Assessment of the effectiveness of regional healthcare using the DEA approach and spatial analysis

The aim of the study is a comparative assessment of the effectiveness of healthcare systems in the RF regions, as well as the identification of spatial effects in the distribution of regional health indicators. The work uses the DEA approach in combination with spatial analysis tools, including global and local one-dimensional and two-dimensional Moran indices. Calculations were performed in the RStudio program using specialized packages. The study is based on official statistics. The analysis includes as indicators of results: life expectancy at birth, infant survival, survival of men and women of working age, and as indicators of resources: unit health care costs, the number of doctors, nursing staff, and hospital beds per 10,000 population. According to calculations, 16 European regions of the Russian Federation were identified as efficient, 42 as inefficient. The least effective regions are the Nenets Autonomous Area, the Komi Republic, the Murmansk Region, the Republic of Karelia, and the Arkhangelsk Region. The Chechen Republic, Sevastopol and the Leningrad Region were the most in demand as a reference, while the Lipetsk and Saratov regions, as well as the Udmurtia Republic, were the least in demand. An analysis of the global Moran indices in terms of resources, results, and efficiency showed significant positive spatial autocorrelation for most indicators, indicating the presence of overflow effects due to interregional interaction. The calculation of the local Moran indices for the level of healthcare efficiency made it possible to identify one cluster of low-efficiency regions located in the northeastern location, which indicates the significant role of the climatic factor in the effectiveness of regional healthcare. The use of the DEA method to evaluate the effectiveness of healthcare facilities has many advantages, including a strong theoretical basis. However, in regional analysis, the premise of the independence of the objects of comparison seems to be untenable. The addition of DEA spatial analysis tools makes it possible to take into account inter-regional interactions.