Inferring Opinions and Behavioral Characteristics of Gay Men with Large Scale Multilingual Text from Blued

Gay Dating Apps Usage, Body Dissatisfaction, and Disordered Eating in Chinese Young Gay Men

There is a paucity of research on the links between gay dating apps usage and body dissatisfaction and disordered eating, especially for muscularity dissatisfaction and muscularity-oriented disordered eating in non-Western contexts. Thus, this study aimed to evaluate the associations between gay dating apps usage, body image inflexibility, body dissatisfaction, and disordered eating with the inclusion of both body fat and muscularity dissatisfaction and both thinness-and muscularity-oriented disordered eating in a sample of Chinese young gay men. A total of 247 Chinese young gay men (mean age: 21.92 ± 2.74 years) were recruited. Gay dating apps usage, body fat dissatisfaction, muscularity dissatisfaction, body image inflexibility, and thinness-and muscularity-oriented disordered eating were evaluated. Pearson correlation and mediation analyses were conducted. Gay dating apps usage was positively correlated with body fat dissatisfaction (r = .20, p = .002), muscularity dissatisfaction (r = .17, p = .006), thinness-oriented disordered eating (r = .21, p < .001), and muscularity-oriented disordered eating (r = .29, p < .001), and these relationships could be mediated by body image inflexibility. Findings support technologically-informed prevention and intervention strategies for eating and body image disturbances in gay men in the Chinese context.

HIV-related stigma, depression and suicidal ideation among HIV-positive MSM in China: a moderated mediation model

BACKGROUND

As the HIV epidemic among MSM in China continues, Chinese men who have sex with men (MSM) face various mental health difficulties, including suicide ideation, depression, and stigma. The current study aims to assess the mechanisms between HIV-related stigma, depression, and suicidal ideation among MSM in China.

METHODS

This national cross-sectional study was completed on the geosocial networking application (GSN) app, Blued, from December 2020 to March 2021. We used the HIV Stigma Scale and the Center for Epidemiologic Studies Depression Scale (CES-D10) to measure HIV stigma and depression, respectively. Suicidal ideation was measured by the suicidal ideation-related item. Descriptive analyses, logistic regression, and structural equation modeling (SEM) were used for data analysis.

RESULTS

A total of 244 HIV-positive MSM were included in the analysis. The mediation model revealed that the direct pathway of perceived HIV-related stigma on suicidal ideation was significant (standardized pathway coefficient = 0.07), and the indirect pathway of perceived HIV-related stigma on suicidal ideation via depression was also significant (standardized pathway coefficient = 0.04). There was a partial mediating effect of depression in the association between perceived HIV-related stigma and suicidal ideation.

CONCLUSIONS

Our study found that both perceived HIV-related stigma and depression were associated with suicidal ideation among HIV-positive MSM in China, and that depression could serve as a mediator between HIV-related stigma and suicidal ideation. Targeted interventions regarding HIV-related stigma and depression should be taken into account to reduce suicidal ideation among HIV-positive MSM in China.

Risky sexual behaviour and HIV testing uptake among male college students: a cross-sectional study in China

OBJECTIVE

To understand the sexual behaviours and HIV testing uptake of sexually experienced male college students in China.

DESIGN

A cross-sectional study was conducted between September and November of 2020 among male college students.

SETTING

Hangzhou, China.

PARTICIPANTS

Male students who had sexual experience in the previous year were investigated.

OUTCOME MEASURES

Sexual risk behaviour was defined as having multiple sexual partners or having unprotected sex.

RESULTS

More than half of the sexually experienced male students (556, 53.2%) had their first sexual intercourse under the age of 18 years old. Among participants, 32.82% (343/1045) had causal sex in the last 6 months; 4.21% (44/1045) had paid sex; 37.32% (390/1045) had sex with other men and had two or more sexual partners in last half year; and 33.33% (130/390) used psychoactive drugs during same-sex intercourse. Only 33.5% (350/1045) of male students had undertaken an HIV test before.

