COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
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SciScore for 10.1101/2021.11.08.21265312: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Cell Line Authentication Authentication: We assessed 270 previously described comorbidities14, across 16 clinical specialities / organ systems, using validated CALIBER phenotypes and data records from 1st of January 1996 until 31st December 2019 from primary care, hospitalisation and procedure data14 (Supplementary Figure 3). Table 2: Resources
Software and Algorithms Sentences Resources A Reporting of studies Conducted using Observational Routinely-collected Data (RECORD) statement can be found in the supplement. RECORDsuggested: (RECORD, RRID:SCR_009097)Data cleaning, … SciScore for 10.1101/2021.11.08.21265312: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Cell Line Authentication Authentication: We assessed 270 previously described comorbidities14, across 16 clinical specialities / organ systems, using validated CALIBER phenotypes and data records from 1st of January 1996 until 31st December 2019 from primary care, hospitalisation and procedure data14 (Supplementary Figure 3). Table 2: Resources
Software and Algorithms Sentences Resources A Reporting of studies Conducted using Observational Routinely-collected Data (RECORD) statement can be found in the supplement. RECORDsuggested: (RECORD, RRID:SCR_009097)Data cleaning, exploratory analysis, phenotype creation and cohort assembly was performed using Python (3.7) and Spark SQL (2.4.5) on Databricks Runtime 6.4 for Machine Learning. Pythonsuggested: (IPython, RRID:SCR_001658)Analysis was performed in RStudio (Professional) Version 1.3.1093.1 RStudiosuggested: (RStudio, RRID:SCR_000432)Figures were constructed using ggplot2 (3.3.3), VennDiagram (1.6.20), igraph (1.2.6), survival (3.2.7) and survminer (0.4.8) packages. ggplot2suggested: (ggplot2, RRID:SCR_014601)VennDiagramsuggested: (VennDiagram, RRID:SCR_002414)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:Strengths and limitations: A key strength of this work using national-scale data is that by definition it is representative of the general population across all age groups, ethnicities, deprivation levels and demographic characteristics. To our knowledge, this is the largest population-wide research study of COVID-19 phenotypes which includes: a) multiple healthcare settings through data linkage at a population level, b) detailed identification of specific ventilatory treatments, c) classification of COVID-19 related deaths, and d) exploration of transitions between COVID-19 events. Using multiple EHR sources spanning different healthcare settings, maximised infection ascertainment and reduced the effects of variable testing and data recording patterns (especially during the first wave). As the focus of this work was to create COVID-19 related phenotypes, and describe the characteristics of individuals experiencing them, we have not conducted multivariable regression analyses to control for confounders. The findings presented are therefore not associative statements and should not be interpreted as causal relationships. However by sharing reproducible phenotype definitions we hope to facilitate further work to address the questions raised in this and other COVID-19 studies exploring national level data, as exemplified by recent research17–19. Whilst our definitions of the pandemic waves differ from others, we believe using non-contiguous dates enabled a balanced comparison ac...
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.
- No funding statement was detected.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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