View More View Less
  • 1 IFT Institut für Therapieforschung, München, Germany
  • | 2 German Youth Institute (DJI), Department of Youth and Youth Services, München, Germany
  • | 3 German University of Health and Sport, Ismaning, Germany
  • | 4 Seeburg Castle University, Seekirchen am Wallersee, Austria
  • | 5 Department of Public Health Sciences, Centre for Social Research on Alcohol and Drugs, Stockholm University, Stockholm, Sweden
  • | 6 Institute of Psychology, ELTE, Eötvös Loránd University, Budapest, Hungary
Open access

Abstract

Background and aim

Evidence on the course of gambling disorder (GD) in clients seeking help from outpatient addiction care facilities is sparse. To close this knowledge gap, this longitudinal one-armed cohort study portrays the development of GD in help-seeking clients over a 3-year timeframe.

Methods

We investigated changes in severity of GD as well as in gambling frequency and intensity in 145 gamblers in outpatient treatment in Bavaria using generalized estimation equations (GEEs). To investigate potentially different trajectories between study participants with and without migration background (MB), additional analyses were applied with time*migration interaction. All analyses were adjusted for age, gender, education, electronic gambling machine (EGM) gambling, MB, GD, related help sought before and treatment status.

Results

Within the entire study population, improvements in severity of GD (reduction of 39.2%), gambling intensity (reduction of 75.6%) and gambling frequency (reduction of 77.0%) were observed between baseline and 36 months of follow-up. The declines were most pronounced between baseline and follow-up 1 and stabilized thereafter. Participants with MB improved consistently less than participants without MB.

Discussion and conclusion

Our study suggests that severity of GD and gambling patterns improve in the context of outpatient treatment. The beneficial results furthermore persist for 36 months after treatment termination. As clients with MB seem to profit less than clients without MB, improvements in outpatient gambling services to the specific needs of this clientele are required.

Abstract

Background and aim

Evidence on the course of gambling disorder (GD) in clients seeking help from outpatient addiction care facilities is sparse. To close this knowledge gap, this longitudinal one-armed cohort study portrays the development of GD in help-seeking clients over a 3-year timeframe.

Methods

We investigated changes in severity of GD as well as in gambling frequency and intensity in 145 gamblers in outpatient treatment in Bavaria using generalized estimation equations (GEEs). To investigate potentially different trajectories between study participants with and without migration background (MB), additional analyses were applied with time*migration interaction. All analyses were adjusted for age, gender, education, electronic gambling machine (EGM) gambling, MB, GD, related help sought before and treatment status.

Results

Within the entire study population, improvements in severity of GD (reduction of 39.2%), gambling intensity (reduction of 75.6%) and gambling frequency (reduction of 77.0%) were observed between baseline and 36 months of follow-up. The declines were most pronounced between baseline and follow-up 1 and stabilized thereafter. Participants with MB improved consistently less than participants without MB.

Discussion and conclusion

Our study suggests that severity of GD and gambling patterns improve in the context of outpatient treatment. The beneficial results furthermore persist for 36 months after treatment termination. As clients with MB seem to profit less than clients without MB, improvements in outpatient gambling services to the specific needs of this clientele are required.

Introduction

Current studies on gambling disorder (GD) report prevalence estimates between 0.3% and 0.5% for the German adult population (Banz, 2019). Considering the severe individual and societal consequences, treatment is indispensable from an individual and societal perspective (Becker, 2011). German outpatient addiction care facilities (OACFs) are mainly community financed offering service for individuals seeking help for addiction-related problems. The service is free of charge and provides a relatively low threshold access to highly need-driven individualized treatment. OACF services greatly differ in content alignment and staff training. The main treatment concepts consist of motivational interviewing techniques and talk therapy. Some OACFs also offer manual-based outpatient rehabilitation. These concepts mandatorily contain individual and group-based psychotherapy, debt-counselling, physician-based care, socio-therapeutic interventions, and relaxation techniques. Whenever required clients are referred to specialist inpatient or outpatient (psycho-therapeutic) interventions (Meyer & Bachmann, 2017). OACFs ease entry into the addiction care system for help-seeking individuals, allowing them to overcome shame and stigmatization (Braun, Ludwig, Kraus, Kroher, & Bühringer, 2013). Against the background of the provision of such heterogeneous treatment services, OACFs are an important key element in the addiction system, considering that they enable individual help to the greatest possible extent. Although there has been increased development and expansion of outpatient care services since the German State Gambling Treaty came into force in 2008, there is still little evidence whether the treatments fulfil their innate purpose to mitigate gambling-related problems. As OACFs are easily accessible for help-seeking clients and thus play a major role in the treatment of GD (Premper & Schulz, 2008), they require continuous and in-depth research to improve care offers, especially as follow-up data are sparse for services other than rehabilitation (Braun et al., 2013).

Metareviews have demonstrated the effectiveness of several psychological outpatient-related treatment approaches (Cowlishaw et al., 2012; Lopez Viets & Miller, 1997; Toneatto & Ladoceur, 2003), but with different methodological caveats (small sample sizes, irregular observation periods, etc.). One of the few longitudinal examples for an outpatient setting was a multimodal GD treatment approach with follow-up assessments after 6 and 12 months. In this study, treatment was associated with improvements in various areas such as gambling frequency, severity of GD, psychosocial condition or financial problems (Stinchfield & Winters, 2001). An earlier Germany-based evaluation indicated that outpatient gambling treatment is helpful, as – after an average follow-up period of 1.9 years – clients reported improved psychosocial wellbeing and mitigated gambling behaviour (J. Petry, 2001).

Several individual and environmental factors have been identified as affecting both severity of GD and GD treatment (Johansson, Grant, Kim, Odlaug, & Gotestam, 2009). In this regard, migration background (MB) plays an important role: studies in Germany found more persistent and maladaptive gambling behaviour among people with MB than among those without MB (Haß, Orth, & Lang, 2012; Kastirke, Rumpf, John, Bischof, & Meyer, 2015). Thus, there might be culture-specific usage, expectations and attitudes towards gambling or acculturation problems (Tuncay, 2010), that impact the trajectories of GD and have different requirements for effective gambling treatment.

Although research suggests that GD is a treatable condition (Cowlishaw et al., 2012; Lopez Viets & Miller, 1997; N.M. Petry 2005), it can be observed that there has been overly strong focus on the evaluation of specific treatment modalities while losing sight of the structural- and process-related variables of the client’s and facilities’ real-world settings (Sulkunen et al., 2018). Re-adjustment to such a level could benefit policy-making, when considering its population-wide implications.

In summary, the outpatient-embedded course of GD during and after treatment has not been comprehensively elucidated – particularly not for people with MB. Closing this knowledge gap might contribute to a better understanding of factors facilitating recovery and might help to identify subgroups of clients with distinct treatment needs. Using longitudinal data from gamblers in outpatient treatment, the present study aims to (1) identify factors that are associated with baseline gambling behaviour and problems (severity of GD, gambling intensity, gambling frequency), (2) analyse longitudinal patterns of gambling behaviour and problems and (3) investigate whether longitudinal profiles differ between people with and without MB.

