Immunological and Clinical Benefits of Maternal Immunization Against Pertussis: A Systematic Review
Bibliographic record
Abstract
Infants are vulnerable to pertussis infection particularly before initiation of pertussis vaccination. Maternal pertussis vaccination during pregnancy has been introduced in a number of countries in order to confer on young infants indirect protection from the disease through transplacental transfer of maternal antibodies. We reviewed the evidence on the immunogenicity and efficacy of maternal pertussis vaccination during pregnancy. A systematic search of PubMed/MEDLINE, EMBASE, Scopus, Cochrane Database of Systematic Reviews, ProQuest, and Science Direct was undertaken to identify studies published between January 1995 and December 2018. This review was not specific to any particular pertussis vaccine but included applicable data on available pertussis vaccines administered to pregnant women. The search identified 40 publications for inclusion in this review. Vaccination during pregnancy elicited robust maternal immune responses against all vaccine antigens and resulted in high placental transfer of pertussis antibodies to the infant that persisted well beyond delivery. Vaccination during the second or early third trimesters was considered ideal for antibody quantity and functionality. Although blunting of immune responses to some antigens in the primary immunization series was documented in neonates born to women vaccinated during pregnancy, there was no apparent adverse effect on vaccine efficacy. Multiple studies conducted in diverse settings have confirmed the effectiveness of maternal pertussis vaccination during pregnancy in preventing pertussis in infants prior to receipt of their first primary vaccine dose and beyond. These findings collectively underscore the value of maternal pertussis vaccination during pregnancy in protecting vulnerable infants too young to be vaccinated.Funding Sanofi Pasteur.Plain Language Summary Plain language summary available for this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".