Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
School of Medicine, Emory University, Atlanta, Georgia.
Am J Epidemiol. 2020 Jul 1;189(7):735-745. doi: 10.1093/aje/kwaa028.
Patterns of transmission of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases worldwide in 2017. Modeling TB transmission networks can provide insight into drivers of transmission, but incomplete sampling of TB cases can pose challenges for inference from individual epidemiologic and molecular data. We assessed the effect of missing cases on a transmission network inferred from Mycobacterium tuberculosis sequencing data on extensively drug-resistant TB cases in KwaZulu-Natal, South Africa, diagnosed in 2011-2014. We tested scenarios in which cases were missing at random, missing differentially by clinical characteristics, or missing differentially by transmission (i.e., cases with many links were under- or oversampled). Under the assumption that cases were missing randomly, the mean number of transmissions per case in the complete network needed to be larger than 20, far higher than expected, to reproduce the observed network. Instead, the most likely scenario involved undersampling of high-transmitting cases, and models provided evidence for super-spreading. To our knowledge, this is the first analysis to have assessed support for different mechanisms of missingness in a TB transmission study, but our results are subject to the distributional assumptions of the network models we used. Transmission studies should consider the potential biases introduced by incomplete sampling and identify host, pathogen, or environmental factors driving super-spreading.
尽管 2017 年全球有超过 50 万例新发病例，但耐药结核病（TB）的传播模式仍未被充分了解。建模 TB 传播网络可以深入了解传播的驱动因素，但对 TB 病例的不完全采样可能会对从个体流行病学和分子数据进行推断构成挑战。我们评估了在南非夸祖鲁-纳塔尔省 2011-2014 年间诊断的广泛耐药结核病病例的结核分枝杆菌测序数据推断的传播网络中缺失病例的影响。我们测试了病例随机缺失、按临床特征差异缺失或按传播差异缺失（即具有许多联系的病例被少采样或多采样）的情况。在假设病例随机缺失的情况下，完整网络中每个病例的平均传播次数需要大于 20，远高于预期，才能重现观察到的网络。相反，最有可能的情况是高传播病例被少采样，并且模型提供了超级传播的证据。据我们所知，这是第一项评估 TB 传播研究中不同缺失机制的支持的分析，但我们的结果受到我们使用的网络模型的分布假设的限制。传播研究应考虑不完全采样引入的潜在偏差，并确定驱动超级传播的宿主、病原体或环境因素。
