Quality healthcare system is a priority for citizens of any country. Citizens' health is also a core EU priority. The objective of this article is application of multidimensional statistical techniques as a tool for information value added on health outcomes data in European countries and their further comparison. To achieve this objective, factor analysis and multidimensional comparison methods have been applied to the matrix of 16 healthcare indicators on 25 selected European countries. The synthetic variable allows transforming the countries described by a variety of healthcare indicators into one-dimensional space that considerably simplify monitoring of healthcare inequalities. The obtained results are compared with the results on the self-perceived health status provided by the citizens of the same countries. The results of this comparison have demonstrated significant similarity between self-reported statuses and objectively measured healthcare statuses. The results are presented in a visual form using tables and graphs.
Efficiently functioning health systems are a prerequisite for high-quality health care and healthy life expectancy. Health care management at all levels requires a lot of information that can be obtained only by relevant analyses of health data. There are collected and regularly updated on-line published a large number of databases and enormous number of indicators about health status, health expenditures and health systems functioning at regional, national, EU member countries, OECD countries and on the world level. Paradoxically, the extent of these data sets is the reason why without at least basic statistical analysis the level of provided information is minimal. Advanced statistical methods aimed at reducing the dimension and quantification of causal relationships can provide significant information added value. The objective of this article is to analyse causal relationships between health status, health expenditures and sources of health care in selected European countries and to identify determinants of health inequalities in European countries by applying multidimensional statistical methods.
Catastrophic events affect various regions of the world with increasing frequency and intensity. The number of catastrophic events and the amount of economic losses is varying in different world regions. Part of these losses is covered by insurance. Catastrophe events in last years are associated with increases in premiums for some lines of business. The article focus on estimating the amount of net premiums that would be needed to cover the total or insured catastrophic losses in different world regions using Bühlmann and Bühlmann-Straub empirical credibility models based on data from Sigma Swiss Re 2010-2016. The empirical credibility models have been developed to estimate insurance premiums for short term insurance contracts using two ingredients: past data from the risk itself and collateral data from other sources considered to be relevant. In this article we deal with application of these models based on the real data about number of catastrophic events and about the total economic and insured catastrophe losses in seven regions of the world in time period 2009-2015. Estimated credible premiums by world regions provide information how much money in the monitored regions will be need to cover total and insured catastrophic losses in next year.
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