Psychiatric Comorbidity and Addiction Severity Differences in Patients With ADHD Seeking Treatment for Cannabis or Cocaine Use Disorders

Abstract

Objective: The objective of this study was to compare psychiatric comorbidity and consumption-related variables in ADHD patients seeking treatment for cocaine, cannabis, or both. Method: Assessment was conducted using European Addiction Severity Index (EuropASI), Conners’ Adult ADHD Diagnostic Interview (CAADID), Structured Clinical Interview for DSM Disorders (SCID), Adult Self-Report Scale (ASRS), Wender Utah Rating Scale (WURS), Barratt Impulsiveness Scale-11 (BIS-11), and FIDI, with statistical analyses of analysis of variance (ANOVA), Student’s t test, chi-square test, and multinomial regression model. Results: In total, 1,538 patients with substance use disorder (SUD) were evaluated for ADHD; 239 (15.5%) had ADHD, with cannabis 41, cannabis/cocaine 36, and cocaine 74. Men represented 80%, with mean age of 32.9 ± 10 years. Significant variables were—in bivariate analysis—more years of cannabis use in cannabis group and younger age for cocaine use disorder in cannabis/cocaine group, and—in multivariate analysis—lifetime anxiety disorder and younger age at onset of any SUD in cannabis group and working affected scale in cannabis and polysubstance use in cannabis/cocaine group. Conclusion: Groups with cannabis use had higher severity. ADHD features were similar in all groups. The assessment of ADHD and comorbid disorders is important.

Keywords

ADHD dual disorders cannabis use disorder cocaine use disorder

Introduction

ADHD and substance use disorder (SUD) comorbidity is a frequent and critical issue in clinical practice (Daigre, Terán, García-Vicent, & Roncero, 2013; Faraone et al., 2000; Mannuzza, Klein, Bessler, Malloy, & LaPadula, 1993; Young & Sedgwick, 2015). The estimated prevalence of ADHD in the general population is 4.4% (Kessler et al., 2006) and 2.5% in a meta-analysis consistent with the age dependent decline in adults (Simon, Czobor, Bálint, Mészáros, & Bitter, 2009); in treatment-seeking patients with SUD, the prevalence of adult ADHD is higher with estimated data between 8% and 46%, which varies according to the main substance studied and methodological factors (Levin, Evans, McDowell, & Kleber, 1998; Schubiner et al., 2000; van Emmerik-van Oortmerssen et al., 2012; van Oortmerssen et al., 2014). In a large multicenter study of treatment-seeking SUDs patients, which were diagnosed using the standard methodological assessment for adult ADHD, the variability was less, with a range from 5.4% to 31.3% and 7.6% to 32.6% with Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013) criteria (van de Glind et al., 2014). Other studies found an estimated prevalence of 23.3%, with a meta-analysis on 12 studies in adult treatment-seeking SUDs patients (van Emmerik-van Oortmerssen et al., 2012). Moreover, it seems that the prevalence of ADHD is different for each substance; the prevalence of ADHD in cocaine consumers has been identified in a range between 15.1% and 25% (Daigre, Roncero, et al., 2013; Martínez-Gras et al., 2016; van de Glind et al., 2014), while the prevalence of ADHD in cannabis consumers varies from 20% to 58% (Notzon et al., 2020; Tims et al., 2002). Regarding each substance, cannabis has been identified as a mediating factor in increasing ADHD symptoms and also increasing the risk of the consumption of other drugs (Fergusson & Boden, 2008). In clinical practice, cannabis use has been associated with an increased risk of psychotic experiences in ADHD patients (Roncero et al., 2017; Vitiello et al., 2017), as well as a factor that increases the risk of acute inpatient care and longer hospitalization (Patel et al., 2018). Similarly, literature has described that ADHD patients who use cocaine have more impairments in cognitive and affective regulation than ADHD patients without any SUD (Miguel et al., 2016); additionally, they are more impulsive (Crunelle, Veltman, van Emmerik-van Oortmerssen, Booij, & van den Brink, 2013) and tend to have a greater prevalence of depressive disorders (Daigre, Roncero, et al., 2013).

