scholarly journals Runaway History and Past 30‐Day Opioid Misuse in Justice‐Involved Adolescents

2021 ◽  
Author(s):  
Micah E. Johnson ◽  
Shawnta L. Lloyd ◽  
Skye C. Bristol ◽  
Giselle Rosel ◽  
Ayodeji A. Otufowora ◽  
...  
Keyword(s):  
Opioid Misuse

Understanding Characteristics Of Users With Signs Of Opioid Misuse On Twitter (Preprint)

2020 ◽  
Author(s):  
Haya Jarad ◽  
Junhua Yang ◽  
Abeed Sarker
Keyword(s):  
United States ◽  
Pain Management ◽  
Religious Orientation ◽  
The United States ◽  
Opioid Abuse ◽  
Management Tool ◽  
Opioid Misuse ◽  
Major Health Problem ◽  
Personal Traits ◽  
And Behaviors

BACKGROUND Opioid misuse is a major health problem in the United States, and can lead to addiction and fatal overdose. The United States is in the midst of an opioid epidemic; in 2018, an average of approximately 130 Americans died daily from an opioid overdose and 2.1 million have an opioid use disorder (OUD). In addition to electronic health records (EHRs), social media have also been harnessed for studying and predicting physical and behavioral outcomes of OUD. Specifically, it has been shown that on Twitter the use of certain language patterns and their frequencies in subjects’ tweets are indicative of significant healthcare outcomes such as opioid misuse/use and suicide ideation. We sought to understand personal traits and behaviors of Twitter chatters relative to the motive of opioid misuse; pain or recreational. OBJECTIVE . METHODS We collected tweets using the Twitter public developer application programming interface (API) between April 13, 2018 – and May 21, 2018. A list of opioid-related keywords were searched for such as methadone, codeine, fentanyl, hydrocodone, vicodin, heroin and oxycodone. We manually annotated tweets into three classes: no-opioid misuse, pain-misuse and recreational-misuse, the latter two representing misuse for pain or recreation/addiction. We computed the coding agreement between the two annotators using the Cohen’s Kappa statistic. We applied the Linguistic Inquiry and Word Count (LIWC) tool on historical tweets, with at least 500 words, of users in the dataset to analyze their language use and learn about their personality raits and behaviors. LIWC is a text processing software that analyzes text narratives and produces approximately 90 variables scored based on word use that pertain to phsycological, emotional, behavioral, and linguistic processes. A multiclass logistic regression model with backward selection based on the BIC criterion was used to identify variables associated with pain and recreational opioid misuse compared to the base class; no-opioid misuse.. The goal was to understand whether personal traits or behaviors differ across different classes. We reported the odd ratios of different variables in both pain and recreational related opioid misuse classes with respect to the no-opioid misuse class. RESULTS The manual annotation resulted in a total of 1,164 opioid related tweets. 229 tweets were assigned to the pain-related class, 769 were in the recreational class, and 166 tweets were tagged with no opioid misuse class. The overall inter-annotator agreement (IAA) was 0.79. Running LIWC on the tweets resulted in 55 variables. We selected the best model based on BIC. We examined the variables with the highest odd ratios to determine those associated with both pain and recreational opioid misuse as compared to the base class. Certain traits such as depression, stress, and melancholy are established in the literature as commonplace amongst opiod abuse indiviuals. In our analysis, these same characteristics, amongst others, were identified as significantly positively associated with both the Pain and Recreational groups compared to the no-opioid misuse group. Despite the different motivaions for opiod abuse, both groups present the same core personality traits. Interestingly, individuals who misuse opioids as a pain management tool exhibited higher odds ratios for psychological processees and personal traits based on their tweet language. These include a strong focus on discipline, as demonstrated by the variables “disciplined”, “cautious” and “work_oriented”. Their tweet language is also indicative of cheerfulness, a variable absent in the recreational misuse group. Variables associated with the reacreational misuse group revolve around external factors. They are generous and motivated by reward, while maintaining a religious orientation. Based on their tweet language, this group is also characterized as “active”; we understand that these individuals are more social and community focused . CONCLUSIONS To our best knowledge, this is the first study to investigate motivations of opioid abuse as it relates to tweet language. Previous studies utilizing Twitter data were limited to simply detecting opiod abuse likelihood through tweets. By delving deeper into the classes of opioid abuse and its motivation, we offer greater insight into opioid abuse behavior. This insight extends beyond simple identification, and explores patterns in motivation. We conclude that user language on Twitter is indicative of significant differences in personal traits and behaviors depending on abuse motivation: pain management or recreation.

