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Enregistrement W3110054227 · doi:10.1016/s2214-109x(20)30425-3

Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study

2020· review· en· W3110054227 sur OpenAlexafffund
Rupert Bourne, Jaimie D Steinmetz, Seth Flaxman, Paul Svitil Briant, Hugh R. Taylor, Serge Resnikoff, Robert J. Casson, Amir Abdoli, Eman Abu‐Gharbieh, Ashkan Afshin, Hamid Ahmadieh, Yonas Akalu, Alehegn Aderaw Alamneh, Wondu Alemayehu, Vahid Alipour, Etsay Woldu Anbesu, Sofia Androudi, Jalal Arabloo, Aries Arditi, Malke Asaad, Eleni Bagli, Atif Amin Baig, Till Bärnighausen, Maurízio Battaglia Parodi, Akshaya Srikanth Bhagavathula, Nikha Bhardwaj, Pankaj Bhardwaj, Krittika Bhattacharyya, Ali Bijani, Mukharram M. Bikbov, Michele Bottone, Tasanee Braithwaite, Alain M. Bron, Zahid A Butt, Ching‐Yu Cheng, Dinh‐Toi Chu, Maria Vittoria Cicinelli, João Coelho, Baye Dagnew, Xiaochen Dai, Reza Dana, Lalit Dandona, Rakhi Dandona, Monte A. Del Monte, Jenny P Deva, Daniel Díaz, Shirin Djalalinia, Laura E. Dreer, Joshua R. Ehrlich, Leon B. Ellwein, Mohammad Hassan Emamian, Arthur Gustavo Fernandes, Florian Fischer, David S. Friedman, João M. Furtado, Abhay Gaidhane, Shilpa Gaidhane, Gus Gazzard, Berhe Gebremichael, Ronnie George, Ahmad Ghashghaee, Mahaveer Golechha, Samer Hamidi, Billy R. Hammond, M. Elizabeth Hartnett, Risky Kusuma Hartono, Simon I Hay, Golnaz Heidari, Hung Chak Ho, Chi Linh Hoang, Mowafa Househ, Segun Emmanuel Ibitoye, Irena Ilić, Milena Ilić, April Ingram, Seyed Sina Naghibi Irvani, Ravi Prakash Jha, Rim Kahloun, Himal Kandel, Ayele Semachew Kasa, John H. Kempen, Maryam Keramati, Moncef Khairallah, Ejaz Ahmad Khan, Rohit C Khanna, Mahalaqua Nazli Khatib, Judy E. Kim, Yun Jin Kim, Sezer Kısa, Adnan Kısa, Ai Koyanagi, Om Kurmi, Van Charles Lansingh, Janet L Leasher, Nicolas Leveziel, Hans Limburg, Marek Majdán, Navid Manafi, Kaweh Mansouri, Colm McAlinden, Seyed-Farzad Mohammadi, Abdollah Mohammadian-Hafshejani, Reza Mohammadpourhodki, Ali H. Mokdad, Delaram Moosavi, Alan R. Morse, Mehdi Naderi, Kovin Naidoo, Vinay Nangia, Cuong Tat Nguyen, Huong Lan Thi Nguyen, Kolawole Ogundimu, Andrew T Olagunju, Samuel M Ostroff, Songhomitra Panda‐Jonas, Konrad Pesudovs, Tünde Pető, Mohammad Hifz Ur Rahman, Pradeep Y. Ramulu, Salman Rawaf, David Laith Rawaf, Nickolas Reinig, Alan L. Robin, Luca Rossetti, Sare Safi, Amirhossein Sahebkar, Abdallah M Samy, Deepak Saxena, Janet B. Serle, Masood Ali Shaikh, Tueng T. Shen, Kenji Shibuya, Jae Il Shin, Juan Carlos Silva, Alexander Silvester, Jasvinder A. Singh, Deepika Singhal, Rita S. Sitorus, Eirini Skiadaresi, Vegard Skirbekk, Amin Soheili, Raúl A. R. C. Sousa, Emma Elizabeth Spurlock, Dwight Stambolian, Eyayou Girma Tadesse, Nina Tahhan, Md. Ismail Tareque, Fotis Topouzis, Bach Xuan Tran, Ravensara S. Travillian, Miltiadis K. Tsilimbaris, Rohit Varma, Gianni Virgili, Ya Xing Wang, Ningli Wang, Sheila K. West, Tien Yin Wong, Zoubida Zaidi, Kaleab Alemayehu Zewdie, Jost B. Jonas, Theo Vos