Regarding subtypes of ADHD, the combined subtype is the most frequent in adults with this comorbidity 72.8% (Kaye et al., 2019), while inattentive subtype patients have less risk to develop a cocaine use disorder (UD; De Alwis, Lynskey, Reiersen, & Agrawal, 2014; Liebrenz, Gamma, Ivanov, Buadze, & Eich, 2016).

Studies on patients with ADHD and SUDs describe a more severe SUDs in terms of becoming addicted at younger ages, the use of more substances (Daigre, Roncero, et al., 2013; Wilens & Upadhyaya, 2007), and poorer SUD treatment outcomes, such as more hospitalization and higher relapse rates after successful addiction treatments (Levin et al., 2004; Wise, Cuffe, & Fischer, 2001). Patients with SUD and ADHD also present higher rates of other psychiatric disorders (Kessler et al., 2005; Wilens & Upadhyaya, 2007).

Until now, literature about ADHD includes all SUDs or only analyzes a specific one. In order that cannabis and cocaine were the most prevalent illegal drugs used in our ADHD patients, we focused on the comparison among cocaine and cannabis addicted patients with comorbid ADHD. To our knowledge, an analysis with this comparison has not been described previously. The hypothesis of this study was that there will be differences in psychiatric comorbidity and in severity of consumption-related variables, depending on the drug used. Therefore, this study aims to compare ADHD patients considering psychiatric comorbidity, consumption-related variables, and clinical factors.

Method

A descriptive study was conducted from January 2009 to April 2017 in the Addiction and Dual Diagnosis Unit at Vall d’Hebron University Hospital in Barcelona, Spain. Inclusion criteria were patients above 18 years old, cocaine or cannabis dependence according to Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000), completion of the psychological assessment, including ADHD diagnosis, and had signed the informed consent prior to participation. Patients who did not meet these criteria were not included in the study. The study was approved by the Ethics Committee of Vall d’Hebron Hospital. Patients did not receive any financial compensation. This study is part of larger research endeavor on comorbidity in patients with substance dependence disorders.

Research and Diagnostic Assessment

Sociodemographic and clinical features

To systematize sociodemographic information, a questionnaire designed ad hoc was used. Sociodemographic variables and variables related to consumption patterns across the lifespan of patients were included.

The following screening tools were used for the diagnostic assessment of ADHD and SUD:

CAADID-II: Conners’ Adult ADHD Diagnostic Interview for DSM-IV (Epstein & Kollins, 2006). The Spanish validated version was used (Ramos-Quiroga et al., 2012).

WURS: Wender Utah Rating Scale (Ward, Wender, & Reimherr, 1993). WURS has shown good psychometric properties in SUD.

ASRS: The Adult Self-Report Scale (Kessler et al., 2005). This instrument has also been studied in SUD patients presenting satisfactory psychometric properties (Ramos-Quiroga et al., 2009; van de Glind et al., 2014).

BIS-11: Barratt Impulsiveness Scale (Patton, Stanford, & Barratt, 1995). The Spanish version was used (Oquendo et al., 2001).

FIDI: Functional and Dysfunctional Impulsivity of Dickman (Adan, 2012; Dickman, 1990).

EuropASI: European Addiction Severity Index (Bobes et al., 2007).

SCID-I: Semi-Structured Clinical Interview for DSM-IV Axis I Disorders: The Spanish version.

SCID-II: Semi-Structured Clinical Interview for Axis II Personality Disorders of the DSM-IV: The Spanish version.

Procedure

The assessment process was performed in four interviews, taking approximately 1 hr for each one. The process of psychological evaluation is conducted at the beginning of the treatment process by psychiatrists and psychologists with previous training in addictions and assessment protocols. The sequence of the interviews occurred as follows: The first interview consisted of explaining the study objectives, signing of the informed consent, and conducting the sociodemographic and clinical features evaluation; the second interview was a diagnostic interview in which EuropASI and CAADID were used to assess childhood and adult ADHD; the third and fourth interviews consisted of using SCID-I and SCID-II to evaluate psychiatric comorbidity. During the first evaluation, the staff gave self-administrated questionnaires, ASRS, WURS, BIS-11, and FIDI, which were to be returned during the evaluation process. The evaluators’ experience and the instruments used support the quality and reliability of the data; when there was diagnostic doubt, the information was compared with the patient’s therapist.

