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Record W4392974519 · doi:10.57187/s.3636

A cohort study of patients hospitalised with SARS-CoV-2 infection in Ontario: patient characteristics and outcomes by wave

2024· article· en· W4392974519 on OpenAlexaffabout
Steven Habbous, Anna Lambrinos, Kirsten Ming, Erik Hellsten

Bibliographic record

VenueSwiss Medical Weekly · 2024
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsPublic Health OntarioWestern University
Fundersnot available
KeywordsMedicinePoisson regressionCohortPopulationLogistic regressionEpidemiologyCohort studyDemographyEmergency medicinePediatricsInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: Each wave of the COVID-19 pandemic exhibited a unique combination of epidemiological, social and structural characteristics. We explore similarities and differences in wave-over-wave characteristics of patients hospitalised with COVID-19. METHODS: This was a population-based study in Ontario province, Canada. Patients hospitalised with SARS-CoV-2 between 26 February 2020 and 31 March 2022 were included. An admission was considered related to SARS-CoV-2 infection if the provincial inpatient or outpatient hospital databases contained the ICD-10 diagnostic codes U071/U072 or the Ontario Laboratories Information System indicated a positive SARS-CoV-2 test result (PCR or rapid antigen testing) during the admission or up to two weeks prior. The primary outcome was 90-day mortality (modified Poisson regression). Secondary outcomes were use of critical care during the admission (logistic regression) and total length-of-stay (linear regression with heteroskedastic-consistent standard-error estimators). All models were adjusted for demographic characteristics, neighbourhood socioeconomic factors and indicators of illness severity. RESULTS: There were 73,201 SARS-CoV-2-related admissions: 6127 (8%) during wave 1 (wild-type), 14,371 (20%) during wave 2 (wild-type), 16,653 (23%) during wave 3 (Alpha), 5678 (8%) during wave 4 (Delta) and 30,372 (42%) during wave 5 (Omicron). SARS-CoV-2 was the most responsible diagnosis for 70% of admissions during waves 1-2 and 42% in wave 5. The proportion of admitted patients who were long-term care residents was 18% (n = 1111) during wave 1, decreasing to 10% (n = 1468) in wave 2 and <5% in subsequent waves. During waves 1-3, 46% of all admitted patients resided in a neighbourhood assigned to the highest ethnic diversity quintile, which declined to 27% during waves 4-5. Compared to wave 1, 90-day mortality was similar during wave 2 (adjusted risk ratio [aRR]: 1.00 [95% CI: 0.95-1.04]), but lower during wave 3 (aRR: 0.89 [0.85-0.94]), wave 4 (aRR: 0.85 [0.79-0.91]) and wave 5 (aRR: 0.83 [0.80-0.88]). Improvements in survival over waves were observed among elderly patients (p-interaction <0.0001). Critical care admission was significantly less likely during wave 5 than previous waves (adjusted odds ratio: 0.50 [0.47-0.54]). The length of stay was a median of 8.5 (3.6-23.8) days during wave 1 and 5.3 (2.2-12.6) during wave 5. After adjustment, the mean length of stay was on average -10.4 (-11.1 to -9.8) days, i.e. shorter, in wave 5 vs wave 1. CONCLUSION: Throughout the pandemic, sociodemographic characteristics of patients hospitalised with SARS-CoV-2 changed over time, particularly in terms of ethnic diversity, but still disproportionately affected patients from more marginalised regions. Improved survival and reduced use of critical care during the Omicron wave are reassuring.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.018
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.358
Teacher spread0.334 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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