An analysis of mortality in Ontario using cremation data: Rise in cremations during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background The impact of coronavirus disease 2019 (COVID-19) on mortality in Ontario is unknown. Cremations are performed for most deaths in Ontario and require coroner certification before the cremation can take place. Our objective was to provide timely analysis of deaths during the COVID-19 pandemic using cremation data. Methods We analyze cremation certificate data from January 1, 2017, to June 30, 2020, in Ontario. 2020 cremation records were compared to historical records from 2017-2019 by age, month, and place of death and COVID-19 status. A time series model was fit to quantify the deviation in cremation trends during the COVID-19 period. Results There have been 39 760 cremations in Ontario in 2020 with the highest number of seen in April (N = 7 527 cremations) when peak COVID-19 cases were seen. Over the study period, the proportion of cremations from deaths in hospitals decreased whereas cremations from long-term care and residences increased. In April there were 1 839 more cremations compared to historical averages over 2017-2019, representing a 32% increase. Time series modelling of cremations from January 2017 demonstrated that cremations in April and May 2020 exceeded the projections based on modelled estimates. Conclusion We demonstrate the utility of cremation data for providing timely mortality information during a public health emergency. Cremations were higher in the pandemic months compared to previous years, and there was a shift in deaths occurring in hospitals to long-term care and residences. These timely estimates of mortality are critical for understanding the impact of COVID-19.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".