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Med. Sci. 2018, 6(2), 26; https://doi.org/10.3390/medsci6020026

Socio-Economic Predictors and Distribution of Tuberculosis Incidence in Beijing, China: A Study Using a Combination of Spatial Statistics and GIS Technology

1,2,3
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1,3,4
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1,3,5
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1,3,6,* and 1,3,*
1
Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No.10 Xitoutiao, Youanmen Wai Street, Fengtai District, Beijing 100069, China
2
NAMS, Bir Hospital, Kathmandu 44600, Nepal
3
Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
4
Department of Evidence-Based Medicine, Xuanwu Hospital, Xicheng District, Beijing 100053, China
5
Fuwai Hospital, Chinese Academy of Medical Science, Xicheng District, Beijing 100037, China
6
School of Medical Sciences, Edith Cowan University, Perth, Joondalup WA6027, Australia
*
Authors to whom correspondence should be addressed.
Received: 17 January 2018 / Revised: 10 March 2018 / Accepted: 12 March 2018 / Published: 21 March 2018
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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 (p < 0.001) and per capita gross domestic product (GDP) (p = 0.025) had a positive effect on TB incidence, whereas population density (p < 0.001) and migrated population (p < 0.001) had an adverse impact on TB incidence in the study settings. High TB incidence districts were detected in urban and densely populated districts in Beijing. Our findings suggested that socio-economic predictors influence TB incidence. These findings may help to guide TB control programs and promote targeted intervention. View Full-Text
Keywords: tuberculosis; socio-economic factors; spatial statistics; Beijing; China tuberculosis; socio-economic factors; spatial statistics; Beijing; China
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Mahara, G.; Yang, K.; Chen, S.; Wang, W.; Guo, X. Socio-Economic Predictors and Distribution of Tuberculosis Incidence in Beijing, China: A Study Using a Combination of Spatial Statistics and GIS Technology. Med. Sci. 2018, 6, 26.

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