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Background:

Methamphetamine use disorder (MUD) is a severe public health problem, accompanying physical and psychological impairment. Depression is a common comorbidity with MUD, which is associated with poor treatment outcome. The objective of this study is to investigate the prevalence of depressive symptoms among male MUD individuals and identify potential risk factors.

Method:

A total of 483 male MUD individuals from six drug rehabilitation centers were included for analysis. The demographic information, drug use history, and clinical performances were analyzed. We conducted multiple regression analysis to identify predictive factors of depression, and examined the difference among MUD individuals with different depression severity.

Result:

89.03% of MUD individuals exhibited depressive symptoms. Length of abstinence (β = -0.006, p = 0.003) and subjective craving (β = 0.052, p < 0.001) were the leading predictive factors of depression. Drug use onset age (R2 = 0.010, r = -0.102, p = 0.024) negatively associated with depression severity. Depressive symptoms also positively associated with anxiety (R2 = 0.171, r = 0.413, p < 0.001), poor sleep quality (R2 = 0.133, r = 0.365, p < 0.001), and impulsivity (R2 = 0.012, r = 0.111, p = 0.015).

Conclusions:

The prevalence of depressive symptoms in MUD individuals is high. Abstinence and Craving days are important predictive factors for depression severity.


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Prevalence and risk factors for depressive symptom in methamphetamine use disorder

Show Author's information Ziqi Liu§Yi Zhang§Ti-Fei Yuan( )
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

§ Ziqi Liu and Yi Zhang contributed equally to this work.

Abstract

Background:

Methamphetamine use disorder (MUD) is a severe public health problem, accompanying physical and psychological impairment. Depression is a common comorbidity with MUD, which is associated with poor treatment outcome. The objective of this study is to investigate the prevalence of depressive symptoms among male MUD individuals and identify potential risk factors.

Method:

A total of 483 male MUD individuals from six drug rehabilitation centers were included for analysis. The demographic information, drug use history, and clinical performances were analyzed. We conducted multiple regression analysis to identify predictive factors of depression, and examined the difference among MUD individuals with different depression severity.

Result:

89.03% of MUD individuals exhibited depressive symptoms. Length of abstinence (β = -0.006, p = 0.003) and subjective craving (β = 0.052, p < 0.001) were the leading predictive factors of depression. Drug use onset age (R2 = 0.010, r = -0.102, p = 0.024) negatively associated with depression severity. Depressive symptoms also positively associated with anxiety (R2 = 0.171, r = 0.413, p < 0.001), poor sleep quality (R2 = 0.133, r = 0.365, p < 0.001), and impulsivity (R2 = 0.012, r = 0.111, p = 0.015).

Conclusions:

The prevalence of depressive symptoms in MUD individuals is high. Abstinence and Craving days are important predictive factors for depression severity.

Keywords:

methamphetamine use disorder, depression, abstinence days, craving
Received: 08 September 2020 Revised: 05 November 2020 Accepted: 28 February 2021 Published: 08 April 2021 Issue date: December 2021
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Publication history
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Publication history

Received: 08 September 2020
Revised: 05 November 2020
Accepted: 28 February 2021
Published: 08 April 2021
Issue date: December 2021

Copyright

© The Author(s) 2021

Acknowledgements

The study is supported by the Guangdong grant "Key Technologies for Treatmert of Brain Disorders" (No. 2018B030331001), Modicine and Engineering Interdisciplinary Research Fund of Shanghai Jiao Tong University (No. ZH2018ZDA30), National Natural Science Foundation of China (NSFC) (Nos. 81822017 and 31771215) to T.-F. Y. We thank Jing Shen, Xi He, Hangbin Zhang, Ziqi Liu, Xue Ma, Yuxuan Zhang, Qian Zhuang, Hui Zheng, Yan Zeng, Ningning Zeng, and Di Zhao for their helps during data collection and management.

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