Spatial temporal analysis of the distribution of pediatric tuberculosis patterns in Kenya

ABSTRACT Tuberculosis is a leading cause of mortality and morbidity globally. Kenya is among the 22 high burden countries that contribute 80% of the global burden and is ranked number 15 worldwide while in Africa, it is ranked number five. Among children, it is a top 10 cause of mortality. However children with TB are given low priority in most national health programs and are a neglected group in this epidemic. This is because from a TB control point of view, they rarely transmit the disease and contribute little to the maintenance of the TB epidemic. This research project presented a GIS approach to analyze the spatial temporal dynamics of pediatric epidemic. The objective of this research was to describe the spatial temporal distribution patterns of notified pediatric cases in Kenya over the years 2009, 2010 and 2011. There is no study that has been done to explore these patterns of pediatric TB in Kenya. Data from the notified pediatric cases were obtained from the Kenyan National TB Control Program. The cases were then stratified by County. After a general statistical analysis, it was found that there is generally an increasing trend in the cases of pediatric TB. Also, urban counties with high population were associated with high incidence of TB and that smear positives cases were least among children. Visualization using the ArcGIS software was then used to provide thematic maps which helped us identify space-time disparities. The global Moran‟s statistic demonstrated an increasing trend towards clustering over the years of study. The LISA statistic showed that majority of the counties had an insignificant relationship with its neighbours but Kitui, Embu, Isiolo and Meru counties were the counties found to be having a high-high relationship with their neighbours implying clustering. A number of counties showed a high – low relationship with their neighbours which could be interpreted as having some tendency towards clustering. Also, the statistic was used to identify the hot spot counties in the spatial data. Tharaka-Nithi and Machakos counties (with z-score> 2.58) were identified as the hot spots for year 2010 while counties surrounding it showed tendency towards becoming hot spots. Baringo, Uasin-gishu, Nandi, Kisumu and Kericho were identified as cold spots in the year 2010. There were no hot spot counties in the year 2011 but Isiolo and Meru showed some tendency towards being hot spots while Baringo showed tendency towards being a cold spot. The spatial-temporal distribution of the disease presented spatial-temporal patterns which provided an understanding of the dynamics of disease spread as tendency of the pediatric cases clustering. The detection of space-time clustering was useful in identifying higher risk areas, where surveillance and control needed to be targeted. Our approach of identifying the hot spots and space-time disparities provided useful and detailed information for guiding policy formulation to succinctly address the burden of TB in children.