Digitalization of healthcare systems is a great opportunity to address inequalities in access to healthcare in the European Union. There is an urgent need to build on what we learned from the COVID-19 pandemic, where digital health technologies were integrated swiftly to limit challenges in healthcare delivery. We created a database for the 27 European Union countries from the European Health Interview Survey (EHIS), the Digital Economy and Society Index (DESI), and other Eurostat databases. We performed k-means cluster analysis to group EU countries along two dimensions: inequalities in access to medical services and level of digitalization. We identified five distinct clusters: two clusters with high, two clusters with moderate, and one cluster with low unmet need for healthcare. Regarding digitalization, only one cluster comprising the Nordic countries, Spain and Cyprus exhibit high digital readiness. A cluster comprising the most developed countries in Western Europe represents moderate levels of both unmet need for healthcare and digitalization. For most EU countries, there is still a need to build digital infrastructure for the healthcare industry, which in the long term may increase the number of digital solutions used by both patients and healthcare professionals. Policy makers across the EU need to consider investing in initiatives that would support digital health solutions as an effective means of healthcare provision and healthcare management.

The aim of our study was a quasi-experimental estimation of the relationship between sports activity and social capital, and an assessment made at an individual level of the impact of sports activity on health, beliefs and the social situation of Polish adults. Matching estimation method and Social Diagnosis data were used. The dataset enables not only an analysis of the self-reported health, but also of social capital. The panel structure of the data and the applied method allow for stratifying the sample using past characteristics, including past sports activity, as well as for assessing outcomes after the treatment—engaging in sports activity—takes place. Both allow for an interpretation of the results as reflections of a causal relationship. Even though publications applying matching estimation or IV estimation to assess the impact of sports activity have recently been increasing in number, ours is the first to concentrate on the effects on multidimensional social capital for adults using panel data and matching estimation. It is also the first to focus on a country that is neither one of the most developed in the world or one of the least developed. The results obtained suggest significant heterogeneity in terms of age, gender and place of living. We also found that sports contributed to building social networks and being socially active. Our research indicates that sports activity enhances social health. It can be used as a tool for a policy of social activation and strengthening the health potential of adults, especially those over 40.

Hitherto empirical research provides insight on particular dimensions or aspects thereof, but analyses that include diverse determinants of the physical activity are scarcer. The aim of the research was to examine the profile of adults who engage in sport activity, using a multi-layer, socio-ecological approach. Combined database: The Social Diagnosis 2015, The Statistics Poland 2014, and the Orliki 2012 was applied. The resulting sample consists of 8,361 adult respondents aged 25–70 years. Sport activity and sport clubs’ membership were measured as dependent variables. According to the Bronfenbrenner’s layered model number of outcome variables were described. While being consistent with fundamental facts known from the literature (e.g. significance of age, education, family, social networks and cultural aspects, the results point to a heterogeneity in determining sport activity. Firstly, women and men differ qualitatively – the set of characteristics important for sport activity is different. For example, while minor health issues seem to instigate activity of women, for men they are rather negatively related. Secondly, different factors are relevant for initiating the activity than for sustaining it. Furthermore, sport activity is rather positively related to other activities related to social life or to the life-long learning. This suggests that a substitution effect, in terms of time, is less important than the general tendency to be engaged in different aspects of life. Lastly, active membership of sports clubs is not only rare, but also qualitatively differs from sports activity in terms of socio-ecological profile of the participants. The obtained results emphasize the need to create and implement nuanced and varied policies to support increase in physical activity in modern societies. Traditional, ‘hard’ measures like providing physical (facilities) or organisational (sports clubs) infrastructure seem to have limited effectiveness.

Social capital (SC) affects quality of life, sport behaviours and health in individual and community context. The aim was to analyse how sports activity (SA) contributes to SC accumulation in a post-transformation country. A combination of four longitudinal, nationwide datasets was used. Instrumental variable method was applied. Results show that in 19+ Poles, on a regional level, SA improves SC. This suggest that sports clubs and at least some types of sports infrastructure can constitute a valid tool for social policies aiming at improving social involvement. The self-triggering character of SA is a valuable asset for social impact regionally and beyond. It is important to estimate the changes in a long-term perspective, due to the inertness of the SC.

Ensuring the health and well-being of workers should be a top priority for employers and governments. The aim of the article is to evaluate and rank the importance of mental health determinants: lifestyle, demographic factors and socio-economic status. The research study is based on EHIS 2013–2015 data for a sample of N = 140,791 employees from 30 European countries. The results obtained using machine learning techniques such as gradient-boosted trees and SHAPley values show that the mental health of European employees is strongly determined by the BMI, age and social support from close people. The next vital features are alcohol consumption, an unmet need for health care and sports activity, followed by the affordability of medicine or treatment, income and occupation. The wide range of variables clearly indicates that there is an important role for governments to play in order to minimize the risk of mental disorders across various socio-economic groups. It is also a signal for businesses to help boost the mental health of their employees by creating holistic, mentally friendly working conditions, such as offering time-management training, implementing morning briefings, offering quiet areas, making employees feel valued, educating them about depression and burnout symptoms, and promoting a healthy lifestyle.

Cancer burden in the European Union (EU) is increasing and has stimulated the European Commission (EC) to develop strategies for cancer control. A common “one size fits all” prevention policy may not be effective in reducing cancer morbidity and mortality. The goal of this paper is to show that EU member states are not homogenous in terms of their exposure to risk factors for cancer (i.e., lifestyle, socio-economic status (SES), air pollution, and vaccination). Data from a variety of sources including Eurostat, the UNESCO Institute for Statistics, the European Health Interview Survey, Eurobarometer, and the European Environment Agency were merged across years 2013–2015 and used to develop a cluster analysis. This work identified four patterns of cancer prevention behaviors in the EU thus making it possible to group EU members states into four distinct country clusters including: sports-engaged countries, tobacco and pollutant exposed nations, unhealthy lifestyle countries, and a stimulant-enjoying cluster of countries. This paper finds that there is a need for closer collaboration among EU countries belonging to the same cluster in order to share best practices regarding health policy measures that might improve cancer control interventions locally and across the EU.