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. 2025 Aug 12;29(14):1307–1318. doi: 10.1177/10870547251357756

Utilization of Mental Health Care Services Among Children and Adolescents with ADHD in Germany: Treatment Satisfaction and Factors Influencing Access

Anne Kaman 1, Martha Gilbert 1, Janine Devine 1, Sophie Möller 1, Robert Schlack 2, Ann-Kristin Beyer 2, Marcel Romanos 3, Thomas Jans 3, Annalena Berner 3, Sophia Weyrich 3; the INTEGRATE-ADHD Study Group, Ulrike Ravens-Sieberer 1,✉
PMCID: PMC12569119  PMID: 40796511

Abstract

Background:

ADHD is one of the most common mental disorders in children and adolescents. While international research on health service utilization, barriers to care, and treatment satisfaction is growing, evidence from Germany remains limited. This study aimed to examine the utilization of mental health care services in a sample of German children and adolescents with an administrative ADHD diagnosis registered with their health insurance company. Treatment satisfaction, belief in treatment efficacy and factors influencing mental health care utilization were examined.

Methods:

As part of the consortium project INTEGRATE-ADHD, data from 4,948 children and adolescents were analyzed. Parents of 7- to 17-year-olds participated in an online survey answering questions about their child’s ADHD health care utilization, treatment satisfaction and efficacy, and factors influencing utilization using established instruments. Sociodemographic factors, geographic characteristics, ADHD symptom severity, and parental psychopathology were also assessed. Descriptive analyses and multivariate logistic regressions were conducted.

Results:

Approximately 40% of the children and adolescents with an administrative ADHD diagnosis were currently receiving ADHD treatment. The majority of parents (76%) were satisfied with the treatment, and 85% considered the treatment effective. Children with more severe ADHD symptoms had a threefold higher likelihood of receiving treatment, while youths with a migration background were less likely to receive mental health care. The most common reasons for not utilizing mental health care included the treatment having already ended, a lack of available treatment options, long waiting times, a lack of motivation among children, or the inability to continue treatment due to the COVID-19 pandemic.

Conclusions:

To overcome the identified barriers in ADHD treatment, we recommend improving access to evidence-based ADHD treatment and expanding its implementation to prevent undertreatment and the associated individual suffering and societal costs.

Keywords: ADHD, youths, health care utilization, barriers, treatment satisfaction

Introduction

Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by symptoms of developmentally inappropriate inattentiveness, impulsivity, and/or hyperactivity. For an ADHD diagnosis, these symptoms must persist for at least 6 months and negatively impact academic, occupational, and social functioning (American Psychiatric Association, 2013; World Health Organization, 2004). ADHD is one of the most common mental disorders among children and adolescents, with meta-reviews estimating a global prevalence of 5% (Polanczyk et al., 2014). When properly diagnosed, ADHD is a highly treatable condition. However, it often remains undetected, which can lead to significant impairments in psychosocial, educational, and occupational functioning, as well as an increased risk of developing comorbid mental health disorders later in life (Rivas-Vazquez et al., 2023). This not only results in prolonged and unnecessary individual suffering but also imposes a substantial economic burden on society (Matza et al., 2005). Therefore, early diagnosis and continuous access to appropriate care are essential to prevent long-term adverse outcomes. To improve early identification and treatment, it is crucial to understand why a considerable number of young people remain undiagnosed or do not receive adequate ADHD treatment. While international—particularly U.S., Canadian, and British—research has addressed patterns of ADHD care utilization, cross-national differences in service structures, and sociocultural perceptions of ADHD make it difficult to generalize findings. Despite a growing body of evidence, there is still a lack of data on health care services utilization, treatment satisfaction, and factors influencing utilization among children and adolescents with ADHD in Germany.

In their recent international review of barriers to ADHD care, Baweja et al. (2021) highlighted that, although several evidence-based psychosocial and pharmacological ADHD treatments exist—and greater utilization of these services is associated with symptom improvement—treatments are often not used or are discontinued prematurely. They identified a variety of structural and attitudinal barriers, including treatment burden (such as cost, time, and travel), limited treatment availability, stigma, lack of parental knowledge, low child motivation, and low self-efficacy to implement change. In an earlier systematic international review, Wright et al. (2015) found that beliefs and perceptions regarding treatment efficacy and acceptable behavior influence help-seeking behavior. Sibley et al. (2022) conducted a mixed-method analysis of audio-recorded therapy sessions in the U.S. and found that common barriers to behavior therapy for adolescent ADHD included low adolescent desire (72.5%), parental failure to monitor skill application (69.4%), adolescent forgetfulness (60.3%), and adolescent belief that change is unnecessary (56.2%). Additional systemic barriers to care for youth with ADHD have also been identified. These include a lack of ADHD education among primary care physicians, which may lead to inadequate recognition and diagnosis and, consequently, low ADHD treatment rates (French et al., 2019; Gamma et al., 2017).