Data Analysis

Bivariate and multivariate analyses were performed. The relationship between comparison groups and continuous variables was tested using analysis of variance (ANOVA) or Student’s t test according the number of groups compared. The chi-square test was used to compare categorical variables. Only variables statistically significant in the bivariate analyses, and that allowed the comparison among the three groups, were included in the multinomial regression model. The enter method was used to build models and a polynomial-dependent variable for comparison groups was used (cannabis group, cocaine group, cocaine/cannabis group). All statistical hypotheses were two-tailed. SPSS, version 20 for Windows, was used for all analyses.

Results

A total of 1,538 patients were evaluated and fulfilled the inclusion criteria; 239 (15.5%) fulfilled the adult ADHD criteria, as measured by the CAADID interview. The final sample consisted of 151 patients with cannabis UD 41 (27%), cocaine UD 74 (49%), and cannabis/cocaine UD 36 (24%).

Table 1 shows that the sample primarily composed of men, with a mean age of 32.9 ± 10 years. The cannabis group was significantly younger than the other groups. Other sociodemographic characteristics were not statistically significant.

Table 1.

Sociodemographic, ADHD, and Impulsivity Variables.

	Total (n = 151)	Cannabis UD (n = 41)	Cannabis/cocaine UD (n = 36)	Cocaine UD (n = 74)	Statistic	p value
	%	%	%	%	Statistic	p value
Sociodemographic variables
Male	80	73. 2	86.1	80.8	2.066	.356
Mean age	32.9 ± 10	29.3 ± 13	33.3 ± 11	34.9 ± 7	4.591	.012
Single	69.6	80.5	61.1	67.6	3.656	.161
Primary education	54.5	50	66.7	50.7	2.872	.238
Secondary or more	45.5	50	33.3	49.3
Working	23.1	17.9	22.2	26.5	1.034	.596
Unemployed/social fair	76.9	82.1	77.8	73.5
Legal record	45.6	42.1	74.4	33.3	5.314	.07
Ever imprisoned	17.3	13	28.6	13.5	2.529	.282
Clinical variables related to ADHD
ADHD CAADID subtypes
Inattentive	19	15.2	26.5	17.1	2.877	.579
Hyperactive/Impulsive	16.8	12.1	14.7	20
Combined	64.2	72.7	58.8	62.9
WURS score	56.8 ± 17.8	57.1 ± 15.7	55.5 ± 18.8	57.3 ± 18.8	0.103	.902
ASRS score	15.8 ± 4.2	16.1 ± 4.2	15.6 ± 3.7	15.8 ± 4.4	0.14	.87
CAADID criteria number
Childhood inattention	5.9 ± 2.4	6.3 ± 2.1	5.8 ± 2.5	5.8 ± 2.5	0.484	.617
Childhood hyperactivity	6.4 ± 2.1	6.6 ± 2.1	5.8 ± 2.6	6.5 ± 1.8	1.451	.238
Adult inattention	5.9 ± 2.4	5.8 ± 2.3	6.1 ± 2.6	5.9 ± 2.5	0.208	.813
Adult hyperactivity	5.9 ± 2.5	5.8 ± 2.4	5.5 ± 2.9	6.1 ± 2.3	6.635	.531
BIS-11 Scale
Cognitive impulsivity	21.1 ± 7.2	22.3 ± 11.1	19.9 ± 4.6	20.9 ± 4.9	0.901	.409
Motor impulsivity	25.6 ± 6.9	25.6 ± 6.9	24.3 ± 6.7	26.3 ± 6.8	0.817	.444
Nonplanning impulsivity	26.8 ± 7.4	25.1 ± 5.4	26.0 ± 6.7	28.1 ± 8.6	1.989	.142
Total	72.4 ± 14.9	70.2 ± 13.2	69.2 ± 15.6	75.3 ± 14.9	2.17	.119
FIDI Scale
Functional impulsivity	33.1 ± 7.4	30.6 ± 7.4	33.2 ± 7.5	34.6 ± 7.1	1.939	.151
Dysfunctional impulsivity	41.4 ± 7.6	38.7 ± 6.8	41.4 ± 8.9	42.9 ± 7.1	2.068	.134

