Pneumococcal Colonization and the Nasopharyngeal Microbiota of Children in Botswana
Bibliographic record
Abstract
BACKGROUND: Nasopharyngeal colonization precedes infections caused by Streptococcus pneumoniae. A more detailed understanding of interactions between S. pneumoniae and the nasopharyngeal microbiota of children could inform strategies to prevent pneumococcal infections. METHODS: We collected nasopharyngeal swabs from children 1 to 23 months of age in Botswana between August 2012 and June 2016. We tested samples for S. pneumoniae and common respiratory viruses using polymerase chain reaction. We sequenced the V3 region of the bacterial 16S ribosomal RNA gene and used random forest models to identify clinical variables and bacterial genera that were associated with pneumococcal colonization. RESULTS: Mean age of the 170 children included in this study was 8.3 months. Ninety-six (56%) children were colonized with S. pneumoniae. Pneumococcal colonization was associated with older age (P = 0.0001), a lack of electricity in the home (P = 0.02) and household use of wood as a cooking fuel (P = 0.002). Upper respiratory symptoms were more frequent in children with S. pneumoniae colonization (60% vs. 32%; P = 0.001). Adjusting for age, nasopharyngeal microbiota composition differed in colonized and noncolonized children (P = 0.001). S. pneumoniae colonization was associated with a higher relative abundance of Moraxella (P = 0.001) and lower relative abundances of Corynebacterium (P = 0.001) and Staphylococcus (P = 0.03). A decision tree model containing the relative abundances of bacterial genera had 81% sensitivity and 85% specificity for the determination of S. pneumoniae colonization status. CONCLUSIONS: S. pneumoniae colonization is associated with characteristic alterations of the nasopharyngeal microbiota of children. Prospective studies should determine if nasopharyngeal microbial composition alters the risk of pneumococcal colonization and thus could be modified as a novel pneumonia prevention strategy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".