key: cord-0929753-labj5l5i
authors: Palmsten, Kristin; Vazquez‐Benitez, Gabriela; Kharbanda, Elyse O.
title: Point: Uncertainty about estimating the risks of COVID‐19 during pregnancy
date: 2021-07-13
journal: Paediatr Perinat Epidemiol
DOI: 10.1111/ppe.12773
sha: e399a1840e4a4668d6ac42ff21b8d2489055622e
doc_id: 929753
cord_uid: labj5l5i

nan

Several observational studies have described the risks of adverse perinatal outcomes following infection during pregnancy with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease . These studies have used retrospective data, typically recorded during health-care encounters. [1] [2] [3] Existing healthcare data are attractive for studying urgent public health questions, including risks associated with COVID-19 during pregnancy, because they can be easily accessed, and may provide rapid results in large populations. For example, using a hospital-based all-payer database of hospitalisations, Jering et al 1 recently compared in-hospital outcomes of 6380 pregnant women giving birth who had a COVID-19 diagnosis versus 400 066 who did not from April-November 2020. Among women with a COVID-19 diagnosis, increased odds of many adverse outcomes were reported including preterm birth (adjusted odds ratio [ Given these factors, differential exposure misclassification should be evaluated in studies relying on COVID-19 diagnosis as has been suggested for studies of maternal influenza and birth outcomes. 7 For example, it is plausible that women with preeclampsia would have more healthcare contacts and thus be more likely to be tested for COVID-19 and correctly classified, as compared to women without preeclampsia. Sensitivity analysis 8 demonstrates that the unadjusted OR of 1.36 reported for preeclampsia in Jering et al 1 could be attenuated to 1.03 upon correction for differential exposure misclassification, assuming correct classification of those with COVID-19 is 90% in women with preeclampsia and is 70% in women without preeclampsia, and that women without COVID-19 are correctly classified.

The biologic plausibility and temporality of a positive association between COVID-19 diagnosis at delivery and preeclampsia should also be considered. Although a symptomatic SARS-CoV-2 infection would lead to a pro-inflammatory state that could increase the preeclampsia risk, it is less clear how an asymptomatic infection would contribute to the development of preeclampsia. It is possible that some women with COVID-19 diagnosed at delivery hospitalisation had subclinical disease onset, that is, placental vascular changes, or even met criteria for preeclampsia before infection. Therefore, reverse causation of preeclampsia resulting in a greater propensity for symptomatic COVID-19 or in differential detection of COVID-19 could be an explanation of an association between COVID-19 diagnosed near delivery and preeclampsia.

Selection bias is a potential concern for studies focusing on COVID-19 at delivery as only pregnancies surviving until delivery are included. Severe disease may cause maternal mortality before delivery, 2 and the impact of COVID-19 on pregnancy loss is unclear.

Another concern for observational studies of COVID-19 and adverse perinatal outcomes is confounding, or residual confound- Moreover, risks should be estimated according to COVID-19 severity. Otherwise, overall risk estimates may depend on the severity case mix. Supporting this, a study comparing asymptomatic pregnant women at delivery who were SARS-CoV-2 positive with those who were SARS-CoV-2 negative reported no increased risk for preterm birth, although results were unadjusted for confounders. 13 Studies with a greater share of asymptomatic infections in their 'exposed' group may produce weaker or null associations when assessing any infection as compared to those with a smaller proportion of infections that are asymptomatic, assuming a greater impact of symptomatic versus asymptomatic infections on perinatal outcomes. As we are in the early phases of understanding the relationship between SARS-CoV-2 infections and pregnancy outcomes, it is even more important to stratify risk estimates by symptomatic and asymptomatic infection.

Although not without limitations, for example, volunteer bias, large prospective studies that carefully collect information on potential confounders and use serology testing throughout pregnancy to capture infection status, such as Assessing the Safety of Pregnancy in the Coronavirus pandemic (ASPIRE) study, 14 the duty to advocate for pregnant women by demonstrating their unique risk, and the concern from personal experience caring for pregnant patients with severe COVID-19, and that these factors can unconsciously impact how data are analyzed, or how results are interpreted. As Spiegelhalter and Riesch stated in their 2011 article on assessing and communicating uncertainties in the analysis of risks, "Communicate the estimates with humility, communicate the uncertainty with confidence." 15 When biases in studies of COVID-19 during pregnancy and adverse perinatal outcomes cannot be minimized through study design and data collection, sensitivity analyses to evaluate the magnitude of misclassification, confounding, and selection bias in measures of association 8, 16 can help us communicate uncertainty in our results with confidence. Journal editors can facilitate this process by requiring such sensitivity analyses.

No conflicts of interest to disclose.

https://orcid.org/0000-0002-0134-6228

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