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Record W3095703806 · doi:10.1017/ice.2020.1189

Variability in Antimicrobial Use Among Hospitals Participating in the Canadian Nosocomial Infection Surveillance Program

2020· article· en· W3095703806 on OpenAlexaffabout
Wallis Rudnick, Linda Pelude, Michelle Science, Daniel J. G. Thirion, Jeannette Comeau, Bruce Dalton, Johan Delport, Rita Dhami, Joanne Embreé, Yannick Émond, Gerald A. Evans, Charles Frenette, Susan Fryters, Greg J. German, Jennifer Grant, Jennifer Happe, Kevin Katz, Pamela Kibsey, Justin Kosar, Joanne M. Langley, Bonita E. Lee, Marie-Astrid Lefebvre, Jerome A. Leis, Susan McKenna, Allison McGeer, Heather Neville, Anada Silva, Andrew E. Simor, Kathryn Slayter, Kathryn N. Suh, Alena Tse‐Chang, Karl Heinz Weiss, John Conly, CNISP PHAC

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsFoothills Medical CentreUniversity of CalgaryUniversity of AlbertaOttawa HospitalSunnybrook Health Science CentreUniversity of TorontoMontreal Children's HospitalHospital for Sick ChildrenNova Scotia Health AuthoritySaskatchewan Health AuthorityRoyal Jubilee HospitalUniversity of British ColumbiaKingston Health Sciences CentreStollery Children's HospitalIzaak Walton Killam Health CentreManitoba HealthHôpital Maisonneuve-RosemontUniversity of ManitobaLondon Health Sciences CentreJewish General HospitalAlberta Health ServicesUniversité de MontréalUniversity of WaterlooHealth PEIPublic Health Agency of CanadaMcGill University Health CentreDalhousie University
Fundersnot available
KeywordsMedicineInterquartile rangeQuartileAntimicrobialEmergency medicinePediatricsInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

Background: The association between antimicrobial use (AMU) and emergence of antimicrobial resistance is well documented. The Canadian Nosocomial Infection Surveillance Program (CNISP) has conducted sentinel surveillance of AMU at participating Canadian hospitals since 2009 resulting in the largest pan-Canadian hospital database of dispensed antimicrobials. Objectives: Describe interhospital variability of AMU across Canada. Methods: Hospitals submit annual AMU data based on patient days (PD). Antimicrobials were measured in defined daily doses (DDD) for adults using the WHO Anatomical Therapeutic Chemical (ATC) system. The AMU data among pediatric patients have been available since 2017 using days of therapy (DOT). Surveillance includes systemic antibacterial agents (J01 ATC codes), oral metronidazole, and oral vancomycin. AMU was assessed using quintiles, interquartile ranges (IQR), and relative IQRs (upper- and lower-quartile values divided by the median). Results: Between 2009 and 2018, 20–26 hospitals participated in adult surveillance each year (35 teaching hospitals and 3 nonteaching hospitals participated in ≥1 year). Over this period, overall AMU decreased by 13% at participating adult hospitals from 645 to 560 DDD per 1,000 PD. AMU varied substantially between hospitals, but this variability decreased over time (Fig. 1). In 2009, the IQRs for overall AMU spanned 309 DDD per 1,000 PD, and in 2018 it spanned only 103 DDD per 1,000 PD. This decrease in variability was due to large decreases in use among hospitals with high use in 2009–2010. Among hospitals in the highest use quintile in 2009–2010, AMU decreased, on average, 44 DDD per 1,000 PD each year. Among hospitals in the lowest use quintile in 2009–2010, AMU increased, on average, 6 DDD per 1,000 PD each year. In 2018, antibiotics with the largest absolute IQR variability were cefazolin (61–113 DDD per 1,000 PD), piperacillin-tazobactam (32–64 DDD per 1,000 PD), and vancomycin (24–49 DDD per 1,000 PD). Among antibiotics with ≥1 DDD per 1,000 PD, antibiotics with the largest relative IQR variability were tobramycin (0.3–6 DDD per 1,000 PD), cefadroxil (0.08–9 DDD per 1,000 PD), and linezolid (0.2–3 DDD per 1,000 PD). In 2018, the IQR for overall pediatric AMU (n = 7 teaching hospitals) was 426–581 DOT per 1,000 PD. Antibiotics with the largest IQRs were vancomycin (0.6–58 DOT per 1,000 PD), cefazolin (33–88 DOT per 1,000 PD), and tobramycin (3–57 DOT per 1,000 PD). Among antibiotics with ≥1 DOT per 1,000 PD in 2018, antibiotics with the largest relative IQRs were tobramycin (3–57 DOT per 1,000 PD), cefuroxime (1–6 DOT per 1,000 PD), and amoxicillin (8–42 DOT per 1,000 PD). Conclusions: There is wide variation in overall antibiotic use across hospitals. Variation between AMU at adult hospitals has decreased between 2009 and 2018; in 2018, antibiotics with the largest IQRs were cefazolin and piperacillin-tazobactam. Benchmarking AMU is crucial for informing antimicrobial stewardship efforts. Funding: CNISP is funded by the Public Health Agency of Canada. Disclosures: Allison McGeer reports funds to her institution from Pfizer and Merck for projects for which she is the principal investigator. She also reports consulting fees from Sanofi-Pasteur, Sunovion, GSK, Pfizer, and Cidara.

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)0.003
metaresearch head score (Gemma)0.013
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 score0.980
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.008
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.275
Teacher spread0.252 · 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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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