Paucity and Disparity of Publicly Available Sex-Disaggregated Data for the COVID-19 Epidemic Hamper Evidence-Based Decision-Making
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Abstract
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SciScore for 10.1101/2020.04.29.20083709: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:It, however, highlighted further limitations, as the data used for the baseline population was almost a year old (and no numerical values were made available). Standardization of data collection and data points needs to be improved: While all methods of graphing and metrics may be valuable for different purposes, comparable, …
SciScore for 10.1101/2020.04.29.20083709: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:It, however, highlighted further limitations, as the data used for the baseline population was almost a year old (and no numerical values were made available). Standardization of data collection and data points needs to be improved: While all methods of graphing and metrics may be valuable for different purposes, comparable, standardized raw data need to be accessible to scholars for meaningful, statistically significant analysis. For continuous variables (e.g. age), actual numerical values are needed. We could not find any stated justification on any governmental website for the wide array of age-binning categories illustrated in Fig. 2. Not only does it make comparison between data sets difficult, it might hide - or artificially create - trends. For example, grouping children aged 10-19, across puberty, is likely to mask hormonally influenced outcomes. Rigorous statistical analysis needs to be applied to raw data to reveal trends (and guide age-binning), rather than the converse. For sex, provision for a third option, beyond the binary, would be useful to accommodate both various country-specific laws and missing data. When this was provided, entities calculated proportions of women/men either out of the total (leaving percentages not adding to 100%, as in WA state) or out of the cases for which sex was known (resulting in information being reported on a limited fraction of total cases - see legend of Table 3). Where countries have put online high-quality disaggregated data...
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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