Drug and Alcohol Dependence 142 (2014) 191–196
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Prevalence and correlates of depressive symptoms during early methamphetamine withdrawal in Han Chinese population Jie Zhang a,d , Ying Xie a , Hang Su a , Jingyan Tao a , Yeming Sun b , Liren Li c , Haiyan Liang d , Ruqian He a , Bin Han a , Yuling Lu a , Haiwei Sun a , Youdan Wei a , Jun Guo e , Xiang Yang Zhang f,g,∗ , Jincai He a,∗∗ a
Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China Department of Psychiatry, New Jersey Medical School, Rutgers University, Piscataway Township, NJ 07103, USA c Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway Township, NJ 08901, USA d Department of Neurology, Taizhou Municipal Hospital, Taizhou 317700, China e Sanyang Detoxification Institute, Wenzhou 325000, China f Beijing HuiLongGuan Hospital, Peking University, Beijing BJ 100096, China g Department of Psychiatry and Behavioral Sciences, Harris County Psychiatric Center, The University of Texas Health Science Center at Houston, Houston, TX, USA b
a r t i c l e
i n f o
Article history: Received 2 April 2014 Received in revised form 25 May 2014 Accepted 12 June 2014 Available online 23 June 2014 Keywords: Methamphetamine Depressive symptoms Withdrawal Prevalence Correlates Chinese
a b s t r a c t Background: Depression, a common comorbidity of drug abuse, is often a core component of withdrawal symptoms; however, risk factors associated with depressive symptoms during the acute stage of withdrawal among methamphetamine (METH) users are not well understood. This study investigated the correlations between several potential risk factors and depressive symptoms during acute METH withdrawal in a Han Chinese population. Methods: A total of 243 eligible Chinese METH users were recruited from Wenzhou Sanyang Detoxification Institute in Zhejiang province from November 2012 to June 2013. A set of self-administrative questionnaires were used to collect information about socio-demographics, drug use history and depression. Thirteen-item Beck Depression Inventory (BDI-13) was used to measure depressive symptoms. Results: METH users had a mean BDI-13 score of 12.39; 157 subjects (64.6%) reported depressive symptoms during METH withdrawal, of which 74 subjects (30.5%) reported moderate depressive symptoms and 83 subjects (34.1%) reported severe depressive symptoms. Higher frequency of drug use and history of METH-use relapse were associated with depressive symptoms (adjusted OR = 2.8; 95% CI = 1.56–5.04) and (adjusted OR = 3.4; 95% CI = 1.36–8.49), respectively. Moderate alcohol drinking was associated with less risk for depressive symptoms during acute withdrawal (adjusted OR = 0.54; 95% CI = 0.31–0.93). Conclusions: Depressive symptoms are common during early METH withdrawal. In addition, several risk factors including frequency of METH use and history of relapse were positively associated with depressive symptoms during that period while moderate alcohol drinking was negatively associated with depressive symptoms. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Amphetamine-type stimulants (ATS) abuse is a global concern. Methamphetamine (METH), a main type of ATS used widely, is
∗ Corresponding author at: Biological Psychiatry Center, Beijing Hui-Long-Guan hospital, Chang-Ping District, Beijing 100096, PR China. Tel.: +86 10 62715511x6464; fax: +86 10 62912169. ∗∗ Corresponding author at: Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, China. Tel.: +86 0577 55579363. E-mail addresses: [email protected] (X.Y. Zhang), [email protected] (J. He). http://dx.doi.org/10.1016/j.drugalcdep.2014.06.021 0376-8716/© 2014 Elsevier Ireland Ltd. All rights reserved.
highly addictive and has a significant impact on the central nervous system when taken repeatedly or at higher doses (Marshall and O’Dell, 2012). Long-term METH use was found to be associated with several serious mental and physical illnesses (Buxton and Dove, 2008; Marshall and Werb, 2010). Depression is a common comorbid condition among ATS users (Bao et al., 2013; Sutcliffe et al., 2009; Zweben et al., 2004). An American epidemiological survey demonstrated that a history of depression was reported by 41.6% of amphetamine adult users (Conway et al., 2006). Previous treatment outcome studies showed that depressed METH users had less benefits from long-term psychotherapy compared to non-depressed subjects
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J. Zhang et al. / Drug and Alcohol Dependence 142 (2014) 191–196
(Kay-Lambkin et al., 2011) and decreased retention in treatment has been related to depressive symptoms following cessation of stimulants use (Leventhal et al., 2008). All above findings suggest that comorbid depression complicates METH abuse and treatment outcomes. Several studies suggest that depression is a core symptom of METH withdrawal symptoms during the first several weeks of abstinence (McGregor et al., 2005; Zorick et al., 2010). Failure to manage METH withdrawal symptoms may lead to the high rates of relapse in the first several weeks of abstinence (Brecht et al., 2000). Until now, there was little information on risk factors associated with depressive symptoms in acute METH withdrawal (first week of abstinence; McGregor et al., 2005). In our study, we focused on the associations of potential risk factors and depressive symptoms in METH-dependent patients during early METH withdrawal. Identifying risk factors that predict depressive symptoms during acute METH withdrawal may help to develop approaches for relapse prevention. 2. Methods 2.1. Subjects and setting The study was conducted at Wenzhou Sanyang Detoxification Institute, which is located in Wenzhou City, Zhejiang province. A total of 243 METH-dependent inpatients were recruited from November, 2012 to June, 2013. At the institute patients have no access to METH, and therefore, the study was carried out in a strictly controlled environment. To be included in the study, patients had to meet the following inclusion criteria: (1) be between 18 and 50 years of age; (2) meet DSM-IV criteria for METH dependence; (3) have had a positive urine test when admitted to the institute; (4) have been abstinent for 1–7 days; (5) and signed informed consent. The subjects were excluded if they met DSM-IV criteria for any other substance dependence disorders (except nicotine), and had significant mental or physical illnesses such as schizophrenia, cardiovascular disease or stroke. The study was approved by the Human Research and Ethics Committee of Wenzhou Medical University. Written informed consents were obtained from all participants.
