All-cause mortality in Canada from 2010 to 2021
This graph comes from Rancourt et al's (2021) analysis of the Canadian government health agency's own data, similar to the CDC data analyzed by Briand (2021) in the United States. It shows that there was no increase in deaths in Canada in 2020 beyond the usual expected yearly increase, and that the "second wave" of covid-19 in winter of 2020-21 actually had lower peak deaths than the winter of 2017-2018 which was considered a bad flu year. Briand demonstrated a similar pattern for US data. Although some regions in the US did have higher deaths than usual in 2020, there were dramatic differences between regions, and most US states did not have any increase over prior years. When she removed data from two of the worst regions, both of which had extremely strict social isolation mandates, the data for the other 48 states was similar to the graph above for Canada with no significant increase over the yearly expected increase (Briand 2021, Graph 70). The two regions she removed were New York City and New Jersey, where catastrophic long-term care staffing crises led to a large spike of deaths immediately after stay-at-home orders were declared.
The graphs of yearly all-cause mortality from Briand and Rancourt et al show that every year there is a winter increase in deaths in the US and Canada, just as in all other countries with cold winters, and each year the overall number goes up slightly due to population increase. The graph above for Canada shows no change in this pattern for the winters of 2019-20 or 2020-21 despite the extremely large number of covid-19 cases and official tallies of deaths supposedly caused by covid-19 in Canada. However, by focusing on only one year, the yearly winter increase in deaths can be made to look alarming, as seen in Figure 4 which zooms in on the winters of 2019-2020 and 2020-21.
Rancourt et al and Briand both point out several aspects of the mortality data for 2020 which suggest that the pandemic response measures were overly aggressive, and likely increased deaths instead of preventing them. For example, they both showed that spikes in mortality occurred simultaneously in regions thousands of miles apart in the two weeks immediately after the WHO declared the pandemic. Viral illnesses normally spread out in sequential fashion, and do not suddenly appear all at once in distant regions. In addition, there is an extreme regional variability for the first 8 months of the pandemic, with most states having no increase over prior years, including the only seven states that never issued stay-at-home orders (Arkansas, Iowa, Nebraska, North Dakota, South Dakota, Utah, and Wyoming). In contrast, many states had extreme spikes in mortality, including some that had the strictest social isolation mandates and healthcare disruption such as New York City and New Jersey, as previously mentioned. If normal high quality care had been available, the outcomes would likely have been similar to most of the other states where no increase or only mild increases were seen. For comparison here are the mortality graphs for two states that did not issue stay-at-home orders, Iowa and South Dakota, as well as the graphs for New York City and New Jersey where very strict protocols were enacted (Iowa, South Dakota, New York City , New Jersey).
A more detailed discussion of response-caused deaths is found in Rancourt et al section 4.5, “Regarding Causes of Response-Induced Deaths” (Pages 41-46). At the top of page 43 begins an excellent summary of effects of the "unprecedented strict mass quarantine and isolation of sick and healthy elderly people" as a "main cause of the 'covid-peak' in Canada." (Page 43-46). They later state: “We conclude that the ‘covid-peak’ was palpably induced by the pandemic response” (Rancourt et al, Page 47).
This graph comes from Rancourt et al's (2021) analysis of the Canadian government health agency's own data, similar to the CDC data analyzed by Briand (2021) in the United States. It shows that there was no increase in deaths in Canada in 2020 beyond the usual expected yearly increase, and that the "second wave" of covid-19 in winter of 2020-21 actually had lower peak deaths than the winter of 2017-2018 which was considered a bad flu year. Briand demonstrated a similar pattern for US data. Although some regions in the US did have higher deaths than usual in 2020, there were dramatic differences between regions, and most US states did not have any increase over prior years. When she removed data from two of the worst regions, both of which had extremely strict social isolation mandates, the data for the other 48 states was similar to the graph above for Canada with no significant increase over the yearly expected increase (Briand 2021, Graph 70). The two regions she removed were New York City and New Jersey, where catastrophic long-term care staffing crises led to a large spike of deaths immediately after stay-at-home orders were declared.
The graphs of yearly all-cause mortality from Briand and Rancourt et al show that every year there is a winter increase in deaths in the US and Canada, just as in all other countries with cold winters, and each year the overall number goes up slightly due to population increase. The graph above for Canada shows no change in this pattern for the winters of 2019-20 or 2020-21 despite the extremely large number of covid-19 cases and official tallies of deaths supposedly caused by covid-19 in Canada. However, by focusing on only one year, the yearly winter increase in deaths can be made to look alarming, as seen in Figure 4 which zooms in on the winters of 2019-2020 and 2020-21.
Rancourt et al and Briand both point out several aspects of the mortality data for 2020 which suggest that the pandemic response measures were overly aggressive, and likely increased deaths instead of preventing them. For example, they both showed that spikes in mortality occurred simultaneously in regions thousands of miles apart in the two weeks immediately after the WHO declared the pandemic. Viral illnesses normally spread out in sequential fashion, and do not suddenly appear all at once in distant regions. In addition, there is an extreme regional variability for the first 8 months of the pandemic, with most states having no increase over prior years, including the only seven states that never issued stay-at-home orders (Arkansas, Iowa, Nebraska, North Dakota, South Dakota, Utah, and Wyoming). In contrast, many states had extreme spikes in mortality, including some that had the strictest social isolation mandates and healthcare disruption such as New York City and New Jersey, as previously mentioned. If normal high quality care had been available, the outcomes would likely have been similar to most of the other states where no increase or only mild increases were seen. For comparison here are the mortality graphs for two states that did not issue stay-at-home orders, Iowa and South Dakota, as well as the graphs for New York City and New Jersey where very strict protocols were enacted (Iowa, South Dakota, New York City , New Jersey).
A more detailed discussion of response-caused deaths is found in Rancourt et al section 4.5, “Regarding Causes of Response-Induced Deaths” (Pages 41-46). At the top of page 43 begins an excellent summary of effects of the "unprecedented strict mass quarantine and isolation of sick and healthy elderly people" as a "main cause of the 'covid-peak' in Canada." (Page 43-46). They later state: “We conclude that the ‘covid-peak’ was palpably induced by the pandemic response” (Rancourt et al, Page 47).