Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis
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....
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| description | 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 (p |
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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 (p<0.0001). Spatial autocorrelation analysis showed the presence of positive spatial autocorrelation for pulmonary TB incidence, SS+TB incidence and SS-TB incidence from 2005 to 2013 (P <0.0001). The Anselin's Local Moran's I identified the "hotspots" which were consistently located in the southwest regions composed of 20 to 28 districts, and the "coldspots" which were consistently located in the north central regions consisting of 21 to 27 districts. Analysis with the Getis-Ord Gi* statistic expanded the scope of "hotspots" and "coldspots" with different intensity; 30 county/districts clustered as "hotspots", while 47 county/districts clustered as "coldspots". OLS regression model included the "proportion of minorities" and the "per capita GDP" as explanatory variables that explained 64% the variation in pulmonary TB incidence (adjR2 = 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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0144010</identifier><identifier>PMID: 26641642</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Autocorrelation ; Care and treatment ; China ; Cluster analysis ; Control programs ; Data analysis ; Data base management systems ; Data processing ; Demographics ; Demography ; Disease control ; Disease prevention ; Epidemiology ; Female ; Fitness ; Geographic information systems ; Gross domestic product ; Hot spots ; Humans ; Incidence ; Infectious diseases ; Male ; Minorities ; Minority & ethnic groups ; Models, Biological ; Population ; Population (statistical) ; Public health ; Pulmonary tuberculosis ; Regression analysis ; Regression models ; Risk factors ; Rural areas ; Satellite navigation systems ; Smear ; Socioeconomic Factors ; Software ; Spatial analysis ; Spatial discrimination ; Spatial distribution ; Sputum ; Statistical analysis ; Studies ; Surveillance ; Tuberculosis ; Tuberculosis, Pulmonary - epidemiology ; Tuberculosis, Pulmonary - transmission</subject><ispartof>PloS one, 2015-12, Vol.10 (12), p.e0144010-e0144010</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Wubuli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Wubuli et al 2015 Wubuli et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-8ec23c3cf32ad811dafc1d90d53d4513fb3dc028ba3e1d10986488fa718e74553</citedby><cites>FETCH-LOGICAL-c692t-8ec23c3cf32ad811dafc1d90d53d4513fb3dc028ba3e1d10986488fa718e74553</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671667/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4671667/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26641642$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Pacheco, Antonio Guilherme</contributor><creatorcontrib>Wubuli, Atikaimu</creatorcontrib><creatorcontrib>Xue, Feng</creatorcontrib><creatorcontrib>Jiang, Daobin</creatorcontrib><creatorcontrib>Yao, Xuemei</creatorcontrib><creatorcontrib>Upur, Halmurat</creatorcontrib><creatorcontrib>Wushouer, Qimanguli</creatorcontrib><title>Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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 (p<0.0001). Spatial autocorrelation analysis showed the presence of positive spatial autocorrelation for pulmonary TB incidence, SS+TB incidence and SS-TB incidence from 2005 to 2013 (P <0.0001). The Anselin's Local Moran's I identified the "hotspots" which were consistently located in the southwest regions composed of 20 to 28 districts, and the "coldspots" which were consistently located in the north central regions consisting of 21 to 27 districts. Analysis with the Getis-Ord Gi* statistic expanded the scope of "hotspots" and "coldspots" with different intensity; 30 county/districts clustered as "hotspots", while 47 county/districts clustered as "coldspots". OLS regression model included the "proportion of minorities" and the "per capita GDP" as explanatory variables that explained 64% the variation in pulmonary TB incidence (adjR2 = 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.</description><subject>Analysis</subject><subject>Autocorrelation</subject><subject>Care and treatment</subject><subject>China</subject><subject>Cluster analysis</subject><subject>Control programs</subject><subject>Data analysis</subject><subject>Data base management systems</subject><subject>Data processing</subject><subject>Demographics</subject><subject>Demography</subject><subject>Disease control</subject><subject>Disease prevention</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Fitness</subject><subject>Geographic information systems</subject><subject>Gross domestic product</subject><subject>Hot spots</subject><subject>Humans</subject><subject>Incidence</subject><subject>Infectious diseases</subject><subject>Male</subject><subject>Minorities</subject><subject>Minority & ethnic groups</subject><subject>Models, Biological</subject><subject>Population</subject><subject>Population (statistical)</subject><subject>Public health</subject><subject>Pulmonary tuberculosis</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Risk factors</subject><subject>Rural areas</subject><subject>Satellite navigation systems</subject><subject>Smear</subject><subject>Socioeconomic Factors</subject><subject>Software</subject><subject>Spatial analysis</subject><subject>Spatial discrimination</subject><subject>Spatial distribution</subject><subject>Sputum</subject><subject>Statistical analysis</subject><subject>Studies</subject><subject>Surveillance</subject><subject>Tuberculosis</subject><subject>Tuberculosis, Pulmonary - epidemiology</subject><subject>Tuberculosis, Pulmonary - 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Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis</title><author>Wubuli, Atikaimu ; Xue, Feng ; Jiang, Daobin ; Yao, Xuemei ; Upur, Halmurat ; Wushouer, Qimanguli</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-8ec23c3cf32ad811dafc1d90d53d4513fb3dc028ba3e1d10986488fa718e74553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Analysis</topic><topic>Autocorrelation</topic><topic>Care and treatment</topic><topic>China</topic><topic>Cluster analysis</topic><topic>Control programs</topic><topic>Data analysis</topic><topic>Data base management systems</topic><topic>Data processing</topic><topic>Demographics</topic><topic>Demography</topic><topic>Disease control</topic><topic>Disease prevention</topic><topic>Epidemiology</topic><topic>Female</topic><topic>Fitness</topic><topic>Geographic information systems</topic><topic>Gross 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Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wubuli, Atikaimu</au><au>Xue, Feng</au><au>Jiang, Daobin</au><au>Yao, Xuemei</au><au>Upur, Halmurat</au><au>Wushouer, Qimanguli</au><au>Pacheco, Antonio Guilherme</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-12-07</date><risdate>2015</risdate><volume>10</volume><issue>12</issue><spage>e0144010</spage><epage>e0144010</epage><pages>e0144010-e0144010</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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 (p<0.0001). Spatial autocorrelation analysis showed the presence of positive spatial autocorrelation for pulmonary TB incidence, SS+TB incidence and SS-TB incidence from 2005 to 2013 (P <0.0001). The Anselin's Local Moran's I identified the "hotspots" which were consistently located in the southwest regions composed of 20 to 28 districts, and the "coldspots" which were consistently located in the north central regions consisting of 21 to 27 districts. Analysis with the Getis-Ord Gi* statistic expanded the scope of "hotspots" and "coldspots" with different intensity; 30 county/districts clustered as "hotspots", while 47 county/districts clustered as "coldspots". OLS regression model included the "proportion of minorities" and the "per capita GDP" as explanatory variables that explained 64% the variation in pulmonary TB incidence (adjR2 = 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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26641642</pmid><doi>10.1371/journal.pone.0144010</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2015-12, Vol.10 (12), p.e0144010-e0144010 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1746586416 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Analysis Autocorrelation Care and treatment China Cluster analysis Control programs Data analysis Data base management systems Data processing Demographics Demography Disease control Disease prevention Epidemiology Female Fitness Geographic information systems Gross domestic product Hot spots Humans Incidence Infectious diseases Male Minorities Minority & ethnic groups Models, Biological Population Population (statistical) Public health Pulmonary tuberculosis Regression analysis Regression models Risk factors Rural areas Satellite navigation systems Smear Socioeconomic Factors Software Spatial analysis Spatial discrimination Spatial distribution Sputum Statistical analysis Studies Surveillance Tuberculosis Tuberculosis, Pulmonary - epidemiology Tuberculosis, Pulmonary - transmission |
| title | Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang, China: A Spatial Analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T02%3A59%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Socio-Demographic%20Predictors%20and%20Distribution%20of%20Pulmonary%20Tuberculosis%20(TB)%20in%20Xinjiang,%20China:%20A%20Spatial%20Analysis&rft.jtitle=PloS%20one&rft.au=Wubuli,%20Atikaimu&rft.date=2015-12-07&rft.volume=10&rft.issue=12&rft.spage=e0144010&rft.epage=e0144010&rft.pages=e0144010-e0144010&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0144010&rft_dat=%3Cgale_plos_%3EA436775626%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1746586416&rft_id=info:pmid/26641642&rft_galeid=A436775626&rft_doaj_id=oai_doaj_org_article_a74796f0548f45f5905d3d18fa4107da&rfr_iscdi=true |