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Record W4206036782 · doi:10.1093/jac/dkab493

Comment on: Measuring the impacts of the <i>Using Antibiotics Wisely</i> campaign on Canadian community utilization of oral antibiotics for respiratory tract infections: a time-series analysis from 2015 to 2019

2021· letter· en· W4206036782 on OpenAlexaffabout
Karen Born, Jerome A. Leis, Wendy Levinson

Bibliographic record

VenueJournal of Antimicrobial Chemotherapy · 2021
Typeletter
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentrePublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsAntibioticsRespiratory tract infectionsMedicineIntensive care medicineRespiratory systemInternal medicineMicrobiologyBiology

Abstract

fetched live from OpenAlex

A recently published paper by Rolf von den Baumen et al.1 detailed the conduct of a population-based study in Canada of community-pharmacy-dispensed antibiotics for respiratory tract infections (RTIs) from January 2015 to December 2019. As leaders of Choosing Wisely Canada, the national campaign that launched Using Antibiotics Wisely, we are delighted to see research on strategies to reduce unnecessary antibiotic prescribing in the community setting in Canada. However, we do not agree that this study can be used to assess this campaign’s impact. First, only a 13 month follow-up period was available following development and early dissemination of Using Antibiotics Wisely, which is an inadequate time frame for expecting antibiotic prescribing practices to change across a country. While Canada has a universal, publicly funded healthcare system, care is decentralized and regionalized and there is significant variation and heterogeneity in modes of primary care delivery. Second, the early goal of Using Antibiotics Wisely was focused on engagement of primary care providers, building relationships with key organizations, including the College of Family Physicians of Canada, and working at local levels to pilot and refine these clinical tools to support practice improvement. It was never the expectation of Choosing Wisely Canada that passive dissemination of these tools would be sufficient to change antibiotic prescribing as they needed to be coupled with context-appropriate implementation strategies.2 The approach of Using Antibiotics Wisely is based on the well-established de-implementation science literature, which underscores that raising awareness and education are necessary, but not sufficient, to change practice.3,4 Notably, a 2016 systematic review on interventions to reduce overuse highlights that the majority of studies combine multiple components in the interventions designed to reduce antibiotics and most combined some form of clinician education with either clinical decision support or provider-specific feedback about prescribing patterns. Changing clinical practice patterns and habits takes considerable time, with a 2021 systematic review of Choosing Wisely implementation highlighting that reducing low-value care requires significant organizational and health system investments over a sustained period of time.5 The study by Rolf von den Baumen et al.1 is therefore a premature evaluation given the expected latency period for seeing any impact of a campaign of this kind deployed at a national scale. Using Antibiotics Wisely was developed and launched cognizant of the formidable challenge of changing antibiotic prescribing in the outpatient setting. Most unnecessary antibiotic use is not related to knowledge or awareness gaps targeted by passive education, but rather to provider-level, patient and contextual factors. There are significant cognitive, psychological and behavioural drivers of unnecessary prescriptions both from the patient and provider perspective that have been targeted by a wide range of interventions, including those leveraging behavioural economics.6,7 Contrary to what is suggested by Rolf von den Baumen et al.,1 the clinical tools developed as part of this campaign do not focus on knowledge alone, but rather address some of these known barriers – through the use of viral prescription and delayed prescription for specific RTI syndromes. In addition to the clinical tools developed by primary care clinicians as part of Using Antibiotics Wisely, more recent efforts have been made to pair tools with further implementation supports. For example, these tools are being integrated into audit and feedback reports, electronic medical records and continuing medical education modules. Illustrative of this is a recently published randomized clinical trial of 3500 primary care physicians in Ontario who received a letter with peer comparison data on antibiotic prescribing alongside Using Antibiotics Wisely tools.8 A single feedback letter resulted in a non-significant trend toward reduced antibiotic prescriptions compared with controls [relative risk = 0.96 (97.5% CI = 0.92 –1.01); P = 0.06]. While not reaching statistical significance within 12 months, this likely reflects the need for more frequent feedback and longitudinal efforts to reduce antibiotic initiation, as compared with changes in antibiotic duration, which are easier to achieve.9 Reducing unnecessary antibiotic prescribing in primary care settings remains a challenge in Canada and indeed globally. Using Antibiotics Wisely draws on efforts and lessons learned to date from countries such as Australia that have launched multipronged interventions, including academic detailing, electronic medical record changes, education and awareness raising.10 A strength of Using Antibiotics Wisely is that it is led by the prescribers, with a strong focus on implementation strategies, rather than knowledge alone, with a long-term time horizon needed to achieve impact. K.B.B., J.A.L. and W.L. receive remuneration for their roles in the Choosing Wisely Canada campaign.

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)0.005
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.530
Threshold uncertainty score0.935

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0330.022
Insufficient payload (model declined to judge)0.0080.005

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 head0.391
GPT teacher head0.466
Teacher spread0.075 · 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 designNot applicable
Domainnot available
GenreCommentary

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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Citations1
Published2021
Admission routes2
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