Note. ADHD = Attention Deficit Hyperactive Disorder; UD = use disorders; CAADID = Conners’ Adult ADHD Diagnostic Interview; WURS = Wender Utah Rating Scale; ASRS = Adult Self-Report Scale; BIS-11 = Barratt Impulsiveness Scale; FIDI = Functional and Dysfunctional Impulsivity of Dickman.

Regarding ADHD clinical features, the combined subtype was the most prevalent in all groups. Even though there were some differences in percentages between WURS and ASRS, there were no statistically significant differences in the ADHD criteria number between groups and scales. There were no significant differences in symptoms of inattention or hyperactivity number criteria. Regarding impulsivity (BIS-11 and FIDI), there were not statistically significance differences in all groups.

Table 2 shows the age at the start of consumption of any drug and the age at onset of dependence of any drug was earlier in the cannabis group, regardless of whether it was the main substance that motivated the search for treatment. In the analysis of other drugs, a trend of alcohol UD history and opiates UD history SUD was found in the cannabis/cocaine group; a statistical significance in the presence of polysubstance use (use of more than three substances) was also found in this group. There were with statistically significance more years of regular consumption of cannabis, younger age at start of cocaine use, and younger age at start of regular consumption of cocaine in the cannabis/cocaine group. The results of severity of addiction based on the EuropASI scale showed that the cannabis group had significantly more problems regarding working status, and the cocaine group had a tendency to suffer more from alcohol issues.

Table 2.

Consumption Variables and Addiction Severity.

	Total (n = 151)	Cannabis UD (n = 41)	Cannabis/cocaine UD (n = 36)	Cocaine UD (n = 74)	Statistic	p value
	%	%	%	%	Statistic	p value
Age at start of consumption any substance	16.5 ± 4.5	14.7 ± 2.0	15.9 ± 2.6	18.1 ± 5.5	9.732	.0001
Age at onset of any SUD	19.7 ± 6.6	16.3 ± 2.8	16.7 ± 3.8	23.2 ± 7.5	23.324	.0001
Lifetime SUD
Alcohol	43.3	27.5	50	48.6	5.587	.061
Sedatives	9.3	5	16.7	8.1	3.307	.191
Opiates	14	5	22.2	14.9	4.758	.093
Polysubstance use (the use of three or more substances)	30.7	12.5	66.7	23	30.212	.0001
Consumption variables
Cannabis cigarettes per day in the last 6 months		9.1 ± 6.5	8.4 ± 5.1		0.311	.757
Cannabis cigarettes per day in the last month		7.6 ± 7.1	9.8 ± 5.5		0.866	.392
Age of onset of cannabis use		14.6 ± 3.3	14.1 ± 1.9		0.658	.513
Age of onset of cannabis UD		15.8 ± 6.3	21.7 ± 24.3		1.388	.171
Years of regular consumption cannabis		9.7 ± 8.2	15.9 ± 6.8		2.712	.009
Amount of cocaine use in the last 6 months grams per week			5.5 ± 4.3	5.9 ± 7.6	0.173	.863
Amount of cocaine use in the last month, gram per week			3.8 ± 3.4	4.7 ± 7.1	0.477	.636
Age of onset of cocaine use			17.6 ± 3.1	20.1 ± 6.3	2.268	.007
Age of onset of cocaine UD			21.5 ± 7.2	24.4 ± 6.8	2.02	.046
Years of regular consumption cocaine			10.4 ± 5.8	9.7 ± 7.6	0.425	.672
Addiction Severity (EuropASI)
Medical	0.22 ± 0.32	0.24 ± 0.35	0.25 ± 0.30	0.20 ± 0.31	0.315	.73
Working	0.59 ± 0.28	0.67 ± 0.28	0.63 ± 0.27	0.52 ± 0.28	4.221	.017
OH	0.19 ± 0.21	0.14 ± 0.19	0.19 ± 0.21	0.23 ± 0.21	2.392	.095
Drugs	0.21± 0.14	0.19 ± 0.09	0.22 ± 0.13	0.20 ± 0.17	0.276	.759
Legal	0.16 ± 0.24	0.13 ± 0.23	0.23 ± 0.25	0.14 ± 0.23	1.89	.155
Family/social	0.39 ± 0.26	0.40 ± 0.25	0.35 ± 0.23	0.42 ± 0.27	0.938	.394
Psychologic	0.45 ± 0.23	0.44 ± 0.24	0.50 ± 0.24	0.42 ± 0.22	1.573	.211