CONCLUSION

Male college students especially men who have sex with men were at risk because they tended to be sexually adventurous, have sex at an early age, have sex with multiple sexual partners and practice unprotected sex. Furthermore, they had a low HIV testing uptake. This highlights the importance of carrying out targeted and timely HIV risk education towards college students.

Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa

INTRODUCTION

High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics.

METHODS

We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies.

RESULTS

Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer's index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be tested to find 55.7% of all males and 50.5% of all females living with HIV.

CONCLUSIONS

We trained a gradient boosting trees algorithm to find 95% of PLHIV with a precision twice higher than with general population testing by using only a limited number of socio-behavioural characteristics. We also successfully identified people at high risk of infection who may be offered pre-exposure prophylaxis or voluntary medical male circumcision. These findings can inform the implementation of new high-yield HIV tests and help develop very precise strategies based on low-resource settings constraints.

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

BACKGROUND

A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods.

OBJECTIVE

This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field.

METHODS

The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings.

RESULTS

We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%).

CONCLUSIONS

ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health.

Location inference for hidden population with online text analysis

Background

Understanding the geographic distribution of hidden population, such as men who have sex with men (MSM), sex workers, or injecting drug users, are of great importance for the adequate deployment of intervention strategies and public health decision making. However, due to the hard-to-access properties, e.g., lack of a sampling frame, sensitivity issue, reporting error, etc., traditional survey methods are largely limited when studying such populations. With data extracted from the very active online community of MSM in China, in this study we adopt and develop location inferring methods to achieve a high-resolution mapping of users in this community at national level.

Methods

We collect a comprehensive dataset from the largest sub-community related to MSM topics in Baidu Tieba, covering 628,360 MSM-related users. Based on users’ publicly available posts, we evaluate and compare the performances of mainstream location inference algorithms on the online locating problem of Chinese MSM population. To improve the inference accuracy, other approaches in natural language processing are introduced into the location extraction, such as context analysis and pattern recognition. In addition, we develop a hybrid voting algorithm (HVA-LI) by allowing different approaches to vote to determine the best inference results, which guarantees a more effective way on location inference for hidden population.

Results

By comparing the performances of popular inference algorithms, we find that the classic gazetteer-based algorithm has achieved better results. And in the HVA-LI algorithms, the hybrid algorithm consisting of the simple gazetteer-based method and named entity recognition (NER) is proven to be the best to deal with inferring users’ locations disclosed in short texts on online communities, improving the inferring accuracy from 50.3 to 71.3% on the MSM-related dataset.

Conclusions

In this study, we have explored the possibility of location inferring by analyzing textual content posted by online users. A more effective hybrid algorithm, i.e., the Gazetteer & NER algorithm is proposed, which is conducive to overcoming the sparse location labeling problem in user profiles, and can be extended to the inference of geo-statistics for other hidden populations.

Network Evolution of a Large Online MSM Dating Community: 2005-2018

Due to multiple sexual partners and low rates of condom use, the HIV infection rate among MSM (men who have sex with men) is much higher than that of the general population. In order to analyze the characteristics of online activities of MSM, and to understand the evolution of their social networks, in this study we collect a comprehensive dataset, covering the period from January 2005 to June 2018, from the largest Chinese online community, Baidu Tieba. We build an online dating network for MSM-related individuals in the gay-bar community, and analyze the network from static and dynamic aspects. It is found that there is a strong homophily regarding the cities where users reside when developing interactions with others, and that most network measurements tend to be stable at the later stages of evolution, while the size of the largest community fluctuates. This is an indication that the network is formed of rapidly flexible interactions which changes quickly. In comparison with studies on heterosexual networks, we find that the MSM dating network shows differences in many aspects, such as the positive degree-degree correlation and high clustering coefficient, suggesting different thinking and measures should be taken in the policy making of public health management towards the MSM population.