriptions dispensed in the United States. We identified a cohort of 277,967 patients newly initiated on long-term opioid therapy during 2018. Patients were stratified into 4 categories based on their mean daily dosage during a 90-day baseline period (<50, 50-89, 90-149, and ≥150 morphine milligram equivalent [MME]) and followed for a 270-day follow-up period. Time-series cluster analysis identified 2 clusters for each of the 3 baseline dosage categories <150 MME and 3 clusters for the baseline dosage category ≥150 MME. One cluster in each baseline dosage category comprised opioid dosage trajectories with decreases in dosage at the end of the follow-up period (80.7%, 98.7%, 98.7%, and 99.0%, respectively), discontinuation (58.5%, 80.0%, 79.3%, and 81.7%, respectively), and rapid tapering (50.8%, 85.8%, 87.5%, and 92.9%, respectively). These findings indicate multiple clusters of patients newly initiated on long-term opioid therapy who experience discontinuation and rapid tapering and highlight potential areas for clinician training to advance evidence-based guideline-concordant opioid prescribing, including strategies to minimize sudden dosage changes, discontinuation, or rapid tapering, and the importance of shared decision-making.

A systems science approach to identifying data gaps in national data sources on adolescent suicidal ideation and suicide attempt in the United States.

BACKGROUND: Suicide is currently the second leading cause of death among adolescents ages 10-14, and third leading cause of death among adolescents ages 15-19 in the United States (U.S). Although we have numerous U.S. based surveillance systems and survey data sources, the coverage offered by these data with regard to the complexity of youth suicide had yet to be examined. The recent release of a comprehensive systems map for adolescent suicide provides an opportunity to contrast the content of surveillance systems and surveys with the mechanisms listed in the map. OBJECTIVE: To inform existing data collection efforts and advance future research on the risk and protective factors relevant to adolescent suicide. METHODS: We examined data from U.S. based surveillance systems and nationally-representative surveys that included (1) observations for an adolescent population and (2) questions or indicators in the data that identified suicidal ideation or suicide attempt. Using thematic analysis, we evaluated the codebooks and data dictionaries for each source to match questions or indicators to suicide-related risk and protective factors identified through a recently published suicide systems map. We used descriptive analysis to summarize where data were available or missing and categorized data gaps by social-ecological level. RESULTS: Approximately 1-of-5 of the suicide-related risk and protective factors identified in the systems map had no supporting data, in any of the considered data sources. All sources cover less than half the factors, except the Adolescent Brain Cognitive Development Study (ABCD), which covers nearly 70% of factors. CONCLUSIONS: Examining gaps in suicide research can help focus future data collection efforts in suicide prevention. Our analysis precisely identified where data is missing and also revealed that missing data affects some aspects of suicide research (e.g., distal factors at the community and societal level) more than others (e.g., proximal factors about individual characteristics). In sum, our analysis highlights limitations in current suicide-related data availability and provides new opportunities to identify and expand current data collection efforts.

Circumstances Contributing to Suicide Among U.S. Adolescents Aged 10-19 Years With and Without a Known Mental Health Condition: National Violent Death Reporting System, 2013-2018.

PURPOSE: Suicide is the second leading cause of death for adolescents in the United States; however, suicide is preventable and a better understanding of circumstances that contribute to death can inform prevention efforts. While the association between adolescent suicide and mental health is well established, multiple circumstances contribute to suicide risk. This study examines characteristics of adolescents who died by suicide and differences in circumstances between those with and without known mental health conditions at the time of death. METHODS: Logistic regression models were used to estimate adjusted odds ratios and 95% confidence intervals of circumstances contributing to suicide between decedents with and without known mental health conditions using data from the 2013 to 2018 National Violent Death Reporting System (analyzed in 2021). RESULTS: Decedents with a known mental health condition were 1.2-1.8 times more likely to experience problematic alcohol misuse, substance misuse, family and other nonintimate relationship problems, and school problems; however, there were no significant differences between those with and without a known mental health condition for the preceding circumstances of arguments or conflicts, criminal or legal problems, or any crisis occurring within the two weeks prior to death. DISCUSSION: A comprehensive suicide prevention approach can address not only mental health conditions as a risk factor but also life stressors and other crises experienced among adolescents without known mental health conditions.

