Risk Factors for Carriage of Antibiotic-resistant Bacteria in Healthy Children in the Community
Bibliographic record
Abstract
BACKGROUND: In addition to health care settings, antibiotic resistance has also been increasing in the community. Healthy children represent an important potential reservoir of antibiotic-resistant (AR) bacteria. However, strategies to reduce the spread of AR bacteria often fail to specifically address the factors that promote the carriage of AR bacteria in this population.The objective of this review was to Identify risk factors for carriage of AR bacteria by healthy children. METHODS: We did a systematic search of MEDLINE, Embase and PubMed for studies in developed (OECD) countries that assessed risk factors for carriage of AR bacteria in healthy children in the community. We excluded studies done before 1998 and studies of AR Streptococcus pneumoniae carriage in the absence of pneumococcal conjugate vaccination. RESULTS: Of 1234 studies identified, 30 were eligible for inclusion. These studies assessed the impact of 49 risk factors on AR strains of S. pneumoniae, Haemophilus influenzae, Staphylococcus aureus, Streptococcus pyogenes and Escherichia coli. The majority of these risk factors were assessed in 2 or fewer studies per bacteria. Recent antibiotic consumption was associated with carriage of resistant respiratory bacteria (S. pneumoniae, H. influenzae); however, it was not consistently associated with carriage of AR bacteria in skin or stool (S. aureus and E. coli). For AR S. aureus, transmission within households appeared to have a greater impact than individual antibiotic use. CONCLUSIONS: The factors that promote carriage of AR bacteria by healthy children differed between bacterial species. To reduce reservoirs of AR bacteria in the community, it is essential for intervention strategies to target the specific risk factors for different bacteria.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".