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Record W2903023530 · doi:10.1186/s13011-018-0180-3

An age-based analysis of nonmedical prescription opioid use among people who use illegal drugs in Vancouver, Canada

2018· article· en· W2903023530 on OpenAlexafffundabout
Tessa Cheng, Will Small, Huiru Dong, Ekaterina Nosova, Kanna Hayashi, Kora DeBeck

Bibliographic record

VenueSubstance Abuse Treatment Prevention and Policy · 2018
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of British ColumbiaCentre for Addiction and Mental HealthSimon Fraser UniversityBritish Columbia Centre on Substance UseProvidence Health Care
FundersCanadian Institutes of Health ResearchNational Institutes of HealthMichael Smith Health Research BCNational Institute on Drug AbuseProvidence Health CareSt. Paul's Foundation
KeywordsMedicineDemographyConfidence intervalOdds ratioPublic healthHeroinPopulationMedical prescriptionProspective cohort studyMultivariate analysisCohort studyGerontologyPsychiatryEnvironmental healthInternal medicineDrug

Abstract

fetched live from OpenAlex

BACKGROUND: Nonmedical prescription opioid use (NMPOU) is a serious public health problem in North America. At a population-level, previous research has identified differences in the prevalence and correlates of NMPOU among younger versus older age groups; however, less is known about age-related differences in NMPOU among people who use illegal drugs. METHODS: Data were collected between 2013 and 2015 from two linked prospective cohort studies in Vancouver, Canada: the At-Risk Youth Study (ARYS) and the Vancouver Injection Drug Users Study (VIDUS). Factors independently associated with NMPOU among younger (ARYS) and older (VIDUS) participants were examined separately using bivariate and multivariate generalized estimating equations. RESULTS: A total of 1162 participants were included. Among 405 eligible younger participants (Median age = 25; Inter-Quartile Range [IQR]: 22-28), 40% (n = 160) reported engaging in NMPOU at baseline; among 757 older participants (Median age = 48, IQR: 40-55), 35% (n = 262) reported engaging in NMPOU at baseline. In separate multivariate analyses of younger and older participants, NMPOU was positively and independently associated with heroin use (younger: Adjusted Odds Ratio [AOR] = 3.12, 95% Confidence Interval [CI]: 2.08-4.68; older: AOR = 2.79, 95% CI: 2.08-3.74), drug dealing (younger: AOR = 2.22, 95% CI: 1.58-3.13; older: AOR = 1.87, 95% CI: 1.40-2.49), and difficulty accessing services (younger: AOR = 1.47, 95% CI: 1.04-2.09; older: AOR = 1.74, 95% CI: 1.32-2.29). Among the youth cohort only, NMPOU was associated with younger age (AOR = 1.12, 95% CI: 1.05-1.19), crack use (AOR = 1.56, 95% CI: 1.06-2.30), and binge drug use (AOR = 1.41, 95% CI: 1.00-1.97); older participants who engaged in NMPOU were more likely to report crystal methamphetamine use (AOR = 1.97, 95% CI: 1.46-2.66), non-fatal overdose (AOR = 1.76, 95% CI: 1.20-2.60) and sex work (AOR = 1.49, 95% CI: 1.00-2.22). DISCUSSION: The prevalence of NMPOU is similar among younger and older people who use drugs, and independently associated with markers of vulnerability among both age groups. Adults who engage in NMPOU are at risk for non-fatal overdose, which highlights the need for youth and adult-specific strategies to address NMPOU that include better access to health and social services, as well as a range of addiction treatment options for opioid use. Findings also underscore the importance of improving pain treatment strategies tailored for PWUD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.283
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2018
Admission routes3
Has abstractyes

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