19 August 2024
1 min reading time
Key findings:
- Younger Asian girls are most likely to develop myopia, while older white boys are least likely to develop myopia.
- Lower farsightedness was also associated with a greater likelihood of developing nearsightedness.
According to a study conducted in Optometry and vision science.
“A prediction model would be useful for sample planning in clinical trials of myopia prevention because it provides estimates of the probability of conversion from emmetropia to myopia, given different sample configurations regarding age, sex, race/ethnicity, baseline refractive error, or other significant covariates,” Donald O. Mutti, OD, PhD, FAAO, The EF Wildermuth Foundation Professor of Optometry at the Ohio State University College of Optometry and colleagues wrote, “A predictive model would help the clinician counsel an individual child and his family about the likelihood of a future refractive error.”
Using data from the multicenter Collaborative Longitudinal Evaluation of Ethnicity and Refractive Error (CLEERE) study, Mutti and colleagues modeled the probability of myopia in children in the United States on an annual and cumulative basis by spherical equivalent of refractive error, horizontal/vertical component of astigmatism, age, sex, race, and ethnicity. They included 4,027 nonmyopic study participants aged 6 to 14 years at baseline (49.9% girls) who were followed for 1 to 7 years through eighth grade.
According to the results, the proportion of children who developed myopia by age 14 was highest among those who were younger at baseline: 18.9% of 6-year-olds and 19.9% of 7-year-olds developed myopia, compared with 5% of 13-year-olds at baseline. Younger Asian girls had the highest likelihood of developing myopia, while older white boys had the lowest.
Less hyperopia and less positive values of the horizontal/vertical component of astigmatism were also associated with a greater likelihood of developing myopia.
“Efforts to harmonize prediction calculators will be useful as more longitudinal studies become available,” Mutti and colleagues wrote. “In addition to providing a consensus prediction model, another aspect of harmonizing different longitudinal data sets would be to determine whether the probabilities associated with different risk factors have changed over time.”