Evidence of the emergence of illicit benzodiazepines from online drug forums.

Illicit or 'designer' benzodiazepines are a growing contributor to overdose deaths. We employed natural language processing (NLP) to study benzodiazepine mentions over 10 years on 270 online drug forums (subreddits) on Reddit. Using NLP, we automatically detected mentions of illicit and prescription benzodiazepines, including their misspellings and non-standard names, grouping relative mentions by quarter. On a collection of 17 861 755 posts between 2012 and 2021, we searched for 26 benzodiazepines (8 prescription; 18 illicit), detecting 173 275 mentions. The rate of posts about both prescription and illicit benzodiazepines increased consistently with increases in deaths involving both drug classes, illustrating the utility of surveillance via Reddit.

Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping.

Suicide is the second leading cause of death among youth ages 10-19 in the USA. While suicide has long been recognized as a multifactorial issue, there is limited understanding regarding the complexities linking adverse childhood experiences (ACEs) to suicide ideation, attempt, and fatality among youth. In this paper, we develop a map of these complex linkages to provide a decision support tool regarding key issues in policymaking and intervention design, such as identifying multiple feedback loops (e.g., involving intergenerational effects) or comprehensively examining the rippling effects of an intervention. We use the methodology of systems mapping to structure the complex interrelationships of suicide and ACEs based on the perceptions of fifteen subject matter experts. Specifically, systems mapping allows us to gain insight into the feedback loops and potential emergent properties of ACEs and youth suicide. We describe our methodology and the results of fifteen one-on-one interviews, which are transformed into individual maps that are then aggregated and simplified to produce our final causal map. Our map is the largest to date on ACEs and suicide among youth, totaling 361 concepts and 946 interrelationships. Using a previously developed open-source software to navigate the map, we are able to explore how trauma may be perpetuated through familial, social, and historical concepts. In particular, we identify connections and pathways between ACEs and youth suicide that have not been identified in prior research, and which are of particular interest for youth suicide prevention efforts.

Signals of increasing co-use of stimulants and opioids from online drug forum data.

BACKGROUND: Despite recent rises in fatal overdoses involving multiple substances, there is a paucity of knowledge about stimulant co-use patterns among people who use opioids (PWUO) or people being treated with medications for opioid use disorder (PTMOUD). A better understanding of the timing and patterns in stimulant co-use among PWUO based on mentions of these substances on social media can help inform prevention programs, policy, and future research directions. This study examines stimulant co-mention trends among PWUO/PTMOUD on social media over multiple years. METHODS: We collected publicly available data from 14 forums on Reddit (subreddits) that focused on prescription and illicit opioids, and medications for opioid use disorder (MOUD). Collected data ranged from 2011 to 2020, and we also collected timelines comprising past posts from a sample of Reddit users (Redditors) on these forums. We applied natural language processing to generate lexical variants of all included prescription and illicit opioids and stimulants and detect mentions of them on the chosen subreddits. Finally, we analyzed and described trends and patterns in co-mentions. RESULTS: Posts collected for 13,812 Redditors showed that 12,306 (89.1%) mentioned at least 1 opioid, opioid-related medication, or stimulant. Analyses revealed that the number and proportion of Redditors mentioning both opioids and/or opioid-related medications and stimulants steadily increased over time. Relative rates of co-mentions by the same Redditor of heroin and methamphetamine, the substances most commonly co-mentioned, decreased in recent years, while co-mentions of both fentanyl and MOUD with methamphetamine increased. CONCLUSION: Our analyses reflect increasing mentions of stimulants, particularly methamphetamine, among PWUO/PTMOUD, which closely resembles the growth in overdose deaths involving both opioids and stimulants. These findings are consistent with recent reports suggesting increasing stimulant use among people receiving treatment for opioid use disorder. These data offer insights on emerging trends in the overdose epidemic and underscore the importance of scaling efforts to address co-occurring opioid and stimulant use including harm reduction and comprehensive healthcare access spanning mental-health services and substance use disorder treatment.

Concerns among people who use opioids during the COVID-19 pandemic: a natural language processing analysis of social media posts.

