Prevalence of blindness and cataract surgical coverage in Narayani Zone, Nepal: a rapid assessment of avoidable blindness (RAAB) study
Bibliographic record
Abstract
BACKGROUND: The 1981 Nepal Blindness Survey first identified the Narayani Zone as one of the regions with the highest prevalence of blindness in the country. Subseuqently, a 2006 survey of the Rautahat District of the Narayani Zone found it to have the country's highest blindness prevalence. This study examines the impact on blind avoidable and treatable eye conditions in this region after significant increase in eye care services in the past decade. METHODS: The rapid assessment of avoidable blindness (RAAB) methodology was used with mobile data collection using the mRAAB smartphone app. Data analysis was done using the standard RAAB software. Based on the 2011 census, 100 clusters of 50 participants aged 50 years or older were randomly sampled proportional to population size. RESULTS: Of the 5000 participants surveyed, 4771 (95.4%) were examined. The age-adjusted and sex-adjusted prevalence of bilateral blindness, severe visual impairment (SVI) and moderate visual impairment (MVI) were 1.2% (95% CI 0.9% to 1.5%), 2.5% (95% CI 2.0% to -3.0%) and 13.2% (95% CI 11.8% to 14.5%), respectively. Cataract remains the primary cause of blindness and SVI despite cataract surgery coverage (CSC) of 91.5% for VA<3/60. Women still account for two-thirds of blindness. CONCLUSION: The prevalence of blindness in people over the age of 50 years has decreased from 6.9% in 2006 to 1.2%, a level in keeping with the national average; however, significant gender inequity persists. CSC has improved but continues to favour men.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".