The changing epidemiology of group B streptococcus bloodstream infection: a multi-national population-based assessment
Bibliographic record
Abstract
BACKGROUND: Population-based studies conducted in single regions or countries have identified significant changes in the epidemiology of invasive group B streptococcus (GBS) infection. However, no studies have concurrently compared the epidemiology of GBS infections among multiple different regions and countries over time. The study objectives were to define the contemporary incidence and determinants of GBS bloodstream infection (BSI) and assess temporal changes in a multi-national population. METHODS: Population-based surveillance for GBS BSI was conducted in nine regions in Australia, Canada, Denmark, Sweden, Finland and the UK during 2000-2010. Incidence rates were age- and gender-standardised to the EU population. RESULTS: During 114 million patient-years of observation, 3464 cases of GBS BSI were identified for an overall annual incidence of 3.4 patients per 100,000 persons. There were marked differences in the overall (range = 1.8-4.1 per 100,000 person-year) and neonatal (range = 0.19-0.83 per 1000 live births) incidences of GBS BSI observed among the study regions. The overall incidence significantly (p = 0.05) increased. Rates of neonatal disease were stable, while the incidence in individuals older than 60 years doubled (p = 0.003). In patients with detailed data (n = 1018), the most common co-morbidity was diabetes (25%). During the study period, the proportion of cases associated with diabetes increased. CONCLUSIONS: While marked variability in the incidence of GBS BSI was observed among these regions, it was consistently found that rates increased among older adults, especially in association with diabetes. The burden of this infection may be expected to continue to increase in ageing populations worldwide.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".