Seasonal, weekly and other cyclical patterns in deaths due to drug poisoning in England and Wales
Bibliographic record
Abstract
BACKGROUND AND AIM: The rate of drug poisoning (or overdose) deaths in England and Wales has risen annually since 2010. We aimed to measure seasonal and other cyclical changes in these deaths within years. METHODS: We used the daily count of deaths due to drug poisoning in England and Wales between 1 January 1993 and 31 December 2018 to investigate variation by season, weekday, week-of-month and public holiday. We used Poisson regression to estimate the count of deaths per day for each of these variables and peak-to-low ratios. We also stratified the analysis by time period and whether an opioid was mentioned on the death certificate. RESULTS: 78 583 deaths occurred between 1993 and 2018, increasing from 5.50 (95% confidence interval [CI] = 5.24-5.77) per day in 1993 to 13.18 (95% CI = 12.66-13.72) per day in 2018. The rate peaked in Spring and was 1.07 (95% CI = 1.04-1.09) times higher in April than in October. This seasonal pattern emerged in the past decade and was only present for opioid-related deaths. The rate at New Year was 1.28 (95% CI = 1.17-1.41) times higher than on non-holidays; and this peak was only present for deaths that were not related to opioids. The rate was higher on Saturday than on other weekdays. We did not find evidence that the number of deaths varied by week-of-month. CONCLUSIONS: Deaths due to drug poisoning in England and Wales are seasonal and peak in Spring and briefly at New Year. This suggests a role of external triggers. These seasonal variations are small compared with long-term increases in drug-related deaths.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".