Estimates of COVID-19 case-fatality risk from individual-level data
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Abstract
When calculated from aggregate data on confirmed cases and deaths, the case-fatality risk (CFR) is a simple ratio between the former and the latter, which is prone to numerous biases. With individual-level data, the CFR can be estimated as a true measure of risk as the proportion of incidence for the disease. We present the first estimates of the CFR for COVID-19 by age and sex based on event history modelling of the risk of dying among confirmed positive individuals in the Canadian province of Ontario, which maintains one of the few individual-level datasets on COVID-19 in the world.
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SciScore for 10.1101/2020.04.16.20067751: (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 These observations were excluded, so that the analysis is based on 8,394 cases, of whom 56.2% (4,716) are females. Table 2: Resources
Software and Algorithms Sentences Resources The CI is estimated after fitting the competing risk regression model, controlling for gender, with stcrreg in Stata/SE (version 12.0, StataCorp, LLC). StataCorpsuggested: (Stata, RRID:SCR_012763)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 LimitationR…SciScore for 10.1101/2020.04.16.20067751: (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 These observations were excluded, so that the analysis is based on 8,394 cases, of whom 56.2% (4,716) are females. Table 2: Resources
Software and Algorithms Sentences Resources The CI is estimated after fitting the competing risk regression model, controlling for gender, with stcrreg in Stata/SE (version 12.0, StataCorp, LLC). StataCorpsuggested: (Stata, RRID:SCR_012763)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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