Association of socioeconomic status with aggressive end-of-life care in patients with cancer before and during the COVID-19 pandemic.
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».