
==== Front
Harm Reduct J
Harm Reduct J
Harm Reduction Journal
1477-7517
BioMed Central London

1079
10.1186/s12954-024-01079-7
Research
ADHD: prevalence and effect on opioid use disorder treatment outcome in a French sample of patients receiving medication for opioid use disorder—the influence of impulsivity as a mediating factor
Beslot Auxane 1
Grall-Bronnec Marie marie.bronnec@chu-nantes.fr

237
Balem Marianne 2
Schreck Benoit 2
Laforgue Edouard-Jules 24
Victorri-Vigneau Caroline 24
Guillou-Landreat Morgane 56
Leboucher Juliette 1
OPAL-GroupBodenez Pierre
Grall-Bronnec Marie
Guillou-Landréat Morgane
Le Geay Bertrand
Martineau Isabelle
Levassor Philippe
Bolo Paul
Guillet Jean-Yves
Guillery Xavier
Dano Corine

Challet-Bouju Gaëlle 12
Cabelguen Clémence 1
1 https://ror.org/03gnr7b55 grid.4817.a 0000 0001 2189 0784 Addiction Medicine and Psychiatry Department, Nantes Université, CHU Nantes, Nantes, 44000 France
2 grid.277151.7 0000 0004 0472 0371 Nantes Université, Univ Tours, CHU Nantes, INSERM, MethodS in Patients Centered Outcomes and HEalth ResEarch, SPHERE, Nantes, 44000 France
3 HUGOPSY Network, Rennes, France
4 https://ror.org/03gnr7b55 grid.4817.a 0000 0001 2189 0784 Pharmacology Department, Nantes Université, CHU Nantes, Nantes, 44000 France
5 grid.411766.3 0000 0004 0472 3249 Addiction Medicine Department, CHU Brest, Brest, France
6 https://ror.org/01b8h3982 grid.6289.5 0000 0001 2188 0893 Université de Bretagne Occidentale, ERCR SPURBO, Brest, France
7 https://ror.org/052w2jw50 grid.414383.9 0000 0001 2173 8408 Addiction Medicine and Psychiatry Department, Saint Jacques Hospital, 85, rue Saint Jacques, Nantes cedex 1, 44093 France
9 9 2024
9 9 2024
2024
21 1652 2 2024
17 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Opioid use disorder (OUD) poses a global health challenge, and despite medications for opioid use disorder (MOUD) and psychosocial interventions, relapse remains a significant concern. Comorbid psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), are one of the major factors associated with poor OUD treatment outcome. We aimed to estimate the frequency of probable ADHD (in childhood and in adulthood) in patients with OUD; to assess the factors associated with this comorbidity; and to explore the factors that mediate the relationship between ADHD and OUD treatment outcome.

Methods

We conducted an observational study using a sample of 229 patients aged 18 years and older who were diagnosed with OUD and had received MOUD for at least six months. Participants were assessed through a structured interview and self-report questionnaires. Multivariate logistic regressions and a mediation analysis were performed.

Results

Almost half of the participants reported probable ADHD in childhood, and ADHD persisted into adulthood among two-thirds of the patients. The factors associated with poor OUD treatment outcome included earlier onset of OUD, lower education, and greater impulsivity. There was no direct effect of probable ADHD in childhood on OUD treatment outcome, but there was an indirect effect through negative urgency, the tendency to respond impulsively to negatively connoted emotional experiences.

Conclusions

The findings suggest that ADHD symptoms, particularly impulsivity, may contribute to vulnerability in opioid use and play a crucial role in treatment outcomes for this population.

Trial registration: ClinicalTrials identifier NCT01847729.

Keywords

Opioid use disorder
Medication for opioid use disorder
Attention deficit hyperactivity disorder
Impulsivity
Mediation
Outcome
French Inter-ministerial Mission for Combating Drugs and Addictive Behaviours (MILDECA) and Université Paris 13PREVDROG issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcBackground

In the past several years, the prevalence of opioid addiction—including both legal and illegal opioids—has been increasing, as shown by the opioid crisis in the United States, which has led to a high mortality rate [1]. According to consensus recommendations, treatment of opioid use disorder (OUD) includes psychosocial interventions combined with the prescription of medications for opioid use disorder (MOUD). Despite the wide use of methadone and buprenorphine, the rate of relapse among patients involved in the process of quitting opioid use is nearly 70% during prolonged observation periods [2, 3], and the worsening of another addictive disorder—mainly alcohol use disorder—occurs for one-third of patients. Therefore, identifying factors associated with a poor clinical outcome of OUD is of major interest to clinicians [4], as these factors could help them screen high-risk patients and focus on risk factor management whenever possible. Psychiatric comorbidities are one of the major factors that have been shown to be associated with less favorable addiction-related outcomes [5, 6]. Thus, OUD is frequently associated with psychiatric disorders, including anxiety disorders, mood disorders, schizophrenia, personality disorders, and neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD) [7].

ADHD affects approximately 4 to 5% of children and 2 to 3% of adults [8]. It was historically classified as a specific behavioral disorder in children and was mainly characterized by a symptomatology of hyperactivity; however, it is now recognized as a multidimensional and polyfactorial neurodevelopmental disorder. The main symptoms include dysregulated attention, impulsivity, lack of inhibition, and hyperactivity. The persistence and impact of ADHD have been established among adults [9, 10].

A strong association between ADHD and substance use disorder (SUD) has been shown in previous studies [11, 12]. The prognosis and severity of both disorders also appear to be related. Indeed, the co-occurrence of ADHD and addictions is frequently associated with more precocious and more severe profiles of addictions and with a greater risk of relapse [8, 13, 14]. ADHD seems to be a major risk factor for the initiation of SUD; however, it is also a modifiable risk factor, as early treatment of ADHD has been shown to be associated with a decreased risk of SUD later in life [15]. Several hypotheses have been proposed to explain the links between ADHD and addictions. Thus, both disorders may share a common genetic vulnerability [16]. The physiopathology of both disorders may also imply alterations in dopaminergic and noradrenergic neurological pathways [17, 18], and similarities in neuroimaging findings were found between patients with ADHD and patients with addiction symptoms such as craving [19]. Finally, patients may also seek to self-medicate symptoms of ADHD with substance use [20], particularly with cocaine use [21], as supported by recent clinical and neurobiological studies [22, 23]. With regard to the intricate and synergic links between ADHD and SUD, it might be important to consider them “dual disorders” [24] rather than separate comorbidities.

Addictions to nicotine, alcohol, cannabis and cocaine in patients with ADHD have been well documented [13, 14], as has addiction to gambling [25]. Conversely, few studies have been conducted on the specific association between OUD and ADHD. The authors showed a greater prevalence of ADHD in patients with OUD, reaching 33% when the AHDH screening tool was used [26–28], as did an earlier onset of the addictive disorder [29, 30], more psychiatric and addictive comorbidities [31, 32], and greater OUD severity [29, 32]. Studies that have explored the prevalence of ADHD in patients with OUD and how ADHD influences the treatment outcome of OUD are scarce [32]. Indeed, the diagnosis of ADHD seems not to be routine in patients with OUD. Several factors could explain this difference. There might be an overlap between symptoms of ADHD and symptoms of opioid intoxication or withdrawal, making it difficult to differentiate between the two. Opioid use could also lessen ADHD symptoms such as hyperactivity [33]. Moreover, clinicians might focus primarily on the immediate issues related to opioid addiction and the prescription of MOUD. Additionally, there might be confusion between ADHD and conduct disorder, which is another frequent comorbidity in patients with OUD [34].

Our work focused on the associations between ADHD and OUD in adult patients. We expected to observe a high prevalence of ADHD in patients with OUD, a more severe clinical profile in patients with comorbidities, and the influence of impulsivity on treatment outcome. Therefore, the objectives of the present study were to estimate the frequency of probable ADHD (in childhood and in adulthood) in patients with OUD; to assess the factors associated with this comorbidity; and to explore the factors that mediate the relationship between ADHD and OUD treatment outcome.

Methods

Procedure

For the present study, we extracted data from the OPAL (“Opiates and PhArmacoLogy”) study (ClinicalTrials identifier: NCT01847729), an observational, cross-sectional, multicenter study conducted in a population of patients with OUD receiving a MOUD. Ten clinical centers located in the western region of France (e.g., hospitals, outpatient facilities and prison health units) were included to account for the heterogeneity of the patients with OUD [4, 35].

Participants

All patients in the OPAL study were aged 18 years and older, were diagnosed with OUD [36] and were receiving MOUD (methadone, buprenorphine, or buprenorphine-naloxone combination) for at least six months. This minimum treatment period was chosen to allow sufficient time for the adjustment and stabilization of the MOUD dosage. The exclusion criteria were difficulty reading or writing French and the presence of a guardianship.

A total of 263 patients were initially included in the OPAL study. For the present analysis, we excluded patients whose ADHD profile could not be determined because of missing data.

Measures

Patients were assessed through a face-to-face structured clinical interview conducted by one of the investigators and through a set of self-report questionnaires. The following data were collected:

Sociodemographic characteristics

The following data were collected via a structural clinical interview: age, sex, education level, marital and parental status, housing, social support status, occupational status and financial status.

ADHD screening

Each patient’s history of ADHD in childhood and its persistence into adulthood were assessed using the Wender Utah Rating Scale-Child (WURS-C) [37, 38] and the Adult Self-Report Scale Symptom Checklist (ASRS v1.1) screener [39]. The WURS-C consists of 25 items that retrospectively assess ADHD symptoms present in childhood, with each item being scored from 0 to 4 on a Likert scale. The ASRS v1.1 is based on all 18 symptoms from the DSM-IV diagnostic criterion A for adult ADHD, and each symptom is scored on a five-point Likert scale according to frequency. The ASRS v1.1 screener includes only the 6 items that captured the highest strength of association with the clinical diagnosis of ADHD; those 6 items assess both the inattentive and hyperactive/impulsive dimensions [40]. Dichotomous scoring of the six items was proposed by Kessler et al., with a cutoff of 4 indicating the best score for detecting ADHD in adulthood [41].

