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Abstract

This study aims to assess the performance of World Health Organization European Region nations in achieving their Health 2020 targets using multi-criteria decision-making approaches and the core indicators defined in Health 2020. These assessments were conducted in two periods based on 2010 and 2019 data, and the relative success of the countries in meeting the targets was compared. The CRiteria Importance Through Inter-criteria Correlation (CRITIC), COmbinative Distance-based ASsessment (CODAS), Evaluation based on Distance from Average Solution (EDAS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which are part of the Multi-Criteria Decision-Making (MCDM) family, as well as the Borda count approach, are used to show the country’s performance based on a set of core indicators. Norway and Iceland were the best-performing countries in both years, according to the Borda ranking, which incorporates the results of three independent approaches. Armenia, Moldova and Kyrgyzstan, on the other hand, did not make any improvement throughout any year and ended up as the poorest countries. Belarus, Estonia and Ireland were among the countries that made the most progress. Cross-country comparisons can be used as a basis for proper policy creation by decision-makers and policymakers in the countries studied.

Keywords

Health performance CODAS CRITIC EDAS TOPSIS Borda

Introduction

The World Health Organization (WHO) regional office for Europe and the 53 European member countries developed and agreed on ‘Health 2020: A European Policy Framework and Strategy for the twenty-first century’, which aims to improve community health and well-being, reduce health inequalities and ensure sustainable people-centred health systems in Europe. Member nations require WHO support in determining and developing policies to address and resolve regional health challenges encountered in the WHO European region, which includes 53 of the WHO’s 194 member countries and affects a population of over 900 million people. The Health 2020 framework emphasises that excellent health benefits everyone and that countries may achieve the highest health standards by working together across government boundaries. For all countries, this policy framework is adaptable and practical (WHO Regional Office for Europe, 2013).

For many years, policymakers and decision-makers have placed a premium on the development of health systems. Furthermore, actions aimed at improving the performance of health systems have received a lot of traction. Reforms are carried out in a variety of areas for this purpose, including reimbursement systems, health insurance and public and private sector laws, to name a few. Another issue that policymakers are concerned about is the success of these reforms. The outcomes of all reforms have been the subject of several studies. Simultaneously, investigations examining the findings have continued. Some indicators, however, are required to track these outcomes (Evans et al., 2001). Pandemics, migration, ageing, chronic diseases and other issues have been addressed by health systems, but conditions continue to pose a challenge. Many countries are focused on their own healthcare systems, yet global issues affect all countries (World Health Organization, 2007).

The health policies established by the countries vary, particularly in terms of disease prevention programmes. Different health strategies show that countries can learn a lot from one another (Rosso et al., 2015). Many studies concentrated on high-income countries. In comparative health systems research, low- and middle-income nations have been put in the background, resulting in an information gap. Furthermore, just a few indicators of nations’ comparative health systems should be included in the study, such as social concerns, technical and clinical variables and methodological concerns (Healy et al., 2018).

Using the basic indicators defined in the Health 2020 document, this study attempts to assess the success of the WHO European Region countries in achieving their Health 2020 targets. Multi-criteria decision-making (MCDM) strategies are utilised for this aim.

Health 2020 Policy Framework

Health 2020 highlights the health challenges faced by countries across the European Region as a whole. While the pattern may differ from country to country, the key overarching issues apply to all. However, Health 2020 focuses on identifying problems as well as potential solutions and areas where evidence suggests that positive action can have important effects (WHO Regional Office for Europe, 2013). As part of the policy framework developed to guide member countries, WHO Regional Office for Europe’s expert team identified 19 core and 18 additional indicators to be followed in achieving two strategic objectives and six targets. Some of these indicators are used in conjunction with more than one target. Health 2020 strategic objectives, targets and core indicators determined are shown in Figure 1 (WHO Regional Office for Europe, 2016).

Figure 1.

Health 2020 Strategic Objectives, Targets and Core Indicators.

Related Works

MCDM is a widespread method and has been used in healthcare management. MCDM has been preferred in many research subjects such as determining priorities, performance evaluation, site selection, supplier selection and technology selection. MCDM is a set of methods developed to identify the best alternative with conflicting criteria. With MCDM, ranking, classification, description, sorting and the choice become possible (Mulliner et al., 2016). MCDM consists of five elements: the goal, decision maker’s preferences, alternatives, criteria and outcomes (Kumar et al., 2017). Many methods have been presented over the years. Some of them can be listed as analytical hierarchy process (AHP, 1970), technique for order preference by similarity to ideal solution (TOPSIS, 1981), CRiteria importance through inter-criteria correlation (CRITIC, 1995), elimination and choice translating reality (ELECTRE, 1965/1968), measuring attractiveness by a categorical based evaluation technique (MACBETH, 1994/1997), complex proportional assessment (COPRAS, 1996) and preference ranking organisation method for enrichment evaluation (PROMETHEE, 1982), which have been used for a long time. In addition, many new methods have been presented in recent years, like multi-objective optimization by ratio analysis (MOORA, 2006), evaluation based on distance from average solution (EDAS, 2016) and COmbinative distance-based assessment (CODAS, 2015) and multi-attributive border approximation area comparison (MABAC, 2015). MCDM is a widespread method and has been used in health and healthcare management. Studies using MCDM methods in the healthcare management literature are summarised in Table 1.

Table 1.

Studies in the Literature with CRITIC, TOPSIS, CODAS, EDAS and BORDA.

