Risk Factors for Illness Severity Among Pregnant Women With Confirmed Severe Acute Respiratory Syndrome Coronavirus 2 Infection—Surveillance for Emerging Threats to Mothers and Babies Network, 22 State, Local, and Territorial Health Departments, 29 March 2020–5 March 2021
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Abstract
Background
Pregnant women with coronavirus disease 2019 (COVID-19) are at increased risk for severe illness compared with nonpregnant women. Data to assess risk factors for illness severity among pregnant women with COVID-19 are limited. This study aimed to determine risk factors associated with COVID-19 illness severity among pregnant women with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
Methods
Pregnant women with SARS-CoV-2 infection confirmed by molecular testing were reported during 29 March 2020–5 March 2021 through the Surveillance for Emerging Threats to Mothers and Babies Network (SET-NET). Criteria for illness severity (asymptomatic, mild, moderate-to-severe, or critical) were adapted from National Institutes of Health and World Health Organization criteria. Crude and adjusted risk ratios for moderate-to-severe or critical COVID-19 illness were calculated for selected demographic and clinical characteristics.
Results
Among 7950 pregnant women with SARS-CoV-2 infection, moderate-to-severe or critical COVID-19 illness was associated with age 25 years and older, healthcare occupation, prepregnancy obesity, chronic lung disease, chronic hypertension, and pregestational diabetes mellitus. Risk of moderate-to-severe or critical illness increased with the number of underlying medical or pregnancy-related conditions.
Conclusions
Older age and having underlying medical conditions were associated with increased risk of moderate-to-severe or critical COVID-19 illness among pregnant women. This information might help pregnant women understand their risk for moderate-to-severe or critical COVID-19 illness and can inform targeted public health messaging.
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SciScore for 10.1101/2021.02.27.21252169: (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 SET-NET is longitudinal surveillance of pregnant women and their infants to understand the effects of emerging and reemerging threats [6]. Table 2: Resources
Software and Algorithms Sentences Resources Analyses were conducted using SAS (version 9.4; SAS Institute). SAS Institutesuggested: (Statistical Analysis System, RRID:SCR_008567)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 …SciScore for 10.1101/2021.02.27.21252169: (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 SET-NET is longitudinal surveillance of pregnant women and their infants to understand the effects of emerging and reemerging threats [6]. Table 2: Resources
Software and Algorithms Sentences Resources Analyses were conducted using SAS (version 9.4; SAS Institute). SAS Institutesuggested: (Statistical Analysis System, RRID:SCR_008567)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:The findings in this report are subject to at least four limitations. First, the clinical criteria for classifying illness severity in this analysis were adapted for surveillance purposes from existing frameworks and used severity indicators that were captured systematically, while other criteria may not have been captured (e.g., respiratory rate and oxygen saturation on room air). Misclassification of illness severity is possible, particularly when data to classify cases into moderate-to-severe or critical illness categories are missing, which might bias towards a lower severity classification and attenuate associations [11]. Similarly, data cannot distinguish between asymptomatic or pre-symptomatic mild infection unless the individual subsequently reported for medical care and information was available in a medical record. Additionally, women who were tested upon hospital admission for delivery may have developed more severe symptoms later on that were not captured by SET-NET. Among women with date of testing and outcome available, 26% were identified within two days of delivery, which could reflect universal screening on admission. Second, a large portion of women could not be categorized for illness severity due to insufficient information, and testing and reporting might be more frequent among women with more severe illness. The ability to detect differences in demographic characteristics between included and excluded women were limited by a large portion of missing demo...
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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