Trends of serotypes and resistance among <i>Streptococcus pneumoniae</i> in the UK and Ireland (1999–2019)
Bibliographic record
Abstract
OBJECTIVES: This study aimed to report the serotype distribution of Streptococcus pneumoniae isolates from UK and Irish patients with bacteraemia or community-associated lower respiratory tract infections (CA-LRTI). Depending upon the year, these were from 23 to 39 sentinel laboratories and were collected between 1999 and 2019, thus spanning the introduction of pneumococcal conjugate vaccines, PCV7 and PCV13. METHODS: Pneumococcal identification, susceptibility testing and serotyping were undertaken by a central laboratory. Changes in serotype distributions and among the predominant types showing antibiotic non-susceptibility were reviewed in relation to vaccine deployment. RESULTS: Following the introduction of PCV7 (2006) and PCV13 (2010), major shifts occurred in serotype prevalence for both bacteraemia and CA-LRTI. PCV7 types and most PCV13 types (but not 3 and 19A) were largely or wholly displaced. Many of the displaced types (e.g. 6B, 9V, 14, 19F and 23B) had been internationally prevalent and were associated with antibiotic resistance. Other serotypes-many included within the older pneumococcal polysaccharide vaccine, PPV23-expanded into the space, with serotype 8 becoming especially prominent in bacteraemia, though not respiratory infections. Further increasingly prevalent types included 9N, 10A, 12F and 22F. Serotype 15A, often multi-resistant, rose then fell in relative importance after deployment of PCV13. Among the currently most prevalent types, serotype 3 is rarely resistant to agents besides tetracyclines and bloodstream serotype 8 isolates mostly are fully susceptible. CONCLUSIONS: These data offer a comparison of serotypes associated with bacteraemia and respiratory disease over two decades in the UK and Ireland.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".