Authors	Year	Aim	Method(s)
Araujo et al. (2018)	2018	Performance analysis of Brazilian public health	TOPSIS, neural networks
Özsarı and Boz (2019)	2019	Ranking countries using health status and macroeconomic indicators	Multi-dimensional scaling, TOPSIS
Wang et al. (2019)	2019	Evaluate the impact of social support on improving self-management of type 2 diabetes mellitus	ANP, CRITIC
Akcan and Güldeş (2019)	2019	Supplier selection problem	AHP, TOPSIS, GRA, ELECTRE, SAW
Mishra et al. (2020)	2020	Assessment of healthcare waste disposal technology	EDAS
Yılmaz and Söyük (2020)	2020	Clustering and ranking World Bank countries according to health risk factor and health status indicators	CRITIC, mean weight, TOPSIS, EDAS, BORDA
Yazdani et al. (2020)	2020	Supplier selection problem in a public procurement system	DEMATEL, BWM, EDAS
Zolfani et al. (2020)	2020	Location selection of a temporary hospital	CRITIC, CoCoSo
Jafari et al. (2020)	2020	Assessment of performance in teaching hospitals	AHP, TOPSIS
Can et al. (2021)	2021	Six sigma project prioritisation	AHP, CODAS
Peng et al. (2021)	2021	Intelligent healthcare management evaluation	CRITIC, CoCoSo
İnce and Güre (2021) <	2021	Rare diseases policy performance evaluation	TOPSIS, MOORA, COPRAS
Aydin and Seker (2021)	2021	Determining the location of isolation hospitals for COVID-19	Delphi, best–worst method, TOPSIS

Note: AHP: Analytical hierarchy process; ANP: Analytic network process; CODAS: COmbinative distance-based assessment; COPRAS: Complex proportional assessment; CRITIC: CRiteria importance through inter-criteria correlation; EDAS: Evaluation based on distance from average solution; ELECTRE: Elimination and choice translating reality; GRA: Gray relational analysis; MOORA: Multi-objective optimization by ratio analysis; TOPSIS: Technique for order preference by similarity to ideal solution.

Data

In this research, it is aimed to evaluate the performance of the WHO European Region countries in reaching their Health 2020 targets, using the core indicators determined in the Health 2020, with MCDM methods. These evaluations were made for two separate time periods based on the data from 2010 and 2019, and the relative performance of the countries in reaching the targets has been compared.

Three out of 19 core indicators (indicators 11, 18 and 19) were not used in this study as they are qualitative indicators. In addition, indicator 14 (social support) was not included in the analysis as it only has data from 2015. When the data from 2010 or 2019 were unavailable, data from the closest year were used for that indicator. Information on the indicators used in the analysis is given in Table 2.

Table 2.

Selected Health 2020 Core Indicators.

Indicators	Description	Topic
1. Pre mort rate	Age-standardised mortality rate (per 100,000 population)	Health status
2. Tobacco	Prevalence of current tobacco use among adults aged 18 years and over	Risk factors
3. Alcohol	Adult (15 years and over) per capita consumption of pure alcohol	Risk factors
4. Overweight	Prevalence of overweight and obesity in people aged 18 years and over, body mass index (BMI) of ≥25 kg/m²	Risk factors
5. Measles	Percentage of children vaccinated against measles	Health service coverage
6. Mort ext causes and inj	Age-standardised mortality rates from external causes of injury and poisoning, all ages per 100,000 population	Health status
7. Life expectancy	Life expectancy at birth (years)	Health status
8. Infant mort	Infant mortality rate (probability of dying between birth and the age of 1 per 1,000 live births)	Health status
9. School enrolment	Proportion of children of official primary school age not enrolled	Social determinants and inequalities
10. Unemployment	Unemployment rate	Social determinants and inequalities
11. GINI	Gini coefficient	Social determinants and inequalities
12. Life satisfaction	Life satisfaction	Population well-being
13. Sanitation	Percentage of population with improved sanitation facilities	Environmental factors
14. Out-of-pocket exp	Private households’ out-of-pocket payments on health as percentage of total health expenditure	Health system resources
15. Health exp	Total health expenditure as percentage of gross domestic product (GDP)	Health system resources

Accordingly, 42 countries whose indicator data in Table 2 were available from 53 countries in the WHO European Region were included in the analysis.

Methods

In this study, the CRITIC method, one of the objective weighting methods, has been used to calculate the weights without the intervention of the decision maker in the weighting of the 15 indicators. In this section, CRITIC, CODAS, EDAS and TOPSIS methods that enable the evaluation and ranking of the alternatives and the Borda Count method for merging the initial rankings to obtain the final ranking are described.

CRITIC Method

Since the subjective methods used in criterion weighting are shaped according to the experience and level of knowledge of the decision makers, there are doubts about the reliability of the results. CRITIC, one of the objective weighting methods, was developed by Diakoulaki et al. (1995) to overcome such problems. The criterion weights obtained by the CRITIC method include both the contrast intensity of each criterion that forms the structure of the decision problem and the conflict between the criteria. The contrast intensity of the criteria is evaluated with the standard deviation and the conflict between the criteria is measured with the Pearson correlation coefficient. The steps of the CRITIC method are given in Table 3 (Jahan et al., 2012).

Table 3.

CRITIC Algorithm.

