Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial

Analyzing the impact of unemployment on mental health among Chinese university graduates: a study of emotional and linguistic patterns on Weibo

Purpose

This study explores the intricate relationship between unemployment rates and emotional responses among Chinese university graduates, analyzing how these factors correlate with specific linguistic features on the popular social media platform Sina Weibo. The goal is to uncover patterns that elucidate the psychological and emotional dimensions of unemployment challenges among this demographic.

Methods

The analysis utilized a dataset of 30,540 Sina Weibo posts containing specific keywords related to unemployment and anxiety, collected from January 2019 to June 2023. The posts were pre-processed to eliminate noise and refine the data quality. Linear regression and textual analyses were employed to identify correlations between unemployment rates for individuals aged 16-24 and the linguistic characteristics of the posts.

Results

The study found significant fluctuations in urban youth unemployment rates, peaking at 21.3% in June 2023. A corresponding increase in anxiety-related expressions was noted in the social media posts, with peak expressions aligning with high unemployment rates. Linguistic analysis revealed that the category of "Affect" showed a strong positive correlation with unemployment rates, indicating increased emotional expression alongside rising unemployment. Other categories such as "Negative emotion" and "Sadness" also showed significant correlations, highlighting a robust relationship between economic challenges and emotional distress.

Conclusion

The findings underscore the profound impact of unemployment on the emotional well-being of university students, suggesting that economic hardships are closely linked to psychological stress and heightened negative emotions. This study contributes to a holistic understanding of the socio-economic challenges faced by young adults, advocating for comprehensive support systems that address both the economic and psychological facets of unemployment.

A Machine Learning Model for Identifying Sexual Health Influencers to Promote the Secondary Distribution of HIV Self-Testing Among Gay, Bisexual, and Other Men Who Have Sex With Men in China: Quasi-Experimental Study

BACKGROUND

Sexual health influencers (SHIs) are individuals actively sharing sexual health information with their peers, and they play an important role in promoting HIV care services, including the secondary distribution of HIV self-testing (SD-HIVST). Previous studies used a 6-item empirical leadership scale to identify SHIs. However, this approach may be biased as it does not consider individuals' social networks.

OBJECTIVE

This study used a quasi-experimental study design to evaluate how well a newly developed machine learning (ML) model identifies SHIs in promoting SD-HIVST compared to SHIs identified by a scale whose validity had been tested before.

METHODS

We recruited participants from BlueD, the largest social networking app for gay men in China. Based on their responses to the baseline survey, the ML model and scale were used to identify SHIs, respectively. This study consisted of 2 rounds, differing in the upper limit of the number of HIVST kits and peer-referral links that SHIs could order and distribute (first round ≤5 and second round ≤10). Consented SHIs could order multiple HIV self-testing (HIVST) kits and generate personalized peer-referral links through a web-based platform managed by a partnered gay-friendly community-based organization. SHIs were encouraged to share additional kits and peer-referral links with their social contacts (defined as "alters"). SHIs would receive US $3 incentives when their corresponding alters uploaded valid photographic testing results to the same platform. Our primary outcomes included (1) the number of alters who conducted HIVST in each group and (2) the number of newly tested alters who conducted HIVST in each. We used negative binomial regression to examine group differences during the first round (February-June 2021), the second round (June-November 2021), and the combined first and second rounds, respectively.

RESULTS

In January 2021, a total of 1828 men who have sex with men (MSM) completed the survey. Overall, 393 SHIs (scale=195 and ML model=198) agreed to participate in SD-HIVST. Among them, 229 SHIs (scale=116 and ML model=113) ordered HIVST on the web. Compared with the scale group, SHIs in the ML model group motivated more alters to conduct HIVST (mean difference [MD] 0.88, 95% CI 0.02-2.22; adjusted incidence risk ratio [aIRR] 1.77, 95% CI 1.07-2.95) when we combined the first and second rounds. Although the mean number of newly tested alters was slightly higher in the ML model group than in the scale group, the group difference was insignificant (MD 0.35, 95% CI -0.17 to -0.99; aIRR 1.49, 95% CI 0.74-3.02).

CONCLUSIONS

Among Chinese MSM, SHIs identified by the ML model can motivate more individuals to conduct HIVST than those identified by the scale. Future research can focus on how to adapt the ML model to encourage newly tested individuals to conduct HIVST.

TRIAL REGISTRATION

Chinese Clinical Trials Registry ChiCTR2000039632; https://www.chictr.org.cn/showprojEN.html?proj=63068.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s12889-021-11817-2.

