International trends in prescribing contact lenses for myopia control (2011–2024): An update
Bibliographic record
Abstract
PURPOSE: The progression of myopia can be slowed using soft myopia control (SMC) and orthokeratology myopia control (OKMC) contact lenses. The purpose of this work is to update an earlier survey (covering years 2011-2018) by describing international trends in SMC and OKMC fitting between 2011 and 2024. METHOD: An annual contact lens prescribing survey was sent to eye care practitioners in up to 71 countries between 2011 and 2024. Data are reported here for 8,578 lens fits undertaken across 20 countries to 6-12-year-old children, comprising 688 SMC fits, 918 OKMC fits, and 6,972 non-myopia control (NMC) fits. To assess current trends, the proportion of fits between 2020 and 2024 for 12 countries returning ≥30 myopia control fits was analysed (n = 2,277 fits). RESULTS: There was a small increase in SMC fitting from 0.0 % in 2011 to 3.9 % in 2016, a significant increase to 29.5 % in 2021, and a slower increase to 30.4 % in 2024 (p < 0.0001). The proportion of OKMC fits, in relation to all rigid lens fits, increased from 40.8 % in 2011 to 86.9 % in 2024 (p < 0.0001). Of all soft lens fits, SMC fits were 7.2 % and 8.7 % to males and females, respectively (p = 0.0008). There was a decrease in the percentage of SMC fits with increasing age (p < 0.0001). No such sex/age associations were evident with OKMC fits. The proportion of all contact lens fits over the past 5 years (2020-2024) were as follows: SMC fits - 24 % (n = 525), OKMC fits - 17 % (n = 320), and NMC lens fits - 59 % (n = 1,332). CONCLUSION: There has been a substantial increase in SMC and OKMC lens fitting over the past 14 years, reflecting a heightened awareness among eye care practitioners and parents of the importance of slowing the progression of myopia in children.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".