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Association of socioeconomic status with aggressive end-of-life care in patients with cancer before and during the COVID-19 pandemic.

2024· article· en· W4399150691 on OpenAlexafffund
Javaid Iqbal, Rahim Moineddin, Robert Fowler, Christopher M. Booth, James Downar, Jenny Lau, Lisa W. Le, Gary Rodin, Hsien Seow, Peter Tanuseputro, Craig C. Earle, Kieran L. Quinn, Monika K. Krzyzanowska, Breffni Hannon, Camilla Zimmermann

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsMount Sinai HospitalHealth Sciences CentreSunnybrook Health Science CentrePrincess Margaret Cancer CentreUniversity of OttawaUniversity Health NetworkUniversity of TorontoMcMaster UniversityQueen's University
FundersCanadian Institutes of Health ResearchPeterborough K. M. Hunter Charitable Foundation
KeywordsMedicineSocioeconomic statusPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakEnd-of-life careSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GerontologyDemographyEnvironmental healthInternal medicinePalliative careDiseaseVirologyOutbreakPopulationNursingInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

1612 Background: Aggressive care near the end-of-life (EOL) reflects poor quality of cancer care.We examined the association between trends in aggressive EOL cancer care and socioeconomic status (SES) before and during the COVID-19 pandemic. Methods: We conducted a population-based cohort study of adults diagnosed with cancer who died from 03/16/2015 to 03/15/2020 (pre-COVID-19 period) and from 03/16/2020 to 03/15/2021 (COVID-19 period). Aggressive EOL care was defined as a composite outcome of percentage (%) of patients with systemic anticancer therapy (SACT) use, >1 ED visit, >1 hospitalization, or ≥1 ICU admission in the last 30 days of life. We conducted an interrupted time series analysis using segmented linear regression, estimating monthly trends before, at the start of, and during the first year of the pandemic. Analyses were stratified by SES, based on area-level material deprivation quintiles (Q1, least; Q3, intermediate; Q5, most deprived). Results: Of 173,915 decedents with cancer (mean [SD] age 72.1 [12.5] years; females 45.9%), 59,613 (34.3% [95% CI, 34.1-34.5]) had aggressive EOL care; 10.3% (10.2-10.5) received SACT, 14.0% (13.8-14.2) had >1 ED visit, 10.3% (10.2-10.5) >1 hospitalization, and 13.4% (13.2-13.5) ≥1 ICU admission within 30 days of death. During the course of the pre-COVID-19 period, patients in Q1 (33.5% [33.0-34.1]) were less likely to receive aggressive care at the EOL than those in Q3 (34.1% [33.5-34.6]) or Q5 (34.8%, 95% CI, 34.3-35.3). Specifically, patients in Q1 were less likely to have ED visits (Q1, 12.9% vs Q3, 14.2% vs Q5, 14.9%), and hospitalizations (9.7% vs 10.3% vs 10.6%) than Q3 and Q5, and less likely to have ICU admissions than Q5 (13.1 vs 13.0% vs 14.4%); however, they were more likely to receive SACT at EOL (11.1% vs 9.9% vs 9.1%). During the pre-COVID-19 period, aggressive care increased by 0.032% (95% CI, 0.026-0.038, P < 0.0001) monthly; this increase was significant in Q1 (P = 0.002)but not in Q3 (P = 0.31) or Q5 (P = 0.21). Within Q1, there was a pre-COVID-19 increase in EOL SACT use (P < 0.0001)and ED visits (P = 0.04) but not inhospitalizations and ICU admissions. In March 2020, aggressiveness of care decreased by 2.37% (95% CI, -2.98 to -1.76, P = 0.0002), which was significant in Q5 (P = 0.04), but not Q1 (P = 0.07)or Q3 (P = 0.81). Within Q5, there was a decrease in EOL ED visits (P = 0.0008) but not in SACT use, hospitalizations, or ICU admissions. Conclusions: More than one third of adults with advanced cancer received aggressive EOL care, which increased in the 5 years prior to COVID-19 pandemic and was attenuated at its onset. Indicators of aggressive care differed by SES, with greatest SACT use in those with highest SES and greatest hospital services use in those with lowest SES. Measures to reduce aggressiveness of care should take into account disparities related to SES.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.122
GPT teacher head0.499
Teacher spread0.377 · 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
Published2024
Admission routes2
Has abstractyes

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