Introduction: Evidence about COVID-19 and pregnancy has rapidly increased since December 2019, making it difficult to make rigorous evidence-based decisions. The objective of this overview of systematic reviews is to conduct a comprehensive analysis of the current evidence on prognosis of COVID-19 in pregnant women. Material and methods: We used Living OVerview of Evidence (L•OVE) platform for COVID-19, which continually retrieves studies from 46 data sources (including Pubmed/MEDLINE, Embase, other electronic databases, clinical trials registries, preprint repositories, among other sources relevant to COVID-19), mapping them into PICO questions. The search covered the period from the inception date of each database to September 13, 2020. We included systematic reviews assessing outcomes of pregnant women with COVID-19 and/or their newborns. Two authors independently screened the titles and abstracts, assessed full-texts to select the studies that met the inclusion criteria, extracted data, and appraised the risk of bias of each included systematic review. We measured the overlap of primary studies included among the selected systematic reviews by building a matrix of evidence, calculating the corrected covered area, and assessing the level of overlapping for every pair of systematic reviews. Results: Our search yielded 1132 references. 52 systematic reviews met inclusion criteria and were included in this overview. Only one review had a low risk of bias, three had an unclear risk of bias, and 48 had a high risk of bias. Most of the included reviews were highly overlapped among each other. In the included reviews, rates of maternal death varied from 0% to 11.1%, admission to intensive care from 2.1% to 28.5%, preterm deliveries before 37 weeks from 14.3% to 61.2%, and cesarean delivery from 48.3% to 100%. Regarding neonatal outcomes, neonatal death varied from 0% to 11.7% while the estimated infection status of the newborn varied between 0% and 11.5%. Conclusions: Only one of 52 systematic reviews had a low risk of bias. Results were heterogenous and the overlap of primary studies was frequently very high between pairs of systematic reviews. High quality evidence syntheses of comparative studies are needed to guide future clinical decisions.

Background Systematic reviews allow health decisions to be informed by the best available research evidence. However, their number is proliferating quickly, and many skills are required to identify all the relevant reviews for a specific question. Methods and findings We screen 10 bibliographic databases on a daily or weekly basis, to identify systematic reviews relevant for health decision-making. Using a machine-based approach developed for this project we select reviews, which are then validated by a network of more than 1000 collaborators. After screening over 1,400,000 records we have identified more than 300,000 systematic reviews, which are now stored in a single place and accessible through an easy-to-use search engine. This makes Epistemonikos the largest database of its kind. Conclusions Using a systematic approach, recruiting a broad network of collaborators and implementing automated methods, we developed a one-stop shop for systematic reviews relevant for health decision making.

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This article belongs to a collaborative methodological series of narrative reviews about biostatistics and clinical epidemiology. The goal is to present basics concepts concerning the systematics reviews of multiple treatments comparisons with network meta-analysis. For clinical ques-tions with several therapeutic alternatives to be compared, the central question is how to classify or rank their effectiveness (benefit and harm) to choose the best option. The network meta-analysis aims to answer questions related to the effectiveness and safety of comparing multiple treatments by the simultaneous analysis of results raised from direct and indirect comparisons. The network geometry is the general graphical representation of the network meta-analysis and allows to understand and assess the strength of comparisons. The network meta-analysis should check several assumptions to be valid, especially the transitivity assumption, which allows assuming that there are no systematic differences among the included comparisons, except their compared interventions. Thus, it is possible to know the relative therapeutic effectiveness of each pair of interventions included in the network meta-analysis and their ranking in terms of categorization. It has been proposed to use a modified Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach considering the distinctive features of network meta-analysis to assess the certainty of the evidence for each comparison and the ranking of interventions.

Background and aims The efficacy of using gloves by the general population to prevent COVID‐19 is unknown. We aim to determine the efficacy of routine glove use by the general healthy population in preventing COVID‐19. This is the protocol of a living systematic review. Methods We adapted an already published common protocol for multiple parallel systematic reviews to the specificities of this question. We will conduct searches in PubMed/Medline, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), grey literature, and in a centralized repository in L·OVE (Living OVerview of Evidence). L·OVE is a platform that maps PICO questions to evidence from Epistemonikos database. In response to the COVID‐19 emergency, L·OVE was adapted to expand the range of evidence it covers and customized to group all COVID‐19 evidence in one place. The search will cover the period until the day before submission to a journal. We will include randomized trials evaluating the effect of use of gloves in healthy population to prevent COVID‐19 disease. Randomized trials evaluating the effect of use of gloves during outbreaks caused by MERS‐CoV and SARS‐CoV, and nonrandomized studies in COVID‐19 will be searched in case no direct evidence from randomized trials is found. Two reviewers will independently screen each study for eligibility, extract data, and assess the risk of bias. We will perform random‐effects meta‐analyses and use GRADE to assess the certainty of the evidence for each outcome. A living, web‐based version of this review will be openly available during the COVID‐19 pandemic. We will resubmit it if the conclusions change or there are substantial updates.