Myopia, or near-sightedness, is a refractive error in which the eyeball grows too long relative to the focusing power of its optical system. When the axial length of the eye exceeds the distance at which the cornea and lens can bring light to a focus, distant images land in front of the retina instead of directly on it, blurring far vision while near vision remains sharp. The condition typically emerges in childhood, progresses through adolescence and often stabilises in early adulthood. Spectacles and contact lenses can correct the blurred vision, but they do nothing to reverse the underlying structural elongation of the eye, which is the true source of long-term risk.

That structural change matters because a longer eye is a more fragile eye. Excessive axial elongation stretches the retina and its supporting tissues, sharply raising the lifetime risk of cataract, open-angle glaucoma, myopic macular degeneration and retinal detachment. Each of these complications can cause irreversible vision loss, and their management places a heavy load on patients and health systems alike. Globally, the burden of vision impairment from uncorrected myopia was estimated at US$244 billion in 2015, a figure expected to climb as prevalence rises. Projections cited in the study suggest that 1.4 billion people were myopic in 2000 and that 4.8 billion, more than half of humanity, could be by 2050.

The new analysis was designed to fill a conspicuous gap. Most large-scale myopia epidemiology has focused on Asian populations, where prevalence in some urban settings reaches 80 to 90 percent among young adults. European data existed but were fragmented: earlier population-based studies reported prevalence ranging from 27 percent in the United Kingdom to 39 percent in France, and a recent meta-analysis of 14 European countries found estimates varying from 11.9 percent in Finland to 49.7 percent in Sweden. To cut through this heterogeneity, the E3 Consortium researchers used individual participant data rather than published summary statistics, harmonising refraction measurements across 28 studies collected between 1990 and 2024.

Methodologically, the team defined myopia as a spherical equivalent of refraction of -0.50 dioptres or worse, following International Myopia Institute criteria, and subdivided cases into low, moderate and high myopia. Participants born between 1900 and 2000 were grouped into birth decades, and pooled prevalence estimates for each decade were derived using random-effects meta-analysis with inverse variance weighting and restricted maximum likelihood estimation. People with artificial lenses or a history of refractive surgery were excluded, as were those with retinal detachment from the axial length analysis, since surgery can alter eye dimensions. Strata with fewer than 50 observations were dropped to keep estimates robust.

The headline result is stark: myopia prevalence more than tripled across twentieth-century birth cohorts, with the steepest acceleration occurring from the 1950s onward. Low myopia climbed from 12.5 percent in the 1900s cohort to 35.0 percent in the 1980s cohort, moderate myopia from 3.3 percent to 11.4 percent, and high myopia from 2.2 percent to 3.9 percent. When the decade-specific estimates were weighted according to the actual age structure of the European population, the standardised prevalence for 2024 came out at 42.7 percent, well above the consortium’s earlier estimate of 30.6 percent and approaching the widely cited global projection of 50 percent by mid-century.

Perhaps the study’s most important technical contribution is its demonstration that the generational rise is written into the physical dimensions of the eye itself. Analysing axial length data from 56,876 participants, the researchers found that every percentile of the distribution, from the 5th to the 95th, shifted longer across birth cohorts from the 1920s to the 1970s. Between the 1920s and 1970s cohorts, mean axial length increased by 0.49 millimetres at the 5th percentile and 1.05 millimetres at the 95th percentile in men, and by 0.37 and 0.67 millimetres respectively in women. This population-wide shift indicates not merely that more people cross the myopia threshold, but that eyes in general are getting bigger, including among people who would otherwise be long-sighted or only mildly myopic.

Some of that elongation could reflect simple growth: axial length correlates with body height, adding roughly 0.03 millimetres per centimetre of stature, and Europeans gained about one centimetre of height per decade over the last century. But that accounts for only 0.03 millimetres of axial growth per decade, whereas the observed increase at the median was 0.07 millimetres. The remainder points to environmental drivers, chief among them the modern lifestyle of prolonged near work, reading and screen use combined with limited time outdoors, patterns amplified by extended years in education. Intriguingly, the sex pattern also shifted: men born in the early twentieth century were slightly more myopic than women, but the trend reversed after the 1950s, a crossover the authors link to rising female participation in higher education and its associated near-work demands.

