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Record W6963352340 · doi:10.20381/ruor-31261

The Health and Economic Impacts of Group B Streptococcus Disease Among Infants in Ontario, Canada

2025· dissertation· en· W6963352340 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2025
Typedissertation
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsnot available
Fundersnot available
KeywordsDiseaseDisease burdenIncidence (geometry)EpidemiologyPublic healthPregnancyGroup BEconomic impact analysisInfant mortalityImmunization

Abstract

fetched live from OpenAlex

Group B Streptococcus (GBS) remains a leading cause of neonatal morbidity and mortality worldwide, despite preventive measures such as intrapartum antibiotic prophylaxis (IAP). Globally, an estimated 20 million pregnant individuals are colonized with GBS each year, leading to nearly 400,000 cases of infant GBS disease and over 90,000 infant deaths annually. While routine screening using rectovaginal swabs in the late third trimester and IAP have reduced the incidence of early-onset GBS disease (infection occurring within the first 6 days of life) in high-income countries like Canada, current strategies have important limitations: they are less effective against later-onset GBS disease (beyond the first week of life), may miss cases due to false-negative screening results or changes in maternal colonization status, and have limited impact on GBS-associated preterm births and stillbirths. Moreover, despite the widespread adoption of these prevention strategies in Canada, studies evaluating their effectiveness remain sparse, often relying on outdated or regionally limited data with small sample sizes. Important gaps also exist in fully understanding the true burden of GBS disease, its long-term neurodevelopmental consequences, and its broader economic impact. As maternal immunization with a GBS vaccine is being explored as a potential future prevention strategy, robust epidemiological and health economic data are critically needed to support vaccine development, inform policy decisions, and guide future implementation efforts. This dissertation aimed to address these gaps by evaluating the epidemiological, clinical, and economic burden of infant GBS disease in Ontario, Canada. Leveraging real-world, population-wide health administrative and registry data, this research pursued three key objectives: (1) to estimate the burden of infant GBS disease, describe epidemiological trends, and assess the effectiveness of current prevention strategies; (2) to evaluate the risk of neurodevelopmental impairments (NDIs) in early childhood following infant GBS disease, while examining sex and prematurity as effect modifiers; and (3) to determine the healthcare costs associated with GBS disease in infancy. The findings from this dissertation highlight the substantial health and economic burden of infant GBS disease in Ontario and reveal ongoing challenges with current prevention efforts. In the first study, despite demonstrating high maternal screening coverage, we identified missed opportunities for IAP administration, and the incidence of later-onset disease remained unchanged across the five-year study period. The second study found that survivors of infant GBS disease had approximately twice the risk of developing neurodevelopmental impairments by five years of age, with particularly elevated risks among preterm infants and males. The last study demonstrated that infant GBS disease was associated with substantial short-term healthcare costs, particularly during the first 30 days after disease onset. Collectively, these findings can support clinical and public health planning, inform cost-effectiveness analyses, and contribute to evidence-based policy development as Canada and other jurisdictions explore the potential introduction of maternal GBS vaccination in the future, should a vaccine become available, licensed, and recommended for use.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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