Global estimates on the number of people blind or visually impaired by glaucoma: A meta-analysis from 2000 to 2020
Bibliographic record
Abstract
OBJECTIVES: To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by glaucoma and their proportion of the total number of vision-impaired individuals. METHODS: A systematic review and meta-analysis of published population studies and grey literature from 2000 to 2020 was carried out to estimate global and regional trends in number of people with vision loss due to glaucoma. Moderate or severe vision loss (MSVI) was defined as visual acuity of 6/60 or better but <6/18 (moderate) and visual acuity of 3/60 or better but <6/60 (severe vision loss). Blindness was defined as presenting visual acuity <3/60. RESULTS: Globally, in 2020, 3.61 million people were blind and nearly 4.14 million were visually impaired by glaucoma. Glaucoma accounted for 8.39% (95% uncertainty intervals [UIs]: 6.54, 10.29) of all blindness and 1.41% (95% UI: 1.10, 1.75) of all MSVI. Regionally, the highest proportion of blindness relating to glaucoma was found in high-income countries (26.12% [95% UI: 20.72, 32.09]), while the region with the highest age-standardized prevalence of glaucoma-related blindness and MSVI was Sub-Saharan Africa. Between 2000 and 2020, global age-standardized prevalence of glaucoma-related blindness among adults ≥50 years decreased by 26.06% among males (95% UI: 25.87, 26.24), and by 21.75% among females (95% UI: 21.54, 21.96), while MSVI due to glaucoma increased by 3.7% among males (95% UI: 3.42, 3.98), and by 7.3% in females (95% UI: 7.01, 7.59). CONCLUSIONS: Within the last two decades, glaucoma has remained a major cause of blindness globally and regionally.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.043 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".