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Record W4387169329 · doi:10.1017/ash.2023.296

Universal COVID-19 screening at hospitals in a large Canadian health region

2023· article· en· W4387169329 on OpenAlexaboutno aff
Matthew Garrod, Katy Short

Bibliographic record

VenueAntimicrobial Stewardship & Healthcare Epidemiology · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePandemicLogistic regressionCoronavirus disease 2019 (COVID-19)Multinomial logistic regressionRetrospective cohort studyEmergency departmentTest (biology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Emergency medicinePediatricsDemographyFamily medicineInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background: Hospitals were affected by COVID-19, with significant concern regarding transmission from unidentified cases. Fraser Health, a Canadian regional health authority, implemented universal testing along with screening questions for emergency department (ED) admissions. We sought to determine which factors were associated with SARS-CoV-2–positive test on admission as well as patient outcome, stratified by screening question responses. Methods: This retrospective analysis included patients aged ≥6 years admitted through 12 hospital EDs between November 1, 2020, and June 30, 2022. Admission, laboratory, and screening data were extracted from electronic health records. Patients who had a first SARS-CoV-2 PCR–positive test in the prior 60 days collected within 48 hours of admission were classified as positive. Covariates included age, geographical region, and SARS-CoV-2 variant era. All questions were modeled using multinomial logistic regression, with components informed through crude analysis in R Studio software. Results: There were 88,511 unique eligible admissions, with 7,642 positive tests (8.6%). The positivity rate over the study period ranged from 0.6% to 21.8%, with a mean of 6.5%. Patients meeting screening criteria were 4.7 times (95% CI, 4.43–4.92) as likely to test positive as those who did not. Patients in the SARS-CoV-2 omicron variant era were 3.2 times (95% CI, 2.98–3.47) as likely to test positive as those in the earlier era of the pandemic. Patients later in the pandemic were less likely to be identified by screening questions than those in earlier eras, with patients in the SARS-CoV-2 omicron variant era only 14% (95% CI, 12%–17%) as likely as in the earlier stages of the pandemic to be identified by screening questions. Patients who tested positive were 1.5 (95% CI, 1.37–1.64) times as likely to die as patients who tested negative, whereas patients in later stages of the pandemic were less likely to die overall. Discussion: Patients who tested positive on admission were more likely to meet screening criteria; however, screening missed half of all positive cases. It is not known whether patients who tested positive without meeting screening criteria would have resulted in transmission. Conclusions: Due to changes in COVID-19 epidemiology, Fraser Health has discontinued universal admission screening. Although universal testing increased resource needs, more than half of patients who tested positive during the study period would not have been identified based on screening criteria alone, allowing for implementation of precaution measures to prevent possible transmission. Ultimately, the decision to conduct universal testing must be a balance of the resources required, community prevalence, and patient population. 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

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 score0.036
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
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 head0.151
GPT teacher head0.429
Teacher spread0.278 · 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
Published2023
Admission routes1
Has abstractyes

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