Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis
Open Access
- 7 December 2015
- journal article
- clinical trial
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 10 (12), e0144010
- https://doi.org/10.1371/journal.pone.0144010
Abstract
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. 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. 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 (pP 2 = 0.64). The SLM model improved the fit of the OLS model with a decrease in AIC value from 883 to 864, suggesting “proportion of minorities” to be the only statistically significant predictor. GWR model also improved the fitness of regression (adj R2 = 0.68, AIC = 871), which revealed that “proportion of minorities” was a strong predictor in the south central regions while “per capita GDP” was a strong predictor for the southwest regions. The SS+TB incidence of Xinjiang had a decreasing trend during 2005–2013, but it still remained higher than the national average in China. Spatial analysis showed significant spatial autocorrelation in pulmonary TB incidence. Cluster analysis detected two clusters—the “hotspots”, which were consistently located in the southwest regions, and the “coldspots”, which were consistently located in the north central regions. The exploration of socio-demographic predictors identified the “proportion of minorities” and the “per capita GDP” as predictors and may help to guide TB control programs and targeting intervention.This publication has 24 references indexed in Scilit:
- NRAMP1, VDR, HLA-DRB1, and HLA-DQB1 Gene Polymorphisms in Susceptibility to Tuberculosis among the Chinese Kazakh Population: A Case-Control StudyBioMed Research International, 2013
- The spatial epidemiology of tuberculosis in Linyi City, China, 2005–2010BMC Public Health, 2012
- Modelling malaria treatment practices in Bangladesh using spatial statisticsMalaria Journal, 2012
- Spatial epidemiology and spatial ecology study of worldwide drug-resistant tuberculosisInternational Journal of Health Geographics, 2011
- Local Indicators of Spatial Association-LISAGeographical Analysis, 2010
- The NRAMPI, VDR and TNF-α gene polymorphisms in Iranian tuberculosis patients: the study on host susceptibilityThe Brazilian Journal of Infectious Diseases, 2009
- Revisiting Robinson: The perils of individualistic and ecologic fallacyInternational Journal of Epidemiology, 2009
- An Ecological Study of Tuberculosis Transmission in CaliforniaAmerican Journal of Public Health, 2006
- Real epidemiologists don't do ecological studies?International Journal of Epidemiology, 2005
- The Assumptions of the Linear Regression ModelTransactions of the Institute of British Geographers, 1971