Using the results of these tests, it was possible to screen for childhood ADHD (WURS-C score ≥ 46/100) and for ADHD likely to persist into adulthood (WURS-C score ≥ 46/100 and ASRS v1.1 screener score ≥ 4/6). This approach permitted the identification of three patient profiles based on their scores on the two ADHD rating scales. The first profile consisted of patients with no ADHD (“No ADHD”), i.e., those who did not have symptoms in childhood (WURS-C score < 46/100), regardless of the ASRS score. In accordance with the DSM diagnostic criteria, we considered that an absence of symptoms in childhood made a diagnosis in adulthood less likely [42]. The other two profiles were obtained from patients who reported probable ADHD in childhood (“probable ADHD in childhood”). The second profile included patients who were considered to have a history of probable ADHD in childhood that resolved in adulthood (“ADHD remission”: WURS-C score ≥ 46/100 but ASRS v1.1 < 4/6). Finally, the third profile was composed of patients who were considered to have a history of probable ADHD in childhood that was still symptomatic in adulthood (“ADHD persistence”) (WURS-C score ≥ 46/100 and ASRS v1.1 ≥ 4/6). We also assessed whether the patients were taking ADHD-specific medication at the time of inclusion.

Impulsivity characteristics

Impulsivity was measured using a short version of the UPPS Impulsive Behavior Scale (UPPS-P) [43, 44]. The UPPS-P is a self-administered questionnaire assessing five dimensions of impulsivity: positive urgency, negative urgency, lack of perseverance, lack of premeditation, and sensation seeking. The positive/negative urgency dimensions assess the tendency to respond impulsively to positively/negatively connoted emotional experiences. A lack of perseverance indicates the inability to persist with tasks despite boredom or fatigue. A lack of premeditation is defined by the inability to consider the consequences of an action before carrying it out. Sensation seeking refers to the tendency to seek intense sensory or emotional experiences. The short version of the UPPS-P is a 20-item version reduced from the original 45-item UPPS [44]. Each dimension is assessed by four questions rated from 1 to 4 on a Likert scale.

OUD characteristics

The data collected covered two distinct time periods: before and after the initiation of the MOUD and included age at first opioid administration, age at onset of OUD, age at first attempt to quit opioids (after withdrawal or initiation of a MOUD), current opioid use despite MOUD (i.e. self-reported use over the previous 6 months, even episodic) or opioid abstinence (i.e. full remission of the OUD), negative consequences related to OUD (financial, socioaffective, psychiatric, medical, professional, or legal), and the presence of drug users in close social circles (family members and friends). Fraudulent ways of obtaining MOUD (through “pharmacy shopping” and “doctor shopping”) were also reported. OUD treatment failure was defined by the persistence of opioid use and/or the worsening of another addictive disorder (regarding substances other than opioid or gambling practice) [4].

Statistical analysis

Continuous variables are described by their means and standard deviations, and categorical variables are described by numbers and percentages. The occasional missing data present in the self-report questionnaires (WURS-C and UPPS-P) were imputed either by the average of the items of the scale (WURS-C) or by the average of the items of the concerned dimension (UPPS-P), only when less than 50% of the items of the scale or of the dimension were missing. When this imputation was not possible due to the excessive number of missing items, the patients were excluded from the analysis.

We first divided the sample into two groups according to their ADHD status in childhood (“No ADHD” and “Probable ADHD in childhood”). Factors associated with probable ADHD in childhood were identified using two-step multivariate logistic regression. All the variables of interest were first compared between the two groups using the chi-square or Fisher test for qualitative variables and Student’s or Wilcoxon test for quantitative variables. P-values were corrected for multiple testing with the Benjamini-Hochberg procedure. Only variables that were significant at a p-value of 0.20 within the corresponding bivariate logistic regressions were subsequently entered into the multivariate model, excluding those for which conditions of independence or collinearity were not verified. Finally, an optimization selection procedure (backward) was applied using the Akaike information criterion (AIC) [45]. The aim of the backward selection was to determine the set of variables that would provide the best fit for the model. At the end of the procedure, only variables that were significant at the 0.05 p-value in the model were interpretable. The corresponding odds ratio and associated 95% confidence interval were estimated.

A mediation analysis was then conducted to explain the effect of probable ADHD in childhood on the failure of OUD treatment, using the variables significantly associated with the “probable ADHD in childhood” profile as mediators. Structural equation modeling (SEM) [46] was used in the form of path analysis. First, the occurrence of a mediating effect was explored with a Satorra-Bentler scaled χ2 difference test [47], which compares a full model that includes all possible indirect paths with a model without any indirect paths. In the case of a significant result, a mediating effect was assumed to be present, and all indirect paths were tested one at a time via an iterative procedure, with the new model tested against the former at each iteration via a Satorra-Bentler scaled χ2 difference test. The procedure was repeated until adding indirect path(s) did not improve the model fit. The fit of the final model was estimated using the root mean square error of approximation (RMSEA) and the comparative fit index (CFI), both of which were computed using Satorra-Bentler corrected χ2 values [47]. A RMSEA < 0.05 and a CFI > 0.95 were considered to indicate a good fit [48]. The strength and direction of the association were estimated with beta coefficients, and we used completely standardized effect sizes to eliminate the various scales of the variables included in the model [49]. The 95% confidence intervals (95% CIs) of the SEM coefficients were estimated via the bootstrap resampling technique (100 replicates).

Finally, the same analysis approach (two-step multivariate logistic regression and mediation analysis) was applied to the subpopulation of “probable ADHD in childhood” patients, who were divided into two groups according to their adulthood ADHD profile (“ADHD remission” and “ADHD persistence”).

Descriptive analyses and logistic regressions were performed with Stata SE 16.0. SEMs were fitted using a robust maximum-likelihood estimator with a Satorra-Bentler correction (MLM) [47] and the lavaan package for R software 4.0.2 [50, 51].

Ethics

The OPAL study was conducted in accordance with the Good Clinical Practice Guidelines and the Declaration of Helsinki. The study was approved by the local ethics committee. The participants were informed about this study, and their written consent was systematically collected.

Results

Description of the sample

A descriptive analysis was performed on a selected sample (N = 229), after excluding patients with missing data (N = 34). MOUD duration were 49.99 months (SD = 52.82) in average, with no significant difference between patients with or without ADHD (p = 0.705).

ADHD profile

Among the 229 selected patients, nearly half reported probable ADHD in childhood (“ADHD+”) (N = 105, 46%). Among these patients, ADHD symptoms resolved for 36 (“ADHD remission”) patients (34% of the “ADHD+” group) and probably persisted for 69 (“ADHD persistence”) patients (66% of the “ADHD+” group). Participant selection and repartition are described in the flow chart provided in Fig. 1.

Fig. 1 Flow chart

No patient was receiving specific pharmacological ADHD treatment at the time of inclusion in the study.

OUD treatment outcome

OUD treatment was considered “successful” for 96 patients and “unsuccessful” for 133 patients (42% and 58%, respectively).

Factors associated with probable ADHD in childhood

Among the 25 candidate variables, 10 had a p-value ≤ 0.2 in the bivariate analysis (Table 1) and were retained for inclusion in the multivariate logistic regression. After the optimization selection procedure, we obtained the final model detailed in Table 2. Factors that were significantly associated with “ADHD+” were earlier age of onset, lower education level, higher negative urgency score, greater (lack of) premeditation score and higher sensation seeking score.

Table 1 Description of the global sample and comparison between the “No ADHD” and “ADHD in childhood” groups (n = 229)

	Global sample
n = 229	“ADHD in childhood”
n = 105	“No ADHD”
n = 124	Adjusted
p-value	
Sociodemographic characteristics					
Sex—women (n (%))	58 (25.33%)	26 (24.76%)	32 (25.81%)	0.890	
Age (µ (σ))	34.59 (7.47)	34.37 (7.91)	34.78 (7.10)	0.755	
Marital status – living alone (n (%))	144 (62.88%)	64 (60.95%)	80 (64.52%)	0.716	
Social circle – close relationships (n (%))	212 (92.58%)	96 (91.43%)	116 (93.55%)	0.705	
Educational level – at least high school graduate (n (%))	89 (38.86)	31 (29.52)	58 (46.77)	0.030	
Professional activity – being inactive (n (%))	121 (52.84%)	61 (58.10%)	60 (48.39%)	0.327	
Stable housing (n (%))	203 (88.65%)	93 (88.57%)	110 (88.71%)	0.974	
OUD characteristics					
OUD duration (µ (σ))	49.99 (52.82)	52.57 (54.54)	47.80 (51.45)	0.705	
OUD treatment failure (n (%))	133 (58.08%)	66 (62.86%)	67 (54.03%)	0.334	
Drug-using family members or friends (n (%))	170 (74.24%)	82 (78.10%)	88 (70.97%)	0.380	
Age of first opioid use (µ (σ))	20.41 (5.17)	19.30 (5.05)	21.36 (5.10)	0.009	
Age of first quitting attempt (µ (σ))	25.84 (5.94)	25.05 (6.30)	26.52 (5.55)	0.179	
Age of onset of OUD (µ (σ))	22.69 (5.62)	21.70 (5.75)	23.52 (5.40)	0.046	
Several prescribers (n (%))	22 (9.61%)	9 (8.57%)	13 (10.48%)	0.739	
Several pharmacies (n (%))	20 (8.73%)	10 (9.52%)	10 (8.06%)	0.755	
OUD negative consequences					
Psychiatric problems (n (%))	161 (70.31%)	77 (73.33%)	84 (67.74%)	0.544	
Somatic problems (n (%))	74 (33.31%)	39 (37.14%)	35 (28.23%)	0.327	
Professional problems (n (%))	128 (55.90%)	51 (58.10%)	67 (54.03%)	0.705	
Socioaffective problems (n (%))	161 (70.31%)	80 (76.19%)	81 (65.32%)	0.190	
Legal problems (n (%))	113 (49.34%)	56 (53.33%)	57 (45.97%)	0.434	
Financial problems (n (%))	160 (69.87%)	78 (74.29%)	82 (66.13%)	0.334	
Impulsivity					
UPPS: Negative urgency [/16] (µ (σ))	10.50 (2.94)	11.42 (2.84)	9.72 (2.80)	0.001	
UPPS: Positive urgency [/16] (µ (σ))	10.39 (2.59)	11.14 (2.24)	9.76 (2.70)	0.001	
UPPS: Lack of premeditation [/16] (µ (σ))	8.37 (2.30)	8.97 (2.37)	7.87 (2.13)	0.002	
UPPS: Lack of perseverance [/16] (µ (σ))	8.41 (2.67)	9.10 (2.74)	7.82 (2.47)	0.002	
UPPS: Sensation seeking [/16] (µ (σ))	10.64 (2.94)	11.32 (2.92)	10.07 (2.85)	0.007	
µ: mean; σ: standard deviation