Table 1 Characteristics of the study group. Characteristics
(n = 243)
Age; mean age (range) Male n (%) Junior high school or less n (%) Family status Intact family n (%) Single parent n (%) Orphan n (%) Living alone n (%) Unmarried n (%) Living in rural n (%) Unemployed n (%) Cigarette smoking n (%) Alcohol drinking n (%) Gambling n (%) Family history of mental illness n (%) Source of drug supply Illegal drug market n (%) Peers or friends n (%) Entertainment places n (%) Cause of first time use Peer influence n (%) Experimenting n (%) Pursuit of euphoria n (%) Boredom n (%) Alleviation of unpleasant emotions n (%) Other n (%) Route of drug administration Smoking n (%) Age of onset; mean (range) Frequency of drug use (2–5 times a week or more) n (%) Poly-substance use n (%) Duration of METH use >12 months n (%) Days of abstinence; mean (range) Number of previous detoxification >1 n (%) Cessation for 12 months or more n (%) History of relapse n (%)
31.88 (18–50) 198 (81.5) 210 (86.4) 175 (72) 58 (23.9) 10 (4.1) 100 (41.2) 116 (47.7) 96 (39.5) 92 (37.9) 220 (90.5) 114 (46.9) 95 (39.1) 14 (5.8) 110 (45.3) 129 (53.1) 3 (1.2) 92 (37.9) 109 (44.9) 6 (2.5) 6 (2.5) 24 (9.9) 6 (2.5) 238 (97.9) 26.58 (12–49) 104 (42.8) 51 (21) 190 (78.2) 4.62 (1–7) 90 (37) 39 (16) 220 (90.5)
2.2. Measures Each subject was interviewed in a separate room and completed a self-administrated case report form (CRF), which included socio-demographic characteristics, drug-use history, cigarette smoking, alcohol drinking and depressive symptoms. Trained interviewers reviewed each question with the subjects to make sure everyone understood the questionnaire. The socio-demographic characteristics included age, gender, education, nationality, marriage, family status, dwelling condition, employment, etc. Information about drug use included age of onset, the route of drug administration, single or multiple drug use and duration of drug use etc. Smoking and alcohol use status were also obtained. The short 13-item Beck Depression Inventory was used to measure depressive symptoms. Each item is scored from 0 to 3 with cumulative scores ranging from 0 to 39. The score of 0–4, 5–7, 8–15, ≥16 were classified as no depression, mild, moderate and severe depression, respectively (Beck et al., 1974). 2.3. Statistics analysis Characteristics of the study sample including demographic characteristics, drug-use history, cigarette smoking, alcohol drinking, BDI score and prevalence of depressive symptoms were summarized using descriptive statistics. Pearson’s chi-square test and bivariate logistic regression were used for the analysis of demographic characteristics, information of drug use, cigarette smoking and alcohol drinking with different depression status. A multivariate logistic regression model was constructed using a forward LR sequence. The significant predictors identified in the univariate logistic regression were then entered in a multivariate logistic regression model controlling for the potential effects of age, gender and education level. All analyses were performed using SPSS software. A two tailed P value of less than 0.05 was considered to be statistically significant.
3. Results 3.1. Characteristics and pattern of drug use A total of 243 METH-dependent inpatients in very early abstinence (1–7 days from last drug use) were recruited from
Wenzhou Sanyang detoxification institute in Wenzhou city, Zhejiang province. Socio-demographic and drug use characteristics of inpatients are listed in Table 1. The mean age for the whole study group was 31.88 years, ranging from 18 to 50 years. The majority of our subjects were male (81.5%). 210 (86.4%) did not have high school education, 175 (72%) were from intact families, 58 (23.9%) were from single parent families and 10 (4.1) were orphans. 116 (47.7%) were unmarried, 100 (41.2%) were living alone, 96 (39.5%) were living in rural area. 92 (37.9%) had no job, 14 (5.8%) reported a family history of mental illness. All recruited subjects were in the state of acute METH withdrawal, average of days of abstinence was 4.62 days ranging from 1 to 7 days. Among the 243 METH-dependent inpatients, the majority (97.9%) used METH by smoking. The average age of onset for METH use was 26.58 years, ranging from 12 to 49 years. Experimenting (44.9%), peer influence (37.9%) and self-medicating to alleviate unpleasant emotions (9.9%) were the main causes of drug use for the first time. Supply from peers or friends (53.1%) and illegal drug market (45.3%) were the main drug resources. One hundred and four (42.8%) subjects used METH 2–5 times a week or more. Fifty-one (51%) subjects reported a history of poly-substance use, including methamphetamine, ecstasy, heroin, ketamine. Most subjects (78.2%) had used METH for more than one year. 37% of subjects had detoxification more than once in the past, not including this study period. The majority (90.5%) reported a history of relapse. About 90.5%, 46.9% and 39.1% METH users reported coexisting cigarette smoking, alcohol drinking (neither abuse nor dependence) and gambling, respectively.
J. Zhang et al. / Drug and Alcohol Dependence 142 (2014) 191–196 Table 2 The values for each of the items in the BDI (n = 243). Scale items
0* , n (%)
1** , n (%)
2*** , n (%)
3**** , n (%)
Mood Pessimism Sense of failure Lack of satisfaction Guilty feeling Self-hate Self punitive wishes Social withdrawal Indecisiveness Body image Work inhibition Fatigability Loss of appetite
97 (39.9) 99 (40.7) 60 (24.7) 76 (31.3) 53 (21.8) 75 (30.9) 173 (71.2) 119 (49.0) 132 (54.3) 91 (37.4) 123 (50.6) 74 (30.5) 155 (63.8)
91 (37.4) 90 (37.0) 84 (34.6) 103 (42.4) 90 (37.0) 104 (42.8) 41 (16.9) 69 (28.4) 53 (21.8) 57 (23.5) 50 (20.6) 99 (40.7) 52 (21.4)
34 (14.0) 33 (13.6) 61 (25.1) 45 (18.5) 79 (32.5) 33 (13.6) 24 (9.9) 33 (13.6) 32 (13.2) 45 (18.5) 53 (21.8) 47 (19.3) 24 (9.9)
21 (8.6) 21 (8.6) 38 (15.6) 19 (7.8) 21 (8.6) 31 (12.8) 5 (2.1) 22 (9.1) 26 (10.7) 50 (20.6) 17 (7.0) 23 (9.5) 12 (4.9)
* ** *** ****
No depression. Mild depression. Moderate depression. Severe depression.
