Corruption kills: estimating the global impact of corruption on children deaths

Abstract

Background: Information on the global risk factors of children mortality is crucial to guide global efforts to improve survival. Corruption has been previously shown to significantly impact on child mortality. However no recent quantification of its current impact is available.

Methods: The impact of corruption was assessed through crude Pearson's correlation, univariate and multivariate linear models coupling national under-five mortality rates in 2008 to the national "perceived level of corruption" (CPI) and a large set of adjustment variables measured during the same period.

Findings: The final multivariable model (adjusted R(2)= 0.89) included the following significant variables: percentage of people with improved sanitation (p.value<0.001), logarithm of total health expenditure (p.value = 0.006), Corruption Perception Index (p.value<0.001), presence of an arid climate on the national territory (p = 0.006), and the dependency ratio (p.value<0.001). A decrease in CPI of one point (i.e. a more important perceived corruption) was associated with an increase in the log of national under-five mortality rate of 0.0644. According to this result, it could be roughly hypothesized that more than 140000 annual children deaths could be indirectly attributed to corruption.

Interpretations: Global response to children mortality must involve a necessary increase in funds available to develop water and sanitation access and purchase new methods for prevention, management, and treatment of major diseases drawing the global pattern of children deaths. However without paying regard to the anti-corruption mechanisms needed to ensure their proper use, it will also provide further opportunity for corruption. Policies and interventions supported by governments and donors must integrate initiatives that recognise how they are inter-related.

Figure 1. World repartition of children deaths and perceived corruption.

A) Deaths of children younger than 5 years per 1000 live births in 2008. Derivated from data compiled by Black and colleagues . B) Corruption Perceptions Index (CPI) in 2008. Derivated from data compiled by Transparency International .

Figure 2. Pearson's correlations between the mortality rate in children and the other variables used in the analysis.

Representation obtained with a Focused Principal Components Analysis. Pearson's correlations between the mortality rate in children and the other variables are represented faithfully. Positive correlations with child mortality are plotted on the left and negative ones on the right. Correlations which are significantly different to zero are inside the pink circle. The relationships between the other variables are interpreted like in a PCA: correlated variables are close or diametrically opposite (for negative correlations), independent variables make a right angle with the origin. Colours indicate the nature of explicative variables (red: political and societal factors, blue: socio economic factors, orange: health and medical factors and green: environmental factors).