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Record W4414425776 · doi:10.1056/evidoa2400108

Peri–Covid-19 Antibiotic Use and Antimicrobial Resistance in Older Adults

2025· article· en· W4414425776 on OpenAlexaffabout
Derek R. MacFadden, Colleen J. Maxwell, Dawn M. E. Bowdish, Susan E. Bronskill, James Brooks, Kevin A. Brown, Lori L. Burrows, Anna E. Clarke, Bradley J. Langford, Elizabeth Leung, Valerie Leung, Douglas G. Manuel, Allison McGeer, Sharmistha Mishra, Andrew M. Morris, Caroline Nott, Sumit Raybardhan, Mia E Sapin, Kevin L. Schwartz, Miranda So, Jean-Paul R. Soucy, Nick Daneman

Bibliographic record

VenueNEJM Evidence · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of OttawaSunnybrook Health Science CentreSt. Michael's HospitalPublic Health OntarioMcMaster UniversityToronto General HospitalOttawa HospitalUniversity Health NetworkToronto East General HospitalUniversity of TorontoNorth York General HospitalUniversity of Waterloo
Fundersnot available
KeywordsAntibiotic resistanceAntibioticsAntimicrobialDrug resistanceAntibiotic therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Antibiotic use during the coronavirus disease 2019 (Covid-19) pandemic was common in the outpatient setting, but was not supported by guidelines. We sought to evaluate the role of this antibiotic use on downstream antibiotic resistance. METHODS: We performed a population-wide cohort study of all nonhospitalized adults 66 years of age or older in Ontario, Canada, from January 1, 2020, to June 30, 2021, with a first identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated the relationship between outpatient peri-Covid-19 antibiotic exposure (within a period of 7 days before or after index SARS-CoV-2 reporting) and downstream isolation of an antibiotic-resistant organism from clinical culture within 6 months. We calculated adjusted odds ratios of the association between peri-Covid-19 prescribing and antibiotic-resistant organism detection, as well as the adjusted attributable fractions of downstream antibiotic-resistant organisms. RESULTS: Of the 53,533 eligible individuals included, 8228 (15%) were prescribed a peri-Covid-19 antibiotic, and 1477 (3%) had a downstream antibiotic-resistant organism identified. The adjusted odds ratio for the presence of any antibiotic-resistant organism with peri-Covid-19 antibiotic use was 1.24 (95% confidence interval [CI], 1.09 to 1.41), while the adjusted odds ratio for the presence of gram-negative antibiotic-resistant organisms was 1.27 (95% CI, 1.11 to 1.46) and for gram-positive antibiotic-resistant organisms it was 1.02 (95% CI, 0.70 to 1.48). Among all individuals who received an antibiotic within 7 days of SARS-CoV-2 diagnosis, the attributable fraction of downstream antimicrobial resistance related to peri-Covid-19 antibiotic use was 17% (95% CI, 7 to 26%). Among all individuals with a SARS-CoV-2 diagnosis, the population-attributable fraction of downstream antimicrobial resistance related to peri-Covid-19 antibiotic use was 4% (95% CI, 2 to 7%). CONCLUSIONS: Peri-Covid-19 antibiotic use was associated with downstream antimicrobial resistance, and particularly the presence of gram-negative antibiotic-resistant organisms. (Funded by the Canadian Institutes of Health Research Operating Grant [grant number 179461] and others).

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.258
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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