3.2. Related factors and depression status during METH withdrawal According to BDI score, 157 subjects (64.6%) had depressive symptoms during METH withdrawal, including 74 (30.5%) with moderate and 83 (34.1%) with severe depressive symptoms. The values for each of the items in the BDI listed in Table 2. The univariate logistic regression analysis showed that depressive symptoms (BDI-13 ≥ 8) were significantly associated with nine factors listed in Table 3, including family status, marital status, employment, history of alcohol drinking, frequency of drug use, poly-substance use, duration of drug use, number of previous detoxifications and history of relapse. Then only these nine factors were entered in the multivariate logistic regression model. The result showed that higher frequency of drug use was significantly associated with depressive symptoms during METH withdrawal. Subjects who used METH 2–5 times a week or more had more risk of depressive symptoms than those once a week or less (adjusted OR = 2.8, 95% CI = 1.56–5.04). History of METH use relapse was also positively associated with depressive symptoms (adjusted OR = 3.4, 95% CI = 1.36–8.49). However, METH users reporting alcohol drinking had lower risk of depressive symptoms during acute withdrawal (adjusted OR = 0.54, 95% CI = 0.31–0.93) compared to those who did not report drinking (Table 4). 4. Discussion To the best of our knowledge, this is the first study to investigate the associations between risk factors and depressive symptoms in METH dependent subjects during the first week of abstinence. In addition, METH dependent subjects reported a high rate (64.6%) of depressive symptoms in the first week following cessation of METH use. Our findings might have important implications in developing measures to prevent METH abuse and to treat depression during acute METH withdrawal period. According to multivariate logistic regression, frequency of drug use, history of relapse and alcohol drinking were associated with moderate to severe depression (BDI-13 ≥ 8) during acute METH withdrawal. Our study suggested those with higher frequency of METH use (2–5 times per week or more) were more likely to experience moderate to severe depression than those with lower frequency use (once a week or less). A previous study showed that depressive symptoms among Chinese ATS users (not during acute withdrawal) correlated with dose of drug use, and might correlate with frequency of drug use (Bao et al., 2013). Another study in Thai
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METH users found that depressive symptoms were associated with frequency of METH use (Celentano et al., 2008). Taken together, these findings indicate that decreasing frequency of METH use will probably reduce risk for depression regardless what stage of drug use. In our study, METH-dependent subjects with history of relapse had higher risk of depression during withdrawal, this finding was consistent with the previous study (Bao et al., 2013). A study conducted in Thailand showed relapse group and persistent-use group had higher levels of depression than the group that had achieved cessation (Sutcliffe et al., 2009). Another study demonstrated that depressive symptoms were significantly associated with craving (Nakama et al., 2008), which was hypothesized to play a critical role in sustaining drug use and relapse (Pickens and Johanson, 1992; Robinson and Berridge, 2003). However, due to cross-sectional design in the present study, the causal relationship between relapse and depression was unable to be determined. Prospective studies are needed to further explore the relationship between relapse and depression. Our study showed that alcohol drinking (neither abuse nor dependence) during the period of METH use decreased the level of depression during acute stage of METH withdrawal; this result seemed inconsistent with findings from former studies. For example, alcohol use disorders were highly prevalent among people with depressive and/or anxiety disorders (Boschloo et al., 2011; Burns and Teesson, 2002; de Graaf et al., 2002; Hasin et al., 2007) and remission of major depressive disorder was less likely achieved in subjects comorbid with alcohol use disorder (Mueller et al., 1994). In addition, a Chinese study showed that alcohol drinking could increase the level of depression among ATS users (Bao et al., 2013). One possible explanation for this discrepancy is the variability in alcohol consumption. A recent study found that the type of alcohol use disorders (abuse or dependence) had different impacts on the depression and anxiety: alcohol dependence, but not abuse, predicted an unfavorable course of depressive and/or anxiety disorders (Boschloo et al., 2012). In our study, subjects who met the criteria of DSM-IV for alcohol abuse or dependence were excluded. Therefore, our findings indicated that moderate alcohol use, but not alcohol abuse or dependence, during the period of METH use may have protective benefit against depression during acute stage of withdrawal. Further research is needed to examine quantity, frequency or duration of alcohol use in order to elucidate the effects of alcohol consumption on depressive symptoms during early METH withdrawal. In addition, our study found high rates (64.4%) of moderate or severe depression in acute METH withdrawal, which is consistent with previous studies. For example, preclinical studies showed that extended access to METH resulted in a depressive-like state in rats during early withdrawal (Jang et al., 2013). Furthermore, a previous study reported that depressive-like behavior (i.e., increased immobility time observed in a tail suspension test) was observed in mice administered with amphetamine released from implanted osmotic mini-pumps for 1 week followed by one day of drug withdrawal. In addition, this study also described that depressive-like behavior (i.e., increased immobility time observed in a forced swimming test) was observed in rats administered with amphetamine 6 days followed by 2–3 days of drug withdrawal (Cryan et al., 2003). Moreover, withdrawal from chronic administration of a psychostimulant had been used as an animal model of depression (Barr et al., 2002; Cryan et al., 2003). In humans, the psychomotor aspect of amphetamine withdrawal symptoms (i.e., depression, inactivity, fatigue, and anhedonia) can last for 2–7 days of the drug abstinence and, more importantly, may disappear thereafter (McGregor et al., 2005; Watson et al., 1972). These findings support a notion that depressive symptoms are major components of METH withdrawal syndromes during early abstinence.
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Table 3 Bivariate analysis of risk factors for depressive symptoms in METH acute withdrawal.
