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Pia M. Wullinger IFT Institut für Therapieforschung, Mental Health and Addition Research, Munich, Germany

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Andreas M. Bickl IFT Institut für Therapieforschung, Mental Health and Addition Research, Munich, Germany

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Johanna K. Loy IFT Institut für Therapieforschung, Mental Health and Addition Research, Munich, Germany

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Ludwig Kraus IFT Institut für Therapieforschung, Mental Health and Addition Research, Munich, Germany
Department of Public Health Sciences, Centre for Social Research on Alcohol and Drugs, Stockholm University, Stockholm, Sweden
Institute of Psychology, ELTE, Eötvöos Loránd University, Budapest, Hungary

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Larissa Schwarzkopf IFT Institut für Therapieforschung, Mental Health and Addition Research, Munich, Germany
Department of Psychiatry and Psychotherapy, Klinikum der Universität München, Munich, Germany

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Abstract

Background and aims

Individuals with gambling disorder (GD) often suffer from psychiatric comorbidities. Previous studies demonstrated greater severity of GD among gamblers with psychiatric comorbidities. However, evidence on the association between psychiatric comorbidity and course of GD severity during and after outpatient treatment is sparse. This study analyses data from a longitudinal one-armed cohort study on outpatient addiction care clients over three years.

Methods

We investigated the course of GD severity using data from 123 clients in 28 outpatient addiction care facilities in Bavaria using generalized estimation equations (GEE). We applied time* interaction analyses to examine different development profiles in participants with and without (1) affective disorders, or (2) anxiety disorders, and (3) to account for the co-occurrence of both.

Results

All participants benefitted from outpatient gambling treatment. Improvement in GD severity was poorer in participants with anxiety disorders compared to participants without anxiety disorders. The co-occurrence of affective and anxiety disorders was linked to a less favourable course of GD than the presence of affective disorders alone. However, the combined occurrence of both disorders was more favourable than the presence of anxiety disorders alone.

Discussion and conclusions

Our study suggests that clients with GD, with and without psychiatric comorbidities, benefit from outpatient gambling care. Psychiatric comorbidity, especially comorbid anxiety disorders, seems to be negatively associated with the course of GD within outpatient gambling care. Addressing psychiatric comorbidity within the treatment of GD and offering individualised help are required to meet the needs of this clientele.

Abstract

Background and aims

Individuals with gambling disorder (GD) often suffer from psychiatric comorbidities. Previous studies demonstrated greater severity of GD among gamblers with psychiatric comorbidities. However, evidence on the association between psychiatric comorbidity and course of GD severity during and after outpatient treatment is sparse. This study analyses data from a longitudinal one-armed cohort study on outpatient addiction care clients over three years.

Methods

We investigated the course of GD severity using data from 123 clients in 28 outpatient addiction care facilities in Bavaria using generalized estimation equations (GEE). We applied time* interaction analyses to examine different development profiles in participants with and without (1) affective disorders, or (2) anxiety disorders, and (3) to account for the co-occurrence of both.

Results

All participants benefitted from outpatient gambling treatment. Improvement in GD severity was poorer in participants with anxiety disorders compared to participants without anxiety disorders. The co-occurrence of affective and anxiety disorders was linked to a less favourable course of GD than the presence of affective disorders alone. However, the combined occurrence of both disorders was more favourable than the presence of anxiety disorders alone.

Discussion and conclusions

Our study suggests that clients with GD, with and without psychiatric comorbidities, benefit from outpatient gambling care. Psychiatric comorbidity, especially comorbid anxiety disorders, seems to be negatively associated with the course of GD within outpatient gambling care. Addressing psychiatric comorbidity within the treatment of GD and offering individualised help are required to meet the needs of this clientele.

Introduction

An estimated three out of four treatment-seeking individuals with gambling disorder (GD) suffer from psychiatric comorbidity (Dowling et al., 2015). More specifically, for major depression, a prevalence of 29.9% was reported, and high rates were also found for anxiety disorders (17.6%), with social phobia (14.9%), generalized anxiety disorder (14.4%), and panic disorder (13.7%) being the most common forms. This comparatively high coincidence of GD and anxiety or affective disorders might to some extent be related to shared underlying genetic contributions (Potenza, Xian, Shah, Scherrer, & Eisen, 2005). Moreover, depression and anxiety disorders were reported to be linked to the development of more problematic gambling behaviour (Hartmann & Blaszczynski, 2018) and to be related to suicidal ideation and suicide attempts among people with GD (Petry & Kiluk, 2002). In a recent longitudinal study among adults with GD, greater severity of depression and posttraumatic stress disorder were found to be associated with intensified gambling over a follow-up period of 48 months. In addition, greater severity of affective as well as anxiety disorders was associated with greater GD severity (Black, Allen, & Bormann, 2021).

Despite the high prevalence rates of psychiatric comorbidities and their demonstrated detrimental association with GD, few studies have addressed the impact of psychiatric comorbidity on the treatment of GD (Buchner & Wodarz, 2011). While it is known that the presence of psychiatric comorbidity increases both treatment dropout among people with GD (Maniaci et al., 2017) and likelihood of relapse after gambling cessation (Hodgins & el-Guebaly, 2010), there is limited evidence on the implications for treatment outcomes. The association of psychiatric comorbidity and the prospective course of GD during and after treatment has only been studied in the inpatient setting so far. There, psychiatric comorbidity was associated with a higher psychological symptom burden of GD and more gambling involvement 6 months after treatment termination (Premper & Schulz, 2007).

For the outpatient setting, evidence on the association between GD and psychiatric comorbidity is lacking, even though in Germany people are more likely to seek help in the outpatient than in the inpatient setting (Künzel, Daubner, Specht, & Braun, 2019). German outpatient addiction care facilities (OACFs) offer highly need-driven individualised treatment and provide a low threshold and free access to a huge variety of help services and treatment modalities (Meyer & Bachmann, 2017). First and foremost, these services consist of counselling services that are intended to make it easier for those affected to handle their everyday live issues and mainly include financial counselling and motivational interviewing, sometimes (elements of) cognitive behavioural therapy are integrated (Schwarzkopf et al., 2021). However, clients with GD and a psychiatric comorbidity may benefit less from gambling-oriented therapy than those without any psychiatric comorbidity (Premper & Schulz, 2007).

