High suicidality predicts overdose events among people with substance use disorder: A latent class analysis

Author:

Schmidt Renae D.,Horigian Viviana E.,Shmueli-Blumberg Dikla,Hefner Kathryn,Feinberg Judith,Kondapaka Radhika,Feaster Daniel J.,Duan Rui,Gonzalez Sophia,Davis Carly,Vena Ashley,Marín-Navarrete Rodrigo,Tross Susan

Abstract

IntroductionSuicide is the tenth leading cause of death in the United States and continues to be a major public health concern. Suicide risk is highly prevalent among individuals with co-occurring substance use disorders (SUD) and mental health disorders, making them more prone to adverse substance use related outcomes including overdose. Identifying individuals with SUD who are suicidal, and therefore potentially most at risk of overdose, is an important step to address the synergistic epidemics of suicides and overdose fatalities in the United States. The current study assesses whether patterns of suicidality endorsement can indicate risk for substance use and overdose.MethodsLatent class analysis (LCA) was used to assess patterns of item level responses to the Concise Health Risk Tracking Self-Report (CHRT-SR), which measures thoughts and feelings associated with suicidal propensity. We used data from 2,541 participants with SUD who were enrolled across 8 randomized clinical trials in the National Drug Abuse Treatment Clinical Trials Network from 2012 to 2021. Characteristics of individuals in each class were assessed, and multivariable logistic regression was performed to examine class membership as a predictor of overdose. LCA was also used to analyze predictors of substance use days.ResultsThree classes were identified and discussed: Class (1) Minimal Suicidality, with low probabilities of endorsing each CHRT-SR construct; Class (2) Moderate Suicidality, with high probabilities of endorsing pessimism, helplessness, and lack of social support, but minimal endorsement of despair or suicidal thoughts; and Class (3) High Suicidality with high probabilities of endorsing all constructs. Individuals in the High Suicidality class comprise the highest proportions of males, Black/African American individuals, and those with a psychiatric history and baseline depression, as compared with the other two classes. Regression analysis revealed that those in the High Suicidality class are more likely to overdose as compared to those in the Minimal Suicidality class (p = 0.04).ConclusionSuicidality is an essential factor to consider when building strategies to screen, identify, and address individuals at risk for overdose. The integration of detailed suicide assessment and suicide risk reduction is a potential solution to help prevent suicide and overdose among people with SUD.

Publisher

Frontiers Media SA

Subject

Public Health, Environmental and Occupational Health

Reference65 articles.

1. Increase in suicide mortality in the United States, 1999-2018;Hedegaard;NCHS Data Brief,2020

2. Suicide prevention strategies revisited: 10-year systematic review;Zalsman;Lancet Psychiatry,2016

3. Understanding links among opioid use, overdose, and suicide;Bohnert;N Engl J Med,2019

4. Overdose and adverse drug event experiences among adult patients in the emergency department;Bohnert;Addict Behav,2018

5. Links between suicidal intent, polysubstance use, and medical treatment after non-fatal opioid overdose;Gicquelais;Drug Alcohol Depend,2020

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3