Drug and Alcohol Dependence 144 (2014) 153–159
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Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep
Long-term cannabis abuse and early-onset cannabis use increase the severity of cocaine withdrawal during detoxification and rehospitalization rates due to cocaine dependence Thiago Wendt Viola a , Saulo Gantes Tractenberg a , Luis Eduardo Wearick-Silva a , Caroline Silva de Oliveira Rosa a , Júlio Carlos Pezzi b , Rodrigo Grassi-Oliveira a,∗ a Centre of Studies and Research in Traumatic Stress, Pontifical Catholic University of Rio Grande do Sul, Av. Ipiranga, 6681, prédio 11, sala 928, 90619-900 Porto Alegre, RS, Brazil b Health Science Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
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Article history: Received 29 January 2014 Received in revised form 14 August 2014 Accepted 4 September 2014 Available online 16 September 2014 Keywords: Cannabis Cocaine Substance withdrawal syndrome Adolescent Addictive behavior Hospitalization
a b s t r a c t Background: Long-term and early-onset cannabis consumption are implicated in subsequent substancerelated problems. The aim of this follow-up study was to investigate whether these patterns of cannabis use could impact cocaine withdrawal severity and cocaine craving intensity during detoxification. In addition, we investigated their impact in the rehospitalization rates due to cocaine dependence 2.5 years after detoxification assessment. Methods: The sample was composed of 93 female cocaine-dependent inpatients who were enrolled in an inpatient detoxification unit. Cocaine withdrawal symptoms were measured at the 4th, 9th and 14th days of detoxification using the cocaine selective severity assessment (CSSA). Data on the age of first years of drug use – alcohol, cannabis and cocaine – and the years of substance abuse were obtained using the Addiction Severity Index (ASI-6). Other relevant clinical variables were also investigated, including a 2.5 years follow-up assessment of number of rehospitalization due to cocaine dependence. Results: Early-onset cannabis use and long-term cannabis abuse were associated with an increase instead of a reduction in the severity of cocaine withdrawal symptoms and craving intensity during detoxification. In addition, long-term cannabis abuse predicted higher number of rehospitalization due to cocaine dependence after 2.5 years of the first detoxification assessment. Conclusions: Early-onset cannabis use and long-term cannabis abuse are associated with a worse detoxification treatment response. Our findings may help to identify patients who will struggle more severely to control cocaine withdrawal syndrome during early drug abstinence, and indicate that cannabis use prior to cocaine withdrawal should be considered an adverse factor. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Early-onset drug use is considered a significant predictor of the subsequent development of drug abuse and dependence (Sintov et al., 2009; Trenz et al., 2012). Around 34% of adolescents in the U.S. reported early-onset use of tobacco, alcohol and illicit substances (Moss et al., 2014), but during this period cannabis is the most popular illicit drug (Chadwick et al., 2013). Therefore, previous studies have shown that a significant percentage of teens (5.6%) who initiate cannabis use before the age of 15 report daily substance use and further drug abuse (Johnston et al., 2004), suggesting that
∗ Corresponding author. Tel.: +55 51 3320 3633; fax: +55 51 3320 3633. E-mail address: [email protected] (R. Grassi-Oliveira). http://dx.doi.org/10.1016/j.drugalcdep.2014.09.003 0376-8716/© 2014 Elsevier Ireland Ltd. All rights reserved.
early-onset cannabis use is a risk factor for further drug-related problems (Lynskey et al., 2003). However, the debate regarding the harmful consequences of cannabis consumption is still ongoing and the main reasons for this include the poor quality of current evidence and the small number of studies on this topic (Calabria et al., 2010). Early-onset cannabis use and abuse are also associated with potential progression to the use of other illicit substances such as cocaine. Estimations regarding the prevalence of cannabis use among primary cocaine users range between 50 and 70% (Hall and Lynskey, 2005; Lynskey et al., 2003; Wagner and Anthony, 2002). Moreover, the treatment of cocaine dependence conventionally involves detoxification programs for the management and reduction of drug craving and abstinence symptoms. However, the severity of withdrawal symptoms can vary among drug users
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(Francke et al., 2013), and multiple factors, including the history of other substance consumption, could impact treatment response (Ahmadi et al., 2008). In this sense, recent evidence indicated that concomitant cocaine and long-term cannabis use might increase craving for cocaine during drug abstinence (Fox et al., 2013). Therefore, a potential role of patterns of cannabis consumption in cocaine dependence-related problems is suggested. Despite that, research has yet to address the consequences of cannabis use on specific clinical manifestations during the initial period of cocaine detoxification. This topic is of particular interest given that the severity of cocaine withdrawal symptoms at the first two weeks of treatment is a robust predictor of the ensuing treatment response (Kampman et al., 2001, 2004). Therefore, the aim of this follow-up study was to investigate whether long-term cannabis abuse and early-onset cannabis use could impact cocaine withdrawal severity and cocaine craving intensity during the first two weeks of detoxification treatment. In addition, we investigated the impact of cannabis consumption patterns in the rehospitalization rates due to cocaine dependence after 2.5 years of assessment. 2. Methods 2.1. Participants One hundred and twenty-two female cocaine-dependent inpatients of a detoxification treatment public hospital unit in Southern Brazil took part in this study. Inclusion criteria were as follows: (1) age 18 to 45; (2) diagnosis of physiological dependence on cocaine (smoked or snorted) according to the Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-IV). Participants were excluded from this study if (1) for any reason they did not report or did not provide reliable information regarding the age at the first year of drug use and patterns of drug use behavior (n = 14); (2) for any reason they were discharged early, before the third week of treatment, resulting in absence from follow-up measurements (n = 12); (3) they presented any severe cognitive impairment that resulted in an altered state of consciousness or agitation (n = 3). Thus, the final sample was composed of 93 participants.
2.2. Measures and procedures Participants were invited to take part in the study during the first three days of treatment and provided written informed consent to participate. The detoxification treatment program consisted of three weeks of drug rehabilitation, including psychoeducation and support groups, nursing care, moderate physical activity, a balanced diet, and medical treatment. The clinical assessment protocol was composed of self-administered questionnaires and semi-structured interviews, applied within the first two weeks of treatment, and comprised of the assessment of relevant variables such as comorbid psychiatric diagnosis, history of drug use and drug-related problems, patterns of multiple drug use, and socio-demographic information. A trained team of psychologists conducted all interviews and instrument application. Data on the age at the first year of drug use – alcohol, cannabis and cocaine – and the number of years of substance abuse were obtained using the Addiction Severity Index (ASI-6; Kessler et al., 2012; McLellan et al., 1980). To characterize early- or lateonset cannabis use, we considered the mean age at the first use of cannabis (15.32 years old) to classify those participants below or above such a mean. Participants were also divided into two groups according to the presence or absence of longterm cannabis abuse—defined as more than 5 years of substance abuse using at least three days per week. Additional data regarding the consequences of substance use were investigated by the ASI-6 subscales of family support problems, legal problems, employment problems and drug-related problems. Cocaine withdrawal symptoms were measured at the 4th, 9th, and 14th days of detoxification using the cocaine selective severity assessment (CSSA; Kampman et al., 1998). The CSSA is an 18-item clinic-based measure of the severity of cocaine abstinence symptoms and each of the 18 individual items is scored on a 0–7 scale, in which 0 represents no symptoms and 7 represents maximum severity. The signs and symptoms included on the CSSA assessment were cocaine craving, depressed mood, appetite changes, sleep disturbances, lethargy, and bradycardia, which are manifestations that commonly occur after abrupt cessation of cocaine use. In addition, items 4 and 5 of the instrument are aimed at investigating the cocaine craving symptoms. Therefore, specific craving symptoms were defined as the sum value of these two items. In order to verify the percentage of variation regarding the severity of the withdrawal symptoms, we calculated the fractional change [((14th day CSSA score − 4th day CSSA score)/4th day CSSA score)×100]. Moreover, if a negative value was derived from the fractional change equation, a reduction in withdrawal symptoms was assumed (Wilson and Hernández-Hall, 2009). Otherwise, in the case
of a positive value an increase of withdrawal symptoms was assumed. Thus, the withdrawal symptoms variation was also categorized into a dichotomous variable. Additional clinical and socio-demographic data were considered. Nicotinedependence severity was assessed by the Fagerstrom test (Heatherton et al., 1991). The effects of the presence of comorbid diagnosis, including mood disorders, anxiety disorders and posttraumatic stress disorder (PTSD) were investigated. Therefore, the Structured Clinical Interview for DSM Disorders (SCID I; Del-Ben et al., 2001) was conducted by a psychiatrist and two psychologists with clinical experience. In addition, analyses of pharmacotherapy effects, such as the use of mood stabilizers, antipsychotics or antidepressants, were performed (benzodiazepines were not prescribed in this treatment protocol). Participants also answered questions about socio-demographic characteristics, including information about years of formal education, estimated income per month, and days of abstinence prior to treatment admission. This study was conducted from December, 2010 to December, 2011. Since all participants belong to the same treatment catchment area, we monitored how many rehospitalizations due to cocaine dependence occurred from the first assessment until June, 2014 (2.5 years). The research protocol was approved by the Ethical Committee of the enrolled institutions. 2.3. Statistical analysis The Shapiro–Wilk test was used for the analysis of normality of data distributions for each variable. Descriptive statistics for psychosocial variables were conducted using chi-square tests or t-tests for independent samples, as well as group comparisons regarding CSSA total scores, cocaine craving intensity, number of rehospitalizations due to cocaine dependence, patterns of drug use behavior, onset of drug use, pharmacotherapy, and the presence of comorbid diagnosis. Fractional change between group effects (long-term versus non-long-term-cannabis abuse and early- versus late-onset cannabis use) was assessed by analysis of covariance (ANCOVA), controlling for mean age. In addition, two multiple logistic regression models were used to analyze the (a) influence of early-onset cannabis use and, (b) the duration of cannabis abuse predicting withdrawal outcomes. In these models the dependent variable was the dichotomous fractional change of CSSA scores during detoxification. We included age as a predicting factor in these models since we found a slight difference between the early- and late-onset cannabis use groups. In the first equation, in order to exclude confusion factors regarding the early use of other drugs, we included in the model whether participants did or did not have early use of alcohol, tobacco and cocaine (also considering the mean age of first use of these drugs in the whole sample). In the second equation, in order to exclude confusion factors regarding the duration of other drug abuse we included the number of years of alcohol, tobacco and cocaine abuse. Finally, we performed a linear multiple regression with one block including whether or not participants had early-onset cannabis use, whether or not they reported long-term cannabis abuse, and number of years of cocaine consumption as predictors of the number of rehospitalizations due to cocaine dependence after 2.5 years since first assessment. All analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 20.0 (SPSS Inc., Chicago, IL, USA), using two-sided tests and a significance level set at 0.05.
