Prevalence of Vision Loss in South and Central Asia in 2020: Magnitude and Temporal Trends
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it