Citation

Yin Y, Workman TE, Blosnich JR, Brandt CA, Skanderson M, Shao Y, Goulet JL, Zeng-Treitler Q. Int. J. Public Health 2024; 69: e1606855.

Copyright

(Copyright © 2024, Holtzbrinck Springer Nature Publishing Group)

DOI

10.3389/ijph.2024.1606855

PMID

38770181

PMCID

PMC11103011

Abstract

OBJECTIVES: Suicide risk is elevated in lesbian, gay, bisexual, and transgender (LGBT) individuals. Limited data on LGBT status in healthcare systems hinder our understanding of this risk. This study used natural language processing to extract LGBT status and a deep neural network (DNN) to examine suicidal death risk factors among US Veterans.

METHODS: Data on 8.8 million veterans with visits between 2010 and 2017 was used. A case-control study was performed, and suicide death risk was analyzed by a DNN. Feature impacts and interactions on the outcome were evaluated.

RESULTS: The crude suicide mortality rate was higher in LGBT patients. However, after adjusting for over 200 risk and protective factors, known LGBT status was associated with reduced risk compared to LGBT-Unknown status. Among LGBT patients, black, female, married, and older Veterans have a higher risk, while Veterans of various religions have a lower risk.

CONCLUSION: Our results suggest that disclosed LGBT status is not directly associated with an increase suicide death risk, however, other factors (e.g., depression and anxiety caused by stigma) are associated with suicide death risks.

Language: en

Keywords

*Artificial Intelligence; *Sexual and Gender Minorities/statistics & numerical data/psychology; *Suicide/statistics & numerical data; *Veterans/psychology/statistics & numerical data; Adult; Aged; Case-Control Studies; deep learning; explainable artificial intelligence; Female; Humans; Male; Middle Aged; Natural Language Processing; risk factors; Risk Factors; sexual and gender minority; suicide mortality; United States/epidemiology