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Record W4405034126 · doi:10.1080/09286586.2024.2374934

Prevalence of Vision Loss in South and Central Asia in 2020: Magnitude and Temporal Trends

2024· article· en· W4405034126 on OpenAlex
Vinay Nangia, Prof Jost B Jonas, Arthur Gustavo Fernandes, Ian Tapply, Maria Vittoria Cicinelli, Paul Svitil Briant, Serge Resnikoff, Tabassom Sedighi, Prof Saira Afzal, Danish Ahmad, Sajjad Ahmad, Tahira Ashraf, Alok Atreya, Atif Amin Baig, Mainak Bardhan, Saurav Basu, Abhishek Bhadra, Devidas S. Bhagat, Pankaj Bhardwaj, Zahid A Butt, Vijay Kumar Chattu, Meghnath Dhimal, Ayesha Fahim, Prof Abhay Motiramji Gaidhane, Syed Amir Gilani, Mahaveer Golechha, Sapna Gupta, Ikramul Hasan, Khezar Hayat, Ramesh Holla, Prof. Dr. Md. Nazrul Islam, Prof Shubha Jayaram, Nitin Joseph, Vidya Kadashetti, Vineet Kumar Kamal, Bhushan Dattatray Kamble, Soujanya Kaup, Navjot Kaur, Himanshu Khajuria, Sudarshan Chandra Khanal, P. Krishan, Nithin Kumar, Chandrakant Lahariya, Kashish Malhotra, Prasanna Mithra, P. Murray, Biswa Prakash Nayak, Robina Khan Niazi, Mamoona Noreen, Jagadish Rao Padubidri, Aslam Pathan, Uttam Paudel, Prof Arokiasamy Perianayagam, Vivek Podder, Pankaja Raghav, Mohammad Hifz Ur Rahman, Mosiur Rahman, Sathish Rajaa, Premkumar Ramasubramani, Sher Zaman Safi, Harihar Sahoo, Muhammad Arif Nadeem Saqib, Ganesh Kumar Saya, Yashendra Sethi, Masood Ali Shaikh, Prof K M Shivakumar, Paramdeep Singh, Saif Ullah, Muhammad Umair, Rehana VR, Jaimie D Steinmetz, Prof Rupert Bourne, Jost B. Jonas, Maria Vittoria Cicinelli, Nicolas Leveziel, Hugh R. Taylor, Mukharram M. Bikbov, Tasanee Braithwaite, Alain M. Bron, Robert J. Casson, Ching‐Yu Cheng, Joshua R. Ehrlich, João M. Furtado, Ronnie George, M. Elizabeth Hartnett, Rim Kahloun, John H. Kempen, Moncef Khairallah, Rohit C Khanna, Van Charles Lansingh, Janet L Leasher, Kovin Naidoo, Michał Nowak, Konrad Pesudovs, Pradeep Y. Ramulu, Nina Tahhan, Fotis Topouzis, Miltiadis K. Tsilimbaris, Rupert Bourne

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOphthalmic Epidemiology · 2024
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of TorontoUniversity of WaterlooUniversity of Calgary
FundersFred Hollows FoundationBrien Holden Vision InstituteSightsavers InternationalUniversität HeidelbergBill and Melinda Gates Foundation
KeywordsMedicineCentral asiaMagnitude (astronomy)OptometryDemographyGeographyPhysical geography

Abstract

fetched live from OpenAlex

PURPOSE: To estimate the prevalence of vision loss for 2020 in South and Central Asia and analyze trends since 1990. METHODS: In a systematic literature review, we estimated the prevalence of blindness, visual impairment (VI) and presbyopia-related VI in 1990,2000,2010, and 2020. RESULTS: The study included 103 population-based studies. In South/Central Asia combined, age-standardized prevalence of blindness, moderate-to-severe VI (MSVI), moderate VI, severe VI, mild VI and presbyopia-related VI for all ages was 0.65% (95% uncertainty interval (UI):0.56/0.74), 5.06 (4.55/5.59), 4.40 (3.91/4.94), 0.65 (0.57/0.74), 3.21 (2.89/3.56), and 8.77 (6.37/11.48), respectively, with higher values for women than men. From 2000 to 2020, changes in age-standardized prevalence in South Asia were -36.85 (-36.94/-36.76), -7.01 (-7.13/-6.90), -5.86 (-5.99/-5.73), -13.96 (-14.09/-13.82), -9.55 (-9.66/-9.44), and -8.62 (-8.93/-8.31), respectively for men, and -38.50 (-38.59/-38.40), -10.12 (-10.22/-10.01), -9.23(-9.36/-9.10), -14.86 (-14.99/-14.73), -9.44 (-9.56/-9.33), and -7.78 (-8.09/-7.48), respectively for women. From 2000/2020, the changes in age-standardized prevalence figures in Central Asia were -21.44 (-21.58/-21.30), -2.75 (-2.87/-2.64), -2.17 (-2.30/-2.04), -7.12 (-7.26/-6.99), -5.36 (-5.48/-5.25), and -3.67(-4.02/-3.32), respectively for men, and -21.13 (-21.27/-20.99), -2.70 (-2.81/-2.58), -2.18 (-2.30/-2.05), -6.93 (-7.07/-6.80), -5.03 (-5.14/-4.91), and -2.65 (-3.00/-2.30), respectively, for women. In 2020, 11.94 million (9.98-14.07) and 0.30 million (0.24-0.36) individuals were blind, and 96.22 million (84.12-110.27) and 2.95 million (2.52-3.43) had MSVI in South Asia and Central Asia, respectively. CONCLUSIONS: Despite a higher decrease between 2000 and 2020, the age-standardized prevalence of blindness and MSVI were higher in South Asia than in Central Asia in 2020. The number of people affected increased due to population growth and improved longevity.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.402
Teacher spread0.357 · 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