Identifying correlates of suicide ideation during the COVID-19 pandemic: A cross-sectional analysis of 148 sociodemographic and pandemic-specific factors

被引:7
|
作者
Dolsen, Emily A. [1 ,2 ,3 ,8 ]
Nishimi, Kristen [1 ,2 ,3 ]
LeWinn, Kaja Z. [2 ]
Byers, Amy L. [2 ,4 ,5 ]
Tripp, Paige [1 ,2 ]
Woodward, Eleanor [1 ,2 ]
Khan, Amanda J. [1 ,2 ]
Marx, Brian P. [6 ,7 ]
Borsari, Brian [1 ,2 ]
Jiha, Ahmad [1 ,2 ]
Neylan, Thomas C. [1 ,2 ,3 ]
O'Donovan, Aoife [1 ,2 ,9 ]
机构
[1] San Francisco VA Med Ctr, Mental Hlth Serv, San Francisco, CA USA
[2] Univ Calif San Francisco, Dept Psychiat & Behav Sci, San Francisco, CA USA
[3] San Francisco Vet Affairs Hlth Care Syst, Mental Illness Res Educ & Clin Ctr, San Francisco, CA USA
[4] San Francisco Vet Affairs Hlth Care Syst, Res Serv, San Francisco, CA USA
[5] Univ Calif San Francisco, Dept Med, Div Geriatr, San Francisco, CA USA
[6] VA Boston Healthcare Syst, Natl Ctr PTSD, Boston, MA USA
[7] Boston Univ, Sch Med, Boston, MA USA
[8] 4150 Clement St,Bldg 8, San Francisco, CA 94121 USA
[9] 4150 Clement St,Bldg 16 116C-1, San Francisco, CA 94121 USA
关键词
Machine learning; Suicide; Covid-19; Posttraumatic stress disorder; Depression; MENTAL-HEALTH; UNITED-STATES; RISK-FACTORS; PTSD; US; PREVALENCE; EXPERIENCE; BEHAVIORS; TRAUMA; PLANS;
D O I
10.1016/j.jpsychires.2022.10.009
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
The coronavirus disease 2019 (COVID-19) pandemic has created a global health crisis, with disproportionate effects on vulnerable sociodemographic groups. Although the pandemic is showing potential to increase suicide ideation (SI), we know little about which sociodemographic characteristics or COVID-19 experiences are asso-ciated with SI. Our United States-based sample (n = 837 adults [mean age = 37.1 years]) completed an online survey during August-September 2020. The study utilized an online convenience sample from a prior study, which was enriched for exposure to trauma and experiences of posttraumatic stress symptoms. We assessed SI using the Beck Depression Inventory-II. Traditional (i.e., logistic regression) and machine learning (i.e., LASSO, random forest) methods evaluated associations of 148 self-reported COVID-19 factors and sociodemographic characteristics with current SI. 234 participants (28.0%) reported SI. Twenty items were significantly associated with SI from logistic regression. Of these 20 items, LASSO identified seven sociodemographic characteristics (younger age, lower income, single relationship status, sexual orientation other than heterosexual as well as specifically identifying as bisexual, non-full-time employment, and living in a town) and six COVID-19 factors (not engaging in protective COVID-19 behaviors, receiving mental health treatment (medication and/or psy-chotherapy) due to the COVID-19 pandemic, socializing during the pandemic, losing one's job due to COVID-19, having a friend with COVID-19, and having an acquaintance with COVID-19) associated with SI. Random forest findings were largely consistent with LASSO. These findings may inform multidisciplinary research and inter-vention work focused on understanding and preventing adverse mental health outcomes such as SI during and in the aftermath of the pandemic.
引用
收藏
页码:186 / 193
页数:8
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