Definition	Formulisation
Step 1. Construct the decision matrix	$D = [\begin{matrix} x_{11} x_{12} x_{13} \dots x_{1 n} \\ x_{21} x_{22} x_{23} \dots x_{2 n} \\ ⋮ ⋮ ⋮ ⋱ ⋮ \\ x_{m 1} x_{m 2} x_{m 3} \dots x_{m n} \end{matrix}]$
Step 2. Calculate the normalised decision matrix (respectively for benefit and cost criteria)	$1 . r_{i j} = \frac{x_{i j} - x_{j}^{m i n}}{x_{j}^{m a x} - x_{j}^{m i n}}$ $2 . r_{i j} = \frac{x_{j}^{m a x} - x_{i j}}{x_{j}^{m a x} - x_{j}^{m i n}}$
Step 3. Calculate correlation values between criteria	$\begin{matrix} ρ_{j k} = \frac{\sum_{i = 1}^{m} (r_{i j -} {\bar{r}}_{j}) (r_{i k -} {\bar{r}}_{k})}{\sqrt{\sum_{i = 1}^{m} {(r_{i j -} {\bar{r}}_{j})}^{2} \sum_{i = 1}^{m} {(r_{i k -} {\bar{r}}_{k})}^{2}}} j, k \\ = 1, \dots, n \end{matrix}$
Step 4. Compute the amount of information for each criteria $C_{j})$	$C_{j} = σ_{j} \sum_{k = 1}^{n} (1 - ρ_{j k}), j = 1, \dots, n$
Step 5. Determine the objective weights	$W_{j} = C_{j} / \sum_{k = 1}^{n} C_{k} j = 1, \dots, n$

CODAS Method

The CODAS method, a relatively new method compared to other MCDM methods, aims to solve multi-criteria group decision-making problems. The method presented by Keshavarz Ghorabaee et al. (2016) measures the alternatives in two different ways: Euclidean distance and Taxicab distance (Peng & Garg, 2018). The steps of the CODAS method consist of eight steps as given in Table 4 (Keshavarz Ghorabaee et al., 2016).

Table 4.

CODAS Algorithm.

Definition	Formulisation
Step 1. Construct the decision matrix	$X_{[x_{i j}] n x m} = [\begin{matrix} x_{11} & x_{12} & . . . & x_{1 m} \\ x_{21} & x_{22} & . . . & x_{2 m} \\ . & . & . & . \\ . & . & . & . \\ . & . & . & . \\ x_{n 1} & x_{n 2} & . . . & x_{n m} \end{matrix}]$
Step 2. Calculate the normalised decision matrix	$n_{i j} = \frac{x_{i j}}{\max x_{i j}} n_{i j} \frac{\min x_{i j}}{x_{i j}}$
Step 3. Calculate the weighted normalised decision matrix.	$r_{i j} = w_{j} n_{i j}$
Step 4. Determine the negative-ideal solution	$n s = {[{n s}_{j}]}_{1 x m}$ ${n s}_{j} = {m i n}_{t} r_{i j}$
Step 5. Calculate the Euclidean and Taxicab distances	$E_{i = \sqrt{\sum_{j = 1}^{m} {(r}_{i j} - {n s}_{j)}^{2}}}$ , $T_{i = \sqrt{\sum_{j = 1}^{m} \|r_{i j} - {n s}_{j}\|}}$
Step 6. Construct the relative assessment matrix	Ra = ${[h_{i k}]}_{n x n}$ , $r_{i j} = (E_{i} - E_{k}) + (¥ ((E_{i} - E_{k}) x (T_{i} - T_{k}))$
Step 7. Calculate the assessment score	$H_{i} = \sum_{k = 1}^{n} h_{i k}$
Step 8. Rank the alternatives	The alternative with the highest score in the ranking is considered the best alternative

EDAS Method

EDAS, presented by Keshavarz Ghorabaee et al. (2015), is based on distance from the average solution. It is used in studies that aim at classification and supplier selection. The method is one of the most preferred MCDM methods because it is useful and practical to calculate, especially in scenarios using conflicting criteria. Although it is a new method, recent studies have demonstrated its consistency (Li et al., 2019). The steps of the EDAS method are shown in Table 5 (Keshavarz Ghorabaee et al., 2015).

Table 5.

EDAS algorithm.

Definition	Formulisation
Step 1. Construct the decision matrix	$X_{[x_{i j}] n x m} = [\begin{matrix} x_{11} & x_{12} & . . . & x_{1 m} \\ x_{21} & x_{22} & . . . & x_{2 m} \\ . & . & . & . \\ . & . & . & . \\ . & . & . & . \\ x_{n 1} & x_{n 2} & . . . & x_{n m} \end{matrix}]$
Step 2. Determine the average solution	${A V}_{j} = {[{A V}_{j}]}_{1 x m}$ ${AV}_{j} = \frac{\sum_{i = 1}^{n} X_{i j}}{n}$
Step 3. Calculate the weighted normalised decision matrix.	$P D A = {[{P D A}_{i j}]}_{n x m}$ $N D A = {[{N D A}_{i j}]}_{n x m}$ In the beneficial criteria; ${PDA}_{i j} = \frac{max (0, (X_{i j} - {AV}_{j))}}{A V_{j}}$ ${NDA}_{i j} = \frac{max (0, ({AV}_{j} - X_{i j}))}{{AV}_{j}}$ In the non-beneficial criteria; ${PDA}_{i j} = \frac{max (0, ({AV}_{j} - X_{i j}))}{{AV}_{j}}$ ${NDA}_{i j} = \frac{max (0, (X_{i j} - {AV}_{j))}}{{AV}_{j}}$
Step 4. Determine the weighted	${S P}_{i} = \sum_{j = 1}^{m} w_{j} {P D A}_{i j}$ $N = \sum_{j = 1}^{m} w_{j} {N D A}_{i j}$
Step 5. Normalise the values	${NSP}_{i} = \frac{{SP}_{i}}{{max}_{i} ({SP}_{i})}$ ${NSN}_{i} = \frac{{SN}_{i}}{{max}_{i} ({SN}_{i})}$
Step 6. Calculate the appraisal score	${A S}_{i} = \frac{1}{2} ({N S P}_{i} + {N S N}_{i})$
Step 7. Rank the alternatives	The alternative with the highest score in the ranking is considered the best alternative.

