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Socioeconomic Status, Palliative Care, and Death at Home Among Patients With Cancer Before and During COVID-19

2024· article· en· W4392190264 on OpenAlexafffundabout
Javaid Iqbal, Rahim Moineddin, Robert Fowler, Monika K. Krzyzanowska, Christopher M. Booth, James Downar, Jenny Lau, Lisa W. Le, Gary Rodin, Hsien Seow, Peter Tanuseputro, Craig C. Earle, Kieran L. Quinn, Breffni Hannon, Camilla Zimmermann

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcMaster UniversityQueen's UniversityUniversity Health NetworkUniversity of TorontoBruyèreUniversity of OttawaPrincess Margaret Cancer Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineSocioeconomic statusPandemicPalliative careCohortDemographyCoronavirus disease 2019 (COVID-19)Cohort studyEnd-of-life careGerontologyEnvironmental healthPopulationInternal medicineDiseaseNursing

Abstract

fetched live from OpenAlex

Importance: The COVID-19 pandemic had a profound impact on the delivery of cancer care, but less is known about its association with place of death and delivery of specialized palliative care (SPC) and potential disparities in these outcomes. Objective: To evaluate the association of the COVID-19 pandemic with death at home and SPC delivery at the end of life and to examine whether disparities in socioeconomic status exist for these outcomes. Design, Setting, and Participants: In this cohort study, an interrupted time series analysis was conducted using Ontario Cancer Registry data comprising adult patients aged 18 years or older who died with cancer between the pre-COVID-19 (March 16, 2015, to March 15, 2020) and COVID-19 (March 16, 2020, to March 15, 2021) periods. The data analysis was performed between March and November 2023. Exposure: COVID-19-related hospital restrictions starting March 16, 2020. Main Outcomes and Measures: Outcomes were death at home and SPC delivery at the end of life (last 30 days before death). Socioeconomic status was measured using Ontario Marginalization Index area-based material deprivation quintiles, with quintile 1 (Q1) indicating the least deprivation; Q3, intermediate deprivation; and Q5, the most deprivation. Segmented linear regression was used to estimate monthly trends in outcomes before, at the start of, and in the first year of the COVID-19 pandemic. Results: Of 173 915 patients in the study cohort (mean [SD] age, 72.1 [12.5] years; males, 54.1% [95% CI, 53.8%-54.3%]), 83.7% (95% CI, 83.6%-83.9%) died in the pre-COVID-19 period and 16.3% (95% CI, 16.1%-16.4%) died in the COVID-19 period, 54.5% (95% CI, 54.2%-54.7%) died at home during the entire study period, and 57.8% (95% CI, 57.5%-58.0%) received SPC at the end of life. In March 2020, home deaths increased by 8.3% (95% CI, 7.4%-9.1%); however, this increase was less marked in Q5 (6.1%; 95% CI, 4.4%-7.8%) than in Q1 (11.4%; 95% CI, 9.6%-13.2%) and Q3 (10.0%; 95% CI, 9.0%-11.1%). There was a simultaneous decrease of 5.3% (95% CI, -6.3% to -4.4%) in the rate of SPC at the end of life, with no significant difference among quintiles. Patients who received SPC at the end of life (vs no SPC) were more likely to die at home before and during the pandemic. However, there was a larger immediate increase in home deaths among those who received no SPC at the end of life vs those who received SPC (Q1, 17.5% [95% CI, 15.2%-19.8%] vs 7.6% [95% CI, 5.4%-9.7%]; Q3, 12.7% [95% CI, 10.8%-14.5%] vs 9.0% [95% CI, 7.2%-10.7%]). For Q5, the increase in home deaths was significant only for patients who did not receive SPC (13.9% [95% CI, 11.9%-15.8%] vs 1.2% [95% CI, -1.0% to 3.5%]). Conclusions and Relevance: These findings suggest that the COVID-19 pandemic was associated with amplified socioeconomic disparities in death at home and SPC delivery at the end of life. Future research should focus on the mechanisms of these disparities and on developing interventions to ensure equitable and consistent SPC access.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.008
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.355
Teacher spread0.327 · 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".

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Citations10
Published2024
Admission routes3
Has abstractyes

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