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Record W4387636926 · doi:10.1371/journal.pone.0292899

Retrospective cohort analysis of outpatient antibiotic prescribing for community-acquired pneumonia in Canadian older adults

2023· article· en· W4387636926 on OpenAlexafffundabout
Ariana Saatchi, Jennifer Reid, Salimah Z. Shariff, Marcus Povitz, Michael E. Silverman, David M. Patrick, Andrew M. Morris, James McCormack, Manon Haverkate, Fawziah Marra

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of TorontoUniversity Health NetworkWestern UniversityUniversity of CalgaryLondon Health Sciences CentreSinai Health SystemLawson Health Research InstituteBC Centre for Disease ControlUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineAntimicrobial stewardshipGuidelineCommunity-acquired pneumoniaRetrospective cohort studyPneumoniaPopulationPoisson regressionCohortDefined daily doseConfidence intervalCohort studyInternal medicinePediatricsMedical prescriptionAntibioticsEmergency medicineAntibiotic resistanceEnvironmental healthPharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: This retrospective cohort study is the first in North America to examine population-level appropriate antibiotic use for community-acquired pneumonia (CAP) in older adults, by agent, dose and duration. With the highest rates of CAP reported in the elderly populations, appropriate antibiotic use is essential to improve clinical outcomes. Given the ongoing crisis of antimicrobial resistance, understanding inappropriate antibiotic prescribing is integral to direct community stewardship efforts. METHODS: All outpatient primary care visits for CAP (aged ≥65 years) were identified using physician billing codes between January 1 2014 to December 31 2018 in British Columbia (BC) and Ontario (ON). Categories of prescribing were derived from existing literature, and constructed for clinical relevance using Canadian and international guidelines available during the study period. Categories were mutually exclusive and included: guideline adherent (first-line agent, adherent dose/duration), clinically appropriate (non-first line agent, presence of comorbidities), effective but unnecessary (first-line agent, excess dose/duration), undertreatment (first-line agent, subtherapeutic dose/duration), and not recommended (non-first line agent, absence of comorbidities). Proportions of prescribing were examined by category. Temporal trends in prescribing were examined using Poisson regression. RESULTS: A total of 436,441 episodes of CAP were identified, with 46% prescribed an antibiotic in BC, and 52% in Ontario. Guideline adherent prescribing was minimal for both provinces (BC: 2%; ON: 1%) however the largest magnitude of increase was reported in this category by the final study year (BC-Rate Ratio [RR]: 3.4, 95% Confidence Interval [CI]: 2.7-4.3; ON-RR: 4.62, 95% CI: 3.4-6.5). Clinically appropriate prescribing accounted for the most antibiotics issued, across all study years (BC: 61%; ON: 74%) (BC-RR: 0.8, 95% CI: 0.8-0.8; ON-RR: 0.9, 95% CI: 0.8-0.9). Excess duration of therapy was the hallmark characteristic for effective but unnecessary prescribing (BC: 92%; ON: 99%). The most common duration prescribed was 7 days, followed by 10. Not recommended prescribing was minimal in both provinces (BC: 4%; ON: 7%) and remained stable by the final study year (BC-RR: 1.1, 95% CI: 0.9-1.2; ON-RR: 0.9, 95% CI: 0.9-1.1). CONCLUSION: Three quarters of antibiotic prescribing for CAP was appropriate in Ontario, but only two thirds in BC. Shortening durations-in line with evidence for 3 to 5-day treatment presents a focused target for stewardship efforts.

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 score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.042
GPT teacher head0.269
Teacher spread0.227 · 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

Citations3
Published2023
Admission routes3
Has abstractyes

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