Time-Varying Vulnerability to Non-Fatal Overdose: A Self-Controlled Case Series
Bibliographic record
Abstract
IntroductionCohort studies have suggested that there are periods of time, including the two weeks following release from prison and the transition on and off opioid agonist therapy (OAT), where the risk of overdose is heightened. However, this research has focused on fatal overdose and may be subject to confounding. Objectives and ApproachThis study aimed to examine the association between time-varying risk factors – release from incarceration, discharge from hospital and emergency department, and use of prescribed OAT, opioids, benzodiazepines and antipsychotics – and non-fatal overdose. People in a 20% random sample of residents in BC, Canada who experienced a non-fatal overdose in 2015-2017 were identified through hospital admissions, physician and emergency department visits, and poison centre and ambulance calls. Risk periods associated with exposure to each time varying risk factor were created using linked administrative data. Using a self-controlled case series design, conditional Poisson regression was used to estimate the incidence rate ratio of non-fatal overdose during the risk periods compared to at other times. Results4149 people experienced a non-fatal overdose during follow-up. People were at increased risk of overdose on the day of admission to prison (adjusted incidence rate ratio (AIRR) 2.8, 95% confidence interval (95%CI) 1.5-5.0), in the two weeks after release from prison (AIRR 2.9, 95%CI 2.4-3.6) and after hospital discharge (AIRR 1.3, 95%CI 1.1-1.6), and during prescription opioid (AIRR 1.3, 95%CI 1.0-1.6) and benzodiazepine (AIRR 1.7, 95%CI 1.3-2.1) use. People were at lower risk of non-fatal overdose during OAT use (AIRR 0.4, 95%CI 0.3-0.5) and while in prison (AIRR 0.1, 95%CI 0.1-0.2). Conclusion / ImplicationsThere are acute, transient periods where a person’s risk of overdose is heightened. These include release from incarceration, discharge from hospital, and while taking prescription opioids and benzodiazepines. These periods of increased risk should be targeted for overdose prevention efforts.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".