Identifying the critical success factors in the coverage of low vision services using the classification analysis and regression tree methodology.

PURPOSE To identify the critical success factors (CSF) associated with coverage of low vision services. METHODS Data were collected from a survey distributed to Vision 2020 contacts, government, and non-government organizations (NGOs) in 195 countries. The Classification and Regression Tree Analysis (CART) was used to identify the critical success factors of low vision service coverage. Independent variables were sourced from the survey: policies, epidemiology, provision of services, equipment and infrastructure, barriers to services, human resources, and monitoring and evaluation. Socioeconomic and demographic independent variables: health expenditure, population statistics, development status, and human resources in general, were sourced from the World Health Organization (WHO), World Bank, and the United Nations (UN). RESULTS The findings identified that having >50% of children obtaining devices when prescribed (χ(2) = 44; P < 0.000), multidisciplinary care (χ(2) = 14.54; P = 0.002), >3 rehabilitation workers per 10 million of population (χ(2) = 4.50; P = 0.034), higher percentage of population urbanized (χ(2) = 14.54; P = 0.002), a level of private investment (χ(2) = 14.55; P = 0.015), and being fully funded by government (χ(2) = 6.02; P = 0.014), are critical success factors associated with coverage of low vision services. CONCLUSIONS This study identified the most important predictors for countries with better low vision coverage. The CART is a useful and suitable methodology in survey research and is a novel way to simplify a complex global public health issue in eye care.