TOPSIS Method

TOPSIS method was first introduced by Hwang and Yoon (1981) and later developed by Chen and Hwang (1992). This method provides the determination of the alternative closest to the positive ideal and the farthest from the negative ideal. Accordingly, while the positive ideal solution expresses the best possible values of the criteria, the negative ideal solution expresses the worst possible values of the criteria. In this method, the best alternative is not only the shortest distance from the positive ideal solution but also the longest distance from the negative ideal solution. In the TOPSIS method, distance calculations are obtained by using the Euclidean distance. The steps of TOPSIS method are given in Table 6 (Chen & Tzeng, 2004; Opricovic & Tzeng, 2004).

Table 6.

TOPSIS Algorithm.

Definition	Formulisation
Step 1. Construct the decision matrix	$D = [\begin{matrix} f_{11} f_{12} f_{13} \dots f_{1 n} \\ f_{21} f_{22} f_{23} \dots f_{2 n} \\ ⋮ ⋮ ⋮ ⋱ ⋮ \\ f_{m 1} f_{m 2} f_{m 3} \dots f_{m n} \end{matrix}]$
Step 2. Calculate the normalised decision matrix	$r_{i j} = \frac{f_{i j}}{\sqrt{\sum_{k = 1}^{m} f_{k j}^{2}}} R_{i j} = [\begin{matrix} r_{11} r_{12} r_{13} \dots r_{1 n} \\ r_{21} r_{22} r_{23} \dots r_{2 n} \\ ⋮ ⋮ ⋮ ⋱ ⋮ \\ r_{m 1} r_{m 2} r_{m 3} \dots r_{m n} \end{matrix}]$
Step 3. Calculate the weighted normalised decision matrix	$\sum_{i = 1}^{n} W_{i} = 1 v_{i j} = [\begin{matrix} {w_{1} r}_{11} w_{2} r_{12} {w_{3} r}_{13} \dots {w_{n} r}_{1 n} \\ w_{1} r_{21} w_{2} r_{22} w_{3} r_{23} \dots w_{n} r_{2 n} \\ ⋮ ⋮ ⋮ ⋱ ⋮ \\ {w_{1} r}_{m 1} w_{2} r_{m 2} {w_{3} r}_{m 3} \dots w_{n} r_{m n} \end{matrix}]$
Step 4. Determine the ideal and negative-ideal solution.	$A^{*} = \{{({m a x}_{j} v_{i j}\| i \in I^{'})}_{,}\| ({m i n}_{j} v_{i j}\| i \in I^{''})\}$ $A^{-} = \{{({m i n}_{j} v_{i j}\| i \in I^{'})}_{,}\| ({m a x}_{j} v_{i j}\| i \in I^{''})\}$ where $I'$ is associated with benefit criteria, and $I^{''}$ is associated with cost criteria
Step 5. Calculate the separation measures, using the n-dimensional Euclidean distance.	$D_{j}^{} = \sqrt{\sum_{j = 1}^{n} {(v_{i j} - v_{i}^{})}^{2}} D_{j}^{-} = \sqrt{\sum_{j = 1}^{n} {(v_{i j} - v_{i}^{-})}^{2}}$
Step 6. Calculate the relative closeness to the ideal solution.	$C_{j}^{} = \frac{D_{j}^{-}}{(D_{j}^{} + D_{j}^{-})};$
Step 7. Rank the alternatives	The alternative with the highest score in the ranking is considered the best alternative.

Borda Count Method

Many MCDM techniques are used together in solving sequencing problems. One of these methods does not have a definite advantage over the other, and generally close results are obtained. Determining the best alternative with these methods is considered an efficiency criterion. The Borda count method is a data integration technique used to combine ordered lists in these cases (Kim et al., 2006). In the Borda count method, each ranking is of equal importance. In this method, zero points are assigned to the least preferred alternative, 1 point for the next alternative and (n − 1) point for the most preferred (n is the number of alternatives). Accordingly, the values assigned for the alternatives in all classes are summed, the total Borda score is calculated and the final ranking is obtained from this score (Wu, 2011).

In order to enhance ranking trustworthiness, this article suggests a hybrid solution based on multiple ranking methods. The proposed solution procedure is shown in Figure 2.

Figure 2.

The Proposed Solution Procedure.

Results

In the first step of research, evaluation indicators were determined, and a decision matrix was created. Following this, the criteria weights were obtained by using the CRITIC method, separately for the years 2010 and 2019. Then, the final weight values

(W_{j})

were obtained by taking the arithmetic average of these two separate weight values. In the MCDM analysis conducted within the data of both years, the arithmetic mean was taken as the important weight of the indicators. The results of the CRITIC analysis are shown in Table 7.

Table 7.

CRITIC Weights of Indicators.

Year	I1	I2	I3	I4	I5	I6	I7	I8	I9	I10	I11	I12	I13	I14	I15
2010	0.071	0.064	0.065	0.090	0.066	0.069	0.065	0.056	0.069	0.080	0.073	0.060	0.058	0.053	0.061
2019	0.067	0.058	0.071	0.109	0.075	0.086	0.046	0.068	0.052	0.075	0.072	0.058	0.050	0.055	0.059
Mean	0.069	0.061	0.068	0.099	0.070	0.077	0.055	0.062	0.060	0.078	0.073	0.059	0.054	0.054	0.060

According to Table 7, while the indicator with the highest importance was overweight, the indicators with the lowest importance were improved sanitation facilities and out-of-pocket expenditures. The importance weights of indicators obtained by the CRITIC method were used in the CODAS, EDAS and TOPSIS methods, and the performance of 42 countries was evaluated for 2010 and 2019. The method steps for Tables 4–6 were respectively used for this process. Finally, to merge different methods’ rankings, the Borda count methodology was used. Performance rankings for 2010 and 2019 were calculated separately. In this study, since it is aimed to determine the relative improvement levels of the performances of the countries based on the years 2010 and 2019, the change in the rankings by the years is also calculated. The findings obtained as a result of these analyses are shown in Table 8.