Note. UD = use disorders; SUD = substance use disorder; EuropASI = European Addiction Severity Index; OH = alcohol.

Table 3 shows that the cannabis group tended to have more axis I disorders and psychotic disorders, but a significant difference was only found in anxiety disorders. No other significant differences between the three groups were found in the presence of Personality disorders.

Table 3.

Psychiatric Comorbidity Variables.

	Total (n = 151	Cannabis UD (n = 41)	Cannabis/Cocaine UD (n = 36)	Cocaine UD (n = 74)	Statistic	p value
	%	%	%	%	Statistic	p value
Lifetime Axis I	63.0	72.5	47.2	66.2	5.736	.057
Major depression	43.2	46.2	37.1	44.6	0.720	.698
Anxiety disorder	25.5	40.0	13.9	23.3	7.169	.028
Psychotic disorders	6.8	7.5	14.3	2.7	5.014	.080
Eating disorders	7.3	5.0	8.3	8.1	0.439	.803
Lifetime Axis II	56.2	63.2	62.1	50.0	2.224	.329
Cluster A	17.8	26.3	16.7	13.9	2.667	.264
Cluster B	43.2	44.7	50.0	38.9	1.261	.532
Cluster C	14.3	18.4	19.4	9.6	2.628	.269
Borderline personality disorder	24.7	23.7	30.6	22.2	0.923	.630
Antisocial personality disorder	28.8	26.3	38.9	25.0	2.410	.300
Childhood conduct disorder	41.8	34.2	48.6	42.6	1.579	.454

Note. UD = use disorders.

Table 4 allows for the pairwise comparison of groups. The resulting model was statistically significant (χ² = 79.285, p ≤ .0001; Cox = 0.454). Principle variables that remained significant were the following: the cannabis group compared with the cocaine group showed significantly younger age at onset of any SUD, more frequency of lifetime anxiety disorder, and more working disabilities evaluated by EuropASI. The cannabis group compared with cocaine/cannabis group showed a higher prevalence of lifetime anxiety disorder and more frequency of polysubstance use in the cannabis/cocaine group. Finally, the cocaine/cannabis group showed earlier onset of any SUD and a higher prevalence of polysubstance use than the cocaine group.

Table 4.

Comparison of Significant Variables With Multivariate Analysis.

	Cannabis UD vs. cocaine UD				Cannabis UD vs. cocaine and cannabis UD				Cocaine UD vs. cocaine and cannabis UD
	Wald	p value	Exp(B)	95% CI	Wald	p value	Exp(B)	95% CI	Wald	p value	Exp(B)	95% CI
Lifetime anxiety disorder	4.582	.032	0.265	[0.078, 0.894]	6.095	.014	6.111	[1.452, 25.720]
Age at onset of any SUD	5.603	.018	0.752	[0.595, 0.952]					6.161	.013	0.810	[0.687, 0.957]
EuropASI working score	4.149	.042	7.458	[1.079, 51.555]
Polysubstance dependence					15.143	.000	0.071	[0.019, 0.270]	8.459	.004	0.210	[0.073, 0.601]

Note. UD = use disorder; CI = confidence interval; SUD = substance use disorder; EuropASI = European Addiction Severity Index.

