The burden of disease profile of residents of Nairobi's slums: Results from a Demographic Surveillance System
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BioMed Central
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The burden of disease profile of residents of Nairobi's slums: Results from a Demographic Surveillance System Catherine Kyobutungi*, Abdhalah Kasiira Ziraba, Alex Ezeh and Yazoumé Yé Address: African Population & Health Research Center, P.O Box 10787, GPO 00100, Nairobi, Kenya Email: Catherine Kyobutungi* - [email protected]; Abdhalah Kasiira Ziraba - [email protected]; Alex Ezeh - [email protected]; Yazoumé Yé - [email protected] * Corresponding author
Published: 10 March 2008 Population Health Metrics 2008, 6:1
doi:10.1186/1478-7954-6-1
Received: 24 May 2007 Accepted: 10 March 2008
This article is available from: http://www.pophealthmetrics.com/content/6/1/1 © 2008 Kyobutungi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract Background: With increasing urbanization in sub-Saharan Africa and poor economic performance, the growth of slums is unavoidable. About 71% of urban residents in Kenya live in slums. Slums are characteristically unplanned, underserved by social services, and their residents are largely underemployed and poor. Recent research shows that the urban poor fare worse than their rural counterparts on most health indicators, yet much about the health of the urban poor remains unknown. This study aims to quantify the burden of mortality of the residents in two Nairobi slums, using a Burden of Disease approach and data generated from a Demographic Surveillance System. Methods: Data from the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) collected between January 2003 and December 2005 were analysed. Core demographic events in the NUHDSS including deaths are updated three times a year; cause of death is ascertained by verbal autopsy and cause of death is assigned according to the ICD 10 classification. Years of Life Lost due to premature mortality (YLL) were calculated by multiplying deaths in each subcategory of sex, age group and cause of death, by the Global Burden of Disease standard life expectancy at that age. Results: The overall mortality burden per capita was 205 YLL/1,000 person years. Children under the age of five years had more than four times the mortality burden of the rest of the population, mostly due to pneumonia and diarrhoeal diseases. Among the population aged five years and above, HIV/AIDS and tuberculosis accounted for about 50% of the mortality burden. Conclusion: Slum residents in Nairobi have a high mortality burden from preventable and treatable conditions. It is necessary to focus on these vulnerable populations since their health outcomes are comparable to or even worse than the health outcomes of rural dwellers who are often the focus of most interventions.
Background Africa is urbanizing at a faster rate than any other region in the world and b
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