The impact of opioid agonist treatment on fatal and non-fatal drug overdose among people with a history of opioid dependence in NSW, Australia, 2001-2018: findings from the OATS retrospective linkage study.
Bibliographic record
Abstract
ObjectivesThere are critical periods of mortality risk at onset and cessation of opioid agonist treatment. We aim to determine whether non-fatal overdose followed the same pattern as fatal overdose, comparing the first 4 weeks of treatment and treatment cessation and the remainder time off treatment, with the remainder treatment time, to determine intervention markers. ApproachRetrospective cohort study of people with a history of opioid agonist treatment using linked New South Wales data. The incidence of non-fatal overdose hospitalization; emergency department presentation; and fatal overdose from national death records were compared. Rates were calculated using generalized estimating equations adjusting for demographics, year, and recent health and incarceration events. ResultsThe rate of an emergency department drug overdose presentation was highest. It was more than three-fold the rate of opioid non-fatal overdose hospitalisation and 14 times higher than fatal opioid overdose. It was also twice the rate of non-opioid non-fatal overdose hospitalisation. Fatal overdose was lowest while in treatment. This differed from the measures of non-fatal overdose, the overdose rate was elevated in the first four weeks in treatment as well as the first four weeks post treatment. ConclusionsRetention on opioid agonist treatment is protective against drug related overdose. There is elevated risk of non-fatal overdose at treatment initiation that is not evident for fatal overdose, however the first month of treatment cessation is a critical period for both non-fatal and fatal overdose. These findings emphasize the importance of treatment retention and interventions for polysubstance overdose at cessation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".