Depression (BDI ≥ 8)
Variables N Age ≤20 21–30 31–40 41–50 Gender Male Female Family status Intact family Single parent Orphan Marital status Unmarried Cohabitating Married Divorced Residence Urban Rural area Dwelling condition Living alone Living in dormitory Living with family Other Education Primary school Junior high school high school or more Employment No Yes Cigarette smoking No Yes Alcohol drinking No Yes Gambling No Yes Family history of mental illness No Yes Route of drug administration Smoking No Yes Frequency of drug use Once a week or less 2–5 times a week or more Poly substance use No Yes Age of onset ≤20 21–30 31–40 41–50 Duration of METH use (m) ≤12 13–60 >60 Days of abstinence ≤4 4–7 Number of previous detoxification ≤1 >1 Pattern of drug use Continue use Cessation 1–12 months Cessation 12 months or more History of relapse No Yes *
P < 0.05.
n
%
OR
95% CI
24 86 90 43
14 48 62 33
58.3 55.8 68.9 76.7
1.0 0.902 1.582 2.357
0.36–2.27 0.63–3.99 0.80–6.92
198 45
127 30
64.1 66.7
1.0 1.118
0.56–2.22
175 58 10
116 38 3
66.3 65.5 30
1.0 0.966 0.218
0.52–1.81 0.05–0.87*
116 16 72 39
67 11 48 31
57.8 68.8 66.7 79.5
1.0 0.35 0.568 0.52
0.149–0.83* 0.15–2.11 0.21–1.29
147 96
88 69
59.9 71.9
1.0 0.584
0.34–1.01
100 10 124 9
64 6 82 5
64 60 66.1 55.6
1.0 0.844 1.10 0.70
0.22–3.12 0.63–1.91 0.18–2.79
68 142 33
43 93 21
63.2 65.5 63.6
1.0 1.10 1.02
0.60–2.02 0.43–2.41
92 151
67 90
72.8 59.6
1.0 0.551
0.31–0.97*
23 220
15 142
65.2 64.5
1.0 0.97
0.39–2.39
129 114
91 66
70.5 57.9
1.0 0.57
0.34–0.98*
148 95
92 65
62.2 68.4
1.0 1.319
0.76–2.28
229 14
147 10
64.2 71.4
1.0 1.40
0.42–4.59
5 238
2 155
40 65.1
1.0 2.80
0.46–17.10
139 104
78 79
56.1 76
1 2.47
1.41–4.33*
192 51
118 39
61.5 76.5
1.0 2.04
1.00–4.14*
51 123 54 15
32 77 38 10
62.7 62.6 70.4 66.7
1.0 0.99 1.41 1.29
0.51–1.95 0.63–3.18 0.35–4.00
53 118 72
28 78 51
52.8 66.1 70.8
1.0 1.74 2.17
0.90–3.37 1.03–4.55*
113 130
78 79
69 60.8
1.0 0.69
0.41–1.18
153 90
91 66
59.5 73.3
1.0 1.87
1.06–3.31*
170 34 39
105 23 29
61.8 67.6 74.4
1.0 1.29 1.80
0.59–2.83 0.82–3.93
23 220
10 147
43.5 66.8
1.0 2.62
1.10–6.25*
J. Zhang et al. / Drug and Alcohol Dependence 142 (2014) 191–196 Table 4 Multivariate logistic regression of risk factors for depressive symptoms during METH early withdrawal. Variables Frequency of drug use Once a week or less 2–5 times a week or more Alcohol drinking No Yes History of relapse No Yes
Adjusted OR (95% CI) for (BDI score ≥ 8)
P value
1.0 2.8 (1.56–5.04)
0.001
1.0 0.54 (0.31–0.93)
0.028
1.0 3.4 (1.36–8.49)
0.009
Some limitations of our study should be noted. First, the use of non-standardized questionnaires is a concern, especially where most measure relied on data derived from patient self-report with no corroborating evidence. We did not apply structured instruments or rating scales for comorbidity assessments of psychotic symptoms, personality disorder and suicidal behavior etc. Some data may be underestimates due to self-report bias. For example, only 5% of subjects report a family history of mental illness. Second, the cross-sectional design of this study limits our ability to confirm a causal relationship between associated factors and depression during early withdrawal among METH users. Third, this investigation was a retrospective study, therefore, recall error and observer bias should not be excluded. Longitudinal studies are needed for future research. Fourth, the size of the sample was relatively small compared to other epidemiological studies. Fifth, subjects in our sample were recruited from compulsory detoxification institutions and may not be similar enough to the greater METH-using community to generalize our results. Sixth, we did not collect the details on the pattern of alcohol use, which may further elucidate the relationship between alcohol use and depressive symptoms during early METH withdrawal. Seventh, we found a history of relapse was a factor influencing depressive symptoms during early METH withdrawal. However, our results must be interpreted with caution due to the fact that the number of the subjects without history of relapse was much smaller than the ones with a history of relapse. Further studies with equal proportions of the two groups with vs without history of relapse are needed to increase the statistical power and to elucidate the relationship of relapse and depression. In summary, the present study indicated METH users during early withdrawal had a high prevalence for depression; several risk factors were associated with depressive symptoms during withdrawal, including frequency of METH use, history of relapse and history of alcohol drinking during the period of METH use. Identifying these factors may help to predict depression during early METH withdrawal and to develop measures to treat depression and to prevent relapse. Role of funding sources This work was funded by the grant from National Key Technology R&D Program in the 11th Five year Plan of China (2009BAI77B06) and Wenzhou Municipal Sci-Tech Bureau Program (H20100021). These sources had no further role in study design, data collection and analysis, decision to publish, or preparation of the article. Contributors Jie Zhang, Jincai He and Xiang Yang Zhang conceived of the study, supervised the statistical analyses, and prepared manuscript, and
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wrote the protocol and the paper. Ying Xie, Hang Su, Jingyan Tao, Yeming Sun, Liren Li, Haiyan Liang, Ruqian He, Bin Han, Yuling Lu, Haiwei Sun, Youdan Wei and Jun Guo were responsible for clinical data collection and the statistical analyses. All authors gave final approval for submission of the manuscript. Dr. Jincai He (the lead author) had full access to all of the data in the study and takes responsibility for the integrity of the data and accuracy of the data analyses.
Conflict of interest statement The authors declare that they have no conflict of interest.
