**5. The results and their interpretation**

The results of computation are shown (**Table 2**).

Column "Score" contains the efficiency score and column "R" the position in the ranking. In the year 2000, the full efficiency (score equal to 1.000) was achieved by the Czech Republic, Hungary, Slovenia and the virtual DMU referred to as DE16, which were among the best also in 2013 (besides Hungary). The full efficiency in 2013 was also reached by Croatia and Ukraine.

In 2013, six countries reduced their efficiency score—in the case of two of them, it was sig‐ nificant. Hungary, with full efficiency in 2000, achieved the level of only 0.627 in 2013, and Slovakia achieved a result of 0.753 in 2013, compared to 0.923 in the year 2000. The other four countries lowered their efficiency score to a negligible degree.

On the other hand, the remaining countries improved their scores, of which 7 to significant degree (above 0.15). The greatest improvement was achieved by Croatia and Ukraine, which reached full efficiency, improving the result by 0.234 and 0.548 respectively. Bosnia and Herzegovina, Estonia, Kazakhstan, Kyrgyzstan and Poland improved their result by 0.15–0.23.

For a more detailed analysis of the causes of these positive and negative changes Hungary, Slovakia, Croatia, Ukraine, Azerbaijan and Georgia were selected. The table includes also additional aggregated data for the most developed economies of Western Europe—DE16, which should be considered to constitute best practice. The source data for the input and out‐ put variables for these countries for the years 2000 and 2013 are presented (**Table 3**).


1000 live births. In 2013, the highest infant mortality rate was reported in Turkmenistan—46.6 infants per 1000 live births. In 2000, it was 17 times higher and in 2013, 20 times higher than

**Year Statistics PR\_TE OOP\_TE GINI LE60 HLE IMR** 2000 Mean 41.2 37.8 32.8 17.9 62.6 23.3

2013 Mean 41.2 36.8 32.3 19.5 66.3 12.3

Mean 2013 − mean 2000 0.0 −1.0 −0.5 1.6 3.7 −11.0

Stand. error 21.3 20.9 3.6 1.1 2.9 19.1 Max 83.0 82.5 40.8 20.4 66.8 74.7 Min 9.7 9.7 27.2 15.7 56.6 4.5

Stand. error 15.9 14.9 5.1 1.5 2.6 11.8 Max 79.2 71.1 44.1 23.4 71.1 46.6 Min 16.7 12.1 24.7 16.4 59.8 2.3

Column "Score" contains the efficiency score and column "R" the position in the ranking. In the year 2000, the full efficiency (score equal to 1.000) was achieved by the Czech Republic, Hungary, Slovenia and the virtual DMU referred to as DE16, which were among the best also in 2013 (besides Hungary). The full efficiency in 2013 was also reached by Croatia and Ukraine. In 2013, six countries reduced their efficiency score—in the case of two of them, it was sig‐ nificant. Hungary, with full efficiency in 2000, achieved the level of only 0.627 in 2013, and Slovakia achieved a result of 0.753 in 2013, compared to 0.923 in the year 2000. The other four

On the other hand, the remaining countries improved their scores, of which 7 to significant degree (above 0.15). The greatest improvement was achieved by Croatia and Ukraine, which reached full efficiency, improving the result by 0.234 and 0.548 respectively. Bosnia and Herzegovina, Estonia, Kazakhstan, Kyrgyzstan and Poland improved their result by 0.15–0.23. For a more detailed analysis of the causes of these positive and negative changes Hungary, Slovakia, Croatia, Ukraine, Azerbaijan and Georgia were selected. The table includes also additional aggregated data for the most developed economies of Western Europe—DE16, which should be considered to constitute best practice. The source data for the input and out‐

put variables for these countries for the years 2000 and 2013 are presented (**Table 3**).

the lowest mortality observed in these years in Slovenia.

**Table 1.** The basic descriptive statistics of variables for years 2000 and 2013.

countries lowered their efficiency score to a negligible degree.

**5. The results and their interpretation**

*Source*: Own computation.

102 Advances in Health Management

The results of computation are shown (**Table 2**).

**Table 2.** Efficiency scores for the years 2000 and 2013.


**Table 3.** Data from selected countries for the years 2000 and 2013.

The primary reason for the decrease of efficiency in Hungary and Slovakia is a very significant change in the financing structure. The PR\_TE variable increased by 7.1 p.p. in Hungary and as much as 19.4 p.p. in Slovakia, whereas the OOP\_TE variable increased by 1.7 p.p. in Hungary and as much as 11.5 p.p. in Slovakia. In the case of Hungary, these negative phenomena coincided with an increase in the income inequalities of the society, illustrated by the change in the GINI index from 27.2 in 2000 to 30.6 in 2013. In the case of Slovakia, the inequalities decreased. It should be noted, however, the all the health outcomes in these two countries improved.

In the case of Croatia, which improved its efficiency score, there was indeed an increase in the share of private expenditure (PR\_TE) but the expenses covered directly by households (OOP\_TE) decreased. The GINI index deteriorated slightly. On the other hand, in the case of Ukraine both private spending (PR\_TE) and the expenditure covered directly by the public (OOP\_TE) decreased. Also the income inequalities in the population (GINI) decreased significantly. The favourable results of Ukraine since 2014 deteriorated due to the ongoing military conflict.

