Does social capital determine health? Empirical evidence from MENA countries

Using data from the World Value Survey (2010–2012) for 18 MENA countries, this paper investigates the causal relationship between social capital and health by applying simultaneous-equations based on structural modeling and IVs regression. Our main findings corroborate the hypothesis of reverse causality between social capital and health i.e. bidirectional causality running from social capital to health and from health to social capital is identified. Furthermore, our empirical findings show that individual-level social capital appears more salient in determining health, while community-level social capital seems less relevant in explaining health differences between individuals. Overall, the present study makes evident that high levels of social capital (i.e. high levels of social participation and high levels of trust) and high individual-level socioeconomic factors (i.e. high levels of income and high levels of education) may generate better health outcomes that policymakers must take into account to improve individual and community health.     

A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk

Accounting for about 290,000–650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.