Hot weather and death related to acute cocaine, opioid and amphetamine toxicity in British Columbia, Canada: a time-stratified case-crossover study
Bibliographic record
Abstract
Background: Previous research has shown that cocaine-associated deaths occur more frequently in hot weather, which has not been described for other illicit drugs or combinations of drugs. The study objective was to evaluate the relation between temperature and risk of death related to cocaine, opioids and amphetamines in British Columbia, Canada. Methods: We extracted data on all deaths with cocaine, opioid or amphetamine toxicity recorded as an underlying or contributing cause from BC vital statistics for 1998–2017. We used a time-stratified case-crossover design to estimate the effect of temperature on the risk of death associated with acute drug toxicity during the warmer months (May through September). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for each 10°C increase in the 2-day average maximum temperature at the residential location. Results: We included 4913 deaths in the analyses. A 10°C increase in the 2-day average maximum temperature was associated with an OR of 1.43 (95% CI 1.11–1.86) for deaths with only cocaine toxicity recorded (n = 561), an OR of 1.15 (95% CI 0.99–1.33) for deaths with opioids only (n = 1682) and an OR of 1.11 (95% CI 0.60–2.04) for deaths with amphetamines only (n = 133). There were also elevated effects when toxicity from multiple drugs was recorded. Sensitivity analyses showed differences in the ORs by sex, by climatic region, and when the location of death was used instead of the location of residence. Interpretation: Increasing temperatures were associated with higher odds of death due to drug toxicity, especially for cocaine alone and combined with other drugs. Targeted interventions are necessary to prevent death associated with toxic drug use during hot weather.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".