Obesity during the COVID-19 pandemic: both cause of high risk and potential effect of lockdown? A population-based electronic health record study
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SciScore for 10.1101/2020.06.22.20137182: (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: 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: Key strengths of this study include use of population-based EHR, large sample size, utilisation of contemporary and clinically relevant measurements of weight and height, records of relevant …
SciScore for 10.1101/2020.06.22.20137182: (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: 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: Key strengths of this study include use of population-based EHR, large sample size, utilisation of contemporary and clinically relevant measurements of weight and height, records of relevant comorbidities and causal inference methods (i.e. the g-formula). Normal weight, overweight and obese individuals have different underlying risk for the occurrence of these chronic conditions, hence it is more appropriate to study them separately. Most observational studies cannot focus on this relationship in this resolution, mainly due to their sample size. Several limitations should be noted. Our observational study cannot exclude unmeasured confounding. Since we use EHR, there may be differences between actual and recorded date of incidence of chronic diseases, which would result in bias due to reverse causation. However, this would actually result in underestimation of BMI gain and development of high-risk conditions, except when BMI loss is a result of the disease itself, which would cause potential increased mortality risk. Using EHR, we made assumptions, e.g. linear trend for weight change between 2 body weight measurements recorded <6 months apart, and the last record for physical activity in the last 4 years.
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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