Secular trends of antibacterial prescribing in UK paediatric primary care
Bibliographic record
Abstract
BACKGROUND: Resistance to antibacterial drugs can be contained by judicious prescribing. In particular, the use of these drugs in children requires ongoing surveillance. While there was a decline in antibacterial prescribing in the UK during the 1990s, recent trends are less well known. OBJECTIVES: To describe antibiotic prescribing patterns and time trends in children in the UK over the last two decades. METHODS: We identified all children ages 0-19 years from 1993 to 2007 and their antibiotic prescriptions from the General Practice Research Database. We used Poisson regression to estimate prescription rates considering the children's age and gender, calendar year and practice. RESULTS: The cohort included 1 751 645 children with 5 835 891 antibacterial prescriptions. The average prescription rate was 511 prescriptions per 1000 person-years [95% confidence interval (CI) 509-513]. As of 1995, the rate decreased to 419/1000 person-years (95% CI 411-426) in 2000, then increased to 568/1000 person-years (95% CI 559-577) in 2007. Between 2000 and 2007, rates increased on average by 4.3% (95% CI 3.7-5.0%) annually, amounting to an increase of 40.7% (95% CI 34.5-47.2%) for all children. Rates were generally higher in girls, except for boys <5 years. Broad-spectrum penicillins were most frequently prescribed; their rate increased on average by 4.6% annually (95% CI 4.0-5.3%) after 2000. This trend was similar in most classes of antibacterials. CONCLUSIONS: Antibacterial prescribing to outpatient children in the UK has been steadily increasing since 2000, consistently for boys and girls, across all ages and antibacterial classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".