Is structural equation modeling applicable in a population health determinants assessment? An experience from the European Union.

The purpose of this study is to examine the relations between health determinants and overall health within population of the European Union by using the structural equation model, particularly given that not all health-determining factors have been theoretically identified. The significance of this issue stems, among other things, from the fact that the factors determining health are essential for policymakers for implementing an effective and accurate intervention in society, particularly considering that not all such factors have yet been theoretically identified. We hypothesized that (1) There is a statistically significant positive relationship between socioeconomic status and the health of the European Union population, (2) The structural equation model (SEM) is applicable for examining the complex interrelationships between socioeconomic status and health outcomes in the European Union population at the regional level. We used a dataset from EUROSTAT covering finally 258 regions at NUTS 2 in European regions for the year 2022 (or the nearest). We employed the SEM in this study due to its ability to simultaneously analyze multiple variables and latent constructs, thereby minimizing measurement error and enhancing the validity of the findings. So, our research has shown that a better social status of communities in European regions is associated with a higher level of health. Furthermore, a better economic situation of a region significantly improves the health of its inhabitants. However, in general economic factors have a stronger impact on health than social status. Thus, these findings have both theoretical and practical significance, as they identified key modifiable socioeconomic determinants of health and provided valuable insights for shaping effective public health policies and targeted interventions aimed at reducing health inequalities across European regions. Moreover, this study demonstrated the utility of SEM as a robust approach for examining complex relationships among health determinants including direct and indirect effects. By applying SEM, the research aligns methodologically with the growing body of literature in health sciences and contributes to a broader understanding of how socioeconomic factors influence health under varying regional conditions.