Joinpoint Regression to Determine the Impact of COVID-19 on Mortality in Europe: A Longitudinal Analysis From 2000 to 2020 in 27 Countries
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Abstract
The novel coronavirus disease 2019 (COVID-19) represented the most extensive health emergency in human history. However, to date, there is still a lot of uncertainty about the exact death toll the pandemic has claimed. In particular, the number of official deaths could be vastly underestimated. Despite this, many conspirationists speculate that COVID-19 is not a dangerous disease. Therefore, in this manuscript, we use joinpoint regression analysis to estimate the impact of COVID-19 in 27 European countries by comparing annual mortality trends from 2000 to 2020. Furthermore, we provide accessible evidence even for a non-expert audience. Siegel (A1) and Holm-Bonferroni (A2) approaches were employed to assess the significance of the results separately. In conclusion, these results estimate that COVID-19 increased the overall mortality in Europe by 10% (A1: P < .001, A2: Adjusted P = .001). In 16 out of 27 countries (59.3%), the excess mortality ranged from 7.4% to 18.5% (A1: P < .003, A2: Adjusted P < .040). Comparison of the excess mortalities’ distribution to the null counterfactual showed that the mortality increase was highly significant across Europe (Adjusted P < .001).
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SciScore for 10.1101/2022.01.19.22269576: (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 Sentences Resources The last subtrend found was then analyzed and confirmed by a graph check and a linear regression analysis performed with the “XLSTAT” tool for Microsoft Excel v.2112 [11]. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)Linear regression: Ordinary least square linear regression from the XLSTAT package was used to model the annual mortality trend from the last joinpoint through 2019. XLSTATsuggested: (XLSTAT, RRID:SCR_016299)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 …
SciScore for 10.1101/2022.01.19.22269576: (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 Sentences Resources The last subtrend found was then analyzed and confirmed by a graph check and a linear regression analysis performed with the “XLSTAT” tool for Microsoft Excel v.2112 [11]. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)Linear regression: Ordinary least square linear regression from the XLSTAT package was used to model the annual mortality trend from the last joinpoint through 2019. XLSTATsuggested: (XLSTAT, RRID:SCR_016299)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.
Results from scite Reference Check: We found no unreliable references.
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