A better understanding of the associations between the presence of psychiatric comorbidity and the course of a pre-existing GD is paramount to better align treatment modalities in outpatient care. Using longitudinal data from clients with GD treated in OACFs, this paper aims (1) to describe and compare socio-demographic and gambling-related characteristics of clients with and without a psychiatric comorbidity, (2) to investigate whether GD severity and its course differ between clients with and without comorbid affective or anxiety disorders, and (3) to analyse the interplay of affective and anxiety disorders in relation to GD.

Methods

Design and setting

Data stem from assessments of the “Katamnese” study, a prospective naturalistic cohort study addressing GD and its development in the context of German OACFs. Participants were recruited between 2014 and 2016 within 28 OACFs in the Bavarian Competence Network for Gambling Issues of the Bavarian Coordination Centre for Gambling Issues. To create a standard framework for high-quality outpatient gambling care across the Bavarian network, which is multifaceted and highly heterogeneous, the Bavarian Coordination Centre for Gambling Issues has developed a 'Practice Guide Gambling'. This guide serves as a set of guidelines for all institutions within the network and provides valuable information on diagnostic procedures, subgroup-specific requirements, counselling elements (such as target setting, debt counselling, and budget planning), as well as exercises for both group and individual counselling sessions.

Individual follow-up covered 36 months. In addition to socio-demographic data, the study assessed information on gambling behaviour, gambling-related problems, and gambling-related consequences. Participants received questionnaires at admission and at 6-, 12-, 24-, and 36-month follow-ups. Moreover, a computer-assisted composite international diagnostic interview (CIDI) was conducted at baseline to collect information on psychiatric comorbidity (Wittchen & Pfister, 1997). To gather information on treatment options sought, questionnaire data were linked to the client's individual OACF routine documentation. Detailed information on the study design has been published elsewhere (Schwarzkopf et al., 2021).

Study sample

Individuals with GD who initiated treatment at one of the participating OACFs and had a minimum of three contacts with their OACF until August 2016 were eligible for the study. Further inclusion criteria were an age of at least 18 years and sufficient German language skills. Of 1,159 clients initiating OACF treatment during the recruitment period, 615 (53.6%) met the inclusion criteria and were invited on a voluntary basis to participate in the study. Out of these, 199 clients (32.4%) provided informed consent, which was subsequently withdrawn by 15 clients (7.5%). After removing one client who could not be contacted for baseline assessment and 38 clients (17.6%) who did not answer the baseline questionnaire, the baseline sample consisted of 145 participants (Fig. 1).

Fig. 1.
Fig. 1.

Participant inclusion/exclusion procedure and study population at each timepoint

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00026

Outcome variable

GD severity was assessed within the participant survey at each assessment point via the validated “Stinchfield criteria” (Stinchfield, 2003). By removing the criterion on criminality, the criteria were aligned with the DSM-5 (American Psychiatric Association, 2013). A sum score of the endorsed DSM-5 criteria was calculated at each assessment point, yielding a GD score ranging from 0 to 9. GD was diagnosed with ≥4 DSM-5 criteria endorsed, and GD severity was classified as mild (4–5 criteria), moderate (6–7 criteria) or severe (8–9 criteria).

Covariables

The covariables included in the analyses were baseline age (in years), sex, psychiatric comorbidity, and migration background; and time-dependent treatment status. Comorbid psychiatric disorders were assessed using the CIDI's standardised questions on mood and anxiety during the last 12 months. Diagnoses of affective disorders included major depression, dysthymic disorder, and bipolar disorders. Anxiety disorders included panic disorder, agoraphobia, specific phobia, social phobia, generalized anxiety disorder, unspecified anxiety disorder, obsessive-compulsive disorder, and post-traumatic stress disorder (World Health Organization [WHO], 2004). As the distinct single conditions were rare, summary variables were created indicating whether at least one of the listed disorders was diagnosed in each group.

As migration background has been identified as a risk factor for a less favourable course of GD severity (Bickl et al., 2021), self-reported migration background status was used as a covariable. In accordance with the definition of the Federal Statistical Office of Germany (Statistisches Bundesamt, 2010) a migration background was present if the participant migrated to Germany him/herself or his/her (grand)parents had immigrated before his/her birth.

Treatment status was obtained from the OACF's routine documentation via the Germany-wide standardised core dataset of addiction care (Deutsche Hauptstelle für Suchtfragen [DHS], 2010) and contained information on whether a participant was still in treatment or had terminated treatment in a regular (i.e., stopped after a full course of outpatient treatment) or irregular (i.e., stopped outpatient treatment before follow-up 2) fashion. We created a trichotomised variable indicating participants' treatment status (still in treatment, regular termination, or irregular termination) at each assessment point. As information on treatment status was only available until follow-up 2, we adopted a conservative perspective by assuming that all participants without information on treatment termination remained in treatment until follow-up 4.

Statistical analyses

First, socio-demographic, gambling-, and comorbidity-related characteristics of participants with and without a psychiatric comorbidity, and of completers (i.e., participants who responded to all follow-up assessments) and dropouts (i.e., participants who did not respond to at least one of the follow-ups), were compared using Χ2 and t-tests. To examine longitudinal changes in GD severity, first we examined the unadjusted mean values of the GD score and its standard deviation (SD) in the total study sample and among participants with and without a psychiatric comorbidity at each assessment point.

Second, considering the within-subject correlation and the unbalanced, unequally spaced nature of our longitudinal data, generalized estimation equations (GEE) with unstructured working correlations (Ghisletta & Spini, 2004) were applied to estimate adjusted GD scores at each assessment point. Autocorrelation matrices are included in Supplemental tables S6–S8. The GEE approach represents a marginal model with robust parameter estimates addressing population averages instead of subject-specific effects (Ballinger, 2004; Zeger & Liang, 1986). Moreover, this approach counteracts missing data as it does not require the full specification of the joint distribution of repeated responses. If data is not available for all measurement points for some participants, GEE approaches solve the problem of missing data by filling the gaps based on the available data (Lipsitz, Fitzmaurice, & Weiss, 2020).

