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Record 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 on 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

Bibliographic record

VenueThe Lancet Global Health · 2020
Typereview
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of ManitobaMcMaster UniversityUniversity of Waterloo
FundersStudent 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
KeywordsVisual impairmentVisual acuityMedicinePopulationPresbyopiaOptometryOphthalmologyEnvironmental healthPsychiatry

Abstract

fetched live from 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 <N6 or <N8 at 40 cm where best-corrected distance visual acuity is ≥6/12). We forecast estimates of vision loss up to 2050. 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.022
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.020
Bibliometrics0.0060.010
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.081
GPT teacher head0.497
Teacher spread0.416 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1,350
Published2020
Admission routes2
Has abstractyes

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