Table 8.

Comparison of the Health 2010 and 2019 Rankings.

Country	$H_{i}$ 2010	CR 2010	${A S}_{i}$ 2010	ER2010	$C_{j}^{*}$ 2010	TR2010	TBS 2010	BR2010	$H_{i}$ 2019	CR2019	${A S}_{i}$ 2010	ER2019	$C_{j}^{*}$ 2019	TR2019	TBS2019	BR2019	BRC	BRD
ARM	−3.649	40	−0.581	37	0.458	41	8	40	−3.541	40	−0.398	42	0.389	42	2	42	dec.	–2
AUT	1.098	18	0.781	7	0.644	25	76	19	0.944	15	0.803	10	0.577	32	69	19	cons.	0
BEL	0.908	21	0.710	12	0.742	13	80	16	1.02	14	0.766	13	0.761	11	88	10	inc.	6
BGR	−2.406	35	0.065	30	0.591	31	30	31	−1.916	34	−0.304	41	0.555	35	16	38	dec.	–7
BLR	−0.033	24	−0.596	38	0.514	34	30	31	1.348	11	0.495	25	0.64	26	64	22	inc.	9
CHE	1.041	20	0.867	4	0.788	5	97	7	0.274	22	0.822	7	0.768	9	88	10	dec.	–3
CYP	1.902	8	0.582	21	0.718	18	79	18	2.845	4	0.599	21	0.698	23	78	17	inc.	1
CZE	2.002	7	0.625	18	0.718	19	82	15	1.627	9	0.721	16	0.73	17	84	15	cons.	0
DEU	1.275	13	0.734	10	0.752	10	93	9	0.708	17	0.816	8	0.772	7	94	8	inc.	1
DNK	1.234	15	0.695	15	0.745	11	85	12	0.691	18	0.876	4	0.807	5	99	7	inc.	5
ESP	1.68	10	0.523	22	0.667	22	72	21	1.354	10	0.438	26	0.629	28	62	24	dec.	–3
EST	0.67	22	0.229	28	0.603	29	47	26	2.293	6	0.525	24	0.699	21	75	18	inc.	8
FIN	2.492	5	0.716	11	0.734	16	94	8	2.563	5	0.734	14	0.745	13	94	8	cons.	0
FRA	1.081	19	0.697	13	0.741	14	80	16	−0.023	26	0.702	18	0.716	19	63	23	dec.	–7
GBR	0.425	23	0.765	9	0.767	7	87	11	0.071	24	0.785	11	0.778	6	85	13	dec.	–2
GEO	−2.351	34	−0.359	36	0.518	33	23	35	−3.274	38	0.257	33	0.544	36	19	35	cons.	0
GRC	1.17	17	0.328	23	0.672	21	65	23	−0.006	25	0.339	30	0.58	31	40	29	dec.	–6
HRV	−0.373	25	0.259	26	0.65	23	52	24	−0.723	29	0.538	23	0.699	22	52	27	dec.	–3
HUN	−0.795	29	0.218	29	0.625	28	40	28	0.369	21	0.355	28	0.665	24	53	26	inc.	2
IRL	1.255	14	0.676	17	0.715	20	75	20	0.943	16	0.768	12	0.752	12	86	12	inc.	8
ISL	3.59	1	0.93	3	0.817	3	119	2	3.707	1	0.925	2	0.808	4	119	2	cons.	0
ISR	1.182	16	0.696	14	0.759	8	88	10	1.122	13	0.867	5	0.808	3	105	3	inc.	7
ITA	1.605	11	0.864	5	0.806	4	106	5	1.197	12	0.622	20	0.736	15	79	16	dec.	–11
KAZ	−3.687	41	−0.295	34	0.467	39	12	38	−3.378	39	0.283	32	0.543	37	18	36	inc.	2
KGZ	−4.546	42	−0.668	40	0.404	42	2	42	−4.724	42	0.093	37	0.442	41	6	40	inc.	2
LTU	−0.692	27	−0.19	32	0.5	36	31	30	0.104	23	0.311	31	0.568	34	38	31	dec.	–1
LUX	2.769	4	0.598	20	0.72	17	85	12	2.021	8	0.712	17	0.733	16	85	13	dec.	–1
LVA	−1.777	32	−0.298	35	0.497	37	22	36	−0.039	27	0.25	34	0.573	33	32	32	inc.	4
MDA	−3.389	39	−0.779	42	0.479	38	7	41	−3.876	41	−0.281	40	0.504	40	5	41	cons.	0
MLT	−0.637	26	0.617	19	0.745	12	69	22	−1.193	30	0.726	15	0.744	14	67	21	inc.	1
NLD	1.69	9	0.989	2	0.827	2	113	3	0.477	19	0.892	3	0.814	2	102	6	dec.	–3
NOR	2.883	3	0.991	1	0.833	1	121	1	3.079	3	0.996	1	0.85	1	121	1	cons.	0
POL	−0.792	28	0.266	25	0.648	24	49	25	−0.341	28	0.544	22	0.711	20	56	25	cons.	0
PRT	1.598	12	0.689	16	0.735	15	83	14	0.38	20	0.669	19	0.727	18	69	19	dec.	–5
ROU	−2.957	38	−0.067	31	0.594	30	27	34	−1.947	35	−0.104	38	0.586	29	24	34	cons.	0
RUS	−2.623	36	−0.598	39	0.465	40	11	39	−1.686	33	0.137	36	0.506	39	18	36	inc.	3
SRB	−1.543	31	−0.194	33	0.561	32	30	31	−1.297	31	0.345	29	0.634	27	39	30	inc.	1
SVK	−1.116	30	0.29	24	0.634	27	45	27	−1.308	32	0.363	27	0.662	25	42	28	dec.	–1
SVN	2.331	6	0.776	8	0.758	9	103	6	3.172	2	0.808	9	0.764	10	105	3	inc.	3
SWE	2.927	2	0.855	6	0.783	6	112	4	2.253	7	0.832	6	0.769	8	105	3	inc.	1
TUR	−2.261	33	0.249	27	0.634	26	40	28	−2.286	36	0.195	35	0.586	30	25	33	dec.	–5
UKR	−2.948	37	−0.673	41	0.509	35	13	37	−2.831	37	−0.209	39	0.511	38	12	39	dec.	–2

