Improving population-level refractive error monitoring via mixture distributions.

INTRODUCTION: Sampling and describing the distribution of refractive error in populations is critical to understanding eye care needs, refractive differences between groups and factors affecting refractive development. We investigated the ability of mixture models to describe refractive error distributions. METHODS: We used key informants to identify raw refractive error datasets and a systematic search strategy to identify published binned datasets of community-representative refractive error. Mixture models combine various component distributions via weighting to describe an observed distribution. We modelled raw refractive error data with a single-Gaussian (normal) distribution, mixtures of two to six Gaussian distributions and an additive model of an exponential and Gaussian (ex-Gaussian) distribution. We tested the relative fitting accuracy of each method via Bayesian Information Criterion (BIC) and then compared the ability of selected models to predict the observed prevalence of refractive error across a range of cut-points for both the raw and binned refractive data. RESULTS: We obtained large raw refractive error datasets from the United States and Korea. The ability of our models to fit the data improved significantly from a single-Gaussian to a two-Gaussian-component additive model and then remained stable with ≥3-Gaussian-component mixture models. Means and standard deviations for BIC relative to 1 for the single-Gaussian model, where lower is better, were 0.89 ± 0.05, 0.88 ± 0.06, 0.89 ± 0.06, 0.89 ± 0.06 and 0.90 ± 0.06 for two-, three-, four-, five- and six-Gaussian-component models, respectively, tested across US and Korean raw data grouped by age decade. Means and standard deviations for the difference between observed and model-based estimates of refractive error prevalence across a range of cut-points for the raw data were -3.0% ± 6.3, 0.5% ± 1.9, 0.6% ± 1.5 and -1.8% ± 4.0 for one-, two- and three-Gaussian-component and ex-Gaussian models, respectively. CONCLUSIONS: Mixture models appear able to describe the population distribution of refractive error accurately, offering significant advantages over commonly quoted simple summary statistics such as mean, standard deviation and prevalence.

Cosmetic Contact Lens-Related Corneal Infections in Asia.

PURPOSE: To explore demographics, disease characteristics, and wear habits in cosmetic contact lens (CL)-related corneal infections in Asia. DESIGN: Prospective multicenter cross-sectional study. METHODS: Cases of CL-related corneal infection presenting over a 12-month period were prospectively identified from 11 centers in 8 countries in Asia. Case demographics, clinical features, microbiology, and compliance characteristics were compared between wearers using CLs for cosmetic purposes and those using CLs for the correction of refractive errors. RESULTS: Six hundred and ninety-four CL wearers with corneal infection presented: 204 cosmetic CL (29.4%) and 490 (70.6%) refractive CL wearers. Cosmetic CL infections comprised 7%-54% of cases across the region. Compared with noncosmetic CL wearers, cosmetic CLs wearers were significantly more likely to be female (90% vs 59%), young (aged <25, 68% vs 44%), and to have a shorter period of wear experience. Lenses worn by cosmetic CL wearers were more likely to be hydrogel materials and manufactured with the pigment located on the back surface of the CL. Presenting disease characteristics and visual outcomes were similar in both groups. Causative organisms were similar between the 2 groups; however, there was a higher rate of Acanthamoeba disease (9%) in cosmetic wearers, compared with refractive wearers (1%; P < .005). CONCLUSIONS: Cosmetic CL infections represent a significant proportion of CL-related infections in Asia. Cosmetic CL users with corneal infections are generally young, female, and wearing hydrogel CLs. Internet supply, quality control, and regulation of the sale of these products provide significant challenges in managing this population of vulnerable wearers.