The Spatial Epidemiology Of Tuberculosis In Gurage Zone Southern Ethiopia.

Background: The global distribution of tuberculosis is skewed heavily toward low-and-middlernincome countries, which accounted for about 87% of all estimated incident cases. Ethiopia is arnlow-income country in east Africa that remains highly afflicted by tuberculosis, with varyingrndegrees of magnitudes across settings. However, there is a dearth of studies clarifying about thernspatial epidemiology of the disease in Ethiopia. Lack of such information may contribute to thernpartial effectiveness of tuberculosis control programs.rnObjectives: The specific objectives of this study were: 1) to detect spatial and space-timernclustering of tuberculosis, 2) to estimate spatial risk of tuberculosis distribution using limitedrnspatial datasets, and 3) to identify ecological factors affecting spatial distribution of tuberculosisrnin Gurage Zone, Southern Ethiopia.rnMethods: The study data were obtained from different sources. Specific objectives 1 and 3rnincluded a total of 15,805 tuberculosis patients diagnosed at health facilities in Gurage Zonernduring 2007 to 2016, whereas specific objective 2 included 1,601 patients diagnosed in 2016.rnThe geo-location and population data were obtained from the Central Statistical Agency ofrnEthiopia (specific objectives 1-3). The altitude data were extracted from global digital elevationrnmodel v2 (specific objective 2). The normalized difference vegetation index data were derivedrnfrom the moderate resolution imaging spectroradiometer imagery, and the temperature andrnrainfall data were obtained from the Meteorological Agency of Ethiopia (specific objective 3).rnThe global Moran’s I, Kulldorff’s scan and Getis-Ordrnstatistics were used to analyze purelyrnspatial and space-time clustering of tuberculosis (specific objective 1). The geostatistical krigingrnapproach was applied to estimate the spatial risk of tuberculosis distribution (specific objectivern2). The spatial panel data analysis was used to estimate the effects of ecological factors on spatialrndistribution of tuberculosis prevalence rate (specific objective 3).rnResults: The prevalence of tuberculosis varied from 70.4 to 155.3 cases per 100,000 populationrnin the Gurage Zone during 2007 to 2016. Eleven purely spatial clusters (relative risk: 1.36–14.52,rnP-value < 0.001) and three space-time clusters (relative risk: 1.46–2.01, P-value < 0.001) forrnhigh occurrence of tuberculosis were detected. The clusters were mainly concentrated in borderrnareas of the zone. The predictive accuracies of ordinary cokriging models have improved withrnthe inclusion of anisotropy, altitude and latitude covariates, the change in detrending patternrnfrom local to global, and the increase in size of spatial dataset (mean-standardized error = 0, rootxirnmean-square-standardized error = 1, and average-standard error ≈ root-mean-square error). Thernspatial risk of tuberculosis was estimated to be higher (i.e., tuberculosis prevalence rate > 100rncases per 100,000 population) at western, northwest, southwest and southeast parts of the studyrnarea, and crossed between high and low at west-central parts. The tuberculosis prevalence raternobserved in a given kebele was determined by both tuberculosis prevalence rate (spatialrnautoregressive coefficient = 0.83) and unobserved factors (spatial autocorrelation coefficient = -rn0.70) in the neighboring kebeles. By controlling the spatial effects, a 1°C rise in temperature wasrnassociated with an increase in the number of tuberculosis prevalence rate by 0.72, and a 1 personrnper square kilometer increase in population density was related to an increase in the number ofrntuberculosis prevalence rate by 1.19.rnConclusions: The spatial and space-time clusters for high occurrence of tuberculosis werernmainly concentrated at border areas of the Gurage Zone. The prevalence rate of tuberculosis in arngiven kebele was determined by both the prevalence rate of tuberculosis and other unobservedrnfactors in its neighboring kebeles in the zone, indicating sustained transmission of the diseasernwithin the communities. The spatial risk of tuberculosis distribution between kebeles in the zonernwas partly explained by spatial variations in temperature, population density, altitude, andrnlatitude. The geostatistical kriging approach can be applied to estimate the spatial risk ofrntuberculosis distribution in data limited settings.rnRecommendations: Tuberculosis control programs should consider the cooperation ofrnneighboring kebeles in the design and implementation of tuberculosis prevention and controlrnstrategies to interrupt the chain of disease transmission between the communities. Moreover, therndesigning of locally effective tuberculosis prevention and control strategies should considerrnspatial locations with higher temperature and population density. Further research is required tornevaluate the effectiveness of geographically targeting tuberculosis prevention and controlrninterventions using the inputs from spatial epidemiological methods.

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