Prevalence and causes of blindness and vision impairment in Western Uganda: Findings from a rapid assessment of avoidable blindness (RAAB) survey
Bibliographic record
Abstract
PURPOSE: To determine the prevalence and causes of blindness and vision impairment (VI) among adults aged ≥50 years in Western Uganda. METHODS: A population-based cross-sectional survey was conducted in Western Uganda (July-August 2023) using RAAB7. Adults aged ≥50 years who had resided in the study districts for at least six months in the past year were eligible. Participants were identified through door-to-door household visits using a two-stage cluster sampling approach. Primary outcomes include prevalence of blindness and VI and its causes. Secondary outcomes include cataract surgical coverage (CSC), effective CSC (eCSC), refractive error coverage (REC), and effective REC (eREC). RESULTS: A total of 3,125 participants were examined (54.1% female). The adjusted prevalence of blindness (presenting visual acuity (PVA) <3/60) was 0.9% (95% CI: 0.5-1.3%). Severe, moderate, and mild VI were found in 0.9% (95% CI: 0.4-1.3%), 4.5% (95% CI: 3.3-5.8%), and 3.8% (95% CI: 3.0-4.6%), respectively. Untreated cataract was the leading cause of bilateral blindness (49.4%). The CSC and eCSC at the < 6/12 threshold were 19.7% and 7.3%, respectively. Only 19.4% of 108 operated eyes achieved good outcomes (PVA ≥ 6/12). The main barriers to cataract surgery included lack of awareness (32.8%), cost (23.9%), and perceived lack of need (20.9%). The adjusted prevalence of uncorrected refractive error as a cause of moderate VI was 1.6% (95% CI: 1.1-2.0%), and mild VI was 2.8% (95% CI: 2.2-3.5%). REC was 1.0%, while eREC was 0.6% (95% CI: 0.0-1.4%). CONCLUSION: Blindness and vision impairment remain major public health issues in Western Uganda, primarily due to untreated cataract and uncorrected refractive error. Poor post-operative outcomes highlight the urgent need to improve surgical quality. These findings may guide targeted interventions and policy to strengthen eye care services.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".