Table 2 Factors associated with probable ADHD in childhood: multivariate analysis (n = 229)

	OR [CI95%]	p-value	
Educational level—at least high school graduate	0.46 [0.25 ; 0.84]	0.011	
Age of first opioid use	0.94 [0.88 ; 0.99]	0.034	
UPPS: Negative urgency	1.16 [1.04 ; 1.30]	0.009	
UPPS: Lack of premeditation	1.14 [1.04 ; 1.30]	0.015	
UPPS: Sensation seeking	1.12 [1.01 ; 1.23]	0.045	
OR [CI95%]: odds ratio and associated 95% confidence interval; p-values indicated in bold are those under 0.05 (level of statistical significance)

Mediation between probable ADHD in childhood and OUD outcome

The results of the iterative procedure comparing the full model, the model without any indirect path and the final model are presented in Table 3. The full model corresponds to the model with five mediating effects—education level, age at first opioid use and three UPPS scores (negative urgency, lack of premeditation and sensation seeking)—for the relationship between probable ADHD in childhood and OUD treatment failure. A significant decrease in model fit was observed between the model without any indirect paths compared to the full model, which indicated the need to search for mediating effect(s). After the iterative procedure, the final model selected displayed a very good fit (RMSEA = 0.000, CFI = 1.000). The final model that best fit the data is schematized in Fig. 2, with estimated beta coefficients indicated for each path. There was no direct effect of probable ADHD in childhood on OUD treatment failure, and only an indirect effect through negative urgency was observed, with a mediation ratio of 44% (Table 4).

Table 3 Results of the iterative procedure used to obtain the final model of the mediation analysis between probable ADHD in childhood and OUD treatment failure (n = 229)

Model	RMSEA	CFI	χ2 value	χ2 difference test	
Full model	0.000	1.000	0.000		
Model without any indirect path	0.162	0.532	27.700	+ 27.700 p < 0.001	
Final model	0.000	1.000	1.826	-25.874 p < 0.001	
CFI: comparative fit index; RMSEA: root mean square error of approximation

Fig. 2 Final model of mediation analysis between probable ADHD in childhood and OUD treatment failure (n = 229)

Table 4 Contribution of the different pathways to the explanation of OUD treatment failure (n = 229)

Probable ADHD in childhood → UPPS Negative urgency → OUD treatment failure	Standardized coefficient [CI95%]	p-value	
Direct effect	0.05 [− 0.09; 0.18]	0.504	
Indirect effect (through UPPS Negative urgency)	0.04 [0.01; 0.09]	0.036	
Total effect	0.09 [− 0.04; 0.22]	0.176	
Proportion mediated	0.44 [0.05; 0.87]	0.041	
[CI95%]: 95% confidence interval; p-values indicated in bold are those under 0.05 (level of statistical significance)

Factors associated with probable ADHD persistence into adulthood

Among the 25 candidate variables, 5 had a p-value ≤ 0.2 in the bivariate analysis (Table 5) and were retained for inclusion in the multivariate logistic regression. After the optimization selection procedure, we obtained the final model detailed in Table 6. Factors that remained significantly associated with “ADHD persistence” were higher lack of premeditation and lack of perseverance scores.

Table 5 Description of the “ADHD in childhood” group and comparison between “ADHD remission” and “ADHD persistence” in adulthood groups (n = 105)

	“ADHD in childhood”
n = 105	“ADHD remission in adulthood”
n = 36	“ADHD persistence
in adulthood”
n = 69	Adjusted
p-value	
Sociodemographic characteristics		
Sex—women (n (%))	26 (24.76%)	9 (25.00%)	17 (24.64%)	0.967	
Age (µ (σ))	34.37 (7.91)	33.44 (7.87)	34.86 (7.94)	0.405	
Marital status – living alone (n (%))	64 (60.95%)	24 (66.67%)	40 (57.97%)	0.415	
Social circle – close relationships (n (%))	96 (91.43%)	32 (88.89%)	64 (92.75%)	0.566	
Educational level – at least high school graduate (n (%))	31 (29.52)	12 (33.33)	19 (27.54)	0.662	
Professional activity – being inactive (n (%))	61 (58.10%)	20 (55.56%)	41 (59.42%)	0.719	
Stable housing (n (%))	93 (88.57%)	31 (86.11%)	62 (89.86%)	0.562	
OUD characteristics			
OUD treatment failure (n (%))	66 (62.86%)	20 (55.56%)	46 (66.67%)	0.315	
OUD duration (µ (σ))	52.57 (54.54)	45.58 (53.89)	56.22 (54.91)	0.567	
Drug-using family members or friends (n (%))	82 (78.10%)	26 (72.22%)	56 (81.16%)	0.315	
Age of first opioid use (µ (σ))	19.30 (5.05)	19.92 (6.04)	18.97 (4.46)	0.386	
Age of first quitting attempt (µ (σ))	25.05 (6.30)	24.92 (6.93)	25.12 (6.00)	0.937	
Age of onset of OUD (µ (σ))	21.70 (5.75)	21.97 (6.05)	21.57 (5.62)	0.819	
Several prescribers (n (%))	9 (8.57%)	5 (13.89%)	4 (5.80%)	0.181	
Several pharmacies (n (%))	10 (9.52%)	3 (8.33%)	7 (10.14%)	0.967	
OUD negative consequences			
Psychiatric problems (n (%))	77 (73.33%)	26 (72.22%)	51 (73.91%)	0.869	
Somatic problems (n (%))	39 (37.14%)	15 (41.67%)	24 (34.78%)	0.503	
Professional problems (n (%))	61 (58.10%)	20 (55.56%)	41 (59.42%)	0.749	
Socioaffective problems (n (%))	80 (76.19%)	27 (75.00%)	53 (77.81%)	0.937	
Legal problems (n (%))	56 (53.33%)	20 (55.56%)	36 (52.17%)	0.776	
Financial problems (n (%))	78 (77.78%)	28 (77.78%)	50 (72.46%)	0.579	
Impulsivity		
UPPS: Negative urgency [/16] (µ (σ))	11.42 (2.84)	10.09 (3.17)	12.11 (2.39)	0.001	
UPPS: Positive urgency [/16] (µ (σ))	11.14 (2.24)	10.89 (2.48)	11.27 (2.11)	0.479	
UPPS: Lack of premeditation [/16] (µ (σ))	8.97 (2.37)	6.94 (1.82)	10.03 (1.89)	0.001	
UPPS: Lack of perseverance [/16] (µ (σ))	9.88 (2.67)	7.59 (2.28)	9.88 (2.65)	0.001	
UPPS: Sensation seeking [/16] (µ (σ))	11.32 (2.92)	10.75 (3.78)	11.62 (2.32)	0.169	
µ: mean; σ: standard deviation

Table 6 Factors associated with probable ADHD persistence in adulthood: multivariate analysis (n = 105)

	OR [CI95%]	p-value	
UPPS: Negative urgency	1.21 [0.99 ; 1.48]	0.581	
UPPS: Lack of premeditation	2.33 [1.57 ; 3.45]	< 0.001	
UPPS: Lack of perseverance	1.25 [1.01 ; 1.54]	0.042	
OR [CI95%]: odds ratio and associated 95% confidence interval; p-values indicated in bold are those under 0.05 (level of statistical significance)

Mediation between probable ADHD persistence into adulthood and OUD outcome

The results of the iterative procedure comparing the full model, the model without any indirect path and the final model are presented in Table 7. The full model corresponds to the model with three mediating effects (UPPS negative urgency, UPPS lack of premeditation and UPPS sensation of seeking) on the relationship between probable ADHD persistence into adulthood and OUD treatment failure. The Satorra-Bentler scaled χ2 difference test between the full model including all of the indirect paths and the model without any indirect path was not significant at the 5% level (χ2 difference = + 1.258, p = 0.258). This finding indicated that there was no significant difference between the models, and thus, there was no interest in adding mediating effects to the relationship between probable ADHD persistence into adulthood and OUD treatment failure.

Table 7 Results of the iterative procedure used to obtain the final model of the mediation analysis between probable ADHD persistence in adulthood and OUD treatment failure (n = 105)

Model	RMSEA	CFI	χ2 value	χ2 difference test	
Full model	0.164	0.904	0.010		
Model without any indirect path	0.000	1.000	1.258	+ 1.258 p = 0.262	
Final model	.	.	.	.	
CFI: comparative fit index; RMSEA: root mean square error of approximation

Discussion

Main results

The purpose of this study was to estimate the prevalence of probable ADHD in childhood and persistence into adulthood in patients with OUD receiving MOUD, to assess factors associated with OUD/ADHD comorbidity, and to explore the factors that mediate the relationship between ADHD and OUD treatment outcome. Several key findings of this work should be highlighted.