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Full length article
Prevalence and correlates of depressive symptoms during early methamphetamine withdrawal in Han Chinese population Jie Zhang a,d , Ying Xie a , Hang Su a , Jingyan Tao a , Yeming Sun b , Liren Li c , Haiyan Liang d , Ruqian He a , Bin Han a , Yuling Lu a , Haiwei Sun a , Youdan Wei a , Jun Guo e , Xiang Yang Zhang f,g,∗ , Jincai He a,∗∗ a
Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China Department of Psychiatry, New Jersey Medical School, Rutgers University, Piscataway Township, NJ 07103, USA c Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway Township, NJ 08901, USA d Department of Neurology, Taizhou Municipal Hospital, Taizhou 317700, China e Sanyang Detoxification Institute, Wenzhou 325000, China f Beijing HuiLongGuan Hospital, Peking University, Beijing BJ 100096, China g Department of Psychiatry and Behavioral Sciences, Harris County Psychiatric Center, The University of Texas Health Science Center at Houston, Houston, TX, USA b
a r t i c l e
i n f o
Article history: Received 2 April 2014 Received in revised form 25 May 2014 Accepted 12 June 2014 Available online 23 June 2014 Keywords: Methamphetamine Depressive symptoms Withdrawal Prevalence Correlates Chinese
a b s t r a c t Background: Depression, a common comorbidity of drug abuse, is often a core component of withdrawal symptoms; however, risk factors associated with depressive symptoms during the acute stage of withdrawal among methamphetamine (METH) users are not well understood. This study investigated the correlations between several potential risk factors and depressive symptoms during acute METH withdrawal in a Han Chinese population. Methods: A total of 243 eligible Chinese METH users were recruited from Wenzhou Sanyang Detoxification Institute in Zhejiang province from November 2012 to June 2013. A set of self-administrative questionnaires were used to collect information about socio-demographics, drug use history and depression. Thirteen-item Beck Depression Inventory (BDI-13) was used to measure depressive symptoms. Results: METH users had a mean BDI-13 score of 12.39; 157 subjects (64.6%) reported depressive symptoms during METH withdrawal, of which 74 subjects (30.5%) reported moderate depressive symptoms and 83 subjects (34.1%) reported severe depressive symptoms. Higher frequency of drug use and history of METH-use relapse were associated with depressive symptoms (adjusted OR = 2.8; 95% CI = 1.56–5.04) and (adjusted OR = 3.4; 95% CI = 1.36–8.49), respectively. Moderate alcohol drinking was associated with less risk for depressive symptoms during acute withdrawal (adjusted OR = 0.54; 95% CI = 0.31–0.93). Conclusions: Depressive symptoms are common during early METH withdrawal. In addition, several risk factors including frequency of METH use and history of relapse were positively associated with depressive symptoms during that period while moderate alcohol drinking was negatively associated with depressive symptoms. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Amphetamine-type stimulants (ATS) abuse is a global concern. Methamphetamine (METH), a main type of ATS used widely, is
∗ Corresponding author at: Biological Psychiatry Center, Beijing Hui-Long-Guan hospital, Chang-Ping District, Beijing 100096, PR China. Tel.: +86 10 62715511x6464; fax: +86 10 62912169. ∗∗ Corresponding author at: Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, China. Tel.: +86 0577 55579363. E-mail addresses: [email protected] (X.Y. Zhang), [email protected] (J. He). http://dx.doi.org/10.1016/j.drugalcdep.2014.06.021 0376-8716/© 2014 Elsevier Ireland Ltd. All rights reserved.
highly addictive and has a significant impact on the central nervous system when taken repeatedly or at higher doses (Marshall and O’Dell, 2012). Long-term METH use was found to be associated with several serious mental and physical illnesses (Buxton and Dove, 2008; Marshall and Werb, 2010). Depression is a common comorbid condition among ATS users (Bao et al., 2013; Sutcliffe et al., 2009; Zweben et al., 2004). An American epidemiological survey demonstrated that a history of depression was reported by 41.6% of amphetamine adult users (Conway et al., 2006). Previous treatment outcome studies showed that depressed METH users had less benefits from long-term psychotherapy compared to non-depressed subjects
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J. Zhang et al. / Drug and Alcohol Dependence 142 (2014) 191–196
(Kay-Lambkin et al., 2011) and decreased retention in treatment has been related to depressive symptoms following cessation of stimulants use (Leventhal et al., 2008). All above findings suggest that comorbid depression complicates METH abuse and treatment outcomes. Several studies suggest that depression is a core symptom of METH withdrawal symptoms during the first several weeks of abstinence (McGregor et al., 2005; Zorick et al., 2010). Failure to manage METH withdrawal symptoms may lead to the high rates of relapse in the first several weeks of abstinence (Brecht et al., 2000). Until now, there was little information on risk factors associated with depressive symptoms in acute METH withdrawal (first week of abstinence; McGregor et al., 2005). In our study, we focused on the associations of potential risk factors and depressive symptoms in METH-dependent patients during early METH withdrawal. Identifying risk factors that predict depressive symptoms during acute METH withdrawal may help to develop approaches for relapse prevention. 2. Methods 2.1. Subjects and setting The study was conducted at Wenzhou Sanyang Detoxification Institute, which is located in Wenzhou City, Zhejiang province. A total of 243 METH-dependent inpatients were recruited from November, 2012 to June, 2013. At the institute patients have no access to METH, and therefore, the study was carried out in a strictly controlled environment. To be included in the study, patients had to meet the following inclusion criteria: (1) be between 18 and 50 years of age; (2) meet DSM-IV criteria for METH dependence; (3) have had a positive urine test when admitted to the institute; (4) have been abstinent for 1–7 days; (5) and signed informed consent. The subjects were excluded if they met DSM-IV criteria for any other substance dependence disorders (except nicotine), and had significant mental or physical illnesses such as schizophrenia, cardiovascular disease or stroke. The study was approved by the Human Research and Ethics Committee of Wenzhou Medical University. Written informed consents were obtained from all participants.