Homoprejudiced Violence Experiences and High-Risk Sexual Behaviors among Chinese Men Who Have Sex with Men: Depression Severity and Recreational Drug Usage as Potential Mediators

Homoprejudiced violence is a type of aggression against an individual or a community based on their actual or perceived sexual orientation. It may be linked to risks of acquiring HIV/STI via psychosocial variables. This study explored the association between homoprejudiced violence experiences and high-risk sexual behaviors, and potential psychosocial mediators. Using cross-sectional survey data collected in China through Blued among men who have sex with men (MSM) in January 2021, this study conducted multiple mediation analyses. Standard instruments were used to collect data on depressive symptoms in the last two weeks (PHQ-9), recreational drug usage in the last three months, and ever experiencing homoprejudiced violence (12-item survey instrument). Dependent variables were having condomless anal sex and having three or more sexual partners in the last three months. Among 1828 MSM, nearly half (847, 46%) had experienced homoprejudiced violence. Twenty-three percent (427) reached a score that suggested moderate or severe depression and 35% (644) had used recreational drugs. In the last three months, 40% (731) had condomless anal sex and 34% (626) had three or more sexual partners. The indirect mediational coefficients through depression on condomless anal sex and multiple sexual partners were 0.04 (95% CI: [0.02, 0.07]) and 0.02 (95% CI: [0.001, 0.05]), respectively. The indirect mediational coefficient of homoprejudiced violence experience on multiple sexual partners through recreational drug use was 0.05 (95% CI: [0.03, 0.08]). These findings suggest that more comprehensive interventions are needed to address the syndemic of homoprejudiced violence, mental health issues, and HIV/STI-related risks.

Factors Associated with the First-time HIV Testing Among Chinese men who have sex with men Who Received HIV Self-tests from Partners or Friends

Secondary distribution of HIV self-testing (HIVST) among individual social networks was an effective approach to expanding HIV testing among men who have sex with men (MSM). However, understanding the factors associated with first-time HIV testing behaviors in the secondary distribution of HIVST programs is limited. Hence, this study aims to identify factors related to first-time testers in the secondary distribution of HIVST. Participants were recruited from five provinces in southern China through Blued, a geo-social gay networking app in China from January 2021 to December 2021. Eligible consented participants (referred to as "seeds") finished a baseline survey and then applied for up to five HIVST kits. They were encouraged to distribute HIVST kits to other MSM (referred to as "alters") and alters were encouraged to scan a QR code to return their photographed testing results. All alters were invited to finish an online survey. In total, 229 seeds reached 292 alters, among whom 126 (43.2%) were first-time testers whereas 166 (56.8%) were non-first-time testers. Importantly, our results demonstrated that the first-time HIV testers were more likely to self-report as heterosexual (aOR = 4.88, 95% CI 1.01-23.61), disclose sexual orientation and/or SSB (aOR = 1.73, 95% CI 1.01-2.96), and receive HIVST knowledge from the seeds (aOR = 3.25, 95% CI 1.02-10.34). However, those who had sex with male partners in the last three months were less likely to be first-time testers (aOR = 0.43, 95% CI 0.23-0.82). Practical implications and limitations were also discussed to improve future HIV prevention programs.

Development of an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care: A study protocol

Background

Cardiovascular disease (CVD) and cancer are the first and second causes of death in over 130 countries across the world. They are also among the top three causes in almost 180 countries worldwide. Cardiovascular complications are often noticed in cancer patients, with nearly 20% exhibiting cardiovascular comorbidities. Physical exercise may be helpful for cancer survivors and people living with cancer (PLWC), as it prevents relapses, CVD, and cardiotoxicity. Therefore, it is beneficial to recommend exercise as part of cardio-oncology preventive care.

Objective

With the progress of deep learning algorithms and the improvement of big data processing techniques, artificial intelligence (AI) has gradually become popular in the fields of medicine and healthcare. In the context of the shortage of medical resources in China, it is of great significance to adopt AI and machine learning methods for prescription recommendations. This study aims to develop an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care, and this paper presents the study protocol.

Methods

This will be a retrospective machine learning modeling cohort study with interventional methods (i.e., exercise prescription). We will recruit PLWC participants at baseline (from 1 January 2025 to 31 December 2026) and follow up over several years (from 1 January 2027 to 31 December 2028). Specifically, participants will be eligible if they are (1) PLWC in Stage I or cancer survivors from Stage I; (2) aged between 18 and 55 years; (3) interested in physical exercise for rehabilitation; (4) willing to wear smart sensors/watches; (5) assessed by doctors as suitable for exercise interventions. At baseline, clinical exercise physiologist certificated by the joint training program (from 1 January 2023 to 31 December 2024) of American College of Sports Medicine and Chinese Association of Sports Medicine will recommend exercise prescription to each participant. During the follow-up, effective exercise prescription will be determined by assessing the CVD status of the participants.

Expected outcomes

This study aims to develop not only an interpretable machine learning model to recommend exercise prescription but also an intelligent system of exercise prescription for precision cardio-oncology preventive care.

Ethics

This study is approved by Human Experimental Ethics Inspection of Guangzhou Sport University.

