Symptoms and syndromes associated with SARS-CoV-2 infection and severity in pregnant women from two community cohorts
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Abstract
We tested whether pregnant and non-pregnant women differ in COVID-19 symptom profile and severity, and we extended previous investigations on hospitalized pregnant women to those who did not require hospitalization. Two female community-based cohorts (18–44 years) provided longitudinal (smartphone application, N = 1,170,315, n = 79 pregnant tested positive) and cross-sectional (web-based survey, N = 1,344,966, n = 134 pregnant tested positive) data, prospectively collected through self-participatory citizen surveillance in UK, Sweden and USA. Pregnant and non-pregnant were compared for frequencies of events, including SARS-CoV-2 testing, symptoms and hospitalization rates. Multivariable regression was used to investigate symptoms severity and comorbidity effects. Pregnant and non-pregnant women positive for SARS-CoV-2 infection were not different in syndromic severity, except for gastrointestinal symptoms. Pregnant were more likely to have received testing, despite reporting fewer symptoms. Pre-existing lung disease was most closely associated with syndromic severity in pregnant hospitalized. Heart and kidney diseases and diabetes increased risk. The most frequent symptoms among non-hospitalized women were anosmia [63% pregnant, 92% non-pregnant] and headache [72%, 62%]. Cardiopulmonary symptoms, including persistent cough [80%] and chest pain [73%], were more frequent among pregnant who were hospitalized. Consistent with observations in non-pregnant populations, lung disease and diabetes were associated with increased risk of more severe SARS-CoV-2 infection during pregnancy.
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SciScore for 10.1101/2020.08.17.20161760: (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 2.2 Pregnancy groups, symptoms, syndromes and outcomes: 2.4 Statistical analysis: A power analysis was conducted to assess the suitability of the samples size. Sex as a biological variable 2.1 Study Populations: We developed a symptom-based prediction method to identify suspected COVID-19 cases among women 18-44 years of age from a discovery cohort. 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 …SciScore for 10.1101/2020.08.17.20161760: (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 2.2 Pregnancy groups, symptoms, syndromes and outcomes: 2.4 Statistical analysis: A power analysis was conducted to assess the suitability of the samples size. Sex as a biological variable 2.1 Study Populations: We developed a symptom-based prediction method to identify suspected COVID-19 cases among women 18-44 years of age from a discovery cohort. 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: Participatory surveillance tools are crucial to epidemiological research and citizen science, as they increase population’s awareness of urgent public health risks, promote public participation into science and enable inclusion in studies of large samples from the community within short time periods. Real-time public health data has been crucial in decision-making during the COVID-19 pandemic. However, user of smartphone applications and web-based surveys may not be representative of the general population, potentially limiting generalizability. Self-reported events may suffer from misclassification bias, which may be differential (e.g. ability to log hospitalization may be higher in less severely affected participants, test results known at the time of cross-sectional symptom reporting may differ). Median app usage was 18 days, which may be insufficient follow-up to ascertain all outcomes. In the discovery cohort, pregnancy status was only queried at the time of registration; women who became pregnant after registration may be misclassified. In addition, gestational age during the infection could not be assessed, as well as whether women were symptomatic at the time of delivery. The replication cohort was designed to be representative of USA population through survey sampling for the active user base and weights with raking to the USA census. Despite the different platforms and country of origin of users, the cross-sectional surveys showed similar ...
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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