Evaluation of the gap in delivery of opioid agonist therapy among individuals with opioid‐related health problems: a population‐based retrospective cohort study
Bibliographic record
Abstract
AIMS: Although opioid-related harms have reached new heights across North America, the size of the gap in opioid agonist therapy (OAT) delivery for opioid-related health problems is unknown in most jurisdictions. This study sought to characterize the gap in OAT treatment using a cascade of care framework, and determine factors associated with engagement and retention in treatment. DESIGN: A population-based retrospective cohort study. SETTING: Ontario, Canada. PARTICIPANTS: Individuals who sought medical care for opioid-related health problems or died from an opioid-related cause between 2005 and 2019. MEASUREMENTS: Monthly treatment status for buprenorphine/naloxone or methadone OAT between 2013 and 2019 (i.e. 'off OAT', 'retained on OAT < 6 months', 'retained on OAT ≥ 6 months'). FINDINGS: Of 122 811 individuals in the cohort, 97 516 (79.4%) received OAT at least once during the study period. There was decreasing 6-month treatment retention over time. Model results indicated that males had higher odds of being on OAT each month [odds ratio (OR) = 1.26, 95% confidence interval (CI) = 1.23-1.28] but lower odds of OAT retention (OR = 0.90, 95% CI = 0.88-0.92), while the reverse was observed for older individuals (monthly: OR = 0.76 per 10-year increase, 95% CI = 0.76-0.77; retention: OR = 1.36 per 10-year increase, 95% CI = 1.34-1.38) and individuals with higher neighbourhood income (e.g. highest income quintile, monthly: OR = 0.79, 95% CI = 0.77-0.82; highest income quintile, retention: OR = 1.15, 95% CI = 1.11-1.20). Individuals residing in rural areas and with a history of mental health diagnoses had poorer outcomes overall, including lower odds of being on OAT each month (rural: OR = 0.75, 95% CI = 0.73-0.78; mental health: OR = 0.89, 95% CI = 0.87-0.92) and OAT retention (rural: OR = 0.79, 95% CI = 0.77-0.82; mental health: OR = 0.81, 95% CI = 0.78-0.83), as well as higher risk of starting/stopping OAT [rural, starting OAT: hazard ratio (HR) = 1.07, 95% CI = 1.05-1.10; mental health, starting OAT: HR = 1.20, 95% CI: 1.18-1.23; rural, stopping OAT: HR = 1.24, 95% CI: = 1.22-1.26; mental health, stopping OAT: HR = 1.11, 95% CI = 1.09-1.13]. Individuals with a history of mental health diagnoses also had a higher risk of death, regardless of OAT status (off OAT death: HR = 1.49, 95% CI = 1.33-1.66; on OAT death: HR = 1.20, 95% CI = 1.09-1.31). CONCLUSIONS: Factors influencing engagement and declining retention in treatment with opioid agonist therapy in Ontario's health system include age, sex and neighbourhood income, as well as mental health diagnoses or residing in rural regions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".