Ahlstrom C, Muellner P, Spencer S E F, Hong S, Saupe A, Rovira A, Hedberg C, Perez A, Muellner U, Alvarez J
Epi-interactive, Wellington, New Zealand.
University of Warwick, Coventry, UK.
Zoonoses Public Health. 2017 Dec;64(8):589-598. doi: 10.1111/zph.12351. Epub 2017 Mar 13.
Salmonella enterica is a global health concern because of its widespread association with foodborne illness. Bayesian models have been developed to attribute the burden of human salmonellosis to specific sources with the ultimate objective of prioritizing intervention strategies. Important considerations of source attribution models include the evaluation of the quality of input data, assessment of whether attribution results logically reflect the data trends and identification of patterns within the data that might explain the detailed contribution of different sources to the disease burden. Here, more than 12,000 non-typhoidal Salmonella isolates from human, bovine, porcine, chicken and turkey sources that originated in Minnesota were analysed. A modified Bayesian source attribution model (available in a dedicated R package), accounting for non-sampled sources of infection, attributed 4,672 human cases to sources assessed here. Most (60%) cases were attributed to chicken, although there was a spike in cases attributed to a non-sampled source in the second half of the study period. Molecular epidemiological analysis methods were used to supplement risk modelling, and a visual attribution application was developed to facilitate data exploration and comprehension of the large multiyear data set assessed here. A large amount of within-source diversity and low similarity between sources was observed, and visual exploration of data provided clues into variations driving the attribution modelling results. Results from this pillared approach provided first attribution estimates for Salmonella in Minnesota and offer an understanding of current data gaps as well as key pathogen population features, such as serotype frequency, similarity and diversity across the sources. Results here will be used to inform policy and management strategies ultimately intended to prevent and control Salmonella infection in the state.
肠炎沙门氏菌因其与食源性疾病的广泛关联而成为全球关注的健康问题。已开发出贝叶斯模型，将人类沙门氏菌病负担归因于特定来源，其最终目标是确定干预策略的优先级。来源归因模型的重要考虑因素包括评估输入数据的质量、评估归因结果是否在逻辑上反映数据趋势以及识别数据中的模式，这些模式可能解释不同来源对疾病负担的详细贡献。在此，对来自明尼苏达州的12000多株源自人类、牛、猪、鸡和火鸡的非伤寒沙门氏菌分离株进行了分析。一种经过改进的贝叶斯来源归因模型（可在一个专用R包中获取），考虑了未采样的感染源，将4672例人类病例归因于此处评估的来源。大多数（60%）病例归因于鸡肉，尽管在研究期后半段，归因于一个未采样来源的病例出现了激增。使用分子流行病学分析方法来补充风险建模，并开发了一个可视化归因应用程序，以促进对这里评估的大型多年数据集的数据探索和理解。观察到大量的源内多样性和源间低相似性，数据的可视化探索为驱动归因建模结果的变异提供了线索。这种支柱性方法的结果提供了明尼苏达州沙门氏菌的首次归因估计，并有助于了解当前的数据差距以及关键病原体群体特征，如各来源的血清型频率、相似性和多样性。这里的结果将用于为最终旨在预防和控制该州沙门氏菌感染的政策和管理策略提供信息。
