The Characteristics and Risk Factors of Web-Based Sexual Behaviors Among Men Who Have Sex With Men in Eastern China: Cross-sectional Study

Chen, Lin; Chen, Wanjun; Jiang, Tingting; Ni, Zhikan; Ma, Qiaoqin

doi:10.2196/25360

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…First, although the questionnaire information was collected through individual interviews between survey subjects and health professionals, some of this occurred in the past, which is subjected to the recall bias. Moreover, the questionnaire needs to be further supplemented due to the absence of some behavioral characteristics (e.g., the number of sexual partners, sex role of accessor/recipient) ( 38 ). Second, we only employed the three most commonly-used machine learning algorithms for classification results prediction, so other useful models and methods can be explored in future research, including natural language processing in unstructured data ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Jiang

et al. 2022

Front. Public Health

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BackgroundContinuously growing of HIV incidence among men who have sex with men (MSM), as well as the low rate of HIV testing of MSM in China, demonstrates a need for innovative strategies to improve the implementation of HIV prevention. The use of machine learning algorithms is an increasing tendency in disease diagnosis prediction. We aimed to develop and validate machine learning models in predicting HIV infection among MSM that can identify individuals at increased risk of HIV acquisition for transmission-reduction interventions.MethodsWe extracted data from MSM sentinel surveillance in Zhejiang province from 2018 to 2020. Univariate logistic regression was used to select significant variables in 2018–2019 data (P < 0.05). After data processing and feature selection, we divided the model development data into two groups by stratified random sampling: training data (70%) and testing data (30%). The Synthetic Minority Oversampling Technique (SMOTE) was applied to solve the problem of unbalanced data. The evaluation metrics of model performance were comprised of accuracy, precision, recall, F-measure, and the area under the receiver operating characteristic curve (AUC). Then, we explored three commonly-used machine learning algorithms to compare with logistic regression (LR), including decision tree (DT), support vector machines (SVM), and random forest (RF). Finally, the four models were validated prospectively with 2020 data from Zhejiang province.ResultsA total of 6,346 MSM were included in model development data, 372 of whom were diagnosed with HIV. In feature selection, 12 variables were selected as model predicting indicators. Compared with LR, the algorithms of DT, SVM, and RF improved the classification prediction performance in SMOTE-processed data, with the AUC of 0.778, 0.856, 0.887, and 0.942, respectively. RF was the best-performing algorithm (accuracy = 0.871, precision = 0.960, recall = 0.775, F-measure = 0.858, and AUC = 0.942). And the RF model still performed well on prospective validation (AUC = 0.846).ConclusionMachine learning models are substantially better than conventional LR model and RF should be considered in prediction tools of HIV infection in Chinese MSM. Further studies are needed to optimize and promote these algorithms and evaluate their impact on HIV prevention of MSM.

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Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Jiang

et al. 2022

Front. Public Health

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Understanding Motivations for Dating App Use Among Gay and Bisexual Men: Validation, Latent Profile Analysis, and Differences in Health Outcomes

He,

Bu,

et al. 2023

Arch Sex Behav

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Post-lockdown Rebounding High-risk Behaviors and HIV Testing Among MSM in China in the Era of the COVID-19 Pandemic

Yang

Qian

et al. 2022

CHR

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Background: Lockdown measures for controlling COVID-19 epidemic were enforced during January and May in 2020 in China. Previous studies showed a decrease in HIV high-risk behaviors (HRBs) and update of testing during lockdown, but little is known about these behaviors during post-lockdown period. Objective: We conducted both quantitative and qualitative assessments of HIV related behaviors among MSM during lockdown and post-lockdown periods in Changsha, south central China. Methods: Face-to-face structured interviews with open-ended questions were conducted using the Timeline Follow back (TLFB) method for collecting retrospective data on frequencies of HRBs and testing. McNemar’s Chi-square test and Wilcoxon signed-rank test were used to compare frequencies of behaviors between lockdown (January-May 2020) and post-lockdown periods (June-October 2020). Content analysis was used to analyze qualitative data on the reasons of rebounding HRBs and testing. Results: Of 159 MSM participants, 64% had at least one HRBs during post-lockdown period. Men had increased condomless sex (from 24% to 35%), multiple partners (23% to 35%), and substance abuse (16% to 27%) between two study periods due to the negative emotions and increased use of social networks during the lockdown. HIV testing frequency also increased from 37% to 66% due to resuming of routine testing service in the community-based organizations and increase in HRBs among MSM during post-lockdown. Conclusions: After lifting the lockdown measures, MSM had rebounding HRBs and uptake of testing. Effective preventive measures and healthcare services should be available to MSM after the lockdown measures are lifted.

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The Characteristics and Risk Factors of Web-Based Sexual Behaviors Among Men Who Have Sex With Men in Eastern China: Cross-sectional Study

Cited by 6 publications

References 27 publications

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation

Understanding Motivations for Dating App Use Among Gay and Bisexual Men: Validation, Latent Profile Analysis, and Differences in Health Outcomes

Post-lockdown Rebounding High-risk Behaviors and HIV Testing Among MSM in China in the Era of the COVID-19 Pandemic

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