Department of Clinical Epidemiology, Biostatistics, and Bioinformatics, Academic Medical Center, University of Amsterdam, the Netherlands.
Am J Epidemiol. 2011 Jul 15;174(2):243-51. doi: 10.1093/aje/kwr061. Epub 2011 May 23.
Molecular epidemiologic studies may use genotypic clustering of isolates as an indicator of recent transmission. It has been shown that missing cases lead to underestimating clustering, and modelling studies suggested that they may also lead to underestimating odds ratios for clustering. Using a national, comprehensive database from the Netherlands covering 15 years between 1993 and 2007 and including over 12,000 patients and their isolates, the authors determined the effects of sampling at random, in time, and by geographic area. As expected, sampling reduced the observed clustering percentages. However, sampling did not reduce the observed odds ratios for clustering. The main explanations for this discrepancy with model outcomes were that a substantial proportion of clustered cases were found in large clusters and that risk factors for clustering tended to be-among clustered cases-also risk factors for large clusters. The authors conclude that, in settings where risk factors for clustering may be interpreted as risk factors for recent transmission, these risk factors are also associated with larger cluster sizes. As a result, odds ratios would show limited sampling bias.
分子流行病学研究可以将分离株的基因型聚类作为近期传播的指标。已经表明，漏报病例会导致低估聚类，模型研究表明，漏报病例也可能导致低估聚类的比值比。利用荷兰一项涵盖 1993 年至 2007 年 15 年的全国性、综合性数据库，作者确定了随机、按时和按地理区域抽样的效果。正如预期的那样，抽样减少了观察到的聚类百分比。然而，抽样并没有降低观察到的聚类比值比。造成这种与模型结果差异的主要原因是，大量的聚类病例是在大的聚类中发现的，而且聚类的危险因素往往也是大聚类的危险因素。作者得出结论，在聚类危险因素可被解释为近期传播危险因素的情况下，这些危险因素也与较大的聚类大小有关。因此，比值比将显示出有限的抽样偏差。
