Socioeconomic disparity in mortality and the burden of cardiovascular disease: analysis of the Prospective Urban Rural Epidemiology (PURE)-China cohort study
Notice bibliographique
Résumé
BACKGROUND: Although socioeconomic inequality in cardiovascular health has long been a public health focus, the differences in cardiovascular-disease burden and mortality between people with different socioeconomic statuses has yet to be adequately addressed. We aimed to assess the effects of socioeconomic status, measured via three socioeconomic-status indicators (ie, education, occupation, and household wealth and a composite socioeconomic-status disparity index, on mortality and cardiovascular-disease burden (ie, incidence, mortality, and admission to hospital) in China. METHODS: For this analysis, we used data from the Prospective Urban Rural Epidemiology (PURE)-China cohort study, which enrolled adults aged 35-70 years from 115 urban and rural areas in 12 provinces in China between Jan 1, 2005, and Dec 31, 2009. Final follow-up was on Aug 30, 2021. Indicators of socioeconomic status were education, occupation, and household wealth; these individual indicators were also used to create an integrated socioeconomic-status index via latent class analysis. Standard questionnaires administered by trained researchers were used to obtain baseline data and were supplemeted by physical measurements. The primary outcomes were all-cause mortality, cardiovascular-disease mortality, non-cardiovascular-disease mortality, major cardiovascular disease, and cardiovascular-disease admission to hospital. Hazard ratios (HRs) and average marginal effects were used to assess the association between the primary outcomes and socioeconomic status. FINDINGS: Of 47 931 participants enrolled in the PURE-China study, 47 278 (98·6%) had complete information on sex and follow-up. After excluding 1189 (2·5%) participants with missing data on education, household wealth, and occupation at baseline, 46 089 participants were included in this analysis. Median follow-up was 11·9 years (IQR 9·5-12·6); 26 860 (58·3%) of 46 089 participants were female and 19 229 (41·7%) were male. Having no or primary education, unskilled occupation, or being in the lowest third of household wealth was associated with a higher risk of all-cause mortality, cardiovascular-disease mortality, non-cardiovascular-disease mortality, major cardiovascular disease, and cardiovascular-disease admission to hospital compared with having higher education, a professional or managerial occupation, or more household wealth. After adjustment for confounders, people categorised as having low integrated socioeconomic status based on the index had a higher risk of all-cause mortality (HR 1·65 [95% CI 1·42-1·92]), cardiovascular-disease mortality (2·19 [1·68-2·85]), non-cardiovascular disease mortality (1·43 [1·18-1·72]), major cardiovascular disease (1·43 [1·27-1·61]) and cardiovascular-disease admission to hospital (1·14 [1·01-1·28]) compared with people categorised as having high integrated socioeconomic status. INTERPRETATION: Socioeconomic-status inequalities in mortality and cardiovascular-disease outcomes exist in China. Targeted policies of equal health-care resource allocation should be promoted to equitably benefit people with fewer years of education and less household wealth. FUNDING: Funding sources are listed at the end of the Article.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,034 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».