3. Results 3.1. Descriptive statistics All participants reported having smoked cocaine and 89% reported having snorted cocaine as well. Overall, 66% of the sample reported early-onset cannabis use and 52% reported long-term cannabis abuse. Participants with early-onset cannabis use presented a higher severity of cocaine withdrawal symptoms and specific cocaine craving symptoms at the 14◦ day of detoxification compared with those with late-onset cannabis use (see Table 1). In addition, a trend association was found between early-onset cannabis use and more craving symptoms at the 4th day of treatment. Similarly, those with long-term cannabis abuse presented higher cocaine withdrawal symptoms at the 9th and 14th days of assessment compared with those that did not report such a pattern of drug use, but no interaction was found regarding craving symptoms (see Table 2). Additional analysis demonstrated that the early-onset cannabis use group also presented early-onset alcohol use and that the longterm cannabis abuse group presented more employment problems related to addiction. In addition, participants with early-onset cannabis use and long-term cannabis abuse presented significantly more total hospitalizations due to cocaine dependence, as well as more rehospitalizations after treatment discharge in a 2.5
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Table 1 Socio-demographic and clinical characteristics of groups related to the onset of cannabis use.
Socio-demographic—mean (SD)/n (%) Age Employed Stable marital relationship Income per month (US$) Pharmacotherapy—n (%) Mood stabilizers/Anticonvulsants Antipsychotics Antidepressants Psychiatric comorbidities—n (%) Mood disorders Anxiety disorders PTSD Age at first drug use—mean (SD) Tobacco Alcohol Cannabis Cocaine Cocaine withdrawal—mean (SD) CSSA total score 4th day CSSA total score 9th day CSSA total score 14th day Cocaine craving symptoms—mean (SD) CSSA craving score 4th day CSSA craving score 9th day CSSA craving score 14th day Nicotine-dependence severity—mean (SD) Fagerstrom total score ASI-6—mean (SD) Drugs Family support Legal problems Employment problems Days of abstinence prior to treatment Cannabis use in the last month Hospitalizations due cocaine dependence—mean (SD) Total hospitalizations Re-hospitalizations after treatment discharge
Early-onset cannabis Use (n = 62)
Late-onset cannabis use (n = 31)
p-Value
27.45 (6.40) 19 (30.64) 17 (27.41) 479.30 (390.330)
30.32 (6.41) 9 (29.03) 10 (32.25) 649.05 (611.49)
0.045 0.87 0.62 0.10
40 (64.51) 40 (64.51) 3 (5.08)
31 (58.06) 31 (58.06) 4 (15.38)
0.54 0.54 0.15
21 (33.87) 3 (4.83) 8 (12.9)
13 (41.93) 4 (12.9) 6 (19.35)
0.44 0.16 0.41
12.96 (2.99) 13.92 (2.8) 13.35 (1.67) 21.54 (6.64)
15.86 (3.68) 16.76 (2.94) 19.25 (4.49) 24.48 (6.98)
0.000 0.000 0.000 0.51
56.04 (18.68) 53.19 (22.36) 50.85 (19.83)
52.48 (15.97) 45.60 (19.16) 42.01 (19.40)
0.36 0.11 0.044
5.48 (4.42) 5.09 (4.52) 5.25 (4.75)
3.64 (3.85) 4.16 (4.10) 2.48 (3.06)
0.05 0.34 0.004
4.87 (2.99)
4.34 (4.00)
0.51
62.92 (10.38) 44.72 (13.88) 64.05 (8.31) 43.62 (7.98) 2.72 (4.86) 27 (43.5)
63.34 (11.24) 44.15 (11.28) 61.22 (9.15) 44.52 (7.61) 2.62 (3.99) 10 (32.2)
0.87 0.84 0.19 0.63 0.92 0.29
6.07 (3.88) 4.85 (3.41)
4.43 (2.60) 3.47 (2.55)
0.022 0.036
Note: n, number of participants; SD, standard deviation; CSSA, cocaine selective severity assessment, ASI-6, Addiction Severity Index.
years follow-up assessment. There were no significant differences between groups regarding cannabis use in the last 30 days prior to treatment enrolment.
3.2. Cocaine withdrawal symptoms variation Considering the whole sample, it is interesting to highlight that 36% (n = 34) showed an increase in cocaine withdrawal symptoms severity after two weeks of detoxification, based on the fractional change data. Specifically, 46% (n = 29) of the participants that reported early-onset cannabis use and 48% (n = 24) of those that reported long-term cannabis abuse presented this increase in withdrawal symptoms (see Fig. 1). The non-long-term cannabis abuse group exhibited 17.91% cocaine withdrawal symptoms reduction during detoxification, while the long-term cannabis abuse group exhibited a reduction of only 3.91% [F(1,90) = 5.004; p = 0.028]. In the same way, the late-onset cannabis use group presented withdrawal symptoms severity reduction of 17.69%, while the early-onset cannabis use group presented a decrease of 6.95%, but no significance in the between group comparison [F(1,90) = 2.440; p = 0.122] was found, probably due to type II error. To identify whether early-onset and long-term cannabis use predict worse abstinence symptoms during cocaine detoxification independently of other substances (tobacco, alcohol and cocaine), we conducted multiple logistic regression models including other substances as covariates in addition to age. Regarding long-term cannabis abuse we found that it independently predicted a worse detoxification treatment response and the logistical regression
model showed adequate goodness-of-fit. However, when we included tobacco variable in the early-onset logistic regression equation, the model was not correctly specified because it failed in the Hosler–Lemeshow test (p = 0.040). Therefore we performed univariate logistic regression including early-onset tobacco use as predictor of cocaine detoxification treatment response, in order to investigate its effects on cocaine withdrawal. We did not find any association between early-onset tobacco use and cocaine detoxification treatment response (Wald = 2.04, df = 1, p = 0.15). In sum,
Fig. 1. Proportion of participants with an increase of cocaine withdrawal symptoms during detoxification depending on the patterns of cannabis use history. Note: Non long-term (22.7%) and long-term cannabis abuse (48.9%) comparison by group regarding the proportion with an increase of withdrawal symptoms severity (chisquare value = 6.88; p = 0.009); late- (16%) and early-onset cannabis use (46.7%) comparison by group regarding the proportion with an increase of withdrawal symptoms severity (chi-square value = 8.36; p = 0.004).
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Table 2 Socio-demographic and clinical characteristics of groups related to years of cannabis abuse.
Socio-demographic—mean (SD)/n (%) Age Employed Stable marital relationship Income per month (US$) Pharmacotherapy—n (%) Mood stabilizers/anticonvulsants Antipsychotics Antidepressants Psychiatric comorbidities—n (%) Mood disorders Anxiety disorders PTSD Years of drug abuse—mean (SD) Tobacco Alcohol Cannabis Cocaine Cocaine withdrawal—mean (SD) CSSA total score 4th day CSSA total score 9th day CSSA total score 14th day Cocaine craving symptoms—mean (SD) CSSA craving score 4th day CSSA craving score 9th day CSSA craving score 14th day Nicotine-dependence severity—mean (SD) Fagerstrom total score ASI-6—mean (SD) Drugs Family support Legal problems Employment problems Days of abstinence prior to treatment Hospitalizations due cocaine dependence—mean (SD) Total hospitalizations Re-hospitalizations after treatment discharge
Long-term cannabis abuse (n = 49)
Not long-term cannabis abuse (n = 44)
p-Value
28.57 (5.11) 13 (26.53) 11 (22.44) 599.84 (423.11)
28.22 (7.84) 15 (34.09) 16 (36.36) 467.39 (531.22)
0.80 0.42 0.14 0.18
28 (57.14) 28 (57.14) 4 (8.16)
30 (68.18) 30 (68.18) 3 (6.81)
0.27 0.27 0.55
19 (38.77) 4 (8.16) 5 (10.2)
15 (34.08) 3 (6.81) 9 (20.45)
0.64 0.80 0.16
14.31 (4.93) 3.43 (5.72) 12.20 (4.99) 7.71 (6.87)
12.25 (7.26) 3.36 (6.14) 1.02 (1.48) 6.47 (11.49)
0.13 0.95 0.004 0.53
56.59 (17.48) 55.73 (22.25) 51.84 (18.44)
52.93 (18.19) 45.00 (19.47) 43.52 (21.00)
0.32 0.016 0.041
5.24 (4.28) 5.24 (4.46) 4.73 (4.77)
4.45 (4.34) 4.27 (4.30) 3.88 (4.06)
0.38 0.29 0.36
4.97 (2.87)
4.29 (3.96)
63.82 (10.17) 45.09 (11.71) 62.95 (8.38) 45.54 (7.37) 2.11 (3.74)
62.16 (11.10) 43.47 (12.52) 63.61 (8.98) 41.97 (8.01) 3.36 (5.34)
0.49 0.54 0.73 0.033 0.21
6.47 (4.01) 5.29 (3.49)
4.41 (2.63) 3.32 (2.47)
0.006 0.003
0.38
Note: n, number of participants; SD, standard deviation; CSSA, cocaine selective severity assessment, ASI-6, Addiction Severity Index.
