Association of neighbourhood-level poverty with outcomes and clinical care following atrial fibrillation diagnosis in a universal healthcare system
Notice bibliographique
Résumé
Abstract Background There are limited data on the association of poverty with outcomes and care patterns after an atrial fibrillation (AF) diagnosis in jurisdictions with universal healthcare. The Canadian province of Ontario provides publicly funded healthcare and prohibits private payment for medically necessary physician and in-hospital care. It also covers prescription medications for residents aged >65 years. Purpose Determine the association of neighbourhood-level poverty with outcomes and processes of care after AF diagnosis in older people within a universal healthcare system. Methods Using linked administrative databases, we conducted a population-based cohort study of community-dwelling adults aged ≥66 years who were newly diagnosed with AF in Ontario between April 1, 2007 and March 31, 2019. The primary exposure was material deprivation of patients' neighborhood of residence. This metric is derived using Canadian census data to estimate inability to access and attain basic material needs. Neighborhoods were categorized by quintile of material deprivation from Q1 (wealthiest) to Q5 (poorest). We used cause-specific hazards regression models to study the association of deprivation quintile with time to the following outcomes over one year from AF diagnosis: death, ischemic stroke, bleeding, heart failure (HF) hospitalization, cardiology services, and AF-specific treatments. Models accounted for clustering by region of residence and adjusted for age, sex, diabetes, HF, stroke/transient ischemic attack, vascular disease, hypertension, bleeding history, rural residence, renal function, and setting of AF diagnosis (hospital, emergency department [ED] or outpatient). Results We studied 350,353 patients with AF (median age 78 years, 48.9% female). People from neighborhoods in higher deprivation quintiles (poorer) were more likely to be diagnosed in hospital/ED than outpatient settings. Relative to people from the wealthiest neighbourhoods (Q1), patients in the poorest neighbourhoods (Q5) had higher prevalence of baseline hypertension, diabetes, HF, vascular disease and other comorbidities. In adjusted analyses (Figure), higher quintiles of neighborhood poverty were associated with greater rates of death, ischemic stroke, bleeding, and HF hospitalization, but lower rates of cardiology visits, cardiac testing, anticoagulation, anti-arrhythmic medications, cardioversion, or AF ablation. Conclusions In a setting of universal healthcare and prescription medication coverage, people living in poorer neighbourhoods had worse baseline health and higher rates of adverse outcomes after an AF diagnosis. Despite this, people in poorer neighbourhoods had less cardiology visits and diagnostic tests and were less likely to receive anticoagulation and rhythm control interventions. This shows that universal healthcare and medication coverage are insufficient to achieve equitable health care and outcomes for people with AF. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): This research was funded by a Canadian Institutes of Health Research Foundation grant; and is supported by ICES (formerly the Institute for Clinical Evaluative Sciences), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC).
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 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 | 0,000 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».