Methods

Design and setting

Data were collected within the ‘Katamnese-Studie’, a prospective, naturalistic cohort study covering GD in the context of German outpatient addiction care. The study was conducted within 28 Bavarian OACFs between 2014 and 2019. Participants received written questionnaires at admission and at 6-, 12-, 24- and 36-month follow-up to assess gambling behaviour, socio-demographic data, gambling-related consequences, and treatment offers sought. These data were linked to client-individual routine documentation for the German Addiction Care Statistical Service. Further details of the study design, instruments used, and methodological approach have been published elsewhere (Schwarzkopf et al., 2021).

Study participation

Adults with sufficient German language skills and a minimum of three contacts with the respective OACF were eligible for participation. During the recruitment period (12/2014-08/2016), 1,159 incident clients were documented in the participating OACFs. Of these, all 615 persons (53.6%) meeting the inclusion criteria were invited to participate in the study. Out of these eligible persons, 199 (32.4%) provided informed consent. Of the recruited participants 15 (7.5%) subsequently withdrew their consent, 38 (17.6%) did not participate in the baseline survey and one person could not be contacted because of an unknown address. Thus, the baseline sample comprised 145 persons. Client-specific data on severity of GD, gambling behaviour (frequency and intensity), gambling activity, MB and socio-demographic data were collected.

Measures

Assessments were conducted at baseline and four consecutive waves covering a patient-individual 3-year interval (Fig. 1). Data were obtained from participants’ self-reports (baseline to follow-up 4), a staff survey with employees from the participating OACFs (baseline) and facilities’ routine documentation of treatment and client characteristics (baseline until end of care episode or follow-up 2, whichever came first).

Fig. 1.
Fig. 1.

Points and types of data assessment

Citation: Journal of Behavioral Addictions 10, 3; 10.1556/2006.2021.00043

Outcome variables

Severity of GD was assessed by calculating the sum score of DSM-5 criteria fulfilled at each assessment point with a maximum score of 9 using a DSM-5 adapted version of the Stinchfield criteria (Stinchfield, 2003). Gambling frequency was assessed by the average number of gambling days per week. Gambling intensity was assessed by the average number of hours spent gambling per gambling day. All measures stem from the participants’ self-reports and refer to time since last assessment point or to the last 12 months (for baseline).

Covariables

Self-reported gambling involvement accounted for electronic gambling machines (EGMs), traditional casino games, lottery tickets, lotteries, pools, television lottery, class lottery, bets on horses, sports bets at licensed retailers, online sports bets, online poker/card games, other forms of online gambling, speculation on the stock exchange, illicit forms of gambling and gambling with family and friends. For each type, a utilization frequency of at least once per week was defined as regular use. As EGMs represent one of the most prominent and harmful gambling types with strong demand within the population of help-seeking individuals (Binde, 2011; Braun et al., 2013), involvement in different gambling types was dichotomized as gambling EGMs versus gambling any other type(s).

Educational background represents a relevant factor in GD development (Scherrer et al., 2007) as well as in treatment utilization (Braun, Ludwig, Sleczka, Buhringer, & Kraus, 2014). Self-reported educational background was assessed using the International Standard Classification of Education (ISCED) Index (UNESCO, 2012) (1. Lower secondary education; 2. Upper secondary education; 3. Post-secondary/non-tertiary education; 4. Tertiary education). Owing to the small sample size, post-secondary and tertiary education were combined into one category.

To address previous contacts with gambling care, self-reported information on GD-related help sought before the study (0. No; 1. Yes) was collected.

Treatment status was obtained from the facilities’ routine documentation via the Germany-wide standardized core dataset of addiction care (German Centre for Addiction Issues, 2010) to differentiate between study dropout and treatment termination. For each assessment point, a trichotomized variable was created with information on clients still in treatment, regular termination and irregular termination. As information on treatment termination was only available until follow-up 2, we adopted a conservative perspective by assuming that all participants without information on date of treatment termination remained under treatment until follow-up 4.

Self-reported MB status indicated whether a participant had migrated to Germany her/himself or whether (s)he was born in Germany as a (grand)child of people who had immigrated into the country (Strupf, de Matos, Soellner, Kraus, & Piontek, 2017).

Self-reported age (in years) and sex were used as standard demographic covariates.

Data analysis

The study population was characterized by summary statistics (means or percentages) at baseline comparing completers and dropouts as well as completers with and without MB regarding demographic and gambling characteristics using χ2- and t-tests. Study dropouts included participants who no longer participated in any of the follow-ups.

To address impact factors at baseline, severity of GD, gambling intensity and gambling frequency were separately regressed on EGM preference, GD-related help sought before, sex (reference: female), MB, age, and education (reference: lower secondary education) using a Poisson regression model.

To address longitudinal changes in severity of GD and gambling behaviour, unadjusted mean values for the distinct outcomes were described at each assessment point. In a second step, model-based changes were estimated and visually examined for time trends. To account for the longitudinal nature of the data, dropout and the intra-subject correlation, a generalized estimation equation (GEE) approach was chosen (Zeger & Liang, 1986), representing a marginal model with robust parameter estimates focusing on population averages instead of subject-specific trajectories (Ballinger, 2016; Zeger & Liang, 1986). As we assumed a stronger correlation of current reported gambling indicators with recent gambling behaviour than with gambling behaviour at previous time points, first-order autoregression was chosen as working correlation (Ghisletta & Spini, 2004). All outcomes were operationalized as count data and analysed using a negative-binomial regression with log-link to reduce overdispersion (Rodrıguez, 2013).

Longitudinal changes were visualized in the form of predicted probabilities (Williams, 2018) and incidence rate ratios (IRR). Analyses were adjusted for treatment status (reference: regular termination), GD-related help sought before, education (reference: lower secondary education), EGM preference, age, and sex (reference: female).

GEE analyses were repeated for the stratum with and the one without MB. Finally, a time*migration interaction term was calculated and tested using Wald χ2-test to analyse whether longitudinal trends differed between participants with and without MB.

To examine the robustness of our results, inverse probability weighted GEE models and random-effects Poisson models were applied as sensitivity analysis (Daza, Hudgens, & Herring, 2017) (Table A2). The inverse probability weighting (IPW) procedure relies on estimating the probability of the exposure observed for a particular person and using the predicted probability as a weight in subsequent analyses to account for loss-to-follow-up bias. All statistical analyses were conducted using Stata/SE 15 (Stata Corp LP; College Station, TX, USA). An alpha level of 0.1 was used to account for the small sample size.

Ethics

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

Results

Study participation and demographic characteristics

Baseline assessment was completed by 145 clients, 105 (72.4%) responded to follow-up 1, 94 (64.8%) to follow-up 2, 88 (60.7%) to follow-up 3 and 73 (50.3%) to follow-up 4. A total of 65 clients participated in all four follow-ups. At baseline, 29.7% (n = 43) had a MB. This proportion was 28.6% (n = 30) at follow-up 1, 28.7% (n = 27) at follow-up 2, 26.1% (n = 23) at follow-up 3 and 23.7% (n = 17) at follow-up 4. This corresponded to dropout rates of 33.8% for clients with MB and 66.2% for clients without MB.