Notice bibliographique

RevueThe Lancet Global Health · 2020
Typereview
Langueen
DomaineMedicine
ThématiqueOphthalmology and Visual Impairment Studies
Établissements canadiensUniversity of ManitobaMcMaster UniversityUniversity of Waterloo
Organismes subventionnairesStudent Research Committee, Tabriz University of Medical SciencesMoorfields Eye CharityJahrom University of Medical SciencesSamara UniversityUniversity of ThessalyUniversity of GondarUniversidad Nacional Autónoma de MéxicoUniversidade de São PauloHaramaya UniversityBabol University of Medical SciencesResearch Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesUniversidad Autónoma de SinaloaUniversity of WaterlooSightsavers InternationalMinistry of Health and Medical EducationBundesministerium für Bildung und ForschungSingapore Eye Research InstituteUniversity of New South WalesUniversität HeidelbergUniversidade do PortoShahid Beheshti University of Medical SciencesĐại học Quốc gia Hà NộiPublic Health Foundation of IndiaIndian Council of Medical ResearchUniversity College LondonShahroud University of Medical SciencesUniversity of CalcuttaDuke-NUS Medical SchoolFred Hollows FoundationXiamen UniversityDebre Markos UniversityUniversiti Tunku Abdul RahmanAerie PharmaceuticalsResearch Management Centre, International Islamic University MalaysiaFight for SightIran University of Medical SciencesNational Eye InstituteNational Institute for Health and Care ResearchBrien Holden Vision InstituteUniversity of UtahInstitute for Health Metrics and EvaluationInternational Glaucoma AssociationFight for Sight UKNational Institutes of HealthAnglia Ruskin UniversityResearch to Prevent BlindnessImperial College LondonBausch and LombUniverzita Karlova v PrazeMoorfields Eye Hospital NHS Foundation TrustUnited Arab Emirates UniversityHarvard UniversityAlexander von Humboldt-StiftungBill and Melinda Gates FoundationUniversity of WashingtonUniversiti Sultan Zainal AbidinMiddlesex University
Mots-clésVisual impairmentVisual acuityMedicinePopulationPresbyopiaOptometryOphthalmologyEnvironmental healthPsychiatry

Résumé

récupéré en direct d'OpenAlex

Background To contribute to the WHO initiative, VISION 2020: The Right to Sight, an assessment of global vision impairment in 2020 and temporal change is needed. We aimed to extensively update estimates of global vision loss burden, presenting estimates for 2020, temporal change over three decades between 1990–2020, and forecasts for 2050. Methods We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. Only studies with samples representative of the population and with clearly defined visual acuity testing protocols were included. We fitted hierarchical models to estimate 2020 prevalence (with 95% uncertainty intervals [UIs]) of mild vision impairment (presenting visual acuity ≥6/18 and <6/12), moderate and severe vision impairment (<6/18 to 3/60), and blindness (<3/60 or less than 10° visual field around central fixation); and vision impairment from uncorrected presbyopia (presenting near vision Findings In 2020, an estimated 43·3 million (95% UI 37·6–48·4) people were blind, of whom 23·9 million (55%; 20·8–26·8) were estimated to be female. We estimated 295 million (267–325) people to have moderate and severe vision impairment, of whom 163 million (55%; 147–179) were female; 258 million (233–285) to have mild vision impairment, of whom 142 million (55%; 128–157) were female; and 510 million (371–667) to have visual impairment from uncorrected presbyopia, of whom 280 million (55%; 205–365) were female. Globally, between 1990 and 2020, among adults aged 50 years or older, age-standardised prevalence of blindness decreased by 28·5% (–29·4 to −27·7) and prevalence of mild vision impairment decreased slightly (–0·3%, −0·8 to −0·2), whereas prevalence of moderate and severe vision impairment increased slightly (2·5%, 1·9 to 3·2; insufficient data were available to calculate this statistic for vision impairment from uncorrected presbyopia). In this period, the number of people who were blind increased by 50·6% (47·8 to 53·4) and the number with moderate and severe vision impairment increased by 91·7% (87·6 to 95·8). By 2050, we predict 61·0 million (52·9 to 69·3) people will be blind, 474 million (428 to 518) will have moderate and severe vision impairment, 360 million (322 to 400) will have mild vision impairment, and 866 million (629 to 1150) will have uncorrected presbyopia. Interpretation Age-adjusted prevalence of blindness has reduced over the past three decades, yet due to population growth, progress is not keeping pace with needs. We face enormous challenges in avoiding vision impairment as the global population grows and ages. Funding Brien Holden Vision Institute, Fondation Thea, Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation, Sightsavers International, and University of Heidelberg.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,053

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,018
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,020
Bibliométrie0,0060,010
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,081
Tête enseignante GPT0,497
Écart entre enseignants0,416 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeMéta-analyse
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1 350
Publié2020
Routes d'admission2
Résumé présentoui

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