Global, regional, and national prevalence and risk factors for peripheral artery disease in 2015: an updated systematic review and analysis
Notice bibliographique
Résumé
BACKGROUND: Peripheral artery disease is a major cardiovascular disease that affected 202 million people worldwide in 2010. In the past decade, new epidemiological data on peripheral artery disease have emerged, enabling us to provide updated estimates of the prevalence and risk factors for peripheral artery disease globally and regionally and, for the first time, nationally. METHODS: For this systematic review and analysis, we did a comprehensive literature search for studies reporting on the prevalence of peripheral artery disease in the general population that were published between Jan 1, 2011, and April 30, 2019, in PubMed, MEDLINE, Embase, the Global Health database, CINAHL, the Global Health Library, the Allied and Complementary Medicine Database, and ProQuest Dissertations and Theses Global. We also included the Global Peripheral Artery Disease Study of 2013 and the China Peripheral Artery Disease Study as sources. Peripheral artery disease had to be defined as an ankle-brachial index lower than or equal to 0·90. With a purpose-built data collection form, data on study characteristics, sample characteristics, prevalence, and risk factors were abstracted from all the included studies identified from the sources. Age-specific and sex-specific prevalence of peripheral artery disease was estimated in both high-income countries (HICs) and low-income and middle-income countries (LMICs). We also did random-effects meta-analyses to pool the odds ratios of 30 risk factors for peripheral artery disease in HICs and LMICs. UN population data were used to generate the number of people affected by the disease in 2015. Finally, we derived the regional and national numbers of people with peripheral artery disease on the basis of a risk factor-based model. FINDINGS: We included 118 articles for systematic review and analysis. The prevalence of peripheral artery disease increased consistently with age. At younger ages, prevalence was slightly higher in LMICs than HICs (4·32%, 95% CI 3·01-6·29, vs 3·54%, 1·17-10·24, at 40-44 years), but the increase with age was greater in HICs than LMICs, leading to a higher prevalence in HICs than LMICs at older ages (21·24%, 15·22-28·90, vs 12·04%, 8·67-16·60, at 80-84 years). In HICs, prevalence was slightly higher in women than in men up to age 75 years (eg, 7·81%, 3·97-14·77, vs 6·60%, 3·74-11·38, at 55-59 years), whereas in LMICs little difference was found between women and men (eg, 6·40%, 5·06-8·05, vs 6·37%, 4·74-8·49, at 55-59 years). Overall, the global prevalence of peripheral artery disease in people aged 25 years and older was 5·56%, 3·79-8·55, and the prevalence estimate was higher in HICs than that in LMICs (7·37%, 4·35-13·66, vs 5·09%, 3·64-7·24). Smoking, diabetes, hypertension, and hypercholesterolaemia were major risk factors for peripheral artery disease. Globally, a total of 236·62 million people aged 25 years and older were living with peripheral artery disease in 2015, among whom 72·91% were in LMICs. The Western Pacific Region had the most peripheral artery disease cases (74·08 million), whereas the Eastern Mediterranean Region had the least (14·67 million). More than two thirds of the global peripheral artery disease cases were concentrated in 15 individual countries in 2015. INTERPRETATION: Peripheral artery disease continues to become an increasingly serious public health problem, especially in LMICs. With the demographic trend towards ageing and projected rise in important risk factors, a larger burden of peripheral artery disease is to be expected in the foreseeable future. FUNDING: None.
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 | 0,011 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,013 |
| Bibliométrie | 0,019 | 0,019 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».