As summarized in Table 1, study participants were on average 36 years old, 86.9% were male and 76.6% and had a lower educational background. At baseline, 78.4% (n = 109) reported gambling EGMs and 85.3% (n = 122) had previously sought GD-related help. Study completers and dropouts were comparable with three exceptions: dropouts were more often gambling on EGM, had a lower education level than completers and a slightly higher gambling frequency (Table 1). At follow-up 1, 14.3% (n = 15 of 105) and at follow-up 2 7.5% (n = 7 of 94) were still in treatment. At baseline, 98.6% (n = 140) fulfilled the DSM-5 criterion for presence of a GD. At follow-up 4, this proportion had decreased to 56.3% (n = 40).

Table 1.

Distribution of demographic variables, gambling type preference, help sought before, treatment status and gambling indicators at baseline for completers and dropouts

VariablesBaseline (n = 145)*Completer (n = 65)*Dropouts (n = 80)*Comparison testAssociated probability
Gender, n (%) of females19 (13.1%)7 (10.8%)12 (15%)0.56b0.453
Agen = 143n = 65n = 781.10a0.273
M (SD)36.2 (10.7)37.3 (11.0)35.3 (10.5)
Educational background, n (%)12.47b0.002
 Lower secondary education29 (20.0%)9 (13.9%)20 (25%)
 Upper secondary education82 (56.6%)32 (49.2%)50 (62.5%)
 Post-secondary non-tertiary education7 (4.8%)6 (9.2%)1 (1.3%)
 Tertiary education27 (18.6%)18 (27.7%)9 (11.2%)
Migration background, n (%)1.43b0.231
 Yes43 (29.7%)16 (24.6%)27 (33.8%)
 No102 (70.3%)49 (75.4%)53 (66.2%)
Most played gambling activity, n (%)n = 139n = 60n = 798.61b0.003
 EGMs109 (78.4%)40 (66.7%)62 (86.3%)
Previous treatment for GD, n (%)n = 143n = 64n = 792.61b0.106
 Yes122 (85.3%)58 (90.6%)64 (81%)
 No21 (14.7%)6 (9.4%)15 (19%)
Treatment status at follow-up 3, n (%)n = 64n = 741.72b0.424
 Still in treatment3 (4.7%)6 (8.1%)
 Regular termination26 (40.6%)23 (31.1%)
 Irregular termination35 (54.7%)45 (60.8%)
Gambling indicators
 Fulfilled criteria of GDn = 142n = 64n = 78–0.60a0.553
M (SD)7.9 (1.4)7.8 (1.5)7.9 (1.2)
 Gambling hours per dayn = 138n = 60n = 78–1.19a0.238
M (SD)6.8 (3.5)6.4 (3.6)7.1 (3.4)
 Gambling days per weekn = 134n = 58n = 762.10a0.038
M (SD)3.7 (1.8)3.1 (2.0)3.4 (1.5)

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

aStudent's t-test for interval variables; bPearson chi-square test for ordinal and nominal variables. GD = Gambling disorder.

Clients with and without MB did not differ with respect to age, gender, baseline gambling indicators and previous treatment. However, they had a lower education level and reported gambling more often on EGM than clients without MB (Table 2). In comparison, 10% (n = 3) of the 30 remaining clients with MB and 16.4% (n = 12) of the remaining 75 clients without MB were still in treatment at follow-up 1. For follow-up 2, the rates dropped to 3.9% (n = 1) for the remaining 27 clients with MB and to 9.2% (n = 6) for the remaining 67 clients without MB.

Table 2.

Distribution of demographic variables, gambling type preference, help sought before, treatment status and gambling indicators for clients with and without MB

VariablesMB (n = 43)*Without MB (n = 102)*Comparison testaAssociated probability
Gender, n (%) of females7 (16.3%)12 (11.85)0.54b0.462
Agen = 43n = 1000.72a0.473
M (SD)35.2 (10.3)36.6 (10.9)
Educational background, n (%)13.01b0.005
 Lower secondary education16 (37.2%)13 (12.8%)
 Upper secondary education22 (51.2%)60 (58.8%)
 Post-secondary non-tertiary education1 (2.3%)6 (5.9%)
 Tertiary education4 (9.3%)23 (22.6%)
Most played gambling activity, n (%)n = 43n = 965.55b0.019
 EGMs39 (90.7%)70 (72.9%)
Previous treatment for GD, n (%)n = 41n = 1020.26b0.609
 Yes34 (82.9%)88 (86.3%)
 No7 (17.1%)14 (13.7%)
Treatment status at follow-up 3, n (%)n = 38n = 1004.00b0.135
 Still in treatment09 (9%)
 Regular termination13 (34.2%)36 (36%)
 Irregular termination25 (65.8%)55 (55%)
Gambling indicators
 Fulfilled criteria of GDn = 43n = 99–1.75a0.083
M (SD)8.2 (1.0)7.8 (1.5)
 Gambling hours per dayn = 43n = 950.41a0.682
M (SD)6.6 (3.3)6.8 (3.6)
 Gambling days per weekn = 40n = 941.56a0.122
M (SD)3.4 (1.8)3.9 (1.7)

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

aStudent's t-test for interval variables; bPearson chi-square test for ordinal and nominal variables. GD = Gambling disorder. MB = Migration background

Factors predicting gambling behaviour and problems at baseline

The results of the Poisson regression of various risk factors are shown in Table 3. Only EGM involvement (IRR = 1.08; P < 0.10) and being male (IRR = 0.95; P < 0.10) were significantly associated with severity of GD. For gambling frequency, associations were found for MB (IRR = 0.84; P < 0.10) and age (IRR = 0.99; P < 0.05). No significant associations were found for gambling intensity.

Table 3.

Poisson regression of demographic variables, gambling involvement and GD-related help sought before on gambling indicators at baseline

VariablesSeverity of GDGambling hours per dayGambling days per week
Poisson (IRR)Poisson (IRR)Poisson (IRR)
Gender
FemaleREFREFREF
Male0.95*0.980.97
(0.03)(0.13)(0.11)
Age1.000.990.99**
(0.00)(0.00)(0.00)
Educational background
Lower secondary educationREFREFREF
Upper secondary education0.98

(0.03)
0.94

(0.11)
0.89

(0.10)
Post-secondary/tertiary education1.03

(0.04)
0.84

(0.12)
1.02

(0.13)
Migration background
NoREFREFREF
Yes1.030.950.84*
(0.03)(0.09)(0.09)
EGMs (Dummy)
Everything besides EGMREFREFREF
EGM player1.08*0.880.94
(0.05)(0.11)(0.10)
Previous treatment for GD
NoREFREFREF
Yes1.020.910.85
(0.03)(0.09)(0.09)
Observations133133129

Standard errors in parentheses; *** P < 0.01, ** P < 0.05, * P < 0.10. GD = Gambling disorder. IRR = Incidence rate ratio

Longitudinal changes in gambling behaviour

Unadjusted mean values of severity of GD, gambling intensity and gambling frequency for the entire sample and stratified for clients with and without MB revealed that the most pronounced changes took place between baseline and follow-up 1 (Table A1). These changes were consistently less pronounced among clients with MB than among clients without MB.

