Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis

Authors

Ying Yin, Washington DC VA Medical Center, United States Department of Veterans Affairs, Washington, DC, United States.
T Elizabeth Workman, Washington DC VA Medical Center, United States Department of Veterans Affairs, Washington, DC, United States.
John R. Blosnich, Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Veterans Health Administration, United States Department of Veterans Affairs, Pittsburgh, PA, United States.
Cynthia A. Brandt, VA Connecticut Healthcare System, Veterans Health Administration, United States Department of Veterans Affairs, West Haven, CT, United States.
Melissa Skanderson, VA Connecticut Healthcare System, Veterans Health Administration, United States Department of Veterans Affairs, West Haven, CT, United States.
Yijun Shao, Washington DC VA Medical Center, United States Department of Veterans Affairs, Washington, DC, United States.
Joseph L. Goulet, Pain, Research, Informatics, Multi-Morbidities, and Education Center, VA Connecticut Healthcare System, West Haven, CT, United States.
Qing Zeng-Treitler, Washington DC VA Medical Center, United States Department of Veterans Affairs, Washington, DC, United States.

Document Type

Journal Article

Publication Date

1-1-2024

Journal

International journal of public health

Volume

69

DOI

10.3389/ijph.2024.1606855

Keywords

deep learning; explainable artificial intelligence; risk factors; sexual and gender minority; suicide mortality

Abstract

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. 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. 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. 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.

APA Citation

Yin, Ying; Workman, T Elizabeth; Blosnich, John R.; Brandt, Cynthia A.; Skanderson, Melissa; Shao, Yijun; Goulet, Joseph L.; and Zeng-Treitler, Qing, "Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis" (2024). GW Authored Works. Paper 4973.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/4973

Department

Clinical Research and Leadership