Discussion

This study evaluated patients seeking treatment for the use of cannabis, cocaine, or both substances that were diagnosed with adult ADHD. Psychiatric comorbidity, consumption-related variables, and clinical factors were compared. Significant differences were observed among the groups regarding lifetime anxiety disorder, evolution of substance use throughout life, working skills, and polysubstance consumption. The groups that presented more severity were those in which cannabis consumption was a common factor.

The high prevalence of ADHD in patients with SUD has been explained by several factors. Neurobiological, genetic, and psychosocial factors have been proposed as causal factors for the development of ADHD and SUD (Lisdahl et al., 2016; Soler Artigas et al., 2020). Some theories found that an imbalance between the inhibitory control and the motivation-reward processing network could increase SUD risk in ADHD (Adisetiyo, 2018). Self-medication theories explain that people with mental disorders choose specific drugs depending on the interaction between the psychopharmacologic action of the drug and the dominant painful feelings with which they struggle. Moreover, psychosocial risk factors are also related to this comorbidity, including parenting styles and norms regarding drug use, childhood sexual abuse, unemployment, and peer pressure (Danielsson, Lundin, Agardh, Allebeck, & Forsell, 2016; Hines et al., 2016; Kosty et al., 2015; Lehn et al., 2007).

Regarding adult ADHD prevalence and its clinical features, ADHD was more prevalent in patients who used cannabis and/or cocaine compared with patients who sought treatment for other SUD. This result is in line with other studies that described a similar prevalence of ADHD in cannabis and cocaine SUD, with a slightly higher percentage in this study (Daigre, Terán, et al., 2013; Faraone et al., 2000; Mannuzza et al., 1993; van de Glind et al., 2014; van Oortmerssen et al., 2014). Combined ADHD subtype was the most prevalent, in accordance with that described in other studies (De Alwis et al., 2014). We did not find differences in terms of ADHD characteristics, such as the subtype of ADHD, impulsivity, inattentive traits, or criteria number even in the ASRS, WURS, or CAADID scales. Hence, this result suggests that ADHD characteristics could be homogeneous among SUD patients independent of the substance used (Kaye et al., 2019). Our results do not support some studies that have described differences in the drug used depending on the subtype of ADHD (Liebrenz et al., 2016).

We divided the sample into three principle groups to analyze drug related and clinical differences. First, we compared the cannabis and cocaine groups, and then we compared the cannabis and cocaine group (as an intermediate group) to the cocaine group and cannabis group.

After multivariate analysis, the cannabis group had an earlier onset of any SUD and earlier start of consumption of any substance. Many studies have described how cannabis is becoming as a first drug of use at younger ages, probably related to the low risk perceived of this substance (Sánchez-Niubò, Sordo, Barrio, Indave, & Domingo-Salvany, 2020). Cannabis has been described as the gateway for the use of other substances in adolescence and as a risk factor for progression to any SUD or polysubstance use (Otten, Mun, & Dishion, 2017; Rioux et al., 2018; Swift et al., 2012; Viola et al., 2014). Earlier age of onset of SUD increases the risk of chronicity, and ADHD is an additional factor that increases the negative impact in mental health (Lisdahl et al., 2016; Silva et al., 2014; Soler Artigas et al., 2020; Tamm et al., 2013).

Regarding the higher frequency of lifetime anxiety disorders, in cannabis group we identified two pathways of influence: on one hand, the frequent comorbidity described between anxiety and ADHD (Asherson, 2017; Faraone et al., 2015; van Oortmerssen et al., 2014), and on the other hand, the high prevalence of anxiety in patients with cannabis UD (Arias et al., 2013; Buckner et al., 2012; Danielsson et al., 2016; Hanna, Perez, & Ghose, 2017; Marel et al., 2019). Lifetime anxiety was significantly higher for the cannabis group compared with the other two groups. The endocannabinoid system is involved in cannabis UD; previous studies described this system as an anxiety modulator. It is possible that alterations or the influence of cannabis in this system explain the higher prevalence of anxiety in these patients (Moreira, Jupp, Belin, & Dalley, 2015; Volkow, Wang, Fowler, & Tomasi, 2012). Comorbidity among cannabis UD, ADHD, and anxiety increased the severity in this group. The anxiety in cannabis UD could be associated with internalization personality characteristics, as this group could be more afraid to use drugs such as cocaine, which are socially considered to be more harmful substances (Fergusson, Boden, & Horwood, 2011; Kedzior & Laeber, 2014; Temple, Driver, & Brown, 2014). Thus, people who seek medical or psychotherapeutic help for problems of cannabis use tend to have a more serious and chronic addiction or worse mental health compared with those who do not seek treatment.

