The Shift to Toxicological-Related Deaths Over Natural During the COVID-19 Pandemic—An Ontario, Canada, Experience
Bibliographic record
Abstract
Study Design: Retrospective review of deaths in Ontario where there was Coroner's investigation and a postmortem examination between 2018 and 2021 to compare year by year changes before and during the COVID-19 pandemic. Objective: To establish the changes in patterns of toxicological deaths over the pandemic. Methods: Using the database of the Office of the Chief Coroner for Ontario to determine the numbers of postmortem examinations for the province of Ontario as well as the primary cause and manner of death. Those with a toxicological primary cause of death were isolated from 2003 to the first half of 2022 and divided by year. For those between the years 2018 and 2021 deaths were divided by manner of death. Further all deaths with either a toxicological primary cause of death or unfinalized investigations which were highly suspicious for a toxicological cause based on circumstance with a positive toxicology were isolated. From these the data on demographics and substances detected were compiled by year for comparison. Results: Comparing two years prior to the COVID-19 pandemic to the following two years there was an increase in total case load of 22%. Comparing the year before the pandemic to the first year of the pandemic deaths from natural causes fell from 52% to 47% of total cases, while drug-related cases increased from 24% to 36%. Fentanyl remained as the most prevalent detected substance in toxicological deaths. Combined opioid toxicity with stimulants increased, as well as the detection of nonpharmaceutical benzodiazepines. Deaths in men increased to comprise 3 in 4 drug-related deaths with the 30 to 39 years age-group remaining the most impacted. Conclusions: There was an increase in numbers and relative proportions of cases attributed to drug-related deaths which remained high over the two years of the pandemic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".