Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study
Bibliographic record
Abstract
Background Many causes of vision impairment can be prevented or treated.With an ageing global population, the demands for eye health services are increasing.We estimated the prevalence and relative contribution of avoidable causes of blindness and vision impairment globally from 1990 to 2020.We aimed to compare the results with the World Health Assembly Global Action Plan (WHA GAP) target of a 25% global reduction from 2010 to 2019 in avoidable vision impairment, defined as cataract and undercorrected refractive error. MethodsWe did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018.We fitted hierarchical models to estimate prevalence (with 95% uncertainty intervals [UIs]) of moderate and severe vision impairment (MSVI; presenting visual acuity from <6/18 to 3/60) and blindness (<3/60 or less than 10° visual field around central fixation) by cause, age, region, and year.Because of data sparsity at younger ages, our analysis focused on adults aged 50 years and older.Findings Global crude prevalence of avoidable vision impairment and blindness in adults aged 50 years and older did not change between 2010 and 2019 (percentage change -0•2% [95% UI -1•5 to 1•0]; 2019 prevalence 9•58 cases per 1000 people [95% IU 8•51 to 10•8], 2010 prevalence 96•0 cases per 1000 people [86•0 to 107•0]).Age-standardised prevalence of avoidable blindness decreased by -15•4% [-16•8 to -14•3], while avoidable MSVI showed no change (0•5% [-0•8 to 1•6]).However, the number of cases increased for both avoidable blindness (10•8% [8•9 to 12•4]) and MSVI (31•5% [30•0 to 33•1]).The leading global causes of blindness in those aged 50 years and older in 2020 were cataract (15•2 million cases [9% IU 12•7-18•0]), followed by glaucoma (3•6 million cases [2•8-4•4]), undercorrected refractive error (2•3 million cases [1•8-2•8]), age-related macular degeneration (1•8 million cases [1•3-2•4]), and diabetic retinopathy (0•86 million cases [0•59-1•23]).Leading causes of MSVI were undercorrected refractive error (86•1 million cases [74•2-101•0]) and cataract (78•8 million cases [67•2-91•4]).Interpretation Results suggest eye care services contributed to the observed reduction of age-standardised rates of avoidable blindness but not of MSVI, and that the target in an ageing global population was not reached.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.029 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".