Clinical trial registration

http://www.chictr.org.cn, identifier ChiCTR2300077887.

Identification of Key Influencers for Secondary Distribution of HIV Self-Testing Kits Among Chinese Men Who Have Sex With Men: Development of an Ensemble Machine Learning Approach

BACKGROUND

HIV self-testing (HIVST) has been rapidly scaled up and additional strategies further expand testing uptake. Secondary distribution involves people (defined as "indexes") applying for multiple kits and subsequently sharing them with people (defined as "alters") in their social networks. However, identifying key influencers is difficult.

OBJECTIVE

This study aimed to develop an innovative ensemble machine learning approach to identify key influencers among Chinese men who have sex with men (MSM) for secondary distribution of HIVST kits.

METHODS

We defined three types of key influencers: (1) key distributors who can distribute more kits, (2) key promoters who can contribute to finding first-time testing alters, and (3) key detectors who can help to find positive alters. Four machine learning models (logistic regression, support vector machine, decision tree, and random forest) were trained to identify key influencers. An ensemble learning algorithm was adopted to combine these 4 models. For comparison with our machine learning models, self-evaluated leadership scales were used as the human identification approach. Four metrics for performance evaluation, including accuracy, precision, recall, and F1-score, were used to evaluate the machine learning models and the human identification approach. Simulation experiments were carried out to validate our approach.

RESULTS

We included 309 indexes (our sample size) who were eligible and applied for multiple test kits; they distributed these kits to 269 alters. We compared the performance of the machine learning classification and ensemble learning models with that of the human identification approach based on leadership self-evaluated scales in terms of the 2 nearest cutoffs. Our approach outperformed human identification (based on the cutoff of the self-reported scales), exceeding by an average accuracy of 11.0%, could distribute 18.2% (95% CI 9.9%-26.5%) more kits, and find 13.6% (95% CI 1.9%-25.3%) more first-time testing alters and 12.0% (95% CI -14.7% to 38.7%) more positive-testing alters. Our approach could also increase the simulated intervention's efficiency by 17.7% (95% CI -3.5% to 38.8%) compared to that of human identification.

CONCLUSIONS

We built machine learning models to identify key influencers among Chinese MSM who were more likely to engage in secondary distribution of HIVST kits.

TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR) ChiCTR1900025433; https://www.chictr.org.cn/showproj.html?proj=42001.

Highlighting and addressing barriers to widespread adaptation of HIV self-testing in the United States

INTRODUCTION

HIV self-testing (HIVST), whereby an individual performs and interprets their own rapid screening test at home, is another tool to increase the proportion of at-risk individuals who know their status. Globally, HIVST has rapidly been adopted through global partnerships to ensure equitable access to tests in low- and middle-income countries (LMIC).

AREA COVERED

This review discusses the regulatory burdens of HIV self-testing within the United States while examining the use of HIV self-tests on a global scale. While the United States only has one approved HIV self-test, numerous tests have been prequalified by the WHO.

EXPERT OPINION

Despite the US Food and Drug Administration (FDA) clearance of the first and only self-test in 2012, there have been no other tests that have undergone FDA consideration due to regulatory barriers. This, in turn, has stifled market competition. Despite existing evidence that such programs are an innovative approach to testing hesitant or hard-to-reach populations, high individual test cost and bulky packaging make large-scale, mail-out, and HIV self-testing programs expensive. COVID-19 pandemic has accelerated the public demand for self-testing - HIV self-test programs should capitalize on this to increase the proportion of at-risk people who know their status and are linked to care to contribute to ending the HIV epidemic.

Optimal resource allocation in HIV self-testing secondary distribution among Chinese MSM: data-driven integer programming models

Human immunodeficiency virus self-testing (HIVST) is an innovative and effective strategy important to the expansion of HIV testing coverage. Several innovative implementations of HIVST have been developed and piloted among some HIV high-risk populations like men who have sex with men (MSM) to meet the global testing target. One innovative strategy is the secondary distribution of HIVST, in which individuals (defined as indexes) were given multiple testing kits for both self-use (i.e.self-testing) and distribution to other people in their MSM social network (defined as alters). Studies about secondary HIVST distribution have mainly concentrated on developing new intervention approaches to further increase the effectiveness of this relatively new strategy from the perspective of traditional public health discipline. There are many points of HIVST secondary distribution in which mathematical modelling can play an important role. In this study, we considered secondary HIVST kits distribution in a resource-constrained situation and proposed two data-driven integer linear programming models to maximize the overall economic benefits of secondary HIVST kits distribution based on our present implementation data from Chinese MSM. The objective function took expansion of normal alters and detection of positive and newly-tested 'alters' into account. Based on solutions from solvers, we developed greedy algorithms to find final solutions for our linear programming models. Results showed that our proposed data-driven approach could improve the total health economic benefit of HIVST secondary distribution. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.