Sick regimes and sick people: A multilevel investigation of the population health consequences of perceived national corruption

Abstract

Objective: There is a paucity of empirical work on the potential population health impact of living under a regime marred by corruption. African countries differ in the extent of national corruption, and we explore whether perceived national corruption is associated with population health across all rungs of society. Methods: World Health Survey data were analysed on 72 524 adults from 20 African countries. The main outcome was self-reported poor general health. Multilevel logistic regression was used to assess the association between poor health and perceived corruption, while jointly accounting for individual- and country-level human development factors. In this research, we use Transparency International's corruption perception index (CPI), which measures 'both administrative and political corruption' on a 0-10 scale. A higher score pertains to a higher rate of perceived corruption within society. We also examined effect modification by gender, age and socio-economic status. Results: Higher national corruption perception was consistently associated with an increase in poor health prevalence, also after multivariable adjustments, with odds ratio (OR) of 1.62 (95% CI: 1.01-2.60). Stratified analyses by age and gender suggested this same pattern in all subgroups. Positive associations between poor health and perceived corruption were evident in all socio-economic groups, with the association being somewhat more positive among less educated people (OR = 1.61, 95% CI: 1.01-2.58) than among more educated people (OR = 1.40, 95% CI: 0.83-2.37). Conclusions: This study is a cautious first step in empirically testing the general health consequences of corruption. Our results suggest that higher perceived national corruption is associated with general health of both men and women within all socio-economic groups across the lifespan. Further research is needed using more countries to assess the magnitude of the health consequences of corruption. © 2013 John Wiley & Sons Ltd.