Prevalence of Vision Loss in South and Central Asia in 2020: Magnitude and Temporal Trends
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".