Table 1 Characteristics of the study group. Characteristics
(n = 243)
Age; mean age (range) Male n (%) Junior high school or less n (%) Family status Intact family n (%) Single parent n (%) Orphan n (%) Living alone n (%) Unmarried n (%) Living in rural n (%) Unemployed n (%) Cigarette smoking n (%) Alcohol drinking n (%) Gambling n (%) Family history of mental illness n (%) Source of drug supply Illegal drug market n (%) Peers or friends n (%) Entertainment places n (%) Cause of first time use Peer influence n (%) Experimenting n (%) Pursuit of euphoria n (%) Boredom n (%) Alleviation of unpleasant emotions n (%) Other n (%) Route of drug administration Smoking n (%) Age of onset; mean (range) Frequency of drug use (2–5 times a week or more) n (%) Poly-substance use n (%) Duration of METH use >12 months n (%) Days of abstinence; mean (range) Number of previous detoxification >1 n (%) Cessation for 12 months or more n (%) History of relapse n (%)
31.88 (18–50) 198 (81.5) 210 (86.4) 175 (72) 58 (23.9) 10 (4.1) 100 (41.2) 116 (47.7) 96 (39.5) 92 (37.9) 220 (90.5) 114 (46.9) 95 (39.1) 14 (5.8) 110 (45.3) 129 (53.1) 3 (1.2) 92 (37.9) 109 (44.9) 6 (2.5) 6 (2.5) 24 (9.9) 6 (2.5) 238 (97.9) 26.58 (12–49) 104 (42.8) 51 (21) 190 (78.2) 4.62 (1–7) 90 (37) 39 (16) 220 (90.5)
2.2. Measures Each subject was interviewed in a separate room and completed a self-administrated case report form (CRF), which included socio-demographic characteristics, drug-use history, cigarette smoking, alcohol drinking and depressive symptoms. Trained interviewers reviewed each question with the subjects to make sure everyone understood the questionnaire. The socio-demographic characteristics included age, gender, education, nationality, marriage, family status, dwelling condition, employment, etc. Information about drug use included age of onset, the route of drug administration, single or multiple drug use and duration of drug use etc. Smoking and alcohol use status were also obtained. The short 13-item Beck Depression Inventory was used to measure depressive symptoms. Each item is scored from 0 to 3 with cumulative scores ranging from 0 to 39. The score of 0–4, 5–7, 8–15, ≥16 were classified as no depression, mild, moderate and severe depression, respectively (Beck et al., 1974). 2.3. Statistics analysis Characteristics of the study sample including demographic characteristics, drug-use history, cigarette smoking, alcohol drinking, BDI score and prevalence of depressive symptoms were summarized using descriptive statistics. Pearson’s chi-square test and bivariate logistic regression were used for the analysis of demographic characteristics, information of drug use, cigarette smoking and alcohol drinking with different depression status. A multivariate logistic regression model was constructed using a forward LR sequence. The significant predictors identified in the univariate logistic regression were then entered in a multivariate logistic regression model controlling for the potential effects of age, gender and education level. All analyses were performed using SPSS software. A two tailed P value of less than 0.05 was considered to be statistically significant.
3. Results 3.1. Characteristics and pattern of drug use A total of 243 METH-dependent inpatients in very early abstinence (1–7 days from last drug use) were recruited from
Wenzhou Sanyang detoxification institute in Wenzhou city, Zhejiang province. Socio-demographic and drug use characteristics of inpatients are listed in Table 1. The mean age for the whole study group was 31.88 years, ranging from 18 to 50 years. The majority of our subjects were male (81.5%). 210 (86.4%) did not have high school education, 175 (72%) were from intact families, 58 (23.9%) were from single parent families and 10 (4.1) were orphans. 116 (47.7%) were unmarried, 100 (41.2%) were living alone, 96 (39.5%) were living in rural area. 92 (37.9%) had no job, 14 (5.8%) reported a family history of mental illness. All recruited subjects were in the state of acute METH withdrawal, average of days of abstinence was 4.62 days ranging from 1 to 7 days. Among the 243 METH-dependent inpatients, the majority (97.9%) used METH by smoking. The average age of onset for METH use was 26.58 years, ranging from 12 to 49 years. Experimenting (44.9%), peer influence (37.9%) and self-medicating to alleviate unpleasant emotions (9.9%) were the main causes of drug use for the first time. Supply from peers or friends (53.1%) and illegal drug market (45.3%) were the main drug resources. One hundred and four (42.8%) subjects used METH 2–5 times a week or more. Fifty-one (51%) subjects reported a history of poly-substance use, including methamphetamine, ecstasy, heroin, ketamine. Most subjects (78.2%) had used METH for more than one year. 37% of subjects had detoxification more than once in the past, not including this study period. The majority (90.5%) reported a history of relapse. About 90.5%, 46.9% and 39.1% METH users reported coexisting cigarette smoking, alcohol drinking (neither abuse nor dependence) and gambling, respectively.
J. Zhang et al. / Drug and Alcohol Dependence 142 (2014) 191–196 Table 2 The values for each of the items in the BDI (n = 243). Scale items
0* , n (%)
1** , n (%)
2*** , n (%)
3**** , n (%)
Mood Pessimism Sense of failure Lack of satisfaction Guilty feeling Self-hate Self punitive wishes Social withdrawal Indecisiveness Body image Work inhibition Fatigability Loss of appetite
97 (39.9) 99 (40.7) 60 (24.7) 76 (31.3) 53 (21.8) 75 (30.9) 173 (71.2) 119 (49.0) 132 (54.3) 91 (37.4) 123 (50.6) 74 (30.5) 155 (63.8)
91 (37.4) 90 (37.0) 84 (34.6) 103 (42.4) 90 (37.0) 104 (42.8) 41 (16.9) 69 (28.4) 53 (21.8) 57 (23.5) 50 (20.6) 99 (40.7) 52 (21.4)
34 (14.0) 33 (13.6) 61 (25.1) 45 (18.5) 79 (32.5) 33 (13.6) 24 (9.9) 33 (13.6) 32 (13.2) 45 (18.5) 53 (21.8) 47 (19.3) 24 (9.9)
21 (8.6) 21 (8.6) 38 (15.6) 19 (7.8) 21 (8.6) 31 (12.8) 5 (2.1) 22 (9.1) 26 (10.7) 50 (20.6) 17 (7.0) 23 (9.5) 12 (4.9)
* ** *** ****
No depression. Mild depression. Moderate depression. Severe depression.
