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90 Impact of the choosing wisely Canada recommendations on potentially inappropriate antibiotic prescribing in emergency medicine across Alberta, Canada: an interrupted time-series analysis

2022· article· en· W4281647658 on OpenAlexaffabout
Jason Black, David Campbell, Kerry McBrien, Paul E. Ronksley, Eddy Lang, Tyler Williamson

Bibliographic record

VenueAbstracts · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineEmergency departmentBronchitisPharmacyBronchiolitisEmergency medicineInterrupted Time Series AnalysisAntibioticsOtitisInterrupted time seriesPediatricsMedical emergencyIntensive care medicineFamily medicinePsychological interventionInternal medicine

Abstract

fetched live from OpenAlex

<h3></h3> Campaigns such as Choosing Wisely Canada have encouraged physicians to consider whether antibiotics are needed when treating presumably viral infections to decrease inappropriate antibiotic prescribing rates; however, the impacts of such campaigns are not well characterized. Objectives In Alberta, Canada, we quantified the rate of potentially inappropriate oral antibiotic prescribing in emergency departments for viral infections or conditions not likely requiring antibiotics from 2010–2020 and assessed the impact of two Choosing Wisely Canada (CWC) campaigns (2015/2016 and 2018) discouraging inappropriate antibiotic prescribing in emergency medicine. <h3>Methods</h3> In Alberta, we linked all emergency department adult and pediatric records from the National Ambulatory Care Reporting System and medication dispensations from community-based pharmacies in the Pharmaceutical Information Network. From January 2010 to February 2020, we identified emergency department visits for 5 conditions that were potentially inappropriately treated using antibiotics per CWC recommendations (bronchitis, asthma, bronchiolitis, pharyngitis, and acute otitis media). We used an interrupted time series design to detect changes in the proportion of emergency departments visits with subsequent antibiotic dispensing by fitting Autoregressive Integrated Moving Average (ARIMA) models to account for secular trends and seasonality, allowing for changes in slope to measure the effect of each CWC intervention. <h3>Results</h3> Antibiotics were commonly prescribed in emergency departments for bronchitis (proportion of visits with antibiotics: 57%) and asthma (22%) in adults; bronchiolitis in children (43%); pharyngitis (39%) and acute otitis media (54%) in adults and children. Based on visual inspection, the proportion of emergency department visits for each condition where antibiotics were dispensed remained relatively consistent. The ARIMA models demonstrated mixed impacts on potentially inappropriate antibiotic prescribing associated with two interruptions: the 2015/2016 CWC recommendations and subsequent 2018 CWC Using Antibiotics Wisely campaign. Following each interruption, antibiotic prescribing was slightly reduced for bronchitis (-1.0%/year,p=0.03; -4.4%/year,p=0.004, respectively) and bronchiolitis (not significant) (-0.7%/year,p=0.57; -2.5%/year,p=0.34), but unchanged for asthma (-0.6%/year,p=0.30; 0.7%/year,p=0.74) and pharyngitis (0.0%/year,p=0.95; -0.2%/year,p=0.93), and slightly increased for acute otitis media (not significant) (1.4%/year,p=0.07; 5.9%/year,p=0.052). <h3>Conclusion</h3> Rates of potentially inappropriate antibiotic prescribing remained constant over the past 10 years in Alberta. Campaigns to rethink antibiotic use in emergency medicine may have resulted in small decreases in antibiotic use for some conditions; however, further initiatives building upon existing campaigns are required to substantially reduce rates of inappropriate antibiotic prescribing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.261
GPT teacher head0.478
Teacher spread0.216 · 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 teacher head, not a consensus.

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
Published2022
Admission routes2
Has abstractyes

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