The national and subnational prevalence and burden of age–related macular degeneration in China
Notice bibliographique
Résumé
BACKGROUND: Age-related macular degeneration (AMD) is the third most common cause of blindness, and the fourth leading cause of visual impairment worldwide, but little is known about the burden of this disease in the most populous country-China. This study provides the first comprehensive estimates of the prevalence and burden of AMD in China from 1990 to 2015, with projections till 2050. METHODS: In this study, a systematic review and meta-analysis was conducted to estimate the prevalence of AMD in China. China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedicine Literature Database (CBM-SinoMed), PubMed, Embase and Medline were searched before September 2016. Multilevel mixed-effect meta-regression was performed to define the prevalence rates of AMD and its subtypes. UN population data were used to estimate and project the number of people affected from 1990 to 2050. Based on different demographic and geographic features, the national burden of AMD in 2000 and 2010 was distributed to different regions in China. RESULTS: Our search returned 2016 citations, of which 25 met the inclusion criteria. The prevalence of any AMD ranged from 2.44% (95% CI = 1.85-3.22) in people aged 45-49 years to 18.98% (95% CI = 15.05-23.66) in people aged 85-89 years. Prevalence of early AMD ranged from 1.79% (95% CI = 1.05-3.02) to 10.05% (95% CI = 6.17-15.97), and, in the case of late AMD, from 0.38% (95% CI = 0.16-0.97) to 3.88% (95% CI = 1.68-9.13). In late AMD, the prevalence of geographic atrophy (GA) was 0.15% (95% CI = 0.05-0.47) in people aged 45-49 years and 1.09% (95% CI = 0.35-3.36) in those aged 85-89 years, and the prevalence of neovascular AMD (NVAMD) ranged between 0.24% (95% CI = 0.11-0.50) and 2.79% (95% CI = 1.33-5.77). The number of people with any AMD was 12.01 million (95% CI = 9.29-15.46) in 1990 and 26.65 million (95% CI = 20.62-34.27) in 2015. Within the same period, the number of people with early AMD increased from 9.44 million (95% CI = 7.74-11.15) to 20.91 million (95% CI = 17.16-24.68), and those with late AMD rose from 2.58 million (95% CI = 1.56-4.30) to 5.74 million (95% CI = 3.46-9.59). In late AMD, the number of people living with GA ranged from 0.87 million (95% CI = 0.40-1.83) in 1990 to 1.93 million (95% CI = 0.89-4.08) in 2015, and NVAMD from 1.71 million (95% CI = 1.16-2.47) to 3.81 million (95% CI = 2.57-5.51). The projected number of people with any AMD in 2020 is 31.23 million (95% CI = 24.18-40.14), increasing to 55.19 million (95% CI = 43.04-70.30) in 2050. Between different regions, the South Central owed the most AMD cases (5.50 million in 2000 and 7.52 million in 2010), whereas the North-West China the least (0.66 million in 2000 and 0.95 million in 2010). CONCLUSIONS: The estimates in this study suggest a substantial burden of AMD in China, with the ageing process in Chinese society, this burden will be increasing in the foreseen future. Primary and secondary prevention and treatment and effective government response are urgently needed. Improved epidemiological studies are also required to better develop eye-care strategies and health services.
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 | 0,006 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,006 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».