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Record W7038394765

Influenza illness and influenza vaccination during pregnancy and risk of preterm birth and fetal death

2016· dissertation· en· W7038394765 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial impacts of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsVaccinationPregnancyInfluenza vaccinePandemicIncidence (geometry)Cohort studyPopulationFetusImmunization
DOInot available

Abstract

fetched live from OpenAlex

Pregnant women are considered a high-risk group for serious influenza illness and influenza-related complications. The World Health Organization and many high-income countries currently advise vaccination of pregnant women with inactivated influenza vaccine in any trimester. Although the primary goal of influenza vaccine recommendations is to directly protect pregnant women from influenza disease, recent observational studies have suggested that maternal influenza immunization could additionally protect against adverse pregnancy outcomes such as preterm birth and fetal death. The biologic plausibility of such findings depends on there being an adverse effect of maternal influenza disease on fetal health, but high‐quality evidence for this association is lacking. The overall aim of my doctoral research was to explore the risk of preterm birth and fetal death in relation to maternal influenza illness and maternal influenza vaccination during pregnancy, with emphasis on the 2009 H1N1 influenza pandemic.The first objective of this thesis was to summarize, through a systematic evidence review, comparative studies evaluating fetal death or preterm birth associated with influenza vaccination during pregnancy. We were unable to perform meta-analyses due to high clinical and statistical heterogeneity, but found that while most studies reported no association between preterm birth or fetal death and influenza vaccination during pregnancy, several reported significant risk reductions. The second objective of this thesis was to assess the association between 2009 pandemic H1N1 (pH1N1) influenza illness during pregnancy and perinatal outcomes using a retrospective cohort study design which accounted for the time-dependent nature of influenza and changing incidence of perinatal outcomes. In the overall obstetrical population of Ontario, there was no association between clinically-diagnosed pH1N1 influenza and preterm birth or spontaneous preterm birth, but among women with pre-existing medical conditions such as asthma, a diagnosis of influenza was associated with increased risk of preterm birth (adjusted hazard ratio [aHR]=1.54, 95% confidence interval [CI]: 1.09–2.17) and spontaneous preterm birth (aHR=1.72, 95% CI: 1.12–2.65), compared with unexposed pregnancy time. Motivated by limitations in individual-level measures of influenza illness and by the distinct temporal features of influenza viral activity, the third objective was to assess the association between an ecologic measure of influenza virus circulation and short-term variation in population-level rates of adverse perinatal outcomes using a time-series study design. Across a ten-year period in Ontario, the rate of preterm birth was not associated with circulating influenza in the week preceding birth (adjusted rate ratio: 1.01, 95% CI: 1.00–1.02), nor with the level of circulating influenza during the first month of gestation. Collectively, the results from this thesis suggest that influenza disease is not a major contributor to preterm birth in the Ontario obstetrical population, including during the 2009 H1N1 pandemic, when the health of pregnant women was of unprecedented high concern. High-quality data on the relationship between maternal influenza disease and adverse perinatal outcomes are critical for clarifying expectations for improved perinatal outcomes following maternal influenza immunization. Although influenza immunization during pregnancy is efficacious in preventing influenza disease in mothers and their newborns, considering the multifactorial etiology of adverse outcomes such as preterm birth, low prevalence of influenza during pregnancy and lack of consistent evidence that fetal health is adversely affected by maternal influenza disease, immunization would not be expected to produce a large improvement in perinatal outcomes to the extent suggested by some studies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes1
Has abstractyes

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