ArticleInfectious diseases of poverty2025
Spatial autocorrelation with environmental factors related to tuberculosis prevalence in Nepal, 2020-2023.
Article in Infectious diseases of poverty, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Global Economic Burden of Tuberculosis: Regional Disparities and Implications.Open forum infectious diseases · 2026Article
- Spatio-temporal trends, distribution and prediction of tuberculosis incidence in Uganda (2020-2025).BMC infectious diseases · 2025Article
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7 authors.
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Abstract
backgroundDespite global efforts to reduce tuberculosis (TB) incidence, Nepal remains burdened by approximately 70,000 new cases annually, with an incidence rate of 229 per 100,000 people in 2022. This study investigated the geographic patterns of TB notifications in Nepal from fiscal year 2020 to 2023, focusing on environmental determinants such as land surface temperature (LST), urbanization, precipitation and cropland coverage.
methodsThis study examined the spatial association between environmental factors and TB prevalence in Nepal at the district level, utilizing Geographic Information System (GIS) techniques, bivariate Local Indicators of Spatial Association (LISA) and spatial regression analyses. The tuberculosis prevalence data were obtained from the National Tuberculosis Control Center (NTCC) Nepal for the fiscal years (FY) 2020-2023.
resultsOver the three fiscal years, high TB prevalence consistently clustered in districts such as Banke, Parsa, and Rautahat, while low prevalence areas included Mustang and Kaski. Significant positive spatial autocorrelation was found between environmental factors and TB prevalence. Moran's I values were as follows: for LST (day), 0.379, 0.424, and 0.423; for LST (night), 0.383, 0.420, and 0.425; for cropland coverage, 0.325, 0.339, and 0.373; for urbanization, 0.197, 0.245, and 0.246; and for precipitation, 0.222, 0.349, and 0.104 across FY 2020-2021, FY 2021-2022 and FY 2022-2023, respectively. Regression analyses, including Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM), demonstrated that Land Surface Temperature Night (LSTN), urbanization, and precipitation significantly influenced TB prevalence, explaining up to 72.1% of the variance in FY 2021-2022 (R
conclusionsEnvironmental factors significantly influence the spatial distribution of TB in Nepal. This underscores the importance of integrating disease management strategies with environmental health policies in effectively addressing TB prevalence.
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