Prevalence, Incidence, and Years Lived With Disability Due to Gout and Its Attributable Risk Factors for 195 Countries and Territories 1990–2017: A Systematic Analysis of the Global Burden of Disease Study 2017
Notice bibliographique
Résumé
OBJECTIVE: To describe the levels and trends of point prevalence, annual incidence, and years lived with disability (YLD) for gout and its attributable risk factors in 195 countries and territories from 1990 to 2017 according to age, sex, and Sociodemographic Index (SDI; a composite of sociodemographic factors). METHODS: Data were extracted from the Global Burden of Disease (GBD) 2017 study. A comprehensive systematic review of databases and the disease-modeled analysis were performed by the GBD team at the Institute for Health Metrics and Evaluation, in collaboration with researchers and experts worldwide, to provide estimates at global, regional, and national levels during 1990 and 2017. Counts and age-standardized rates per 100,000 population, along with 95% uncertainty intervals (95% UIs), were reported for point prevalence, annual incidence, and YLD. RESULTS: Globally, there were ~41.2 million (95% UI 36.7 million, 46.1 million) prevalent cases of gout, with 7.4 million incident cases per year (95% UI 6.6 million, 8.5 million) and almost 1.3 million YLD (95% UI 0.87 million, 1.8 million) in 2017. The global age-standardized point prevalence estimates and annual incidence rates in 2017 were 510.6 (95% UI 455.6, 570.3) and 91.8 (95% UI 81.3, 104.1) cases per 100,000 population, respectively, an increase of 7.2% (95% UI 6.4%, 8.1%) and 5.5% (95% UI 4.8%, 6.3%) from 1990. The corresponding age-standardized YLD rate was 15.9 (95% UI 10.7, 21.8) cases per 100,000 persons, a 7.2% increase (95% UI 5.9%, 8.6%) from 1990. In 2017, the global point prevalence estimates for gout were higher in males, and higher prevalence was seen in older age groups and increased with age for both males and females. The burden of gout was generally highest in developed regions and countries. The 3 countries with the highest age-standardized point prevalence estimates of gout in 2017 were New Zealand (1,394.0 cases [95% UI 1,290.1, 1,500.9]), Australia (1,171.4 cases [95% UI 1,038.1, 1,322.9]), and the US (996.0 cases [95% UI 923.1, 1,076.8]). The countries with the highest increases in age-standardized point prevalence estimates of gout from 1990 to 2017 were the US (34.7% [95% UI 27.7%, 43.1%]), Canada (28.5% [95% UI 21.9%, 35.4%]), and Oman (28.0% [95% UI 21.5%, 34.8%]). Globally, high body mass index and impaired kidney function accounted for 32.4% (95% UI 18.7%, 49.2%) and 15.3% (95% UI 13.5%, 17.1%), respectively, of YLD due to gout in the 2017 estimates. The YLD attributable to these risk factors were higher in males. CONCLUSION: The burden of gout increased across the world from 1990 to 2017, with variations in point prevalence, annual incidence, and YLD between countries and territories. Besides improving the clinical management of disease, prevention and health promotion in communities to provide basic knowledge of the disease, risk factors, consequences, and effective treatment options (tailoring to high-risk groups such as the middle-aged male population) are crucial to avoid disease onset and hence to decrease the global disease burden.
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 | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,007 |
| Bibliométrie | 0,008 | 0,015 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».