Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020
Bibliographic record
Abstract
Abstract Objectives To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by diabetic retinopathy and their proportion of the total number of vision-impaired individuals. Methods Data from population-based studies on eye diseases between 1980 to 2018 were compiled. Meta-regression models were performed to estimate the prevalence of blindness (presenting visual acuity <3/60) and moderate or severe vision impairment (MSVI; <6/18 to ≥3/60) attributed to DR. The estimates, with 95% uncertainty intervals [UIs], were stratified by age, sex, year, and region. Results In 2020, 1.07 million (95% UI: 0.76, 1.51) people were blind due to DR, with nearly 3.28 million (95% UI: 2.41, 4.34) experiencing MSVI. The GBD super-regions with the highest percentage of all DR-related blindness and MSVI were Latin America and the Caribbean (6.95% [95% UI: 5.08, 9.51]) and North Africa and the Middle East (2.12% [95% UI: 1.55, 2.79]), respectively. Between 2000 and 2020, changes in DR-related blindness and MSVI were greater among females than males, predominantly in the super-regions of South Asia (blindness) and Southeast Asia, East Asia, and Oceania (MSVI). Conclusions Given the rapid global rise in diabetes and increased life expectancy, DR is anticipated to persist as a significant public health challenge. The findings emphasise the need for gender-specific interventions and region-specific DR healthcare policies to mitigate disparities and prevent avoidable blindness. This study contributes to the expanding body of literature on the burden of DR, highlighting the need for increased global attention and investment in this research area.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.043 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".