Peer-reviewed prevalence rates, primary risk factors, and documented interventions across 21 study populations worldwide. Data from 6 published sources including the IMI 2021 Digest and NIH/PMC epidemiological reviews.
| Region / Country | Population Group | Prevalence Rate | Primary Risk Factors | Interventions Documented |
|---|---|---|---|---|
| East Asia China (Metropolitan) |
15-year-olds | 78.1% | Academic demands, screen exposure >3 hrs/day, reduced outdoor time | — |
| East Asia Taiwan |
High schoolers | 84% | Near-work activities, educational pressure | 'Tian-Tian 120' — 120 min/day outdoor time in schools |
| East Asia South Korea |
Young adults | 90% | Intensive educational frameworks, rapid urbanization, lifestyle shifts | — |
| East Asia Japan (Tokyo) |
Students aged 6–14 | 76.5% (primary) → 94.9% (junior high) | Educational institutions, lifestyle (environmental) | — |
| East Asia Korea |
Children aged 5–18 | 64.6% | Parental myopia, female gender | — |
| East Asia China (Guangzhou) |
1st-grade children | 39.5% control vs 30.4% intervention (3-yr cumulative) | Insufficient time outdoors | Outdoor time intervention (school-based) |
| East Asia China (East) |
Third-year high school (mean age 18.4) | 79.5% (2001) → 87.7% (2015) | Female gender | — |
| East Asia China (East) |
Children aged 5–20 (mean age 12.3) | 63.1% | Moderate to high school workload, female gender | — |
| East Asia China (Hong Kong) |
Children | — | — | — |
| SE Asia Singapore |
12-year-olds | 65% | Education intensity, urban environments | National Myopia Prevention Program (NMPP), school-based screenings |
| SE Asia Vietnam |
Secondary students | 56.3% | Urbanization, self-study >2 hrs/day, extra classes | — |
| South Asia India (North) |
Schoolchildren aged 5–15 | 21.1% | Low outdoor activity (<1.5 hrs/day), reading/writing >4 hrs/day, video games >2 hrs/day | — |
| North America United States |
Adults (20+) | 36.2% | Lifestyle, ancestry-linked susceptibility | — |
| North America United States |
Children aged 6–11 | — | Accommodative lag, near esophoria | — |
| North America Mexico |
Outpatients | 44.4% | Astigmatism identified as major risk factor | — |
| North America Canada |
11–13 year-olds | 28.9% | — | — |
| Europe Norway |
Adolescents aged 16–19 | 13.4% | Less sport/outdoor time | — |
| Europe Ireland |
Schoolchildren aged 6–7 and 12–13 | 3.7% → 22.8% (by age) | Paternal myopia, screen use >3 hrs/day, frequent reading, high BMI | — |
| Middle East Israel |
5-year-olds (post-COVID) | 12.6% | COVID-19 lockdowns, increased screen time, reduced outdoor activity | — |
| Global Multi-centre |
Children | — | — | — |
| Global 2050 Projection |
General population | 9.8% (high myopia) | Urbanization, axial elongation | — |
East Asia leads globally — rates of 60–94% in adolescent populations, driven by high academic intensity, near-work load, and reduced outdoor time. Taiwan's 'Tian-Tian 120' policy (mandatory 120 min/day outdoor time in schools) represents the most documented population-level intervention, associated with the Guangzhou RCT showing a 9.1 percentage-point reduction in cumulative 3-year myopia incidence.
Europe and North America show a wide spread — from 3.7% in young Irish children to 44.4% in Mexican outpatients. Risk factors in these regions emphasize screen use, paternal myopia, and reading habits rather than systemic academic pressure.
The COVID-19 effect is documented — Israel shows 12.6% in 5-year-olds post-COVID, attributed to lockdown-driven increases in screen time and reduction of outdoor activity. This cohort (aged 5–9 in 2020) is now 11–15 and in clinical practice.
Enter age + axial length to see where your patient sits vs age-matched normative data. Free, no login.
Open Calculator →Sources: [1] The Global Myopia Pandemic — Comprehensive Epidemiological Analysis (multiple regions) · [2] High myopia and its risks — PMC/NIH · [3] Onset and Progression of Myopia — NCBI Bookshelf · [4] Recent Epidemiology Study Data of Myopia — PMC/NIH · [5] Myopia — StatPearls/NCBI · [6] IMI 2021 Reports and Digest — PMC · Rows with no prevalence data reflect populations where study design focused on risk factor identification rather than prevalence estimation.