Characteristics and transmission dynamics of COVID-19 in healthcare workers at a London teaching hospital
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SciScore for 10.1101/2020.07.10.20149237: (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
Software and Algorithms Sentences Resources Occupational health data and staff records were combined to identify proven COVID-19 and sickness rates from March to April 2020 and analysed using Microsoft Excel™. Microsoft Excel™suggested: (Microsoft Excel, RRID:SCR_016137)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 …SciScore for 10.1101/2020.07.10.20149237: (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
Software and Algorithms Sentences Resources Occupational health data and staff records were combined to identify proven COVID-19 and sickness rates from March to April 2020 and analysed using Microsoft Excel™. Microsoft Excel™suggested: (Microsoft Excel, RRID:SCR_016137)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:Limitations of our data include a lack of information on disease severity and clinical outcomes as well as the effect of staff redeployment to COVID-19 wards and ICUs. We also have less data available for contracted services, which includes many domestic and cleaning staff. The true rate of COVID-19 in different staff groups may be masked by selective and changing testing criteria. This was addressed by analysing overall staff sickness episodes. When the COVID-19 pandemic began, there was global concern about the risks to HCWs and the adequacy of PPE. Front-line clinical staff were perceived to be at greatest risk, and this (along with concerns about diagnostic capacity) informed the initial staff testing strategy. However, the matching epidemic curves of proven staff and patient infections along with the large numbers of infections in non-clinical staff supports a community source for a significant proportion of staff. Nevertheless, the delayed peak in clinical staff sickness episodes cannot be ignored. The most plausible explanation is that at least some of the staff infections are related to patient exposure, with some transmission within individual clinical departments. Department-specific data does support a hypothesis of some localised clusters of infection (Table II). This is not surprising given viral infectivity and necessary close contact of staff in a busy work environment. The possible second smaller peak in staff sickness may represent increased detection due to ...
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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