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. 2015 Jan 27;12(2):1425-48.
doi: 10.3390/ijerph120201425.

A spatial, social and environmental study of tuberculosis in China using statistical and GIS technology

Wenyi Sun  1 , Jianhua Gong  2 , Jieping Zhou  3 , Yanlin Zhao  4 , Junxiang Tan  5 , Abdoul Nasser Ibrahim  6 , Yang Zhou  4
Affiliations

A spatial, social and environmental study of tuberculosis in China using statistical and GIS technology

Wenyi Sun et al. Int J Environ Res Public Health. .

Abstract

Tuberculosis (TB) remains a major public health problem in China, and its incidence shows certain regional disparities. Systematic investigations of the social and environmental factors influencing TB are necessary for the prevention and control of the disease. Data on cases were obtained from the Chinese Center for Disease and Prevention. Social and environmental variables were tabulated to investigate the latent factor structure of the data using exploratory factor analysis (EFA). Partial least square path modeling (PLS-PM) was used to analyze the complex causal relationship and hysteresis effects between the factors and TB prevalence. A geographically weighted regression (GWR) model was used to explore the local association between factors and TB prevalence. EFA and PLS-PM indicated significant associations between TB prevalence and its latent factors. Altitude, longitude, climate, and education burden played an important role; primary industry employment, population density, air quality, and economic level had hysteresis with different lag time; health service and unemployment played a limited role but had limited hysteresis. Additionally, the GWR model showed that each latent factor had different effects on TB prevalence in different areas. It is necessary to formulate regional measures and strategies for TB control and prevention in China according to the local regional effects of specific factors.

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Figures

Figure 1
Figure 1
Average annual notification rate (per 100,000 population) for TB in China in 2007.
Figure 2
Figure 2
PLS path models of TB prevalence with its latent risk factors. (a) TB prevalence (2007) with factors (2007); (b) TB prevalence (2007) with factors (2006); (c) TB prevalence (2007) with factors (2005); (d) TB prevalence (2007) with factors (2004); (e) TB prevalence (2007) with factors (2003); (f) TB prevalence (2007) with factors (2002).
Figure 2
Figure 2
PLS path models of TB prevalence with its latent risk factors. (a) TB prevalence (2007) with factors (2007); (b) TB prevalence (2007) with factors (2006); (c) TB prevalence (2007) with factors (2005); (d) TB prevalence (2007) with factors (2004); (e) TB prevalence (2007) with factors (2003); (f) TB prevalence (2007) with factors (2002).
Figure 2
Figure 2
PLS path models of TB prevalence with its latent risk factors. (a) TB prevalence (2007) with factors (2007); (b) TB prevalence (2007) with factors (2006); (c) TB prevalence (2007) with factors (2005); (d) TB prevalence (2007) with factors (2004); (e) TB prevalence (2007) with factors (2003); (f) TB prevalence (2007) with factors (2002).
Figure 2
Figure 2
PLS path models of TB prevalence with its latent risk factors. (a) TB prevalence (2007) with factors (2007); (b) TB prevalence (2007) with factors (2006); (c) TB prevalence (2007) with factors (2005); (d) TB prevalence (2007) with factors (2004); (e) TB prevalence (2007) with factors (2003); (f) TB prevalence (2007) with factors (2002).
Figure 3
Figure 3
Spatial heterogeneity of the factor coefficients for TB prevalence derived from the GWR model. ((a1–d1) distribution of coefficients of “Air quality”, “Altitude factor”, “Climatic factor”, and “Economic level”. (a2–d2) distribution of p values of coefficients).
Figure 3
Figure 3
Spatial heterogeneity of the factor coefficients for TB prevalence derived from the GWR model. ((a1–d1) distribution of coefficients of “Air quality”, “Altitude factor”, “Climatic factor”, and “Economic level”. (a2–d2) distribution of p values of coefficients).
Figure 4
Figure 4
Spatial heterogeneity of the factor coefficients for TB prevalence derived from the GWR model ((a1–d1) distribution of coefficients of “Education burden”, “Health service”, “Longitude factor”, and “Population density”. (a2–d2) distribution of p values of coefficients).
Figure 4
Figure 4
Spatial heterogeneity of the factor coefficients for TB prevalence derived from the GWR model ((a1–d1) distribution of coefficients of “Education burden”, “Health service”, “Longitude factor”, and “Population density”. (a2–d2) distribution of p values of coefficients).
Figure 5
Figure 5
Spatial heterogeneity of the factor coefficients for TB prevalence derived from the GWR model. ((a1–c1) distribution of coefficients of “Primary industry employment”, “Rainy day factor”, and “Unemployment level”. (a2–c2) distribution of p values of coefficients).
Figure 5
Figure 5
Spatial heterogeneity of the factor coefficients for TB prevalence derived from the GWR model. ((a1–c1) distribution of coefficients of “Primary industry employment”, “Rainy day factor”, and “Unemployment level”. (a2–c2) distribution of p values of coefficients).
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