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Record W3015716813 · doi:10.1093/cid/ciaa403

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

2020· article· en· W3015716813 on OpenAlexafffund
Tiffany Fitzpatrick, William Malcolm, Jim McMenamin, Arlene Reynolds, Astrid Guttmann, Pia Hardelid

Bibliographic record

VenueClinical Infectious Diseases · 2020
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationPublic Health OntarioUniversity of Toronto
FundersSickkids Research InstituteNational Institute for Health and Care Research
KeywordsRhinovirusMedicineHuman metapneumovirusAntibioticsPopulationHuman Parainfluenza VirusRespiratory tract infectionsMedical prescriptionPediatricsVirusInternal medicineVirologyRespiratory systemBiologyMicrobiologyEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.098
GPT teacher head0.401
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2020
Admission routes2
Has abstractyes

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