Table 3 Multiple logistic regression models. Withdrawal symptoms increase during detoxification (n = 33) OR (95% CI)
p-Value
1.16 (0.39, 3.42) 3.97 (1.15, 13.71) 1.82 (0.49, 6.73) 1.01 (0.90, 1.08)
0.78 0.020 0.36 0.95
1.04 (0.95, 1.13) 2.84 (1.06, 7.59) 1.02 (0.91, 1.15) 0.99 (0.99, 1.01) 0.94 (0.87, 1.03)
0.36 0.037 0.66 0.48 0.23
1
Early-onset drug use effects Alcohol (n = 80) Cannabis (n = 93) Cocaine (n = 93) Age (n = 93) Years of drug abuse effects2 Alcohol (n = 80) Cannabis (n = 93) Cocaine (n = 93) Tobacco (n = 84) Age (n = 93)
Note: Logistic regression models used to analyze the influence of patterns of cannabis use predicting withdrawal outcomes. 1 Wald = 5.01; df = 1; p = 0.025. 2 Wald = 6.37; df = 1; p = 0.012.
we documented that early-onset and long-term cannabis use predicted worse abstinence symptoms during cocaine detoxification independently of early-onset and long-term tobacco, alcohol and cocaine use. (see Table 3). 3.3. Effects of patterns of cannabis use on subsequent rehospitalizations due to cocaine dependence Next, when we modeled a linear regression for the effects of early-onset cannabis use and long-term cannabis abuse in the number of subsequent rehospitalizations due to cocaine dependence (2.5 years follow-up), we found that long-term cannabis abuse significantly predicted rehospitalizations for cocaine detoxification, independently of lifetime years of cocaine use and early-onset cannabis use history (see Table 4). 4. Discussion The current study shows that long-term cannabis abuse and early-onset cannabis use are associated with a worse detoxification
Table 4 Multiple linear regression predicting re-hospitalization due cocaine dependence after 2.5 years of first detoxification assessment. R 0.35 Long-term cannabis abuse Early-onset cannabis use Years of cocaine consumption
R2 0.12
F
df
3.95
3.85
Beta 0.28 0.16 −0.07
p 0.011 0.008 0.12 0.45
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treatment response in a cohort of female inpatients with cocaine dependence. More specifically, patients who exhibit more severe cocaine withdrawal symptoms at the end of two weeks of detoxification also reported early-onset cannabis use or a prolonged history of cannabis abuse. Such histories of cannabis consumption were associated with an increase, instead of a reduction, in cocaine withdrawal symptoms during detoxification, regardless of age and patterns of alcohol or cocaine use. In addition, longterm cannabis abuse predicted higher rehospitalization rates due to cocaine dependence after 2.5 years follow-up assessment. Therefore, taking into account specific patterns of cannabis use may help to identify patients that will struggle more severely to treat cocaine dependence and to control cocaine craving and abstinence syndrome during detoxification. These findings corroborate prior research suggesting that cannabis use might be a feature that underlies a vulnerability to further substance-related problems (Aharonovich et al., 2006). The cannabis “gateway hypothesis” proposes that early-onset cannabis use and regular cannabis consumption are strong predictors of other drug use (Fergusson et al., 2006). In addition, some studies have also demonstrated that early- and late-onset substance users might differ in withdrawal symptoms severity during early abstinence and this is important regarding treatment effectiveness (Kampman et al., 2001). For example, early cannabis use was associated with the occurrence and the severity of psychotic symptoms during cocaine intoxication (Trape et al., 2014). Furthermore, despite the independent effects of cannabis in treatment response, we found that within the early-onset cannabis group the age of first use of tobacco and alcohol was lower than the comparison group, which is in agreement with previous evidence (Lynskey et al., 2003). Babor et al. (1992) proposed a typology to distinguish drug users based on the history and patterns of substance use, suggesting a “Type B” kind of patient with alcohol dependence, who was more likely to exhibit drug-related problems and severe clinical manifestation in response to substance withdrawal. Similar patterns were found regarding the association between cocaine withdrawal symptoms severity at the beginning of detoxification treatment and characteristics of Type B cocaine-dependent patients (Ahmadi et al., 2008). Specifically, in the Ahmadi study, higher withdrawal symptoms during early abstinence predicted worse pharmacological or psychosocial treatment outcomes. Interestingly, the authors assessed the cocaine withdrawal syndrome using the same instrument as used in our study, the CSSA. Therefore, the data reported here supports the idea that specific patterns of cannabis use history are also factors related to Type B patients with cocaine dependence. The lack of group differences regarding other clinical factors, such as pharmacotherapy treatment and comorbid psychiatric disorders, suggest that the patterns of cannabis consumption affect the symptoms of cocaine withdrawal particularly. Also, it is worth mentioning that the association found between long-term cannabis abuse and more employment problems is supported by previous research. For example, a comprehensive longitudinal study conducted with cannabis abusers reported that cannabis consumption during adolescence and early adulthood is related with several adverse outcomes in later life, including poorer educational outcomes, lower income, unemployment and lower life satisfaction, demonstrating the adverse consequences of heavy cannabis abuse before adulthood (Fergusson and Boden, 2008). Another body of research has been investigating the biological mechanisms involved in the effects of cannabis use and further drug-related problems. Recent data showed that stress system changes (i.e., hypothalamic–pituitary–adrenal axis) are associated with cannabis dependence and such changes can increase craving for cocaine during drug (Fox et al., 2013). Furthermore, the psychoactive effects of cannabis are predominantly mediated by tetrahydrocannabinol (THC), through its interaction with the
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brain endocannabinoid system. Interestingly, the endocannabinoid system seems to regulate several processes involved in brain development and neuroplasticity, and early cannabis exposure has been suggested to enhance the risk for substance-related problems via neuroadaptations associated with endocannabinoid signaling and changes in brain reward system functioning (Chadwick et al., 2013). Additionally, emerging evidence from animal studies revealed a higher sensitivity to cocaine effects due to early THC exposure, especially during the beginning of rodent adolescence, and the endocannabinoid system partially mediated such effect (Hernandez et al., 2013; Orio et al., 2009; Wiskerke et al., 2008). Hence, from a neurobiological perspective, higher cocaine withdrawal and craving symptoms could be associated with brain endocannabinoid system functioning alterations due to early cannabis exposure. The results of the current study should be interpreted in light of its limitations. One limitation is the lack of assessment of the quantity of drug consumption, which could give us a better estimation regarding the variability in cannabis use severity. However, to estimate the precise amount is a complex issue because of the indiscriminate polysubstance use of our sample. Moreover, consumption assessment instruments require individuals to nominate both the frequency and quantity of cannabis used and the few tools that assess quantity of cannabis provide conflicting guidelines for quantity measurement (Robinson et al., 2014). Another limitation is related to the sample size, but it is difficult to recruit participants for a longitudinal research. Nevertheless, such a design added quality to our study. In addition, the participants were all female and the findings cannot be generalized to male samples. In this regard, important differences between females and males have been found regarding the initiation and the frequency of substance use (Fattore et al., 2008), craving and abstinence symptoms severity (Becker and Hu, 2008), probably due neurobiological specificities related to drug addiction among males and females (Becker et al., 2012). For instance, there are sex differences in the number and firing rates of mescencephalic dopamine neurons (Murray et al., 2003) and female gonadal hormones seem to selectively influence regional dopamine neurotransmission, affecting the sensitivity to drug rewarding/aversive effects and certain aspects of drug-seeking behavior (Becker et al., 2012). Furthermore, clinical studies showed that women start using cocaine earlier than do men and the rate of drug use escalation is greater for women than for men (Dluzen and McDermott, 2008). Women also report higher craving severity and greater amounts consumed when compared to men seeking treatment (Elman et al., 2001). Moreover, epidemiological data support important sex differences regarding crack cocaine dependence in Brazil, showing that men are more likely to use crack cocaine, while female crack cocaine users report higher craving severity and are more vulnerable to develop dependence (Abdalla et al., 2014). Therefore, a replication of this study with larger samples, including male subjects, should be undertaken. Despite these limitations, the current findings can provide clinicians with a detailed understanding of the characteristics of individuals who are most likely to present severe cocaine withdrawal syndrome at detoxification entry, helping to anticipate the detoxification treatment outcomes based on factors related to the history of cannabis use. Furthermore, we detected that higher rehospitalizations due to cocaine dependence was predicted by long-term cannabis abuse. These current results extend earlier findings showing that chronic cannabis use can increase the rates of relapse in polysubstance abusers and that postdischarge cannabis consumption can substantially reduce the chances of a stable remission from use of any substance, including cocaine (Aharonovich et al., 2005; Alessi et al., 2011). In this sense, clinicians strongly should consider the patterns of cannabis use when planning treatment aftercare for cocaine dependence. Finally, our
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findings raise doubts about policies, programs and evidences proposing cannabis use as a therapeutic factor and a potential substitute for cocaine use for individuals with cocaine dependence (Lucas, 2012). Thus, rather than encouraging cannabis consume among cocaine dependents, health professionals should be very cautious about its worsening effects on abstinence symptoms. In addition, the prevention of cannabis use in adolescence would appear to be a priority for health policies, given the potential harm that it can cause regarding the reward system (De Bellis et al., 2013; Volkow and Baler, 2014), especially to those who go on to develop cocaine dependence later in life. Author disclosures Role of funding source This study was supported by MCT/CT-Saúde—DECIT/SCTIE/MS, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Grant number 402723/2010-4) and Fundac¸ão de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) (Grant number 11/1302-7). The funding source had no involvement in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the paper for publication. Contributors Authors Viola T.W., J.C. Pezzi and Grassi-Oliveira R. designed the study and wrote the protocol. Authors Viola T.W., Tractenberg S.G., Wearick-Silva, L.E. and Rosa, C.S.O., managed the literature searches and summaries of previous related work. Authors Viola T.W., Wearick-Silva, L.E. and Grassi-Oliveira R. undertook the statistical analysis, and authors Viola T.W. and Tractenberg S.G. wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest All authors declare that they have no conflicts of interest. Acknowledgements The authors would like to thank all members of GNCD and Sistema de Saúde Mãe de Deus. References Abdalla, R.R., Madruga, C.S., Ribeiro, M., Pinsky, I., Caetano, R., Laranjeira, R., 2014. Prevalence of cocaine use in Brazil: data from the II Brazilian National Alcohol and Drugs Survey (BNADS). Addict. Behav. 39, 297–301. Aharonovich, E., Garawi, F., Bisaga, A., Brooks, D., Raby, W.N., Rubin, E., Nunes, E.V., Levin, F.R., 2006. Concurrent cannabis use during treatment for comorbid ADHD and cocaine dependence: effects on outcome. Am. J. Drug Alcohol Abuse 32, 629–635, http://dx.doi.org/10.1080/00952990600919005. Aharonovich, E., Liu, X., Samet, S., Nunes, E., Waxman, R., Hasin, D., 2005. Postdischarge cannabis use and its relationship to cocaine, alcohol, and heroin use: a prospective study. Am. J. Psychiatry 162, 1507–1514, http://dx.doi.org/10.1176/appi.ajp.162.8.1507. Ahmadi, J., Kampman, K., Dackis, C., Sparkman, T., Pettinati, H., 2008. Cocaine withdrawal symptoms identify “Type B” cocaine-dependent patients. Am. J. Addict. 17, 60–64, http://dx.doi.org/10.1080/10550490701755999. Alessi, S.M., Rash, C., Petry, N.M., 2011. Contingency management is efficacious and improves outcomes in cocaine patients with pretreatment marijuana use. Drug Alcohol Depend. 118, 62–67, http://dx.doi.org/10.1016/j.drugalcdep.2011.03.001. Babor, T.F., Hofmann, M., DelBoca, F.K., Hesselbrock, V., Meyer, R.E., Dolinsky, Z.S., Rounsaville, B., 1992. Types of alcoholics, I. Evidence for an empirically derived typology based on indicators of vulnerability and severity. Arch. Gen. Psychiatry 49, 599–608. Becker, J.B., Hu, M., 2008. Sex differences in drug abuse. Front. Neuroendocrinol. 29, 36–47, http://dx.doi.org/10.1016/j.yfrne.2007.07.003.