As the GD score (i.e., number of DSM-5 criteria fulfilled) is calculated from count data, a Poisson regression with log-link and robust Huber/White/Sandwich estimators of standard errors was applied to account for overdispersion (Rodríguez, 2013).

To analyse whether longitudinal changes in GD severity differed between participants with and without a psychiatric comorbidity, we implemented three different models: (1) model 1 contrasts changes in GD scores between participants with and without affective disorders using a time*affective disorders interaction term; (2) model 2 contrasts changes in GD scores between participants with and without anxiety disorders using a time*anxiety disorders interaction term; (3) model 3 accounts for concurrent affective and anxiety disorders using a time*affective disorders*anxiety disorders interaction term. Interaction terms were examined using Wald tests to analyse whether longitudinal changes from baseline to follow-up 4 differed between participants with and without each respective psychiatric comorbidity. Longitudinal changes were described using predicted probabilities (Williams, 2012) and incidence rate ratios (IRR), and were visualised using margins plots. Analyses were adjusted for age, sex (reference: female), migration background (reference: no), and treatment status (reference: still in treatment). Presence of affective/anxiety disorders was not included as a covariable in models 1 and 2 as we wanted to model the isolated influence of the individual disorders on GD scores.

Two different sensitivity analyses were conducted to assess the robustness of the results. To account for loss-to-follow-up bias, the original three models were repeated with exclusively completers (SA-1). As we compared participants with affective/anxiety disorders to participants without these disorders in the main analyses, we re-ran models 1 and 2 to compare participants with each psychiatric disorder of interest to those without any psychiatric comorbidity (SA-2).

All statistical analyses were conducted using Stata/SE 15 (Stata Corp LP; College Station, TX, USA). An alpha level of 0.05 was set.

Ethics

This study received ethical approval from the ethics committee of the German Association of Psychology (application number: LK092014).

Results

Study participation and participants' baseline characteristics

Of the 145 eligible participants, 22 were excluded due to lacking baseline information on psychiatric comorbidity or GD score. Of the 123 participants included, 78.9% (n = 97) participated at follow-up 1, 70.7% (n = 87) at follow-up 2, 66.7% (n = 82) at follow-up 3, and 56.1% (n = 69) at follow-up 4 (Fig. 1), while 49.6% (n = 61) responded at all four follow-ups.

At baseline, the average age of participants was 36.7 years (SD = 10.8), 88.6% (n = 109) were male, and 28.5% (n = 35) had a migration background (Table 1). The mean GD score was 7.9 (SD = 1.4) and 61.8% (n = 76) of participants presented with a psychiatric comorbidity. More specifically, the participants' diagnostic rates were 55.3% (n = 68) for affective disorders and 33.3% (n = 41) for anxiety disorders. Whilst 26.8% (n = 33) presented with both types of disorder, 28.5% (n = 35) were affected only by affective disorders and 6.5% (n = 8) were affected only by anxiety disorders. At follow-ups 1 through 4, psychiatric comorbidity was present in 60.8% (n = 59), 58.6% (n = 51), 59.8% (n = 49) and 57.8% (n = 40) of participants, respectively.

Table 1.

Baseline characteristics of participants

VariablesBaseline 123 (100)
Age in years, M (SD)36.7 (10.8)
Sex, n (%)
Male109 (88.6)
Migration background, n (%)
Yes35 (28.5)
Severity of GD, M (SD)
Fulfilled criteria of GD7.9 (1.4)
Affective disorders, n (%)68 (55.3)
Depressive disorders61 (49.6)
Major Depression – single episodes28 (22.8)
Major Depression – recurrent22 (17.9)
Dysthymic disorder32 (26.0)
Bipolar disorders10 (8.1)
Bipolar I disorder7 (5.7)
Bipolar II disorder3 (2.4)
Anxiety disorders, n (%)41 (33.3)
Panic disorder8 (6.5)
Agoraphobia3 (2.4)
Specific phobia28 (22.8)
Social phobia10 (8.1)
Generalized anxiety disorder2 (1.6)
Unspecified anxiety disorder2 (1.6)
Obsessive-compulsive disorder10 (8.1)
Post-traumatic stress disorder6 (4.9)
Affective disorders only, n (%)35 (28.5)
Anxiety disorders only, n (%)8 (6.5)
Both disorders, n (%)33 (26.8)

GD = gambling disorder, M = mean value, n = participant count, SD = standard deviation.

At baseline, participants with and without a psychiatric comorbidity did not differ regarding age, sex, and migration background, but the mean GD score was higher in participants with a psychiatric comorbidity, to a statistically significant degree (Table 2). Treatment status at follow-up 2 was also comparable. Study completers and dropouts presented similar demographic and GD severity (i.e., GD score) characteristics (Table S1).

Table 2.

Distribution of demographic variables and GD severity at baseline and treatment status at follow-up 2 for participants with and without a psychiatric comorbidity

With psychiatric comorbidityWithout any psychiatric comorbidityComparison testp-value
Variables76 (61.8)a47 (38.2)a
Age in years, M (SD)36.0 (10.9)37.9 (10.5)0.93b0.352
Sex, n (%)
Male65 (85.5)44 (93.6)1.88c0.170
Migration background, n (%)
Yes26 (34.2)9 (19.2)3.23c0.072
Treatment status (at follow-up 2), n (%)n = 71n = 47
Still in treatment2 (2.8)5 (10.6)3.36c0.187
Regular termination28 (39.4)15 (31.9)
Irregular termination41 (57.8)27 (57.5)
GD severity, M (SD)
Fulfilled criteria of GD8.2 (1.1)7.4 (1.6)−2.98b0.003

GD = gambling disorder, M = mean value, n = participant count, SD = standard deviation.

aFor some analyses, n differ due to missing data and are reported separately.

bStudent's t-test for interval variables.

cPearson Χ2 test for ordinal and nominal variables.

