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探索与预测四川广元地区HIV-1持续传播的驱动因素

Exploring and Predicting the Drivers of Ongoing HIV-1 Transmission in Guangyuan, Sichuan.

作者信息

Zhang Yan, Jiang Haolin, Xiang Wenkai, Zhu Jun, Hou Xueqin, Liang Shu, Yuan Dan, Zhou Chang, Su Ling

机构信息

Center for AIDS/STD Control and Prevention, Sichuan Center for Disease Control and Prevention, Chengdu, People's Republic of China.

Department of Epidemiology and Statistics, Chengdu Medical College, Chengdu, People's Republic of China.

出版信息

Infect Drug Resist. 2023 Dec 6;16:7467-7484. doi: 10.2147/IDR.S421763. eCollection 2023.

Abstract

PURPOSE

Guangyuan was selected as the first pilot city of molecular transmission network in Sichuan Province to implement dynamic monitoring. This study aim to insight the characteristics of HIV-1 molecular epidemiology and explore the influencing factors of transmission dynamics. Furthermore, it predict the driving factors of network expansion by established a transmission risk prediction model.

PATIENTS AND METHODS

A longitudinal cohort study was conducted to obtain a total of 1434 plasma samples from newly diagnosed HIV-infected patients from 2010 to June 2022. Phylogenetic relationship and cluster analysis were performed using HIV-1 polymerase () gene sequences to study the risk factors of clustering. We applied Logistic ML algorithms to establish a transmission risk prediction model, and model performance was checked using 10-fold cross-validation in the training set and receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 1360 sequences linked demographics obtained in this study cover approximately 94.8% of newly notified infections from 2010 to June 2022. The major epidemic genotypes were CRF07_BC, CRF01_AE, CRF08_BC and B subtypes, accounting for 93.82% of all. The differences of some clinical and demographic factors (eg, age, marital status) were statistically significant (<0.05). We identified 136 clusters containing 654 HIV-1 sequences and observed that some characteristics (eg, over 50 years, married) were more likely to associated to the clusters (<0.05). The predictive model showed excellent predictive ability to forecast cluster growth.

CONCLUSION

The epidemic genotypes were relatively complex and diverse in Guangyuan. There was a potential transmission association caused widely spread in local area after the new strains entering. The transmission risk prediction model showed excellent predictive ability to forecast cluster growth which can predict the risk factors causing clusters expansion and provide a guidance for precise intervention strategies.

摘要

目的

广元市被选为四川省首个分子传播网络动态监测试点城市。本研究旨在深入了解HIV-1分子流行病学特征,探索传播动态的影响因素。此外,通过建立传播风险预测模型来预测网络扩展的驱动因素。

患者与方法

开展一项纵向队列研究,共收集了2010年至2022年6月新诊断的HIV感染患者的1434份血浆样本。利用HIV-1聚合酶()基因序列进行系统发育关系和聚类分析,以研究聚类的危险因素。我们应用逻辑ML算法建立传播风险预测模型,并在训练集中使用10倍交叉验证和受试者工作特征(ROC)曲线分析来检验模型性能。

结果

本研究中获得的1360个与人口统计学相关的序列涵盖了2010年至2022年6月新报告感染病例的约94.8%。主要流行基因型为CRF07_BC、CRF01_AE、CRF08_BC和B亚型,占全部的93.82%。一些临床和人口统计学因素(如年龄、婚姻状况)的差异具有统计学意义(<0.05)。我们识别出136个包含654个HIV-1序列的聚类,并观察到某些特征(如50岁以上、已婚)更有可能与聚类相关(<0.05)。预测模型对聚类增长显示出优异的预测能力。

结论

广元市的流行基因型相对复杂多样。新毒株进入后在当地引起了广泛传播的潜在传播关联。传播风险预测模型对聚类增长显示出优异的预测能力,可预测导致聚类扩展的危险因素,并为精准干预策略提供指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ff8/10710947/3ee46f379af0/IDR-16-7467-g0001.jpg

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