Mapping the distribution of tuberculosis cases and associated factors identified through routine program implementation and community-based active screening in Central Ethiopia

Abstract

Abstract Background Smear-positive TB patients greatly contribute to community-level transmission of this disease. Locating hotspots would make it easier to prioritize and target control interventions. This study aimed to assess the spatial distribution of smear-positive index TB cases and their secondary cases and the predictors of clustering of smear-positive TB cases. Methods This study was conducted in the Silti Zone of Central Ethiopia from 2020 to 2022. The data of smear-positive index TB patients were collected from the unit TB registries of healthcare facilities. Contacts of all index TB patients were screened in the community and tested to identify secondary TB patients. We performed spatial analysis, including Moran’s I statistic, the Getis-Ord Gi* statistic and geographically weighted regression (GWR), to assess the global distribution, local clustering and predictors of clustering of smear-positive TB patients, respectively. Additionally, we used inverse distance weighting (IDW) interpolation to predict the distribution of smear-positive TB cases and develop a continuous raster map for places with no data. Results Spatial autocorrelation analysis revealed that the distribution of smear-positive TB patients exhibited significant clustering (Moran’s I = 0.70029; p value < 0.000). The Getis-Ord Gi* output indicated the presence of statistically significant hotspots as well as cold spots in the study area. Significant hotspots were found in 11 Kebeles of the Silti, Dalocha and Misrak Silti districts. Significant coldspots were also found in five kebeles of the Silti and Misrak districts. GWR analysis revealed that no education, primary education, family size and thatched roof houses were significant predictors of the spatial clustering of smear-positive TB cases. We also found that the majority of the secondary TB cases were found in hotspots identified through spatial analysis. Conclusion The study revealed a heterogenous distribution of smear positive TB in the study area and it could act as a model that can be replicated in other regions. The identified hotspots of TB could be targeted through location-based interventions such as systematic active screening in the form of outreach programs to improve the performance of TB prevention and control, including reducing the transmission of TB. Educational status, family size and housing type were some of the factors that significantly influenced the spatial distribution of smear-positive TB in the study area. The distribution of secondary TB cases found through household contact screening coincided with the identified hotspots, indicating greater transmission of the disease in these places.

Keywords

• Spatial analysis
• Hotspots
• Coldspots
• Clustering
• Tuberculosis
• Ethiopia