Studying social determinants of health using fuzzy-set Qualitative Comparative Analysis: A worked example

Using fuzzy-set Qualitative Comparative Analysis (fsQCA), we present an alternative method for studying the social determinants of health (SDHs) that focuses on their configurational paths leading to population health outcomes. In our worked example, we examine the macrosocial determinants of infant mortality based on data covering 149 countries. First, we applied regression techniques to assess the net effects of key macrosocial determinants. Second, we used fsQCA to analyze the same data and identify the configurational paths. We calibrated the macrosocial determinants in terms of both advantages and disadvantages and revealed the configurations of (dis)advantages consistently linked to high infant mortality rates and low infant mortality rates. The regression analysis showed that the net effects of national economic performance, democracy level, inequality, and women's autonomy were all statistically significant. Together, they explained 83% of the variance in infant mortality rates between countries. Following the fuzzy-set analysis, the two main configurational paths to achieve low infant mortality rates were high women's autonomy together with high economic performance and high women's autonomy together with low inequality and full democracy. The main paths that left countries burdened with high infant mortality rates were low economic performance together with either low women's autonomy or high inequality. We conclude that different SDH configurations may lead to the same health outcomes. Therefore, it may not always be sufficient to say which variables matter the most universally, and by using fsQCA, it is possible to move from treating SDHs as competing independent variables to using them in configurations to explain health outcomes.