3.2. Related factors and depression status during METH withdrawal According to BDI score, 157 subjects (64.6%) had depressive symptoms during METH withdrawal, including 74 (30.5%) with moderate and 83 (34.1%) with severe depressive symptoms. The values for each of the items in the BDI listed in Table 2. The univariate logistic regression analysis showed that depressive symptoms (BDI-13 ≥ 8) were significantly associated with nine factors listed in Table 3, including family status, marital status, employment, history of alcohol drinking, frequency of drug use, poly-substance use, duration of drug use, number of previous detoxifications and history of relapse. Then only these nine factors were entered in the multivariate logistic regression model. The result showed that higher frequency of drug use was significantly associated with depressive symptoms during METH withdrawal. Subjects who used METH 2–5 times a week or more had more risk of depressive symptoms than those once a week or less (adjusted OR = 2.8, 95% CI = 1.56–5.04). History of METH use relapse was also positively associated with depressive symptoms (adjusted OR = 3.4, 95% CI = 1.36–8.49). However, METH users reporting alcohol drinking had lower risk of depressive symptoms during acute withdrawal (adjusted OR = 0.54, 95% CI = 0.31–0.93) compared to those who did not report drinking (Table 4). 4. Discussion To the best of our knowledge, this is the first study to investigate the associations between risk factors and depressive symptoms in METH dependent subjects during the first week of abstinence. In addition, METH dependent subjects reported a high rate (64.6%) of depressive symptoms in the first week following cessation of METH use. Our findings might have important implications in developing measures to prevent METH abuse and to treat depression during acute METH withdrawal period. According to multivariate logistic regression, frequency of drug use, history of relapse and alcohol drinking were associated with moderate to severe depression (BDI-13 ≥ 8) during acute METH withdrawal. Our study suggested those with higher frequency of METH use (2–5 times per week or more) were more likely to experience moderate to severe depression than those with lower frequency use (once a week or less). A previous study showed that depressive symptoms among Chinese ATS users (not during acute withdrawal) correlated with dose of drug use, and might correlate with frequency of drug use (Bao et al., 2013). Another study in Thai
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METH users found that depressive symptoms were associated with frequency of METH use (Celentano et al., 2008). Taken together, these findings indicate that decreasing frequency of METH use will probably reduce risk for depression regardless what stage of drug use. In our study, METH-dependent subjects with history of relapse had higher risk of depression during withdrawal, this finding was consistent with the previous study (Bao et al., 2013). A study conducted in Thailand showed relapse group and persistent-use group had higher levels of depression than the group that had achieved cessation (Sutcliffe et al., 2009). Another study demonstrated that depressive symptoms were significantly associated with craving (Nakama et al., 2008), which was hypothesized to play a critical role in sustaining drug use and relapse (Pickens and Johanson, 1992; Robinson and Berridge, 2003). However, due to cross-sectional design in the present study, the causal relationship between relapse and depression was unable to be determined. Prospective studies are needed to further explore the relationship between relapse and depression. Our study showed that alcohol drinking (neither abuse nor dependence) during the period of METH use decreased the level of depression during acute stage of METH withdrawal; this result seemed inconsistent with findings from former studies. For example, alcohol use disorders were highly prevalent among people with depressive and/or anxiety disorders (Boschloo et al., 2011; Burns and Teesson, 2002; de Graaf et al., 2002; Hasin et al., 2007) and remission of major depressive disorder was less likely achieved in subjects comorbid with alcohol use disorder (Mueller et al., 1994). In addition, a Chinese study showed that alcohol drinking could increase the level of depression among ATS users (Bao et al., 2013). One possible explanation for this discrepancy is the variability in alcohol consumption. A recent study found that the type of alcohol use disorders (abuse or dependence) had different impacts on the depression and anxiety: alcohol dependence, but not abuse, predicted an unfavorable course of depressive and/or anxiety disorders (Boschloo et al., 2012). In our study, subjects who met the criteria of DSM-IV for alcohol abuse or dependence were excluded. Therefore, our findings indicated that moderate alcohol use, but not alcohol abuse or dependence, during the period of METH use may have protective benefit against depression during acute stage of withdrawal. Further research is needed to examine quantity, frequency or duration of alcohol use in order to elucidate the effects of alcohol consumption on depressive symptoms during early METH withdrawal. In addition, our study found high rates (64.4%) of moderate or severe depression in acute METH withdrawal, which is consistent with previous studies. For example, preclinical studies showed that extended access to METH resulted in a depressive-like state in rats during early withdrawal (Jang et al., 2013). Furthermore, a previous study reported that depressive-like behavior (i.e., increased immobility time observed in a tail suspension test) was observed in mice administered with amphetamine released from implanted osmotic mini-pumps for 1 week followed by one day of drug withdrawal. In addition, this study also described that depressive-like behavior (i.e., increased immobility time observed in a forced swimming test) was observed in rats administered with amphetamine 6 days followed by 2–3 days of drug withdrawal (Cryan et al., 2003). Moreover, withdrawal from chronic administration of a psychostimulant had been used as an animal model of depression (Barr et al., 2002; Cryan et al., 2003). In humans, the psychomotor aspect of amphetamine withdrawal symptoms (i.e., depression, inactivity, fatigue, and anhedonia) can last for 2–7 days of the drug abstinence and, more importantly, may disappear thereafter (McGregor et al., 2005; Watson et al., 1972). These findings support a notion that depressive symptoms are major components of METH withdrawal syndromes during early abstinence.
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Table 3 Bivariate analysis of risk factors for depressive symptoms in METH acute withdrawal.
