Correlates of past year suicidal thoughts among sexual and gender minority young adults: A machine learning analysis

Kundu, Anasua; Fu, Rui; Grace, Daniel; Logie, Carmen H.; Abramovich, Alex; Baskerville, Bruce; Yager, Christina; Schwartz, Robert; Mitsakakis, Nicholas; Planinac, Lynn; Chaiton, Michael

doi:10.1016/j.jpsychires.2022.06.013

Cited by 16 publications

(7 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, Table 1 also shows that Watson et al ( Watson et al, 2022 ) did not examine adverse effects resulting from the socio-economic consequences of NPIs ( Brenner and Bhugra, 2020 , Kundu et al, 2022 ) and vaccine toxicity ( Kostoff et al, 2021 , Seneff and Nigh, 2021 , Walach et al, 2021 , Fraiman et al, 2022 , Yamamoto, 2022 ); these effects may induce secondary deaths that should be co-estimated together with the putatively prevented Covid-19 deaths by NPIs or vaccination campaigns predicted by epidemic forecasting models in order to obtain the “big picture, covering multiple dimensions” ( Ioannidis et al, 2022 ).…”

Section: Reasons For Forecasting Failures – Insights From Critical Re...mentioning

confidence: 99%

SEIR models in the light of Critical Realism – A critique of exaggerated claims about the effectiveness of Covid 19 vaccinations

Klement

Walach

2023

Futures

View full text Add to dashboard Cite

Section: Reasons For Forecasting Failures – Insights From Critical Re...mentioning

confidence: 99%

SEIR models in the light of Critical Realism – A critique of exaggerated claims about the effectiveness of Covid 19 vaccinations

Klement

Walach

2023

Futures

View full text Add to dashboard Cite

“…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.…”

Section: Illustrative Examplementioning

confidence: 99%

“…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 ].…”

Section: Illustrative Examplementioning

confidence: 99%

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

Dharma

Chaiton

2023

IJERPH

Self Cite

View full text Add to dashboard Cite

There is a lack of rigorous methodological development for descriptive epidemiology, where the goal is to describe and identify the most important associations with an outcome given a large set of potential predictors. This has often led to the Table 2 fallacy, where one presents the coefficient estimates for all covariates from a single multivariable regression model, which are often uninterpretable in a descriptive analysis. We argue that machine learning (ML) is a potential solution to this problem. We illustrate the power of ML with an example analysis identifying the most important predictors of alcohol abuse among sexual minority youth. The framework we propose for this analysis is as follows: (1) Identify a few ML methods for the analysis, (2) optimize the parameters using the whole data with a nested cross-validation approach, (3) rank the variables using variable importance scores, (4) present partial dependence plots (PDP) to illustrate the association between the important variables and the outcome, (5) and identify the strength of the interaction terms using the PDPs. We discuss the potential strengths and weaknesses of using ML methods for descriptive analysis and future directions for research. R codes to reproduce these analyses are provided, which we invite other researchers to use.

show abstract

“…Our recent scoping review of machine learning applications in mental health and substance use issues among SGM people found only 4 prediction modelling studies up to 2020 [ 12 ]. We recently updated the review and detected 3 additional studies [ 13 – 15 ] that applied machine learning-based predictive modelling and none of them explored the wanting to seek help behaviours for mental health and substance use issues among this population. Hence, we addressed this research gap by this current study.…”

Section: Introductionmentioning

confidence: 99%

“…Recent data suggests that 84% and 66% of Canadian homeless 2SLGBTQI+ young adults experienced severe anxiety and moderately severe depression respectively, while 57% of these young adults reported problematic substance use during the pandemic [ 20 ]. Moreover, unemployment and urbanicity have been found to increase the risk of suicidal thoughts among SGM youth who experienced mental health challenges or social stigma [ 13 ]. It is important to understand how these interactions of SGM status with different socio-economic identities influence needs of seeking help.…”

Section: Introductionmentioning

confidence: 99%

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

Kundu

Fu²,

Grace³

et al. 2022

PLoS ONE

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Correlates of past year suicidal thoughts among sexual and gender minority young adults: A machine learning analysis

Cited by 16 publications

References 45 publications

SEIR models in the light of Critical Realism – A critique of exaggerated claims about the effectiveness of Covid 19 vaccinations

SEIR models in the light of Critical Realism – A critique of exaggerated claims about the effectiveness of Covid 19 vaccinations

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

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

Contact Info

Product

Resources

About