Increase in Myopia Prevalence in Clinic‐Based Populations Across a Century
Bibliographic record
Abstract
PURPOSE: The study's aim was to report prevalence estimates and age-related trends in refractive error in a clinic-based series and compare them to results from studies of a similar nature conducted since 1892. METHODS: Refractive error, patient age, and sex were collected from the files of all patients seen at the University of Waterloo School of Optometry and Vision Science clinic between January 2007 and January 2008. Patients were categorized as having myopia (<-0.5D mean ocular refraction [MOR]), hyperopia (>0.5D MOR), emmetropia, astigmatism (<-0.5D), and/or anisometropia (>1.0D difference between eyes). The prevalence of all refractive components was determined overall and in 1- and 5-year age groups, and then compared to data from older clinic-based studies. Refractive trends over time were noted. The prevalence of myopia and hyperopia were compared to older studies. RESULTS: The lowest prevalence of myopia was 5% at 0 to 5 years of age, after which it increased to 72% at 20 to 30 years of age and then decreased to 22% in patients older than 70 years. A myopic peak occurred at 24 years of age. The prevalence of hyperopia followed opposite trends with a minimum prevalence of 6% at 25 to 30 years of age. Peaks in emmetropia prevalence were 55% at 5 to 10 years of age and 37% at 45 to 50 years of age. The prevalence of astigmatism and anisometropia increased with age. The Waterloo Eye Study showed a higher prevalence of myopia across all ages compared to the older studies with a peak prevalence of 72% compared to 21% in the oldest (Herrnheiser) study from 1892. CONCLUSIONS: In the last 100 years, there appears to have been a myopic shift in clinic-based populations and myopia prevalence appears to follow a predictable pattern with age.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".