BACKGROUND: Timely data from official sources regarding the impact of the COVID-19 pandemic on people who use prescription and illegal opioids is lacking. We conducted a large-scale, natural language processing (NLP) analysis of conversations on opioid-related drug forums to better understand concerns among people who use opioids. METHODS: In this retrospective observational study, we analyzed posts from 14 opioid-related forums on the social network Reddit. We applied NLP to identify frequently mentioned substances and phrases, and grouped the phrases manually based on their contents into three broad key themes: (i) prescription and/or illegal opioid use; (ii) substance use disorder treatment access and care; and (iii) withdrawal. Phrases that were unmappable to any particular theme were discarded. We computed the frequencies of substance and theme mentions, and quantified their volumes over time. We compared changes in post volumes by key themes and substances between pre-COVID-19 (1/1/2019-2/29/2020) and COVID-19 (3/1/2020-11/30/2020) periods. RESULTS: Seventy-seven thousand six hundred fifty-two and 119,168 posts were collected for the pre-COVID-19 and COVID-19 periods, respectively. By theme, posts about treatment and access to care increased by 300%, from 0.631 to 2.526 per 1000 posts between the pre-COVID-19 and COVID-19 periods. Conversations about withdrawal increased by 812% between the same periods (0.026 to 0.235 per 1,000 posts). Posts about drug use did not increase (0.219 to 0.218 per 1,000 posts). By substance, among medications for opioid use disorder, methadone had the largest increase in conversations (20.751 to 56.313 per 1,000 posts; 171.4% increase). Among other medications, posts about diphenhydramine exhibited the largest increase (0.341 to 0.927 per 1,000 posts; 171.8% increase). CONCLUSIONS: Conversations on opioid-related forums among people who use opioids revealed increased concerns about treatment and access to care along with withdrawal following the emergence of COVID-19. Greater attention to social media data may help inform timely responses to the needs of people who use opioids during COVID-19.

Dose tapering, increases, and discontinuity among patients on long-term high-dose opioid therapy in the United States, 2017-2019.

BACKGROUND: While reduced exposure to prescription opioids may decrease risks, including overdose and opioid use disorder, abrupt tapering or discontinuation may pose new risks. OBJECTIVES: To examine potentially unsafe tapering and discontinuation among dosage changes in opioid prescriptions dispensed to US patients on high-dose long-term opioid therapy. DESIGN: Longitudinal observational study of adults (≥18 years) on stable high-dose (≥50 oral morphine milligram equivalents [MME] daily dosage) long-term opioid therapy during a 180-day baseline and a 360-day follow-up using all-payer pharmaceutical claims data, 2017-2019. MEASURES: Dosage tapering, increases, and/or stability during follow-up; sustained dosage stability, reductions, or discontinuation at the end of follow-up; and tapering rate. Patients could experience more than one outcome during follow-up. RESULTS: Among 595,078 patients receiving high-dose long-term opioid therapy in the sample, 26.7% experienced sustained dosage reductions and 9.3% experienced discontinuation. Among patients experiencing tapering, 62.0% experienced maximum taper rates between > 10-40% reductions per month and 36.1% experienced monthly rates ≥ 40%. Among patients with mean baseline daily dosages ≥ 150 MME, 47.7% experienced a maximum taper rate ≥ 40% per month. Relative to baseline, 19.7% of patients experiencing tapering had long-term dosage reductions ≥ 40% per month at the end of follow-up. IMPLICATIONS: Dosage changes for patients on high-dose long-term opioid therapy may warrant special attention, particularly over shorter intervals, to understand how potentially sudden tapering and discontinuation can be reduced while emphasizing patient safety and shared decision-making. Rapid discontinuation of opioids can increase risk of adverse outcomes including opioid withdrawal.

Congruence of opioid prescriptions and dispensing using electronic records and claims data.

