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Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis

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  • Atikaimu Wubuli
  • Feng Xue
  • Daobin Jiang
  • Xuemei Yao
  • Halmurat Upur
  • Qimanguli Wushouer

Abstract

Objectives: Xinjiang is one of the high TB burden provinces of China. A spatial analysis was conducted using geographical information system (GIS) technology to improve the understanding of geographic variation of the pulmonary TB occurrence in Xinjiang, its predictors, and to search for targeted interventions. Methods: Numbers of reported pulmonary TB cases were collected at county/district level from TB surveillance system database. Population data were extracted from Xinjiang Statistical Yearbook (2006~2014). Spatial autocorrelation (or dependency) was assessed using global Moran’s I statistic. Anselin’s local Moran’s I and local Getis-Ord statistics were used to detect local spatial clusters. Ordinary least squares (OLS) regression, spatial lag model (SLM) and geographically-weighted regression (GWR) models were used to explore the socio-demographic predictors of pulmonary TB incidence from global and local perspectives. SPSS17.0, ArcGIS10.2.2, and GeoDA software were used for data analysis. Results: Incidence of sputum smear positive (SS+) TB and new SS+TB showed a declining trend from 2005 to 2013. Pulmonary TB incidence showed a declining trend from 2005 to 2010 and a rising trend since 2011 mainly caused by the rising trend of sputum smear negative (SS-) TB incidence (p

Suggested Citation

  • Atikaimu Wubuli & Feng Xue & Daobin Jiang & Xuemei Yao & Halmurat Upur & Qimanguli Wushouer, 2015. "Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
    • Handle: RePEc:plo:pone00:0144010
    DOI: 10.1371/journal.pone.0144010
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    References listed on IDEAS

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    1. Luc Anselin & Sanjeev Sridharan & Susan Gholston, 2007. "Using Exploratory Spatial Data Analysis to Leverage Social Indicator Databases: The Discovery of Interesting Patterns," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 82(2), pages 287-309, June.
    2. Myers, W.P. & Westenhouse, J.L. & Flood, J. & Riley, L.W., 2006. "An ecological study of tuberculosis transmission in California," American Journal of Public Health, American Public Health Association, vol. 96(4), pages 685-690.
    3. Anselin, Luc & Getis, Arthur, 1992. "Spatial Statistical Analysis and Geographic Information Systems," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 26(1), pages 19-33, April.
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    Cited by:

    1. Dan Kibuuka & Charles Mpofu & Penny Neave & Samuel Manda, 2021. "A Spatial Analysis of Tuberculosis Related Mortality in South Africa," IJERPH, MDPI, vol. 18(22), pages 1-10, November.
    2. Long Viet Bui & Zohar Mor & Daniel Chemtob & Son Thai Ha & Hagai Levine, 2018. "Use of Geographically Weighted Poisson Regression to examine the effect of distance on Tuberculosis incidence: A case study in Nam Dinh, Vietnam," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-12, November.
    3. Ling Zhang & Hui Zhang & Hao Yang, 2018. "Spatial Distribution Pattern of the Headquarters of Listed Firms in China," Sustainability, MDPI, vol. 10(7), pages 1-20, July.
    4. Kang Ma & Yuesheng Lin & Xiaopeng Zhang & Fengman Fang & Yong Zhang & Jiajia Li & Youru Yao & Lei Ge & Huarong Tan & Fei Wang, 2022. "Spatiotemporal Distribution and Evolution of Digestive Tract Cancer Cases in Lujiang County, China since 2012," IJERPH, MDPI, vol. 19(12), pages 1-13, June.
    5. Kustanto, Andi, 2020. "The role of socioeconomic and environmental factors on the number of tuberculosis cases in Indonesia," OSF Preprints ad3uv, Center for Open Science.

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