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Record W3097605078 · doi:10.1017/ice.2020.566

Prevalence of Healthcare-Associated Infections and Antimicrobial Resistance in Rural Alberta Acute-Care Facilities

2020· article· en· W3097605078 on OpenAlexaffabout
Jennifer Ellison, Uma Chandran, Jennifer Happe, Ye Shen, Jayson Shurgold, Geoffrey Taylor, Kathryn Bush

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of AlbertaPublic Health Agency of CanadaGlenrose Rehabilitation HospitalAlberta Health Services
Fundersnot available
KeywordsMedicineAntibiotic resistanceCeftriaxoneAcute careAntimicrobialAntibioticsEmergency medicineInternal medicineHealth careIntensive care medicineMicrobiology

Abstract

fetched live from OpenAlex

Background: Antibiotic-resistant organisms (AROs) are associated with greater disease severity and poor outcomes. Previous studies have investigated AROs and healthcare-associated infections (HAIs) within larger urban acute-care settings, but similar data for rural settings are scarce. In this stud, we aimed to fill this gap. Methods : Data on antimicrobial resistance (AMR), additional precautions and HAI were collected from 8 rural Alberta acute-care facilities over a 24-hour period from February 4–28, 2019. Data were gathered as part of the national Canadian, Rural, and Northern Acute Care Point Prevalence (CNAPP) survey. All inpatients on included units were reviewed. CNAPP protocol surveillance definitions were used. Results : In total, 961 patients were surveyed, of whom 94 of 961 (9.8%) were on additional precautions. Contact precautions only were most common (54 of 94, 57.4%) and were predominantly in place for MRSA (30 of 94, 31.9%). Of 961 patients, 100 (~10%) met the surveillance definitions for any infection. The most common infections were skin and soft-tissue infections (29 of 100, 29.0%) and bloodstream infections (28 of 100, 28.0%). An HAI occurred in 30 of 961 patients (3.1%); the most common HAIs were surgical site infections (8 of 30, 26.7%) and urinary tract infections (8 of 30, 26.7%). An antimicrobial was prescribed to 333 of 961 patients (34.6%) at the time of the survey, with ceftriaxone the most commonly prescribed (68 of 333, 20.4%). Most patients receiving an antimicrobial (237 of 333, 71.2%) did not meet the surveillance definition for any infection. The most common reason for any antimicrobial administration was empiric therapy (167 of 333, 50.1%). Conclusions: Investigations into antimicrobial use and the burden of HAIs in rural acute-care settings have been limited. In this study, we (1) established provincial baseline data for burden of disease in these facilities due to HAIs and (2) demonstrated that antimicrobial use is common, though most patients who were prescribed an antimicrobial did not meet study definitions for infection. It will be important to continue this type of surveillance in this understudied population to monitor the burden of HAIs over time, to establish antimicrobial utilization trends, and to continue to identify potential antimicrobial stewardship initiatives. Funding: None Disclosures: None

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Labeled directly by 2 models reading the full record.

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

Citations0
Published2020
Admission routes2
Has abstractyes

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