Neonatal and Maternal Outcome of COVID-19 positive women in Sri Lanka: Secondary Analysis using National COVID-19 Positive Pregnant Women Surveillance

  1. Malith Kumarasinghe
  2. Kaushalya Kasturiaratchi
  3. Hemali Jayakody
  4. Shakira Irfaan
  5. Wasana Samarasinghe
  6. Harendra Dassanayake
  7. Sanjeeva Godakandage
  8. Chithramalee de Silva

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Abstract

Objectives

This study aims to describe the population level data on neonatal and maternal outcomes of COVID-19 positive pregnant women of Sri Lanka by secondary analysis using National COVID-19 Positive Pregnant Women Surveillance.

Design

Secondary analysis of surveillance data from the National COVID-19 positive pregnant women surveillance, Sri Lanka. Data of all pregnant women whose maternal and neonatal outcomes were reported in National Surveillance from 1st March 2020 to 31st October 2021 were included in the study. Associated factors for maternal and neonatal outcomes, namely POA at delivery, mode of delivery, birthweight, immediate place of newborn care, congenital abnormalities, and condition of neonate at completion of one month were calculated using univariate and multivariate Odds ratios.

Results

Maternal COVID-19 infection reported preterm birth rate of 11.9%, LSCS rate of 54.5%, low birthweight rate16.5% and 8.3% of the newborns requiring intensive care. Neonatal mortality rate was 9 per 1000 live births. Pre-pregnancy overweight and obesity increased the risk of preterm delivery compared to pregnant women with normal BMI by 46.7% (AOR=1.467, CI=1.111-1.938, P=0.007). In contrast, the risk of preterm delivery reduced by 82.4% (AOR=0.176, CI=0.097-0.317, p<0.001) and presence of any type of congenital abnormalities in newborns by 72.4% among the COVID-19 positive women who required only inward treatment in comparison to women with severe COVID-19 infection requiring intensive care (AOR=0.276, CI=0.112-0.683, p=0.005).

Conclusion

Increased severity of maternal COVID-19 infection and pre-pregnancy overweight/ obesity were associated with many adverse pregnancy and neonatal outcomes. Therefore, close observation and aggressive management of COVID-19 among the pregnant women should be considered to reduce the risk of progressing to severe illness.

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  1. ScreenIT

    SciScore for 10.1101/2022.02.10.22270732: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableSecondary analysis was conducted using the data available at National COVID-19 positive pregnant women surveillance in Sri Lanka.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data was initially transferred to MS Excel software; they were coded and analysed using Statistical Package for Social Sciences (SPSS) 22 version.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

    About 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 checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2022.02.10.22270732v1 on medRxiv