Becker, J.B., Perry, A.N., Westenbroek, C., 2012. Sex differences in the neural mechanisms mediating addiction: a new synthesis and hypothesis. Biol. Sex Differ. 3, 14, http://dx.doi.org/10.1186/2042-6410-3-14. Calabria, B., Degenhardt, L., Hall, W., Lynskey, M., 2010. Does cannabis use increase the risk of death? Systematic review of epidemiological evidence on adverse effects of cannabis use. Drug Alcohol Rev. 29, 318–330, http://dx.doi.org/10.1111/j.1465-3362.2009.00149.x. Chadwick, B., Miller, M.L., Hurd, Y.L., 2013. Cannabis use during adolescent development: susceptibility to psychiatric illness. Front. Psychiatry 4, 129, http://dx.doi.org/10.3389/fpsyt.2013.00129. De Bellis, M.D., Wang, L., Bergman, S.R., Yaxley, R.H., Hooper, S.R., Huettel, S.A., 2013. Neural mechanisms of risky decision-making and reward response in adolescent onset cannabis use disorder. Drug Alcohol Depend. 133, 134–145, http://dx.doi.org/10.1016/j.drugalcdep.2013.05.020. Del-Ben, C.M., Vilela, J.A., Crippa, J.A., Hallak, J.E.C., Labati, C.M., Zuardi, A.W., 2001. Reliability of the structured clinical interview for dsm-iv—clinical version translated into portuguese. Braz. J. Psychiatry 23, 156–159. Dluzen, D.E., McDermott, J.L., 2008. Sex differences in dopamine- and vesicular monoamine-transporter functions. Ann. N.Y. Acad. Sci. 1139, 140–150, http://dx.doi.org/10.1196/annals.1432.010. Elman, I., Karlsgodt, K.H., Gastfriend, D.R., 2001. Gender differences in cocaine craving among non-treatment-seeking individuals with cocaine dependence. Am. J. Drug Alcohol Abuse 27, 193–202. Fattore, L., Altea, S., Fratta, W., 2008. Sex differences in drug addiction: a review of animal and human studies. Womens Health (London Engl.) 4, 51–65, http://dx.doi.org/10.2217/17455057.4.1.51. Fergusson, D.M., Boden, J.M., 2008. Cannabis use and later life outcomes. Addiction 103, 969–976, http://dx.doi.org/10.1111/j.1360-0443.2008.02221.x (discussion 977-968). Fergusson, D.M., Boden, J.M., Horwood, L.J., 2006. Cannabis use and other illicit drug use: testing the cannabis gateway hypothesis. Addiction 101, 556–569, http://dx.doi.org/10.1111/j.1360-0443.2005.01322.x. Fox, H.C., Tuit, K.L., Sinha, R., 2013. Stress system changes associated with marijuana dependence may increase craving for alcohol and cocaine. Hum. Psychopharmacol. 28, 40–53, http://dx.doi.org/10.1002/hup.2280. Francke, I.D., Viola, T.W., Tractenberg, S.G., Grassi-Oliveira, R., 2013. Childhood neglect and increased withdrawal and depressive severity in crack cocaine users during early abstinence. Child Abuse Negl. 37, 883–889, http://dx.doi.org/10.1016/j.chiabu.2013.04.008. Hall, W.D., Lynskey, M., 2005. Is cannabis a gateway drug? Testing hypotheses about the relationship between cannabis use and the use of other illicit drugs. Drug Alcohol Rev. 24, 39–48, http://dx.doi.org/10.1080/09595230500126698. Heatherton, T.F., Kozlowski, L.T., Frecker, R.C., Fagerstrom, K.O., 1991. The Fagerstrom test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br. J. Addict. 86, 1119–1127. Hernandez, G., Oleson, E.B., Gentry, R.N., Abbas, Z., Bernstein, D.L., Arvanitogiannis, A., Cheer, J.F., 2013. Endocannabinoids promote cocaine-induced impulsivity and its rapid dopaminergic correlates. Biol. Psychiatry 75, 487–498, http://dx.doi.org/10.1016/j.biopsych.2013.09.005. Johnston, L.D., O’Malley, P.M., Bachman, J.G., 2004. Monitoring the Future National Survey Results on Drug Use, 1975–2003. Volume I: Secondary School Students National Institute on Drug Abuse. NIH Publications (04-5507-55045508). Kampman, K.M., Alterman, A.I., Volpicelli, J.R., Maany, I., Muller, E.S., Luce, D.D., Mulholland, E.M., Jawad, A.F., Parikh, G.A., Mulvaney, F.D., Weinrieb, R.M., O’Brien, C.P., 2001. Cocaine withdrawal symptoms and initial urine toxicology results predict treatment attrition in outpatient cocaine dependence treatment. Psychol. Addict. Behav. 15, 52–59. Kampman, K.M., Pettinati, H.M., Volpicelli, J.R., Oslin, D.M., Lipkin, C., Sparkman, T., O‘Brien, C.P., 2004. Cocaine dependence severity predicts outcome in outpatient detoxification from cocaine and alcohol. Am. J. Addict. 13, 74–82. Kampman, K.M., Volpicelli, J.R., McGinnis, D.E., Alterman, A.I., Weinrieb, R.M., D’Angelo, L., Epperson, L.E., 1998. Reliability and validity of the cocaine selective severity assessment. Addict. Behav. 23, 449–461. Kessler, F., Cacciola, J., Alterman, A., Faller, S., Souza-Formigoni, M.L., Santos Cruz, M., Brasilliano, S., Pechansky, F., 2012. Psychometric properties of the sixth version of the Addiction Severity Index (ASI-6) in Brazil. Rev. Bras. Psiquiatr. 34, 24–33, S1516-44462012000100006 [pii]. Lucas, P., 2012. Cannabis as an adjunct to or substitute for opiates in the treatment of chronic pain. J. Psychoactive Drugs 44, 125–133. Lynskey, M.T., Heath, A.C., Bucholz, K.K., Slutske, W.S., Madden, P.A., Nelson, E.C., Statham, D.J., Martin, N.G., 2003. Escalation of drug use in early-onset cannabis users vs co-twin controls. JAMA 289, 427–433. McLellan, A.T., Luborsky, L., Woody, G.E., O’Brien, C.P., 1980. An improved diagnostic evaluation instrument for substance abuse patients. The Addiction Severity Index. J. Nerv. Ment. Dis. 168, 26–33. Moss, H.B., Chen, C.M., Yi, H.Y., 2014. Early adolescent patterns of alcohol, cigarettes, and marijuana polysubstance use and young adult substance use outcomes in a nationally representative sample. Drug Alcohol Depend. 136, 51–62, http://dx.doi.org/10.1016/j.drugalcdep.2013.12.011, Epub 2013 Dec 31. Murray, H.E., Pillai, A.V., McArthur, S.R., Razvi, N., Datla, K.P., Dexter, D.T., Gillies, G.E., 2003. Dose- and sex-dependent effects of the neurotoxin 6-hydroxydopamine on the nigrostriatal dopaminergic pathway of adult rats: differential actions of estrogen in males and females. Neuroscience 116, 213–222. Orio, L., Edwards, S., George, O., Parsons, L.H., Koob, G.F., 2009. A role for the endocannabinoid system in the increased motivation for
T.W. Viola et al. / Drug and Alcohol Dependence 144 (2014) 153–159 cocaine in extended-access conditions. J. Neurosci. 29, 4846–4857, http://dx.doi.org/10.1523/JNEUROSCI.0563-09.2009. Robinson, S.M., Sobell, L.C., Sobell, M.B., Leo, G.I., 2014. Reliability of the Timeline Followback for cocaine, cannabis, and cigarette use. Psychol. Addict. Behav. 28, 154–162, http://dx.doi.org/10.1037/a0030992. Sintov, N.D., Kendler, K.S., Walsh, D., Patterson, D.G., Prescott, C.A., 2009. Predictors of illicit substance dependence among individuals with alcohol dependence. J. Stud. Alcohol Drugs 70, 269–278. Trape, S., Charles-Nicolas, A., Jehel, L., Lacoste, J., 2014. Early cannabis use is associated with severity of cocaine-induced psychosis among cocaine smokers in Martinique, French West Indies. J. Addict. Med. 8, 33–39, http://dx.doi.org/10.1097/ADM.0000000000000003. Trenz, R.C., Scherer, M., Harrell, P., Zur, J., Sinha, A., Latimer, W., 2012. Early onset of drug and polysubstance use as predictors of injection drug use among adult drug users. Addict. Behav. 37, 367–372, http://dx.doi.org/10.1016/j.addbeh.2011.11.011.
159
Volkow, N.D., Baler, R.D., 2014. Addiction science: uncovering neucomplexity. Neuropharmacology 76 Pt B, 235–249, robiological http://dx.doi.org/10.1016/j.neuropharm.2013.05.007. Wagner, F.A., Anthony, J.C., 2002. From first drug use to drug dependence; developmental periods of risk for dependence upon marijuana, and alcohol. Neuropsychopharmacology 26, 479–488, cocaine, http://dx.doi.org/10.1016/S0893-133X(01)00367-0. Wilson, J., Hernández-Hall, C., 2009. The thermal coefficient of linear expansion. In: Wilson, J., Hernández-Hall, C. (Eds.), Physics Laboratory Experiments. Cengage Learning, New York, NY (251 pp.). Wiskerke, J., Pattij, T., Schoffelmeer, A.N., De Vries, T.J., 2008. The role of CB1 receptors in psychostimulant addiction. Addict. Biol. 13, 225–238, http://dx.doi.org/10.1111/j.1369-1600.2008.00109.x.
