Pearl D L, Louie M, Chui L, Doré K, Grimsrud K M, Martin S W, Michel P, Svenson L W, McEwen S A
Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada.
Epidemiol Infect. 2008 Apr;136(4):483-91. doi: 10.1017/S0950268807008904. Epub 2007 Jun 13.
Using multivariable models, we compared whether there were significant differences between reported outbreak and sporadic cases in terms of their sex, age, and mode and site of disease transmission. We also determined the potential role of administrative, temporal, and spatial factors within these models. We compared a variety of approaches to account for clustering of cases in outbreaks including weighted logistic regression, random effects models, general estimating equations, robust variance estimates, and the random selection of one case from each outbreak. Age and mode of transmission were the only epidemiologically and statistically significant covariates in our final models using the above approaches. Weighing observations in a logistic regression model by the inverse of their outbreak size appeared to be a relatively robust and valid means for modelling these data. Some analytical techniques, designed to account for clustering, had difficulty converging or producing realistic measures of association.
我们使用多变量模型，比较了报告的暴发病例和散发病例在性别、年龄、疾病传播方式和部位方面是否存在显著差异。我们还确定了这些模型中行政、时间和空间因素的潜在作用。我们比较了多种方法来处理暴发中病例的聚集情况，包括加权逻辑回归、随机效应模型、广义估计方程、稳健方差估计以及从每次暴发中随机选择一个病例。在使用上述方法的最终模型中，年龄和传播方式是仅有的在流行病学和统计学上具有显著意义的协变量。在逻辑回归模型中，用暴发规模的倒数对观测值进行加权，似乎是对这些数据进行建模的一种相对稳健且有效的方法。一些旨在处理聚集情况的分析技术在收敛或产生现实的关联度量方面存在困难。