Longitudinal changes in GD severity

Unadjusted mean GD scores at each assessment point were consistently higher among participants with a psychiatric comorbidity compared to participants without any psychiatric comorbidity (for all assessment points, p < 0.05 or lower; see Table S2).

Model-based predictions for GD severity

For all three models (Table 3), we observed statistically significant reductions of GD scores between baseline and follow-up 4 (−56.5% in model 1, −58.9% in model 2, −56.8% in model 3), with the most pronounced improvements between baseline and follow-up 1 (Table S3). Thereafter, GD scores remained almost stable (Fig. 2a–c).

Table 3.

Model-based estimates of effects of psychiatric comorbidities, time, demographic variables, and treatment status on GD severity (i.e., fulfilled DSM-5 criteria) over time

Model 1Model 2Model 3
VariablesIRR (SD)IRR (SD)IRR (SD)
Time
BaselineREFREFREF
Follow-up 10.42 (0.08)***0.40 (0.08)***0.37 (0.08)***
Follow-up 20.38 (0.09)***0.38 (0.08)***0.33 (0.09)***
Follow-up 30.37 (0.09)***0.35 (0.08)***0.30 (0.08)***
Follow-up 40.32 (0.09)***0.32 (0.08)***0.24 (0.08)***
Affective disorders
NoREF-REF
Yes1.07 (0.04)-1.12 (0.04)**
Anxiety disorders
No-REFREF
Yes-1.01 (0.36)1.06 (0.06)
Time*affective disorders
Follow-up 11.15 (0.19)-1.26 (0.26)
Follow-up 21.37 (0.24)-1.45 (0.32)
Follow-up 31.37 (0.25)-1.48 (0.37)
Follow-up 41.64 (0.34)*-1.94 (0.54)*
Time*anxiety disorders
Follow-up 1-1.31 (0.21)1.88 (0.44)**
Follow-up 2-1.44 (0.23)*2.01 (0.49)**
Follow-up 3-1.68 (0.27)***2.51 (0.57)***
Follow-up 4-1.87 (0.31)***3.31 (0.85)***
Time*affective disorders*anxiety disorders
Follow-up 1--0.57 (0.17)
Follow-up 2--0.57 (0.18)
Follow-up 3--0.52 (0.16)*
Follow-up 4--0.37 (0.12)**
Affective disorders*anxiety disorders--0.90 (0.06)
Age1.00 (0.00)1.00 (0.00)1.00 (0.00)
Sex
FemaleREFREFREF
Male1.11 (0.07)1.07 (0.08)1.11 (0.08)
Migration background
NoREFREFREF
Yes1.22 (0.65)***1.20 (0.07)***1.15 (0.06)**
Treatment status
Still in treatmentREFREFREF
Regular termination1.07 (0.19)1.06 (0.20)1.02 (0.19)
Irregular termination1.35 (0.28)1.48 (0.30)1.39 (0.27)
Wald test (Baseline – Follow-up 4)
Time*affective disorders6.42; p = 0.17-6.43; p = 0.17
Time*anxiety disorders-16.40; p = 0.002524.49; p = 0.0001
Time*affective disorders*anxiety disorders--10.69; p = 0.03
Number of observations443443443
Number of groups123123123

Note. Model 1 analysed whether severity of GD developed differently between participants with and without affective disorders and therefore included an interaction term: time*affective disorders. Model 2 analysed whether severity of GD developed differently between participants with and without anxiety disorders and therefore included an interaction term: time*anxiety disorders. Model 3 accounted for the combined occurrence of affective and anxiety disorders and therefore included an interaction term: time*affective disorders*anxiety disorders.

IRR = incidence rate ratio, REF = reference, SD = standard deviation.

*p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 2.
Fig. 2.

Adjusted predictions of GD severity. a: Model-based trajectories for GD severity with adjustment for affective disorders. b: Model-based trajectories for GD severity with adjustment for anxiety disorders. c: Model-based trajectories for GD severity with adjustment for affective and anxiety disorders

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00026

In terms of how GD scores changed, differences between participants with a psychiatric comorbidity and those without were apparent (Fig. 3 a–c). Whilst participants without affective disorders achieved a reduction of 67.8% until follow-up 4, the reduction was only 47.2% in participants with affective disorder. For participants without and with anxiety disorders, reductions were 67.6% and 39.4%, respectively.

Fig. 3.
Fig. 3.

Trajectories for GD severity. a: Model-based trajectories for GD severity by presence of affective disorders. b: Model-based trajectories for GD severity by presence of anxiety disorders. c: Model-based trajectories for GD severity with consideration of interplay between affective and anxiety disorders

Citation: Journal of Behavioral Addictions 12, 2; 10.1556/2006.2023.00026

Participants without affective disorders experienced a sustained decrease in GD score until follow-up 4, whereas participants with affective disorders experienced a less pronounced improvement between baseline and follow-up 1, followed by a mostly steady increase in GD score (Fig. 3a). However, these differences were only statistically significant at follow-up 4 (p < 0.05) and the Wald test for the entire observation period was not statistically significant. Similar profiles were observed for participants with and without anxiety disorders (Fig. 3b), with statistically significant differences at follow-ups 2 through 4 (p < 0.05 at follow-up 2, p = 0.001 at follow-up 3, p < 0.001 at follow-up 4) and a statistically significant Wald test for the entire observation period (p < 0.01). Combined anxiety and affective disorders were linked to a less favourable course of the GD score than the presence of affective disorders alone (Fig. 3c). However, the GD score of participants with combined affective and anxiety disorders developed more favourably than the GD score of participants who were affected only by anxiety disorders. Corresponding differences were statistically significant at follow-up 3 (p < 0.05) and follow-up 4 (p < 0.01), and so was the Wald test for the entire observation period (p < 0.05).

The only statistically significant covariate within all models was migration background, which was consistently associated with a higher GD score (model 1: p < 0.001, model 2: p = 0.001, model 3: p < 0.01).