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Long-term cannabis abuse and early-onset cannabis use increase the severity of cocaine withdrawal during detoxification and rehospitalization rates due to cocaine dependence Thiago Wendt Viola a , Saulo Gantes Tractenberg a , Luis Eduardo Wearick-Silva a , Caroline Silva de Oliveira Rosa a , Júlio Carlos Pezzi b , Rodrigo Grassi-Oliveira a,∗ a Centre of Studies and Research in Traumatic Stress, Pontifical Catholic University of Rio Grande do Sul, Av. Ipiranga, 6681, prédio 11, sala 928, 90619-900 Porto Alegre, RS, Brazil b Health Science Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, RS, Brazil
a r t i c l e
i n f o
Article history: Received 29 January 2014 Received in revised form 14 August 2014 Accepted 4 September 2014 Available online 16 September 2014 Keywords: Cannabis Cocaine Substance withdrawal syndrome Adolescent Addictive behavior Hospitalization
a b s t r a c t Background: Long-term and early-onset cannabis consumption are implicated in subsequent substancerelated problems. The aim of this follow-up study was to investigate whether these patterns of cannabis use could impact cocaine withdrawal severity and cocaine craving intensity during detoxification. In addition, we investigated their impact in the rehospitalization rates due to cocaine dependence 2.5 years after detoxification assessment. Methods: The sample was composed of 93 female cocaine-dependent inpatients who were enrolled in an inpatient detoxification unit. Cocaine withdrawal symptoms were measured at the 4th, 9th and 14th days of detoxification using the cocaine selective severity assessment (CSSA). Data on the age of first years of drug use – alcohol, cannabis and cocaine – and the years of substance abuse were obtained using the Addiction Severity Index (ASI-6). Other relevant clinical variables were also investigated, including a 2.5 years follow-up assessment of number of rehospitalization due to cocaine dependence. Results: Early-onset cannabis use and long-term cannabis abuse were associated with an increase instead of a reduction in the severity of cocaine withdrawal symptoms and craving intensity during detoxification. In addition, long-term cannabis abuse predicted higher number of rehospitalization due to cocaine dependence after 2.5 years of the first detoxification assessment. Conclusions: Early-onset cannabis use and long-term cannabis abuse are associated with a worse detoxification treatment response. Our findings may help to identify patients who will struggle more severely to control cocaine withdrawal syndrome during early drug abstinence, and indicate that cannabis use prior to cocaine withdrawal should be considered an adverse factor. © 2014 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Early-onset drug use is considered a significant predictor of the subsequent development of drug abuse and dependence (Sintov et al., 2009; Trenz et al., 2012). Around 34% of adolescents in the U.S. reported early-onset use of tobacco, alcohol and illicit substances (Moss et al., 2014), but during this period cannabis is the most popular illicit drug (Chadwick et al., 2013). Therefore, previous studies have shown that a significant percentage of teens (5.6%) who initiate cannabis use before the age of 15 report daily substance use and further drug abuse (Johnston et al., 2004), suggesting that
∗ Corresponding author. Tel.: +55 51 3320 3633; fax: +55 51 3320 3633. E-mail address: [email protected] (R. Grassi-Oliveira). http://dx.doi.org/10.1016/j.drugalcdep.2014.09.003 0376-8716/© 2014 Elsevier Ireland Ltd. All rights reserved.
early-onset cannabis use is a risk factor for further drug-related problems (Lynskey et al., 2003). However, the debate regarding the harmful consequences of cannabis consumption is still ongoing and the main reasons for this include the poor quality of current evidence and the small number of studies on this topic (Calabria et al., 2010). Early-onset cannabis use and abuse are also associated with potential progression to the use of other illicit substances such as cocaine. Estimations regarding the prevalence of cannabis use among primary cocaine users range between 50 and 70% (Hall and Lynskey, 2005; Lynskey et al., 2003; Wagner and Anthony, 2002). Moreover, the treatment of cocaine dependence conventionally involves detoxification programs for the management and reduction of drug craving and abstinence symptoms. However, the severity of withdrawal symptoms can vary among drug users
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(Francke et al., 2013), and multiple factors, including the history of other substance consumption, could impact treatment response (Ahmadi et al., 2008). In this sense, recent evidence indicated that concomitant cocaine and long-term cannabis use might increase craving for cocaine during drug abstinence (Fox et al., 2013). Therefore, a potential role of patterns of cannabis consumption in cocaine dependence-related problems is suggested. Despite that, research has yet to address the consequences of cannabis use on specific clinical manifestations during the initial period of cocaine detoxification. This topic is of particular interest given that the severity of cocaine withdrawal symptoms at the first two weeks of treatment is a robust predictor of the ensuing treatment response (Kampman et al., 2001, 2004). Therefore, the aim of this follow-up study was to investigate whether long-term cannabis abuse and early-onset cannabis use could impact cocaine withdrawal severity and cocaine craving intensity during the first two weeks of detoxification treatment. In addition, we investigated the impact of cannabis consumption patterns in the rehospitalization rates due to cocaine dependence after 2.5 years of assessment. 2. Methods 2.1. Participants One hundred and twenty-two female cocaine-dependent inpatients of a detoxification treatment public hospital unit in Southern Brazil took part in this study. Inclusion criteria were as follows: (1) age 18 to 45; (2) diagnosis of physiological dependence on cocaine (smoked or snorted) according to the Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSM-IV). Participants were excluded from this study if (1) for any reason they did not report or did not provide reliable information regarding the age at the first year of drug use and patterns of drug use behavior (n = 14); (2) for any reason they were discharged early, before the third week of treatment, resulting in absence from follow-up measurements (n = 12); (3) they presented any severe cognitive impairment that resulted in an altered state of consciousness or agitation (n = 3). Thus, the final sample was composed of 93 participants.
2.2. Measures and procedures Participants were invited to take part in the study during the first three days of treatment and provided written informed consent to participate. The detoxification treatment program consisted of three weeks of drug rehabilitation, including psychoeducation and support groups, nursing care, moderate physical activity, a balanced diet, and medical treatment. The clinical assessment protocol was composed of self-administered questionnaires and semi-structured interviews, applied within the first two weeks of treatment, and comprised of the assessment of relevant variables such as comorbid psychiatric diagnosis, history of drug use and drug-related problems, patterns of multiple drug use, and socio-demographic information. A trained team of psychologists conducted all interviews and instrument application. Data on the age at the first year of drug use – alcohol, cannabis and cocaine – and the number of years of substance abuse were obtained using the Addiction Severity Index (ASI-6; Kessler et al., 2012; McLellan et al., 1980). To characterize early- or lateonset cannabis use, we considered the mean age at the first use of cannabis (15.32 years old) to classify those participants below or above such a mean. Participants were also divided into two groups according to the presence or absence of longterm cannabis abuse—defined as more than 5 years of substance abuse using at least three days per week. Additional data regarding the consequences of substance use were investigated by the ASI-6 subscales of family support problems, legal problems, employment problems and drug-related problems. Cocaine withdrawal symptoms were measured at the 4th, 9th, and 14th days of detoxification using the cocaine selective severity assessment (CSSA; Kampman et al., 1998). The CSSA is an 18-item clinic-based measure of the severity of cocaine abstinence symptoms and each of the 18 individual items is scored on a 0–7 scale, in which 0 represents no symptoms and 7 represents maximum severity. The signs and symptoms included on the CSSA assessment were cocaine craving, depressed mood, appetite changes, sleep disturbances, lethargy, and bradycardia, which are manifestations that commonly occur after abrupt cessation of cocaine use. In addition, items 4 and 5 of the instrument are aimed at investigating the cocaine craving symptoms. Therefore, specific craving symptoms were defined as the sum value of these two items. In order to verify the percentage of variation regarding the severity of the withdrawal symptoms, we calculated the fractional change [((14th day CSSA score − 4th day CSSA score)/4th day CSSA score)×100]. Moreover, if a negative value was derived from the fractional change equation, a reduction in withdrawal symptoms was assumed (Wilson and Hernández-Hall, 2009). Otherwise, in the case
of a positive value an increase of withdrawal symptoms was assumed. Thus, the withdrawal symptoms variation was also categorized into a dichotomous variable. Additional clinical and socio-demographic data were considered. Nicotinedependence severity was assessed by the Fagerstrom test (Heatherton et al., 1991). The effects of the presence of comorbid diagnosis, including mood disorders, anxiety disorders and posttraumatic stress disorder (PTSD) were investigated. Therefore, the Structured Clinical Interview for DSM Disorders (SCID I; Del-Ben et al., 2001) was conducted by a psychiatrist and two psychologists with clinical experience. In addition, analyses of pharmacotherapy effects, such as the use of mood stabilizers, antipsychotics or antidepressants, were performed (benzodiazepines were not prescribed in this treatment protocol). Participants also answered questions about socio-demographic characteristics, including information about years of formal education, estimated income per month, and days of abstinence prior to treatment admission. This study was conducted from December, 2010 to December, 2011. Since all participants belong to the same treatment catchment area, we monitored how many rehospitalizations due to cocaine dependence occurred from the first assessment until June, 2014 (2.5 years). The research protocol was approved by the Ethical Committee of the enrolled institutions. 2.3. Statistical analysis The Shapiro–Wilk test was used for the analysis of normality of data distributions for each variable. Descriptive statistics for psychosocial variables were conducted using chi-square tests or t-tests for independent samples, as well as group comparisons regarding CSSA total scores, cocaine craving intensity, number of rehospitalizations due to cocaine dependence, patterns of drug use behavior, onset of drug use, pharmacotherapy, and the presence of comorbid diagnosis. Fractional change between group effects (long-term versus non-long-term-cannabis abuse and early- versus late-onset cannabis use) was assessed by analysis of covariance (ANCOVA), controlling for mean age. In addition, two multiple logistic regression models were used to analyze the (a) influence of early-onset cannabis use and, (b) the duration of cannabis abuse predicting withdrawal outcomes. In these models the dependent variable was the dichotomous fractional change of CSSA scores during detoxification. We included age as a predicting factor in these models since we found a slight difference between the early- and late-onset cannabis use groups. In the first equation, in order to exclude confusion factors regarding the early use of other drugs, we included in the model whether participants did or did not have early use of alcohol, tobacco and cocaine (also considering the mean age of first use of these drugs in the whole sample). In the second equation, in order to exclude confusion factors regarding the duration of other drug abuse we included the number of years of alcohol, tobacco and cocaine abuse. Finally, we performed a linear multiple regression with one block including whether or not participants had early-onset cannabis use, whether or not they reported long-term cannabis abuse, and number of years of cocaine consumption as predictors of the number of rehospitalizations due to cocaine dependence after 2.5 years since first assessment. All analyses were performed using the Statistical Package for the Social Sciences (SPSS) version 20.0 (SPSS Inc., Chicago, IL, USA), using two-sided tests and a significance level set at 0.05.
