Associations between declining antimicrobial use in primary care in Scotland and patient satisfaction and hospitalisation due to infection: a longitudinal study of greater than five million patients
Bibliographic record
Abstract
Background. Scottish antimicrobial prescribing in the community has fallen since 2012, but this could have unintended consequences. The aim was to examine associations between changes in antibiotic prescribing in primary care and hospital admissions and patient satisfaction. Methods. Data for 877 Scottish general practices with 5.1 million patients were provided by NHS National Services Scotland. Practices were classified into four equal groups (quartiles) in terms of change in total antibiotic prescribing (rate/1000 registered patients in each quarter) 2012-2018. Changes in hospital admission with infection were examined comparing the four groups. Multivariate regression examined associations between change in antibiotic prescribing and patient satisfaction with the practice using national survey data. Results. Across Scotland, primary care antibiotic prescribing decreased by 15% from 194.1 (95%CI 193.8-194.4) in Q1 2012 to 165.3 (95%CI 165.0-165.6 ) in Q2 2012, with considerable variation between practices (non-significant increase of 0.22 prescriptions/1000/quarter [p=0.49] for the quartile of practices with least reduction in antibiotic prescribing, vs reduction of -2.95 prescriptions/1000/quarter [p<0.001] for quartile with the largest reduction). Rates of hospital admissions with infection increased over the time period but there were no significant association with changes in antibiotic prescribing. Patient satisfaction decreased over the period, but change in antibiotic prescribing was not associated with patient satisfaction. Conclusion. There have been clinically significant reductions in Scottish primary care antibiotic use since 2012, varying considerably between practices. Longitudinal analysis of Scotland-wide practice level data found no associations between practice-level reductions in primary care antibiotic prescribing and hospital admissions or patient satisfaction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".