Weisent Jennifer, Rohrbach Barton, Dunn John R, Odoi Agricola
Department of Comparative and Experimental Medicine, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN 37996, USA.
Geospat Health. 2011 Nov;6(1):65-76. doi: 10.4081/gh.2011.158.
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.
弯曲杆菌病是美国和许多其他发达国家细菌性肠胃炎的主要病因。了解这种疾病的空间分布并确定高危地区对于集中资源采取预防和控制措施至关重要。此外,确定监测数据地理分析的适当尺度是流行病学家和公共卫生官员关注的一个领域。本研究的目的是:(i)比较田纳西州弯曲杆菌病在三个不同地理尺度(普查区、邮政编码区和县级分区)上的标准化风险估计值;(ii)在这三个地理尺度上识别和调查弯曲杆菌病的高危空间聚集情况,以确定聚集是否依赖尺度。在所有三个空间尺度上均检测到显著的高危聚集区(P<0.05)。在所有三个地理层面上,高危区域和聚集区都存在重叠。在普查区层面,空间分析识别出分辨率更高的较小聚集区,且检测到的聚集区比其他两个层面更多。然而,邮政编码区或县级分区的数据汇总得出了类似的结果。这一系列研究的重要性在于创建一个框架,通过提高地理精度和风险检测,使经济高效的疾病控制策略更易于实现。准确识别弯曲杆菌病的疾病聚集区可使公共卫生人员将稀缺资源集中用于针对最高危人群的预防和控制项目。多个空间层面的一致结果凸显了本研究中所使用地理空间技术的稳健性。此外,在邮政编码区和县级分区层面进行分析,在无法获得地址层面信息(分辨率更高的数据)时可能会很有用。这些程序也可用于帮助识别弯曲杆菌病的区域特定风险因素。