For Germany specifically, Döpfner et al. (2010) suggested that there is a lack of locally available behavioral therapy for ADHD patients. Additionally, therapy interruptions during the transition from adolescence to adulthood, due to a lack of continuity between pediatric and adult care in Germany, increase the risk of negative outcomes for ADHD patients (Bachmann et al., 2017; Libutzki et al., 2019). The decline in ADHD prevalence in adulthood likely results from under-recognition and limited specialized care (Bachmann et al., 2017; Philipsen & Döpfner, 2020). If ADHD remains untreated during childhood, adolescence, and adulthood, it can lead to a higher risk of poor academic performance, mental health and substance use disorders (e.g., depression), criminal behavior (e.g., arrests), and unemployment in adulthood (Erskine et al., 2016). Therefore, it is crucial to better understand the scope and determinants of health service utilization among children and adolescents with ADHD to develop effective interventions that improve the care and well-being of those affected.

With regard to sociodemographic factors associated with the utilization of mental health care services among children and adolescents with ADHD, Bussing et al. (1998) reported that girls had more unmet ADHD care needs than boys. In contrast, a study by Cuffe et al. (2009) found no gender difference in ADHD care utilization. Cuffe et al. (2009) also examined the impact of age on ADHD care utilization, finding that younger children between the ages of 9 and 13 were more likely to visit a medical professional than those aged 14 to 17. Studies on the association between socioeconomic status (SES) and mental health care utilization show mixed results: While Schlack et al. (2007) found that children with low SES were diagnosed with ADHD twice as often, they did not investigate health care utilization. A Canadian study by Brownell et al. (2006) found that SES was inversely related to medication use, whereas other studies found no association between SES and medication use (Russell et al., 2019). Additional studies found that lower SES was associated with less medication use (Froehlich et al., 2007) and lower engagement in and adherence to parental ADHD training (Chacko et al., 2016). Studies on the association between migration background and mental health care utilization indicate that in some countries, children with a migration background have lower rates of health care utilization than non-migrant children (Eiraldi et al., 2006; Yang et al., 2022). In the U.S., a recent review reported that medication use was particularly less frequent among Black, Hispanic, and Asian children (Baweja et al., 2021). However, results are mixed; for example, Cuffe et al. (2009) found no association between race and ADHD care utilization. For Germany, Schlack et al. (2007) found that children from families with a migration background were less likely to report an ADHD diagnosis, potentially due to migrant-specific underdiagnosis or differences in health care utilization between migrants and non-migrants. However, to date, there are no findings on whether migration background affects ADHD care utilization in Germany.

Regarding geographic factors, living in rural areas has found to be associated with a 30% higher likelihood of an ADHD diagnosis in children (Danielson et al., 2018) but with lower health care utilization compared to living in urban sites (Danielson et al., 2018; Howell & McFeeters, 2008; Janicke & Davis, 2011). Moreover, Sayal et al. (2015) found in the UK that the more ADHD symptoms a child exhibits, the more health care they receive. However, this has not yet been replicated for German youth. Finally, research indicates that parental mental health problems are associated with higher mental health service use for children with ADHD (Cuffe et al., 2009; Sayal et al., 2015).

In conclusion, despite growing international research, there is a substantial lack of representative data on how children and adolescents with ADHD in Germany access and experience mental health care. Specifically, little is known about how sociodemographic, geographic, health-related, and parental factors are associated with service utilization, and which barriers contribute to insufficient treatment. Addressing these gaps is essential to inform strategies that improve access, continuity, and effectiveness of ADHD care in the German health system.

Thus, the present study aims to enhance the understanding of ADHD-specific mental health care utilization in Germany by examining sociodemographic and health-related factors, using quantitative data from the consortium project INTEGRATE-ADHD. The consortium project INTEGRATE-ADHD was established to compare and integrate administrative and epidemiological ADHD diagnostic data for children and adolescents in Germany through clinical assessment. The aims of the present study are as follows:

  • (1) To describe the utilization of mental health care services among children and adolescents with ADHD, parental treatment satisfaction, parental belief in treatment efficacy, and factors influencing utilization in Germany.

  • (2) To investigate whether sociodemographic factors (age, gender, parental education, migration background), geographic characteristics, and health-related factors (ADHD symptom severity and parental psychopathology) are associated with mental health care utilization among children and adolescents with ADHD and parental satisfaction with treatment.