Sensitivity analyses

The sensitivity analyses by and large confirmed the profiles of the main analyses (Tables S4 & S5). In SA-1, which included exclusively study completers, differences between participants with affective or anxiety disorders and those without were less pronounced (Figure S1 a–c). Moreover, in models 1 and 3, sex gained statistical significance with a less favourable course of the GD score for men, and in model 2 the course of the GD score was significantly less favourable for participants with irregular treatment termination.

In SA-2, in model 1 (Figure S2 a–b), the comparison of participants with affective disorders to those without any psychiatric comorbidity showed less favourable changes in GD scores among those with affective disorders (Wald test p < 0.05), with statistically significant differences at follow-ups 2 through 4 (p < 0.05 at follow-ups 2 and 3, p < 0.01 at follow-up 4). Further, migration background lost statistical significance whilst sex gained statistical significance in model 2, with a less favourable course of the GD score for men.

Discussion

Using longitudinal data from the “Katamnese” study, we contrasted gamblers with and without a psychiatric comorbidity and investigated the impact of baseline affective and anxiety disorders on the course of GD severity over a 3-year period. Though they had similar socio-demographic characteristics, the baseline GD score was significantly higher among study participants with comorbid affective or anxiety disorders and their GD score developed less favourably. Contrary to our expectation, we found a more favourable course of the GD score in participants with combined affective and anxiety disorders than in participants with anxiety disorders alone. Indeed, the GD score course was, as we expected, less favourable in those with combined affective and anxiety disorders than in participants with affective disorders alone.

A previous German study on inpatients with GD estimated the prevalence of comorbid affective disorders at 51.1% and comorbid anxiety disorder prevalence at 47.5% (Premper & Schulz, 2008). In our outpatient sample, the prevalence of comorbid affective disorders was similar (55.3%, n = 68) but the prevalence of comorbid anxiety disorders was substantially lower (33.3%, n = 41). These estimates exceed those of a systematic literature review which reported mean prevalence rates of 23.1% for any affective disorder and 17.6% for any anxiety disorder in help-seeking individuals with GD. However, noteworthily, there was high between-study heterogeneity in the evidence used for the estimates for individual disorders (range: 2.0%–41.3%) due to differences in sample characteristics, methodological approach, or treatment factors (Dowling et al., 2015). Hence, the results of our study can reasonably be placed in the context of existing evidence.

At all assessment points, we observed (at times significantly) higher mean GD scores among study participants with affective or anxiety disorders compared to those among study participants without psychiatric disorders. This supports previous findings of a disadvantageous correlation between psychiatric comorbidity and GD severity in the general population (Barry, Stefanovics, Desai, & Potenza, 2011; Kong, Smith, Pilver, Hoff, & Potenza, 2016; Pilver, Libby, Hoff, & Potenza, 2013) as well as among help-seeking individuals with GD (Shek, Chan, & Wong, 2012). A possible explanation may be that gambling serves as an escape from problems (Blanco, Hasin, Petry, Stinson, & Grant, 2006) that may occur due to psychiatric comorbidity. Furthermore, gambling can act as a coping strategy in individuals with depressive disorders to compensate for feelings of emptiness and inability to feel pleasure, leading to a stronger urge to gamble and increased GD severity (Rømer Thomsen, Callesen, Linnet, Kringelbach, & Møller, 2009).

According to our models, the GD score improved significantly in both participants with and without a psychiatric comorbidity over the 3-year observation period. Participants with psychiatric comorbidity, thereof particularly those with anxiety disorder, had poorer treatment success than participants without. This is in line with a previous study in adults with GD, reporting greater GD severity in participants with a psychiatric comorbidity at all assessments over a 48 months study period (Black et al., 2021).

In our sample, we found a corresponding detriment, particularly in participants with anxiety disorders, who achieved minor and less stable improvements than participants without anxiety disorders in our study. This does not suggest that outpatient gambling care does not meet the treatment needs of individuals with comorbid anxiety disorders. As both groups face different starting conditions, similar achievements in GD score development for individuals with and without a psychiatric comorbidity are not to be expected. Within a previous study, participants with comorbid anxiety disorders presented with significantly higher GD-related psychological symptom burden during treatment and in the post-treatment period compared to participants without anxiety disorders. After treatment termination, participants with comorbid anxiety disorders were more strongly involved in gambling, and relapse was more likely compared to participants without comorbid anxiety disorders (Premper & Schulz, 2007).

We observed a less pronounced detriment within individuals with affective disorders. A possible explanation for higher GD scores in gamblers with anxiety disorders than in gamblers with affective disorders may be the disorder-specific symptomatology and its impact on gambling behaviour. While people with anxiety disorders often experience symptoms such as restlessness, difficulty concentrating, and sleep disturbances, common symptoms of affective disorders include listlessness, inability to experience pleasure, and lack of energy (American Psychiatric Association, 2013). Thus, individuals with affective disorders might often not even have the energy to participate in gambling during acute episodes of their comorbid psychiatric disorder, whereas people with anxiety disorders may increasingly use gambling as a distraction from anxiety-related thoughts when anxiety disorder symptoms are most severe.

Unlike in previous studies (Black et al., 2021; Premper & Schulz, 2007), we found no statistically significant difference in GD scores between clients with and without affective disorders. As there appears to be a correlation between severity of depressive symptoms and GD severity (Rømer Thomsen et al., 2009), the initial degree of severity of affective disorders might have been low in our sample, which might explain the lack of a difference between participants with and without affective disorders in our findings. Another explanation is that the group of individuals without affective disorders contains both individuals without any psychiatric comorbidity and individuals with an anxiety disorder. Hence, considering the elevated GD scores among gamblers with anxiety disorders mentioned above, GD-score estimates for the group without affective disorders may be overestimated. SA-2, which compares participants with affective disorders and participants without any psychiatric comorbidity, supports this hypothesis, as it shows a statistically significant detriment in the first group.

