Retrospective study of non-natural manners of death in Ontario: Effects of the COVID-19 pandemic and related public health measures
Bibliographic record
Abstract
BACKGROUND: The effects of the COVID-19 pandemic on non-natural manners of death in Ontario is not known. Understanding the indirect consequences of the pandemic and related public health measures (i.e. lockdown) fills a vital need to inform best practice in public health and guide policy decisions. METHODS: The Office of the Chief Coroner and the Ontario Forensic Pathology Service (OCC-OFPS) investigate sudden and unexpected deaths in the province of Ontario. The number of homicides, suicides, and accidental deaths (non-natural deaths=77,655) were extracted from the centralized Coroner's Information System database (total deaths=197,966), across four provincially defined stages of lockdown related to the COVID-19 pandemic (March 17 to December 31, 2020), and crude rates (per 100,000 people) were compared to the previous eleven years. FINDINGS: There was no major change to the rate of homicides during 2020 compared to 2009-2019 (RR 1⋅1, 95% CI 0⋅95-1⋅2; p=0⋅19; estimated annual effect=21 more deaths in 2020). The rate of suicides also did not show an overall major change in 2020 (RR 1⋅02, 95% CI 0⋅96-1⋅1; p=0⋅50; estimated annual effect=27 more deaths in 2020). However, during the first stage of lockdown (Stage 0), there was a decrease in the rate of suicides compared to all combinations of recent years from 2013 onwards (RRs 0⋅82-0⋅86, combined 95% CI 0⋅69-0⋅99; max p=0⋅039; estimated effect of 30 less deaths in Stage 0). There was an excess of over 1,500 accidental drug-related deaths that occurred during 2020 (RR 2⋅5, 95% CI 2⋅4-2⋅7; p<0⋅001). This finding held up to 'interrupted time series' robustness testing, indicating that 2020 had substantially more drug-related deaths, even when accounting for the linear increasing trend over time. Although motor vehicle collision associated fatalities appeared to decrease slightly in 2020 (RR 0⋅89, 95% CI 0⋅81-0⋅96; p=0⋅0039; estimated annual effect of 78 less deaths), we could not conclude any lockdown-associated effect, particularly when compared to 2019 (RR 0⋅26, 95% CI 0⋅75-1⋅1; p=0⋅26). INTERPRETATION: In Ontario, the short-term effects of the COVID-19 pandemic did not greatly increase homicide or suicide rates, nor decrease motor vehicle collision fatality rates; however, the longer-term impact of the pandemic remains to be elucidated and ongoing vigilance is warranted in the event that other trends emerge. Accidental drug-related fatalities substantially increased during all stages of the lockdown, marking an urgent need for consideration in policy. These results highlight the vital role of death investigation systems in providing high quality and timely data to inform public health recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".