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Record W3099003860 · doi:10.1101/2020.11.06.20227140

Temporal Variations in the Intensity of Care Provided to Community and Nursing Home Residents Who Died of COVID-19 in Ontario, Canada

2020· preprint· en· W3099003860 on OpenAlexaffabout
Kevin A. Brown, Nick Daneman, Sarah A. Buchan, Adrienne K. Chan, Nathan M. Stall

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of TorontoPublic Health Ontario
Fundersnot available
KeywordsMedicineResidencePandemicInterquartile rangeDemographyPopulationCoronavirus disease 2019 (COVID-19)GerontologyNursing homesNursingEnvironmental healthDisease

Abstract

fetched live from OpenAlex

Introduction - Worldwide, nursing home residents have experienced disproportionately high COVID-19 mortality due to the intersection of congregate living, multimorbidity, and advanced age. Among 12 OECD countries, Canada has had the highest proportion of COVID-19 deaths in nursing home residents (78%), raising concerns about a skewed pandemic response that averted much transmission and mortality in community-dwelling residents, but did not adequately protect those in nursing homes. To investigate this, we measured temporal variations in hospitalizations among community and nursing home-dwelling decedents with COVID-19 during the first and second waves of the pandemic. Methods - We conducted a population-based cohort study of residents of Ontario, Canada with COVID-19 who died between March 11, 2020 (first COVID-19 death in Ontario) and October 28, 2020. We examined hospitalization prior to death as a function of 4 factors: community (defined as all non-nursing home residents) vs. nursing home residence, age in years (<70, 70-79, 80-89, ≥90), gender, and month of death (1st wave: March-April [peak], May, June-July 2020 [nadir], 2nd wave: August-October 2020). Results - A total of 3,114 people with confirmed COVID-19 died in Ontario from March to October, 2020 (Table 1), of whom 1,354 (43.5%) were hospitalized prior to death (median: 9 days before death, interquartile range: 4-19). Among nursing home decedents (N=2000), 22.4% were admitted to hospital prior to death, but this varied substantially from a low of 15.5% in March-April (peak of wave 1) to a high of 41.2% in June-July (nadir of wave 1). Among community-dwelling decedents (N=1,114), admission to acute care was higher (81.4%) and remained relatively stable throughout the first and second waves. Similar temporal trends for nursing home versus community decedents were apparent in age-stratified analyses (Figure 1). Women who died were less likely to have been hospitalized compared to men in both community (80% women vs 84% men) and nursing home (21% women vs 24% men) settings. Discussion - Only a minority of Ontario nursing home residents who died of COVID-19 were hospitalized prior to death, and that there were substantial temporal variations, with hospitalizations reaching their lowest point when overall COVID-19 incidence peaked in mid-April, 2020. While many nursing home residents had pre-pandemic advance directives precluding hospitalization, the low admission rate observed in March-April 2020 (15.5%) was inconsistent with both higher admission rates in subsequent months (>30%), and comparatively stable rates among community-dwelling adults. Our findings substantiate reports suggesting that hospitalizations for nursing home residents with COVID-19 were low during the peak of the pandemic’s first wave in Canada, which may have contributed to the particularly high concentration of COVID-19 mortality in Ontario’s nursing homes.

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)0.002
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 score0.025
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.083
GPT teacher head0.382
Teacher spread0.299 · 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".

Quick stats

Citations7
Published2020
Admission routes2
Has abstractyes

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