Combined affective and anxiety disorders were associated with more favourable GD score development than the presence of anxiety disorders alone, but with a less favourable course than the presence of affective disorders alone, which we did not anticipate. This might be explained by the fact that anxiety disorders predominantly manifest before the development of GD and that anxiety disorder-related symptomatology might encourage gambling behaviour. In contrast, affective disorders often develop as sequelae of gambling, further encouraging GD (Premper & Schulz, 2008). Furthermore, symptoms of affective disorders such as listlessness and lack of energy might moderate the symptoms of anxiety disorders such as restlessness which potentially drive intensified gambling behaviour.

Limitations and strengths

In addition to the usual caveats of cohort studies (e.g., selection bias due to voluntary participation, information bias due to self-reported data), the following limitations ought to be considered. As the “Katamnese” study was an one-armed observational study, our results indicate associations and do not allow one to make a causal attribution between OACF treatment and GD score development. First, although the dropout rate was satisfactory (43.9% at follow-up 4), it led to small sample sizes and consequently large confidence intervals. Hence, it appears possible that our models failed to detect the true differences between participant subgroups with and without a psychiatric comorbidity. Second, our models are limited by overdispersion and we could not appropriately account for the distribution of our count data by assuming a negative binomial distribution because of convergency issues (Rodríguez, 2013). Thus, GD scores are probably overestimated in our results. We strongly believe that this issue affects group-specific levels of GD score more so than the between-group differences in GD score development. Third, we had no information on severity of psychiatric disorders, potential treatment of psychiatric disorders, and presence/new occurrence of psychiatric disorders during the follow-up period. There is evidence that the severity of GD changes according to the severity of psychiatric disorders over time (Black et al., 2021), which could have been intertwined with the development of GD severity in our analyses. Finally, information on treatment status was only available until follow-up 2. Hence treatment termination for individuals still in treatment at follow-up 2 could not be verified for subsequent follow-ups, and we imputed the follow-up 2 status instead, which results in a not fully precise covariate specification. As we had to impute treatment status for only 7 study participants, we are strongly convinced that this imprecision has not crucially affected our results.

On the other hand, the main strength of this study is its unique focus on the development of GD score in individuals with and without a psychiatric comorbidity who were seeking outpatient gambling care. Therefore, our study population is rather unselected, as OACFs offer a broad range of addiction support services, and we presumably achieved a high degree of external validity. Moreover, we were able to investigate associations between comorbid affective and anxiety disorders with the course of GD severity over a three year period, which exceeds the short-term follow-up of previous research (Premper & Schulz, 2007). Additionally, our analyses revealed realistic results by including not only study completers but also dropouts. SA-1 suggests that dropout of participants without any psychiatric comorbidity occurred mainly among those with less GD severity, whilst dropout of participants with a psychiatric comorbidity mainly occurred among those with greater GD severity. This indicates that we avoided underestimating the negative influence of psychiatric comorbidity on severity of GD by analysing the data of dropouts in the main models. Finally, our advanced modeling techniques (GEE) counteracted missing data and misspecification of within-group correlation, yielding robust results.

Conclusions and further research

Our study suggests that individuals with GD and psychiatric comorbidity benefit from outpatient gambling care, though to a lesser degree than individuals with GD but without any psychiatric comorbidity. Hence, the broad spectrum of treatment services offered by OACFs might not yet fully address the needs of clients with psychiatric comorbidity. Due to the dual burden of GD and psychiatric comorbidity, affected individuals most probably require comprehensive treatment services that outpatient gambling care alone might not provide. Therefore, establishing cooperative networks with facilities that offer, for instance, psychotherapy and pharmacotherapy, could support outpatient treatment care. Such combined efforts could support continuous management of both GD and psychiatric disorders, which might in turn help clients to better exploit their resources to overcome GD. To assess the comprehensiveness of treatment services, further research is necessary to understand existing treatment modalities and cooperative networks. Additional longitudinal research on interactions between the course of psychiatric comorbidity and the course of GD severity is paramount.

Funding sources

The “Katamnese” study was conducted in the context of the Bavarian Coordination Centre for Gambling Issues (Bayerische Landesstelle Gluecksspielsucht (LSG)). The LSG is funded by the Bavarian State Ministry of Public Health and Care Services. The State of Bavaria provides gambling services (lotteries, sports betting, and casino games) within the State gambling monopoly via the State Lottery Administration and provided funding for the LSG as an unrestricted grant. LK was also supported by the Swedish program grant “Responding to and Reducing Gambling Problems—Studies in Help-seeking, Measurement, Comorbidity and Policy Impacts” (REGAPS), funded by the Swedish Research Council for Health, Working Life and Welfare (Forte; grant number 2016-07091).

Authors’ contribution

LS developed the research question and the analytical approach. Together with AB, she supervised the carrying out of the analyses. PW analysed the data, interpreted the results, and wrote the manuscript. AB provided continuous content- and programming-related support. JL and LK provided advice and support in conducting the study and drafting the paper. All authors contributed substantially to earlier drafts of the manuscript and approved its final version.

Conflict of interest

The authors report no financial or other relationship relevant to the subject of this article.

Acknowledgements

The authors thank the participating outpatient addiction care facilities of the Bavarian Competence Network of Gambling Issues for their cooperation in the context of conducting the study. We especially acknowledge the study participants' willingness to take part in the “Katamnese” study from enrolment to the end of follow-up. We furthermore express our heartfelt thanks to Bettina Grüne, Barbara Braun-Michl and Ludwig Kraus for designing and executing the “Katamnese” study. Furthermore, we thank our anonymous reviewers, whose constructive suggestions and remarks substantially improved precision and quality of this paper.

Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1556/2006.2023.00026.

