The effect of person, treatment and prescriber characteristics on retention in opioid agonist treatment: a 15‐year retrospective cohort study
Bibliographic record
Abstract
BACKGROUND AND AIMS: There is limited evidence on the relationship between retention in opioid agonist treatment for opioid dependence and characteristics of treatment prescribers. This study estimated retention in buprenorphine and methadone treatment and its relationship with person, treatment and prescriber characteristics. DESIGN: Retrospective longitudinal study. SETTING: New South Wales, Australia. PARTICIPANTS: People entering the opioid agonist treatment programme for the first time between August 2001 and December 2015. MEASUREMENTS: Time in opioid agonist treatment (primary outcome) was modelled using a generalized estimating equation model to estimate associations with person, treatment and prescriber characteristics. FINDINGS: The impact of medication type on opioid agonist treatment retention reduced over time; the risk of leaving treatment when on buprenorphine compared with methadone was higher among those who entered treatment earlier [e.g. 2001-03: odds ratio (OR) = 1.59, 95% confidence interval (CI) = 1.45-1.75] and lowest among those who entered most recently (2013-15: OR = 1.23, 95% CI = 1.11-1.36). In adjusted analyses, risk of leaving was reduced among people whose prescriber had longer tenure of prescribing (e.g. 3 versus 8 years: OR = 0.94, 95% CI = 0.93-0.95) compared with prescribers with shorter tenure. Aboriginal and Torres Strait Islander people, being of younger age, past-year psychosis disorder and having been convicted of more criminal charges in the year prior to treatment entry were associated with increased risk of leaving treatment. CONCLUSION: In New South Wales, Australia, retention in buprenorphine treatment for opioid dependence, compared with methadone, has improved over time since its introduction in 2001. Opioid agonist treatment retention is affected not only by characteristics of the person and his or her treatment, but also of the prescriber, with those of longer prescribing tenure associated with increased retention of people in opioid agonist treatment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".