Prevalence of Visual Impairment and Blindness in Upper Egypt: A Gender-based Perspective
Bibliographic record
Abstract
PURPOSE: To estimate the prevalence, causes of and risk factors for vision loss in Upper Egypt. METHODS: In this cross-sectional study, four villages in Upper Egypt were randomly selected; within these four villages, households were randomly selected and within the selected households all residents aged ≥ 40 years were enumerated and enrolled. Door-to-door eye examinations of household members were conducted. Data on relevant demographic and socioeconomic characteristics were collected. The prevalence and causes of vision loss and associated risk factors were assessed. Sex differences in prevalence and determinants were also evaluated. RESULTS: The prevalence of best eye presenting visual impairment, severe visual impairment, and blindness were 23.9%, 6.4%, and 9.3% respectively. The prevalence of blindness among women significantly exceeded that among men (11.8% vs. 5.4%, respectively, p = 0.021). The prevalence of cataract was 22.9% (higher in women, 26.5% than men 17.2%, p = 0.018). The prevalence of trachomatous trichiasis was 9.7% (higher among women, 12.5%, than men, 5.4%, p = 0.012). The principal causes of blindness were cataract (60%), uncorrected refractive errors (16%) and corneal opacities (12%). Age, sex, family size, illiteracy, unemployment, water source and sanitation methods and living conditions were the major risk factors for vision loss. CONCLUSION: The prevalence of visual impairment remains high in Egypt, particularly among women. Risk factors for blindness may differ between men and women. There is a need for qualitative investigations to better understand the causes behind the excess in prevalence of blindness among women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".