Spatial-temporal analysis of malaria and the effect of environmental factors on its incidence in Yongcheng, China, 2006–2010

Zhang, Yan; Liu, Qi Yong; Lu, Xin; Liu, Xiaobo; Zhou, Guang Chao; Jiang, Jing; Li, Hong Sheng; Li, Zhi Fang

doi:10.1186/1471-2458-12-544

Cited by 32 publications

(26 citation statements)

References 39 publications

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“…Other targeted approaches to vector control such as larviciding, environmental management, community education and mobilization are applied wherever appropriate based on scientific evidence. Recently, the applications and uses of Geographic Information Systems (GIS) and Remotes Sensing (RS) have been applied in mapping of the spatio-temporal risk factors of malaria in order to predict the impact of control interventions, possible outbreaks and monitor the vectorial density in any given areas [69-72]. …”

Section: Resultsmentioning

confidence: 99%

Scaling up impact of malaria control programmes: a tale of events in Sub-Saharan Africa and People’s Republic of China

et al. 2012

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This review aims at providing synthetic information with scientific evidence on the trends in the malaria events from 1960 to 2011, with the hope that it will help policy makers to take informed decisions on public health issues and intervention designs on malaria control towards elimination in both Sub-Sahara Africa and in the People’s Republic of China by highlighting the achievements, progress and challenges in research on moving malaria from epidemic status towards elimination. Our findings showed that since 1960, malaria control programmes in most countries have been disjointed and not harmonized. Interestingly, during the last decade, the causal factors of the unprecedented and substantial decline in malaria morbidity and mortality rates in most vulnerable groups in these endemic areas are multifaceted, including not only the spread of malaria and its related effects but also political and financial willingness, commitment and funding by governments and international donors. The benefits of scaling up the impact of malaria coverage interventions, improvement of health system approaches and sustained commitment of stakeholders are highlighted, although considerable efforts are still necessary in Sub-Sahara Africa. Furthermore, novel integrated control strategies aiming at moving malaria from epidemic status to control towards elimination, require solid research priorities both for sustainability of the most efficient existing tools and intervention coverage, and in gaining more insights in the understanding of the epidemiology, pathogenesis, vector dynamics, and socioeconomic aspects of the disease. In conclusion, political commitment and financial investment of stakeholders in sustaining the scaling up impact of malaria control interventions, networking between African and Chinese scientists, and their Western partners are urgently needed in upholding the recent gains, and in translating lessons learnt from the Chinese malaria control achievements and successes into practical interventions in malaria endemic countries in Africa and elsewhere.

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Section: Resultsmentioning

confidence: 99%

Scaling up impact of malaria control programmes: a tale of events in Sub-Saharan Africa and People’s Republic of China

et al. 2012

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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%

“…These techniques detect disease clusters while adjusting for varying population size among spatial and temporal scales under study. In China, these have been used to identify high-risk areas and periods of malaria in some endemic provinces [18–22, 24, 25]. However, few studies have analysed the spatial and space–time distribution of both P. vivax and P. falciparum malaria at the national level.…”

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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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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“…Furthermore, patient care factors [49], as well as poor health care and TB control services may also contribute to the high rate of the disease occurrence Spatial analysis has been extensively utilized to detect the distribution patterns of various communicable diseases along with non-communicable diseases by GIS technology, ArcGIS, SaTScan and other relevant software, and assimilated meaningful results [51]. Investigators can identify the patterns and relationships in the data based on geography through spatial analysis [52]. Significant hotspot clusters including most likely clusters and secondary clusters were displayed in this study prioritized for public health action according to the statistical analysis [53].…”

Section: Discussionmentioning

confidence: 99%

Space-Time Cluster Analysis of Tuberculosis Incidence in Beijing, China

Mahara¹,

Karki²,

Yang³

et al. 2018

JTR

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Tuberculosis is one of the top killer diseases in the globe. The aim of this study was to explore the geographic distribution patterns and clustering characteristics of the disease incidence in terms of both space and time with high relative risk locations for tuberculosis incidence in Beijing area. A retrospective space-time clustering analysis was conducted at the districts level in Beijing area based on reported cases of sputum smear-positive pulmonary tuberculosis (TB) from 2005 to 2014. Global and local Moran's I, autocorrelation analysis along with Ord (Gi*) statistics was applied to detect spatial patterns and the hotspot of TB incidence. Furthermore, the Kuldorff's scan statistics were used to analyze space-time clusters. A total of 40,878 TB cases were reported in Beijing from 2005 to 2014. The annual average incidence rate was 22.11 per 100,000 populations (ranged from 16.55 to 25.71). The seasonal incidence occurred from March to July until late autumn. A higher relative risk area for TB incidence was mainly detected in urban and some rural districts of Beijing. The significant most likely space-time clusters and secondary clusters of TB incidence were scattered diversely in Beijing districts in each study year. The risk population was mainly scattered in urban and dense populated districts, including in few rural districts.

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Spatial-temporal analysis of malaria and the effect of environmental factors on its incidence in Yongcheng, China, 2006–2010

Cited by 32 publications

References 39 publications

Scaling up impact of malaria control programmes: a tale of events in Sub-Saharan Africa and People’s Republic of China

Scaling up impact of malaria control programmes: a tale of events in Sub-Saharan Africa and People’s Republic of China

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

Space-Time Cluster Analysis of Tuberculosis Incidence in Beijing, China

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