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Enregistrement W4392190264 · doi:10.1001/jamanetworkopen.2024.0503

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

2024· article· en· W4392190264 sur 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

Notice bibliographique

RevueJAMA Network Open · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensMcMaster UniversityQueen's UniversityUniversity Health NetworkUniversity of TorontoBruyèreUniversity of OttawaPrincess Margaret Cancer Centre
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésMedicineSocioeconomic statusPandemicPalliative careCohortDemographyCoronavirus disease 2019 (COVID-19)Cohort studyEnd-of-life careGerontologyEnvironmental healthPopulationInternal medicineDiseaseNursing

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,355
Écart entre enseignants0,327 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2024
Routes d'admission3
Résumé présentoui

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