OBJECTIVE: To quantify discrepancies between opioid prescribing and dispensing via the percentage of patients with Electronic Medical Record (EMR) prescriptions who subsequently filled the prescription within 90 days, defined as congruence, and compared opioid congruence with related medications. DATA SOURCES: Deidentified data from the IBM MarketScan Explorys Claims-EMR Dataset. STUDY DESIGN: In this retrospective, observational study, we examined congruence for commonly prescribed controlled substances-opioids, stimulants, and benzodiazepines. Congruence was stratified by age group and sex. DATA COLLECTION/EXTRACTION METHODS: Continuously enrolled adults aged 18-64 years with an EMR encounter (excluding inpatient settings) and ≥ 1 prescription for selected classes between 1/1/2016 and 10/2/2017. PRINCIPAL FINDINGS: During the study period, 1,353,478 adults had ≥1 EMR encounter. Patients with stimulants prescriptions had the highest congruence (83%) corresponding to 7151 claims for 8,635 EMR prescriptions, followed by opioids (66%; 62,766/95,690) and benzodiazepines (64%; 30,181/47,408). Chi-square testing showed congruence differed by age group within opioids (P < .0001) and benzodiazepines (P < .0001) and was higher among females within benzodiazepines (P < .0001). CONCLUSIONS: These findings demonstrate that relying on claims data alone for opioid prescribing measures might underestimate actual prescribing magnitude by as much as one-third in these data. Combined EMR and claims data can help future research better understand characteristics associated with congruence or incongruence between prescribing and dispensing.

Adipose-Derived Inflammatory and Coagulant Mediators in Patients With Sepsis.

ABSTRACT: Results from preclinical sepsis studies using rodents are often criticized as not being reproducible in humans. Using a murine model, we previously reported that visceral adipose tissues (VAT) are highly active during the acute inflammatory response, serving as a major source of inflammatory and coagulant mediators. The purpose of this study was to determine whether these findings are recapitulated in patients with sepsis and to evaluate their clinical significance. VAT and plasma were obtained from patients undergoing intra-abdominal operations with noninflammatory conditions (control), local inflammation, or sepsis. In mesenteric and epiploic VAT, gene expression of pro-inflammatory (TNFα, IL-6, IL-1α, IL-1ß) and pro-coagulant (PAI-1, PAI-2, TSP-1, TF) mediators was increased in sepsis compared with control and local inflammation groups. In the omentum, increased expression was limited to IL-1ß, PAI-1, and PAI-2, showing a depot-specific regulation. Histological analyses showed little correlation between cellular infiltration and gene expression, indicating a resident source of these mediators. Notably, a strong correlation between PAI-1 expression in VAT and circulating protein levels was observed, both being positively associated with markers of acute kidney injury (AKI). In another cohort of septic patients stratified by incidence of AKI, circulating PAI-1 levels were higher in those with versus without AKI, thus extending these findings beyond intra-abdominal cases. This study is the first to translate upregulation of VAT mediators in sepsis from mouse to human. Collectively, the data suggest that development of AKI in septic patients is associated with high plasma levels of PAI-1, likely derived from resident cells within VAT.

Mapping the Complexity of Suicide by Combining Participatory Modeling and Network Science.

Suicide rates are steadily increasing among youth in the USA. Although several theories and frameworks of suicide have been developed, they do not account for some of the features that define suicide as a complex problem, such as a large number of interrelationships and cycles. In this paper, we create the first c omprehensive m ap o f a dverse c hildhood experiences (ACEs) and suicide for youth, by combining a participatory approach (involving 15 subject-matter experts) and network science. This results in a map of 946 edges and 361 concepts, in which we identify ACEs to be the most important factor (per degree centrality). The map is openly shared with the community to support further network analyses (e.g., decomposition into clusters). Similarly to the high-impact Foresight Map developed in the context of obesity, the largest map on suicide and ACEs to date presented in this paper can start a discussion at the crossroad of suicide research and network science, thus bringing new means to address a complex public health challenge.

U.S. National 90-Day Readmissions After Opioid Overdose Discharge.

