Comparative effectiveness of urine drug testing schedules alongside opioid agonist treatment: Emulation of a population‐based target trial in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND AND AIM: Urine drug testing is often utilized alongside opioid agonist treatment to assess client progress by validating self-reported substance use, monitoring for diversion and supporting clinical decisions for take-home dosing. However, there is a paucity of evidence to support the practice of urine drug testing. We aimed to determine the association of alternative urine drug testing frequencies with opioid agonist treatment discontinuation, compared with no monitoring, among individuals receiving methadone or buprenorphine/naloxone treatment. DESIGN: Population-based retrospective cohort study and target trial emulation based on nine-linked administrative databases. SETTING: British Columbia, Canada, between 1 January 2010 and 17 March 2020. PARTICIPANTS: Individuals with no history of cancer or palliative care, aged 18 or older and no indication of pregnancy who initiated methadone or buprenorphine/naloxone. A total of 18 988 methadone and 11 910 buprenorphine/naloxone recipients were included in the incident user design (individuals with no past opioid agonist treatment experience). MEASUREMENTS: We used a clone-censor-weight approach to estimate hazard ratios with 95% compatibility ("confidence") intervals for treatment discontinuation (lasting at least 5 and 6 days for methadone and buprenorphine, respectively) and all-cause mortality on treatment within 12 months for static urine drug testing strategies. FINDINGS: Under static monitoring strategies, weekly urine drug testing was associated with a slightly reduced risk of discontinuation in the first year of continuous retention in treatment [methadone: adjusted hazard ratio (aHR) = 0.96, 95% compatibility interval (CI) = (0.95-0.98); buprenorphine/naloxone: aHR = 0.95 (0.94-0.97)] compared with no monitoring. The estimated associations of weekly urine drug testing with all-cause mortality were similar in size but extremely imprecise [methadone: aHR = 0.95 (0.78-1.15), buprenorphine/naloxone: aHR = 0.99 (0.62-1.58)]. Less frequent testing demonstrated no observed difference on treatment discontinuation or all-cause mortality compared with no monitoring. CONCLUSION: Compared with no urine drug testing, weekly urine drug testing may be associated with improved opioid agonist treatment retention; however, the high costs attributable to frequent testing may not be cost-effective and requires further evaluation. There was no improvement associated with less frequent testing compared with no monitoring.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".