Model-based predictions for the three gambling indicators

For all three indicators, statistically significant reductions between baseline and follow-up 4 were observed with changes being most pronounced between baseline and follow-up 1 (P < 0.05) (Table A2). In the subsequent follow-ups, values stabilized at around the level of follow-up 1 (Fig. 2a–c).

Fig. 2.
Fig. 2.

a: Model-based trajectory of severity of GD. b: Model-based trajectory of gambling intensity. c: Model-based trajectory of gambling frequency

Citation: Journal of Behavioral Addictions 10, 3; 10.1556/2006.2021.00043

Moreover, there was a statistically significant time*MB interaction for severity of GD (P < 0.01) and gambling intensity (P < 0.05), but not for gambling frequency (P > 0.1), indicating different profiles of clients with and without MB. As portrayed in Fig. 3a and b, clients without MB experienced a more sustained decline in severity of GD and gambling intensity. In contrast, clients with MB experienced a minor decline between baseline and follow-up 1, but values stayed rather stable across the subsequent follow-ups. As depicted in Fig. 3c, the reduction in gambling frequency was less pronounced in clients with MB. Only clients without MB experienced a sustained reduction after follow-up 1, whereas there was a slight upwards trend in clients with MB from follow-up 2 onwards. However, these differences were not statistically significant.

Fig. 3.
Fig. 3.

a: Model-based trajectory of severity of GD by MB status. b: Model-based trajectory of gambling intensity by MB status. c: Model-based trajectory of gambling frequency by MB status

Citation: Journal of Behavioral Addictions 10, 3; 10.1556/2006.2021.00043

Sensitivity analysis

The weighted GEE models and random-effects Poisson models by and large confirmed the results of the main analysis with some exceptions (Table A2).

Weighted GEE: For gambling severity, being still in treatment gained statistical significance, education levels lost statistical significance and MB and gender lost statistical significance. For gambling intensity, EGM preference gained statistical significance and an irregular termination lost statistical significance. For gambling frequency, being still in treatment gained statistical significance, MB gained statistical significance and age lost statistical significance. Wald χ2-tests for the time*migration interaction term became more pronounced.

Random-effects Poisson model: For gambling severity, previous treatment and education levels lost statistical significance. For gambling intensity, EGM preference gained statistical significance and previous treatment and irregular termination lost statistical significance. For gambling frequency, MB gained statistical significance and age lost statistical significance. Again, Wald χ2-tests for the time*migration interaction term became more pronounced.

Discussion

The present study investigated trends in gambling behaviour and severity of GD in clients seeking help for gambling-related problems in Bavarian OACFs over a period of 3 years. Analyses found that EGM preference was associated with higher severity of GD at baseline, whereas MB was associated with lower gambling frequency. Longitudinal observations demonstrated short- as well as medium-term reduction in severity of GD and gambling involvement among gamblers in outpatient addiction treatment. These reductions were less pronounced in clients with MB than in clients without MB.

Explanations of these findings must consider the design-related caveat of the Katamnese study as a one-armed observational study, rendering the resulting trends suggestive rather than causal in nature. Different reviews in (un-)controlled settings have already demonstrated that GD is a treatable condition and that individuals respond well to treatment modalities. Thus, it was concluded that behavioural changes are most likely to be attributable to treatment (Ladouceur, 1994; Lopez Viets & Miller, 1997; Toneatto & Ladoceur, 2003). However, the phenomenon of spontaneous remission, which is not unusual among people with GD (Slutske, Blaszczynski, & Martin, 2009), may introduce the risk of overestimating longitudinal changes in uncontrolled designs. According to the literature, spontaneous remission appears to be more likely among people with GD who have little or no recourse to professional help at all (with low rates of 10–20% for people with previous recourse to professional help) and for those with only minor signs of pathological gambling behaviour (Meyer et al., 2011; Slutske, 2006; Toneatto et al., 2009). As the characteristics of GD and the psychosocial burden of people seeking outpatient treatment resemble those of people in inpatient treatment (Braun et al., 2013), spontaneous remission presumably played a minor role in our sample, especially when looking at our DSM-5 score of 7.9 at baseline. Based on previous evidence and acknowledging the possibility of spontaneous remission, we assume that the reductions in gambling indicators observed in our study mark a threshold for the effect of the outpatient intervention under controlled conditions.

In our sample, completers had a higher education status than dropouts. Regarding the association between ongoing treatment status and education, this results rather from the generally long study period than higher problem awareness among the better educated, as indicated by our sensitivity analyses. In addition, results indicate that those with MB had a slightly higher problem severity at baseline than those without MB, which is consistent with the general observation that there is a higher proportion of problematic gamblers among individuals who themselves or whose antecedents immigrated than among individuals who did not (Williams, Volberg, & Stevens, 2012).

Our findings support existing evidence on the high-risk potential of EGMs for the development of problematic gambling behaviour over time (Breen & Zimmerman, 2002; Dowling, Smith, & Thomas, 2005), as an association was found for gambling severity. Game characteristics such as event frequency, short payoff intervals, control illusions and near misses disguised as wins are evident in this form of gambling, enhancing problematic gambling behaviour (Schüll, 2012). However, no associations between EGM involvement and gambling intensity or frequency were found, which can be explained in the sense that the characteristics of EGMs do not automatically entice to more frequent and intense gambling sessions and that a more differentiated view on gambling types within their context might be the key. However, lower baseline frequency was found for clients with MB than for clients without MB. The reason for this counterintuitive association remains unclear, considering that particular offers of the gambling industry seem to fit well with the leisure time behaviour of certain cultural milieus, such as for example the tearoom-resembling design of betting rooms (Tuncay, 2010). Nevertheless, different characteristics of milieu- and culture-specific conditions (e.g. disposable income, social networks, language barriers, cultural dimensions such as stigmatization) in association with culture-specific gambling practices could lead to such differences.

Our longitudinal analysis identified sustained improvements in gambling frequency, gambling intensity and a reduction in the severity of GD, with changes being most pronounced 6 months after enrolment and reductions remaining rather stable over subsequent follow-ups. Similar improvements were observed in other longitudinal substance disorder treatment studies such as the MATCH study (Babor & Del Boca, 2003). Motivation and willingness to change were reported to be significant predictors of change. Since these are particularly enhanced by motivational therapy approaches often used in outpatient therapy, an immediate learning effect can be theorized, which is likely to manifest itself in behavioural changes in the early stage of treatment before stabilization takes place. The subsequent stabilization presumably reflects a tapping of client-individual potentials and a completion of the treatment-related learning curve.

Our results can also be compared with an earlier outpatient study, using a multidimensional success criterion (Klepsch et al., 1989). Patients were classified as successful when, subjectively and retrospectively, both their gambling behaviour and their psychosocial adjustment improved by at least 25% compared with the time before outpatient treatment. Based on three samples (1. n = 28; 2. n = 84; 3. n = 50), success rates of 40–54% were found in relation to the overall sample. Even though our reduction rates of 39.2% for gambling severity, 75.6% for gambling intensity and 77% for gambling frequency might be overoptimistic considering the imperfectly solved issue of model overdispersion, they point in a similar direction. Given the sparse research body on medium-term outcomes of outpatient gambling treatment, our results indicate an empirically evident success of outpatient treatment.

