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Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015

BACKGROUND: Tuberculosis (TB) is still one of the most serious infectious diseases in the mainland of China. So it was urgent for the formulation of more effective measures to prevent and control it. METHODS: The data of reported TB cases in 340 prefectures from the mainland of China were extracted...

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Autores principales: Liu, Meng-Yang, Li, Qi-Huan, Zhang, Ying-Jie, Ma, Yuan, Liu, Yue, Feng, Wei, Hou, Cheng-Bei, Amsalu, Endawoke, Li, Xia, Wang, Wei, Li, Wei-Min, Guo, Xiu-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195697/
https://www.ncbi.nlm.nih.gov/pubmed/30340513
http://dx.doi.org/10.1186/s40249-018-0490-8
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author Liu, Meng-Yang
Li, Qi-Huan
Zhang, Ying-Jie
Ma, Yuan
Liu, Yue
Feng, Wei
Hou, Cheng-Bei
Amsalu, Endawoke
Li, Xia
Wang, Wei
Li, Wei-Min
Guo, Xiu-Hua
author_facet Liu, Meng-Yang
Li, Qi-Huan
Zhang, Ying-Jie
Ma, Yuan
Liu, Yue
Feng, Wei
Hou, Cheng-Bei
Amsalu, Endawoke
Li, Xia
Wang, Wei
Li, Wei-Min
Guo, Xiu-Hua
author_sort Liu, Meng-Yang
collection PubMed
description BACKGROUND: Tuberculosis (TB) is still one of the most serious infectious diseases in the mainland of China. So it was urgent for the formulation of more effective measures to prevent and control it. METHODS: The data of reported TB cases in 340 prefectures from the mainland of China were extracted from the China Information System for Disease Control and Prevention (CISDCP) during January 2005 to December 2015. The Kulldorff’s retrospective space-time scan statistics was used to identify the temporal, spatial and spatio-temporal clusters of reported TB in the mainland of China by using the discrete Poisson probability model. Spatio-temporal clusters of sputum smear-positive (SS+) reported TB and sputum smear-negative (SS-) reported TB were also detected at the prefecture level. RESULTS: A total of 10 200 528 reported TB cases were collected from 2005 to 2015 in 340 prefectures, including 5 283 983 SS- TB cases and 4 631 734 SS + TB cases with specific sputum smear results, 284 811 cases without sputum smear test. Significantly TB clustering patterns in spatial, temporal and spatio-temporal were observed in this research. Results of the Kulldorff’s scan found twelve significant space-time clusters of reported TB. The most likely spatio-temporal cluster (RR = 3.27, P <  0.001) was mainly located in Xinjiang Uygur Autonomous Region of western China, covering five prefectures and clustering in the time frame from September 2012 to November 2015. The spatio-temporal clustering results of SS+ TB and SS- TB also showed the most likely clusters distributed in the western China. However, the clustering time of SS+ TB was concentrated before 2010 while SS- TB was mainly concentrated after 2010. CONCLUSIONS: This study identified the time and region of TB, SS+ TB and SS- TB clustered easily in 340 prefectures in the mainland of China, which is helpful in prioritizing resource assignment in high-risk periods and high-risk areas, and to formulate powerful strategy to prevention and control TB. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40249-018-0490-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-61956972018-10-30 Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015 Liu, Meng-Yang Li, Qi-Huan Zhang, Ying-Jie Ma, Yuan Liu, Yue Feng, Wei Hou, Cheng-Bei Amsalu, Endawoke Li, Xia Wang, Wei Li, Wei-Min Guo, Xiu-Hua Infect Dis Poverty Research Article BACKGROUND: Tuberculosis (TB) is still one of the most serious infectious diseases in the mainland of China. So it was urgent for the formulation of more effective measures to prevent and control it. METHODS: The data of reported TB cases in 340 prefectures from the mainland of China were extracted from the China Information System for Disease Control and Prevention (CISDCP) during January 2005 to December 2015. The Kulldorff’s retrospective space-time scan statistics was used to identify the temporal, spatial and spatio-temporal clusters of reported TB in the mainland of China by using the discrete Poisson probability model. Spatio-temporal clusters of sputum smear-positive (SS+) reported TB and sputum smear-negative (SS-) reported TB were also detected at the prefecture level. RESULTS: A total of 10 200 528 reported TB cases were collected from 2005 to 2015 in 340 prefectures, including 5 283 983 SS- TB cases and 4 631 734 SS + TB cases with specific sputum smear results, 284 811 cases without sputum smear test. Significantly TB clustering patterns in spatial, temporal and spatio-temporal were observed in this research. Results of the Kulldorff’s scan found twelve significant space-time clusters of reported TB. The most likely spatio-temporal cluster (RR = 3.27, P <  0.001) was mainly located in Xinjiang Uygur Autonomous Region of western China, covering five prefectures and clustering in the time frame from September 2012 to November 2015. The spatio-temporal clustering results of SS+ TB and SS- TB also showed the most likely clusters distributed in the western China. However, the clustering time of SS+ TB was concentrated before 2010 while SS- TB was mainly concentrated after 2010. CONCLUSIONS: This study identified the time and region of TB, SS+ TB and SS- TB clustered easily in 340 prefectures in the mainland of China, which is helpful in prioritizing resource assignment in high-risk periods and high-risk areas, and to formulate powerful strategy to prevention and control TB. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40249-018-0490-8) contains supplementary material, which is available to authorized users. BioMed Central 2018-10-20 /pmc/articles/PMC6195697/ /pubmed/30340513 http://dx.doi.org/10.1186/s40249-018-0490-8 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Liu, Meng-Yang
Li, Qi-Huan
Zhang, Ying-Jie
Ma, Yuan
Liu, Yue
Feng, Wei
Hou, Cheng-Bei
Amsalu, Endawoke
Li, Xia
Wang, Wei
Li, Wei-Min
Guo, Xiu-Hua
Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015
title Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015
title_full Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015
title_fullStr Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015
title_full_unstemmed Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015
title_short Spatial and temporal clustering analysis of tuberculosis in the mainland of China at the prefecture level, 2005–2015
title_sort spatial and temporal clustering analysis of tuberculosis in the mainland of china at the prefecture level, 2005–2015
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195697/
https://www.ncbi.nlm.nih.gov/pubmed/30340513
http://dx.doi.org/10.1186/s40249-018-0490-8
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