First, the proportion of patients with probable ADHD in childhood was very high—46%. A high prevalence of ADHD symptoms among patients with OUD has already been found in several studies, which reported heterogeneous results ranging from 11 to 58%, with most studies reporting a prevalence of approximately 20%. The prevalence of ADHD was systematically greater among individuals with OUD than in the general population [26, 32, 52]. In our study, the presence of probable ADHD in childhood, whether it persisted into adulthood or not, was significantly associated with an earlier onset of opioid use. This result is congruent with those found in two other studies [26, 31] and, more generally, with studies showing earlier onset of SUD in patients with ADHD [12, 53, 54]. Several hypotheses have been proposed to explain these results. First, direct symptoms of ADHD, such as impulsivity and sensation seeking, might be determinant factors of vulnerability to substance experimentation. We can also hypothesize that patients self-medicate their symptoms of ADHD with the use of substances [52], for instance, sedative substances, to alleviate hyperactivity, although this has not been confirmed [55]. Frequent psychiatric comorbidities of ADHD, such as anxiety disorders or depressive disorders [56], could also increase the use of opioid substances [57]. We also found that a lower level of education was associated with probable ADHD in childhood. This association has also been found in previous studies on ADHD [58, 59]. This could result from direct symptoms of ADHD, such as inattention, which can impair learning abilities in childhood. Finally, an impulsivity profile with higher “negative urgency”, “lack of premeditation”, and “sensation seeking” scores also emerged as significant in patients with lifetime ADHD symptoms. Impulsivity is the clinical reflection of an inhibitory control deficit and is a core mechanism in ADHD. It is found at various levels: cognitive, emotional, and behavioral. These different expressions can be assessed by the different dimensions of the UPPS-P. A lack of premeditation is related to impairments in decision-making and engagement in risky behavior, which are clinical manifestations of an executive dysfunction involving a deficiency in the capacities of anticipation and planning [60]. Anomalies in the dopaminergic pathways in patients with ADHD, especially in the reward system, may develop and might underlie the presence of sensation seeking [61, 62]. Another hypothesis is the frequent co-occurrence of sensory modulation dysfunction and ADHD and the link between sensory modulation and sensation seeking [63]. This could explain the vulnerability to experimental substance use, such as opioid use, as a way to seek intense sensorial and emotional experiences. The negative urgency dimension is important to emphasize because emotional dysregulation is increasingly being described as an integral component of ADHD [64]. A recent study carried out among adults newly diagnosed with ADHD concluded that emotional dysregulation is characterized by emotional instability and emotional impulsivity, which our results tend to support [65]. Furthermore, negative urgency probably plays an important role in the development of addictive disorders. This dimension of impulsivity could negatively impact an individual’s ability to effectively adapt to adverse life events and lead to dysfunctional coping mechanisms such as substance use and, in turn, to the development of alcohol, tobacco or cannabis use disorders [66]. The use of sedative and anxiolytic substances such as opioids may be favored by a dysregulation of the response to stress and negative emotions. As negative urgency scores were not significantly different between patients with probable ADHD persistence and patients with ADHD remission, we can assume that this trait can remain a “scar” of childhood ADHD rather than a consequence of a symptom of current ADHD. Negative urgency was also found to be the single factor that significantly mediates the association between probable ADHD in childhood and OUD treatment failure. We can hypothesize that patients with greater negative urgency scores will show less capacity for inhibition when confronted with adversities during OUD treatment, such as withdrawal symptoms or craving, thus increasing the risk of relapse. Therefore, negative urgency can be considered a key risk factor for relapse or for switching addiction and should be closely monitored during OUD treatment in this population.

Second, the probable persistence of ADHD in adulthood was observed in two-thirds of the patients who were screened for ADHD in childhood. It is well established that ADHD persists most of the time even after childhood, and a recent study further clarified that the course of ADHD is often marked by fluctuating symptoms between childhood and young adulthood, with sustained remission in less than 10% of the cases [67]. The “lack of premeditation” and “lack of perseverance” impulsivity dimensions were the only factors we identified as associated with the persistence of probable ADHD. A lack of perseverance could be related to executive dysfunction or increased fatigability due to ADHD symptoms [68]. As previously mentioned, the lack of premeditation is linked to decision-making impairments and risk-taking behaviors [69], which are clinical correlates of executive dysfunction. In particular, inhibitory control deficits are considered a phenotype in adults with ADHD [70].

Both the precociousness of opioid use and the small differences between patients who were screened for childhood ADHD that persisted or resolved suggested that the influence of ADHD on OUD outcome occurred beginning in childhood and not via the direct influence of symptoms in adulthood. Many complex mechanisms are likely involved and intertwined. Impulsivity and emotional dysregulation in ADHD might lead to greater risk seeking and inefficient coping strategies when confronted with adversity. Psychiatric comorbidities of ADHD might also participate in the occurrence of addiction and response to treatment.

Finally, one important lesson from the study was the absence of specific treatment for ADHD in all patients with probable ADHD persistence into adulthood, despite the theoretical indication for treatment. It is important to note that no medication had marketing authorization in adults in France at the time of recruitment, with the exception of those already prescribed since childhood, without interruption. Although few studies have been conducted on this topic and the effectiveness of psychostimulant medication for ADHD seems unclear [71, 72], no adverse effects were found in the literature when psychostimulant treatment was used in patients with OUD or treated by MOUD when contraindications were respected. The risk of misuse of psychostimulants seems low [55]. This underprescription of psychostimulants in these patients could be related to a lack of identification of ADHD or to a lack of training or reluctance of prescribers concerning the management of the comorbidity of OUD and ADHD. It is important to specify that methylphenidate for adult patients with ADHD has only been authorized in France since 2021 [73].

Strengths and limitations of the study

These results must be viewed within the context of several limitations. The main limitation of the study is its cross-sectional nature, as we cannot establish a causal hypothesis between probable ADHD in childhood or adulthood and the associated factors. Moreover, since part of the data collection was declarative and retrospective, recall bias cannot be ruled out. Defining success or failure of OUD treatment was a tough challenge. We chose to be stringent about criteria of OUD success to ensure that we isolated truly improved patients in the “success” group. Abstaining from any opioid use thus corresponded to the definition of full remission of the disorder according to the ICD-11. However, this choice is debatable: in the context of OUD, episodic use and/or relapses are to be expected. If transient, they do not necessarily constitute a treatment failure. We cannot exclude the possibility that some patients, treated for a long time, may have experienced multiple states (success or failure) at different times during their follow-up. Indeed, patients had been treated on average for several years, and MOUD duration constitute a potential bias when analyzing relapses, that are expected to occur more frequently. The validity of the ADHD diagnosis is a limitation frequently found in studies in this population. The scale used in the present study to assess the presence of probable ADHD in adulthood was the ASRS v1.1 screener, which is a screening scale [39]. Therefore, there is a risk of over-diagnosis among patients with symptoms suggestive of ADHD but possibly not specific enough to allow a diagnosis to be made. A diagnosis should normally be made through a structured interview such as the DIVA [74]. However, to increase the reliability of the ADHD diagnosis, the results of the WURS, which assesses ADHD in childhood, were also taken into account. Indeed, because ADHD is a neurodevelopmental disorder, we considered that a diagnosis of probable ADHD implied the presence of preexisting symptoms in childhood. Thus, we limited the risk of considering patients with attentional or hyperactivity symptoms related to causes other than ADHD, such as existing SUD or mood and anxiety comorbidities common in this population, to be ADHD [56, 75]. The combination of the WURS-C and ASRS v1.1 screener questionnaires is considered by several studies to be sufficient for reliably suggesting a diagnosis of ADHD [76]. The fact that patients had been treated and stabilized on MOUD for six months also helped to avoid confounding bias, as symptoms of inattention or hyperactivity could also be directly related to the effects of opioid intoxication or withdrawal. Another limitation is the absence of a clinical assessment of psychiatric comorbidities and associated treatments. As an association between ADHD and psychiatric comorbidities has been well demonstrated [56, 77, 78] and the presence of psychiatric disorders is associated with a greater frequency and severity of addictive disorders, there may be confounding bias regarding the characteristics of OUD and the outcome of treatment. However, psychiatric problems reported as negative consequences of OUD were not significantly different between patients with and without ADHD symptoms. These findings suggest that the prevalence of psychiatric disorders was similar. Finally, the acceptance rate to participate in the OPAL study would have been interesting to calculate. Unfortunately, the study protocol did not include recording the number of eligible patients, the number of patients refusing to participate, and the number of patients agreeing to participate at each center. Indeed, we aimed to streamline the procedure as much as possible for the investigators, who in most centers, lacked research experience and were primarily involved in clinical activities.

These limitations are compensated for by the strengths of the study. This is one of the few studies examining the relationship between ADHD and OUD, including both patients with or without probable ADHD in childhood and patients with ADHD that probably persisted into adulthood or resolved in adulthood. As mentioned earlier, this wide perspective facilitated the identification of patients with ADHD but also permitted the analysis of the impact of childhood ADHD on OUD later in life. The large number of subjects allowed for sufficient power of the statistical analyses.

Perspectives

This study is a reminder of the significant prevalence of ADHD among patients with OUD. The development of OUD seems to depend more on ADHD in childhood and its consequences than on the direct influence of ADHD in adulthood. These results reinforce the need for early and appropriate treatment in children with ADHD, especially since it has been shown that pharmacological treatment in childhood has a protective effect on addictive disorders in adulthood [79, 80].

The influence of one of the impulsivity dimensions on OUD treatment failure in patients with ADHD underscores the value of systematically screening and possibly treating this disorder and assessing the negative urgency dimension in all patients with OUD and a history of ADHD. Specific management of negative urgency through adapted psychotherapeutic care could be a way to address this issue. The place for pharmacological treatment for ADHD in managing negative urgency and, more globally, emotional dysregulation has yet to be determined. A few studies have shown positive effects of psychostimulants on cognitive impulsivity and decision-making in individuals with ADHD [81–83], although additional studies should be conducted in adults with SUD in the future. Longitudinal studies may also be conducted in the future to assess the influence of ADHD on OUD development and perpetuation and on the response to treatment, as well as the interplay with other psychiatric comorbidities.

Abbreviations

ADHD Attention Deficit Hyperactivity Disorder

AIC Akaike Information Criterion

ASRS v1.1 Adult Self-Report Scale Symptom Checklist

CFI Comparative Fit Index

CIs Confidence Intervals

MLM Maximum-Likelihood estimator with a Satorra-Bentler correction

MOUD Medication for opioid use disorderOPAL: Opiates and PhArmacoLogy

OUD Opioid Use Disorder

RMSEA Root Mean Square Error of Approximation

SEM Structural Equation Modeling

SUD Substance Use Disorder

UPPS-P UPPS Impulsive Behavior Scale

WURS-C Wender Utah Rating Scale-Child

Acknowledgements

We would like to thank the members of the OPAL-Group for their valuable contribution to the recruitment of the participants. Members of the OPAL-Group are: Pierre Bodenez, Marie Grall-Bronnec, Morgane Guillou-Landréat, Bertrand Le Geay, Isabelle Martineau, Philippe Levassor, Paul Bolo, Jean-Yves Guillet, Xavier Guillery and Corine Dano.

Author contributions

AB: conceptualization, writing—original draft preparation, MGB: funding acquisition, supervision, data collection, conceptualization, writing—original draft preparation, MB: analysis, writing—original draft preparation, BS: writing—review and editing, EJL: writing—review and editing, CVV: writing—review and editing, MGL: data collection, writing—review and editing, JL: conceptualization, writing—review and editing, OPAL-Group: data collection, GCB: conceptualization, writing—original draft preparation, CC: conceptualization, writing—original draft preparation. All authors read and approved the final manuscript.

Funding

The OPAL study was supported jointly by the Mission Interministérielle de Lutte contre les Drogues et les Conduites Addictives (MILDECA) and the Université Paris 13, as part of the call for research projects “PREVDROG” launched by these two organizations in 2011. This research was conducted at the initiative of and coordinated by the UIC “Psychiatrie et Santé Mentale” of Nantes University Hospital. Nantes University Hospital is the sponsor of this study. There were no constraints on publishing.

