Latent profile analysis for the classification of OECD countries with health indicators

Abstract

Aims: Health indicators provide up-to-date information on the health status of a population. This study aimed to classify the Organization for Economic Co-operation and Development (OECD) countries according to health indicators and assess their status. Methods: The dataset was obtained from the OECD and World Bank databases. The most recent data from 2018 to 2022 were used. The dataset included the number of hospital beds, computed tomography scanners, magnetic resonance imaging (MRI) units, mammography machines, and radiotherapy machines as indicators of health equipment and the number of doctors, nurses, medical graduates, and nursing graduates as indicators of healthcare workers. The classification was performed using latent profile analysis (LPA). Estimated classes were compared using ANOVA or the Kruskal-Wallis test. Results: Three distinct classes were obtained from the models constructed with LPA (Akaike information criteria: 1674.91, Bayesian information criteria: 1726.87, Lo-Mendell-Rubin adjusted likelihood ratio test: p<0.001). The number of countries in the classes was 11, 14, and 4, respectively. The number of MRI units was the most prominent variable in separating the classes (p=0.001). Türkiye was in the same class as Canada, Chile, the Czech Republic, Estonia, Hungary, Israel, Luxembourg, Mexico, Poland, and Slovenia. The numbers for all indicators in Türkiye were below the average of its class, except for the numbers of MRI units and medical graduates. Conclusions: This study found the number of MRI units to be the most prominent indicator in categorizing OECD countries into three different classes, whereas the number of hospital beds and nurses did not differ across the defined classes.

Keywords

• classification
• health equipment
• healthcare workers
• latent profile analysis
• oecd