Effective refractive error coverage in adults: a systematic review and meta-analysis of updated estimates from population-based surveys in 76 countries modelling the path towards the 2030 global target
Bibliographic record
Abstract
BACKGROUND: In 2024, WHO included effective refractive error coverage (eREC) into the results framework of the 14th General Programme of Work, which sets a road map for global health and guides WHO's work between 2025 and 2028. eREC is a measure of both the availability and quality of refractive correction in a population. This study aimed to model global and regional estimates of eREC as of 2023 and evaluate progress towards the WHO global target of a 40 percentage-point absolute increase in eREC by 2030. METHODS: For this systematic review and meta-analysis, the Vision Loss Expert Group analysed data from 237 population-based eye surveys conducted in 76 countries since 2000, comprising 815 273 participants, to calculate eREC (met need / met need + undermet need + unmet need]) and the relative quality gap between eREC and REC ([REC - eREC] / REC × 100, where REC = [met + undermet need] / [met need + undermet need + unmet need]). An expert elicitation process was used to choose covariates for a Bayesian logistic regression model used to estimate eREC by country-age-sex grouping among adults aged 50 years and older. Country-age-sex group estimates were aggregated to provide estimates according to Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) super-regions. FINDINGS: Global eREC was estimated to be 65·8% (95% uncertainty interval [UI] 64·7-66·8) in 2023, 6 percentage points higher than in 2010 (eREC 59·8% [59·4-60·2]). There were marked differences in eREC between GBD super-regions in 2023, ranging from 84·0% (95% UI 83·0-85·0) in high-income countries to 28·3% (26·4-30·4) in sub-Saharan Africa. In all super-regions, eREC was lower in females than males, and decreased with increasing age among adults aged ≥50 years. Since 2000, the relative increase in eREC was 60·2% in sub-Saharan Africa, 45·7% in North Africa and the Middle East, 41·5% in southeast Asia, east Asia and Oceania, 40·3% in south Asia, 16·2% in Latin America and the Caribbean, 8·3% in central Europe, eastern Europe and central Asia, and 6·8% in the high-income super-region. The relative quality gap ranged from 2·9% to 78·3% across studies, with larger gaps characteristically in regions of lower eREC. Globally, the percentage of those with a refractive need that was undermet reduced between 2000 and 2023, from 10·0% (95% UI 9·5-10·5) to 5·3% (5·1-5·5). INTERPRETATION: The current trajectory of improvement in eREC and the relative quality gap are insufficient to meet the 2030 target. Global efforts to equitably increase spectacle coverage, such as the WHO SPECS 2030 initiative, and to address equity failings associated with geography, age, and sex, are crucial to accelerating progress towards the 2030 targets. No region is close to achieving universal coverage. FUNDING: WHO, Sightsavers, The Fred Hollows Foundation, Fondation Thea, University of Heidelberg, German Federal Ministry for Education and Research. TRANSLATIONS: For the French, Chinese and Spanish translations of the abstract see Supplementary Materials section.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".