Data availability

The datasets generated and analysed during the current study are not publicly available because data generated included sensitive data according to the French Data Protection Authority (CNIL), that could not be transferred to other researchers to guarantee participants’ anonymity. But they are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. The OPAL study was approved by the local ethics committee. The participants were informed about this study, and their written consent was systematically collected.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Kolodny A Courtwright DT Hwang CS Kreiner P Eadie JL Clark TW The prescription opioid and heroin crisis: a public health approach to an epidemic of addiction Annu Rev Public Health 2015 36 559 74 10.1146/annurev-publhealth-031914-122957 25581144
Kolodny A, Courtwright DT, Hwang CS, Kreiner P, Eadie JL, Clark TW, et al. The prescription opioid and heroin crisis: a public health approach to an epidemic of addiction. Annu Rev Public Health. 2015;36:559–74.25581144 10.1146/annurev-publhealth-031914-122957
2. Clark RE Baxter JD Aweh G O’Connell E Fisher WH Barton BA Risk factors for Relapse and higher costs among Medicaid members with opioid dependence or abuse: opioid agonists, comorbidities, and treatment history J Subst Abuse Treat 2015 57 75 80 10.1016/j.jsat.2015.05.001 25997674
Clark RE, Baxter JD, Aweh G, O’Connell E, Fisher WH, Barton BA. Risk factors for Relapse and higher costs among Medicaid members with opioid dependence or abuse: opioid agonists, comorbidities, and treatment history. J Subst Abuse Treat. 2015;57:75–80.25997674 10.1016/j.jsat.2015.05.001
3. Hser YI Evans E Grella C Ling W Anglin D Long-term course of opioid addiction Harv Rev Psychiatry 2015 23 2 76 89 10.1097/HRP.0000000000000052 25747921
Hser YI, Evans E, Grella C, Ling W, Anglin D. Long-term course of opioid addiction. Harv Rev Psychiatry. 2015;23(2):76–89.25747921 10.1097/HRP.0000000000000052
4. Grall-Bronnec M Laforgue EJ Challet-Bouju G Cholet J Hardouin JB Leboucher J Prevalence of coaddictions and rate of successful treatment among a French sample of opioid-dependent patients with Long-Term Opioid Substitution Therapy: the OPAL Study Front Psychiatry 2019 10 726 10.3389/fpsyt.2019.00726 31681038
Grall-Bronnec M, Laforgue EJ, Challet-Bouju G, Cholet J, Hardouin JB, Leboucher J, et al. Prevalence of coaddictions and rate of successful treatment among a French sample of opioid-dependent patients with Long-Term Opioid Substitution Therapy: the OPAL Study. Front Psychiatry. 2019;10:726.31681038 10.3389/fpsyt.2019.00726
5. Ross S Peselow E Co-occurring psychotic and addictive disorders: Neurobiology and diagnosis Clin Neuropharmacol 2012 35 5 235 43 10.1097/WNF.0b013e318261e193 22986797
Ross S, Peselow E. Co-occurring psychotic and addictive disorders: Neurobiology and diagnosis. Clin Neuropharmacol. 2012;35(5):235–43.22986797 10.1097/WNF.0b013e318261e193
6. Strakowski S The co-occurrence of bipolar and substance use disorders Clin Psychol Rev 2000 20 2 191 206 10.1016/S0272-7358(99)00025-2 10721497
Strakowski S. The co-occurrence of bipolar and substance use disorders. Clin Psychol Rev. 2000;20(2):191–206.10721497 10.1016/S0272-7358(99)00025-2
7. Krausz M Verthein U Degkwitz P Psychiatric comorbidity in opiate addicts Eur Addict Res 1999 5 2 55 62 10.1159/000018966 10394034
Krausz M, Verthein U, Degkwitz P. Psychiatric comorbidity in opiate addicts. Eur Addict Res. 1999;5(2):55–62.10394034 10.1159/000018966
8. Kessler RC Adler L Barkley R Biederman J Conners CK Demler O The prevalence and correlates of adult ADHD in the United States: results from the National Comorbidity Survey Replication Am J Psychiatry 2006 163 4 716 23 10.1176/ajp.2006.163.4.716 16585449
Kessler RC, Adler L, Barkley R, Biederman J, Conners CK, Demler O, et al. The prevalence and correlates of adult ADHD in the United States: results from the National Comorbidity Survey Replication. Am J Psychiatry. 2006;163(4):716–23.16585449 10.1176/ajp.2006.163.4.716
9. Faraone SV Biederman J Mick E The age-dependent decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies Psychol Med 2006 36 2 159 65 10.1017/S003329170500471X 16420712
Faraone SV, Biederman J, Mick E. The age-dependent decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies. Psychol Med. 2006;36(2):159–65.16420712 10.1017/S003329170500471X
10. Barbaresi WJ Weaver AL Voigt RG Killian JM Katusic SK Comparing methods to determine persistence of Childhood ADHD into Adulthood: a prospective, Population-based study J Atten Disord 2018 22 6 571 80 10.1177/1087054715618791 26700793
Barbaresi WJ, Weaver AL, Voigt RG, Killian JM, Katusic SK. Comparing methods to determine persistence of Childhood ADHD into Adulthood: a prospective, Population-based study. J Atten Disord. 2018;22(6):571–80.26700793 10.1177/1087054715618791
11. Capusan AJ Bendtsen P Marteinsdottir I Larsson H Comorbidity of adult ADHD and its subtypes with Substance Use Disorder in a large Population-based epidemiological study J Atten Disord 2019 23 12 1416 26 10.1177/1087054715626511 26838558
Capusan AJ, Bendtsen P, Marteinsdottir I, Larsson H. Comorbidity of adult ADHD and its subtypes with Substance Use Disorder in a large Population-based epidemiological study. J Atten Disord. 2019;23(12):1416–26.26838558 10.1177/1087054715626511
12. Wilens TE Biederman J Mick E Faraone SV Spencer T Attention deficit hyperactivity disorder (ADHD) is associated with early onset substance use disorders J Nerv Ment Dis 1997 185 8 475 82 10.1097/00005053-199708000-00001 9284860
Wilens TE, Biederman J, Mick E, Faraone SV, Spencer T. Attention deficit hyperactivity disorder (ADHD) is associated with early onset substance use disorders. J Nerv Ment Dis. 1997;185(8):475–82.9284860 10.1097/00005053-199708000-00001
13. Lee SS Humphreys KL Flory K Liu R Glass K Prospective association of childhood attention-deficit/hyperactivity disorder (ADHD) and substance use and abuse/dependence: a meta-analytic review Clin Psychol Rev 2011 31 3 328 41 10.1016/j.cpr.2011.01.006 21382538
Lee SS, Humphreys KL, Flory K, Liu R, Glass K. Prospective association of childhood attention-deficit/hyperactivity disorder (ADHD) and substance use and abuse/dependence: a meta-analytic review. Clin Psychol Rev. 2011;31(3):328–41.21382538 10.1016/j.cpr.2011.01.006
14. Charach A Yeung E Climans T Lillie E Childhood Attention-Deficit/Hyperactivity disorder and future substance Use disorders: comparative Meta-analyses J Am Acad Child Adolesc Psychiatry 2011 50 1 9 21 10.1016/j.jaac.2010.09.019 21156266
Charach A, Yeung E, Climans T, Lillie E. Childhood Attention-Deficit/Hyperactivity disorder and future substance Use disorders: comparative Meta-analyses. J Am Acad Child Adolesc Psychiatry. 2011;50(1):9–21.21156266 10.1016/j.jaac.2010.09.019
15. Quinn PD Chang Z Hur K Gibbons RD Lahey BB Rickert ME ADHD medication and substance-related problems Am J Psychiatry 2017 174 9 877 85 10.1176/appi.ajp.2017.16060686 28659039
Quinn PD, Chang Z, Hur K, Gibbons RD, Lahey BB, Rickert ME, et al. ADHD medication and substance-related problems. Am J Psychiatry. 2017;174(9):877–85.28659039 10.1176/appi.ajp.2017.16060686
16. Young SE Stallings MC Corley RP Krauter KS Hewitt JK Genetic and environmental influences on behavioral disinhibition Am J Med Genet 2000 96 5 684 95 10.1002/1096-8628(20001009)96:5<684::AID-AJMG16>3.0.CO;2-G 11054778
Young SE, Stallings MC, Corley RP, Krauter KS, Hewitt JK. Genetic and environmental influences on behavioral disinhibition. Am J Med Genet. 2000;96(5):684–95.11054778 10.1002/1096-8628(20001009)96:5<684::AID-AJMG16>3.0.CO;2-G
17. Faraone SV Biederman J Neurobiology of attention-deficit hyperactivity disorder Biol Psychiatry 1998 44 10 951 8 10.1016/S0006-3223(98)00240-6 9821559
Faraone SV, Biederman J. Neurobiology of attention-deficit hyperactivity disorder. Biol Psychiatry. 1998;44(10):951–8.9821559 10.1016/S0006-3223(98)00240-6
18. Koob GF Volkow ND Neurobiology of addiction: a neurocircuitry analysis Lancet Psychiatry 2016 3 8 760 73 10.1016/S2215-0366(16)00104-8 27475769
Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. 2016;3(8):760–73.27475769 10.1016/S2215-0366(16)00104-8
19. Frodl T Comorbidity of ADHD and substance use disorder (SUD): a neuroimaging perspective J Atten Disord 2010 14 2 109 20 10.1177/1087054710365054 20495160
Frodl T. Comorbidity of ADHD and substance use disorder (SUD): a neuroimaging perspective. J Atten Disord. 2010;14(2):109–20.20495160 10.1177/1087054710365054
20. Wilens TE Adamson J Sgambati S Whitley J Santry A Monuteaux MC Do individuals with ADHD self-medicate with cigarettes and substances of abuse? Results from a controlled family study of ADHD Am J Addict 2007 16 s1 14 23 10.1080/10550490601082742 17453603
Wilens TE, Adamson J, Sgambati S, Whitley J, Santry A, Monuteaux MC, et al. Do individuals with ADHD self-medicate with cigarettes and substances of abuse? Results from a controlled family study of ADHD. Am J Addict. 2007;16(s1):14–23.17453603 10.1080/10550490601082742