3. Results 3.1. Descriptive statistics All participants reported having smoked cocaine and 89% reported having snorted cocaine as well. Overall, 66% of the sample reported early-onset cannabis use and 52% reported long-term cannabis abuse. Participants with early-onset cannabis use presented a higher severity of cocaine withdrawal symptoms and specific cocaine craving symptoms at the 14◦ day of detoxification compared with those with late-onset cannabis use (see Table 1). In addition, a trend association was found between early-onset cannabis use and more craving symptoms at the 4th day of treatment. Similarly, those with long-term cannabis abuse presented higher cocaine withdrawal symptoms at the 9th and 14th days of assessment compared with those that did not report such a pattern of drug use, but no interaction was found regarding craving symptoms (see Table 2). Additional analysis demonstrated that the early-onset cannabis use group also presented early-onset alcohol use and that the longterm cannabis abuse group presented more employment problems related to addiction. In addition, participants with early-onset cannabis use and long-term cannabis abuse presented significantly more total hospitalizations due to cocaine dependence, as well as more rehospitalizations after treatment discharge in a 2.5
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Table 1 Socio-demographic and clinical characteristics of groups related to the onset of cannabis use.
Socio-demographic—mean (SD)/n (%) Age Employed Stable marital relationship Income per month (US$) Pharmacotherapy—n (%) Mood stabilizers/Anticonvulsants Antipsychotics Antidepressants Psychiatric comorbidities—n (%) Mood disorders Anxiety disorders PTSD Age at first drug use—mean (SD) Tobacco Alcohol Cannabis Cocaine Cocaine withdrawal—mean (SD) CSSA total score 4th day CSSA total score 9th day CSSA total score 14th day Cocaine craving symptoms—mean (SD) CSSA craving score 4th day CSSA craving score 9th day CSSA craving score 14th day Nicotine-dependence severity—mean (SD) Fagerstrom total score ASI-6—mean (SD) Drugs Family support Legal problems Employment problems Days of abstinence prior to treatment Cannabis use in the last month Hospitalizations due cocaine dependence—mean (SD) Total hospitalizations Re-hospitalizations after treatment discharge
Early-onset cannabis Use (n = 62)
Late-onset cannabis use (n = 31)
p-Value
27.45 (6.40) 19 (30.64) 17 (27.41) 479.30 (390.330)
30.32 (6.41) 9 (29.03) 10 (32.25) 649.05 (611.49)
0.045 0.87 0.62 0.10
40 (64.51) 40 (64.51) 3 (5.08)
31 (58.06) 31 (58.06) 4 (15.38)
0.54 0.54 0.15
21 (33.87) 3 (4.83) 8 (12.9)
13 (41.93) 4 (12.9) 6 (19.35)
0.44 0.16 0.41
12.96 (2.99) 13.92 (2.8) 13.35 (1.67) 21.54 (6.64)
15.86 (3.68) 16.76 (2.94) 19.25 (4.49) 24.48 (6.98)
0.000 0.000 0.000 0.51
56.04 (18.68) 53.19 (22.36) 50.85 (19.83)
52.48 (15.97) 45.60 (19.16) 42.01 (19.40)
0.36 0.11 0.044
5.48 (4.42) 5.09 (4.52) 5.25 (4.75)
3.64 (3.85) 4.16 (4.10) 2.48 (3.06)
0.05 0.34 0.004
4.87 (2.99)
4.34 (4.00)
0.51
62.92 (10.38) 44.72 (13.88) 64.05 (8.31) 43.62 (7.98) 2.72 (4.86) 27 (43.5)
63.34 (11.24) 44.15 (11.28) 61.22 (9.15) 44.52 (7.61) 2.62 (3.99) 10 (32.2)
0.87 0.84 0.19 0.63 0.92 0.29
6.07 (3.88) 4.85 (3.41)
4.43 (2.60) 3.47 (2.55)
0.022 0.036
Note: n, number of participants; SD, standard deviation; CSSA, cocaine selective severity assessment, ASI-6, Addiction Severity Index.
years follow-up assessment. There were no significant differences between groups regarding cannabis use in the last 30 days prior to treatment enrolment.
3.2. Cocaine withdrawal symptoms variation Considering the whole sample, it is interesting to highlight that 36% (n = 34) showed an increase in cocaine withdrawal symptoms severity after two weeks of detoxification, based on the fractional change data. Specifically, 46% (n = 29) of the participants that reported early-onset cannabis use and 48% (n = 24) of those that reported long-term cannabis abuse presented this increase in withdrawal symptoms (see Fig. 1). The non-long-term cannabis abuse group exhibited 17.91% cocaine withdrawal symptoms reduction during detoxification, while the long-term cannabis abuse group exhibited a reduction of only 3.91% [F(1,90) = 5.004; p = 0.028]. In the same way, the late-onset cannabis use group presented withdrawal symptoms severity reduction of 17.69%, while the early-onset cannabis use group presented a decrease of 6.95%, but no significance in the between group comparison [F(1,90) = 2.440; p = 0.122] was found, probably due to type II error. To identify whether early-onset and long-term cannabis use predict worse abstinence symptoms during cocaine detoxification independently of other substances (tobacco, alcohol and cocaine), we conducted multiple logistic regression models including other substances as covariates in addition to age. Regarding long-term cannabis abuse we found that it independently predicted a worse detoxification treatment response and the logistical regression
model showed adequate goodness-of-fit. However, when we included tobacco variable in the early-onset logistic regression equation, the model was not correctly specified because it failed in the Hosler–Lemeshow test (p = 0.040). Therefore we performed univariate logistic regression including early-onset tobacco use as predictor of cocaine detoxification treatment response, in order to investigate its effects on cocaine withdrawal. We did not find any association between early-onset tobacco use and cocaine detoxification treatment response (Wald = 2.04, df = 1, p = 0.15). In sum,
Fig. 1. Proportion of participants with an increase of cocaine withdrawal symptoms during detoxification depending on the patterns of cannabis use history. Note: Non long-term (22.7%) and long-term cannabis abuse (48.9%) comparison by group regarding the proportion with an increase of withdrawal symptoms severity (chisquare value = 6.88; p = 0.009); late- (16%) and early-onset cannabis use (46.7%) comparison by group regarding the proportion with an increase of withdrawal symptoms severity (chi-square value = 8.36; p = 0.004).
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Table 2 Socio-demographic and clinical characteristics of groups related to years of cannabis abuse.
Socio-demographic—mean (SD)/n (%) Age Employed Stable marital relationship Income per month (US$) Pharmacotherapy—n (%) Mood stabilizers/anticonvulsants Antipsychotics Antidepressants Psychiatric comorbidities—n (%) Mood disorders Anxiety disorders PTSD Years of drug abuse—mean (SD) Tobacco Alcohol Cannabis Cocaine Cocaine withdrawal—mean (SD) CSSA total score 4th day CSSA total score 9th day CSSA total score 14th day Cocaine craving symptoms—mean (SD) CSSA craving score 4th day CSSA craving score 9th day CSSA craving score 14th day Nicotine-dependence severity—mean (SD) Fagerstrom total score ASI-6—mean (SD) Drugs Family support Legal problems Employment problems Days of abstinence prior to treatment Hospitalizations due cocaine dependence—mean (SD) Total hospitalizations Re-hospitalizations after treatment discharge
Long-term cannabis abuse (n = 49)
Not long-term cannabis abuse (n = 44)
p-Value
28.57 (5.11) 13 (26.53) 11 (22.44) 599.84 (423.11)
28.22 (7.84) 15 (34.09) 16 (36.36) 467.39 (531.22)
0.80 0.42 0.14 0.18
28 (57.14) 28 (57.14) 4 (8.16)
30 (68.18) 30 (68.18) 3 (6.81)
0.27 0.27 0.55
19 (38.77) 4 (8.16) 5 (10.2)
15 (34.08) 3 (6.81) 9 (20.45)
0.64 0.80 0.16
14.31 (4.93) 3.43 (5.72) 12.20 (4.99) 7.71 (6.87)
12.25 (7.26) 3.36 (6.14) 1.02 (1.48) 6.47 (11.49)
0.13 0.95 0.004 0.53
56.59 (17.48) 55.73 (22.25) 51.84 (18.44)
52.93 (18.19) 45.00 (19.47) 43.52 (21.00)
0.32 0.016 0.041
5.24 (4.28) 5.24 (4.46) 4.73 (4.77)
4.45 (4.34) 4.27 (4.30) 3.88 (4.06)
0.38 0.29 0.36
4.97 (2.87)
4.29 (3.96)
63.82 (10.17) 45.09 (11.71) 62.95 (8.38) 45.54 (7.37) 2.11 (3.74)
62.16 (11.10) 43.47 (12.52) 63.61 (8.98) 41.97 (8.01) 3.36 (5.34)
0.49 0.54 0.73 0.033 0.21
6.47 (4.01) 5.29 (3.49)
4.41 (2.63) 3.32 (2.47)
0.006 0.003
0.38
Note: n, number of participants; SD, standard deviation; CSSA, cocaine selective severity assessment, ASI-6, Addiction Severity Index.
