Analysis of Health Care System Development in the Regions amidst the Economic Inclusiveness and Social Determinants of Health

Abstract

The article proposes a neural network-based Kohonen's self-organized maps cluster analysis of Ukraine’s health care system at regional level. At analysis, economic patterns and social determinants of health are considered. The research aims to estimate regional security at the public health level. For that, behavioral and social patterns determine a regions’ potential resistance to public health risks. The authors identify the strengths and weaknesses of each region and assess the effectiveness of health care as it is provided. Interestingly, the clustering algorithm fits multidimensional space design into spaces with a lower dimension. Additionally, similar vectors in the source space appear closely on the resulting map. The algorithm design, stages of evaluation, and input groups of indicators by components are described. The data set reflects the 22 regions of Ukraine. The rationing of indicators is calculated to make the data comparable. Data are checked for quality, sparsity, duplicates, and inconsistencies. Five clusters are generated based on development of patterns within regions as well as the information value of healthcare-related socio-economic indicators. The residents of regions that belong to the first cluster systematically assess their health. Demographically, these residents are more physically active compared with residents in clusters of other regions. Findings also indicate that residents in the first cluster monitor their nutrition. The second cluster is informative on residents’ behavioral components. In the third cluster are grouped regions with financially secure residents. The fourth cluster includes leader regions. The fifth cluster includes outsider regions. The proposed model can easily fit to new data, to identify new patterns and to graphically represent new results. The model can also analyze computationally complex approach based on a complete set of multidirectional indicators relating to the country's medical system at a state of risk. Moreover, this cluster-based approach can identify areas that require increased attention by state public health agencies.