Modifiable risk factors associated with cardiovascular disease and mortality in China: a PURE substudy
Notice bibliographique
Résumé
AIMS: To examine the incidence of cardiovascular disease (CVD) and mortality in China and in key subpopulations, and to estimate the population-level risks attributable to 12 common modifiable risk factors for each outcome. METHODS AND RESULTS: In this prospective cohort of 47 262 middle-aged participants from 115 urban and rural communities in 12 provinces of China, it was examined how CVD incidence and mortality rates varied by sex, by urban-rural area, and by region. In participants without prior CVD, population-attributable fractions (PAFs) for CVD and for death related to 12 common modifiable risk factors were assessed: four metabolic risk factors (hypertension, diabetes, abdominal obesity, and lipids), four behavioural risk factors (tobacco, alcohol, diet quality, and physical activity), education, depression, grip strength, and household air pollution. The mean age of the cohort was 51.1 years. 58.2% were female, 49.2% were from urban areas, and 59.6% were from the eastern region of China. The median follow-up duration was 11.9 years. The CVD was the leading cause of death in China (36%). The rates of CVD and death were 8.35 and 5.33 per 1000 person-years, respectively, with higher rates in men compared with women and in rural compared with urban areas. Death rates were higher in the central and western regions of China compared with the eastern region. The modifiable risk factors studied collectively contributed to 59% of the PAF for CVD and 56% of the PAF for death in China. Metabolic risk factors accounted for the largest proportion of CVD (PAF of 41.7%), and hypertension was the most important risk factor (25.0%), followed by low education (10.2%), high non-high-density lipoprotein cholesterol (7.8%), and abdominal obesity (6.9%). The largest risk factors for death were hypertension (10.8%), low education (10.5%), poor diet (8.3%), tobacco use (7.5%), and household air pollution (6.1%). CONCLUSION: Both CVD and mortality are higher in men compared with women, and in rural compared with urban areas. Large reductions in CVD could potentially be achieved by controlling metabolic risk factors and improving education. Lowering mortality rates will require strategies addressing a broader range of risk factors.
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 | 0,004 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».