Newhouse V F, Choi K, Holman R C, Thacker S B, D'Angelo L J, Smith J D
Public Health Rep. 1986 Jul-Aug;101(4):419-28.
For the period of 1961 through 1975, 10 geographic and sociologic variables in each of the 159 counties of Georgia were analyzed to determine how they were correlated with the occurrence of Rocky Mountain spotted fever (RMSF). Combinations of variables were transformed into a smaller number of factors using principal-component analysis. Based upon the relative values of these factors, geographic areas of similarity were delineated by cluster analysis. It was found by use of these analyses that the counties of the State formed four similarity clusters, which we called south, central, lower north and upper north. When the incidence of RMSF was subsequently calculated for each of these regions of similarity, the regions had differing RMSF incidence; low in the south and upper north, moderate in the central, and high in the lower north. The four similarity clusters agreed closely with the incidence of RMSF when both were plotted on a map. Thus, when analyzed simultaneously, the 10 variables selected could be used to predict the occurrence of RMSF. The most important variables were those of climate and geography. Of secondary, but still major importance, were the changes over the 15-year period in variables associated with humans and their environmental alterations. Detailed examination of these factors has permitted quantitative evaluation of the simultaneous impacts of the geographic and sociologic variables on the occurrence of RMSF in Georgia. These analyses could be updated to reflect changes in the relevant variables and tested as a means of identifying new high risk areas for RMSF in the State. More generally, this method might be adapted to clarify our understanding of the relative importance of individual variables in the ecology of other diseases or environmental health problems.
在1961年至1975年期间,对佐治亚州159个县中的每一个县的10个地理和社会变量进行了分析,以确定它们与落基山斑疹热(RMSF)的发生之间的相关性。使用主成分分析将变量组合转化为数量较少的因子。基于这些因子的相对值,通过聚类分析划定相似的地理区域。通过这些分析发现,该州的县形成了四个相似性聚类,我们称之为南部、中部、北部下游和北部上游。随后计算这些相似性区域中每个区域的RMSF发病率时,各区域的RMSF发病率不同;南部和北部上游较低,中部适中,北部下游较高。当将这四个相似性聚类和RMSF发病率同时绘制在地图上时,二者非常吻合。因此,当同时进行分析时,所选择的10个变量可用于预测RMSF的发生。最重要的变量是气候和地理变量。其次但仍然很重要的是与人类及其环境变化相关的变量在15年期间的变化。对这些因素的详细研究使得能够对地理和社会变量对佐治亚州RMSF发生的同时影响进行定量评估。这些分析可以更新以反映相关变量的变化,并作为确定该州RMSF新的高风险区域的一种方法进行检验。更一般地说,这种方法可能适用于阐明我们对其他疾病生态或环境卫生问题中各个变量相对重要性的理解。