Global Incidence and Prevalence of Keratoconus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To estimate global incidence and prevalence trends of keratoconus (KC). METHODS: A systematic review and meta-analysis was conducted using MedLine, Embase, and Scopus databases up to January 2024, including all age groups, sexes, and geographic regions. Pooled incidence and prevalence were estimated using random-effects models, with heterogeneity assessed by χ2 and I2 statistics. RESULTS: This study included 55 studies (53 in meta-analysis). Pooled KC prevalence was 289.1 per 100,000 persons [95% confidence interval (CI) 124.2-671.1] or 0.24% of the population. The pooled incidence was 4.0 per 100,000 person-years (95% CI 2.5-6.3). Males had higher odds of KC compared with females (odds ratio 1.10; 95% CI 1.07-1.13). The 20 to 29 age group had the highest prevalence (525.5 per 100,000 persons; 95% CI 92.6-2822.6) and incidence (20.8 per 100,000 persons-years; 95% CI 12.7-24.1). Prevalence was highest in Africa (2414.2 per 100,000 persons; 95% CI 110.1-1421.5). Prevalence estimates varied significantly across Asian subregions, with lowest prevalence observed in East Asia (12.7 per 100,000 persons; 95% CI 2.81-57.0) and substantially higher rates in West (682.0 per 100,000 persons; 95% CI 141.8-3213.1) and South Asia (1374.5 per 100,000 persons; 95% CI 537.2-3471.5). Prevalence and incidence increased over time, with highest prevalence post-2020 (1155.2 per 100,000 persons; 95% CI 32.4-29682.7) and highest incidence in 2015 to 2019 (15.23 per 100,000 person-years; 95% CI, 8.5-27.3). CONCLUSIONS: Over 23.7 million individuals globally are affected by KC, highlighting an increasing global burden of KC and emphasizing the need for further research into temporal and regional patterns to inform public health strategies and optimize patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".