21. Mariani JJ Khantzian EJ Levin FR The self-medication hypothesis and psychostimulant treatment of Cocaine Dependence: an update Am J Addict Am Acad Psychiatr Alcohol Addict 2014 23 2 189 93
Mariani JJ, Khantzian EJ, Levin FR. The self-medication hypothesis and psychostimulant treatment of Cocaine Dependence: an update. Am J Addict Am Acad Psychiatr Alcohol Addict. 2014;23(2):189–93.
22. Manni C Cipollone G Pallucchini A Maremmani AGI Perugi G Maremmani I Remarkable reduction of Cocaine Use in Dual Disorder (adult attention deficit hyperactive Disorder/Cocaine Use Disorder) patients treated with medications for ADHD Int J Environ Res Public Health 2019 16 20 3911 10.3390/ijerph16203911 31618876
Manni C, Cipollone G, Pallucchini A, Maremmani AGI, Perugi G, Maremmani I. Remarkable reduction of Cocaine Use in Dual Disorder (adult attention deficit hyperactive Disorder/Cocaine Use Disorder) patients treated with medications for ADHD. Int J Environ Res Public Health. 2019;16(20):3911.31618876 10.3390/ijerph16203911
23. Carli G Cavicchioli M Martini AL Bruscoli M Manfredi A Presotto L Neurobiological dysfunctional substrates for the self-medication hypothesis in adult individuals with attention-deficit hyperactivity disorder and Cocaine Use Disorder: a fluorine-18-Fluorodeoxyglucose Positron Emission Tomography Study Brain Connect 2023 13 7 370 82 10.1089/brain.2022.0076 37097207
Carli G, Cavicchioli M, Martini AL, Bruscoli M, Manfredi A, Presotto L, et al. Neurobiological dysfunctional substrates for the self-medication hypothesis in adult individuals with attention-deficit hyperactivity disorder and Cocaine Use Disorder: a fluorine-18-Fluorodeoxyglucose Positron Emission Tomography Study. Brain Connect. 2023;13(7):370–82.37097207 10.1089/brain.2022.0076
24. Szerman N, Parro-Torres C, Didia-Attas J, El-Guebaly N. Dual Disorders: Addiction and Other Mental Disorders. Integrating Mental Health. In: Javed A, Fountoulakis KN, editors. Advances in Psychiatry [Internet]. Cham: Springer International Publishing; 2019 [cited 2022 Dec 22]. pp. 109–27. 10.1007/978-3-319-70554-5_7
25. Fatseas M Alexandre JM Vénisse JL Romo L Valleur M Magalon D Gambling behaviors and psychopathology related to Attention-Deficit/Hyperactivity disorder (ADHD) in problem and non-problem adult gamblers Psychiatry Res 2016 239 232 8 10.1016/j.psychres.2016.03.028 27031593
Fatseas M, Alexandre JM, Vénisse JL, Romo L, Valleur M, Magalon D, et al. Gambling behaviors and psychopathology related to Attention-Deficit/Hyperactivity disorder (ADHD) in problem and non-problem adult gamblers. Psychiatry Res. 2016;239:232–8.27031593 10.1016/j.psychres.2016.03.028
26. Peles E Schreiber S Sutzman A Adelson M Attention deficit hyperactivity disorder and obsessive-compulsive disorder among former heroin addicts currently in Methadone Maintenance Treatment Psychopathology 2012 45 5 327 33 10.1159/000336219 22796643
Peles E, Schreiber S, Sutzman A, Adelson M. Attention deficit hyperactivity disorder and obsessive-compulsive disorder among former heroin addicts currently in Methadone Maintenance Treatment. Psychopathology. 2012;45(5):327–33.22796643 10.1159/000336219
27. Fiksdal Abel K Ravndal E Clausen T Bramness JG Attention deficit hyperactivity disorder symptoms are common in patients in opioid maintenance treatment Eur Addict Res 2017 23 6 298 305 10.1159/000484240 29320768
Fiksdal Abel K, Ravndal E, Clausen T, Bramness JG. Attention deficit hyperactivity disorder symptoms are common in patients in opioid maintenance treatment. Eur Addict Res. 2017;23(6):298–305.29320768 10.1159/000484240
28. Liao YT Chen CY Ng MH Huang KY Shao WC Lin TY Depression and severity of substance dependence among heroin dependent patients with ADHD symptoms Am J Addict 2017 26 1 26 33 10.1111/ajad.12487 27997065
Liao YT, Chen CY, Ng MH, Huang KY, Shao WC, Lin TY, et al. Depression and severity of substance dependence among heroin dependent patients with ADHD symptoms. Am J Addict. 2017;26(1):26–33.27997065 10.1111/ajad.12487
29. Karlstad Ø Furu K Skurtveit S Selmer R Prescribing of drugs for attention-deficit hyperactivity disorder in opioid maintenance treatment patients in Norway Eur Addict Res 2014 20 2 59 65 10.1159/000353969 24080771
Karlstad Ø, Furu K, Skurtveit S, Selmer R. Prescribing of drugs for attention-deficit hyperactivity disorder in opioid maintenance treatment patients in Norway. Eur Addict Res. 2014;20(2):59–65.24080771 10.1159/000353969
30. Arias AJ Gelernter J Chan G Weiss RD Brady KT Farrer L Correlates of co-occurring ADHD in drug-dependent subjects: prevalence and features of substance dependence and psychiatric disorders Addict Behav 2008 33 9 1199 207 10.1016/j.addbeh.2008.05.003 18558465
Arias AJ, Gelernter J, Chan G, Weiss RD, Brady KT, Farrer L, et al. Correlates of co-occurring ADHD in drug-dependent subjects: prevalence and features of substance dependence and psychiatric disorders. Addict Behav. 2008;33(9):1199–207.18558465 10.1016/j.addbeh.2008.05.003
31. Modestin J Matutat B Würmle O Antecedents of opioid dependence and personality disorder: attention-deficit/hyperactivity disorder and conduct disorder Eur Arch Psychiatry Clin Neurosci 2001 251 1 42 7 10.1007/s004060170067 11315518
Modestin J, Matutat B, Würmle O. Antecedents of opioid dependence and personality disorder: attention-deficit/hyperactivity disorder and conduct disorder. Eur Arch Psychiatry Clin Neurosci. 2001;251(1):42–7.11315518 10.1007/s004060170067
32. Carpentier PJ van Gogh MT Knapen LJM Buitelaar JK De Jong CAJ Influence of attention deficit hyperactivity disorder and Conduct Disorder on Opioid Dependence Severity and Psychiatric Comorbidity in Chronic Methadone-maintained patients Eur Addict Res 2011 17 1 10 20 10.1159/000321259 20881401
Carpentier PJ, van Gogh MT, Knapen LJM, Buitelaar JK, De Jong CAJ. Influence of attention deficit hyperactivity disorder and Conduct Disorder on Opioid Dependence Severity and Psychiatric Comorbidity in Chronic Methadone-maintained patients. Eur Addict Res. 2011;17(1):10–20.20881401 10.1159/000321259
33. van Emmerik-van Oortmerssen K Vedel E Kramer FJ Koeter MW Schoevers RA van den Brink W Diagnosing ADHD during active substance use: feasible or flawed? Drug Alcohol Depend 2017 180 371 5 10.1016/j.drugalcdep.2017.07.039 28957778
van Emmerik-van Oortmerssen K, Vedel E, Kramer FJ, Koeter MW, Schoevers RA, van den Brink W. Diagnosing ADHD during active substance use: feasible or flawed? Drug Alcohol Depend. 2017;180:371–5.28957778 10.1016/j.drugalcdep.2017.07.039
34. Carpentier PJ Knapen LJM van Gogh MT Buitelaar JK De Jong CAJ Addiction in Developmental Perspective: influence of Conduct Disorder Severity, Subtype, and attention-deficit hyperactivity disorder on Problem Severity and Comorbidity in adults with opioid dependence J Addict Dis 2012 31 1 45 59 10.1080/10550887.2011.642756 22356668
Carpentier PJ, Knapen LJM, van Gogh MT, Buitelaar JK, De Jong CAJ. Addiction in Developmental Perspective: influence of Conduct Disorder Severity, Subtype, and attention-deficit hyperactivity disorder on Problem Severity and Comorbidity in adults with opioid dependence. J Addict Dis. 2012;31(1):45–59.22356668 10.1080/10550887.2011.642756
35. Victorri-Vigneau C Verstuyft C Bouquié R Laforgue E Hardouin J Leboucher J Relevance of CYP2B6 and CYP2D6 genotypes to methadone pharmacokinetics and response in the OPAL study Br J Clin Pharmacol 2019 85 7 1538 43 10.1111/bcp.13936 30907440
Victorri-Vigneau C, Verstuyft C, Bouquié R, Laforgue E, Hardouin J, Leboucher J, et al. Relevance of CYP2B6 and CYP2D6 genotypes to methadone pharmacokinetics and response in the OPAL study. Br J Clin Pharmacol. 2019;85(7):1538–43.30907440 10.1111/bcp.13936
36. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-IV-TR. Ward. 2000. 1002 p.
37. Ward MF Wender PH Reimherr FW The Wender Utah rating scale: an aid in the retrospective diagnosis of childhood attention deficit hyperactivity disorder Am J Psychiatry 1993 150 6 885 90 8494063
Ward MF, Wender PH, Reimherr FW. The Wender Utah rating scale: an aid in the retrospective diagnosis of childhood attention deficit hyperactivity disorder. Am J Psychiatry. 1993;150(6):885–90.8494063
38. Baylé F Martin C Wender P Version française de la Wender Utah rating scale (WURS) Can J Psychiatry 2003 48 2 132 132 10.1177/070674370304800220 12655921
Baylé F, Martin C, Wender P. Version française de la Wender Utah rating scale (WURS). Can J Psychiatry. 2003;48(2):132–132.12655921 10.1177/070674370304800220
39. Kessler RC Adler LA Gruber MJ Sarawate CA Spencer T Van Brunt DL Validity of the World Health Organization adult ADHD self-report scale (ASRS) screener in a representative sample of health plan members Int J Methods Psychiatr Res 2007 16 2 52 65 10.1002/mpr.208 17623385
Kessler RC, Adler LA, Gruber MJ, Sarawate CA, Spencer T, Van Brunt DL. Validity of the World Health Organization adult ADHD self-report scale (ASRS) screener in a representative sample of health plan members. Int J Methods Psychiatr Res. 2007;16(2):52–65.17623385 10.1002/mpr.208