INTRODUCTION: U.S. hospital discharges for opioid overdose increased substantially during the past two decades. This brief report describes 90-day readmissions among patients discharged from inpatient stays for opioid overdose. METHODS: In 2018, survey-weighted analysis of hospital stays in the 2016 Healthcare Cost and Utilization Project National Readmissions Database yielded the national estimated proportion of patients with opioid overdose stays that had all-cause readmissions within ≤90 days. A multivariable logistic regression model assessed index stay factors associated with readmission by type (opioid overdose or not). Number of readmissions per patient was assessed. RESULTS: More than 24% (n=14,351/58,850) of patients with non-fatal index stays for opioid overdose had at least one all-cause readmission ≤90 days of index stay discharge and 3% (n=1,658/58,850) of patients had at least one opioid overdose readmission. Less than 0.2% (n=104/58,850) of patients had more than one readmission for opioid overdose. Patient demographic characteristics (e.g., male, older age), comorbidities diagnosed during the index stay (e.g., drug use disorder, chronic pulmonary disease, psychoses), and other index stay factors (Medicare or Medicaid primary payer, discharge against medical advice) were significantly associated with both opioid overdose and non-opioid overdose readmissions. Nearly 30% of index stays for opioid overdose included heroin, which was significantly associated with opioid overdose readmissions. CONCLUSIONS: A quarter of opioid overdose patients have ≤90 days all-cause readmissions, although opioid overdose readmission is uncommon. Effective strategies to reduce readmissions will address substance use disorder as well as comorbid physical and mental health conditions.

Identifying opioid prescribing patterns for high-volume prescribers via cluster analysis.

OBJECTIVE: Despite recent decreases in opioid prescribing rates, evidence suggests there is substantial variation in the way opioids are prescribed by providers. This study aims to identify patterns in high-volume opioid prescribing. METHODS: We conducted partitioning-around-medoids cluster analysis using the IQVIA Prescriber Profile dataset, including the number of opioid prescriptions filled at US retail pharmacies aggregated at the prescriber-level from July 2016 through June 2017. Clustering was used to identify prescription patterns within a sample of 10,000 high-volume opioid prescribers (defined as the top 10% of prescribers by number of opioid prescriptions during the 12-month period). Clustering variables included prescription counts by opioid type, and prescriber specialty, age, and region. RESULTS: Family medicine (32%), internal medicine (23%), and orthopedics (11%) were the most common high-volume prescribing specialties. Across specialties, hydrocodone and oxycodone were the most-frequently prescribed opioid types. Thirty-five clusters of prescribers were obtained, consistently comprised of a single majority specialty and region. All majority high-prescribing specialties were represented in Southern clusters, indicating consistently high volume opioid prescribing across specialties in the region. Prescribing patterns varied by drug type and region - across every Northeastern cluster, oxycodone prescribing was higher than hydrocodone. While clusters of pain medicine specialists had the highest median total prescriptions, emergency medicine specialist clusters had some of the lowest. CONCLUSIONS: These results provide a clearer picture of current patterns among high-volume prescribers, who accounted for almost two-thirds of all opioid prescriptions. In light of the ongoing opioid overdose epidemic, this knowledge is critical for prevention activities.

Diabetes and the hospitalized patient : A cluster analytic framework for characterizing the role of sex, race and comorbidity from 2006 to 2011.

In the US, one in four adults has two or more chronic conditions; this population accounts for two thirds of healthcare spending. Comorbidity, the presence of multiple simultaneous health conditions in an individual, is increasing in prevalence and has been shown to impact patient outcomes negatively. Comorbidities associated with diabetes are correlated with increased incidence of preventable hospitalizations, longer lengths of stay (LOS), and higher costs. This study focuses on sex and race disparities in outcomes for hospitalized adult patients with and without diabetes. The objective is to characterize the impact of comorbidity burden, measured as the Charlson Weighted Index of Comorbidities (WIC), on outcomes including LOS, total charges, and disposition (specifically, probability of routine discharge home). Data from the National Inpatient Sample (2006-2011) were used to build a cluster-analytic framework which integrates cluster analysis with multivariate and logistic regression methods, for several goals: (i) to evaluate impact of these covariates on outcomes; (ii) to identify the most important comorbidities in the hospitalized population; and (iii) to create a simplified WIC score. Results showed that, although hospitalized women had better outcomes than men, the impact of diabetes was worse for women. Also, non-White patients had longer lengths of stay and higher total charges. Furthermore, the simplified WIC performed equivalently in the generalized linear models predicting standardized total charges and LOS, suggesting that this new score can sufficiently capture the important variability in the data. Our findings underscore the need to evaluate the differential impact of diabetes on physiology and treatment in women and in minorities.