COVID-19 Excess Deaths in the United States, New York City, and Michigan During April 2020
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Abstract
Background
It has been suggested that many of those who died from COVID-19 were older, had more comorbidities, and would have died within a short period anyway. We estimated the number and percent of excess deaths due to COVID-19 In April 2020 in the United States, New York City, and Michigan.
Methods
For each locale we calculated attributable fractions in the exposed comparing observed COVID-19 deaths and expected deaths. In addition, we estimated the number of months it would take for the excess deaths to occur without the virus and the proportions of the populations that were infected leading to the April deaths. We compared the excess deaths from the attributable fraction method to those obtained by comparing weekly deaths in 2019 and 2020.
Results
Using an assumed infection fatality rate of 1%, the percentages of excess deaths were 95%, 97%, and 95% in the US, NYC, and MI equivalent to 54,560; 14,951; and 3,338 deaths, respectively. Absent the virus these deaths would have occurred over 21.0, 29.2, and 18.4 months in the respective locations. An estimated 1.7% of the US population was infected between March 13 and April 10, 2020. Nearly 19% were infected in NYC.
Conclusions
Over 75% of COVID-19 deaths in April 2020 were excess deaths meaning they would not have occurred in April without SARS-CoV-2 but would have been spread out over the ensuing 18 to 29 months. Confirmed cases in the US under-report the actual number of infections by at least an order of magnitude. Excess death numbers calculated using the attributable fraction in the exposed are similar to those obtained from weekly mortality reports.
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SciScore for 10.1101/2020.04.02.20051532: (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
No key resources detected.
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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2020.04.02.20051532: (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
No key resources detected.
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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
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SciScore for 10.1101/2020.04.02.20051532: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Although males may have higher fatality rates, the data are not yet conclusive.28 Moreover, we have no data on possible differences in infection rates by age or gender. Table 2: Resources
Software and Algorithms Sentences Resources We used Microsoft Excel Solver to find the average CIR that would produce the projected number of COVID-19 deaths during April 2020. Microsoft Excelsuggested: (Microsoft Excel, SCR_016137)Similarly, Solver was used to determine the number of months … SciScore for 10.1101/2020.04.02.20051532: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable Although males may have higher fatality rates, the data are not yet conclusive.28 Moreover, we have no data on possible differences in infection rates by age or gender. Table 2: Resources
Software and Algorithms Sentences Resources We used Microsoft Excel Solver to find the average CIR that would produce the projected number of COVID-19 deaths during April 2020. Microsoft Excelsuggested: (Microsoft Excel, SCR_016137)Similarly, Solver was used to determine the number of months needed to generate the excess deaths. Solversuggested: (Solver, SCR_008510)4 Conclusions During April 2020, over 90% of the deaths predicted in the US population for those infected with SARS-CoV-2 will be excess deaths, i.e., deaths that would not have occurred during a single month without the increased fatality rates due to SARS-CoV-2. SARS-CoV-2suggested: (Sino Biological Cat# 40143-R019, AB_2827973)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).
About SciScore
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