Understanding Health Perception across Europe

Victor Cazac
5 Min Read

Eurostat provides an evaluation of the public’s perception of good health, considering individuals aged 16 years and above. Health conditions are gauged on a scale varying from “very good” to “very bad”. The index reflects the proportion of the population professing “good” or “very good” health. This information acts as a predictor of future health care consumption and mortality rates.

The 2021 ranking of European countries, based on perceived health value, puts Switzerland at the forefront with a score of 81.8%, trailed closely by Ireland (81.2%) and Greece (78.3%). Mid-ranking countries include Spain (71.2%), Finland (70.1%), and Slovenia (69.1%). Portugal (50.2%), Latvia (49.8%), and Lithuania (47.9%) are at the lower end of the scale. The average perceived good health score across the surveyed countries in 2021 was 68.5%.

The ranking also takes into account the percentage change in perceived health between 2010 and 2021. Croatia leads this category with a 30.29% increase (14.6 units), followed by Hungary (17.6% or 9.7 units) and Slovenia (15.75% or 9.4 units). Mid-ranking countries include Greece (3.43% or 2.6 units), Finland (2.34% or 1.6 units), and Portugal (1.83% or 0.9 units). Countries that observed a decline include Denmark (-5.9% or -4.2 units), the Netherlands (-6.03% or -4.7 units), and Sweden (-7.65% or -6 units). The average increase in health perception for the surveyed countries was 4.24% (or 2.3 units) between 2010 and 2021.

Clustering using the K-means algorithm, optimized with the silhouette coefficient, revealed two distinct groups: • Cluster 1: Belgium, Spain, Netherlands, Greece, Malta, Denmark, Luxembourg, Cyprus, Sweden, Austria, Romania, Switzerland, Finland, Ireland, Italy, France, Bulgaria, Germany, Slovenia • Cluster 2: Portugal, Latvia, Estonia, Lithuania, Croatia, Hungary, Poland, Czech Republic The median perceived health value for Cluster 1 was 72.8 units, while for Cluster 2, it was 60.55 units. Generally, Eastern European countries exhibited lower health perception by about 12.3 points compared to their Western European counterparts.

Network analysis using Euclidean distance identified two network structures – complex and simplified. The complex structure features connections between Spain and Belgium, Belgium and the Netherlands, the Netherlands and Sweden, Luxembourg and Denmark, Denmark and Austria, Austria and Romania, Romania and Finland, Finland and France, and France and Germany. The simplified structure shows a connection between Hungary and Poland.

In conclusion, the perception of good health across the European nations surveyed has, on average, risen by 4% from 2010 to 2021. However, several countries, including Spain, Ireland, Germany, Lithuania, Denmark, the Netherlands, and Sweden, saw a decline in this period. Typically, Eastern Europe records a lower health perception than Western Europe. This perception anticipates the population’s demand for health services. As the perception of health deteriorates, it is likely that the demand for health services will rise, and vice versa. These insights can be leveraged at regional and urban levels to forecast future health service demand, manage service provision through the health network, and prevent congestion, contributing to the efficient health economy of public and private organizations.


Data Accessibility: The data utilized in this research can be procured upon request from the corresponding author.

Funding: This study was undertaken without any financial backing for the research, authorship, or publication of the article.

Conflict of Interest Statement: The author affirms that there are no conflicts of interest concerning the publication of this manuscript. Additionally, ethical considerations, including plagiarism, informed consent, misconduct, and data fabrication or falsification, double publication, have been duly addressed.

Software: The authors employed Gretl for econometric models, Orange for clusterization and network analysis, and KNIME for machine learning and predictions. All these are open-source versions and do not require licenses.

Acknowledgements: I extend my gratitude towards the faculty at LUM University “Giuseppe Degennaro” and the administration of LUM Enterprise s.r.l. Their unwavering inspiration has been instrumental in the continuous progress of our scientific research work.

Reference: https://www.researchgate.net/

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