The burden of infant group B streptococcal infections in Ontario: Analysis of administrative data to estimate the potential benefits of new vaccines
Bibliographic record
Abstract
Group B streptococcus (GBS) is a leading bacterial cause of neonatal sepsis and meningitis in many countries as well as an important cause of disease in pregnant women. Currently, serotype-specific conjugate vaccines are being developed. We conducted an epidemiological analysis of health administrative data to estimate the burden of infant GBS disease in Ontario, Canada and combined these estimates with literature on serotype distribution to estimate the burden of disease likely to be vaccine-preventable. Between 1st January 2005 and 31st December 2015, 907 of 64320 health care encounters in Ontario in patients under 1 year old had codes specifically identifying GBS as the cause of the disease, of which 717 were under one month of age. In addition, application of epidemiological data to the remaining patients allowed us to estimate a further 2322 cases and among them 1822 were under one month of age. In the same period, 579 confirmed neonatal invasive GBS cases in patients up to one month of age were reported to public health. Depending on serotype distribution, vaccination coverage and early versus late onset disease (0-6 days and 7-90 days of age respectively), the preventable fraction ranged widely. With a vaccine that is 90% effective and 60% immunization coverage, up to 52% of early and late onset disease could be prevented by forthcoming vaccines. GBS is under-reported in Ontario. Uncertainty about the potential impact of vaccine indicates that further analysis and research may be needed to prepare for policy-decision making, including clinical validation studies and an economic evaluation of GBS vaccination in Ontario.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".