Pneumococcal infections and homelessness
Bibliographic record
Abstract
Objective: To assess the prevalence of pneumococcal nasopharyngeal carriage, the role of potential risk factors, and the pneumococcal vaccination coverage among sheltered homeless people in Marseille, France. Methods: During the winters 2015-2018, we enrolled 571 sheltered homeless males and 54 non-homeless controls. Streptococcus pneumoniae was directly searched from nasal/pharyngeal samples using real-time polymerase chain reaction. Results: The homeless people were mostly migrants from African countries, with a mean age of 43 years. Pneumococcal vaccination coverage was low (3.1%). The overall pneumococcal carriage rate was 13.0% and was significantly higher in homeless people (15.3% in 2018) than in controls (3.7%), with p = 0.033. Among homeless people, being aged ≥ 65 years (1.97, 95% CI; 1.01-3.87), living in a specific shelter (OR = 1.80, 95% CI: 1.06-3.05), and having respiratory signs and symptoms at the time of enrolment (OR = 2.55, 95% CI: 1.54-4.21) were independently associated with pneumococcal carriage. Conclusion: Pneumococcal nasopharyngeal carriage, which is a precursor for pneumococcal disease in at-risk individuals, is frequent among French homeless people. Studies conducted in other countries have also reported outbreaks of pneumococcal infections in homeless people. Pneumococcal vaccination should be systematically considered for sheltered homeless people in France, as is being done in Canada since 2008.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".