It must be noted that, at the end of the study, more than half the participants (56.3%) still fulfilled the diagnostic criteria for GD, with an even higher rate (93.8%) for clients with MB. When considering the high dropout rates in the outpatient sector (Braun et al., 2013), it becomes clear that outpatient therapy seems to have its limits. Owing to the serious nature of the problem, it can be assumed that further treatment is needed in most cases.

Clients with and without MB profited from outpatient gambling treatment. However, clients with MB improved less and were also less stable than clients without MB, particularly between follow-up 2 and follow-up 4. Using the prevalence of individuals with MB in the general population as reference, Rommel and Köppen (2016) reported German born clients with MB to be overrepresented in OACFs. This suggests that MB per se is not an obstacle to accessing outpatient gambling care. Rather, treatment offers may not fully match the (culture-)specific needs of clients with MB (Rommel & Köppen, 2016). For instance, in the treatment of gamblers from an oriental background, community-driven life constellations, role patterns, cultural standards of conduct, concepts such as honour, shame and religion as a value-giving system ought to be addressed in the treatment setting to foster effective understanding, trust building and change in behaviour (Bensel & Tuncay, 2013). Furthermore, language barriers often impede identification of the client’s needs, which limits treatment success. Improvements may be achieved through the provision of culture-specific and multilingual information material, telephone hotlines and online offer-raising. Finally, the integration of therapists specialized in culturally sensitive therapy could reduce language and cultural barriers (Raylu & Oei, 2004).

Limitations and strengths

In addition to the observational uncontrolled design, other limitations of our study need to be considered. During recruitment in 28 Bavarian OACFs over one year, clients with insufficient language skills and clients with less than three contacts had to be excluded from the study resulting in a baseline sample of only 145 clients. The latter criterion was chosen to exclude clients with ambiguous willingness to undergo comprehensive care. Although the dropout rate was satisfactory (50.3% at follow-up 4), prediction models resulted in quite large confidence intervals and thus not significant results, even though visualizations of the longitudinal gambling patterns suggest structural differences. Moreover, although we accounted for the distribution of our count data outcomes by choosing a negative-binomial assumption, our models suffered from overdispersion resulting in overestimation of baseline values. Hence, the level of change is most probably overestimated. Third, in the absence of full information on treatment termination, we cannot fully exclude the possibility that the observed stabilization of all three gambling indicators in the mid-term was the result of ongoing treatment rather than an indicator of the sustainability of short-term treatment. However, as the comparison of completers and dropouts regarding treatment status as well as additionally checked treatment status*time interaction terms showed no statistical significance, this influence is considered negligible.

The main strength of our study is its unique focus on outpatient gambling care, contributing insight to the entire spectrum of care services provided within the German health and social care system. Furthermore, it provides information over a period of 3 years after treatment initiation, which is a longer follow-up period than usually applied in research on inpatient gambling care (Müller et al., 2017; J. Petry 2001). Moreover, the naturalistic design of our study has a high degree of external validity as it follows help-seeking clients in a real-world setting. Finally, with a time span of 36 months of follow-up, the study mirrors a longer follow-up period than most previous studies (J. Petry 2001; Steffen, Werle, Steffen, Steffen, & Steffen, 2012).

Conclusions and further research

In conclusion, our study revealed that severity of GD as well as gambling frequency and intensity declined in the context of outpatient gambling treatment. These improvements also persisted medium-term, suggesting that outpatient gambling care has beneficial implications even after termination. As these beneficial effects can only be interpreted in terms of relative improvements, it is important to clarify which clients need more intensive treatment than others. The focus switch on treatment regimes instead of on gross effects could therefore help to enable a more targeted treatment offer.

Finally, the broad range of different outpatient care offers apparently does not yet address the needs of individuals with MB equally as well as those without MB. Further understanding of socio-cultural background conditions for people with MB and potential barriers to successful gambling care are therefore of utmost importance to further develop meaningful care concepts for this target group.

Funding sources

This 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 Bavarian Coordination Centre for Gambling Issues as an unrestricted grant. Support for LK and JCÖ came from the Swedish programme grant ‘Responding to and Reducing Gambling Problems – Studies in Help-seeking, Measurement, Comorbidity and Policy Impacts’ financed by the Swedish Research Council for Health, Working Life and Welfare (Forte), grant number 2016-07091.

Authors’ contribution

LK, BG and BBM designed the study including data to be assessed and measures to be applied. AB and LS conducted the statistical analyses and wrote the first draft of the manuscript. All authors critically contributed to earlier drafts of the manuscript and approved the final version of the paper.

Conflict of interest

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

Acknowledgments

The authors thank the participating outpatient care facilities of the Bavarian Competence Network of Gambling Issues for smooth cooperation in the context of conducting the study. We especially acknowledge the study participants’ willingness to take part in the ‘Katamnese-Studie’ from enrolment to end of follow-up. We furthermore express our heartfelt thanks to our colleagues Lucia Sedlacek, Francesca Linke and Marieke Neyer, who provided advice and support in conducting the study or drafting this paper.

Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1556/2006.2021.00043.

References

  • Babor, T. F. , & Del Boca, F. K. (2003). Treatment matching in alcoholism. Cambridge University Press.

  • Ballinger, G. A. (2016). Using generalized estimating equations for longitudinal data analysis. Organizational Research Methods, 7(2), 127150. https://doi.org/10.1177/1094428104263672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banz, M. (2019). Glücksspielverhalten und Glücksspielsucht in Deutschland. Ergebnisse des Surveys 2019 und Trends (BZgA-Forschungsbericht). Köln: Bundeszentrale für gesundheitliche Aufklärung. https://doi.org/10.17623/BZGA:225-GS-SY19-1.0.

    • Search Google Scholar
    • Export Citation
  • Becker, T. (2011). Soziale Kosten des Glücksspiels in Deutschland. Retrieved from Hohenheim: https://gluecksspiel.uni-hohenheim.de/fileadmin/einrichtungen/gluecksspiel/Oekonomie/SozialeKostenDesGluecksspiels_Internet.pdf.

    • Search Google Scholar
    • Export Citation
  • Bensel, W. , & Tuncay, M. (2013). Beratung und Behandlung von Glücksspielern mit türkisch-orientalischem Migrationshintergrund. Differentielle Behandlungsstrategien bei pathologischem Glücksspielen. Lambertus: Freiburg i. Br, 156168.

    • Search Google Scholar
    • Export Citation
  • Binde, P. (2011). What are the most harmful forms of gambling? Analyzing problem gambling prevalence surveys. Rapport nr.: CEFOS Working Papers 12.

    • Search Google Scholar
    • Export Citation
  • Braun, B. , Ludwig, M. , Kraus, L. , Kroher, M. , & Bühringer, G. (2013). Ambulante Suchthilfe für pathologische Glücksspieler in Bayern: Passung zwischen Behandlungsbedarf und -angebot. Suchttherapie, 14(01), 3745. https://doi.org/10.1055/s-0032-1323802.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braun, B. , Ludwig, M. , Sleczka, P. , Buhringer, G. , & Kraus, L. (2014). Gamblers seeking treatment: Who does and who doesn’t? Journal of Behavioral Addictions, 3(3), 189198. https://doi.org/10.1556/JBA.3.2014.3.7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Breen, R. B. , & Zimmerman, M. (2002). Rapid onset of pathological gambling in machine gamblers. Journal of Gambling Studies, 18(1), 3143. https://doi.org/10.1023/A:1014580112648.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cowlishaw, S. , Merkouris, S. , Dowling, N. , Anderson, C. , Jackson, A. , & Thomas, S. (2012). Psychological therapies for pathological and problem gambling. Cochrane Database Systematic Review, 11, CD008937. https://doi.org/10.1002/14651858.CD008937.pub2.

