Prevalence of refractive errors in Nepalese children and adults: a systematic review with meta-analysis
Bibliographic record
Abstract
CLINICAL RELEVANCE: Country-specific estimates of the prevalence of refractive errors are important to formulate national eye health policies for refractive care services. BACKGROUND: The purpose of this study was to systematically synthesise available literature and estimate the prevalence of refractive errors in the Nepalese population. METHODS: PubMed, Scopus, and Web of Science databases were systematically searched for articles on refractive errors and presbyopia published in English language until 27 September 2022. Population and school-based quantitative, cross-sectional prevalence studies and Rapid Assessment of Avoidable Blindness survey repository data were included. The quality of the included studies was assessed using the Newcastle Ottawa scale adapted for cross-sectional studies. Data extraction was performed with consensus among the reviewers. Meta-analysis of the prevalence was performed using the Random effects model to estimate the pooled proportions. RESULTS: A total of 38 studies with 101 701 participants were included: 18 studies in children (n = 31 596) and 20 in adults (n = 70 105). In children, the estimated pooled prevalence of overall refractive errors was 8.4% (95% CI: 4.8 to 12.9) with myopia, hypermetropia and astigmatism prevalent in 7.1% (95% CI: 3.7 to 11.4), 1.0% (95% CI: 0.7 to 1.3) and 2.2% (95% CI: 0.9 to 3.9), respectively. In adults, the prevalence of refractive errors, uncorrected refractive errors, and uncorrected presbyopia were 11.2% (95% CI: 8.0 to 14.9), 7.3% (95% CI: 5.4 to 9.5) and 78.9% (95% CI: 69.1 to 87.3), respectively. CONCLUSIONS: The pooled prevalence of refractive errors is relatively low while uncorrected refractive errors and presbyopia are high in Nepalese population suggesting a need for better access to refractive care services in the country. The paucity of quality evidence on prevalence of refractive errors, particularly in children, indicates a need for a well-designed population-based study to accurately estimate the current prevalence of refractive errors.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".