scholarly journals Confronting Two Crises: The COVID-19 Pandemic, the Opioid Epidemic, and the Industrial Hygienist

2021 ◽  
pp. 104829112110295
Author(s):  
Jonathan Rosen ◽  
Peter Harnett
Keyword(s):  
Disease Control ◽  
Drug Overdose ◽  
Industrial Hygiene ◽  
Prevention Programs ◽  
Opioid Epidemic ◽  
Opioid Misuse ◽  
Opioid Crisis ◽  
The Impact ◽  
Overdose Deaths ◽  
Industrial Hygienist

This article was originally written for and published in the January 2021 issue of The Synergist, a monthly publication of the American Industrial Hygiene Association. The article addresses the convergence of the COVID-19 and opioid crises, the impact of the opioid crisis on the workplace and workers, and the role that industrial hygienists can play in developing workplace programs to prevent and respond to opioid misuse. While the article is specifically written for industrial hygienists, the review and recommendations will be useful to others who are developing workplace opioid prevention programs. Note that the data presented in this article were current as of January 2021. Centers for Disease Control and Prevention’s latest available data are for the twelve-month period ending October 2020 and include 88,990 total overdose deaths and 91,862 predicted, when reporting is completed. Source: https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm (accessed on 15 June 2021).

scholarly journals External validation of an opioid misuse machine learning classifier in hospitalized adult patients

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Majid Afshar ◽  
Brihat Sharma ◽  
Sameer Bhalla ◽  
Hale M. Thompson ◽  
Dmitriy Dligach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Screening Program ◽  
External Validation ◽  
Hospital Setting ◽  
Opioid Misuse ◽  
Original Cohort ◽  
Learning Classifier ◽  
Observational Cohort ◽  
Drug Abuse Screening Test ◽  
Screening Barriers

Abstract Background Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse. Methods An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort. Results Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99–0.99) across the encounter and 0.98 (95% CI 0.98–0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77–0.84) and 0.72 (95% CI 0.68–0.75). For the first 24 h, they were 0.75 (95% CI 0.71–0.78) and 0.61 (95% CI 0.57–0.64). Conclusions Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.

Association of opioid misuse with anxiety and depression: A systematic review of the literature

2021 ◽  
Vol 84 ◽  
pp. 101978
Author(s):  
Andrew H. Rogers ◽  
Michael J. Zvolensky ◽  
Joseph W. Ditre ◽  
Julia D. Buckner ◽  
Gordon J.G. Asmundson
Keyword(s):  
Systematic Review ◽  
Anxiety And Depression ◽  
Opioid Misuse ◽  
Review Of The Literature

scholarly journals Consensus-based approach to managing opioids, including opioid misuse and opioid use disorder, in patients with serious illness: protocol for a modified Delphi process

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e045402
Author(s):  
Caroline King ◽  
Robert Arnold ◽  
Emily Dao ◽  
Jennifer Kapo ◽  
Jane Liebschutz ◽  
...  
Keyword(s):  
Palliative Care ◽  
Opioid Therapy ◽  
Opioid Use Disorder ◽  
Opioid Misuse ◽  
Opioid Use ◽  
Serious Illness ◽  
Modified Delphi ◽  
Delphi Approach ◽  
Institutional Review ◽  
Management Guidance