References

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    • Search Google Scholar
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  • Petry, N. M., & Kiluk, B. D. (2002). Suicidal ideation and suicide attempts in treatment-seeking pathological gamblers. The Journal of Nervous and Mental Disease, 190(7), 462469. https://doi.org/10.1097/00005053-200207000-00007.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Pilver, C. E., Libby, D. J., Hoff, R. A., & Potenza, M. N. (2013). Problem gambling severity and the incidence of Axis I psychopathology among older adults in the general population. Journal of Psychiatric Research, 47(4), 534541. https://doi.org/10.1016/j.jpsychires.2012.12.013.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Potenza, M. N., Xian, H., Shah, K., Scherrer, J. F., & Eisen, S. A. (2005). Shared genetic contributions to pathological gambling and major depression in men. Archives of General Psychiatry, 62(9), 10151021. https://doi.org/10.1001/archpsyc.62.9.1015.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Premper, V., & Schulz, W. (2007). Behandlungserfolg und prospektiver Krankheitsverlauf bei pathologischen Glücksspielern in Abhängigkeit von Komorbidität und Spielkarriere. Suchttherapie, 8(3), 108114. https://doi.org/10.1055/s-2007-985390.

    • Search Google Scholar
    • Export Citation
  • Premper, V., & Schulz, W. (2008). Komorbidität bei Pathologischem Glücksspiel. SUCHT, 54(3), 131140. https://doi.org/10.1024/2008.03.03.

    • Search Google Scholar
    • Export Citation
  • Rodríguez, G. (2013). Lecture notes on generalized linear models: Models for count data with overdispersion. Retrieved from https://data.princeton.edu/wws509/notes.

    • Search Google Scholar
    • Export Citation
  • Rømer Thomsen, K., Callesen, M. B., Linnet, J., Kringelbach, M. L., & Møller, A. (2009). Severity of gambling is associated with severity of depressive symptoms in pathological gamblers. Behavioural Pharmacology, 20(5–6), 527536. https://doi.org/10.1097/FBP.0b013e3283305e7a.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Schwarzkopf, L., Loy, J. K., Braun-Michl, B., Grüne, B., Sleczka, P., & Kraus, L. (2021). Gambling disorder in the context of outpatient counselling and treatment: Background and design of a prospective German cohort study. International Journal of Methods in Psychiatric Research, 30(2), e1867. https://doi.org/10.1002/mpr.1867.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Shek, D. T. L., Chan, E. M. L., & Wong, R. H. Y. (2012). Associations between pathological gambling and psychiatric comorbidity among help-seeking populations in Hong Kong. The Scientific World Journal, 2012, 571434. https://doi.org/10.1100/2012/571434.

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    • Search Google Scholar
    • Export Citation
  • Zeger, S. L., & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42(1), 121. https://doi.org/10.2307/2531248.

    • PubMed
    • Search Google Scholar
    • Export Citation

Supplementary Materials

  • American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (5. ed.). Washington, DC: American Psychiatric Publishing.

    • Search Google Scholar
    • Export Citation
  • Ballinger, G. A. (2004). Using generalized estimating equations for longitudinal data analysis. Organizational Research Methods, 7(2), 127150. https://doi.org/10.1177/1094428104263672.

    • Search Google Scholar
    • Export Citation
  • Barry, D. T., Stefanovics, E. A., Desai, R. A., & Potenza, M. N. (2011). Gambling problem severity and psychiatric disorders among Hispanic and white adults: Findings from a nationally representative sample. Journal of Psychiatric Research, 45(3), 404411. https://doi.org/10.1016/j.jpsychires.2010.07.010.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Bickl, A. M., Schwarzkopf, L., Loy, J. K., Grüne, B., Braun-Michl, B., Sleczka, P., … Kraus, L. (2021). Changes in gambling behaviour and related problems in clients seeking help in outpatient addiction care: Results from a 36-month follow-up study in Bavaria. Journal of Behavioral Addictions, 10(3), 690700. https://doi.org/10.1556/2006.2021.00043.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Black, D. W., Allen, J., & Bormann, N. L. (2021). Are comorbid disorders associated with changes in gambling activity? A longitudinal study of younger and older subjects with DSM-IV pathological gambling. Journal of Gambling Studies, 37(4), 12191230. https://doi.org/10.1007/s10899-021-10000-x.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Blanco, C., Hasin, D. S., Petry, N., Stinson, F. S., & Grant, B. F. (2006). Sex differences in subclinical and DSM-IV pathological gambling: Results from the national epidemiologic survey on alcohol and related conditions. Psychological Medicine, 36(7), 943953. https://doi.org/10.1017/S0033291706007410.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Buchner, U. G., & Wodarz, N. (2011). Pathologisches Glücksspielen — aktueller Stand des Wissens [Pathological gambling—what do we know?]. Psychotherapie, Psychosomatik, Medizinische Psychologie, 61(8), 341346. https://doi.org/10.1055/s-0030-1262784.

    • Search Google Scholar
    • Export Citation
  • Deutsche Hauptstelle für Suchtfragen (2010). Deutscher Kerndatensatz zur Dokumentation im Bereich der Suchtkrankenhilfe: Definitionen und Erläuterungen zum Gebrauch. [German core data set for documentation in the field of addiction care. Definitions and explanations of use]. Retrieved from https://www.suchthilfestatistik.de/fileadmin/user_upload_dshs/02_kds/KDS_Manual_10_2010.pdf.

    • Search Google Scholar
    • Export Citation
  • Dowling, N. A., Cowlishaw, S., Jackson, A. C., Merkouris, S. S., Francis, K. L., & Christensen, D. R. (2015). Prevalence of psychiatric co-morbidity in treatment-seeking problem gamblers: A systematic review and meta-analysis. The Australian and New Zealand Journal of Psychiatry, 49(6), 519539. https://doi.org/10.1177/0004867415575774.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Ghisletta, P., & Spini, D. (2004). An introduction to generalized estimating equations and an application to assess selectivity effects in a longitudinal study on very old individuals. Journal of Educational and Behavioral Statistics, 29(4), 421437. https://doi.org/10.3102/10769986029004421.

    • Search Google Scholar
    • Export Citation
  • Hartmann, M., & Blaszczynski, A. (2018). The longitudinal relationships between psychiatric disorders and gambling disorders. International Journal of Mental Health and Addiction, 16(1), 1644. https://doi.org/10.1007/s11469-016-9705-z.

