| Title |
Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016
|
|---|---|
| Published in |
BMC Infectious Diseases, August 2018
|
| DOI | 10.1186/s12879-018-3240-4 |
| Pubmed ID | |
| Authors |
Guoqi Yu, Rencong Yang, Yi Wei, Dongmei Yu, Wenwen Zhai, Jiansheng Cai, Bingshuang Long, Shiyi Chen, Jiexia Tang, Ge Zhong, Jian Qin |
| Abstract |
The resurgence of mumps around the world occurs frequently in recent years. As the country with the largest number of cases in the world, the status of mumps epidemics in China is not yet clear. This study, taking the relatively serious epidemic province of Guangxi as the example, aimed to examine the spatiotemporal pattern and epidemiological characteristics of mumps, and provide a scientific basis for the effective control of this disease and formulation of related health policies. Geographic information system (GIS)-based spatiotemporal analyses, including spatial autocorrelation analysis, Kulldorff's purely spatial and space-time scan statistics, were applied to detect the location and extent of mumps high-risk areas. Spatial empirical Bayesian (SEB) was performed to smoothen the rate for eliminating the instability of small-area data. A total of 208,470 cases were reported during 2005 and 2016 in Guangxi. Despite the fluctuations in 2006 and 2011, the overall mumps epidemic continued to decline. Bimodal seasonal distribution (mainly from April to July) were found and students aged 5-9 years were high-incidence groups. Though results of the global spatial autocorrelation based on the annual incidence largely varied, the spatial distribution of the average annual incidence of mumps was nonrandom with the significant Moran's I. Spatial cluster analysis detected high-value clusters, mainly located in the western, northern and central parts of Guangxi. Spatiotemporal scan statistics identified almost the same high-risk areas, and the aggregation time was mainly concentrated in 2009-2012. The incidence of mumps in Guangxi exhibited spatial heterogeneity in 2005-2016. Several spatial and spatiotemporal clusters were identified in this study, which might assist the local government to develop targeted health strategies, allocate health resources reasonably and increase the efficiency of disease prevention. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Venezuela, Bolivarian Republic of | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 67 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 20 | 30% |
| Student > Ph. D. Student | 12 | 18% |
| Researcher | 10 | 15% |
| Student > Bachelor | 5 | 7% |
| Student > Doctoral Student | 1 | 1% |
| Other | 4 | 6% |
| Unknown | 15 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 15% |
| Computer Science | 6 | 9% |
| Biochemistry, Genetics and Molecular Biology | 5 | 7% |
| Nursing and Health Professions | 5 | 7% |
| Social Sciences | 5 | 7% |
| Other | 17 | 25% |
| Unknown | 19 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 August 2018.
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#20,529,980
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Outputs from BMC Infectious Diseases
#6,542
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Outputs of similar age
#288,979
of 331,122 outputs
Outputs of similar age from BMC Infectious Diseases
#128
of 166 outputs
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