Estimating the Proportion of Antibiotics Attributable to Common Paediatric Respiratory Viruses: An Example Leveraging Unique Population-Based Prescribing and Laboratory Data
Bibliographic record
Abstract
IntroductionInappropriate antibiotic prescribing, such as that for viral illness, is common in primary care. This is of growing interest given concerns around antimicrobial resistance and harms associated with unnecessary treatment; however, current data limitations have hindered population-based estimates of the proportion of community-prescribed antibiotics attributable to common respiratory viruses. Objectives and ApproachTo estimate the proportion of antibiotics prescribed in primary care to young children attributable to common respiratory viruses, including respiratory syncytial virus (RSV), influenza, human metapneumovirus (HuMPV) and parainfluenza. We leveraged two unique sources of comprehensive, linked population-based administrative data on dispensed antibiotic prescriptions and laboratory tests for respiratory viruses for all Scottish children (<5 years). We fit time series negative binomial models to predict weekly antibiotic prescribing rates from positive viral tests rates for the period April 1, 2009 through Dec 27, 2017. Using linked demographic and hospitalization data, we stratified our analysis by age, presence of high-risk chronic medical conditions, and antibiotic class. ResultsWe included data on over 6 million antibiotic prescriptions among nearly 800,000 children. An estimated 6.9% (95% CI: 5.6,8.3), 2.4% (1.7,3.1), and 2.3% (0.8,3.9) of prescribed antibiotics were attributable to RSV, influenza and HuMPV, respectively. RSV was consistently associated with the highest proportion of antibiotics prescribed across all analyses but particularly among children without chronic conditions [4.30% (3.19, 5.41)] and for amoxicillin [8.10% (6.43, 9.76)] and macrolide prescriptions [7.65% (6.14, 9.16)]. Conclusion / ImplicationsNearly 14% of antibiotics prescribed to Scottish children in this study were attributable to common viral pathogens such as RSV for which antibiotics are not recommended. This highlights clear targets for antibiotic stewardship programs and suggests antibiotic prescribing could be reduced once an RSV vaccine is introduced.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".