ArticleGlobal health action2020
Identifying geographical heterogeneity of pulmonary tuberculosis in southern Ethiopia: a method to identify clustering for targeted interventions.
Article in Global health action, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
- Spatiotemporal epidemiology, geographic hotspots, and risk factor associations of drug-resistant tuberculosis incidence in Indonesia: a Bayesian hierarchical modelling approach.Infectious diseases of poverty · 2026Article
- Geospatial analysis of shared risks for tuberculosis transmission in an urban cohort.Scientific reports · 2025Article
- Comprehensive Spatial Investigation of Tuberculosis Dynamics and Affecting Factors in Şanlıurfa, Türkiye (2016-2023).GeoHealth · 2025Article
- Clustering and risk factor analysis of pulmonary tuberculosis in a district in Ethiopia: a population-based cohort study.BMJ open · 2025Article
- Article
- Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China.Preventive medicine reports · 2023Article
- A Spatio-temporal Bayesian model to estimate risk and influencing factors related to tuberculosis in Chongqing, China, 2014-2020.Archives of public health = Archives belges de sante publique · 2023Article
- Spatial Co-Clustering of Tuberculosis and HIV in Ethiopia.Diseases (Basel, Switzerland) · 2022Article
- Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression.Tropical medicine and infectious disease · 2022Article
- How many of persistent coughers have pulmonary tuberculosis? Population-based cohort study in Ethiopia.BMJ open · 2022Article
- Specific urban units identified in tuberculosis epidemic using a geographical detector in Guangzhou, China.Infectious diseases of poverty · 2022Article
- Large local variations in the use of health services in rural southern Ethiopia: An ecological study.PLOS global public health · 2022Article
- Spatial clustering and genetic diversity of Mycobacterium tuberculosis isolate among pulmonary tuberculosis suspected patients, Arsi Zone, Ethiopia.BMC pulmonary medicine · 2021Article
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrevious 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.
objectiveTo 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.
methodsThe 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).
resultsThe 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, R
conclusionWe 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.
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