40. Hesse M The ASRS-6 has two latent factors: attention deficit and hyperactivity J Atten Disord 2013 17 3 203 7 10.1177/1087054711430330 22262467
Hesse M. The ASRS-6 has two latent factors: attention deficit and hyperactivity. J Atten Disord. 2013;17(3):203–7.22262467 10.1177/1087054711430330
41. Kessler RC Adler L Ames M Demler O Faraone S Hiripi E The World Health Organization adult ADHD self-report scale (ASRS): a short screening scale for use in the general population Psychol Med 2005 35 2 245 56 10.1017/S0033291704002892 15841682
Kessler RC, Adler L, Ames M, Demler O, Faraone S, Hiripi E, et al. The World Health Organization adult ADHD self-report scale (ASRS): a short screening scale for use in the general population. Psychol Med. 2005;35(2):245–56.15841682 10.1017/S0033291704002892
42. Taylor LE Kaplan-Kahn EA Lighthall RA Antshel KM Adult-Onset ADHD A critical analysis and alternative explanations Child Psychiatry Hum Dev 2022 53 4 635 53 10.1007/s10578-021-01159-w 33738692
Taylor LE, Kaplan-Kahn EA, Lighthall RA, Antshel KM, Adult-Onset ADHD. A critical analysis and alternative explanations. Child Psychiatry Hum Dev. 2022;53(4):635–53.33738692 10.1007/s10578-021-01159-w
43. Whiteside SP Lynam DR The five factor model and impulsivity: using a structural model of personality to understand impulsivity Personal Individ Differ 2001 30 4 669 89 10.1016/S0191-8869(00)00064-7
Whiteside SP, Lynam DR. The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personal Individ Differ. 2001;30(4):669–89.10.1016/S0191-8869(00)00064-7
44. Billieux J Rochat L Ceschi G Carré A Offerlin-Meyer I Defeldre AC Validation of a short French version of the UPPS-P Impulsive Behavior Scale Compr Psychiatry 2012 53 5 609 15 10.1016/j.comppsych.2011.09.001 22036009
Billieux J, Rochat L, Ceschi G, Carré A, Offerlin-Meyer I, Defeldre AC, et al. Validation of a short French version of the UPPS-P Impulsive Behavior Scale. Compr Psychiatry. 2012;53(5):609–15.22036009 10.1016/j.comppsych.2011.09.001
45. Chowdhury MZI Turin TC Variable selection strategies and its importance in clinical prediction modelling Fam Med Community Health 2020 8 1 e000262 10.1136/fmch-2019-000262 32148735
Chowdhury MZI, Turin TC. Variable selection strategies and its importance in clinical prediction modelling. Fam Med Community Health. 2020;8(1):e000262.32148735 10.1136/fmch-2019-000262
46. Raykov T, Marcoulides GA. A first course in structural equation modeling, 2nd ed. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers; 2006. ix, 238 p. (A first course in structural equation modeling, 2nd ed).
47. Bryant FB Satorra A Principles and practice of scaled difference chi-square testing Struct Equ Model 2012 19 3 372 98 10.1080/10705511.2012.687671
Bryant FB, Satorra A. Principles and practice of scaled difference chi-square testing. Struct Equ Model. 2012;19(3):372–98.10.1080/10705511.2012.687671
48. Schermelleh-Engel K Moosbrugger H Müller H Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures Methods Psychol Res 2003 8 2 23 74
Schermelleh-Engel K, Moosbrugger H, Müller H. Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures. Methods Psychol Res. 2003;8(2):23–74.
49. Preacher KJ Kelley K Effect size measures for mediation models: quantitative strategies for communicating indirect effects Psychol Methods 2011 16 2 93 115 10.1037/a0022658 21500915
Preacher KJ, Kelley K. Effect size measures for mediation models: quantitative strategies for communicating indirect effects. Psychol Methods. 2011;16(2):93–115.21500915 10.1037/a0022658
50. Rosseel Y Lavaan: an R Package for Structural equation modeling J Stat Softw 2012 48 1 36 10.18637/jss.v048.i02
Rosseel Y. Lavaan: an R Package for Structural equation modeling. J Stat Softw. 2012;48:1–36.10.18637/jss.v048.i02
51. Dalgaard PR, Development Core, Team. (2010): R: A language and environment for statistical computing. 2010 [cited 2024 Jan 9]; https://research.cbs.dk/en/publications/r-development-core-team-2010-r-a-language-and-environment-for-sta
52. van Emmerik-van Oortmerssen K van de Glind G van den Brink W Smit F Crunelle CL Swets M Prevalence of attention-deficit hyperactivity disorder in substance use disorder patients: a meta-analysis and meta-regression analysis Drug Alcohol Depend 2012 122 1 11 9 10.1016/j.drugalcdep.2011.12.007 22209385
van Emmerik-van Oortmerssen K, van de Glind G, van den Brink W, Smit F, Crunelle CL, Swets M, et al. Prevalence of attention-deficit hyperactivity disorder in substance use disorder patients: a meta-analysis and meta-regression analysis. Drug Alcohol Depend. 2012;122(1):11–9.22209385 10.1016/j.drugalcdep.2011.12.007
53. Levin FR Evans SM Vosburg SK Horton T Brooks D Ng J Impact of attention-deficit hyperactivity disorder and other psychopathology on treatment retention among cocaine abusers in a therapeutic community Addict Behav 2004 29 9 1875 82 10.1016/j.addbeh.2004.03.041 15530732
Levin FR, Evans SM, Vosburg SK, Horton T, Brooks D, Ng J. Impact of attention-deficit hyperactivity disorder and other psychopathology on treatment retention among cocaine abusers in a therapeutic community. Addict Behav. 2004;29(9):1875–82.15530732 10.1016/j.addbeh.2004.03.041
54. Humfleet GL Prochaska JJ Mengis M Cullen J Muñoz R Reus V Preliminary evidence of the association between the history of childhood attention-deficit/hyperactivity disorder and smoking treatment failure Nicotine Tob Res off J Soc Res Nicotine Tob 2005 7 3 453 60 10.1080/14622200500125310
Humfleet GL, Prochaska JJ, Mengis M, Cullen J, Muñoz R, Reus V, et al. Preliminary evidence of the association between the history of childhood attention-deficit/hyperactivity disorder and smoking treatment failure. Nicotine Tob Res off J Soc Res Nicotine Tob. 2005;7(3):453–60.10.1080/14622200500125310
55. Pérez de los Cobos J Siñol N Pérez V Trujols J Pharmacological and clinical dilemmas of prescribing in co-morbid adult attention-deficit/hyperactivity disorder and addiction: adult ADHD and addiction Br J Clin Pharmacol 2014 77 2 337 56 10.1111/bcp.12045 23216449
Pérez de los Cobos J, Siñol N, Pérez V, Trujols J. Pharmacological and clinical dilemmas of prescribing in co-morbid adult attention-deficit/hyperactivity disorder and addiction: adult ADHD and addiction. Br J Clin Pharmacol. 2014;77(2):337–56.23216449 10.1111/bcp.12045
56. Katzman MA Bilkey TS Chokka PR Fallu A Klassen LJ Adult ADHD and comorbid disorders: clinical implications of a dimensional approach BMC Psychiatry 2017 17 302 10.1186/s12888-017-1463-3 28830387
Katzman MA, Bilkey TS, Chokka PR, Fallu A, Klassen LJ. Adult ADHD and comorbid disorders: clinical implications of a dimensional approach. BMC Psychiatry. 2017;17:302.28830387 10.1186/s12888-017-1463-3
57. Garey L Olofsson H Garza T Rogers AH Kauffman BY Zvolensky MJ Directional effects of anxiety and depressive disorders with Substance Use: a review of recent prospective research Curr Addict Rep 2020 7 3 344 55 10.1007/s40429-020-00321-z
Garey L, Olofsson H, Garza T, Rogers AH, Kauffman BY, Zvolensky MJ. Directional effects of anxiety and depressive disorders with Substance Use: a review of recent prospective research. Curr Addict Rep. 2020;7(3):344–55.10.1007/s40429-020-00321-z
58. de Graaf R Kessler RC Fayyad J Have M ten, Alonso J Angermeyer M The prevalence and effects of adult attention-deficit/hyperactivity disorder (ADHD) on the performance of workers: results from the WHO World Mental Health Survey Initiative Occup Environ Med 2008 65 12 835 42 10.1136/oem.2007.038448 18505771
de Graaf R, Kessler RC, Fayyad J, Have M, ten, Alonso J, Angermeyer M, et al. The prevalence and effects of adult attention-deficit/hyperactivity disorder (ADHD) on the performance of workers: results from the WHO World Mental Health Survey Initiative. Occup Environ Med. 2008;65(12):835–42.18505771 10.1136/oem.2007.038448
59. Polderman TJC Boomsma DI Bartels M Verhulst FC Huizink AC A systematic review of prospective studies on attention problems and academic achievement Acta Psychiatr Scand 2010 122 4 271 84 10.1111/j.1600-0447.2010.01568.x 20491715
Polderman TJC, Boomsma DI, Bartels M, Verhulst FC, Huizink AC. A systematic review of prospective studies on attention problems and academic achievement. Acta Psychiatr Scand. 2010;122(4):271–84.20491715 10.1111/j.1600-0447.2010.01568.x
60. Roselló B Berenguer C Baixauli I Mira Á Martinez-Raga J Miranda A Empirical examination of executive functioning, ADHD associated behaviors, and functional impairments in adults with persistent ADHD, remittent ADHD, and without ADHD BMC Psychiatry 2020 20 134 10.1186/s12888-020-02542-y 32204708
Roselló B, Berenguer C, Baixauli I, Mira Á, Martinez-Raga J, Miranda A. Empirical examination of executive functioning, ADHD associated behaviors, and functional impairments in adults with persistent ADHD, remittent ADHD, and without ADHD. BMC Psychiatry. 2020;20:134.32204708 10.1186/s12888-020-02542-y
61. Norbury A Husain M Sensation-seeking: dopaminergic modulation and risk for psychopathology Behav Brain Res 2015 288 79 93 10.1016/j.bbr.2015.04.015 25907745
