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21.05.2018 | Original Paper | Ausgabe 7/2018

AIDS and Behavior 7/2018

Risk Prediction Score for HIV Infection: Development and Internal Validation with Cross-Sectional Data from Men Who Have Sex with Men in China

AIDS and Behavior > Ausgabe 7/2018
Lu Yin, Yuejuan Zhao, Meridith Blevins Peratikos, Liang Song, Xiangjun Zhang, Ruolei Xin, Zheya Sun, Yunan Xu, Li Zhang, Yifei Hu, Chun Hao, Yuhua Ruan, Yiming Shao, Sten H. Vermund, Han-Zhu Qian
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Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s10461-018-2129-y) contains supplementary material, which is available to authorized users.
Lu Yin, Yuejuan Zhao and Meridith Blevins Peratikos These authors contribute equally to this work.


Receptive anal intercourse, multiple partners, condomless sex, sexually transmitted infections (STIs), and drug/alcohol addiction are familiar factors that correlate with increased human immunodeficiency virus (HIV) risk among men who have sex with men (MSM). To improve estimation to HIV acquisition, we created a composite score using questions from routine survey of 3588 MSM in Beijing, China. The HIV prevalence was 13.4%. A risk scoring tool using penalized maximum likelihood multivariable logistic regression modeling was developed, deploying backward step-down variable selection to obtain a reduced-form model. The full penalized model included 19 sexual predictors, while the reduced-form model had 12 predictors. Both models calibrated well; bootstrap-corrected c-indices were 0.70 (full model) and 0.71 (reduced-form model). Non-Beijing residence, short-term living in Beijing, illegal drug use, multiple male sexual partners, receptive anal sex, inconsistent condom use, alcohol consumption before sex, and syphilis infection were the strongest predictors of HIV infection. Discriminating higher-risk MSM for targeted HIV prevention programming using a validated risk score could improve the efficiency of resource deployment for educational and risk reduction programs. A valid risk score can also identify higher risk persons into prevention and vaccine clinical trials, which would improve trial cost-efficiency.

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