Trends in shigellosis notifications in England, January 2016 to March 2023
Bibliographic record
Abstract
Abstract We reviewed all diagnoses of Shigella species notified to the UK Health Security Agency from January 2016 to March 2023. An overall increase in notifications of shigellosis was seen between 2016 (n = 415/quarter) and 2023 (n = 1 029/quarter). However, notifications dramatically declined between March 2020 and September 2021 during the COVID-19 pandemic (n = 208/quarter) highlighting the impact of travel and social distancing restrictions on transmission. S. sonnei diagnoses were more affected by lockdown restrictions than S. flexneri, most likely due to a combination of species-specific characteristics and host attributes. Azithromycin resistance continued to be associated with epidemics of sexually transmissible S. flexneri (adult males = 45.6% vs. adult females = 8.7%) and S. sonnei (adult males = 59.5% vs. adult females = 14.6%). We detected resistance to ciprofloxacin in S. sonnei from adult male cases not reporting travel at a higher frequency (79.4%) than in travel-associated cases (61.7%). Extensively drug-resistant Shigella species associated with sexual transmission among men almost exclusively had ESBL encoded by blaCTX-M-27, whereas those associated with returning travellers had blaCTX-M-15. Given the increasing incidence of infections and AMR, we recommend that enhanced surveillance is used to better understand the impact of travel and sexual transmission on the acquisition and spread of MDR and XDR Shigella species.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.006 | 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".