Notes: Hi CODAS score; CR: CODAS rank; ${A S}_{i} :$ EDAS score; ER: EDAS rank; $C_{j}^{*} :$ TOPSIS score; TR: TOPSIS rank; TBS: Total Borda score; BR: Borda rank; BRC: Borda rank change; BRD: Borda rank differences; cons.: constant; dec.: decreased; inc.: increased.

According to the Health 2010 rankings, in the EDAS and TOPSIS methods, the top three countries were Norway, the Netherlands and Iceland, while Sweden was in the top three instead of the Netherlands in the CODAS ranking. Kyrgyzstan was the only country that appeared at the bottom of all three rankings. According to the Borda ranking obtained by combining these three rankings, Norway, Iceland and the Netherlands are in the top three, respectively, while Kyrgyzstan, Moldova and Armenia are at the bottom of the list.

According to the Health 2019 rankings, Norway was the only common country at the top three of EDAS, CODAS and TOPSIS rankings. Moldova and Armenia were the common countries in the last three places according to the three rankings. According to the Borda ranking, respectively, Norway and Iceland are in the top two, similar to the 2010 Borda ranking. Israel, Slovenia and Sweden shared third place with the same Borda score. Finally, the last three countries were Kyrgyzstan, Moldova and Armenia, similar to the 2010 Borda ranking.

According to the CODAS ranking, the countries that fell the most were the Netherlands, Greece and Portugal, respectively, while the countries that rose the most were Estonia, Belarus and Hungary. According to the EDAS ranking, the countries that fell the most were Italy, Bulgaria and Turkey, respectively, while the countries that rose the most were Belarus, Denmark and Israel. According to the TOPSIS ranking, the countries that fell the most were Italy, Bulgaria and France, respectively, while the countries that rose the most were Belarus, Estonia and Ireland. According to the Borda ranking obtained by the combination of these three rankings, the countries that fell the most were Italy, France and Bulgaria, respectively, while the countries that rose the most were Belarus, Estonia and Ireland. According to these four rankings, Belarus is the only country with the highest rise, while Estonia is among the countries with the highest rise in three rankings. Italy and Bulgaria, on the other hand, were among the countries that fell the most according to the three rankings.

When the indicators that cause this result are examined, indicators 3, 4, 9, 10, 11, 14 and 15 for Italy showed negative changes. For Bulgaria, especially indicators 3, 4, 5, 9 and 11 showed negative changes. Belarus, on the other hand, has achieved a remarkable improvement over the base year, especially in indicators 1, 2, 3, 6, 7, 8, 9, 10, 11 and 14. The 2010 and 2019 Borda ranking changes for the countries are also shown in Figure 3.

Figure 3.

The Change in Borda Health 2010 and 2019 Rankings.

Discussion

In recent years, important developments in health and well-being have been recorded throughout the world, but the inequalities among countries make health, social and economic challenges inevitable. The Health 2020 policy framework addresses these challenges by developing leadership that engages governments in achieving the goal of health for all. This study aims to obtain an empirical finding for the policy framework determined by the WHO. For this purpose, the relative level of achievement of 42 countries was evaluated by taking into account the indicators determined by the WHO European Region within the scope of the Health 2020 targets. In this context, it is important to emphasise that the results of the research are limited to the countries covered, as the assessment of the level of achievement is made on the basis of the countries included in the analysis. In addition, since the methodologies of the MCDM techniques used in the research are decisive in determining the level of achievement, it should be taken into account that the research results are limited to the methodology of the methods used. Accordingly, in the Borda ranking, which combines the rankings obtained by three different methods, Norway and Iceland were the countries in the best condition in 2 separate years. Armenia, Moldova and Kyrgyzstan, on the other hand, failed to make any progress in either period and remained the countries with the worst conditions.

Another aim of the study is to compare the ranking changes of the countries of the WHO European Region. In this context, as a result of the analysis, Belarus, Estonia and Ireland were the countries that showed the best progress. It is possible to say that Belarus and Estonia are below average in terms of many indicators, especially in the year they progressed. Although these countries have values below the average, it is clear that they have made significant improvements compared to the initial year. According to the ranking changes, the worst performing countries are Italy, Bulgaria and France. Although Italy and France are in good condition compared to other countries in terms of many indicators, it can be interpreted that they did not achieve the desired progress according to the base year. Bulgaria, on the other hand, was one of the worst-performing countries in both years, in regard to baseline and change.

