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Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach

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  • Isadora Celine Rodrigues Carneiro

    (Instituto Carlos Chagas, Fundação Oswaldo Cruz, Curitiba 81310-020, Brazil)

  • Sofia Galvão Feronato

    (Instituto Carlos Chagas, Fundação Oswaldo Cruz, Curitiba 81310-020, Brazil)

  • Guilherme Ferreira Silveira

    (Instituto Carlos Chagas, Fundação Oswaldo Cruz, Curitiba 81310-020, Brazil)

  • Alexandre Dias Porto Chiavegatto Filho

    (Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo 01246-904, Brazil)

  • Hellen Geremias dos Santos

    (Instituto Carlos Chagas, Fundação Oswaldo Cruz, Curitiba 81310-020, Brazil)

Abstract

COVID-19 has been widely explored in relation to its symptoms, outcomes, and risk profiles for the severe form of the disease. Our aim was to identify clusters of pregnant and postpartum women with severe acute respiratory syndrome (SARS) due to COVID-19 by analyzing data available in the Influenza Epidemiological Surveillance Information System of Brazil (SIVEP-Gripe) between March 2020 and August 2021. The study’s population comprised 16,409 women aged between 10 and 49 years old. Multiple correspondence analyses were performed to summarize information from 28 variables related to symptoms, comorbidities, and hospital characteristics into a set of continuous principal components (PCs). The population was segmented into three clusters based on an agglomerative hierarchical cluster analysis applied to the first 10 PCs. Cluster 1 had a higher frequency of younger women without comorbidities and with flu-like symptoms; cluster 2 was represented by women who reported mainly ageusia and anosmia; cluster 3 grouped older women with the highest frequencies of comorbidities and poor outcomes. The defined clusters revealed different levels of disease severity, which can contribute to the initial risk assessment of the patient, assisting the referral of these women to health services with an appropriate level of complexity.

Suggested Citation

  • Isadora Celine Rodrigues Carneiro & Sofia Galvão Feronato & Guilherme Ferreira Silveira & Alexandre Dias Porto Chiavegatto Filho & Hellen Geremias dos Santos, 2022. "Clusters of Pregnant Women with Severe Acute Respiratory Syndrome Due to COVID-19: An Unsupervised Learning Approach," IJERPH, MDPI, vol. 19(20), pages 1-13, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13522-:d:946728
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    References listed on IDEAS

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    1. Caroline Benski & Daria Di Filippo & Gianmarco Taraschi & Michael R. Reich, 2020. "Guidelines for Pregnancy Management During the COVID-19 Pandemic: A Public Health Conundrum," IJERPH, MDPI, vol. 17(21), pages 1-17, November.
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