Abstract
Background: From a causal point of view, there exists a set of socioeconomic indicators concerning life expectancy. The objective of this paper is to determine the indicators which exhibit a relation of causality with life expectancy at birth. Methods: Our analysis applies the Granger causality test, more specifically its version by Dumitrescu–Hurlin, starting from the information concerning life expectancy at birth and a set of socioeconomic variables corresponding to 17 Spanish regions, throughout the period 2006–2016. To do this, we used the panel data involving the information provided by the Spanish Ministry of Health, Consumer Affairs and Social Welfare (MHCSW) and the National Institute of Statistics (NIS). Results: Per capita income, and the rate of hospital beds, medical staff and nurses Granger-cause the variable “life expectancy at birth”, according to the Granger causality test applied to panel data (Dumitrescu–Hurlin’s version). Conclusions: Life expectancy at birth has become one of the main indicators able to measure the performance of a country’s health system. This analysis facilitates the identification of those factors which exhibit a unidirectional Granger-causality relationship with life expectancy at birth. Therefore, this paper provides useful information for the management of public health resources from the point of view of the maximization of social benefits.
Highlights
Life expectancy at birth has become an aggregate variable which reflects the influence of a wide variety of indicators [1] on the working of modern health systems.On the other hand, the current complex health context, characterized by the constant interrelation of a large number of variables of different types, motivates the causal analysis of these variables which can indicate, among other aspects, the degree to which the resources available to public authorities contribute to the efficient performance of health policies.A review of the existing literature on this topic shows that, to the extent of our knowledge, no papers have opted to employ a causal analysis
This research has analyzed the socioeconomic indicators which have exhibited a relation of causality with the life expectancy at birth in Spanish regions
Our findings have concluded that, according to the Granger causality test for panel data (Dumitrescu–Hurlin version), the explanatory according to the Granger causality test for panel data (Dumitrescu–Hurlin version), the explanatory variables hospital beds, medical staff in specialized care, medical staff in primary care, nursing staff variables hospital beds, medical staff in specialized care, medical staff in primary care, nursing staff in in specialized care, nursing staff in primary care, and per specialized care, nursing staff in primary care, and per capita income cause the variable “life expectancy at birth”
Summary
Life expectancy at birth has become an aggregate variable which reflects the influence of a wide variety of indicators (social, economic, environmental, etc.) [1] on the working of modern health systems.On the other hand, the current complex health context, characterized by the constant interrelation of a large number of variables of different types, motivates the causal analysis of these variables which can indicate, among other aspects, the degree to which the resources available to public authorities contribute to the efficient performance of health policies.A review of the existing literature on this topic (see Section 2) shows that, to the extent of our knowledge, no papers have opted to employ a causal analysis. The research question is to address a causal approach to the determinants of health outcome by applying, for the first time in this field, the methodology put forward by Dumitrescu–Hurlin for panel data [4]. This novel procedure is a non-homogeneous causality test, that is to say, a causal empirical. Methods: Our analysis applies the Granger causality test, its version by Dumitrescu–Hurlin, starting from the information concerning life expectancy at birth and a set of socioeconomic variables corresponding to 17 Spanish regions, throughout the period 2006–2016. This analysis facilitates the identification of those factors which exhibit a unidirectional
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