Özsarı and Boz (2019) analysed the performance of countries with indicators in both health status and macroeconomics. The country performances were ranked separately from the analysis of the indicators. Ireland ranked first in the ranking obtained by the TOPSIS method using macroeconomic indicators. It was followed by Luxembourg, Switzerland, Norway and the Netherlands. Japan ranked first in the analysis obtained by the TOPSIS method, where health status indicators were used. Finland, Sweden, Iceland and France were followed by Japan. In this study, indicators such as macroeconomic, health status and health risk indicators were analysed together in accordance with the framework determined by the WHO. Yılmaz and Söyük (2020) aimed to classify the member countries of the World Bank according to health risk factors and to rank the countries according to health status indicators. Mean weight, CRITIC, TOPSIS and EDAS methods were used for ranking in the study. At the end of the study, countries were classified and ranked to achieve homogeneity. The ranking was obtained by using different weighting methods among homogeneous groups. Norway ranked first in the Borda count ranking obtained using the mean weight. This country has been followed by Australia, Luxembourg, Sweden, Japan and Switzerland, respectively. Kyrgyzstan ranked 76th, Moldova 68th, Armenia 64th, Russia 57th and Kazakhstan 58th in the cluster of 84 countries. The difference of this study in this respect is that it is an empirical study for a specific region for the WHO European Region countries, without the need for cluster analysis to ensure homogeneity in cross-country comparisons. In the meantime, this study includes the ranking changes of countries on a yearly basis. In this study, Norway ranked first in the evaluations for 2010 and 2019. In addition, the ranking of Kazakhstan, Russia, Armenia, Moldova and Kyrgyzstan, which are in the last, shows similarity in both studies. Zuidberg et al. (2020) aimed to analyse the progress of 50 member countries in the WHO European Region according to the Health 2020 indicators between 2005, 2010 and 2015. In the study, an index called the Health 2020 Index was obtained. The ranking was determined according to this index score. The top five countries in the ranking were Sweden, Norway, Iceland, Switzerland and the Netherlands, respectively. The last five countries were shared by Turkmenistan, Georgia, Ukraine, Russia and Bulgaria. Kyrgyzstan ranked 21st, Moldova 44th, Armenia 41st and Russia 47th among 50 countries. Although some of the results are similar on the basis of country rankings, this study summarises many different methods, comparing the findings and showing how the countries progressed in reaching the determined targets by monitoring the changes in the rankings with more actual data.

Conclusion

Since the scope of the research includes relatively homogeneous countries located in a specific region, the findings obtained by cross-country comparisons are important. For decision makers and policymakers in the countries included in the analysis, cross-country comparisons can be used as a basis for appropriate policy development. The active involvement of governments at all levels, international, national and local, is required in tackling these health challenges. In this respect, it is important for countries to provide various improvements with action plans and control programmes for issues such as a tobacco control programme, an alcohol policy, an obesity policy, a vaccination programme, a health insurance scheme, and to periodically evaluate their current situation in terms of indicators determined under the leadership of WHO. It should be designed by considering all dimensions, such as intrasectoral, inter-sectoral and multisectoral dimensions. It is important for the countries of the WHO European Region to cooperate in order to achieve better health and well-being by developing appropriate policies.

In future studies, different methods can be used to weight the criteria. It can be used for weighing from qualitative and quantitative methods. Different methods can be used for ranking in subsequent studies. In addition, qualitative objectives that are not included in this study can be used in further studies. Also, countries that cannot be included in this study due to a lack of data and data access problems may be included in future studies. It is quite clear that comparative analyses are needed for best practices. For this reason, the analysis of health systems should be studied more comprehensively with different methods and by considering the intrasectoral, inter-sectoral and multi-sectoral dimensions in future studies.

Footnotes

Declaration of Conflict of Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Faruk Yılmaz

Özgür İnce

References

Akcan

, & Güldeş

(2019). Integrated multicriteria decision-making methods to solve supplier selection problem: A case study in a hospital. Journal of Healthcare Engineering , 2019, 5614892.

Araujo

C. A. S.

, Wanke

, & Siqueira

M. M.

(2018). A performance analysis of Brazilian public health: TOPSIS and neural networks application. International Journal of Productivity and Performance Management , 67(9), 1526–1549.

Aydin

, & Seker

(2021). Determining the location of isolation hospitals for COVID-19 via Delphi-based MCDM method. International Journal of Intelligent Systems , 36(6), 3011–3034.

Can

G. F.

, Toktaş

, & Pakdil

(2021). Six sigma project prioritization and selection using AHP–CODAS integration: A case study in healthcare industry. IEEE Transactions on Engineering Management , 70, 3587–3600.

Chen

M. F.

, & Tzeng

G. H.

(2004). Combining grey relation and TOPSIS concepts for selecting an expatriate host country. Mathematical and Computer Modelling , 40(13), 1473–1490.

Chen

S. J.

, & Hwang

C. L.

(1992). Fuzzy multiple attribute decision making: Methods and applications . Springer.

Diakoulaki

, Mavrotas

, & Papayannakis

(1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research , 22(7), 763–770.

Evans

D. B.

, Tandon

, Murray

C. J.

, & Lauer J.

(2001). Comparative efficiency of national health systems: Cross national econometric analysis. BMJ , 323(7308), 307–310.

Healy

, Tang

, Patcharanarumol

, & Annear

(2018). A framework for comparative analysis of health systems: experiences from the Asia Pacific Observatory on Health Systems and Policies. WHO South East Asia Journal of Public Health , 7(1), 5–12.

10.

Hwang

C. L.

, & Yoon

(1981). Multiple attribute decision making: A state of the art survey. In Lecture notes in economics and mathematical systems (Vol. 186). Springer.

