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Regarding the process of selection of indicators, it started with an extensive review of the literature concerning the whole set of determinants of population health. This was followed by an enchained 2-round Delphi process 51 Consortium experts and 30 stakeholders participated in this exercise and two meetings and one decision conference of the Project Steering Group [ 4 , 5 ]. The second function is proxied by medical doctors per , inhabitants. Being a supply side variable, it reflects the capacity of health care systems to deliver adequate care to their populations, but, as noted above, although human resources are the most relevant inputs, we should bear in mind what they can provide and how this also depends on physical inputs and consumables.

Finally, proxies used for the financing function include not only total health expenditure but also public expenditure and out-of-pocket payments.

Health system

The breakdown of total health expenditure gives an indication of access to health care as direct payments are a regressive mode of financing and can thus act as relevant barriers to access. Additional indicators have also been used to describe clusters. Our statistical analysis of the data collected followed a 3-step procedure.

In the first step, using factor analysis with a varimax with Kaiser normalization for rotation method, we searched for three factors which could express the common variance of the original indicators used as proxies for health systems functions appearing in the WHO framework. In this step, the KMO measure of sampling adequacy was estimated and the Bartlett test of sphericity was performed. The factor analysis was based on the correlation matrix, and the factor scores, to be used in the following cluster analysis, were obtained using the Bartlett method.

The second step was a cluster analysis of the factors found in the previous step for each country in order to identify groups of objects that are similar to each other but different from objects from other groups. The clustering performed here was based on a hierarchical method, which may be visualized in a dendrogram. The third part of this work was descriptive. It aimed to characterize the clusters found in the previous step.

The characteristics used to present the clusters were grouped in socioeconomic characteristics and health outcome indicators. As we can observe, most bivariate correlations are not statistically significant, except for those relating to the funding function of health systems.

These two tests guaranteed that the sample of data was adequate for factor analysis. Factor analysis assumes an extraction method by the principal component analysis of three factors and a varimax rotation method with Kaiser normalization. The first factor, named funding, aggregates the indicators related to health systems financing THEG, THE, OOP ; the second factor includes medical doctors MD and it includes resources; and finally, the third factor is represented by hospital discharges HD and includes provision. Using the three factors previously found, we clustered the countries by using a hierarchical procedure.

This procedure considers the Ward clustering method based on the Euclidean distance. In the absence of an objective method to optimally select the number of clusters, we determined that 5 clusters represented the reasonable and preferable number to reflect well observed reality, to allow for easy labelling, and to enable international comparisons.

The five clusters of countries are displayed in Fig. The clusters labels do not indicate any correlation between geographical positioning and health system characteristics; their only purpose is to simplify identification. The difference between Eastern countries clusters lies in the provision function. Otherwise, with respect to funding and resources, both clusters have high values.

Finally, we described the clusters of EU health systems created above according to certain socioeconomic characteristics and health outcome indicators. The figures presented are unweighted averages for each cluster with respect to the selected indicators, with the purpose being to perform a description and a comparison across the clusters. For its part, growth in EU economies during this same period of time was seen to be very slow. The individual labor markets of all EU countries display features which vary significantly. Despite an identical level of development across the 28 EU countries, certain health outcomes are not so similar.

There is an observable difference in infant mortality rates across clusters. This fact can also be confirmed by the outstanding values of DALY. A common shortcoming across these countries points to the low level of financing and thus a lack of financial protection; in addition, the general economic context of these nations is not very favorable, as shown by low GDP per capita, although growth rates seem to indicate some dynamism.

Higher hospital discharges can reflect higher levels of hospital activities in , for instance, hospital discharge rates in general were highest in Austria and Germany [ 16 ]. However, hospital activities are affected not only by the capacity of hospitals to treat patients but also by the ability of the primary care sector to prevent avoidable hospital admissions and by the availability of post-acute care settings to provide rehabilitative and long-term care services.

As for human resources, there is some degree of input substitution between medical doctors and other health personnel, namely nurses, meaning that some countries might compensate for a lower number of medical doctors with a higher number of other health personnel. Indeed, in , countries such as Finland, Denmark, Luxembourg and Ireland were among the countries with highest ratios of nurses to doctors [ 16 ].

Two Eastern countries clusters were created based on the main difference found with respect to the provision function. The economic context is characterized by high levels of unemployment and negative growth. Also, despite the good health outcomes, authorities should not lose sight of potential long term effects from the economic crisis that affected Southern countries. The main objective of this work was, nonetheless, to propose a classification of the EU health systems with no intention to advance conclusions regarding health systems performance.

