Aim A comprehensive approach to health highlights its close relationship with the social and economic conditions, physical environment and individual lifestyles. However, this relationship is not exempt from methodological problems that may bias the establishment of direct effects between the variables studied. Thus, further research is necessary to investigate the role of socioeconomic variables, their composition and distribution according to health status, particularly on non-communicable diseases. Subjects and methods To shed light on this field, here a systematic review is performed using PubMed, the Cochrane Library and Web of Science. A 7-year retrospective horizon was considered until 21 July 2017. Results Twenty-six papers were obtained from the database search. Additionally, results from “hand searching” were also included, where a wider horizon was considered. Five of the 26 studies analyzed used aggregated data compared to 21 using individual data. Eleven considered income as a study variable, while 17 analyzed the effect of income inequality on health status (2 of the studies considered both the absolute level and distribution of income). The most used indicator of inequality in the literature was the Gini index. Conclusion Although different types of analysis produce very different results concerning the role of health determinants, the general conclusion is that income distribution is related to health where it represents a measure of the differences in social class in the society. The effect of income inequality is to increase the gap between social classes or to widen differences in status.

The increase in the proportion of elderly people in developed societies has several consequences, such as the rise in demand for long-term care (LTC). Due to cost, inequalities may arise and punish low-income households. Our objective is to examine socioeconomic inequalities in LTC utilization in Europe. We use the last wave from the Survey of Health, Aging, and Retirement in Europe SHARE (Munich Center for the Economics of Ageing, Munich, Germany), dated 2017, to analyze the impact of socioeconomic status (SES) on LTC. For this purpose, we construct logistic models and control for socioeconomic/household characteristics, health status, and region. Then, concentration indices are calculated to assess the distribution of LTC. Moreover, we also analyze horizontal inequity by using the indirect need-standardization process. We use two measures of SES (household net total income and household net wealth) to obtain robust results. Our findings demonstrate that informal care is concentrated among low-SES households, whereas formal care is concentrated in high-SES households. The results for horizontal concentration indices show a pro-rich distribution in both formal and informal LTC. We add new empirical evidence by showing the dawning of deep social inequalities in LTC utilization. Policymakers should implement policies focused on people who need care to tackle socioeconomic inequalities in LTC.

Several stakeholders have undertaken initiatives to propose solutions towards a more sustainable health system and Spain, as an example of a European country affected by austerity measures, is looking for ways to cut healthcare budgets. The aim of this paper is to study the effect of private health insurance on health care utilization using the latest micro-data from the European Community Household Panel (ECHP), the Spanish National Health Survey (SNHS) and the European Union Statistics on Income and Living Conditions (EU-SILC). We use matching techniques based on propensity score methods: single match, four matches, bias-adjustment and allowing for heteroskedasticity. The results demonstrate that people with a private health insurance, use the public health system less than individuals without double health insurance coverage. Our conclusions are useful when policy makers design public-private partnership policies.

In current times, we coexist with technology, using it every day. However, in older people, the use and employability of technology on a day-to-day basis is often more complicated or even null due to a lack of knowledge. Background/Objectives: The youngest generation were born surrounded by technology, which has given them superior capabilities when it comes to handling technology compared to elderly people. In short, older people have grown up without technology and only later in life have they crossed paths with it. Therefore, these circumstances can produce what is known as a “digital divide”, an unequal distribution in the access, use, or impact of information and communication technologies among social groups. Thus, the aim of this study is to examine whether there is a digital divide among European older adults and to show its effect on the mental health of individuals. In this way, we analyze how technological characteristics (digital divide) cause worsen mental health. Additionally, we examine whether, over time, the digital divide has had a greater impact on the mental health of older adults. Methods: For this purpose, recently published data from the Survey on Health, Aging, and Retirement in Europe are used. Results: Our analysis has revealed that the digital divide, driven by age, negatively affects the mental health of older adults in Europe. Thus, we have analyzed how technological characteristics related to the digital divide lead to poorer mental health among this population. Additionally, we have examined whether the digital divide has increasingly impacted older adults’ mental health over time. Conclusions: These findings highlight the need to address the digital divide as a public health issue, promoting greater digital inclusion to improve the psychological well-being of older adults.

An open debate these days is about how national income inequality could affect individuals’ health outcomes. Therefore, the present study aims to provide new evidence regarding life expectancy determinants and how they are related to the income inequality hypothesis. Precisely, it is provided new evidence on this relationship for 26 European countries during the period 1995–2014. The analysis is based on panel data techniques, with the latest data from both Eurostat and the OECD Health Statistics. Furthermore, data from the World Bank is also applied. Besides, we have tested the sensitivity of the estimates in our empirical analysis using three clusters of countries. Our results suggest that income inequality does not significantly reduce health in developed societies, like the European ones. Notwithstanding, as income inequality can be sometimes harmful for population health, these issues must be taken into account in order to improve health care policies.