Prediction Model for the Risk of HIV Infection among MSM in China: Validation and Stability

Yinqiao Dong, Shangbin Liu, Danni Xia, Chen Xu, Xiaoyue Yu, Hui Chen, Rongxi Wang, Yujie Liu, Jingwen Dong, Fan Hu, Yong Cai and Ying Wang
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Yinqiao Dong: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Shangbin Liu: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Danni Xia: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Chen Xu: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Xiaoyue Yu: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Hui Chen: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Rongxi Wang: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Yujie Liu: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Jingwen Dong: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Fan Hu: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Yong Cai: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Ying Wang: School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

IJERPH, 2022, vol. 19, issue 2, 1-14

Abstract: The impact of psychosocial factors on increasing the risk of HIV infection among men who have sex with men (MSM) has attracted increasing attention. We aimed to develop and validate an integrated prediction model, especially incorporating emerging psychosocial variables, for predicting the risk of HIV infection among MSM. We surveyed and collected sociodemographic, psychosocial, and behavioral information from 547 MSM in China. The participants were split into a training set and a testing set in a 3:1 theoretical ratio. The prediction model was constructed by introducing the important variables selected with the least absolute shrinkage and selection operator (LASSO) regression, applying multivariate logistic regression, and visually assessing the risk of HIV infection through the nomogram. Receiver operating characteristic curves (ROC), Kolmogorov–Smirnov test, calibration plots, Hosmer–Lemeshow test and population stability index (PSI) were performed to test validity and stability of the model. Four of the 15 selected variables—unprotected anal intercourse, multiple sexual partners, involuntary subordination and drug use before sex—were included in the prediction model. The results indicated that the comprehensive prediction model we developed had relatively good predictive performance and stability in identifying MSM at high-risk for HIV infection, thus providing targeted interventions for high-risk MSM.

Keywords: men who have sex with men; nomogram; machine learning; HIV infection; model validation; psychosocial factors; involuntary subordination (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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