About: Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009

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About: Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016 Goto Sponge NotDistinct Permalink

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
has title	Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016
Creator	Zhang, Xi Shi, Xinyu Rao, Huaxiang
Source	Medline; PMC
abstract	BACKGROUND: Although the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the planning and implementing key preventative measures. METHODS: We extracted data of reported TB cases in the Qinghai province from the China Information System for Disease Control and Prevention (CISDCP) during January 2009 to December 2016. The Kulldorff’s retrospective space-time scan statistics, calculated by using the discrete Poisson probability model, was used to identify the temporal, spatial, and spatio-temporal clusters of TB at the county level in Qinghai. RESULTS: A total of 48,274 TB cases were reported from 2009 to 2016 in Qinghai. Results of the Kulldorff’s scan revealed that the TB cases in Qinghai were significantly clustered in spatial, temporal, and spatio-temporal distribution. The most likely spatio-temporal cluster (LLR = 2547.64, RR = 4.21, P < 0.001) was mainly concentrated in the southwest of Qinghai, covering seven counties and clustered in the time frame from September 2014 to December 2016. CONCLUSION: This study identified eight significant space-time clusters of TB in Qinghai from 2009 to 2016, which could be helpful in prioritizing resource assignment in high-risk areas for TB control and elimination in the future. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-017-2643-y) contains supplementary material, which is available to authorized users.
has issue date	2017-08-21(xsd:dateTime)
bibo:doi	10.1186/s12879-017-2643-y
bibo:pmid	28826399
has license	cc-by
sha1sum (hex)	e1be06ec4d2cc4190334ac135f85090e6d3f380f
schema:url	https://doi.org/10.1186/s12879-017-2643-y
resource representing a document's title	Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016
has PubMed Central identifier	PMC5563899
has PubMed identifier	28826399
schema:publication	BMC Infect Dis
resource representing a document's body	covid:e1be06ec4d2cc4190334ac135f85090e6d3f380f#body_text
is schema:about of	named entity 'REPORTED' named entity 'TIME' named entity '27S' named entity 'TO IDENTIFY' named entity 'PROBABILITY' named entity 'PLANNING' named entity 'MEASURES' named entity 'PREVENTION' named entity 'DATA' named entity 'CALCULATED' named entity 'EXTRACTED' named entity 'RETROSPECTIVE' named entity 'HEALTH PROBLEM' named entity 'SPATIAL' named entity 'PUBLIC HEALTH' named entity 'STUDY' named entity 'BACKGROUND' named entity 'China' named entity 'December' named entity 'Qinghai' named entity 'areas' named entity 'TUBERCULOSIS' named entity 'PARTS' named entity 'LEVEL' named entity 'USED' named entity 'HELPFUL' named entity 'MODEL' named entity 'SCAN' named entity 'USING' named entity 'COULD BE' named entity 'TUBERCULOSIS' named entity 'AIMS' named entity 'DISCRETE' named entity 'TEMPORAL' named entity 'PATTERNS' named entity 'PREVENTATIVE' named entity 'TO DETECT' named entity 'CLUSTERS' named entity 'STATISTICAL ANALYSIS' named entity 'USING' named entity 'SCAN' named entity 'A MAJOR' named entity 'CLUSTERS' named entity 'CASES' named entity 'INCIDENCE' named entity 'DISEASE CONTROL' named entity 'COUNTY' named entity 'SPACE' named entity 'AREAS' named entity 'STATISTICS' named entity 'METHODS' named entity 'JANUARY' named entity 'TEMPORAL' named entity '27S' named entity 'INFORMATION SYSTEM' named entity 'UNDER CONTROL' named entity 'QINGHAI PROVINCE' named entity 'KEY' named entity 'REVEAL' named entity 'DECEMBER' named entity 'CHINA' covid:arg/e1be06ec4d2cc4190334ac135f85090e6d3f380f named entity 'aims' named entity 'Prevention' named entity 'System' named entity 'tuberculosis' named entity 'spatial autocorrelation' named entity 'window covering' named entity 'multiple testing' named entity 'space-time' named entity 'high-risk' named entity 'space-time' named entity 'space-time' named entity 'Qinghai' named entity 'high-risk' named entity 'ultraviolet rays' named entity 'high-risk' named entity 'early summer' named entity 'highly complex'

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