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Maintaining Maternal-Newborn Safety During the COVID-19 Pandemic.

ABSTRACT: COVID-19, the disease caused by the SARS-CoV-2 virus, was declared a global pandemic by the World Health Organization on March 11, 2020. In addition to older individuals and those with underlying chronic health conditions, maternal and newborn populations were also identified as being at greater risk. It became critical for hospitals and clinicians to maintain the safety of individuals in the facility and minimize the transmission of COVID-19 while continuing to strive for optimized outcomes by providing family-centered care. Rapid change during the pandemic made it appropriate to use the plan-do-study-act (PDSA) cycle to continually evaluate proposed and standard practices. Our team established an obstetric COVID-19 unit for women and newborns, developed guidelines for visitation and for the use of personal protective equipment, initiated universal COVID-19 testing, and provided health education to emphasize shared decision making.

SUBMITTER: Patrick NA

PROVIDER: S-EPMC7983449 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceThe study team previously showed that maternal mRNA COVID-19 vaccination during pregnancy confers protection against SARS-CoV-2 infection and COVID-19-related hospital admission in newborns and young infants. In this study, the study team evaluated newborn and early infant safety outcomes following maternal messenger RNA (mRNA) COVID-19 vaccination during pregnancy, for which there is limited comparative epidemiological evidence.ObjectiveTo determine if maternal mRNA COVID-19 vaccination during pregnancy is associated with adverse newborn and early infant outcomes.Design, setting, and participantsThis population-based retrospective cohort study took place in Ontario, Canada, using multiple linked health administrative databases. Singleton live births with an expected delivery date between May 1, 2021, and September 2, 2022, were included. Data were analyzed from January 2023 through March 2023.ExposureMaternal mRNA COVID-19 vaccination (1 or more doses) during pregnancy.Main outcomes and measuresSevere neonatal morbidity (SNM), neonatal death, neonatal intensive care unit (NICU) admission, neonatal readmission, and hospital admission up to 6 months of age. The study team calculated inverse probability of treatment weighted risk ratios (RRs) and fit weighted Cox proportional hazards regression models comparing outcomes in infants of mothers who received COVID-19 vaccination during pregnancy with those who received no COVID-19 vaccine doses before delivery.ResultsIn total, 142 006 infants (72 595 male [51%]; mean [SD] gestational age at birth, 38.7 [1.7] weeks) were included; 85 670 were exposed to 1 or more COVID-19 vaccine doses in utero (60%). Infants of vaccinated mothers had lower risks of SNM (vaccine exposed 7.3% vs vaccine unexposed 8.3%; adjusted RR [aRR], 0.86; 95% CI, 0.83-0.90), neonatal death (0.09% vs 0.16%; aRR, 0.47; 95% CI, 0.33-0.65), and NICU admission (11.4% vs 13.1%; aRR, 0.86; 95% CI, 0.83-0.89). There was no association between maternal vaccination during pregnancy and neonatal readmission (5.5% vs 5.1%; adjusted hazard ratio, 1.03; 95% CI, 0.98-1.09) or 6-month hospital admission (8.4% vs 8.1%; adjusted hazard ratio, 1.01; 95% CI, 0.96-1.05).Conclusions and relevanceIn this population-based cohort study in Ontario, Canada, maternal mRNA COVID-19 vaccination during pregnancy was associated with lower risks of SNM, neonatal death, and NICU admission. In addition, neonatal and 6-month readmissions were not increased in infants of mothers vaccinated during pregnancy.

Project description:Background Maternal and newborn healthcare providers are essential professional groups vulnerable to physical and psychological risks associated with the COVID-19 pandemic. This study uses machine learning algorithms to create a predictive tool for maternal and newborn healthcare providers’ perception of being safe in the workplace globally during the pandemic. Methods We used data collected between 24 March and 5 July 2020 through a global online survey of maternal and newborn healthcare providers. The questionnaire was available in 12 languages. To predict healthcare providers’ perception of safety in the workplace, we used features collected in the questionnaire, in addition to publicly available national economic and COVID-19-related factors. We built, trained and tested five machine learning models: Support Vector Machine (SVM), Random Forest (RF), XGBoost, CatBoost and Artificial Neural Network (ANN) for classification and regression. We extracted from RF models the relative contribution of features in output prediction. Results Models included data from 941 maternal and newborn healthcare providers from 89 countries. ML models performed well in classification and regression tasks, whereby RF had 82% cross-validated accuracy for classification, and CatBoost with 0.46 cross-validated root mean square error for regression. In both classification and regression, the most important features contributing to output prediction were classified as three themes: (1) information accessibility, clarity and quality; (2) availability of support and means of protection; and (3) COVID-19 epidemiology. Conclusion This study identified salient features contributing to maternal and newborn healthcare providers perception of safety in the workplace. The developed tool can be used by health systems globally to allow real-time learning from data collected during a health system shock. By responding in real-time to the needs of healthcare providers, health systems could prevent potential negative consequences on the quality of care offered to women and newborns. Supplementary Information The online version contains supplementary material available at 10.1186/s12960-022-00758-5.

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Maintaining Maternal-Newborn Safety During the COVID-19 Pandemic.

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