    • Search Google Scholar
    • Export Citation
  • Hodgins, D. C., & El-Guebaly, N. (2010). The influence of substance dependence and mood disorders on outcome from pathological gambling: Five-year follow-up. Journal of Gambling Studies, 26(1), 117127. https://doi.org/10.1007/s10899-009-9137-9.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Kong, G., Smith, P. H., Pilver, C., Hoff, R., & Potenza, M. N. (2016). Problem-gambling severity and psychiatric disorders among American-Indian/Alaska native adults. Journal of Psychiatric Research, 74, 5562. https://doi.org/10.1016/j.jpsychires.2015.12.004.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Künzel, J., Daubner, H., Specht, S., & Braun, B. (2019). Klienten/Patienten mit Hauptdiagnose Pathologisches Glücksspielen in ambulanter und stationärer Suchtbehandlung (No. 1/2019). München. Retrieved from https://www.ift.de/publikationen/berichte.html#c4786.

    • Search Google Scholar
    • Export Citation
  • Lipsitz, S. R., Fitzmaurice, G. M., & Weiss, R. D. (2020). Using multiple imputation with GEE with non-monotone missing longitudinal binary outcomes. Psychometrika, 85(4), 890904. https://doi.org/10.1007/s11336-020-09729-y.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Maniaci, G., La Cascia, C., Picone, F., Lipari, A., Cannizzaro, C., & La Barbera, D. (2017). Predictors of early dropout in treatment for gambling disorder: The role of personality disorders and clinical syndromes. Psychiatry Research, 257, 540545. https://doi.org/10.1016/j.psychres.2017.08.003.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Meyer, G., & Bachmann, M. (2017). Spielsucht: Ursachen, Therapie und Prävention von glücksspielbezogenem Suchtverhalten. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-54839-4.

    • Search Google Scholar
    • Export Citation
  • Petry, N. M., & Kiluk, B. D. (2002). Suicidal ideation and suicide attempts in treatment-seeking pathological gamblers. The Journal of Nervous and Mental Disease, 190(7), 462469. https://doi.org/10.1097/00005053-200207000-00007.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Pilver, C. E., Libby, D. J., Hoff, R. A., & Potenza, M. N. (2013). Problem gambling severity and the incidence of Axis I psychopathology among older adults in the general population. Journal of Psychiatric Research, 47(4), 534541. https://doi.org/10.1016/j.jpsychires.2012.12.013.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Potenza, M. N., Xian, H., Shah, K., Scherrer, J. F., & Eisen, S. A. (2005). Shared genetic contributions to pathological gambling and major depression in men. Archives of General Psychiatry, 62(9), 10151021. https://doi.org/10.1001/archpsyc.62.9.1015.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Premper, V., & Schulz, W. (2007). Behandlungserfolg und prospektiver Krankheitsverlauf bei pathologischen Glücksspielern in Abhängigkeit von Komorbidität und Spielkarriere. Suchttherapie, 8(3), 108114. https://doi.org/10.1055/s-2007-985390.

    • Search Google Scholar
    • Export Citation
  • Premper, V., & Schulz, W. (2008). Komorbidität bei Pathologischem Glücksspiel. SUCHT, 54(3), 131140. https://doi.org/10.1024/2008.03.03.

    • Search Google Scholar
    • Export Citation
  • Rodríguez, G. (2013). Lecture notes on generalized linear models: Models for count data with overdispersion. Retrieved from https://data.princeton.edu/wws509/notes.

    • Search Google Scholar
    • Export Citation
  • Rømer Thomsen, K., Callesen, M. B., Linnet, J., Kringelbach, M. L., & Møller, A. (2009). Severity of gambling is associated with severity of depressive symptoms in pathological gamblers. Behavioural Pharmacology, 20(5–6), 527536. https://doi.org/10.1097/FBP.0b013e3283305e7a.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Schwarzkopf, L., Loy, J. K., Braun-Michl, B., Grüne, B., Sleczka, P., & Kraus, L. (2021). Gambling disorder in the context of outpatient counselling and treatment: Background and design of a prospective German cohort study. International Journal of Methods in Psychiatric Research, 30(2), e1867. https://doi.org/10.1002/mpr.1867.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Shek, D. T. L., Chan, E. M. L., & Wong, R. H. Y. (2012). Associations between pathological gambling and psychiatric comorbidity among help-seeking populations in Hong Kong. The Scientific World Journal, 2012, 571434. https://doi.org/10.1100/2012/571434.

    • Search Google Scholar
    • Export Citation
  • StataCorp (2017). Stata statistical software: Release 15. College Station, TX: StataCorp LLC.

  • Statistisches Bundesamt (2010). Demographische Standards Ausgabe 2010: Eine gemeinsame Empfehlung des ADM Arbeitskreis Deutscher Markt- und Sozialforschungsinstitute e.V., der Arbeitsgemeinschaft Sozialwissenschaftlicher Institute e.V. (ASI) und des Statistischen Bundesamtes. [Demographic Standards Edition 2010]. (5., überarb. und erw. Aufl.). Statistik und Wissenschaft (Vol. 17). Wiesbaden: Statistisches Bundesamt.

    • Search Google Scholar
    • Export Citation
  • Stinchfield, R. (2003). Reliability, validity, and classification accuracy of a measure of DSM-IV diagnostic criteria for pathological gambling. The American Journal of Psychiatry, 160(1), 180182. https://doi.org/10.1176/appi.ajp.160.1.180.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • Williams, R. (2012). Using the margins command to estimate and interpret adjusted predictions and marginal effects. The Stata Journal: Promoting Communications on Statistics and Stata, 12(2), 308331. https://doi.org/10.1177/1536867X1201200209.

    • Search Google Scholar
    • Export Citation
  • Wittchen, H.-U., & Pfister, H. (1997). DIA-X-Interviews: Manual für Screening-Verfahren und Interview. Frankfurt: Interviewheft.

  • World Health Organization (2004). ICD-10: International statistical classification of diseases and related health problems. Geneva: World Health Organization.

    • Search Google Scholar
    • Export Citation
  • Zeger, S. L., & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42(1), 121. https://doi.org/10.2307/2531248.

    • PubMed
    • Search Google Scholar
    • Export Citation
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Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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