At first, all 53 critical factors were included in the CART.
However, conflicting information was found in the following service-related critical factors: .
Only two variables were detected, with only one variable showing statistical significance.
These findings were affected by the amount of missing data; for instance, not every country had data or collected data on the main outcome—coverage—and not every question in the survey was answered, such as the numbers of low vision health professionals in the country. As logistic regression does not allow full exploration of the data, an alternative method was sought.
In terms of handling missing values, the CHAID growing method first generated categories using valid values, and then decided whether to merge the missing category with its most similar (valid) category or keep it as a separate category. This table indicates the overall predictive performance of the model (i.e., percentage of countries correctly classified with respect to each category of the dependent variable coverage) as well as the sensitivity and the specificity.