Kapur Abhinav, Schneider John A, Heard Daniel, Mukherjee Sayan, Schumm Phil, Oruganti Ganesh, Laumann Edward O
Pritzker School of Medicine, University of Chicago, Chicago, Illinois, United States of America.
Department of Medicine, University of Chicago, Chicago, Illinois, United States of America; Department of Health Studies, University of Chicago, Illinois, Chicago, United States of America.
PLoS One. 2014 Jul 3;9(7):e101416. doi: 10.1371/journal.pone.0101416. eCollection 2014.
Improve the ability to infer sex behaviors more accurately using network data.
A hybrid network analytic approach was utilized to integrate: (1) the plurality of reports from others tied to individual(s) of interest; and (2) structural features of the network generated from those ties. Network data was generated from digitally extracted cell-phone contact lists of a purposeful sample of 241 high-risk men in India. These data were integrated with interview responses to describe the corresponding individuals in the contact lists and the ties between them. HIV serostatus was collected for each respondent and served as an internal validation of the model's predictions of sex behavior.
We found that network-based model predictions of sex behavior and self-reported sex behavior had limited correlation (54% agreement). Additionally, when respondent sex behaviors were re-classified to network model predictions from self-reported data, there was a 30.7% decrease in HIV seroprevalence among groups of men with lower risk behavior, which is consistent with HIV transmission biology.
Combining the relative completeness and objectivity of digital network data with the substantive details of classical interview and HIV biomarker data permitted new analyses and insights into the accuracy of self-reported sex behavior.
提高利用网络数据更准确推断性行为的能力。
采用一种混合网络分析方法来整合：（1）与感兴趣个体相关的来自他人的多个报告；以及（2）由这些关系产生的网络的结构特征。网络数据来自对印度241名高危男性的有目的样本的数字提取的手机联系人列表。这些数据与访谈回复相结合，以描述联系人列表中的相应个体以及他们之间的关系。为每位受访者收集了HIV血清学状态，并用作模型对性行为预测的内部验证。
我们发现基于网络的性行为模型预测与自我报告的性行为之间的相关性有限（一致性为54%）。此外，当将受访者的性行为从自我报告数据重新分类为网络模型预测时，低风险行为男性群体中的HIV血清阳性率下降了30.7%，这与HIV传播生物学一致。
将数字网络数据的相对完整性和客观性与经典访谈的实质性细节以及HIV生物标志物数据相结合，使得对自我报告性行为的准确性有了新的分析和见解。
