MétaCan
Menu
Back to cohort
Record W3213876314 · doi:10.1136/bmj-2021-066965

Prescription opioid treatment for non-cancer pain and initiation of injection drug use: large retrospective cohort study

2021· article· en· W3213876314 on OpenAlexafffundabout
James Wilton, Younathan Abdia, Mei Chong, Mohammad Ehsanul Karim, Stanley Wong, Aaron MacInnes, R Balshaw, Bin Zhao, Tara Gomes, Amanda Yu, Maria Alvarez, Richard C. Dart, Mel Krajden, Jane A. Buxton, Naveed Z. Janjua, Roy Purssell

Bibliographic record

VenueBMJ · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of ManitobaUniversity of TorontoGeorge & Fay Yee Centre for Healthcare InnovationFraser HealthSt. Michael's HospitalCentre for Advancing Health OutcomesSt. Paul's HospitalUniversity of British ColumbiaBC Centre for Disease Control
FundersCanadian Institutes of Health ResearchBritish Columbia Centre for Disease ControlMinistry of Health, British ColumbiaProvincial Health Services Authority
KeywordsMedicineRetrospective cohort studyMedical prescriptionCohortOpioidPharmacyCohort studyChronic painInternal medicinePsychiatryFamily medicinePharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the association between long term prescription opioid treatment medically dispensed for non-cancer pain and the initiation of injection drug use (IDU) among individuals without a history of substance use. DESIGN: Retrospective cohort study. SETTING: Large administrative data source (containing information for about 1.7 million individuals tested for hepatitis C virus or HIV in British Columbia, Canada) with linkage to administrative health databases, including dispensations from community pharmacies. PARTICIPANTS: Individuals age 11-65 years and without a history of substance use (except alcohol) at baseline. MAIN OUTCOME MEASURES: Episodes of prescription opioid use for non-cancer pain were identified based on drugs dispensed between 2000 and 2015. Episodes were classified by the increasing length and intensity of opioid use (acute (lasting <90 episode days), episodic (lasting ≥90 episode days; with <90 days' drug supply and/or <50% episode intensity), and chronic (lasting ≥90 episode days; with ≥90 days' drug supply and ≥50% episode intensity)). People with a chronic episode were matched 1:1:1:1 on socioeconomic variables to those with episodic or acute episodes and to those who were opioid naive. IDU initiation was identified by a validated administrative algorithm with high specificity. Cox models weighted by inverse probability of treatment weights assessed the association between opioid use category (chronic, episodic, acute, opioid naive) and IDU initiation. RESULTS: 59 804 participants (14 951 people from each opioid use category) were included in the matched cohort, and followed for a median of 5.8 years. 1149 participants initiated IDU. Cumulative probability of IDU initiation at five years was highest for participants with chronic opioid use (4.0%), followed by those with episodic use (1.3%) and acute use (0.7%), and those who were opioid naive (0.4%). In the inverse probability of treatment weighted Cox model, risk of IDU initiation was 8.4 times higher for those with chronic opioid use versus those who were opioid naive (95% confidence interval 6.4 to 10.9). In a sensitivity analysis limited to individuals with a history of chronic pain, cumulative risk for those with chronic use (3.4% within five years) was lower than the primary results, but the relative risk was not (hazard ratio 9.7 (95% confidence interval 6.5 to 14.5)). IDU initiation was more frequent at higher opioid doses and younger ages. CONCLUSIONS: The rate of IDU initiation among individuals who received chronic prescription opioid treatment for non-cancer pain was infrequent overall (3-4% within five years) but about eight times higher than among opioid naive individuals. These findings could have implications for strategies to prevent IDU initiation, but should not be used as a reason to support involuntary tapering or discontinuation of long term prescription opioid treatment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.326
Teacher spread0.305 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueBMJSame topicOpioid Use Disorder TreatmentFrench-language works237,207