Risk factors, cardiovascular disease, and mortality in South America: a PURE substudy
Notice bibliographique
Résumé
AIMS: In a multinational South American cohort, we examined variations in CVD incidence and mortality rates between subpopulations stratified by country, by sex and by urban or rural location. We also examined the contributions of 12 modifiable risk factors to CVD development and to death. METHODS AND RESULTS: This prospective cohort study included 24 718 participants from 51 urban and 49 rural communities in Argentina, Brazil, Chile, and Colombia. The mean follow-up was 10.3 years. The incidence of CVD and mortality rates were calculated for the overall cohort and in subpopulations. Hazard ratios and population attributable fractions (PAFs) for CVD and for death were examined for 12 common modifiable risk factors, grouped as metabolic (hypertension, diabetes, abdominal obesity, and high non-HDL cholesterol), behavioural (tobacco, alcohol, diet quality, and physical activity), and others (education, household air pollution, strength, and depression). Leading causes of death were CVD (31.1%), cancer (30.6%), and respiratory diseases (8.6%). The incidence of CVD (per 1000 person-years) only modestly varied between countries, with the highest incidence in Brazil (3.86) and the lowest in Argentina (3.07). There was a greater variation in mortality rates (per 1000 person-years) between countries, with the highest in Argentina (5.98) and the lowest in Chile (4.07). Men had a higher incidence of CVD (4.48 vs. 2.60 per 1000 person-years) and a higher mortality rate (6.33 vs. 3.96 per 1000 person-years) compared with women. Deaths were higher in rural compared to urban areas. Approximately 72% of the PAF for CVD and 69% of the PAF for deaths were attributable to 12 modifiable risk factors. For CVD, largest PAFs were due to hypertension (18.7%), abdominal obesity (15.4%), tobacco use (13.5%), low strength (5.6%), and diabetes (5.3%). For death, the largest PAFs were from tobacco use (14.4%), hypertension (12.0%), low education (10.5%), abdominal obesity (9.7%), and diabetes (5.5%). CONCLUSIONS: Cardiovascular disease, cancer, and respiratory diseases account for over two-thirds of deaths in South America. Men have consistently higher CVD and mortality rates than women. A large proportion of CVD and premature deaths could be averted by controlling metabolic risk factors and tobacco use, which are common leading risk factors for both outcomes in the region.
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 | 0,002 | 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 | 0,000 | 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 ».