Correlates of wanting to seek help for mental health and substance use concerns by sexual and gender minority young adults during the COVID-19 pandemic: A machine learning analysis

Abstract

The COVID-19 pandemic has worsened the mental health and substance use challenges among many people who are Two Spirit, lesbian, gay, bisexual, transgender, queer, questioning, and intersex (2SLGBTQI+). We aimed to identify the important correlates and their effects on the predicted likelihood of wanting to seek help among 2SLGBTQI+ young adults for mental health or substance use concerns during the pandemic. A cross-sectional survey was conducted in 2020–2021 among 2SLGBTQI+ young adults aged 16–29 living in two Canadian provinces (Ontario and Quebec). Among 1414 participants, 77% (n = 1089) wanted to seek help for their mental health or substance use concerns during the pandemic, out of these, 69.8% (n = 760) reported delay in accessing care. We built a random forest (RF) model to predict the status of wanting to seek help, which achieved moderately high performance with an area under the receiver operating characteristic curve (AUC) of 0.85. The top 10 correlates of wanting to seek help were worsening mental health, age, stigma and discrimination, and adverse childhood experiences. The interactions of adequate housing with certain sexual orientations, gender identities and mental health challenges were found to increase the likelihood of wanting to seek help. We built another RF model for predicting risk of delay in accessing care among participants who wanted to seek help (n = 1089). The model identified a similar set of top 10 correlates of delay in accessing care but lacked adequate performance (AUC 0.61). These findings can direct future research and targeted prevention measures to reduce health disparities for 2SLGBTQI+ young adults.

A Machine Learning Approach Reveals Distinct Predictors of Vaping Dependence for Adolescent Daily and Non-Daily Vapers in the COVID-19 Era

“…However, there is no data on the particular association between substance abuse and having a questioning sexual orientation. As such, our analysis helped fill this literature gap by suggesting that young people who are in the process of figuring out their sexual orientation may be at exceedingly high risk of using vaping as a coping mechanism, a harmful decision for their health [39,40]. We also reached similar findings among baseline daily vapers as being gender non-conforming was identified to be one of the most important predictors of vaping dependence in this cohort.…”

“…We then generated separate plots for non-daily and daily vapers, displaying the combined variable importance for each category. Following this, partial dependence plots were generated for the top 20 predictors to illustrate their marginal effect on the predicted vaping dependence score [8,[38][39][40]. Analyses were conducted using R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria).…”

Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table

“…Baseline data were collected from November 2020 to January 2021 during the coronavirus disease 2019 (COVID-19) pandemic. Previous analysis of this data and recruitment information can be found elsewhere [ 26 , 27 ]. There were 1414 participants used in this analysis.…”

“…Finally, we explored the potential two-way interactions between the variables. We focused on the interactions between the demographic variables (i.e., age, sexual orientation, gender, education, and income) as well as the interactions between these demographic variables and the top 10 most important predictors identified by each respective ML model [ 26 , 27 ]. Identification of the interactions was performed on the final optimized model through the NCV; the strength of the interactions was determined using Greenwell’s PDP method [ 14 , 38 ].…”