Page 113 - Contributed Paper Session (CPS) - Volume 8

CPS2210 Justyna Majewska et al.
ensures that the rates of change of the future mortality rates are the same for
the two populations, and thus avoids crossovers.
The crucial issue was to derive similar countries to Poland. Thus, the analysis
was preceded by an idenfitifaction of homogenous spatial clusters of EU
countries according to the following demographic and economic variables
(details can be found in Majewska and Trzpiot, 2019):
– Human Development Index – developed by the United Nations to measure
and rank countries’ levels of social and economic development,
– Air pollution – greenhouse gas emissions in tons per capita,
– Social protection expenditures measured as percentage of GDP,
– Doctors providing direct care to patients per 1000 inhabitants,
– Alcohol – annual sales of pure alcohol in liters per person aged 15 years
and older,
– Cigarettes – a percentage of daily smokers of the population aged 15 years
and over,
– Obesity – a percentage of obese inhabitants in population; obesity is
measured by the body mass index.
Cluster with Poland contains also the following countries: Czech Republic,
Malta, Latvia, Lithuania, Slovakia, Croatia, Hungary, Romania, Bulgaria, Greece,
Cyprus. For comparison Slovakia, Czech Republic, Lithuania and Hungary are
selected.
The dataset comprises the number of deaths and the number of exposures for
male and female in above-mentioned countries since the beginning of 1950
until 2015.

3. Result
Trends in mortality for the countries grouped in a spatial cluster are
presented in figure 1. A visual inspection of figure 1 suggests the possibility of
common stochastic trends in mortality.
Fig. 1. Log mortality rates for male in selected European countries

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