背景:结核病被认为是主要的健康问题之一,尤其是在发展中国家。这项研究旨在可视化,统计模型,并描述加权网络,以调查与结核病相关的社会接触强度。
方法:在本病例对照研究中,我们应用加权网络分析来评估在商店中花费的人员时间网络,工作场所,餐馆,清真寺,警察基地,homes,医院,学院,理发师,学校,联系家园,健康中心,电影院,公园,和市场。将基于拓扑重叠矩阵中的变量之间的相似性来确定模块。考虑到每个变量和模块特征值之间的关联,将找到最重要的变量。
结果:结果显示了提取的位置模块,该模块基于每个地点的连接时间以及人员时间。TB和绿松石之间的相关性(p值),蓝色,棕色模块为0.058(0.351),0.004(0.943),和0.117(0.039),分别。棕色模块是最重要的一个,证明了家庭之间的重要联系,联系家园,健康中心,和医院。因此,发现四个地点的人-次与结核病的发生之间存在关联.
结论:这项研究的发现表明,结核病感染的大多数传播发生在家庭中,联系家园,健康中心,和医院。这些地方评估可以识别有更多接触和需要筛查的人,如此关键,导致识别更多的活动性结核病患者。
BACKGROUND: Tuberculosis is known as one of the principal health problems, especially in developing countries. This study aimed to visualize, statistically model, and describe the weighted networks to investigate the intensity of social contacts associated with tuberculosis.
METHODS: In this case-control study, we applied weighted network analysis to assess the network of person-time spent in stores, workplaces, restaurants, mosques, Police bases, homes, hospitals, colleges, hairdressers, schools, contact homes, health centers, cinemas, parks, and markets. Modules will be determined based on the similarities between the variables in a topology overlap matrix. The most important variables will be found considering the association between each variable and module eigenvalues.
RESULTS: The result shows the extracted modules of locations based on the connectivity followed by the person-time at each place. The correlation (p-value) between TB and the turquoise, blue, and brown modules was 0.058 (0.351), 0.004 (0.943), and 0.117 (0.039), respectively. The brown module is the most important one, demonstrating a significant connection between homes, contact homes, health centers, and hospitals. Therefore, an association was found between person-time in four places and the occurrence of TB.
CONCLUSIONS: The finding of this study showed that most transmission of tuberculosis infection occurs in homes, contact homes, health centers, and hospitals. These place evaluations allow the identification of people with more contact and in need of screening, so critically leading to the identification of more patients with active TB.