Epidemiology of Group B <i>Streptococcus</i>: Maternal Colonization and Infant Disease in Kampala, Uganda
Bibliographic record
Abstract
Abstract Background Child survival rates have improved globally, but neonatal mortality due to infections, such as group B Streptococcus (GBS), remains a significant concern. The global burden of GBS-related morbidity and mortality is substantial. However, data from low and middle-income countries are lacking. Vaccination during pregnancy could be a feasible strategy to address GBS-related disease burden. Methods We assessed maternal rectovaginal GBS colonization and neonatal disease rates in a prospective cohort of 6062 women–infant pairs. Surveillance for invasive infant disease occurred in parallel at 2 Kampala hospital sites. In a nested case-control study, we identified infants <90 days of age with invasive GBS disease (iGBS) (n = 24) and healthy infants born to mothers colonized with GBS (n = 72). We measured serotype-specific anticapsular immunoglobulin G (IgG) in cord blood/infant sera using a validated multiplex Luminex assay. Results We found a high incidence of iGBS (1.0 per 1000 live births) within the first 90 days of life across the surveillance sites, associated with a high case fatality rate (18.2%). Maternal GBS colonization prevalence was consistent with other studies in the region (14.7% [95% confidence interval, 13.7%–15.6%]). IgG geometric mean concentrations were lower in cases than controls for serotypes Ia (0.005 vs 0.12 µg/mL; P = .05) and III (0.011 vs 0.036 µg/mL; P = .07) and in an aggregate analysis of all serotypes (0.014 vs 0.05 µg/mL; P = .02). Conclusions We found that GBS is an important cause of neonatal and young infant disease in Uganda and confirmed that maternally derived antibodies were lower in early-onset GBS cases than in healthy exposed controls.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".