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IntroductionCoronary artery disease (CAD) has been a leading cause of death in the western world for the last few decades, despite significant improvements in treatment and management. Diagnostic algorithms for the evaluation of patients with suspected CAD are based on available guidelines.AimTo evaluate the impact of geographical distances to coronary angiography laboratories on the patient evaluation pathways in patients with suspected CAD, from a population-based study in Hungary.Material and methodsDepersonalised data of 29,202 patients identified by their pseudo-social security number were analysed. All patients underwent coronary angiography as an initial direct invasive investigation (DI) following an at least half-year-long stable period between 1 January 2004 and 31 December 2008.ResultsOne hundred and thirty-five dominant primary cardiology centres (PCC) have been identified, from which 85 proved to have sample size more than 100 DIs in tertiary cardiology centres (TCC). The frequency of DIs showed a close correlation with PCC-TCC distances (r = –0.44, p < 0.001). A negative correlation could be demonstrated between the age of patients and PCC-TCC distances (r = –0.45, p < 0.001). Without significant change in the absolute mortality, the relative mortality increased with the increase in PCC-TCC distance (r = 0.25, p < 0.05).ConclusionsThe PCC-TCC distance has an important effect on patient pathways in subjects with suspected CAD.

Social network analysis is increasingly applied to modeling regional relationships. However, in this scenario, we cannot ignore the geographical economic and technological nature of the relationships. In this study, the tools of social network analysis and the gravity model are combined. Our study is based on the Amadeus database of European organizations, which includes 24 million companies. The ownership of parent subsidiaries was modeled using economic, technological, and geographic factors. Ownership was aggregated to the NUTS 3 regional level, to which average corporate profitability indicators, the GDP per capita characterizing the economic environment, and the number of patents, which is a proxy of the technological environment, were assigned to NUTS 3 regions. The formation of the ownership network between 2010 and 2018 was characterized using this dataset. As the proposed model accurately describes the formation of ownership relationships marked with edges, it is possible to estimate network properties, such as modularity and centrality.