The Global Burden of Visual Difficulty in Low, Middle, and High Income Countries
Bibliographic record
Abstract
PURPOSE: Using a world-wide, population-based dataset of adults, we sought to determine the frequency of far visual difficulty and its associated risk factors. METHODS: The World Health Survey (WHS) was conducted in 70 countries throughout the world in 2003 using a random, multi-stage, stratified, cluster sampling design of adults ages 18 years and older. Far vision was assessed by asking "In the last 30 days, how much difficulty did you have in seeing and recognizing a person you know across the road (i.e. from a distance of about 20 meters)?". Responses included none, mild, moderate, severe, or extreme/unable. The income status of countries was estimated using gross national income per capita data from 2003 from the World Bank. Prevalence and regression estimates were adjusted to account for the complex sample design. RESULTS: 21% of adults reported any visual difficulty. The rate varied by the income status of the country with the percentage who had any visual difficulty being 24%, 23%, and 13% in low, middle, and high income countries, respectively. Five percent of people reported severe or extreme visual difficulty with rates in low, middle, and high income countries of 6%, 5%, and 2% respectively. Risk factors for visual difficulty included older age, female sex, poorer socioeconomic status, little to no formal education, and diabetes (P<0.05). CONCLUSIONS: One out of five adults in the WHS reported some degree of far visual difficulty. Given the importance of vision to living an independent life, better access to quality eye care services and life course factors affecting vision health (e.g. repeated eye infections, diet lacking vitamin A) must receive adequate attention and resources, especially in low and middle income countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".