The cannabis group compared with cocaine group showed impairment in the work field. Previous studies on ADHD and SUD have also described more difficulties in employment areas (Daigre, Roncero, et al., 2013; Martínez-Gras et al., 2016; van de Glind et al., 2014). Factors like earlier onset of consumption, anxiety throughout life, and cognitive and social effects of cannabis could explain this relationship with difficulty in labor stability (Fergusson & Boden, 2008).

As expected, the cannabis and cocaine UD group consumed three or more substances more frequently. Several studies have described a higher psychopathology severity when both substances are consumed (O’Brien, Comment, Liang, & Anthony, 2012; Swift et al., 2012; Viola et al., 2014). Accordingly, the cannabis and cocaine group showed greater severity in variables related to use of drugs thorough life (younger age at onset of any SUD, younger age for the start of cocaine use, and the onset of cocaine UD) compared with the cocaine group. There is consistent evidence that cannabis onset is a proximal trigger for cocaine use, with genetic influences, environmental conditions, and processes (O’Brien et al., 2012; Swift et al., 2012).

This study has to be analyzed considering some of its limitations. Due to the recruitment period, we had to adapt the criteria for DSM-IV-TR to DSM-5, which was possible because the psychological evaluation was exhaustive. SUD of other substances (e.g., nicotine, alcohol) was not an exclusion criterion; therefore, it could influence the addiction course. However, other SUD were controlled in the data analysis, and the sample represented patients treated in clinical daily practice. Another limitation is that we did not analyze the transition from the first substance used to the use or dependence of other substances, but it is a promise for a future research line. Highlighting the strengths of this study, we pointed out that this is a naturalistic study that focused on the comparison between two substances frequently used in ADHD patients, representing the real daily practice in a drug outpatient treatment center. The evaluation process were exhaustive to give a good interrater reliability.

Many dilemmas have been described in the treatment of patients with SUD and ADHD, an uncertain ADHD diagnosis in SUD patients, the priorities of SUD treatment, ADHD or other mental disorders, and the professionals experience for SUD and ADHD comorbidity treatment. Thus, it is necessary to take an integral approach, individualizing the needs of each patient (Pérez De Los Cobos, Siñol, Pérez, & Trujols, 2012).

Finally, we confirmed that in the selected sample for this study, ADHD was more prevalent in patients with cannabis and/or cocaine consumption compared with patients who seek treatment for other SUD. These patients frequently have comorbidities with other mental disorders. When the groups were compared, anxiety disorders were more frequent in the cannabis UD group. Thus, this result highlights the necessity to perform specific interventions in patients with ADHD and cannabis UD. Finally, differences between the evolution and severity of the addiction emerged, such as younger age of the start of consumption and SUD in the groups in which cannabis use was a common factor. With these results, we underline the importance of diagnosing comorbidity in patients with ADHD and SUD to achieve an accurate intervention and better outcomes.

Our study describes the differences between cannabis and cocaine UD in patients with ADHD, showing the importance of assessing clinical risks and vulnerability factors to promote preventive strategies and early interventions.