Azerbaijan and Georgia reduced the share of private spending by 2.2 and 4.5 p.p., respec‐ tively. These expenses are about two times higher than the average for post‐communist countries and almost four times higher than the average for developed countries of Western Europe. The share of direct expenditure in Azerbaijan increased by 7.8 p.p., while in Georgia it decreased by 20.6 p.p. The income inequalities fell by 4.7 percentage points in Azerbaijan and increased slightly by 0.9 p.p. in Georgia. The health outcomes improved.

**Figure 1** is the illustration of the efficiency scores shown in **Table 3** of the changes described above. The efficiency scores are shown in descending order, which allows for the analysis of the direction and magnitude of change.

The conducted analysis allows for indicating several typical situations. The countries that achieved better health outcomes are those in which there is a low level of private spend‐ ing, such as e.g. the Czech Republic (16.7%). The higher share of private spending is seen in Croatia and Slovenia, but these countries have low share of out‐of‐pocket expenses—62.4% and 42.7% respectively. These are the only two post‐communist countries in which the vol‐ untary private insurances operate effectively. Increasing the share of private spending while increasing direct expenditure affected the health results achieved by Hungary and Slovakia negatively. A very high share of private expenditure and at the same time a high share out of pocket payments contributes to the achievement of worse health outcomes.

The share of private expenditure in the total expenditure (PR\_TE) on healthcare and the share of patients' out‐of‐pocket payments (OOP\_TE) are the variables which indirectly characterize the barriers in access to healthcare services. Of course, the obtained results should not be interpreted as meaning that a change in the financing structure has a direct impact on the improvement of health outcomes. However, the indirect effect has been demonstrated, which confirms the results of other authors dealing with research on the availability of medical services for patients.

The primary reason for the decrease of efficiency in Hungary and Slovakia is a very significant change in the financing structure. The PR\_TE variable increased by 7.1 p.p. in Hungary and as much as 19.4 p.p. in Slovakia, whereas the OOP\_TE variable increased by 1.7 p.p. in Hungary and as much as 11.5 p.p. in Slovakia. In the case of Hungary, these negative phenomena coincided with an increase in the income inequalities of the society, illustrated by the change in the GINI index from 27.2 in 2000 to 30.6 in 2013. In the case of Slovakia, the inequalities decreased. It should be noted, however, the all the health outcomes in these two countries

**Country Year PR\_TE OOP\_TE GINI LE60 HLE ISR** Hungary 2000 29.3 26.3 27.2 18.3 63.7 990.3

Slovakia 2000 10.6 10.6 28.9 18.3 64.9 989.8

Croatia 2000 13.9 13.9 31.3 19.3 66.4 992.8

Ukraine 2000 48.2 44.1 29.1 16.7 60.6 984.2

Azerbaijan 2000 81.4 63.3 36.5 16.8 59.3 939.3

Georgia 2000 83.0 82.5 40.5 18.6 64.1 968.8

DE\_16 2000 24.0 16.7 30.9 21.9 69.0 995.5

2013 36.4 27.5 30.6 20.1 67.4 994.8

2013 30.0 22.1 26.1 20.3 68.1 994.0

2013 20.0 12.5 32.5 21.2 69.4 996.2

2013 45.5 42.8 24.7 18.1 64.1 991.4

2013 79.2 71.1 31.8 18.5 64.7 970.1

2013 78.5 61.9 41.4 19.7 66.4 988.3

2013 21.9 15.3 30.5 24.1 71.9 997.1

In the case of Croatia, which improved its efficiency score, there was indeed an increase in the share of private expenditure (PR\_TE) but the expenses covered directly by households (OOP\_TE) decreased. The GINI index deteriorated slightly. On the other hand, in the case of Ukraine both private spending (PR\_TE) and the expenditure covered directly by the public (OOP\_TE) decreased. Also the income inequalities in the population (GINI) decreased significantly. The favourable results of Ukraine since 2014 deteriorated due to the ongoing military conflict.

Azerbaijan and Georgia reduced the share of private spending by 2.2 and 4.5 p.p., respec‐ tively. These expenses are about two times higher than the average for post‐communist countries and almost four times higher than the average for developed countries of Western Europe. The share of direct expenditure in Azerbaijan increased by 7.8 p.p., while in Georgia it decreased by 20.6 p.p. The income inequalities fell by 4.7 percentage points in Azerbaijan

and increased slightly by 0.9 p.p. in Georgia. The health outcomes improved.

improved.

*Source*: Own computation.

104 Advances in Health Management

**Table 3.** Data from selected countries for the years 2000 and 2013.

**Figure 1.** Comparison of the effectiveness results in the years 2000 and 2013. Source: Own elaboration.

The next step of the analysis is to provide a projection, that is the directions and magnitudes of changes that should be introduced by the inefficient countries in order to achieve the efficiency of leaders. This is illustrated in **Table 4**. The calculations were carried out for the year 2013.


**Table 4.** Projection of changes in the inefficient countries for the year 2013.

The DATA columns contain the values of the respective variables registered in 2013. The CHANGE columns present the percentage change, the introduction of which would lead to achieving full efficiency in individual countries. The direction of these changes is the same for all variables and countries, and the size varies. The changes are for PR\_TE from 0.184 to 0.795, for OOP\_TE from 0.165 to 0.785 and for GINI from 0.003 to 0.409.

In order to achieve full efficiency, these countries should change the structure of financing and income inequalities, e.g. Azerbaijan should reduce PR\_TE by 79.5%, OOP\_TE by 78.5% and GINI by 20.0%, whereas Georgia should reduce PR\_TE by 78.9%, OOP\_TE by 74.8% and GINI by 37.4%, which to reduce the proportion of people at risk of catastrophic health expen‐ ditures in this countries [31].
