A digital network approach to infer sex behavior in emerging HIV epidemics.
A digital network approach to infer sex behavior in emerging HIV epidemics.
复制标题
DOI：
10.1371/journal.pone.0101416
复制
发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Laumann EO
中科院分区：
文献类型：
--
作者：
Kapur A;Schneider JA;Heard D;Mukherjee S;Schumm P;Oruganti G;Laumann EO
提高使用网络数据更准确地推断性行为的能力。利用混合网络分析方法来整合：(1)与感兴趣的个人(S)有关的其他人的多份报告；(2)由这些联系产生的网络结构特征。网络数据是从数字提取的手机联系人名单中产生的，该名单是对印度241名高危男性进行有目的的抽样。这些数据与面谈答复相结合，以描述联系人名单中的相应个人以及他们之间的联系。收集了每个受访者的HIV血清状态，并作为模型对性行为预测的内部验证。我们发现，基于网络的模型对性行为的预测和自我报告的性行为的相关性有限(54%的一致性)。此外，当从自我报告的数据中将受访者的性行为重新分类为网络模型预测时，在风险行为较低的男性群体中，艾滋病毒血清阳性率下降了30.7%，这与艾滋病毒传播生物学一致。将数字网络数据的相对完整性和客观性与经典访谈和艾滋病毒生物标志物数据的实质性细节结合起来，可以对自我报告的性行为的准确性进行新的分析和洞察。
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.
登录
查看更多内容
影响因子：
3.8
作者：
Dandona, L;Dandona, R;Bertozzi, SM
通讯作者：
Bertozzi, SM
影响因子：
56.3
作者：
Boily, Marie-Claude;Baggaley, Rebecca F.;Wang, Lei;Masse, Benoit;White, Richard G.;Hayes, Richard J.;Alary, Michel
通讯作者：
Alary, Michel
影响因子：
3.1
作者：
KRACKHARDT, D
通讯作者：
KRACKHARDT, D
影响因子：
3.1
作者：
Brewer, DD;Rothenberg, RB;Potterat, JJ
通讯作者：
Potterat, JJ
影响因子：
56.9
作者：
Kossinets, G;Watts, DJ
通讯作者：
Watts, DJ