IntroductionManagement of opioid misuse and opioid use disorder (OUD) among individuals with serious illness is an important yet understudied issue. Palliative care clinicians caring for individuals with serious illness, many of whom may live for months or years, describe a complex tension between weighing the benefits of opioids, which are considered a cornerstone of pain management in serious illness, and serious opioid-related harms like opioid misuse and OUD. And yet, little literature exists to inform the management of opioid misuse and OUDs among individuals with serious illness. Our objective is to provide evidence-based management guidance to clinicians caring for individuals with serious illness who develop opioid misuse or OUD.Methods and analysisWe chose a modified Delphi approach, which is appropriate when empirical evidence is lacking and expert input must be used to shape clinical guidance. We sought to recruit 60 clinicians with expertise in palliative care, addiction or both to participate in this study. We created seven patient cases that capture important management challenges in individuals with serious illness prescribed opioid therapy. We used ExpertLens, an online platform for conducting modified Delphi panels. Participants completed three rounds of data collection. In round 1, they rated and commented on the appropriateness of management choices for cases. In round 2, participants reviewed and discussed their own and other participants’ round 1 numerical responses and comments. In round 3 (currently ongoing), participants again reviewed rounds 1 and 2, and are allowed to change their final numerical responses. We used ExpertLens to automatically identify whether there is consensus, or disagreement, among responses in panels. Only round 3 responses will be used to assess final consensus and disagreement.Ethics and disseminationThis project received ethical approval from the University of Pittsburgh’s Institutional Review Board (study 19110301) and the RAND Institutional Research Board (study 2020-0142). Guidance from this work will be disseminated through national stakeholder networks to gain buy-in and endorsement. This study will also form the basis of an implementation toolkit for clinicians caring for individuals with serious illness who are at risk of opioid misuse or OUD.

Pharmacists’ stigma toward patients engaged in opioid misuse: When “social distance” does not mean disease prevention

2021 ◽  
pp. 1-8
Author(s):  
Amy Werremeyer ◽  
Sydney Mosher ◽  
Heidi Eukel ◽  
Elizabeth Skoy ◽  
Jayme Steig ◽  
...  
Keyword(s):  
Disease Prevention ◽  
Social Distance ◽  
Opioid Misuse

scholarly journals Health(care) in the Crisis: Reflections in Science and Society on Opioid Addiction

2021 ◽  
Vol 18 (1) ◽  
pp. 341
Author(s):  
Roxana Damiescu ◽  
Mita Banerjee ◽  
David Y. W. Lee ◽  
Norbert W. Paul ◽  
Thomas Efferth
Keyword(s):  
Chronic Pain ◽  
Lived Experience ◽  
Narrative Analysis ◽  
Opioid Abuse ◽  
Societal Implications ◽  
Public Health Approach ◽  
Opioid Misuse ◽  
Post Surgery ◽  
Patient Narratives ◽  
The Relationship

Opioid abuse and misuse have led to an epidemic which is currently spreading worldwide. Since the number of opioid overdoses is still increasing, it is becoming obvious that current rather unsystematic approaches to tackle this health problem are not effective. This review suggests that fighting the opioid epidemic requires a structured public health approach. Therefore, it is important to consider not only scientific and biomedical perspectives, but societal implications and the lived experience of groups at risk as well. Hence, this review evaluates the risk factors associated with opioid overdoses and investigates the rates of chronic opioid misuse, particularly in the context of chronic pain as well as post-surgery treatments, as the entrance of opioids in people’s lives. Linking pharmaceutical biology to narrative analysis is essential to understand the modulations of the usual themes of addiction and abuse present in the opioid crisis. This paper shows that patient narratives can be an important resource in understanding the complexity of opioid abuse and addiction. In particular, the relationship between chronic pain and social inequality must be considered. The main goal of this review is to demonstrate how a deeper transdisciplinary-enriched understanding can lead to more precise strategies of prevention or treatment of opioid abuse.

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