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Record W4311614187 · doi:10.1093/ofid/ofac492.014

89. On-going impact of the SARS-CoV-2 pandemic on the evolution of carbapenemase-producing <i>Enterobacterales</i> in Ontario, Canada

2022· article· en· W4311614187 on OpenAlexaffabout
Mohammad Mozafarihashjin, Alainna Jamal, Christopher Kandel, Philipp Köhler, Laura Mataseje, Michael R. Mulvey, Vanessa Allen, Kevin R. Barker, Mahin Baqi, Sergio Borgia, Brenda L. Coleman, Amna Faheem, Lubna Farooqi, Jennie Johnstone, Kevin Katz, Roberto G. Melano, Matthew Muller, Samira Mubareka, Samir Patel, Susan M. Poutanen, David Richardson, Angel Li, Zoë Zhong, Allison McGeer

Bibliographic record

VenueOpen Forum Infectious Diseases · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsMount Sinai HospitalHealth Sciences CentrePublic Health OntarioSunnybrook Health Science CentreSinai Health SystemNorth York General HospitalWilliam Osler Health SystemTrillium Health CentreUniversity of Toronto
Fundersnot available
KeywordsIncidence (geometry)MedicinePandemicOutbreakPopulationKlebsiella pneumoniaeBacteremiaClinical microbiologyEnterobacterMicrobiologyInternal medicineVirologyCoronavirus disease 2019 (COVID-19)BiologyDiseaseGeneEnvironmental healthInfectious disease (medical specialty)Escherichia coliAntibiotics

Abstract

fetched live from OpenAlex

Abstract Background The spread of carbapenemase-producing Enterobacterales (CPE) is global threat. Numerous outbreaks of CPE have been reported during the COVID-19 pandemic. We describe the impact of of the SARS-CoV-2 pandemic on the emergence of CPE in south-central Ontario, Canada. Incidence of clinical isolates of CPE and isolates with different CPE genes in Toronto/Peel region, 2017–2021. The upper panel shows the incidence of patients with clinical isolates of CPE by year and quarter from q4 2007 to q1 2022. The lower panel shows the incidence of patients with clinical isolates with different carbapenemase genes by fiscal year during the same period. Methods TIBDN has performed population-based surveillance for CPE in Toronto/Peel region (pop 4.5M) from first identified isolate in 2007. All laboratories test/refer all carbapenem non-susceptible Enterobacterial isolates for identification of CPE. Hospital charts are reviewed and patients/physicians interviewed. Population data are obtained from Statistics Canada. Results From 10/2007 to 3/31/2022, 1367 persons colonized or infected with CPE were identified. Their median age was 68.7yrs (IQR 54–78yrs); 761 (56%) were male. 772 (56%) were colonized when first identified; 115 (8.4%) were bacteremic at identification or subsequently developed bacteremia. The most common organisms were E. coli (651, 48%), K. pneumoniae (436, 32%), Enterobacter spp. (146, 11%), Citrobacter spp (62, 5%); the most common genes were NDM±OXA-48 (722, 53%), OXA-48-like (341, 25%), KPC (225, 16%), VIM (44, 3%). The incidence of CPE infections increased steadily until 3/2020 then declined by 61% and remained stable until 3/2022 (Figure, upper panel). The decline was greater for E. coli (56% decrease), K. pneumoniae (62%) than for Enterobacter spp. (30%) and other species (19%). It occurred in all genes in 2020; however, KPC containing organisms increased again in 2021 (Figure, lower panel). Conclusion The advent of the COVID-19 pandemic was associated with an immediate, substantial decline in the incidence of patients with CPE in our population area. This decline occurred in both isolates with genes usually occurring in cases imported from other countries, and in those usually occurring in cases associated with transmission within Canadian hospitals. Decreased travel and enhanced infection prevention and control in hospitals may both have contributed to reductions in CPE during the pandemic. Disclosures All Authors: No reported disclosures.

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)0.001
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 score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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
Published2022
Admission routes2
Has abstractyes

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