Cost-effectiveness of Universal School- and Community-Based Vision Testing Strategies to Detect Amblyopia in Children in Ontario, Canada

Cost-effectiveness of screening for amblyopia among kindergarten children in China

Objective

Current cost-effectiveness analyses of amblyopia screening are mainly from western countries. It remains unclear whether it is cost-effective to implement a preschool amblyopia screening programme in China. Our study aimed to evaluate the cost-effectiveness of a hypothetical kindergarten-based amblyopia screening versus non-screening among 3-year-old children.

Methods

We developed a decision tree combined with a Markov model to compare the cost and effectiveness of screening versus non-screening for 3-year-old children from a third-party payment perspective. The primary outcomes were quality-adjusted life years (QALYs) and the incremental cost-effectiveness ratio (ICER). Costs were obtained from expert opinions in different regions of China. Transition probabilities and health utilities were mainly based on published literature and open sources. Sensitivity analyses were performed to assess the impact of parameters' uncertainty on results.

Results

Base-case analysis demonstrated that the ICER of screening versus non-screening was $17,466/QALY, well below the WTP threshold ($38,223/QALY) for China. One-way sensitivity analysis showed that the prevalence of amblyopia, the transition probability per year from untreated amblyopia to healthy, and the discount rate were the top three factors. The likelihood of cost-effectiveness of screening compared with non-screening was 92.56%, according to probabilistic sensitivity analysis. Scenario analysis also indicated that ICER was lower than the WTP threshold even if the time horizon was shortened or the screening was delayed to the age of 4 or 5.

Conclusions

Amblyopia screening could be considered a cost-effective strategy compared to non-screening for 3-year-old children in China. Screening for children at the age of 4 or 5 may even yield better results.

Artificial intelligence-based screening for amblyopia and its risk factors: comparison with four classic stereovision tests

Introduction

The development of costs-effective and sensitive screening solutions to prevent amblyopia and identify its risk factors (strabismus, refractive problems or mixed) is a significant priority of pediatric ophthalmology. The main objective of our study was to compare the classification performance of various vision screening tests, including classic, stereoacuity-based tests (Lang II, TNO, Stereo Fly, and Frisby), and non-stereoacuity-based, low-density static, dynamic, and noisy anaglyphic random dot stereograms. We determined whether the combination of non-stereoacuity-based tests integrated in the simplest artificial intelligence (AI) model could be an alternative method for vision screening.

Methods

Our study, conducted in Spain and Hungary, is a non-experimental, cross-sectional diagnostic test assessment focused on pediatric eye conditions. Using convenience sampling, we enrolled 423 children aged 3.6-14 years, diagnosed with amblyopia, strabismus, or refractive errors, and compared them to age-matched emmetropic controls. Comprehensive pediatric ophthalmologic examinations ascertained diagnoses. Participants used filter glasses for stereovision tests and red-green goggles for an AI-based test over their prescribed glasses. Sensitivity, specificity, and the area under the ROC curve (AUC) were our metrics, with sensitivity being the primary endpoint. AUCs were analyzed using DeLong's method, and binary classifications (pathologic vs. normal) were evaluated using McNemar's matched pair and Fisher's nonparametric tests.

Results

Four non-overlapping groups were studied: (1) amblyopia (n = 46), (2) amblyogenic (n = 55), (3) non-amblyogenic (n = 128), and (4) emmetropic (n = 194), and a fifth group that was a combination of the amblyopia and amblyogenic groups. Based on AUCs, the AI combination of non-stereoacuity-based tests showed significantly better performance 0.908, 95% CI: (0.829-0.958) for detecting amblyopia and its risk factors than most classical tests: Lang II: 0.704, (0.648-0.755), Stereo Fly: 0.780, (0.714-0.837), Frisby: 0.754 (0.688-0.812), p < 0.02, n = 91, DeLong's method). At the optimum ROC point, McNemar's test indicated significantly higher sensitivity in accord with AUCs. Moreover, the AI solution had significantly higher sensitivity than TNO (p = 0.046, N = 134, Fisher's test), as well, while the specificity did not differ.

Discussion

The combination of multiple tests utilizing anaglyphic random dot stereograms with varying parameters (density, noise, dynamism) in AI leads to the most advanced and sensitive screening test for identifying amblyopia and amblyogenic conditions compared to all the other tests studied.