    • Search Google Scholar
    • Export Citation
  • Daza, E. J. , Hudgens, M. G. , & Herring, A. H. (2017). Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command. The Stata Journal, 17(2), 253278. https://doi.org/10.1177/1536867X1701700202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowling, N. , Smith, D. , & Thomas, T. (2005). Electronic gaming machines: Are they the “crack‐cocaine”of gambling? Addiction, 100(1), 3345. https://doi.org/10.1111/j.1360-0443.2005.00962.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • German Centre for Addiction Issues. (2010). Deutscher Kerndatensatz zur Dokumentation im Bereich der Suchtkrankenhilfe. Definitionen und Erläuterungen zum Gebrauch. Retrieved from https://www.suchthilfestatistik.de/fileadmin/user_upload_dshs/methode/KDS/Manual_KDS_bis_2017.pdf.

    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haß, W. , Orth, B. , & Lang, P. (2012). Zusammenhang zwischen verschiedenen Glücksspielformen und glücksspielassoziierten Problemen: Ergebnisse aus drei repräsentativen Bevölkerungs-Surveys der Bundeszentrale für gesundheitliche Aufklärung (BZgA). Sucht, 58(5), 333345. https://doi.org/10.1007/s10899-008-9088-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johansson, A. , Grant, J. E. , Kim, S. W. , Odlaug, B. L. , & Gotestam, K. G. (2009). Risk factors for problematic gambling: A critical literature review. Journal of Gambling Studies, 25(1), 6792. https://doi.org/10.1007/s10899-008-9088-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kastirke, N. , Rumpf, H. J. , John, U. , Bischof, A. , & Meyer, C. (2015). Demographic risk factors and gambling preference may not explain the high prevalence of gambling problems among the population with migration background: Results from a German Nationwide survey. Journal of Gambling Studies, 31(3), 741757. https://doi.org/10.1007/s10899-014-9459-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klepsch, R. , Hand, I. , Wlazlo, Z. , Fischer, M. , Friedrich, B. , & Bodek, D. (1989). Langzeiteffekte multimodaler Verhaltenstherapie bei krankhaftem Glücksspielen. III.: Zweite prospektive Katamnese der Hamburger Projekt-Studie. Suchtgefahren, 35, 3549.

    • Search Google Scholar
    • Export Citation
  • Ladouceur, R. (1994). The psychology of gambling: By Michael B. New York: Walker Pergamon Press, 1992. In: Pergamon.

  • Lopez Viets, V. C. , & Miller, W. R. (1997). Treatment approaches for pathological gamblers. Clinical Psychology Review, 17(7), 689702. https://doi.org/10.1016/s0272-7358(97)00031-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meyer, G. , & Bachmann, M. (2017). Spielsucht. Ursachen, Therapie und Prävention von glücksspielbezogenem Suchtverhalten (4th ed.). Berlin: Springer.

    • Search Google Scholar
    • Export Citation
  • Meyer, C. , Rumpf, H. J. , Kreuzer, A. , de Brito, S. , Glorius, S. , Jeske, C. , Westram, A. (2011). Pathologisches Glücksspielen und Epidemiologie (PAGE): Entstehung, Komorbidität, Remission und Behandlung. Endbericht an das Hessische Ministerium des Innern und für Sport. Lübeck: University of Greifswald.

    • Search Google Scholar
    • Export Citation
  • Müller, K. , Wölfling, K. , Dickenhorst, U. , Beutel, M. , Medenwaldt, J. , & Koch, A. (2017). Recovery, relapse, or else? Treatment outcomes in gambling disorder from a multicenter follow-up study. European Psychiatry, 43, 2834. https://doi.org/10.1016/j.eurpsy.2017.01.326.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petry, J. (2001). Übersicht aller katamnestischer Studien zur ambulanten und stationären Behandlung von “Pathologischen Glücksspielern” in Deutschland. [Review of follow-up studies of outpatient and inpatient “pathological gamblers” in Germany]. Verhaltenstherapie & Verhaltensmedizin, 22(2), 103121.

    • Search Google Scholar
    • Export Citation
  • Petry, N. M. (2005). Pathological gambling: Etiology, comorbidity, and treatment (Vol. 2). Washington, DC: American Psychological Association.

  • Premper, V. , & Schulz, W. (2008). Komorbidität bei Pathologischem Glücksspiel. Sucht, 54(3), 131140. https://doi.org/10.1463/2008.03.03.

  • Raylu, N. , & Oei, T. P. (2004). Role of culture in gambling and problem gambling. Clinical Psychology Review, 23(8), 10871114. https://doi.org/10.1016/j.cpr.2003.09.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodrıguez, G. (2013). Models for count data with overdispersion. Addendum to the WWS, 509.

  • Rommel, A. , & Köppen, J. (2016). Migration und Suchthilfe–Inanspruchnahme von Leistungen durch Menschen mit Migrationshintergrund. Psychiatrische Praxis, 43(02), 8288. https://doi.org/10.1055/s-0034-1387291.

    • Search Google Scholar
    • Export Citation
  • Scherrer, J. F. , Slutske, W. S. , Xian, H. , Waterman, B. , Shah, K. R. , Volberg, R. , & Eisen, S. A. (2007). Factors associated with pathological gambling at 10-year follow-up in a national sample of middle-aged men. Addiction, 102(6), 970978. https://doi.org/10.1111/j.1360-0443.2007.01833.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schüll, N. D. (2012). Addiction by design: Machine gambling in Las Vegas. Princeton, NJ: Princeton University Press.

  • Schwarzkopf, L. , Loy, J. K. , Braun-Michl, B. , Grune, 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, n/a(n/a), e1867. https://doi.org/10.1002/mpr.1867.

    • Search Google Scholar
    • Export Citation
  • Slutske, W. S. (2006). Natural recovery and treatment-seeking in pathological gambling: Results of two US national surveys. American Journal of Psychiatry, 163(2), 297302. https://doi.org/10.1176/appi.ajp.163.2.297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slutske, W. S. , Blaszczynski, A. , & Martin, N. G. (2009). Sex differences in the rates of recovery, treatment-seeking, and natural recovery in pathological gambling: Results from an Australian community-based twin survey. Twin Research and Human Genetics, 12(5), 425432. https://doi.org/10.1375/twin.12.5.425.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steffen, D. V. , Werle, L. , Steffen, R. , Steffen, M. , & Steffen, S. (2012). Long-term effectiveness of psychodynamic outpatient treatment of addiction. Fortschritte der Neurologie-psychiatrie, 80(7), 394401. https://doi.org/10.1055/s-0031-1299081.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stinchfield, R. , & Winters, K. C. (2001). Outcome of Minnesota’s gambling treatment programs. Journal of Gambling Studies, 17(3), 217245. https://doi.org/10.1023/A:1012268322509.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strupf, M. , de Matos, E. G. , Soellner, R. , Kraus, L. , & Piontek, D. (2017). Drinking behaviour among individuals of different origins in Germany: A comparison of individuals with and without a migration background. Suchttherapie, 18(2), 9097. https://doi.org/10.1055/s-0042-111197.

