Disease pattern and risk factors of antimicrobial resistance in patients with pneumococcal infection in the Hong Kong population
Bibliographic record
Abstract
OBJECTIVES: Antimicrobial resistance (AMR) presents significant challenges for the effective treatment of pneumococcal disease (PD), disease prevalence, and vaccine effectiveness caused by S. pneumoniae. We aimed to describe the pattern of AMR among isolates from patients with PD reported in the Hong Kong population from 2012-2021, and to explore the risk factors associated with AMR among patients hospitalized with PD compared to those with susceptible isolates. METHODS: PD-related hospitalizations were identified and grouped into invasive PD (IPD) or non-IPD patients. Electronic health records were collected to calculate the healthcare resource utilization relevant to each IPD/non-IPD patient. We compared the characteristics of patients with IPD/non-IPD caused by non-susceptible isolates (cases) and those without (controls) using a multivariable logistic regression model, looking for risk factors for AMR. RESULTS: The PD incidence trend was stable from 2012 to 2019 with a sudden decrease in 2020, coinciding with the beginning of the COVID-19 pandemic. Overall, 80% of patients had S. pneumoniae non-susceptible to ≥1 antibiotic. The percentage of non-susceptibility found to tetracyclines, macrolides, penicillin, and fluoroquinolones, were 85%, 79%, 23% and 2%, respectively. Overall, 46% of the patients with serotyping results were serotype 3. Significantly increased odds of AMR infection were found among the non-IPD patients aged 2-17 years when compared to older patients (18-64 years). CONCLUSIONS: Measures to reduce non-susceptible S. pneumoniae infections should focus on children and adolescents of school age. Despite the introduction of PCV13 in 2011, serotype 3 and AMR continued to threaten people in the community. Serotype 3-infected patients accounted for nearly half of the patients with PD with serotyping results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".