Effects of Environmental Factors on Severity and Mortality of COVID-19
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Abstract
Background: Most respiratory viruses show pronounced seasonality, but for SARS-CoV-2, this still needs to be documented.
Methods: We examined the disease progression of COVID-19 in 6,914 patients admitted to hospitals in Europe and China. In addition, we evaluated progress of disease symptoms in 37,187 individuals reporting symptoms into the COVID Symptom Study application.
Findings: Meta-analysis of the mortality risk in seven European hospitals estimated odds ratios per 1-day increase in the admission date to be 0.981 (0.973–0.988, p < 0.001) and per increase in ambient temperature of 1°C to be 0.854 (0.773–0.944, p = 0.007). Statistically significant decreases of comparable magnitude in median hospital stay, probability of transfer to the intensive care unit, and need for mechanical ventilation were also observed in most, but not all hospitals. The analysis of individually reported symptoms of 37,187 individuals in the UK also showed the decrease in symptom duration and disease severity with time.
Interpretation: Severity of COVID-19 in Europe decreased significantly between March and May and the seasonality of COVID-19 is the most likely explanation.
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SciScore for 10.1101/2020.07.11.20147157: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: , ASST Papa Giovanni XXIII° Hospital in Bergamo, Hospital del Mar in Barcelona and Helsinki University Hospital local ethics committees approved this retrospective study of COVID-19 patient data.
Consent: Importantly, participants enrolled in ongoing epidemiologic studies, clinical cohorts, or clinical trials, can provide informed consent to link data collected through the app in a HIPPA and GDPR-compliant manner with extant study data they have previously provided or may provide in the future.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable The following patient characteristics, and hospitalization … SciScore for 10.1101/2020.07.11.20147157: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: , ASST Papa Giovanni XXIII° Hospital in Bergamo, Hospital del Mar in Barcelona and Helsinki University Hospital local ethics committees approved this retrospective study of COVID-19 patient data.
Consent: Importantly, participants enrolled in ongoing epidemiologic studies, clinical cohorts, or clinical trials, can provide informed consent to link data collected through the app in a HIPPA and GDPR-compliant manner with extant study data they have previously provided or may provide in the future.Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable The following patient characteristics, and hospitalization episode co-variates were explored: Died/discharged outcome was used as dependent variable and admission as independent variable along with age (in years) and gender (female/male). Table 2: Resources
Software and Algorithms Sentences Resources All statistical analyses were performed in R programming software (version 3.6.3), with exception of logistic and linear regressions on Milano cohort data which are performed in Stata Statistical Software (version 12) and the COVID Symptom Study cohort for which linear regression were performed using python statsmodels package (version 0.11.1). pythonsuggested: (IPython, RRID:SCR_001658)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: Potential sampling bias is the main limitation of this study. By focusing on individual progression of the disease in already hospitalized patients we excluded effects of the unknown number of true infections on national mortality rates, and we still cannot exclude the possibility that some other unidentified external factors (including confinement and social distancing, improvement and compliance of prevention and environmental hygiene protocols and even decreased air-pollution could have progressively affected the severity of patients arriving to the hospital) were affecting composition of hospitalized patient cohorts and contributing to the decreased COVID-19 severity and mortality. Therefore, it is important that tracking of individual symptoms in 37,187 UK patients are showing the same trend, since these are individuals voluntary reporting symptoms and potential sampling bias there is independent from bias in hospitalization. The choice to include imputed positives was mostly motivated by the restriction in testing access that were observed over the first wave before being relaxed in May and June. Accounting only for PCR tested positive reporting to the app would have unduly biased the results towards higher severity in the early days. We adopted instead the model developed by Menni et al. (12) that achieved a reasonable performance in prediction of positive cases (ROC-AUC 76%).
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.
- Thank you for including a protocol registration statement.
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SciScore for 10.1101/2020.07.11.20147157: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement ASST Papa Giovanni XXIII° Hospital in Bergamo, Hospital del Mar in Barcelona and Helsinki University Hospital local ethics committees approved this retrospective study of COVID-19 patient data. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable The following patient characteristics, and hospitalization episode co-variates were explored: Died/discharged outcome was used as dependent variable and admission as independent variable along with age (in years) and gender (female/male). Table 2: Resources
Software and Algorithms Sentences Resources Funding This work was supported in part by the European … SciScore for 10.1101/2020.07.11.20147157: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement ASST Papa Giovanni XXIII° Hospital in Bergamo, Hospital del Mar in Barcelona and Helsinki University Hospital local ethics committees approved this retrospective study of COVID-19 patient data. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable The following patient characteristics, and hospitalization episode co-variates were explored: Died/discharged outcome was used as dependent variable and admission as independent variable along with age (in years) and gender (female/male). Table 2: Resources
Software and Algorithms Sentences Resources Funding This work was supported in part by the European Structural and Investment Funds grant for the National Centre of Competence in Molecular Diagnostics (#KK.01.2.2.03.0006), National Centre of Research Excellence in Personalized Healthcare grant (#KK.01.1.1.01.0010) and IP CORONA-2020-04 grant from the Croatian Science Foundation. Personalized Healthcaresuggested: NoneAbout SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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