ArticleMedical sciences (Basel, Switzerland)2018
Socio-Economic Predictors and Distribution of Tuberculosis Incidence in Beijing, China: A Study Using a Combination of Spatial Statistics and GIS Technology.
Article in Medical sciences (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Spatial spillover effects and regional differences of tuberculosis health education levels among China's floating population: a spatial lag model study (2014-2017).BMC public health · 2026Article
- 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
- Treatment Outcomes of Tuberculosis in the Eastern Cape: Clinical and Socio-Demographic Predictors from Two Rural Clinics.International journal of environmental research and public health · 2025Article
- Article
- A retrospective study of tuberculosis prevalence and associated factors among HIV-positive key populations in Nigeria.PLOS global public health · 2024Article
- Latent Tuberculosis Infection Diagnosis Using QuantiFERON-TB Gold Plus Kit Among Correctional Workers: A Cross-Sectional Study in Francisco Beltrão-PR, Brazil.Journal of community health · 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
- Development and validation of a structured questionnaire for assessing risk factors of medication non-adherence among pulmonary tuberculosis patients in Indonesia.Frontiers in pharmacology · 2023Article
- 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
- Reclaiming independence in spatial-clustering datasets: A series of data-driven spatial weights matrices.Statistics in medicine · 2022Article
- Influence of Public Health Services on the Goal of Ending Tuberculosis: Evidence From Panel Data in China.Frontiers in public health · 2022Article
- Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018.BMC public health · 2021Article
- The association between internal migration and pulmonary tuberculosis in China, 2005-2015: a spatial analysis.Infectious diseases of poverty · 2020Article
- Tuberculosis associated with the living conditions in an endemic municipality in the North of Brazil.Revista latino-americana de enfermagem · 2020Article
- Article
- A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States.International journal of environmental research and public health · 2019Article
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5 authors.
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Abstract
Evidence shows that multiple factors, such as socio-economic status and access to health care facilities, affect tuberculosis (TB) incidence. However, there is limited literature available with respect to the correlation between socio-economic/health facility factors and tuberculosis incidence. This study aimed to explore the relationship between TB incidence and socio-economic/health service predictors in the study settings. A retrospective spatial regression analysis was carried out based on new sputum smear-positive pulmonary TB cases in Beijing districts. Global Moran's I analysis was adopted to detect the spatial dependency followed by spatial regression models (spatial lag model, and spatial error model) along with the ordinary least square model were applied to examine the correlation between TB incidence and predictors. A high incidence of TB was seen in densely populated districts in Beijing, e.g., Haidian, Mentougou, and Xicheng. After comparing the R², log-likelihood, and Akaike information criterion (AIC) values among three models, the spatial error model (R² = 0.413; Log Likelihood = -591; AIC = 1199.76) identified the best model fit for the spatial regression model. The study showed that the number of beds in health institutes (
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