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 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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| 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) | 0,001 | 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 ».