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This paper investigates the impact and non-linear effects of urbanization on health care expenditure in China. The results indicate that urbanization in both Eastern and Central regions can significantly increase health care expenditure. But the impact of urbanization is not significant, which is related to the backward economic development level and low urbanization rate in the Western region. Taking population aging into consideration, the results of the panel threshold regression model imply that the positive relationship between urbanization and health care expenditure becomes greater when the level of population aging exceeds 10.72% in the Eastern region and 7.00% in the Western region. Therefore, in the urbanization process, the government should pay attention to the positive effect of urbanization on health care expenditure, provide more financial support for the construction of medical facilities, and expand the coverage of medical services and security for residents, especially for elderly people.

The recent worldwide COVID-19 pandemic has highlighted the importance of a performant public sector in terms of health. To achieve greater use and efficiency of health information and communication technology (ICT), the whole community of European states needs a model to develop a common strategy to support the implementation of e-health and reduce decision-making difficulties. Our research suggests such a model, starting from the level of adoption to the implementation of e-health and points out the existing disparities in the European countries regarding the difficulties of adopting e-health. We draw a composite index to assess the inequalities present in the quality of life, the public health system, and the adoption of e-health. Furthermore, to return to a hierarchy of European countries, the relative distance method (RDM) is applied by combining various classification criteria. The results identify the European countries with the highest levels of adoption (Denmark, Estonia, Spain, Sweden, Finland, and the United Kingdom), where e-health is routine, and the countries with the lowest levels of adoption (Greece, Lithuania, Luxembourg, Malta, Romania, and Slovakia), where e-health is not widespread. These results reveal critical implications in identifying solutions to reduce the gaps between countries, identifying public policies to support the adoption of e-health, and reducing difficulties in decision-making.