The authors are careful about the limits of their data. Between-study heterogeneity was substantial, with the I-squared statistic often exceeding 90 percent, meaning pooled figures should be read as averages across diverse settings rather than a single number applicable everywhere. Non-cycloplegic refraction can slightly overestimate myopia through residual accommodation, though recent work suggests the effect, around 0.31 dioptres in young adults, is unlikely to have changed classification across the broad severity categories. And because cohorts born after 2000 were excluded, the youngest generations, whose eyes may not yet have finished elongating, remain outside the analysis; an apparent dip in axial length in the most recent cohorts likely reflects incomplete growth rather than a genuine reversal.

Even with those caveats, the trajectory is unmistakable and the implications are serious. If prevalence continues to climb, Europe faces not only more spectacle prescriptions but a growing reservoir of high myopia and its attendant complications, threatening irreversible vision loss in working-age adults and straining ophthalmic services. The study’s authors argue that the evidence now justifies preventive strategies targeting modifiable environmental risk factors, particularly increasing time spent outdoors and reducing prolonged near work in children, alongside continued evaluation of clinical interventions designed to slow myopia progression. What was once an inconvenience corrected by a pair of glasses is, on current trends, becoming one of the century’s most consequential chronic eye diseases, and the generations now in school will determine just how heavy that burden becomes.

Subject of Research: Temporal trends in myopia prevalence and axial length across European birth cohorts

Article Title: The growing burden of myopia in Europe: evidence from population-based cohorts

Article References: Tiemens, A., van Hemert, D. J., Lemma, S. T., Quist, S. W., Shi – van Wielink, K., Agius, D., Arnould, L., Berendschot, T. T. J. M., Beuse, A., Bikbov, M. M., Birtel, J., Bountziouka, V., Breteler, M. M. B., Carbonaro, F., Chakravarthy, U., Coimbra, R., de Vries, V. A., Delcourt, C., Farinha, C., … Lotery, A. J. (2026). The growing burden of myopia in Europe: evidence from population-based cohorts. European Journal of Epidemiology. https://doi.org/10.1007/s10654-026-01435-w

Image Credits: AI Generated

DOI: 10.1007/s10654-026-01435-w

Keywords: myopia, axial length, refractive error, epidemiology, birth cohorts, E3 Consortium, Europe, public health, high myopia, vision impairment, meta-analysis, eye health

The research, published in the Journal of Translational Medicine, drew on 4,220 screening records from 2,373 children aged six to thirteen, collected between 2018 and 2021. Rather than chasing exotic data sources, the team deliberately restricted itself to the kind of information already gathered in ordinary pediatric vision screening. Their goal was ambitious: to predict six distinct outcomes at once, including high myopia defined as a spherical equivalent of minus 3.00 diopters or worse at three different time horizons, mild-to-moderate progression, first onset of myopia, and rapid progression of existing myopia.

The headline finding is the model’s striking accuracy for the outcome that matters most to eye health. For predicting which children would reach high myopia within one year, the model achieved an area under the curve of 0.878, with a confidence interval running from 0.815 to 0.931. Performance gracefully declined with longer horizons, reaching 0.828 at two years and 0.795 at three years, while mild-to-moderate progression was predicted at 0.782 and myopia onset at 0.739. In practical terms, the simplest possible model built from routine data performed better at forecasting severe nearsightedness than many far more complicated approaches.

Perhaps the most provocative result, however, is a negative one. Modern vision screening devices automatically capture a suite of biometric features, including pupil diameter, interpupillary distance, and gaze deviation. Health systems have increasingly wondered whether these automated measurements could sharpen risk prediction. The answer, according to this study, is a resounding no. Across every outcome tested, adding the biometric features changed the AUC by no more than 0.005 for any single feature, a difference so small it carries no clinical meaning. The researchers found that these measurements are so strongly correlated with age and refractive status that they add essentially nothing once those dominant predictors are known.

