A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology

Wenyi Sun, Jianhua Gong, Jieping Zhou, Yanlin Zhao, Junxiang Tan, Abdoul Nasser Ibrahim and Yang Zhou
Additional contact information
Wenyi Sun: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Jianhua Gong: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Jieping Zhou: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Yanlin Zhao: The Chinese Center for Disease Control and Prevention, Beijing 102206, China
Junxiang Tan: Center for Airborne Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Abdoul Nasser Ibrahim: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Yang Zhou: The Chinese Center for Disease Control and Prevention, Beijing 102206, China

IJERPH, 2015, vol. 12, issue 2, 1-24

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.

Keywords: tuberculosis; partial least squares path model; geographically weighted regression (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1660-4601/12/2/1425/pdf (application/pdf)
https://www.mdpi.com/1660-4601/12/2/1425/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:12:y:2015:i:2:p:1425-1448:d:45151

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().