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Enregistrement W4379768987 · doi:10.1001/jamanetworkopen.2023.17247

Place of Death From Cancer in US States With vs Without Palliative Care Laws

2023· article· en· W4379768987 sur OpenAlexfundno aff
Main Lin Quan Vega, Stanford Chihuri, Deven Lackraj, Komal Patel Murali, Guohua Li, May Hua

Notice bibliographique

RevueJAMA Network Open · 2023
Typearticle
Langueen
DomaineMedicine
ThématiquePalliative Care and End-of-Life Issues
Établissements canadiensnon disponible
Organismes subventionnairesMailman School of Public Health, Columbia UniversityNational Institute of General Medical SciencesSchool of Medicine and Health Sciences, George Washington UniversityYork UniversityGeorge Washington University
Mots-clésPalliative careLegislationDeath certificateMedicineCancerEthnic groupEnd-of-life careCause of deathFamily medicineDemographyLawGerontologyNursingDiseasePolitical scienceInternal medicine

Résumé

récupéré en direct d'OpenAlex

Importance: In the US, improving end-of-life care has become increasingly urgent. Some states have enacted legislation intended to facilitate palliative care delivery for seriously ill patients, but it is unknown whether these laws have any measurable consequences for patient outcomes. Objective: To determine whether US state palliative care legislation is associated with place of death from cancer. Design, Setting, and Participants: This cohort study with a difference-in-differences analysis used information about state legislation combined with death certificate data for 50 US states (from January 1, 2005, to December 31, 2017) for all decedents who had any type of cancer listed as the underlying cause of death. Data analysis for this study occurred between September 1, 2021, and August 31, 2022. Exposures: Presence of a nonprescriptive (relating to palliative and end-of-life care without prescribing particular clinician actions) or prescriptive (requiring clinicians to offer patients information about care options) palliative care law in the state-year where death occurred. Main Outcomes and Measures: Multilevel relative risk regression with state modeled as a random effect was used to estimate the likelihood of dying at home or hospice for decedents dying in state-years with a palliative care law compared with decedents dying in state-years without such laws. Results: This study included 7 547 907 individuals with cancer as the underlying cause of death. Their mean (SD) age was 71 (14) years, and 3 609 146 were women (47.8%). In terms of race and ethnicity, the majority of decedents were White (85.6%) and non-Hispanic (94.1%). During the study period, 553 state-years (85.1%) had no palliative care law, 60 state-years (9.2%) had a nonprescriptive palliative care law, and 37 state-years (5.7%) had a prescriptive palliative care law. A total of 3 780 918 individuals (50.1%) died at home or in hospice. Most decedents (70.8%) died in state-years without a palliative care law, while 15.7% died in state-years with a nonprescriptive law and 13.5% died in state-years with a prescriptive law. Compared with state-years without a palliative care law, the likelihood of dying at home or in hospice was 12% higher for decedents in state-years with a nonprescriptive palliative care law (relative risk, 1.12 [95% CI 1.08-1.16]) and 18% higher for decedents in state-years with a prescriptive palliative care law (relative risk, 1.18 [95% CI, 1.11-1.26]). Conclusions and Relevance: In this cohort study of decedents from cancer, state palliative care laws were associated with an increased likelihood of dying at home or in hospice. Passage of state palliative care legislation may be an effective policy intervention to increase the number of seriously ill patients who experience their death in such locations.

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 enseignants0,017
Score d'incertitude au seuil0,988

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
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 Opus0,104
Tête enseignante GPT0,427
Écart entre enseignants0,322 · 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

Citations9
Publié2023
Routes d'admission1
Résumé présentoui

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