    • Search Google Scholar
    • Export Citation
  • Sulkunen, P. , Babor, T. F. , Ornberg, J. C. , Egerer, M. , Hellman, M. , Livingstone, C. , Room, R. (2018). Setting limits: Gambling, science and public policy. Oxford University Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toneatto, T. , Cunningham, J. , Hodgins, D. , Adams, M. , Turner, N. , & Koski-Jannes, A. (2009). Recovery from problem gambling without formal treatment. Addiction Research & Theory, 16(2), 111120. https://doi.org/10.1080/16066350801923638.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toneatto, T. , & Ladoceur, R. (2003). Treatment of pathological gambling: A critical review of the literature. Psychological Addictive Behavior, 17(4), 284292. https://doi.org/10.1037/0893-164X.17.4.284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tuncay, M. (2010). Wettbüro statt Teestube. Glücksspiel bei Migranten aus dem orientalischen Kulturraum. Konturen. Fachzeitschrift zu Sucht und sozialen Fragen, 5, 2010.

    • Search Google Scholar
    • Export Citation
  • UNESCO. (2012). International standard classification of education: ISCED 2011. UNESCO Institute for Statistics Montreal.

  • Williams, R. (2018). 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, R. J. , Volberg, R. A. , & Stevens, R. M. G. (2012). The population prevalence of problem gambling: Methodological influences, standardized rates, jurisdictional differences, and worldwide trends. Ontario: Problem Gambling Research Centre and the Ontario Ministry of Health and Long Term Care.

    • Search Google Scholar
    • Export Citation
  • Zeger, S. L. , & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 121130. https://doi.org/10.2307/2531248.

    • Crossref
    • Search Google Scholar
    • Export Citation

Supplementary Materials

  • Babor, T. F. , & Del Boca, F. K. (2003). Treatment matching in alcoholism. Cambridge University Press.

  • Ballinger, G. A. (2016). Using generalized estimating equations for longitudinal data analysis. Organizational Research Methods, 7(2), 127150. https://doi.org/10.1177/1094428104263672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banz, M. (2019). Glücksspielverhalten und Glücksspielsucht in Deutschland. Ergebnisse des Surveys 2019 und Trends (BZgA-Forschungsbericht). Köln: Bundeszentrale für gesundheitliche Aufklärung. https://doi.org/10.17623/BZGA:225-GS-SY19-1.0.

    • Search Google Scholar
    • Export Citation
  • Becker, T. (2011). Soziale Kosten des Glücksspiels in Deutschland. Retrieved from Hohenheim: https://gluecksspiel.uni-hohenheim.de/fileadmin/einrichtungen/gluecksspiel/Oekonomie/SozialeKostenDesGluecksspiels_Internet.pdf.

    • Search Google Scholar
    • Export Citation
  • Bensel, W. , & Tuncay, M. (2013). Beratung und Behandlung von Glücksspielern mit türkisch-orientalischem Migrationshintergrund. Differentielle Behandlungsstrategien bei pathologischem Glücksspielen. Lambertus: Freiburg i. Br, 156168.

    • Search Google Scholar
    • Export Citation
  • Binde, P. (2011). What are the most harmful forms of gambling? Analyzing problem gambling prevalence surveys. Rapport nr.: CEFOS Working Papers 12.

    • Search Google Scholar
    • Export Citation
  • Braun, B. , Ludwig, M. , Kraus, L. , Kroher, M. , & Bühringer, G. (2013). Ambulante Suchthilfe für pathologische Glücksspieler in Bayern: Passung zwischen Behandlungsbedarf und -angebot. Suchttherapie, 14(01), 3745. https://doi.org/10.1055/s-0032-1323802.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braun, B. , Ludwig, M. , Sleczka, P. , Buhringer, G. , & Kraus, L. (2014). Gamblers seeking treatment: Who does and who doesn’t? Journal of Behavioral Addictions, 3(3), 189198. https://doi.org/10.1556/JBA.3.2014.3.7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Breen, R. B. , & Zimmerman, M. (2002). Rapid onset of pathological gambling in machine gamblers. Journal of Gambling Studies, 18(1), 3143. https://doi.org/10.1023/A:1014580112648.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cowlishaw, S. , Merkouris, S. , Dowling, N. , Anderson, C. , Jackson, A. , & Thomas, S. (2012). Psychological therapies for pathological and problem gambling. Cochrane Database Systematic Review, 11, CD008937. https://doi.org/10.1002/14651858.CD008937.pub2.

    • Search Google Scholar
    • Export Citation
  • Daza, E. J. , Hudgens, M. G. , & Herring, A. H. (2017). Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command. The Stata Journal, 17(2), 253278. https://doi.org/10.1177/1536867X1701700202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowling, N. , Smith, D. , & Thomas, T. (2005). Electronic gaming machines: Are they the “crack‐cocaine”of gambling? Addiction, 100(1), 3345. https://doi.org/10.1111/j.1360-0443.2005.00962.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • German Centre for Addiction Issues. (2010). Deutscher Kerndatensatz zur Dokumentation im Bereich der Suchtkrankenhilfe. Definitionen und Erläuterungen zum Gebrauch. Retrieved from https://www.suchthilfestatistik.de/fileadmin/user_upload_dshs/methode/KDS/Manual_KDS_bis_2017.pdf.

    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haß, W. , Orth, B. , & Lang, P. (2012). Zusammenhang zwischen verschiedenen Glücksspielformen und glücksspielassoziierten Problemen: Ergebnisse aus drei repräsentativen Bevölkerungs-Surveys der Bundeszentrale für gesundheitliche Aufklärung (BZgA). Sucht, 58(5), 333345. https://doi.org/10.1007/s10899-008-9088-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johansson, A. , Grant, J. E. , Kim, S. W. , Odlaug, B. L. , & Gotestam, K. G. (2009). Risk factors for problematic gambling: A critical literature review. Journal of Gambling Studies, 25(1), 6792. https://doi.org/10.1007/s10899-008-9088-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kastirke, N. , Rumpf, H. J. , John, U. , Bischof, A. , & Meyer, C. (2015). Demographic risk factors and gambling preference may not explain the high prevalence of gambling problems among the population with migration background: Results from a German Nationwide survey. Journal of Gambling Studies, 31(3), 741757. https://doi.org/10.1007/s10899-014-9459-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klepsch, R. , Hand, I. , Wlazlo, Z. , Fischer, M. , Friedrich, B. , & Bodek, D. (1989). Langzeiteffekte multimodaler Verhaltenstherapie bei krankhaftem Glücksspielen. III.: Zweite prospektive Katamnese der Hamburger Projekt-Studie. Suchtgefahren, 35, 3549.

    • Search Google Scholar
    • Export Citation
  • Ladouceur, R. (1994). The psychology of gambling: By Michael B. New York: Walker Pergamon Press, 1992. In: Pergamon.