Depression (BDI ≥ 8)
Variables N Age ≤20 21–30 31–40 41–50 Gender Male Female Family status Intact family Single parent Orphan Marital status Unmarried Cohabitating Married Divorced Residence Urban Rural area Dwelling condition Living alone Living in dormitory Living with family Other Education Primary school Junior high school high school or more Employment No Yes Cigarette smoking No Yes Alcohol drinking No Yes Gambling No Yes Family history of mental illness No Yes Route of drug administration Smoking No Yes Frequency of drug use Once a week or less 2–5 times a week or more Poly substance use No Yes Age of onset ≤20 21–30 31–40 41–50 Duration of METH use (m) ≤12 13–60 >60 Days of abstinence ≤4 4–7 Number of previous detoxification ≤1 >1 Pattern of drug use Continue use Cessation 1–12 months Cessation 12 months or more History of relapse No Yes *
P < 0.05.
n
%
OR
95% CI
24 86 90 43
14 48 62 33
58.3 55.8 68.9 76.7
1.0 0.902 1.582 2.357
0.36–2.27 0.63–3.99 0.80–6.92
198 45
127 30
64.1 66.7
1.0 1.118
0.56–2.22
175 58 10
116 38 3
66.3 65.5 30
1.0 0.966 0.218
0.52–1.81 0.05–0.87*
116 16 72 39
67 11 48 31
57.8 68.8 66.7 79.5
1.0 0.35 0.568 0.52
0.149–0.83* 0.15–2.11 0.21–1.29
147 96
88 69
59.9 71.9
1.0 0.584
0.34–1.01
100 10 124 9
64 6 82 5
64 60 66.1 55.6
1.0 0.844 1.10 0.70
0.22–3.12 0.63–1.91 0.18–2.79
68 142 33
43 93 21
63.2 65.5 63.6
1.0 1.10 1.02
0.60–2.02 0.43–2.41
92 151
67 90
72.8 59.6
1.0 0.551
0.31–0.97*
23 220
15 142
65.2 64.5
1.0 0.97
0.39–2.39
129 114
91 66
70.5 57.9
1.0 0.57
0.34–0.98*
148 95
92 65
62.2 68.4
1.0 1.319
0.76–2.28
229 14
147 10
64.2 71.4
1.0 1.40
0.42–4.59
5 238
2 155
40 65.1
1.0 2.80
0.46–17.10
139 104
78 79
56.1 76
1 2.47
1.41–4.33*
192 51
118 39
61.5 76.5
1.0 2.04
1.00–4.14*
51 123 54 15
32 77 38 10
62.7 62.6 70.4 66.7
1.0 0.99 1.41 1.29
0.51–1.95 0.63–3.18 0.35–4.00
53 118 72
28 78 51
52.8 66.1 70.8
1.0 1.74 2.17
0.90–3.37 1.03–4.55*
113 130
78 79
69 60.8
1.0 0.69
0.41–1.18
153 90
91 66
59.5 73.3
1.0 1.87
1.06–3.31*
170 34 39
105 23 29
61.8 67.6 74.4
1.0 1.29 1.80
0.59–2.83 0.82–3.93
23 220
10 147
43.5 66.8
1.0 2.62
1.10–6.25*
J. Zhang et al. / Drug and Alcohol Dependence 142 (2014) 191–196 Table 4 Multivariate logistic regression of risk factors for depressive symptoms during METH early withdrawal. Variables Frequency of drug use Once a week or less 2–5 times a week or more Alcohol drinking No Yes History of relapse No Yes
Adjusted OR (95% CI) for (BDI score ≥ 8)
P value
1.0 2.8 (1.56–5.04)
0.001
1.0 0.54 (0.31–0.93)
0.028
1.0 3.4 (1.36–8.49)
0.009
Some limitations of our study should be noted. First, the use of non-standardized questionnaires is a concern, especially where most measure relied on data derived from patient self-report with no corroborating evidence. We did not apply structured instruments or rating scales for comorbidity assessments of psychotic symptoms, personality disorder and suicidal behavior etc. Some data may be underestimates due to self-report bias. For example, only 5% of subjects report a family history of mental illness. Second, the cross-sectional design of this study limits our ability to confirm a causal relationship between associated factors and depression during early withdrawal among METH users. Third, this investigation was a retrospective study, therefore, recall error and observer bias should not be excluded. Longitudinal studies are needed for future research. Fourth, the size of the sample was relatively small compared to other epidemiological studies. Fifth, subjects in our sample were recruited from compulsory detoxification institutions and may not be similar enough to the greater METH-using community to generalize our results. Sixth, we did not collect the details on the pattern of alcohol use, which may further elucidate the relationship between alcohol use and depressive symptoms during early METH withdrawal. Seventh, we found a history of relapse was a factor influencing depressive symptoms during early METH withdrawal. However, our results must be interpreted with caution due to the fact that the number of the subjects without history of relapse was much smaller than the ones with a history of relapse. Further studies with equal proportions of the two groups with vs without history of relapse are needed to increase the statistical power and to elucidate the relationship of relapse and depression. In summary, the present study indicated METH users during early withdrawal had a high prevalence for depression; several risk factors were associated with depressive symptoms during withdrawal, including frequency of METH use, history of relapse and history of alcohol drinking during the period of METH use. Identifying these factors may help to predict depression during early METH withdrawal and to develop measures to treat depression and to prevent relapse. Role of funding sources This work was funded by the grant from National Key Technology R&D Program in the 11th Five year Plan of China (2009BAI77B06) and Wenzhou Municipal Sci-Tech Bureau Program (H20100021). These sources had no further role in study design, data collection and analysis, decision to publish, or preparation of the article. Contributors Jie Zhang, Jincai He and Xiang Yang Zhang conceived of the study, supervised the statistical analyses, and prepared manuscript, and
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wrote the protocol and the paper. Ying Xie, Hang Su, Jingyan Tao, Yeming Sun, Liren Li, Haiyan Liang, Ruqian He, Bin Han, Yuling Lu, Haiwei Sun, Youdan Wei and Jun Guo were responsible for clinical data collection and the statistical analyses. All authors gave final approval for submission of the manuscript. Dr. Jincai He (the lead author) had full access to all of the data in the study and takes responsibility for the integrity of the data and accuracy of the data analyses.
Conflict of interest statement The authors declare that they have no conflict of interest.
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