Non-COVID-19 deaths in the United States during the imposition of sheltering-in-place
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Abstract
Public debate over imposed sheltering-in-place in the United States (US) includes the claim that non-COVID-19 deaths increased above those expected from history and from such deaths in Sweden. We test this claim by applying Box-Jenkins transfer function modeling to weekly deaths between December 29, 2013 and May 16, 2020 in the US and Sweden. In the eight weeks of imposed sheltering-in-place in the US – assumed begun in the week of March 22 – an average of 715 fewer non-COVID-19 deaths (95% CI: −1428, −2) occurred per week implying a total of 5720 less than expected from those in Sweden and from history. Results changed little (i.e., 5472 fewer deaths than expected) when we excluded deaths in New York City. Contrary to speculation, non-COVID-19 mortality appeared to decline in the US after shelter-in-place orders.
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SciScore for 10.1101/2020.07.26.20162396: (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 not detected. 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: We detected the following sentences addressing limitations in the study:Weaknesses include that the data do not provide enough detail to identify the causes of death that fell below expected levels. We further acknowledge that our results do not describe the sequelae of the “loosening” of …
SciScore for 10.1101/2020.07.26.20162396: (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 not detected. 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: We detected the following sentences addressing limitations in the study:Weaknesses include that the data do not provide enough detail to identify the causes of death that fell below expected levels. We further acknowledge that our results do not describe the sequelae of the “loosening” of shelter-in-place restrictions after our test period.
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 10.1101/2020.07.26.20162396: (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 not detected. 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: We detected the following sentences addressing limitations in the study:
Weaknesses include that the data do no provide enough detail to identify the causes of death that fell below expected levels. We further acknowledge that our results do not describe the sequelae of the “loosenin of …
SciScore for 10.1101/2020.07.26.20162396: (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 not detected. 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: We detected the following sentences addressing limitations in the study:
Weaknesses include that the data do no provide enough detail to identify the causes of death that fell below expected levels. We further acknowledge that our results do not describe the sequelae of the “loosenin of shelter-in-place restrictions after our test period. Figure 1. Counts of non-Covid-19 deaths in the US for the 332 weeks beginning Dece 2020. 70000 60000 55000 50000 45000 40000 35000 30000 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 Non-Covid-19 US Deaths 65000 Week Figure 2. Observed less expected non-COVID-19 deaths in the United States and the weeks starting 5/20/2019 and ending 5/17/2020. Expected values derived Sweden and from autocorrelation for 324 weeks before the imposition of so Oserved less expected non-Covid-19 deaths 6000 4000 2000 0 -2000 -4000 -6000 -8000 -10000 1 6 11 16 21 26 Week 31
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.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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SciScore for 10.1101/2020.07.26.20162396: (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 not detected. 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: We detected the following sentences addressing limitations in the study:
Weaknesses include that the data do no provide enough detail to identify the causes of death that fell below expected levels. We further acknowledge that our results do not describe the sequelae of the “loosenin of …
SciScore for 10.1101/2020.07.26.20162396: (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 not detected. 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: We detected the following sentences addressing limitations in the study:
Weaknesses include that the data do no provide enough detail to identify the causes of death that fell below expected levels. We further acknowledge that our results do not describe the sequelae of the “loosenin of shelter-in-place restrictions after our test period. Figure 1. Counts of non-Covid-19 deaths in the US for the 332 weeks beginning Dece 2020. 70000 60000 55000 50000 45000 40000 35000 30000 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 Non-Covid-19 US Deaths 65000 Week Figure 2. Observed less expected non-COVID-19 deaths in the United States and the weeks starting 5/20/2019 and ending 5/17/2020. Expected values derived Sweden and from autocorrelation for 324 weeks before the imposition of so Oserved less expected non-Covid-19 deaths 6000 4000 2000 0 -2000 -4000 -6000 -8000 -10000 1 6 11 16 21 26 Week 31
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.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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