The national and subnational prevalence of cataract and cataract blindness in China: a systematic review and meta-analysis
Notice bibliographique
Résumé
The national and subnational prevalence of cataract and cataract blindness in China: a systematic review and meta-analysis Background Cataract is the second leading cause of visual impairment and the first of blindness globally.However, for the most populous country, China, much remains to be understood about the scale of cataract and cataract blindness.We aimed to investigate the prevalence of cataract and cataract blindness in China at both the national and subnational levels, with projections till 2050. MethodsIn this systematic review and meta-analysis, China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedicine Literature Database (CBM-SinoMed), PubMed, Embase, and Medline were searched using a comprehensive search strategy to identify all relevant articles on the prevalence of cataract or cataract blindness in Chinese population published from January 1990 onwards.We fitted a multilevel mixed-effects meta-regression model to estimate the prevalence of cataract, and a random-effects meta-analysis model to pool the overall prevalence of cataract blindness.The United Nations Population Division (UNPD) data were used to estimate and project the number of people with cataract and cataract blindness from 1990 to 2050.According to different demographic and geographic features in the six geographic regions in China, the national numbers of people with cataract in the years 2000 and 2010 were distributed to each region. ResultsIn males, the prevalence of any cataract (including post-surgical cases) ranged from 6.71% (95% CI = 5.06-8.83) in people aged 45-49 years to 73.01%(95% CI = 65.78-79.2) in elderly aged 85-89 years.In females, the prevalence of any cataract increased from 8.39% (95% CI = 6.36-10.98) in individuals aged 45-49 years to 77.51% (95% CI = 71.00-82.90) in those aged 85-89 years.For age-related cataract (ARC, including post-surgical cases), in males, the prevalence rates ranged from 3.23% (95% CI = 1.51-6.80) in adults aged 45-49 years to 65.78% (95% CI = 46.72-80.82) in those aged 85-89 years.The prevalence of ARC in females was 4.72% (95% CI = 2.22-9.76) in the 45-49 years age group and 74.03% (95% CI = 56.53-86.21) in the 85-89 years age group.The pooled prevalence rate of cataract blindness (including post-surgical cases) by best corrected visual acuity (BCVA)<0.05among middle-aged and older Chinese was 2.30% (95% CI = 1.72-3.07),and those of cataract blindness by BCVA<0.10 and cataract blindness by presenting visual acuity (PVA)<0.10 were 2.56% (95% CI = 1.94-3.38)and 4.51% (95% CI = 3.53-5.75)respectively.In people aged 45-89 years, the number of any cataract cases was 50.75 million (95% CI = 42.17-60.37)in 1990 and 111.74 million (95% CI = 92.94-132.84) in 2015, and that of ARC rose from 35.77 million (95% CI = 19.81-59.55) in 1990 to 79.04 million (95% CI = 44.14-130.85) in 2015.By 2050, it is projected that the number of people (45-89 years of age) affected by any cataract will be 240.83 million (95% CI = 206.07-277.35),and that of those with ARC will be 187.26 million (95% CI = 113.17-281.23).During 2000 and 2010, South Central China consistently owed the most cases of any cataract, whereas Northwest China the least. ConclusionsThe prevalence of cataract and cataract blindness in China was unmasked.In the coming decades, cataract and cataract blindness will continue to be a leading public-health issue in China due to the ageing population.Future work should be prioritized to the promotion of high-quality epidemiological studies on cataract.
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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 | 0,008 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,015 | 0,029 |
| Bibliométrie | 0,005 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».