Spatial epidemiology of tuberculosis in Siaya and Kisumu counties, western Kenya, 2013
SIFUNA, Peter M
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Kenya is among the 22 high burden countries that contribute 80% of the world’s TB and is ranked 15th globally, while in Africa, it is ranked number five. Strategies for reduction of TB rely on knowledge of where, when and to what degree the disease is present. Information on the geographical occurrence of TB in the high burden regions of Kisumu and Siaya Counties, Western Kenya remains scanty. As such, the main objective of this study was to analyze the spatial distribution of TB and identify geographic factors associated with its occurrence in Kisumu and Siaya Counties, Western Kenya. The study area has a high TB prevalence with a mixture of urban and rural areas, with Kisumu County being the most urbanized. The specific objectives were to determine the presence of TB clusters and hotspots in Kisumu and Siaya Counties, Western Kenya and to correlate the occurrence of TB and residency (urban or rural), population density and slum dwelling in Kisumu and Siaya Counties. In a cross-sectional study, data on the notified TB cases for the year 2013 were extracted from the Kenyan National TB Control Program database. Since November of 2012, Kenya has been implementing an electronic TB register system and complete baseline data was available for the year 2013. The TB cases were stripped off personal identifiers and geocoded at the County Ward level based on the physical address provided in the register data. The ArcGIS® software was used to visualize and generate choropleth maps which helped to identify geographical patterns of TB. A spatial autocorrelation indicator, Global Moran's I index was used describe the pattern of TB. The GetisOrd Gi* static was used to identify TB hotspots and cold spots while the Local Moran's I statistic was used to identify statistically significant TB cluster and TB outliers. A simple histogram was used to test for data normality. TB rates per 100,000 populations were computed for each county Ward grouped by level of urbanization and population density. Computed means were used to compare TB occurrence in the urban and rural areas. Spearman's test was used to correlate TB rate and population density. A total of 5,568 TB cases were abstracted from 237 TB clinics in Kisumu and Siaya Counties. Of these, 5,063 (91.0%) were linked to the 65 Wards within the study area. The notified TB rate of 278 cases per 100,000 populations with variations at the Ward level (76 per 100, 000 population) in South Uyoma Ward to (813 per 100, 000 population) in Nyalenda A Ward. Moran’s indices were positive (Moran’s I=0.423, p<0.001) indicating a clustered characteristic of the distribution. The study revealed distinct TB hotspots regions (with z-score> 2.58) and 7 County Wards high-high relationship with its neighbors (clusters). There was a positive correlation between population density and the rate of TB, which was statistically significant (rs=0.5739, p=0.0001). The study also found higher TB mean (3.9 and 2.2) in the urban Wards and rural Wards, respectively (Wilcoxon rank-sum, p=0.0001). In conclusion, it is clear that TB occurrence in the high burden Counties of Siaya and Kisumu varies geographically at the small area level. The study revealed that the distribution of TB in Siaya and Kisumu Counties is nonrandom and clustered with the significant TB clusters and hotspots identified for the year 2013. Urbanization and high population density areas were found to be positivelycorrelated with TB occurrence. The current study has added knowledge that is being utilized currently in the allocation of TB resources in the identified high TB regions in Siaya and Kisumu Counties. The identification of TB clusters and hotspots can now be used by TB programs in targeting prevention strategies in the identified areas. The correlation of TB with urban population and population density will be utilized in designing of specific interventions that would target these areas. In general, these findings will help inform TB prevention and control strategies and in allocation of resources towards these efforts in Western Kenya.
- Community Health