11.

İnce

Ö.

, & Güre

M. D.

(2021). Evaluation of rare diseases policy performance of OECD countries using MCDM methods. Health Policy and Technology , 10(3), 100537.

12.

Jafari

, Seyedjavadi

, & Zaboli

(2020). Assessment of performance in teaching hospitals: Using multicriteria decision-making techniques. Journal of Education and Health Promotion , 9(1), 214.

13.

Jahan

, Mustapha

, Sapuan

S. M.

, Ismail

M. Y.

, & Bahraminasab

(2012). A framework for weighting of criteria in ranking stage of material selection process. International Journal of Advanced Manufacturing Technology , 58(1–4), 411–420.

14.

Keshavarz Ghorabaee

, Zavadskas

E. K.

, Olfat

, & Turskis

(2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica (Vilnius) , 26(3), 435–451.

15.

Keshavarz Ghorabaee

, Zavadskas

E. K.

, Turskis

, & Antucheviciene

(2016). A new combinative distance-based assessment (CODAS) method for multi-criteria decision-making. Economic Computation & Economic Cybernetics Studies & Research , 50(3), 25–44.

16.

Kim

M.J.

, Min

S. H.

, & Han

(2006). An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Systems and Applications , 31(2), 241–247.

17.

Kumar

, Sah

, Singh

A. R.

, Deng

, He

, Kumar

, & Bansal

R. C.

(2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews , 69, 596–609.

18.

Y. Y.

, Wang

J. Q.

, & Wang

T. L.

(2019). A linguistic neutrosophic multi-criteria group decision-making approach with EDAS method. Arabian Journal of Science and Engineering , 44(3), 2737–2749.

19.

Mishra

A. R.

, Mardani

, Rani

, & Zavadskas

E. K.

(2020). A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures. Journal of Cleaner Production , 272, 122807.

20.

Mulliner

, Malys

, & Maliene

(2016). Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega , 59, 146–156.

21.

Opricovic

, & Tzeng

G. H.

(2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research , 156(2), 445–455.

22.

Özsarı

S. H.

, & Boz

(2019). Comparison of health status and macroeconomic indicators in organization for economic cooperation and development countries using multidimensional scaling and TOPSIS. Journal of Health Science and Profession , 6(3), 545–554.

23.

Peng

, & Garg

(2018). Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Computers and Industrial Engineering , 119, 439–452.

24.

Peng

, Krishankumar

, & Ravichandran

K. S.

(2021). A novel interval-valued fuzzy soft decision-making method based on CoCoSo and CRITIC for intelligent healthcare management evaluation. Soft Computing , 25(6), 4213–4241.

25.

Rosso

, Marzuillo

, Massimi

, De Vito

, de Belvis

A. G.

, La Torre

, Federici

, Ricciardi

, & Villari

(2015). Policy and planning of prevention in Italy: Results from an appraisal of prevention plans developed by regions for the period 2010-2012. Health Policy , 119(6), 760–769.

26.

Wang

, He

, Zhu

, Zhang

, Xin

, Xu

, & Guan

(2019). An integrated model to evaluate the impact of social support on improving self-management of type 2 diabetes mellitus. BMC Medical Informatics and Decision Making , 19(1), 197.

27.

WHO Regional Office for Europe. (2013). Health 2020 a European policy framework and strategy for the 21st century .

28.

WHO Regional Office for Europe. (2016). Targets and indicators for Health 2020 (3rd ed.).

29.

World Health Organization. (2007). Strengthening health systems to improve health outcomes: WHO’s framework for action .

30.

W. W.

(2011). Beyond travel & tourism competitiveness ranking using DEA, GST, ANN and Borda count. Expert Systems with Applications , 38(10), 12974–12982.

31.

Yazdani

, Torkayesh

A. E.

, & Chatterjee

(2020). An integrated decision-making model for supplier evaluation in public healthcare system: The case study of a Spanish hospital. Journal of Enterprise Information Management , 33(5), 965–989.

32.

Yılmaz

, & Söyük

(2020). Clustering countries according to health risk factors and analysis of health status indicators with multi-criteria decision making techniques. Social Security , 17, 283–320.

33.

Zolfani

S. H.

, Yazdani

, Torkayesh

A. E.

, & Derakhti

(2020). Application of a gray-based decision support framework for location selection of a temporary hospital during COVID-19 pandemic. Symmetry , 12, 886.

34.

Zuidberg

M. R. J.

, Shriwise

, de Boer

L. M.

, & Johansen

A. S.

(2020). Assessing progress under Health 2020 in the European Region of the World Health Organization. European Journal of Public Health , 30(6), 1072–1077.

A Comparative Analysis of the WHO European Region Countries’ Health 2020 Performance Using MCDM Methods

Abstract

Keywords

Introduction

Health 2020 Policy Framework

Health 2020 Strategic Objectives, Targets and Core Indicators.

Related Works

Studies in the Literature with CRITIC, TOPSIS, CODAS, EDAS and BORDA.

Data

Selected Health 2020 Core Indicators.

Methods

CRITIC Method

CRITIC Algorithm.

CODAS Method

CODAS Algorithm.

EDAS Method

EDAS algorithm.

TOPSIS Method

TOPSIS Algorithm.

Borda Count Method

The Proposed Solution Procedure.

Results

CRITIC Weights of Indicators.

Comparison of the Health 2010 and 2019 Rankings.

The Change in Borda Health 2010 and 2019 Rankings.

Discussion

Conclusion

Footnotes

Declaration of Conflict of Interests

Funding

ORCID iDs

References