The impact of the COVID‐19 pandemic on antibiotic consumption and prevalence of pathogens in primary and secondary healthcare settings in Northern Ireland
Bibliographic record
Abstract
AIM: To evaluate the impact of the COVID-19 pandemic on the patterns of antimicrobial use and the incidence of pathogens in primary and secondary healthcare settings in Northern Ireland. METHODS: Data were collected on antibiotic use and Gram-positive and Gram-negative pathogens from primary and secondary healthcare settings in Northern Ireland for the period before (January 2015-March 2020) and during (April 2020-December 2021) the pandemic. Time series intervention analysis methods were utilized. RESULTS: In the hospital setting, the mean total hospital antibiotic consumption during the pandemic was 1864.5 defined daily doses (DDDs) per 1000 occupied-bed days (OBD), showing no significant change from pre-pandemic (P = .7365). During the pandemic, the use of second-generation cephalosporins, third-generation cephalosporins, co-amoxiclav and levofloxacin increased, there was a decrease in the percentage use of the hospital Access group (P = .0083) and an increase in the percentage use of Watch group (P = .0040), and the number of hospital Klebsiella oxytoca and methicillin-susceptible Staphylococcus aureus cases increased. In primary care, the mean total antibiotic consumption during the COVID-19 pandemic was 20.53 DDDs per 1000 inhabitants per day (DID), compared to 25.56 DID before the COVID-19 pandemic (P = .0071). During the pandemic, there was a decrease in the use of several antibiotic classes, an increase in the percentage use of the Reserve group (P = .0032) and an increase in the number of community-onset Pseudomonas aeruginosa cases. CONCLUSION: This study provides details of both changes in antibiotic consumption and the prevalence of infections in hospitals and primary care before and during the COVID-19 pandemic that emphasize the importance of antimicrobial stewardship in pandemic situations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".