Abstract
ABSTRACT Background Previous studies from Ethiopia detected disease clustering using broader geographic settings, but limited information exists on the spatial distribution of the disease using residential locations. An assessment of predictors of spatial variations of TB at community level could fill the knowledge gaps, and helps in devising tailored interventions to improve TB control. Objective To assess the pattern of spatial distribution of pulmonary tuberculosis (PTB) based on geographic locations of individual cases in the Dale district and Yirga Alem town in southern Ethiopia. Methods The socio-demographic characteristics of PTB cases were collected using a structured questionnaire, and spatial information was collected using geographic position systems. We carried out Getis and Ord (Gi*) statistics and scan statistics to explore the pattern of spatial clusters of PTB cases, and geographically weighted regression (GWR) was used to assess the spatial heterogeneities in relationship between predictor variables and PTB case notification rates (CNRs). Results The distribution of PTB varied by enumeration areas within the kebeles, and we identified areas with significant hotspots in various areas ineach year. In GWR analysis, the disease distribution showed a geographic heterogeneity (non-stationarity) in relation to physical access (distance to TB control facilities) and population density (AICc = 5591, R2 = 0.3359, adjusted R2 = 0.2671). The model explained 27% of the variability in PTB CNRs (local R2 ranged from 0.0002–0.4248 between enumeration areas). The GWR analysis showed that areas with high PTB CNRs had better physical accessibility to TB control facilities and high population density. The effect of physical access on PTB CNRs changed after the coverage of TB control facilities was improved. Conclusion We report a varying distribution of PTB in small and different areas over 10 years. Spatial and temporal analysis of disease distribution can be used to identify areas with a high burden of disease and predictors of clustering, which helps in making policy decisions and devising targeted interventions.
Highlights
Previous studies from Ethiopia detected disease clustering using broader geo graphic settings, but limited information exists on the spatial distribution of the disease using residential locations
Twenty smear-negative pulmonary tuberculosis (PTB) and 8 smear-positive PTB cases’ locations were from the same households, from the same family corresponding to the same geographic location
We found the spatial variations and local clusters of PTB in the study area
Summary
Previous studies from Ethiopia detected disease clustering using broader geo graphic settings, but limited information exists on the spatial distribution of the disease using residential locations. The GWR analysis showed that areas with high PTB CNRs had better physical accessibility to TB control facilities and high population density. Spatial and temporal analysis of disease distribution can be used to identify areas with a high burden of disease and predictors of clustering, which helps in making policy decisions and devising targeted interventions. Adverse socio-economic conditions [9,10,11,12,13], a high prevalence of HIV infection [14,15,16,17], geo graphic factors such as altitude [18,19,20,21] and access to TB control facilities are all related to variations in the burden of disease. The variations indicate the importance of targeted interventions and resource allo cation at local level based on the burden of the disease
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
