Variation in neonatal mortality and its relation to country characteristics in sub-Saharan Africa: an ecological study

• Published in: BMJ global health Vol. 2; no. 4; p. e000209
• Main Authors: Kayode, Gbenga Ayodele, Grobbee, Diederick E, Amoakoh-Coleman, Mary, Ansah, Evelyn, Uthman, Olalekan A, Klipstein-Grobusch, Kerstin
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group LTD 01-10-2017; BMJ Publishing Group
• Subjects: Childrens health; Clustering; Control charts; Data analysis; Ecological studies; Expenditures; Global health; Health care; Health services; HIV; Human immunodeficiency virus; Human resources; Infant mortality; Neonates; Newborn babies; Population; Poverty; Public health; Purchasing power parity; Regression analysis; Sanitation; Socioeconomic factors; Socioeconomics; Spatial analysis; Spatial data; Statistical analysis; United States > US; Sub-Saharan Africa; health systems; child health
• ISSN: 2059-7908; 2059-7908
• DOI: 10.1136/bmjgh-2016-000209

BackgroundA substantial reduction in neonatal mortality is the main priority to reduce under-five mortality. A clear understanding of the variation in neonatal mortality and the underlying causes is important for targeted intervention. We aimed to explore variation in neonatal mortality and identify underlying causes of variation in neonatal mortality in sub-Saharan Africa (SSA).MethodsThis ecological study used 2012 publicly available data from WHO, the US Agency for International Development and the World Bank. Variation in neonatal mortality across 49 SSA countries was examined using control chart and explanatory spatial data analysis. Associations between country-level characteristics and neonatal mortality were examined using linear regression analysis.ResultsThe control chart showed that 28 (57%) SSA countries exhibited special-cause variation, 14 countries were below and 14 above the 99.8% control-limits. The remaining 21 (43%) SSA countries showed common-cause variation. No spatial clustering was observed for neonatal mortality (Global Moran’s I statistic −0.10; p=0.74). Linear regression analysis showed HIV/AIDS prevalence among the population of reproductive age to be positively associated with neonatal mortality (β=0.463; 95% CI 0.135 to 0.790; p<0.01). Declining socioeconomic deprivation (β=−0.234; 95% CI −0.424 to −0.044; p<0.05) and high quality of healthcare governance (β=−1.327, 95% CI −2.073 to −0.580; p<0.01) were inversely associated with neonatal mortality.ConclusionThis study shows a wide variation in neonatal mortality in SSA. A substantial part of this variation can be explained by differences in the quality of healthcare governance, prevalence of HIV and socioeconomic deprivation. Future studies should validate our findings using more rigorous epidemiological study designs.