Table 3 Multiple logistic regression models. Withdrawal symptoms increase during detoxification (n = 33) OR (95% CI)
p-Value
1.16 (0.39, 3.42) 3.97 (1.15, 13.71) 1.82 (0.49, 6.73) 1.01 (0.90, 1.08)
0.78 0.020 0.36 0.95
1.04 (0.95, 1.13) 2.84 (1.06, 7.59) 1.02 (0.91, 1.15) 0.99 (0.99, 1.01) 0.94 (0.87, 1.03)
0.36 0.037 0.66 0.48 0.23
1
Early-onset drug use effects Alcohol (n = 80) Cannabis (n = 93) Cocaine (n = 93) Age (n = 93) Years of drug abuse effects2 Alcohol (n = 80) Cannabis (n = 93) Cocaine (n = 93) Tobacco (n = 84) Age (n = 93)
Note: Logistic regression models used to analyze the influence of patterns of cannabis use predicting withdrawal outcomes. 1 Wald = 5.01; df = 1; p = 0.025. 2 Wald = 6.37; df = 1; p = 0.012.
we documented that early-onset and long-term cannabis use predicted worse abstinence symptoms during cocaine detoxification independently of early-onset and long-term tobacco, alcohol and cocaine use. (see Table 3). 3.3. Effects of patterns of cannabis use on subsequent rehospitalizations due to cocaine dependence Next, when we modeled a linear regression for the effects of early-onset cannabis use and long-term cannabis abuse in the number of subsequent rehospitalizations due to cocaine dependence (2.5 years follow-up), we found that long-term cannabis abuse significantly predicted rehospitalizations for cocaine detoxification, independently of lifetime years of cocaine use and early-onset cannabis use history (see Table 4). 4. Discussion The current study shows that long-term cannabis abuse and early-onset cannabis use are associated with a worse detoxification
Table 4 Multiple linear regression predicting re-hospitalization due cocaine dependence after 2.5 years of first detoxification assessment. R 0.35 Long-term cannabis abuse Early-onset cannabis use Years of cocaine consumption
R2 0.12
F
df
3.95
3.85
Beta 0.28 0.16 −0.07
p 0.011 0.008 0.12 0.45
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treatment response in a cohort of female inpatients with cocaine dependence. More specifically, patients who exhibit more severe cocaine withdrawal symptoms at the end of two weeks of detoxification also reported early-onset cannabis use or a prolonged history of cannabis abuse. Such histories of cannabis consumption were associated with an increase, instead of a reduction, in cocaine withdrawal symptoms during detoxification, regardless of age and patterns of alcohol or cocaine use. In addition, longterm cannabis abuse predicted higher rehospitalization rates due to cocaine dependence after 2.5 years follow-up assessment. Therefore, taking into account specific patterns of cannabis use may help to identify patients that will struggle more severely to treat cocaine dependence and to control cocaine craving and abstinence syndrome during detoxification. These findings corroborate prior research suggesting that cannabis use might be a feature that underlies a vulnerability to further substance-related problems (Aharonovich et al., 2006). The cannabis “gateway hypothesis” proposes that early-onset cannabis use and regular cannabis consumption are strong predictors of other drug use (Fergusson et al., 2006). In addition, some studies have also demonstrated that early- and late-onset substance users might differ in withdrawal symptoms severity during early abstinence and this is important regarding treatment effectiveness (Kampman et al., 2001). For example, early cannabis use was associated with the occurrence and the severity of psychotic symptoms during cocaine intoxication (Trape et al., 2014). Furthermore, despite the independent effects of cannabis in treatment response, we found that within the early-onset cannabis group the age of first use of tobacco and alcohol was lower than the comparison group, which is in agreement with previous evidence (Lynskey et al., 2003). Babor et al. (1992) proposed a typology to distinguish drug users based on the history and patterns of substance use, suggesting a “Type B” kind of patient with alcohol dependence, who was more likely to exhibit drug-related problems and severe clinical manifestation in response to substance withdrawal. Similar patterns were found regarding the association between cocaine withdrawal symptoms severity at the beginning of detoxification treatment and characteristics of Type B cocaine-dependent patients (Ahmadi et al., 2008). Specifically, in the Ahmadi study, higher withdrawal symptoms during early abstinence predicted worse pharmacological or psychosocial treatment outcomes. Interestingly, the authors assessed the cocaine withdrawal syndrome using the same instrument as used in our study, the CSSA. Therefore, the data reported here supports the idea that specific patterns of cannabis use history are also factors related to Type B patients with cocaine dependence. The lack of group differences regarding other clinical factors, such as pharmacotherapy treatment and comorbid psychiatric disorders, suggest that the patterns of cannabis consumption affect the symptoms of cocaine withdrawal particularly. Also, it is worth mentioning that the association found between long-term cannabis abuse and more employment problems is supported by previous research. For example, a comprehensive longitudinal study conducted with cannabis abusers reported that cannabis consumption during adolescence and early adulthood is related with several adverse outcomes in later life, including poorer educational outcomes, lower income, unemployment and lower life satisfaction, demonstrating the adverse consequences of heavy cannabis abuse before adulthood (Fergusson and Boden, 2008). Another body of research has been investigating the biological mechanisms involved in the effects of cannabis use and further drug-related problems. Recent data showed that stress system changes (i.e., hypothalamic–pituitary–adrenal axis) are associated with cannabis dependence and such changes can increase craving for cocaine during drug (Fox et al., 2013). Furthermore, the psychoactive effects of cannabis are predominantly mediated by tetrahydrocannabinol (THC), through its interaction with the
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brain endocannabinoid system. Interestingly, the endocannabinoid system seems to regulate several processes involved in brain development and neuroplasticity, and early cannabis exposure has been suggested to enhance the risk for substance-related problems via neuroadaptations associated with endocannabinoid signaling and changes in brain reward system functioning (Chadwick et al., 2013). Additionally, emerging evidence from animal studies revealed a higher sensitivity to cocaine effects due to early THC exposure, especially during the beginning of rodent adolescence, and the endocannabinoid system partially mediated such effect (Hernandez et al., 2013; Orio et al., 2009; Wiskerke et al., 2008). Hence, from a neurobiological perspective, higher cocaine withdrawal and craving symptoms could be associated with brain endocannabinoid system functioning alterations due to early cannabis exposure. The results of the current study should be interpreted in light of its limitations. One limitation is the lack of assessment of the quantity of drug consumption, which could give us a better estimation regarding the variability in cannabis use severity. However, to estimate the precise amount is a complex issue because of the indiscriminate polysubstance use of our sample. Moreover, consumption assessment instruments require individuals to nominate both the frequency and quantity of cannabis used and the few tools that assess quantity of cannabis provide conflicting guidelines for quantity measurement (Robinson et al., 2014). Another limitation is related to the sample size, but it is difficult to recruit participants for a longitudinal research. Nevertheless, such a design added quality to our study. In addition, the participants were all female and the findings cannot be generalized to male samples. In this regard, important differences between females and males have been found regarding the initiation and the frequency of substance use (Fattore et al., 2008), craving and abstinence symptoms severity (Becker and Hu, 2008), probably due neurobiological specificities related to drug addiction among males and females (Becker et al., 2012). For instance, there are sex differences in the number and firing rates of mescencephalic dopamine neurons (Murray et al., 2003) and female gonadal hormones seem to selectively influence regional dopamine neurotransmission, affecting the sensitivity to drug rewarding/aversive effects and certain aspects of drug-seeking behavior (Becker et al., 2012). Furthermore, clinical studies showed that women start using cocaine earlier than do men and the rate of drug use escalation is greater for women than for men (Dluzen and McDermott, 2008). Women also report higher craving severity and greater amounts consumed when compared to men seeking treatment (Elman et al., 2001). Moreover, epidemiological data support important sex differences regarding crack cocaine dependence in Brazil, showing that men are more likely to use crack cocaine, while female crack cocaine users report higher craving severity and are more vulnerable to develop dependence (Abdalla et al., 2014). Therefore, a replication of this study with larger samples, including male subjects, should be undertaken. Despite these limitations, the current findings can provide clinicians with a detailed understanding of the characteristics of individuals who are most likely to present severe cocaine withdrawal syndrome at detoxification entry, helping to anticipate the detoxification treatment outcomes based on factors related to the history of cannabis use. Furthermore, we detected that higher rehospitalizations due to cocaine dependence was predicted by long-term cannabis abuse. These current results extend earlier findings showing that chronic cannabis use can increase the rates of relapse in polysubstance abusers and that postdischarge cannabis consumption can substantially reduce the chances of a stable remission from use of any substance, including cocaine (Aharonovich et al., 2005; Alessi et al., 2011). In this sense, clinicians strongly should consider the patterns of cannabis use when planning treatment aftercare for cocaine dependence. Finally, our
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findings raise doubts about policies, programs and evidences proposing cannabis use as a therapeutic factor and a potential substitute for cocaine use for individuals with cocaine dependence (Lucas, 2012). Thus, rather than encouraging cannabis consume among cocaine dependents, health professionals should be very cautious about its worsening effects on abstinence symptoms. In addition, the prevention of cannabis use in adolescence would appear to be a priority for health policies, given the potential harm that it can cause regarding the reward system (De Bellis et al., 2013; Volkow and Baler, 2014), especially to those who go on to develop cocaine dependence later in life. Author disclosures Role of funding source This study was supported by MCT/CT-Saúde—DECIT/SCTIE/MS, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Grant number 402723/2010-4) and Fundac¸ão de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS) (Grant number 11/1302-7). The funding source had no involvement in study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the paper for publication. Contributors Authors Viola T.W., J.C. Pezzi and Grassi-Oliveira R. designed the study and wrote the protocol. Authors Viola T.W., Tractenberg S.G., Wearick-Silva, L.E. and Rosa, C.S.O., managed the literature searches and summaries of previous related work. Authors Viola T.W., Wearick-Silva, L.E. and Grassi-Oliveira R. undertook the statistical analysis, and authors Viola T.W. and Tractenberg S.G. wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest All authors declare that they have no conflicts of interest. Acknowledgements The authors would like to thank all members of GNCD and Sistema de Saúde Mãe de Deus. References Abdalla, R.R., Madruga, C.S., Ribeiro, M., Pinsky, I., Caetano, R., Laranjeira, R., 2014. Prevalence of cocaine use in Brazil: data from the II Brazilian National Alcohol and Drugs Survey (BNADS). Addict. Behav. 39, 297–301. Aharonovich, E., Garawi, F., Bisaga, A., Brooks, D., Raby, W.N., Rubin, E., Nunes, E.V., Levin, F.R., 2006. Concurrent cannabis use during treatment for comorbid ADHD and cocaine dependence: effects on outcome. Am. J. Drug Alcohol Abuse 32, 629–635, http://dx.doi.org/10.1080/00952990600919005. Aharonovich, E., Liu, X., Samet, S., Nunes, E., Waxman, R., Hasin, D., 2005. Postdischarge cannabis use and its relationship to cocaine, alcohol, and heroin use: a prospective study. Am. J. Psychiatry 162, 1507–1514, http://dx.doi.org/10.1176/appi.ajp.162.8.1507. Ahmadi, J., Kampman, K., Dackis, C., Sparkman, T., Pettinati, H., 2008. Cocaine withdrawal symptoms identify “Type B” cocaine-dependent patients. Am. J. Addict. 17, 60–64, http://dx.doi.org/10.1080/10550490701755999. Alessi, S.M., Rash, C., Petry, N.M., 2011. Contingency management is efficacious and improves outcomes in cocaine patients with pretreatment marijuana use. Drug Alcohol Depend. 118, 62–67, http://dx.doi.org/10.1016/j.drugalcdep.2011.03.001. Babor, T.F., Hofmann, M., DelBoca, F.K., Hesselbrock, V., Meyer, R.E., Dolinsky, Z.S., Rounsaville, B., 1992. Types of alcoholics, I. Evidence for an empirically derived typology based on indicators of vulnerability and severity. Arch. Gen. Psychiatry 49, 599–608. Becker, J.B., Hu, M., 2008. Sex differences in drug abuse. Front. Neuroendocrinol. 29, 36–47, http://dx.doi.org/10.1016/j.yfrne.2007.07.003.
