Changes in ideal cardiovascular health among Malawian adults from 2009 to 2017
Notice bibliographique
Résumé
Abstract Ideal Cardiovascular Health (CVH) is a concept defined by the American Heart Association (AHA) as part of its 2020 Impact Goals. Until now, changes in ideal CVH have been poorly evaluated in Sub-Saharan African populations. We aimed to investigate changes in the prevalence of ideal CVH and its components in a population of Malawian adults. Secondary analysis was done on cross-sectional data from 2009 to 2017, obtained from the Malawi STEPS surveys which included 5730 participants aged 25–64 years. CVH metrics categorized into “ideal (6–7 ideal metrics)”, “intermediate (3–5 ideal metrics)” and “poor (0–2 ideal metrics)” were computed using blood pressure, body mass index (BMI), fasting glycaemia, fruit and vegetable intake, physical activity, smoking, and total cholesterol. Sampling weights were used to account for the sampling design, and all estimates were standardised by age and sex using the direct method. The mean participant age across both periods was 40.1 ± 12.4 years. The prevalence of meeting ≥ 6 ideal CVH metrics increased substantially from 9.4% in 2009 to 33.3% in 2017, whereas having ≤ 2 ideal CVH metrics decreased from 7.6% to 0.5% over this time. For the individual metrics, desirable levels of smoking, fruit and vegetable intake, physical activity, blood pressure (BP), total cholesterol and fasting glucose all increased during the study period whilst achievable levels of BMI (< 25 kg/m 2 ) declined. From 2009 to 2017, the mean number of ideal CVH metrics was higher in women compared to men (from 2.1% to 5.1% vs 2.0% to 5.0%). However, poor levels of smoking and fruit and vegetable intake were higher in men compared to women (from 27.9% to 23.6% vs. 7.4%% to 1.9% , and from 33.7% to 42.9% vs 30.8% to 34.6%, respectively). Also, whilst achievable levels of BMI rose in men (from 84.4% to 86.2%) the proportion reduced in women (from 72.1% to 67.5% ). Overall, CVH improved in Malawian adults from 2009 to 2017 and was highest in women. However, the prevalence of poor fruit and vegetable intake, and poor smoking remained high in men whilst optimal levels of BMI was declined in women. To improve this situation, individual and population-based strategies that address body mass, smoking and fruit and vegetable intake are warranted for maximal health gains in stemming the development of cardiovascular events.
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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 | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,000 |
| 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 ».