ArticleInternational journal of health geographics2025
Multiscale geographically weighted modeling of tuberculosis incidence in China: integrating geographic perspectives into epidemiological analysis.
Article in International journal of health geographics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Article
- Mobile signaling big data, healthcare-seeking flows, and multidimensional healthcare access in Shanghai: implications for healthcare supply-demand matching and medical security governance.Frontiers in public health · 2026Article
- Older adults life expectancy in China: Bayesian mortality estimation and spatially heterogeneous determinants.Frontiers in public health · 2026Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTuberculosis (TB) is a major global health problem, and the pathogenesis of TB is determined by multiple variables. The complicated relationship between geographic determinants and incidence rates is poorly understood, and multicollinearity and spatial heterogeneity were not considered when exploring this relationship.
methodsIn this study, the factors influencing the incidence of TB in China were investigated, considering spatial heterogeneity, to develop a multidimensional TB indicator system that incorporates geographic factors. A comprehensive linear-nonlinear two-stage feature screening model was developed to identify key factors contributing to TB. The ordinary least squares model was constructed at the national scale using these key indicators to understand the macro-relationships between TB incidence rates and key indicators. A geographically weighted regression (GWR) model was constructed at a provincial scale, and a multiscale geographically weighted regression (MGWR) model was developed to conduct an in-depth comparative analysis of the fitting effects of the GWR and MGWR models on the TB incidence rates. The goal of this study is to investigate the impact of the GWR and MGWR models on TB incidence. The adjustable bandwidth mechanism of the MGWR model was compared with the fixed bandwidth mechanism of the GWR model to determine the best model for geographical analysis of TB incidence.
resultsThe MGWR model had the best fit (R
conclusionThese results provide the theoretical foundation for developing TB prevention and control measures.
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