The burden of ischemic heart disease and the epidemiologic transition in the Eastern Mediterranean Region: 1990–2019
Notice bibliographique
Résumé
It has been estimated that in the next decade, IHD prevalence, DALYs and deaths will increase more significantly in EMR than in any other region of the world. This study aims to provide a comprehensive description of the trends in the burden of ischemic heart disease (IHD) across the countries of the Eastern Mediterranean Region (EMR) from 1990 to 2019. Data on IHD prevalence, disability-adjusted life years (DALYs), mortality, DALYs attributable to risk factors, healthcare access and quality index (HAQ), and universal health coverage (UHC) were extracted from the Global Burden of Disease (GBD) database for EMR countries. The data were stratified based on the social demographic index (SDI). Information on cardiac rehabilitation was obtained from publications by the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR), and additional country-specific data were obtained through advanced search methods. Age standardization was performed using the direct method, applying the estimated age structure of the global population from 2019. Uncertainty intervals were calculated through 1000 iterations, and the 2.5th and 97.5th percentiles were derived from these calculations. The age-standardized prevalence of IHD in the EMR increased from 5.0% to 5.5% between 1990 and 2019, while it decreased at the global level. In the EMR, the age-standardized rates of IHD mortality and DALYs decreased by 11.4% and 15.4%, respectively, during the study period, although both rates remained higher than the global rates. The burden of IHD was found to be higher in males compared to females. Bahrain exhibited the highest decrease in age-standardized prevalence (-3.7%), mortality (-65.0%), and DALYs (-69.1%) rates among the EMR countries. Conversely, Oman experienced the highest increase in prevalence (14.5%), while Pakistan had the greatest increase in mortality (30.0%) and DALYs (32.0%) rates. The top three risk factors contributing to IHD DALYs in the EMR in 2019 were high systolic blood pressure, high low-density lipoprotein cholesterol, and particulate matter pollution. The trend analysis over the 29-year period (1990-2019) revealed that high fasting plasma glucose (64.0%) and high body mass index (23.4%) exhibited increasing trends as attributed risk factors for IHD DALYs in the EMR. Our findings indicate an increasing trend in the prevalence of IHD and a decrease in mortality and DALYs in the EMR. These results emphasize the need for well-planned prevention and treatment strategies to address the risk factors associated with IHD. It is crucial for the countries in this region to prioritize the development and implementation of programs focused on health promotion, education, prevention, and medical care.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 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 ».