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East African Medical Journal

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Spatial and temporal distribution of notified tuberculosis cases in Nairobi County, Kenya, between 2012 and 2016

Norah K. Maore, Nicholas K. Kirui, Omanwa Kireki, Omondi Ogutu, Moses Owino, Thomas Ogaro, Elizabeth Mueni, Martin Mulonzi, Jacqueline C. Louis, Chelimo Japhet, Kamene Kimenye, Eunice Omesa, Enos Masini, Paul W. Wekunda, Murima Ng’ang’a

Abstract


Background: Tuberculosis (TB) is an infectious disease of major public health concern globally. The disease has showed space‐time variations across settings. Spatial temporal assessment can be used to understand the distribution and variations of TB disease.

Objective: To determine the spatial and temporal distribution of notified TB cases in Nairobi County, Kenya, between 2012 and 2016

Design: A cross sectional study

Setting: Nairobi County, Kenya

Subjects: Tuberculosis cases notified in Tuberculosis Information for Basic Units from 2012 to 2016

Results: A total of 70,505 cases of TB were notified in Nairobi County between 2012 and 2016, with male to female ratio of 3:2 and HIV coinfection rate of 38%.The temporal analysis showed a declining trend of the notified cases. The spatial clusters showed stability in most areas while others varied annually during the study period. The space‐time analysis also detected the four most likely clusters or hotspots. Cluster 1 which covered the informal settlements of Kibera, Kawangware and Kangemi with 4,011observed cases against 2,977expected notified TB cases(relative risk (RR) 1.37, p<0.001). Further, Cluster 2 covered Starehe and parts of Kamukunji, Mathare, Makadara, Kibra and Dagoretti North Constituencies (RR 1.93, p<0.001; observed and expected cases were 4,206 and 2,242, respectively.

Conclusion: This study identified high TB case notifications, declining temporal trends and clustering of TB cases in Nairobi. Evidence of clustering of TB cases indicates the need for focused interventions in the hotspot areas. Strategies should be devised for continuous TB surveillance and evidence based decision making.




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