Spatial, temporal, and spatiotemporal analysis of malaria in Hubei Province, China from 2004–2011

Xia, Jing; Cai, Shumin; Zhang, Huaxun; Wen, Liang; Fan, Yunzhou; Qiu, Juan; Sun, Liqian; Bianrong, Chang; Zhang, Zhijie; Nie, Shaoping

doi:10.1186/s12936-015-0650-2

Cited by 54 publications

(54 citation statements)

References 25 publications

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“…These techniques were designed particularly to perform spatial clustering of disease or health related events, and to test the statistical significance of clustering under the null hypothesis of a random distribution of the diseases in space, time and space–time [66, 67]. These techniques are most effective at identifying disease clusters [68], and have been widely used in fields of epidemiology for similar purpose [18, 19, 21, 24, 25]. …”

Section: Discussionmentioning

confidence: 99%

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Spatial and space–time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005–2014

Hundessa

Williams

et al. 2016

Malar J

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BackgroundDespite the declining burden of malaria in China, the disease remains a significant public health problem with periodic outbreaks and spatial variation across the country. A better understanding of the spatial and temporal characteristics of malaria is essential for consolidating the disease control and elimination programme. This study aims to understand the spatial and spatiotemporal distribution of Plasmodium vivax and Plasmodium falciparum malaria in China during 2005–2009.MethodsGlobal Moran’s I statistics was used to detect a spatial distribution of local P. falciparum and P. vivax malaria at the county level. Spatial and space–time scan statistics were applied to detect spatial and spatiotemporal clusters, respectively.ResultsBoth P. vivax and P. falciparum malaria showed spatial autocorrelation. The most likely spatial cluster of P. vivax was detected in northern Anhui province between 2005 and 2009, and western Yunnan province between 2010 and 2014. For P. falciparum, the clusters included several counties of western Yunnan province from 2005 to 2011, Guangxi from 2012 to 2013, and Anhui in 2014. The most likely space–time clusters of P. vivax malaria and P. falciparum malaria were detected in northern Anhui province and western Yunnan province, respectively, during 2005–2009.ConclusionThe spatial and space–time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Contrary to P. vivax, the high-risk areas for P. falciparum malaria shifted from the west to the east of China. Further studies are required to examine the spatial changes in risk of malaria transmission and identify the underlying causes of elevated risk in the high-risk areas.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1646-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

“…The distribution of malaria in China shows considerable variation at fine spatial resolution such as county [18, 19]. A better understanding of the spatiotemporal change in disease distribution is crucial for improving control interventions and health resource allocation.…”

Section: Introductionmentioning

confidence: 99%

Spatial and space–time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005–2014

Hundessa

Williams

et al. 2016

Malar J

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“…A P-value <0.05 indicates a significantly high risk inside of the scan window, which might be a potential cluster of a high risk of TB. The relative risk (RR) of TB in each cluster was calculated to evaluate the risk of TB in the cluster areas [12,21,22].…”

Section: Methodsmentioning

confidence: 99%

“…It is a known fact that time is a critical confounder that might directly bias the determination of the high-risk regions of TB. The Kulldorff 's space-time scan statistical method can detect the distribution characteristics in both the temporal and spatial axes, bringing them closer to real-world conditions [12][13][14][15]. Additionally, the relative risk of disease in a cluster area can be estimated by comparing it with the area outside the cluster area.…”

Section: Introductionmentioning

confidence: 99%

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

Rao

Shi

2017

BMC Infect Dis

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BackgroundAlthough 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.MethodsWe 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.ResultsA 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.ConclusionThis 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 materialThe online version of this article (doi:10.1186/s12879-017-2643-y) contains supplementary material, which is available to authorized users.

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“…Drawing data across a time span of 10 years, this study attempts to delineate the time-geography of STIs in China. Among all of the indicators for spatial autocorrelation, Moran's I is one of the universally adopted indicators to explore the spatial distribution of infectious diseases and has been widely used in other studies [17][18][19][20][21][22][23]. Specifically, the global and local Moran's I are both employed …”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Notifiable Sexually Transmitted Infections in China: Epidemiologic Trends and Spatial Changing Patterns

Zhu

Liu

et al. 2017

Sustainability

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Sexually transmitted infections (STIs) have become one of the major public health threats to the sustainable development of human beings. Among all of the STIs in China, three are listed as the notifiable infectious diseases, i.e., gonorrhea, syphilis, and HIV/AIDS, which demand more attention. This study aims to detect, describe, and compare the spatial-temporal clustering of these notifiable STIs in China and to relate spatial analysis results to epidemiologic trends during the past decade. A descriptive epidemiology analysis and a spatial autocorrelation analysis (global and local) are adopted to study the epidemiologic trends and spatial changing patterns of STIs respectively. The results indicated that there were regional disparities and spatial clusters in the spatial distribution of notifiable STIs in China. However, the incidence rates of the three notifiable STIs displayed relatively different characteristics in epidemiologic trends and the agglomeration level. Overall, the Yangtze River Delta region, the southwestern border area, and some other border regions are the places demanding more attention. In the end, we propose a three-dimensional prevention and control strategy, which focuses on not only the most-at-risk populations, but also the most-at-risk areas and most-at-risk timings. Besides, some measures targeting more than one STI should also be formulated.

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Spatial, temporal, and spatiotemporal analysis of malaria in Hubei Province, China from 2004–2011

Cited by 54 publications

References 25 publications

Spatial and space–time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005–2014

Spatial and space–time distribution of Plasmodium vivax and Plasmodium falciparum malaria in China, 2005–2014

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

Notifiable Sexually Transmitted Infections in China: Epidemiologic Trends and Spatial Changing Patterns

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