Eye care utilization by older adults in low, middle, and high income countries
Bibliographic record
Abstract
BACKGROUND: The risk of visual impairment increases dramatically with age and therefore older adults should have their eyes examined at least every 1 to 2 years. Using a world-wide, population-based dataset, we sought to determine the frequency that older people had their eyes examined. We also examined factors associated with having a recent eye exam. METHODS: The World Health Surveys were conducted in 70 countries throughout the world in 2002-2003 using a random, multi-stage, stratified, cluster sampling design. Participants 60 years and older from 52 countries (n = 35,839) were asked "When was the last time you had your eyes examined by a medical professional?". The income status of countries was estimated using gross national income per capita data from 2003 from the World Bank website. Prevalence estimates were adjusted to account for the complex sample design. RESULTS: Overall, only 18% (95% CI 17, 19) of older adults had an eye exam in the last year. The rate of an eye exam in the last year in low, lower middle, upper middle, and high income countries was 10%, 24%, 22%, and 37% respectively. Factors associated with having an eye exam in the last year included older age, female gender, more education, urban residence, greater wealth, worse self-reported health, having diabetes, and wearing glasses or contact lenses (p < 0.05). CONCLUSIONS: Given that older adults often suffer from age-related but treatable conditions, they should be seen on a regular basis to prevent visual impairment and its disabling consequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".