African Population Studies
Union for African Population Studies
Vol. 32, No. 2, 2018, pp. 4376-4395
Bioline Code: ep18043
Full paper language: English
Document type: Research Article
Document available free of charge
African Population Studies, Vol. 32, No. 2, 2018, pp. 4376-4395
© Copyright 2018 - African Population Studies
Spatial analysis of child mortality and welfare differentials in South Africa: evidences from the 2011 Census
Zewdie, Samuel Abera & Adjiwanou, Visseho
Background: Welfare differential is a common phenomenon among South African population which can be manifested in terms of various economic and health outcomes. Using child mortality (CM) as one of a key measure of the country’s health system, the study attempted to show its spatial distribution and the association with economic disparities in the country.
Methods: The study primarily aimed to derive estimates of CM rates for the municipalities and provinces of South Africa and assessed the results in relation to some welfare measures such as poverty and inequality. The estimation of CM rates was achieved through the use of direct synthetic cohort methods with Bayesian spatial smoothing. The smoothing process helped to generate accurate municipal level estimates of CM. The model utilized information from neighboring municipalities by controlling the effects of women’s education and HIV.
Results: It was found that there were clear spatial differentials of CM in the country, where at province level under-five mortality (U5M) rate (deaths per 1000 live births) ranges from 26 in Western Cape to 71 in KwaZulu-Natal. At municipal level, it ranges from 24 in City of Cape Town to 109 in uPhongolo. It was also shown that CM was higher in poorer and more unequal areas, although there were cases which had inverse relationship. For instance, several municipalities in Limpopo province scored relatively lower child mortality rates though the level of poverty is very high
Conclusions: The study revealed significant spatial differentials of CM in the country, which were also associated with the level of poverty and income inequality. The findings may help local and national government to implement policies more effectively and make more focused decisions for a better health outcome.
Spatial demography; family health; Bayesian smoothing; poverty; inequality.
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