About: Space-Time Clustering Characteristics of Tuberculosis in China, 2005-2011

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Space-Time Clustering Characteristics of Tuberculosis in China, 2005-2011
Creator	Zhang, H Cheng, S He, G Huang, F Zhao, F
source	Medline; PMC
abstract	OBJECTIVES: China is one of the 22 tuberculosis (TB) high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics. METHODS: Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+) notification rate and total TB notification rate. Global Moran’s I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011. RESULTS: Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01). Global Moran’s I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01). The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces. CONCLUSION: The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.
has issue date	2013-12-19(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0083605
bibo:pmid	24367604
has license	cc-by
sha1sum (hex)	2be4d8ed799f2556c53e6946720e3033d41060e2
schema:url	https://doi.org/10.1371/journal.pone.0083605
resource representing a document's title	Space-Time Clustering Characteristics of Tuberculosis in China, 2005-2011
has PubMed Central identifier	PMC3868653
has PubMed identifier	24367604
schema:publication	PLoS One
resource representing a document's body	covid:2be4d8ed799f2556c53e6946720e3033d41060e2#body_text
is schema:about of	named entity 'Characteristics' named entity 'China' covid:arg/2be4d8ed799f2556c53e6946720e3033d41060e2 named entity 'CHINA' named entity 'HIGH' named entity 'A MAJOR' named entity 'WORLD' named entity 'SPATIAL ANALYSIS' named entity 'COUNTRIES' named entity 'HEALTH PROBLEM' named entity 'BURDEN' named entity 'EPIDEMICS' named entity 'CLUSTERS' named entity 'PUBLIC HEALTH' named entity 'GEOGRAPHIC DISTRIBUTION' named entity 'COULD BE' named entity 'TUBERCULOSIS' named entity 'SPACE-TIME CLUSTERING' named entity 'ONE OF' named entity 'OBJECTIVES' named entity 'APPLIED' named entity 'INTERVENTION' named entity 'CHARACTERISTICS' named entity 'CHINA' named entity 'TO DETECT' named entity 'TARGETED' named entity 'TUBERCULOSIS' named entity 'IS A' named entity 'China' named entity 'China' named entity 'epidemic' named entity 'Jiangsu' named entity 'China' named entity 'China' named entity 'Xinjiang Autonomous Region' named entity 'geocoded' named entity 'open source' named entity 'Jilin' named entity 'China' named entity 'mainland China' named entity 'Henan' named entity 'probability model' named entity 'China' named entity 'spatial autocorrelation' named entity 'public health' named entity 'epidemics' named entity 'clusters' named entity 'spatial analysis' named entity 'public health' named entity 'WHO' named entity 'Severe Acute Respiratory Syndrome' named entity 'infection control' named entity 'likelihood function' named entity 'sputum' named entity 'Gangsu' named entity 'sputum' named entity 'Hohhot' named entity 'data set' named entity '1 million' named entity 'Taiyuan' named entity 'China' named entity 'Qinghai province' named entity 'data based' named entity 'sputum' named entity 'pleurisy' named entity 'spatial autocorrelation' named entity 'China' named entity 'spatial autocorrelation' named entity 'Spatial autocorrelation' named entity 'sputum' named entity 'South-East Asia' named entity 'Inner Mongolia' named entity 'microscopy' named entity 'China' named entity 'infectious diseases' named entity 'sputum' named entity 'Qinghai'

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