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Record W4405349268 · doi:10.4212/cjhp.3595

Antimicrobial Use among Adult Inpatients in Northern Canada, 2019–2021

2024· article· en· W4405349268 on OpenAlexafffundvenueabout
Wallis Rudnick, Joëlle Cayen, Jessica J Bartoszko, Chris Bessey, John Conly, Ginette Dutrisac, Edith Lee, Darren Pasay, Linda Pelude, Alicia Rahier, Daniel J. G. Thirion

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsMcGill University Health CentreMcGill UniversityNunavut Research InstituteFoothills Medical CentreAlberta Health ServicesUniversity of Northern British ColumbiaGovernment of Northwest TerritoriesHealth Sciences NorthPublic Health Agency of Canada
FundersCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of CanadaWorld Health OrganizationPfizer
KeywordsAntimicrobialMedicineGeographyFamily medicineBiologyMicrobiology

Abstract

fetched live from OpenAlex

Background: Antimicrobial use data from inpatients in northern Canada suitable to inform stewardship programs are limited. Objective: As a special project of the Canadian Nosocomial Infection Surveillance Program, to describe antimicrobial use for inpatients in northern Canadian acute care hospitals. Methods: Participating acute care hospitals serving adult or mixed adult and pediatric populations in northern Canada submitted annual data on the use of all systemic antimicrobials from 2019 to 2021. Patient-day denominators were also submitted. Northern Canada was defined as the territories and Statistics Canada’s provincial north. Data were analyzed in terms of defined daily doses per 1000 patient days (DDD/1000pd), as per the Anatomical Therapeutic Chemical classification system. Antimicrobials were categorized using the World Health Organization’s AWaRe (Access/ Watch/Reserve) classification system. Results: Each year, 42–47 hospitals participated. More than 90% of participating hospitals were in Alberta or British Columbia. There was large variation in overall antimicrobial use between hospitals (e.g., interquartile range 429 to 779 DDD/1000pd in 2021). From 2019 to 2021, there was a 49% relative increase in antimicrobial use, from 401 to 596 DDD/1000pd (p = 0.11). Over the same period, the use of third- and first-generation cephalosporins increased by 80% and 64%, respectively; antimicrobials in the “Reserve” category increased from 0.4% to 2% of overall use. Conclusions: This study represents the largest collection of antimicrobial use data for inpatients in northern Canada to date. From 2019 to 2021, there was an increase in antimicrobial use of 195 DDD/1000pd, largely driven by increases in the use of third- and first-generation cephalosporins. The findings should be interpreted with caution, as results may not be generalizable to all northern hospitals. Keywords: antimicrobial use, hospital, surveillance RÉSUMÉ Contexte : Les données concernant l’utilisation d’agents antimicrobiens chez les patients hospitalisés dans le nord du Canada pouvant éclairer les programmes de gestion sont limitées. Objectif : Projet spécial de Programme canadien de surveillance des infections nosocomiales visant à décrire l’utilisation d’agents antimicrobiens chez les patients hospitalisés dans des hôpitaux de soins aigus dans le nord du Canada. Méthodologie : Les hôpitaux de soins aigus ayant participé au projet et qui servent une population adulte ou une population mixte composée d’adultes et d’enfants dans le nord du Canada ont soumis leurs données annuelles concernant l’utilisation de tous les agents antimicrobiens systémiques de 2019 à 2021. Les dénominateurs de jours-patients ont également été soumis. Le nord du Canada était défini comme les territoires et le nord provincial de Statistique Canada. Les données ont été analysées en termes de doses journalières définies par 1000 jours-patients (DJD/1000 jours-patients), selon le Système de classification anatomique, thérapeutique et chimique. Les agents antimicrobiens ont été répartis à l’aide du système de classification AWaRe de l’Organisation mondiale de la Santé : accès aux antibiotiques; antibiotiques à surveiller; antibiotiques de réserve. Résultats : Chaque année, entre 42 et 47 hôpitaux ont participé. Plus de 90 % de ces hôpitaux se situaient en Alberta ou en Colombie-Britannique. De grandes variations quant à l’utilisation d’agents antimicrobiens de manière générale ont été observées d’un hôpital à l’autre (par exemple, l’intervalle interquartile était de 429 à 779 DJD/1000 jours-patients en 2021). Entre 2019 et 2021, on a observé une augmentation relative de 49 % de l’utilisation d’agents antimicrobiens, qui est passée de 401 à 596 DJD/1000 jours-patients (p = 0,11). Pendant la même période, l’utilisation des céphalosporines de troisième et de première génération a augmenté respectivement de 80 % et 64 %; les agents antimicrobiens de la catégorie « Réserve » ont quant à eux augmenté, passant de 0,4 % à 2 % de l’utilisation globale. Conclusions : Cette étude constitue la plus grande collecte de données sur l’utilisation d’agents antimicrobiens chez les patients hospitalisés dans le nord du Canada à ce jour. Entre 2019 et 2021, on a observé une augmentation de 195 DJD/1000 jours-patients de l’utilisation de ces agents, une augmentation principalement due à une hausse de l’utilisation des céphalosporines de troisième et de première génération. Il convient d’interpréter ces résultats avec prudence, car ils peuvent ne pas être généralisables à tous les hôpitaux du Nord. Mots-clés : utilisation d’agents antimicrobiens, hôpital, surveillance

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)0.000
metaresearch head score (Gemma)0.002
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 score0.033
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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
Published2024
Admission routes4
Has abstractyes

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