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Record W4393986258 · doi:10.1136/bmjgh-2023-014247

Effectiveness and safety of COVID-19 vaccines on maternal and perinatal outcomes: a systematic review and meta-analysis

2024· review· en· W4393986258 on OpenAlexfundno aff
Silvia Fernández-García, Laura del Campo-Albendea, Dharshini Sambamoorthi, Jameela Sheikh, Karen Lau, Nana Osei-Lah, Anoushka Ramkumar, Harshitha Naidu, Nicole Stoney, Paul Sundaram, Paulomi Sengupta, Samay Mehta, Shruti Attarde, Sophie Maddock, Millie Manning, Zainita Meherally, Kehkashan Ansari, Heidi Lawson, Magnus Yap, Tania Kew, Andriya Punnoose, Chloe Knight, Eyna Sadeqa, Jiya Cherian, Sangamithra Ravi, Wentin Chen, Kate F. Walker, Keelin O’Donoghue, Madelon van Wely, Elisabeth van Leeuwen, Elena Kostova, Heinke Kunst, Asma Khalil, Vanessa Brizuela, Edna Kara, Caron Rahn Kim, Anna Thorson, Olufemi T. Oladapo, Lynne Mofenson, Sami L. Gottlieb, Mercedes Bonet, Ngawai Moss, Javier Zamora, John Allotey, Shakila Thangaratinam

Bibliographic record

VenueBMJ Global Health · 2024
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
FundersBirmingham Biomedical Research CentreDepartment of Health and Social CareNational Institute for Health and Care ResearchAdvanced Research Projects AgencyGovernment of CanadaBundesministerium für GesundheitBarts CharityUNICEFWorld Health Organization
KeywordsMedicineReactogenicityPregnancyMeta-analysisOdds ratioCaesarean sectionCohort studyObstetricsInternal medicineImmunologyImmunization

Abstract

fetched live from OpenAlex

Objective To assess the effects of COVID-19 vaccines in women before or during pregnancy on SARS-CoV-2 infection-related, pregnancy, offspring and reactogenicity outcomes. Design Systematic review and meta-analysis. Data sources Major databases between December 2019 and January 2023. Study selection Nine pairs of reviewers contributed to study selection. We included test-negative designs, comparative cohorts and randomised trials on effects of COVID-19 vaccines on infection-related and pregnancy outcomes. Non-comparative cohort studies reporting reactogenicity outcomes were also included. Quality assessment, data extraction and analysis Two reviewers independently assessed study quality and extracted data. We undertook random-effects meta-analysis and reported findings as HRs, risk ratios (RRs), ORs or rates with 95% CIs. Results Sixty-seven studies (1 813 947 women) were included. Overall, in test-negative design studies, pregnant women fully vaccinated with any COVID-19 vaccine had 61% reduced odds of SARS-CoV-2 infection during pregnancy (OR 0.39, 95% CI 0.21 to 0.75; 4 studies, 23 927 women; I 2 =87.2%) and 94% reduced odds of hospital admission (OR 0.06, 95% CI 0.01 to 0.71; 2 studies, 868 women; I 2 =92%). In adjusted cohort studies, the risk of hypertensive disorders in pregnancy was reduced by 12% (RR 0.88, 95% CI 0.82 to 0.92; 2 studies; 115 085 women), while caesarean section was reduced by 9% (OR 0.91, 95% CI 0.85 to 0.98; 6 studies; 30 192 women). We observed an 8% reduction in the risk of neonatal intensive care unit admission (RR 0.92, 95% CI 0.87 to 0.97; 2 studies; 54 569 women) in babies born to vaccinated versus not vaccinated women. In general, vaccination during pregnancy was not associated with increased risk of adverse pregnancy or perinatal outcomes. Pain at the injection site was the most common side effect reported (77%, 95% CI 52% to 94%; 11 studies; 27 195 women). Conclusion COVID-19 vaccines are effective in preventing SARS-CoV-2 infection and related complications in pregnant women. PROSPERO registration number CRD42020178076.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.044
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0230.037
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.113
GPT teacher head0.521
Teacher spread0.408 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations36
Published2024
Admission routes1
Has abstractyes

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