Norbury A, Husain M. Sensation-seeking: dopaminergic modulation and risk for psychopathology. Behav Brain Res. 2015;288:79–93.25907745 10.1016/j.bbr.2015.04.015
62. Blum K Chen ALC Braverman ER Comings DE Chen TJ Arcuri V Attention-deficit-hyperactivity disorder and reward deficiency syndrome Neuropsychiatr Dis Treat 2008 4 5 893 918 19183781
Blum K, Chen ALC, Braverman ER, Comings DE, Chen TJ, Arcuri V, et al. Attention-deficit-hyperactivity disorder and reward deficiency syndrome. Neuropsychiatr Dis Treat. 2008;4(5):893–918.19183781
63. Assayag N Berger I Parush S Mell H Bar-Shalita T Attention-Deficit/Hyperactivity disorder symptoms, Sensation-Seeking, and sensory modulation dysfunction in Substance Use Disorder: A Cross Sectional two-group comparative study Int J Environ Res Public Health 2022 19 5 2541 10.3390/ijerph19052541 35270233
Assayag N, Berger I, Parush S, Mell H, Bar-Shalita T. Attention-Deficit/Hyperactivity disorder symptoms, Sensation-Seeking, and sensory modulation dysfunction in Substance Use Disorder: A Cross Sectional two-group comparative study. Int J Environ Res Public Health. 2022;19(5):2541.35270233 10.3390/ijerph19052541
64. Shaw P Stringaris A Nigg J Leibenluft E Emotion dysregulation in attention deficit hyperactivity disorder Am J Psychiatry 2014 171 3 276 93 10.1176/appi.ajp.2013.13070966 24480998
Shaw P, Stringaris A, Nigg J, Leibenluft E. Emotion dysregulation in attention deficit hyperactivity disorder. Am J Psychiatry. 2014;171(3):276–93.24480998 10.1176/appi.ajp.2013.13070966
65. Martz E Weiner L Weibel S Identifying different patterns of emotion dysregulation in adult ADHD Borderline Personal Disord Emot Dysregulation 2023 10 28 10.1186/s40479-023-00235-y
Martz E, Weiner L, Weibel S. Identifying different patterns of emotion dysregulation in adult ADHD. Borderline Personal Disord Emot Dysregulation. 2023;10:28.10.1186/s40479-023-00235-y
66. Roberts W Peters JR Adams ZW Lynam DR Milich R Identifying the facets of impulsivity that explain the relation between ADHD symptoms and substance use in a nonclinical sample Addict Behav 2014 39 8 1272 7 10.1016/j.addbeh.2014.04.005 24813555
Roberts W, Peters JR, Adams ZW, Lynam DR, Milich R. Identifying the facets of impulsivity that explain the relation between ADHD symptoms and substance use in a nonclinical sample. Addict Behav. 2014;39(8):1272–7.24813555 10.1016/j.addbeh.2014.04.005
67. Sibley MH Arnold LE Swanson JM Hechtman LT Kennedy TM Owens E Variable patterns of remission from ADHD in the Multimodal treatment study of ADHD Am J Psychiatry 2022 179 2 142 51 10.1176/appi.ajp.2021.21010032 34384227
Sibley MH, Arnold LE, Swanson JM, Hechtman LT, Kennedy TM, Owens E, et al. Variable patterns of remission from ADHD in the Multimodal treatment study of ADHD. Am J Psychiatry. 2022;179(2):142–51.34384227 10.1176/appi.ajp.2021.21010032
68. Griffin SA Lynam DR Samuel DB Dimensional conceptualizations of impulsivity Personal Disord Theory Res Treat 2018 9 4 333 45 10.1037/per0000253
Griffin SA, Lynam DR, Samuel DB. Dimensional conceptualizations of impulsivity. Personal Disord Theory Res Treat. 2018;9(4):333–45.10.1037/per0000253
69. Rochat L Billieux J Gagnon J Van der Linden M A multifactorial and integrative approach to impulsivity in neuropsychology: insights from the UPPS model of impulsivity J Clin Exp Neuropsychol 2018 40 1 45 61 10.1080/13803395.2017.1313393 28398126
Rochat L, Billieux J, Gagnon J, Van der Linden M. A multifactorial and integrative approach to impulsivity in neuropsychology: insights from the UPPS model of impulsivity. J Clin Exp Neuropsychol. 2018;40(1):45–61.28398126 10.1080/13803395.2017.1313393
70. Senkowski D, Ziegler T, Singh M, Heinz A, He J, Silk T et al. Assessing Inhibitory Control deficits in adult ADHD: a systematic review and Meta-analysis of the stop-signal Task. Neuropsychol Rev. 2023.
71. Willcutt EG The prevalence of DSM-IV Attention-Deficit/Hyperactivity disorder: a Meta-Analytic Review Neurotherapeutics 2012 9 3 490 9 10.1007/s13311-012-0135-8 22976615
Willcutt EG. The prevalence of DSM-IV Attention-Deficit/Hyperactivity disorder: a Meta-Analytic Review. Neurotherapeutics. 2012;9(3):490–9.22976615 10.1007/s13311-012-0135-8
72. Abel KF Bramness JG Martinsen EW Stimulant medication for ADHD in opioid maintenance treatment J Dual Diagn 2014 10 1 32 8 10.1080/15504263.2013.867657 25392060
Abel KF, Bramness JG, Martinsen EW. Stimulant medication for ADHD in opioid maintenance treatment. J Dual Diagn. 2014;10(1):32–8.25392060 10.1080/15504263.2013.867657
73. Haute Autorité de Santé. Haute Autorité de Santé. 2022 [cited 2024 Jan 10]. Ritaline LP (methylphenidate). https://www.has-sante.fr/jcms/p_3305318/fr/ritaline-lp-methylphenidate-tdah
74. Kooij JJS, Adult ADHD. Diagnostic assessment and treatment, 3rd ed. New York, NY, US: Springer-Verlag Publishing/Springer Nature; 2013. xvii, 292 p. (Adult ADHD: Diagnostic assessment and treatment, 3rd ed).
75. Biederman J Ball SW Monuteaux MC Mick E Spencer TJ McCREARY M New insights into the Comorbidity between ADHD and Major Depression in adolescent and young adult females J Am Acad Child Adolesc Psychiatry 2008 47 4 426 34 10.1097/CHI.0b013e31816429d3 18388760
Biederman J, Ball SW, Monuteaux MC, Mick E, Spencer TJ, McCREARY M, et al. New insights into the Comorbidity between ADHD and Major Depression in adolescent and young adult females. J Am Acad Child Adolesc Psychiatry. 2008;47(4):426–34.18388760 10.1097/CHI.0b013e31816429d3
76. Brevik EJ, Lundervold AJ, Haavik J, Posserud M. Validity and accuracy of the Adult Attention-Deficit/Hyperactivity Disorder (ADHD) Self‐Report Scale (ASRS) and the Wender Utah Rating Scale (WURS) symptom checklists in discriminating between adults with and without ADHD. Brain Behav [Internet]. 2020 Apr 13 [cited 2021 Mar 9];10(6). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303368/
77. Biederman J Attention-Deficit/Hyperactivity disorder: a selective overview Biol Psychiatry 2005 57 11 1215 20 10.1016/j.biopsych.2004.10.020 15949990
Biederman J. Attention-Deficit/Hyperactivity disorder: a selective overview. Biol Psychiatry. 2005;57(11):1215–20.15949990 10.1016/j.biopsych.2004.10.020
78. Torgersen T Gjervan B Rasmussen K ADHD in adults: a study of clinical characteristics, impairment and comorbidity Nord J Psychiatry 2006 60 1 38 43 10.1080/08039480500520665 16500798
Torgersen T, Gjervan B, Rasmussen K. ADHD in adults: a study of clinical characteristics, impairment and comorbidity. Nord J Psychiatry. 2006;60(1):38–43.16500798 10.1080/08039480500520665
79. Maia CRM Cortese S Caye A Deakin TK Polanczyk GV Polanczyk CA Long-term efficacy of Methylphenidate Immediate-Release for the treatment of Childhood ADHD J Atten Disord 2017 21 1 3 13 10.1177/1087054714559643 25501355
Maia CRM, Cortese S, Caye A, Deakin TK, Polanczyk GV, Polanczyk CA, et al. Long-term efficacy of Methylphenidate Immediate-Release for the treatment of Childhood ADHD. J Atten Disord. 2017;21(1):3–13.25501355 10.1177/1087054714559643
80. Coghill DR Seth S Pedroso S Usala T Currie J Gagliano A Effects of methylphenidate on cognitive functions in children and adolescents with attention-deficit/hyperactivity disorder: evidence from a systematic review and a meta-analysis Biol Psychiatry 2014 76 8 603 15 10.1016/j.biopsych.2013.10.005 24231201
Coghill DR, Seth S, Pedroso S, Usala T, Currie J, Gagliano A. Effects of methylphenidate on cognitive functions in children and adolescents with attention-deficit/hyperactivity disorder: evidence from a systematic review and a meta-analysis. Biol Psychiatry. 2014;76(8):603–15.24231201 10.1016/j.biopsych.2013.10.005
81. DeVito EE Blackwell AD Kent L Ersche KD Clark L Salmond CH The effects of methylphenidate on decision making in attention-deficit/hyperactivity disorder Biol Psychiatry 2008 64 7 636 9 10.1016/j.biopsych.2008.04.017 18504036
DeVito EE, Blackwell AD, Kent L, Ersche KD, Clark L, Salmond CH, et al. The effects of methylphenidate on decision making in attention-deficit/hyperactivity disorder. Biol Psychiatry. 2008;64(7):636–9.18504036 10.1016/j.biopsych.2008.04.017
82. Crunelle CL van den Brink W Dom G Booij J Dopamine transporter occupancy by methylphenidate and impulsivity in adult ADHD Br J Psychiatry J Ment Sci 2014 204 6 486 7 10.1192/bjp.bp.113.132977
Crunelle CL, van den Brink W, Dom G, Booij J. Dopamine transporter occupancy by methylphenidate and impulsivity in adult ADHD. Br J Psychiatry J Ment Sci. 2014;204(6):486–7.10.1192/bjp.bp.113.132977
83. Campez M Raiker JS Little K Altszuler AR Merrill BM Macphee FL An evaluation of the effect of methylphenidate on working memory, time perception, and choice impulsivity in children with ADHD Exp Clin Psychopharmacol 2022 30 2 209 19 10.1037/pha0000446 33475395
Campez M, Raiker JS, Little K, Altszuler AR, Merrill BM, Macphee FL, et al. An evaluation of the effect of methylphenidate on working memory, time perception, and choice impulsivity in children with ADHD. Exp Clin Psychopharmacol. 2022;30(2):209–19.33475395 10.1037/pha0000446
