An analysis of mortality in Ontario using cremation data: Rise in cremations during the COVID-19 pandemic

  1. Gemma Postill
  2. Regan Murray
  3. Andrew S. Wilton
  4. Richard A. Wells
  5. Renee Sirbu
  6. Mark J. Daley
  7. Laura C Rosella

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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.

Article activity feed

  1. Version published to 10.1101/2020.07.22.20159913v3 on medRxiv
  2. Version published to 10.1101/2020.07.22.20159913v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.07.22.20159913: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All analysis was done using Python 3.7.0.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are some important limitations to this analysis that should be considered. First, specific cause-of-death data were not included because of the nature of the data structure (open text fields) and the heterogeneity in the classification of cause of death as immediate and antecedent, which prevents rapid analysis. Additional cremation records may be recorded for the 2020 time period, but this is not expected to change the findings reported in this study. As discussed, while cremation data represent the majority of deaths in Ontario, they do not represent all deaths. Certain segments of the population may be more or less likely to be cremated. This would be an issue in the comparison if the number of deaths during the pandemic period was affected disproportionately in a group with an intrinsically higher or lower rate of cremation (e.g. age group, burial traditions, geographic regions). Therefore, we acknowledge that at least some of the changes observed may be due to differences in preferences for cremation during the pandemic. However, we do not anticipate this to be the main reason for the increase given the already very high numbers of cremations before the pandemic (>70%), the magnitude of excess cremations (>30% during the peak month of COVID-19 activity) and the congruence of the increase and subsequent decreases with the confirmed COVID-19 activity. Using long-term care data we further demonstrate that the cremation rate is not meaningfully different than in previo...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  4. Version published to 10.1101/2020.07.22.20159913v1 on medRxiv