Community-Based Antibiotic Prescribing Attributable to Respiratory Syncytial Virus and Other Common Respiratory Viruses in Young Children: A Population-Based Time-series Study of Scottish Children
Bibliographic record
Abstract
BACKGROUND: Inappropriate antibiotic prescribing, such as for viral illness, remains common in primary care. The objective of this study was to estimate the proportion of community-prescribed antibiotics to children aged less than 5 years attributable to common respiratory viruses. METHODS: We fitted time-series negative binomial models to predict weekly antibiotic prescribing rates from positive viral pathogen tests for the period 1 April 2009 through 27 December 2017 using comprehensive, population-based administrative data for all children (<5 years) living in Scotland. Multiple respiratory viral pathogens were considered, including respiratory syncytial virus (RSV), influenza, human metapneumovirus (HMPV), rhinovirus, and human parainfluenza (HPIV) types 1-4. We estimated the proportion of antibiotic prescriptions explained by virus circulation according to type of virus, by age group, presence of high-risk chronic conditions, and antibiotic class. RESULTS: We included data on 6 066 492 antibiotic prescriptions among 452 877 children. The antibiotic-prescribing rate among all Scottish children (<5 years) was 609.7 per 1000 child-years. Our final model included RSV, influenza, HMPV, HPIV-1, and HPIV-3. An estimated 6.9% (95% confidence interval, 5.6-8.3%), 2.4% (1.7-3.1%), and 2.3% (.8-3.9%) of antibiotics were attributable to RSV, influenza, and HMPV, respectively. RSV was consistently associated with the highest proportion of prescribed antibiotics, particularly among children without chronic conditions and for amoxicillin and macrolide prescriptions. CONCLUSIONS: Nearly 14% of antibiotics prescribed to children in this study were estimated to be attributable to common viruses for which antibiotics are not recommended. A future RSV vaccine could substantially reduce unnecessary antibiotic prescribing among children.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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".