This was done by simply interpreting figures for the indicators collected in the light of the clusters generated. But the framework used in this work and the groups generated may well prove to be quite useful to guide future health policy assessments and policy recommendations within each health system cluster.

Our classification shows some similarities with the clusters proposed by Wendt [ 17 ], who also used indicators related with health expenditure total, public and out-of-pocket payments. In our case, Austria and Germany appear in a separate cluster whereas in his work they come together with the countries that essentially comprise our Central and Northern countries cluster. Two other differences concern the positions occupied by Finland and Italy. Also, Netherlands and Greece were left out from the clusters in Wendt [ 9 ], but in our work, they were placed as expected: in Central and Northern countries and Southern countries, respectively.

Comparisons with other studies are even less straightforward, not only due to the number of countries analyzed but also due to the nature of the indicators considered. Some limitations are reflected in our results, as often occurs with the application of a method. Firstly, the results from factor analysis may be subjective, always depending on the variables included and on the prior choice of the number of factors. For this reason, it does not yield a unique solution [ 18 ]. Secondly, cluster analysis algorithms rely on the following initial assumptions with implicit implications [ 19 ] on the good performance of the algorithm: i spherical clusters, meaning that clusters are not very different in size, density and globular shape, ii the prior probability of the k clusters is the same, that is, each cluster has a roughly equal number of observations, and iii error variances are cluster invariant and equal across variables.

It may be argued as to whether these features are indeed observable in the data because the results are different-sized clusters. The final limitation to be mentioned concerns the set of indicators selected to create the clusters of EU health systems. The indicators are mainly associated with the supply side, which implies that the classification proposed here is grouping countries according to their health system supply structures. This may have implications both for policy or managerial analytical perspectives as any performance comparison across countries based on our classification must exercise caution as costs and quality are distorting and influencing features in these types of comparisons.

However, in spite of these potential drawbacks, we defend that an acceptable alternative for an EU-country typology has been achieved. The main contribution of this work is the proposal of a simple, updated and easy classification for 28 EU health systems into four homogeneous groups. The purpose of this paper was to contribute to the discussion on the classification of the 28 European health systems. Our proposal was based on three functions of the health systems, namely, provision, resource generation and financing. The methods used for this analysis have been the factor and the cluster analyses for 28 EU countries and referring to or the latest available year for the statistical indicator in question.

The variables used were hospital discharges, medical doctors, total health expenditure, public health expenditure, and out-of-pocket expenditures, collected within the scope of the EURO-HEALTHY research project. An overview of the clusters reveals that there is a diversity of socioeconomic characteristics across clusters. This work has provided a review on some of the health system typologies that may be found in the literature. Despite the limitations of the statistical analysis, it is our conviction that the proposal offered in this work contributes positively to the discussion of this topic in the literature and is grounds for potential comparison across the EU health systems.

Moreover, the clustering of all EU health systems allows for performance and policy assessments and for policy recommendations within each cluster. Healthcare systems: improving performance. World Health Report. Geneve: WHO; Geneva: WHO; European Parliament. Directorate general for research; Euro-healthy website. Euro-healthy Consortium. Promoting population health and equity in Europe: from evidence to policy: University of Coimbra Press; Financing health care in the European Union, challenges and policy responsews.

In: Observatory Studies Series European Observatory on Health Systems and Policies. Reibling N. Healthcare systems in Europe: towards an incorporation of patient access. J Eur Soc Policy. Classifying OECD healthcare systems: a deductive approach. TranState Working Papers. Wendt C. Mapping European healthcare systems: a comparative analysis of financing, service provision and access to healthcare. Health care systems: efficiency and institutions. Economics department working papers no. Paris: OECD; Eurostat Database Accessed 10 Sept Kaiser HF, Rice J.

Little jiffy, mark IV. Educ Psychol Meas. Hatcher L. Indrayan A, Holt MP. Concise Encyclopaedia of biostatistics for medical professionals, vol. Health at a glance OECD indicators. Healthcare system types: a conceptual framework for comparison. Soc Pol Admin. Creasy M. Some criticisms of factor analysis with suggestions for alternative methods. Br J Psychiatry. Magidson J, Vermunt JK. Chapter Latent class models. In: Kaplan D, editor. The SAGE handbook of quantitative methodology for the social sciences. Field M.

Soc Sci Med. Financing and delivering healthcare: a comparative analysis of OECD countries. Donaldson C, Gerard K.

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