Literature DB >> 22109864

Detection of high risk campylobacteriosis clusters at three geographic levels.

Jennifer Weisent1, Barton Rohrbach, John R Dunn, Agricola Odoi.   

Abstract

Campylobacteriosis is a leading cause of bacterial gastroenteritis in the United States and many other developed countries. Understanding the spatial distribution of this disease and identifying high-risk areas is vital to focus resources for prevention and control measures. In addition, determining the appropriate scale for geographical analysis of surveillance data is an area of concern to epidemiologists and public health officials. The purpose of this study was to (i) compare standardized risk estimates for campylobacteriosis in Tennessee over three distinct geographical scales (census tract, zip code and county subdivision), and (ii) identify and investigate high-risk spatial clustering of campylobacteriosis at the three geographical scales to determine if clustering is scale dependent. Significant high risk clusters (P <0.05) were detected at all three spatial scales. There were overlaps in regions of high-risk and clusters at all three geographic levels. At the census tract level, spatial analysis identified smaller clusters of finer resolution and detected more clusters than the other two levels. However, data aggregation at zip code or county subdivision yielded similar findings. The importance of this line of research is to create a framework whereby economically efficient disease control strategies become more attainable through improved geographical precision and risk detection. Accurate identification of disease clusters for campylobacteriosis can enable public health personnel to focus scarce resources towards prevention and control programmes on the most at-risk populations. Consistent results at multiple spatial levels highlight the robustness of the geospatial techniques utilized in this study. Furthermore, analyses at the zip code and county subdivision levels can be useful when address level information (finer resolution data) are not available. These procedures may also be used to help identify regionally specific risk factors for campylobacteriosis.

Entities:  

Mesh:

Year:  2011        PMID: 22109864     DOI: 10.4081/gh.2011.158

Source DB:  PubMed          Journal:  Geospat Health        ISSN: 1827-1987            Impact factor:   1.212


  5 in total

1.  Using geovisual analytics in Google Earth to understand disease distribution: a case study of campylobacteriosis in the Czech Republic (2008-2012).

Authors:  Lukáš Marek; Pavel Tuček; Vít Pászto
Journal:  Int J Health Geogr       Date:  2015-01-28       Impact factor: 3.918

2.  Area-level global and local clustering of human Salmonella Enteritidis infection rates in the city of Toronto, Canada, 2007-2009.

Authors:  Csaba Varga; David L Pearl; Scott A McEwen; Jan M Sargeant; Frank Pollari; Michele T Guerin
Journal:  BMC Infect Dis       Date:  2015-08-21       Impact factor: 3.090

3.  Evaluating area-level spatial clustering of Salmonella Enteritidis infections and their socioeconomic determinants in the greater Toronto area, Ontario, Canada (2007 - 2009): a retrospective population-based ecological study.

Authors:  Csaba Varga; David L Pearl; Scott A McEwen; Jan M Sargeant; Frank Pollari; Michele T Guerin
Journal:  BMC Public Health       Date:  2013-11-15       Impact factor: 3.295

4.  Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

Authors:  Yue Ma; Fei Yin; Tao Zhang; Xiaohua Andrew Zhou; Xiaosong Li
Journal:  PLoS One       Date:  2016-01-28       Impact factor: 3.240

5.  Using the maximum clustering heterogeneous set-proportion to select the maximum window size for the spatial scan statistic.

Authors:  Wei Wang; Tao Zhang; Fei Yin; Xiong Xiao; Shiqi Chen; Xingyu Zhang; Xiaosong Li; Yue Ma
Journal:  Sci Rep       Date:  2020-03-17       Impact factor: 4.379

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.