Abstract
BackgroundIn Ethiopia where tuberculosis epidemic remains high, studies that describe hotspots of the disease are unavailable. This study tried to detect the spatial distribution and clustering of smear-positive tuberculosis cases in Dabat, Ethiopia.Methods and FindingsA population-based cross sectional study conducted in the Dabat Health and Demographic Surveillance System site from October 2010 to September 2011 identified smear-positive tuberculosis cases. Trained field workers collected demographic and location data from each study participant through house-to-house visits. A spatial scan statistic was used to identify purely spatial and space–time clusters of tuberculosis among permanent residents. Two significant (p<0.001) spatial and space-time clusters were identified in the study district.ConclusionTuberculosis is concentrated in certain geographic locations in Dabat, Ethiopia. This kind of clustering can be common in the country, so the National Tuberculosis Control Program can be more effective by identifying such clusters and targeting interventions.
Highlights
Tuberculosis (TB) is responsible for a significant morbidity and mortality worldwide, especially in low-income countries
Tuberculosis is concentrated in certain geographic locations in Dabat, Ethiopia
To conduct a GISbased analysis on the spatial distribution of TB, the kebele-level polygon digital map at 1:100,000 scale was obtained from the Central Statistical Agency of Ethiopia (CSA), on which the kebelelevel point layer containing information regarding latitudes and longitudes of central points of each kebele was created
Summary
A population-based cross sectional study conducted in the Dabat Health and Demographic Surveillance System site from October 2010 to September 2011 identified smear-positive tuberculosis cases. Trained field workers collected demographic and location data from each study participant through house-to-house visits. A spatial scan statistic was used to identify purely spatial and space–time clusters of tuberculosis among permanent residents. Two significant (p,0.001) spatial and space-time clusters were identified in the study district
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