  • Lopez Viets, V. C. , & Miller, W. R. (1997). Treatment approaches for pathological gamblers. Clinical Psychology Review, 17(7), 689702. https://doi.org/10.1016/s0272-7358(97)00031-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meyer, G. , & Bachmann, M. (2017). Spielsucht. Ursachen, Therapie und Prävention von glücksspielbezogenem Suchtverhalten (4th ed.). Berlin: Springer.

    • Search Google Scholar
    • Export Citation
  • Meyer, C. , Rumpf, H. J. , Kreuzer, A. , de Brito, S. , Glorius, S. , Jeske, C. , Westram, A. (2011). Pathologisches Glücksspielen und Epidemiologie (PAGE): Entstehung, Komorbidität, Remission und Behandlung. Endbericht an das Hessische Ministerium des Innern und für Sport. Lübeck: University of Greifswald.

    • Search Google Scholar
    • Export Citation
  • Müller, K. , Wölfling, K. , Dickenhorst, U. , Beutel, M. , Medenwaldt, J. , & Koch, A. (2017). Recovery, relapse, or else? Treatment outcomes in gambling disorder from a multicenter follow-up study. European Psychiatry, 43, 2834. https://doi.org/10.1016/j.eurpsy.2017.01.326.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petry, J. (2001). Übersicht aller katamnestischer Studien zur ambulanten und stationären Behandlung von “Pathologischen Glücksspielern” in Deutschland. [Review of follow-up studies of outpatient and inpatient “pathological gamblers” in Germany]. Verhaltenstherapie & Verhaltensmedizin, 22(2), 103121.

    • Search Google Scholar
    • Export Citation
  • Petry, N. M. (2005). Pathological gambling: Etiology, comorbidity, and treatment (Vol. 2). Washington, DC: American Psychological Association.

  • Premper, V. , & Schulz, W. (2008). Komorbidität bei Pathologischem Glücksspiel. Sucht, 54(3), 131140. https://doi.org/10.1463/2008.03.03.

  • Raylu, N. , & Oei, T. P. (2004). Role of culture in gambling and problem gambling. Clinical Psychology Review, 23(8), 10871114. https://doi.org/10.1016/j.cpr.2003.09.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodrıguez, G. (2013). Models for count data with overdispersion. Addendum to the WWS, 509.

  • Rommel, A. , & Köppen, J. (2016). Migration und Suchthilfe–Inanspruchnahme von Leistungen durch Menschen mit Migrationshintergrund. Psychiatrische Praxis, 43(02), 8288. https://doi.org/10.1055/s-0034-1387291.

    • Search Google Scholar
    • Export Citation
  • Scherrer, J. F. , Slutske, W. S. , Xian, H. , Waterman, B. , Shah, K. R. , Volberg, R. , & Eisen, S. A. (2007). Factors associated with pathological gambling at 10-year follow-up in a national sample of middle-aged men. Addiction, 102(6), 970978. https://doi.org/10.1111/j.1360-0443.2007.01833.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schüll, N. D. (2012). Addiction by design: Machine gambling in Las Vegas. Princeton, NJ: Princeton University Press.

  • Schwarzkopf, L. , Loy, J. K. , Braun-Michl, B. , Grune, 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, n/a(n/a), e1867. https://doi.org/10.1002/mpr.1867.

    • Search Google Scholar
    • Export Citation
  • Slutske, W. S. (2006). Natural recovery and treatment-seeking in pathological gambling: Results of two US national surveys. American Journal of Psychiatry, 163(2), 297302. https://doi.org/10.1176/appi.ajp.163.2.297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slutske, W. S. , Blaszczynski, A. , & Martin, N. G. (2009). Sex differences in the rates of recovery, treatment-seeking, and natural recovery in pathological gambling: Results from an Australian community-based twin survey. Twin Research and Human Genetics, 12(5), 425432. https://doi.org/10.1375/twin.12.5.425.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steffen, D. V. , Werle, L. , Steffen, R. , Steffen, M. , & Steffen, S. (2012). Long-term effectiveness of psychodynamic outpatient treatment of addiction. Fortschritte der Neurologie-psychiatrie, 80(7), 394401. https://doi.org/10.1055/s-0031-1299081.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stinchfield, R. , & Winters, K. C. (2001). Outcome of Minnesota’s gambling treatment programs. Journal of Gambling Studies, 17(3), 217245. https://doi.org/10.1023/A:1012268322509.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Strupf, M. , de Matos, E. G. , Soellner, R. , Kraus, L. , & Piontek, D. (2017). Drinking behaviour among individuals of different origins in Germany: A comparison of individuals with and without a migration background. Suchttherapie, 18(2), 9097. https://doi.org/10.1055/s-0042-111197.

    • Search Google Scholar
    • Export Citation
  • Sulkunen, P. , Babor, T. F. , Ornberg, J. C. , Egerer, M. , Hellman, M. , Livingstone, C. , Room, R. (2018). Setting limits: Gambling, science and public policy. Oxford University Press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toneatto, T. , Cunningham, J. , Hodgins, D. , Adams, M. , Turner, N. , & Koski-Jannes, A. (2009). Recovery from problem gambling without formal treatment. Addiction Research & Theory, 16(2), 111120. https://doi.org/10.1080/16066350801923638.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toneatto, T. , & Ladoceur, R. (2003). Treatment of pathological gambling: A critical review of the literature. Psychological Addictive Behavior, 17(4), 284292. https://doi.org/10.1037/0893-164X.17.4.284.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tuncay, M. (2010). Wettbüro statt Teestube. Glücksspiel bei Migranten aus dem orientalischen Kulturraum. Konturen. Fachzeitschrift zu Sucht und sozialen Fragen, 5, 2010.

    • Search Google Scholar
    • Export Citation
  • UNESCO. (2012). International standard classification of education: ISCED 2011. UNESCO Institute for Statistics Montreal.

  • Williams, R. (2018). 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, R. J. , Volberg, R. A. , & Stevens, R. M. G. (2012). The population prevalence of problem gambling: Methodological influences, standardized rates, jurisdictional differences, and worldwide trends. Ontario: Problem Gambling Research Centre and the Ontario Ministry of Health and Long Term Care.

    • Search Google Scholar
    • Export Citation
  • Zeger, S. L. , & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 121130. https://doi.org/10.2307/2531248.

    • Crossref
    • Search Google Scholar
    • Export Citation
The author instruction is available in PDF.
Please, download the file from HERE

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

Indexing and Abstracting Services:

  • Web of Science [Science Citation Index Expanded (also known as SciSearch®)
  • Journal Citation Reports/Science Edition
  • Social Sciences Citation Index®
  • Journal Citation Reports/ Social Sciences Edition
  • Current Contents®/Social and Behavioral Sciences
  • EBSCO
  • GoogleScholar
  • PsycINFO
  • PubMed Central
  • SCOPUS
  • Medline
  • CABI

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 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
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

  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Luke CLARK (University of British Columbia, Canada)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

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)
  • 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)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Jan 2022 0 56 42
Feb 2022 0 25 18
Mar 2022 0 32 28
Apr 2022 0 57 37
May 2022 0 26 33
Jun 2022 0 34 25
Jul 2022 0 2 2