Becker, J.B., Perry, A.N., Westenbroek, C., 2012. Sex differences in the neural mechanisms mediating addiction: a new synthesis and hypothesis. Biol. Sex Differ. 3, 14, http://dx.doi.org/10.1186/2042-6410-3-14. Calabria, B., Degenhardt, L., Hall, W., Lynskey, M., 2010. Does cannabis use increase the risk of death? Systematic review of epidemiological evidence on adverse effects of cannabis use. Drug Alcohol Rev. 29, 318–330, http://dx.doi.org/10.1111/j.1465-3362.2009.00149.x. Chadwick, B., Miller, M.L., Hurd, Y.L., 2013. Cannabis use during adolescent development: susceptibility to psychiatric illness. Front. Psychiatry 4, 129, http://dx.doi.org/10.3389/fpsyt.2013.00129. De Bellis, M.D., Wang, L., Bergman, S.R., Yaxley, R.H., Hooper, S.R., Huettel, S.A., 2013. Neural mechanisms of risky decision-making and reward response in adolescent onset cannabis use disorder. Drug Alcohol Depend. 133, 134–145, http://dx.doi.org/10.1016/j.drugalcdep.2013.05.020. Del-Ben, C.M., Vilela, J.A., Crippa, J.A., Hallak, J.E.C., Labati, C.M., Zuardi, A.W., 2001. Reliability of the structured clinical interview for dsm-iv—clinical version translated into portuguese. Braz. J. Psychiatry 23, 156–159. Dluzen, D.E., McDermott, J.L., 2008. Sex differences in dopamine- and vesicular monoamine-transporter functions. Ann. N.Y. Acad. Sci. 1139, 140–150, http://dx.doi.org/10.1196/annals.1432.010. Elman, I., Karlsgodt, K.H., Gastfriend, D.R., 2001. Gender differences in cocaine craving among non-treatment-seeking individuals with cocaine dependence. Am. J. Drug Alcohol Abuse 27, 193–202. Fattore, L., Altea, S., Fratta, W., 2008. Sex differences in drug addiction: a review of animal and human studies. Womens Health (London Engl.) 4, 51–65, http://dx.doi.org/10.2217/17455057.4.1.51. Fergusson, D.M., Boden, J.M., 2008. Cannabis use and later life outcomes. Addiction 103, 969–976, http://dx.doi.org/10.1111/j.1360-0443.2008.02221.x (discussion 977-968). Fergusson, D.M., Boden, J.M., Horwood, L.J., 2006. Cannabis use and other illicit drug use: testing the cannabis gateway hypothesis. Addiction 101, 556–569, http://dx.doi.org/10.1111/j.1360-0443.2005.01322.x. Fox, H.C., Tuit, K.L., Sinha, R., 2013. Stress system changes associated with marijuana dependence may increase craving for alcohol and cocaine. Hum. Psychopharmacol. 28, 40–53, http://dx.doi.org/10.1002/hup.2280. Francke, I.D., Viola, T.W., Tractenberg, S.G., Grassi-Oliveira, R., 2013. Childhood neglect and increased withdrawal and depressive severity in crack cocaine users during early abstinence. Child Abuse Negl. 37, 883–889, http://dx.doi.org/10.1016/j.chiabu.2013.04.008. Hall, W.D., Lynskey, M., 2005. Is cannabis a gateway drug? Testing hypotheses about the relationship between cannabis use and the use of other illicit drugs. Drug Alcohol Rev. 24, 39–48, http://dx.doi.org/10.1080/09595230500126698. Heatherton, T.F., Kozlowski, L.T., Frecker, R.C., Fagerstrom, K.O., 1991. The Fagerstrom test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br. J. Addict. 86, 1119–1127. Hernandez, G., Oleson, E.B., Gentry, R.N., Abbas, Z., Bernstein, D.L., Arvanitogiannis, A., Cheer, J.F., 2013. Endocannabinoids promote cocaine-induced impulsivity and its rapid dopaminergic correlates. Biol. Psychiatry 75, 487–498, http://dx.doi.org/10.1016/j.biopsych.2013.09.005. Johnston, L.D., O’Malley, P.M., Bachman, J.G., 2004. Monitoring the Future National Survey Results on Drug Use, 1975–2003. Volume I: Secondary School Students National Institute on Drug Abuse. NIH Publications (04-5507-55045508). Kampman, K.M., Alterman, A.I., Volpicelli, J.R., Maany, I., Muller, E.S., Luce, D.D., Mulholland, E.M., Jawad, A.F., Parikh, G.A., Mulvaney, F.D., Weinrieb, R.M., O’Brien, C.P., 2001. Cocaine withdrawal symptoms and initial urine toxicology results predict treatment attrition in outpatient cocaine dependence treatment. Psychol. Addict. Behav. 15, 52–59. Kampman, K.M., Pettinati, H.M., Volpicelli, J.R., Oslin, D.M., Lipkin, C., Sparkman, T., O‘Brien, C.P., 2004. Cocaine dependence severity predicts outcome in outpatient detoxification from cocaine and alcohol. Am. J. Addict. 13, 74–82. Kampman, K.M., Volpicelli, J.R., McGinnis, D.E., Alterman, A.I., Weinrieb, R.M., D’Angelo, L., Epperson, L.E., 1998. Reliability and validity of the cocaine selective severity assessment. Addict. Behav. 23, 449–461. Kessler, F., Cacciola, J., Alterman, A., Faller, S., Souza-Formigoni, M.L., Santos Cruz, M., Brasilliano, S., Pechansky, F., 2012. Psychometric properties of the sixth version of the Addiction Severity Index (ASI-6) in Brazil. Rev. Bras. Psiquiatr. 34, 24–33, S1516-44462012000100006 [pii]. Lucas, P., 2012. Cannabis as an adjunct to or substitute for opiates in the treatment of chronic pain. J. Psychoactive Drugs 44, 125–133. Lynskey, M.T., Heath, A.C., Bucholz, K.K., Slutske, W.S., Madden, P.A., Nelson, E.C., Statham, D.J., Martin, N.G., 2003. Escalation of drug use in early-onset cannabis users vs co-twin controls. JAMA 289, 427–433. McLellan, A.T., Luborsky, L., Woody, G.E., O’Brien, C.P., 1980. An improved diagnostic evaluation instrument for substance abuse patients. The Addiction Severity Index. J. Nerv. Ment. Dis. 168, 26–33. Moss, H.B., Chen, C.M., Yi, H.Y., 2014. Early adolescent patterns of alcohol, cigarettes, and marijuana polysubstance use and young adult substance use outcomes in a nationally representative sample. Drug Alcohol Depend. 136, 51–62, http://dx.doi.org/10.1016/j.drugalcdep.2013.12.011, Epub 2013 Dec 31. Murray, H.E., Pillai, A.V., McArthur, S.R., Razvi, N., Datla, K.P., Dexter, D.T., Gillies, G.E., 2003. Dose- and sex-dependent effects of the neurotoxin 6-hydroxydopamine on the nigrostriatal dopaminergic pathway of adult rats: differential actions of estrogen in males and females. Neuroscience 116, 213–222. Orio, L., Edwards, S., George, O., Parsons, L.H., Koob, G.F., 2009. A role for the endocannabinoid system in the increased motivation for
T.W. Viola et al. / Drug and Alcohol Dependence 144 (2014) 153–159 cocaine in extended-access conditions. J. Neurosci. 29, 4846–4857, http://dx.doi.org/10.1523/JNEUROSCI.0563-09.2009. Robinson, S.M., Sobell, L.C., Sobell, M.B., Leo, G.I., 2014. Reliability of the Timeline Followback for cocaine, cannabis, and cigarette use. Psychol. Addict. Behav. 28, 154–162, http://dx.doi.org/10.1037/a0030992. Sintov, N.D., Kendler, K.S., Walsh, D., Patterson, D.G., Prescott, C.A., 2009. Predictors of illicit substance dependence among individuals with alcohol dependence. J. Stud. Alcohol Drugs 70, 269–278. Trape, S., Charles-Nicolas, A., Jehel, L., Lacoste, J., 2014. Early cannabis use is associated with severity of cocaine-induced psychosis among cocaine smokers in Martinique, French West Indies. J. Addict. Med. 8, 33–39, http://dx.doi.org/10.1097/ADM.0000000000000003. Trenz, R.C., Scherer, M., Harrell, P., Zur, J., Sinha, A., Latimer, W., 2012. Early onset of drug and polysubstance use as predictors of injection drug use among adult drug users. Addict. Behav. 37, 367–372, http://dx.doi.org/10.1016/j.addbeh.2011.11.011.
159
Volkow, N.D., Baler, R.D., 2014. Addiction science: uncovering neucomplexity. Neuropharmacology 76 Pt B, 235–249, robiological http://dx.doi.org/10.1016/j.neuropharm.2013.05.007. Wagner, F.A., Anthony, J.C., 2002. From first drug use to drug dependence; developmental periods of risk for dependence upon marijuana, and alcohol. Neuropsychopharmacology 26, 479–488, cocaine, http://dx.doi.org/10.1016/S0893-133X(01)00367-0. Wilson, J., Hernández-Hall, C., 2009. The thermal coefficient of linear expansion. In: Wilson, J., Hernández-Hall, C. (Eds.), Physics Laboratory Experiments. Cengage Learning, New York, NY (251 pp.). Wiskerke, J., Pattij, T., Schoffelmeer, A.N., De Vries, T.J., 2008. The role of CB1 receptors in psychostimulant addiction. Addict. Biol. 13, 225–238, http://dx.doi.org/10.1111/j.1369-1600.2008.00109.x.