The rigor of the validation pipeline deserves attention, because predictive models in medicine have a notorious habit of looking brilliant in development and collapsing in practice. The team used patient-grouped cross-validation to prevent any single child’s records from leaking into both training and test sets, applied patient-clustered bootstrap resampling for uncertainty estimates, and ran DeLong tests with Holm-Bonferroni correction to compare their model against six competitors in a statistically disciplined way. Temporal validation confirmed that the model retained its discrimination when applied across different time periods rather than being tuned to a single snapshot of the data.

Fairness was also assessed in advance rather than as an afterthought. A pre-specified sex-stratified analysis found no performance disparity between boys and girls, an important check given that myopia prevalence and progression rates differ between the sexes. The team also followed the TRIPOD + AI reporting guidelines, the emerging standard for transparently documenting how clinical prediction models involving machine learning are developed and validated. The study was approved by the Institutional Review Board of Tianjin Medical University Eye Hospital, and written informed consent was obtained from all participants.

Discrimination, the ability to rank risky children above safer ones, is only half the story of a useful clinical model. The other half is calibration, whether the predicted probabilities actually match reality. After applying isotonic recalibration, a technique that maps raw model outputs onto observed event rates, the researchers used decision curve analysis to demonstrate net clinical benefit across a realistic range of decision thresholds. This analysis matters because it shows the model would help clinicians make better decisions, not merely produce impressive statistics.

The practical stakes are illustrated by one striking number. At a 30 percent risk threshold, the two-year high myopia model achieved 83.4 percent sensitivity while maintaining a 97.1 percent negative predictive value. In plain language, if the model tells a family their child is unlikely to become highly myopic within two years, that reassurance is correct almost 97 times out of 100. That kind of reliable reassurance is exactly what screening programs need, because it lets scarce clinical resources, from axial length monitoring to myopia-control interventions such as specialized spectacle lenses or low-dose atropine, be concentrated on the children who genuinely need them.

The elegance of the approach lies in its parsimony. Logistic regression, one of the oldest and most interpretable tools in statistics, outperformed or matched six comparator models, and no alternative was significantly superior. In an era when medical AI headlines often celebrate sprawling deep learning architectures trained on millions of images, this study makes the counterintuitive argument that a transparent, three-variable model can already capture most of the predictable signal in pediatric myopia progression. That simplicity is not a limitation; it is a feature, making the model auditable, deployable on existing screening data, and easy to explain to families.

With myopia on track to affect half the world’s population by mid-century, and high myopia carrying elevated risks of retinal detachment, myopic macular degeneration, and permanent vision loss, tools that convert routine screening data into actionable risk estimates could reshape pediatric eye care. The caveats remain real: the model was developed and internally validated within a single screening program in Tianjin, and external validation in other populations would be needed before widespread adoption. But the study’s central message is already resonant. The data needed to identify which children will develop the most dangerous form of nearsightedness may have been sitting in screening records all along, waiting for someone to read it properly.

Subject of Research: Risk prediction of pediatric myopia progression and high myopia from routine vision screening data

Article Title: Predicting pediatric myopia progression from single screening encounters: a multi-outcome development and validation study

Article References: Wei, N., Li, C., Moutari, S., Usama, M., Li, J., Chen, Q., Pazo, E. E., & Qian, X. (2026). Predicting pediatric myopia progression from single screening encounters: a multi-outcome development and validation study. Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-09003-2

Image Credits: AI Generated

DOI: 10.1186/s12967-026-09003-2

Keywords: pediatric myopia, high myopia, risk prediction, vision screening, logistic regression, biometric features, decision curve analysis, myopia progression, TRIPOD + AI, predictive medicine, refractive error, risk stratification