Footnotes

Author’s Note

Constanza Daigre is also affiliated with Biomedical Network Research Center on Mental Health (CIBERSAM), Spain.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: N.M-.L. received fees to give talks for INDIVIOR, Exeltis Healthcare, and Lundbekc. F.P-.A. received fees to give talks for Exeltis, Lundbekc, and MSD. L.G-.L. has received fees to give talks for Janssen-Cilag, Lundbeck, and Exeltis. She has no other relevant affiliations or financial involvement with any organization or entity that has a financial interest in or a financial conflict with the subject matter or materials discussed in the article apart from those disclosed. C.R. has received fees to give lectures for Janssen-Cilag, Ferrer-Brainfarma, Indivior, Lundbeck, Otsuka, Servier, GSK, Astra, Gilead, MSD, Sanofi, Exeltis, Abbvie, Takeda, and Rubio. He has received financial compensation for his participation as a board member of the Janssen-Cilag, Lundbeck, Gilead, MSD, Mundipharm, INDIVIOR, Exeltis, Martindale, and Camurus board. He has carried out the PROTEUS project, which was funded by a grant from Reckitt-Benckisert/Indivior. He received two medical education grants by Gilead. J.A.R-.Q. was on the speakers’ bureau and/or acted as consultant for Eli-Lilly, Janssen-Cilag, Novartis, Shire, Lundbeck, Almirall, Braingaze, Sincrolab, Medice, and Rubió in the last 5 years. He also received travel awards (air tickets + hotel) for taking part in psychiatric meetings from Janssen-Cilag, Rubió, Shire, Medice, and Eli- Lilly. The Department of Psychiatry chaired by him received unrestricted educational and research support from the following companies in the last 5 years: Eli-Lilly, Lundbeck, Janssen-Cilag, Actelion, Shire, Ferrer, Oryzon, Roche, Psious, and Rubió. C.D., M.P-.O., and E.C-.P. have no conflict of interest.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nieves Martínez-Luna

Felipe Palma-Álvarez

Carlos Roncero

Author Biographies

Nieves Martínez-Luna, MD, is a psychiatrist in the Department of Psychiatry at Vall d’Hebron University Hospital. She works at the section of addiction and dual pathology of Vall d’Hebrón University Hospital. Her research interest is in the study of psychiatric and substance use disorders and ADHD.

Constanza Daigre, PhD, is a psychologist in the Department of Psychiatry at Vall d’Hebron University Hospital and the group of Psychiatry, Mental Health and Addiction, Vall d’ Hebron Research Institute (VIHR). She works at the section of addiction and dual pathology of Vall d’Hebron University Hospital. Her research interest is in the study of ADHD, psychiatric and substance use disorders.

Felipe Palma-Álvarez, MD, is a psychiatrist in the Department of Psychiatry at Vall d’Hebron University Hospital. He works at the section of addiction and dual pathology of Vall d’Hebrón University Hospital. His research interest is in the study of psychiatric and substance use disorders.

Marta Perea-Ortueta, MS, is a psychologist in the Department of Psychiatry at Vall d’Hebron University Hospital. She works at the section of addiction and dual pathology of Vall d’Hebron University Hospital. Her research interest is in the study of psychiatric and substance use disorders.

Lara Grau-López, MD, PhD, is a psychiatrist in the Department of Psychiatry at Vall d’Hebron University Hospital. She is head of the section of addiction and dual pathology, all in Vall d’Hebron University Hospital. She is also an associate professor in the Department of Psychiatry and Legal Medicine at the Universitat Autònoma de Barcelona. Her research interest is in the study of substance use disorders and dual disorders.

Carlos Roncero, MD, PhD, is an associate professor in the Department of Psychiatry at the University of Salamanca and the head of psychiatric services in the University of Salamanca Health Care Complex, Salamanca, Spain, and Institute of Biomedicine of Salamanca in the University of Salamanca (Spain). His research interests include the study of psychiatric and substance use disorders and dual disorders.

Eudald Castell-Panisello, MS, is a psychologist in the Department of Psychiatry at the Vall d’Hebron University Hospital. His research interests include the study of psychiatric disorders, substance use disorders, and behavioral research.

Josep Antoni Ramos-Quiroga, MD, PhD, is an associate professor in the Department of Psychiatry and Legal Medicine at the Universitat Autònoma de Barcelona, Spain, and head of the Department of Psychiatry at the Vall d’Hebron University Hospital. His research interests include the study of ADHD across the life span, psychiatric disorders and dual disorders.

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