Methods

Study Design

The consortium project INTEGRATE-ADHD was designed as a cross-sectional survey of parents of children and adolescents with an administratively documented ADHD diagnosis registered with their health insurance company (ICD-10 F90.0-9) in at least one quarter of 2020 (the so-called M1Q criterion). The survey included parents of children who (a) were insured with the third-largest nationwide German statutory health insurance company, “DAK-Gesundheit”, in 2020; (b) were 0 to 17 years old at that time; and (c) had an administrative ADHD diagnosis, marked with the additional designation “G”, which indicates a confirmed diagnosis in the German health insurance coding system. DAK-Gesundheit provides coverage for approximately 5.5 million people and insures a demographically broad population. The survey was conducted online, using modified questionnaires from the epidemiological German Health Interview and Examination Survey for Children and Adolescents (KiGGS study) (Kurth et al., 2008; Mauz et al., 2017, 2020) and its mental health module (BELLA study) (Klasen et al., 2017; Otto et al., 2021). A subsample of the participating families also underwent guideline-based clinical diagnostics in accordance with the German AWMF-S3 guideline on ADHD (Deutsche Gesellschaft für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie e.V. (DGKJP), Deutsche Gesellschaft für Psychiatrie und Psychotherapie, Psychosomatik und Nervenheilkunde (DGPPN), & Deutsche Gesellschaft für Sozialpädiatrie und Jugendmedizin (DGSPJ), 2017). The INTEGRATE-ADHD project is unique in that it links administrative health insurance data with primary epidemiological survey data and, for a subsample, clinical diagnostic assessments. This combination enables a comprehensive view of ADHD diagnosis, care structures, and unmet needs in a large and diverse sample (Schlack et al., 2024).

Of a total of 24,877 invited parents (gross sample), 5,919 participated in the online survey. Subsequently, 458 participants were excluded for formal and substantive reasons, such as having more than 50% missing data or inconsistencies in age and gender information between the administrative and epidemiological data sets. This resulted in a net sample of 5,461 participants. The response rate, calculated according to AAPOR’s Standard Definitions, was 21.5% (The American Association for Public Opinion Research, 2020). For details on sampling and response, please see Beyer et al. (2025).

In the present study, we analyzed data from the nationwide INTEGRATE-ADHD epidemiological online survey. Participants with an existing administrative ADHD diagnosis in 2020 were included in the analyses if they were between 7 and 17 years old and currently enrolled in school. The final sample under analysis included n = 4,948 parents of children and adolescents aged 7 to 17 years.

Instruments

Current mental health care utilization, factors influencing utilization, treatment satisfaction, and belief in treatment efficacy were examined using established items from the KiGGS and BELLA studies. Current mental health care utilization was measured as a dichotomous variable, asking whether the child or adolescent was currently receiving psychological, psychotherapeutic, or psychiatric treatment for ADHD. This question was directed only to parents who had previously indicated that their child had an ADHD diagnosis. A filter-related missing in this variable (because the parents did not report an ADHD diagnosis for their child) was categorized as no current mental health care utilization. Among children and adolescents receiving mental health care, treatment satisfaction was assessed by asking parents to rate their satisfaction with the treatment on a 4-point Likert scale (“not at all” (1), “a little” (2), “fairly/quite” (3), “very” (4)). Parental belief in treatment efficacy was measured on a 4-point Likert scale, ranging from “not at all effective” (1) to “very effective” (4).

Factors influencing mental health care utilization were assessed using an established item from the BELLA study, asking about reasons for not seeking mental health care services. Multiple answers were allowed. This question was presented only to parents who had previously indicated that their child had an ADHD diagnosis but who did not report any use of mental health care services. The available response options are listed in Figure 1.

Figure 1.

In this bar chart, the factors influencing mental health care utilization is represented in percentage. “Treatment has already ended” is by far the highest at 37.7% followed by “My child is being treated my pediatrician/family doctor” at 23.5 %.

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Factors influencing mental health care utilization (subsample not currently receiving mental health care; n = 1,591).

Sociodemographic information, including children’s and adolescents’ gender and age, as well as the families’ SES and migration background, was collected. SES was determined using the Comparative Analysis of Social Mobility in Industrial Nations (CASMIN) classification (Brauns et al., 2003), which categorizes SES based on parental education. Based on two items assessing the highest academic and vocational qualifications of both parents, parents were categorized into three groups reflecting low (primary education), medium (secondary education), and high (tertiary education) levels of education. Participants were identified as having a migration background if they had immigrated to Germany and had at least one parent born in a country other than Germany, or if both parents had immigrated to Germany or did not hold German citizenship (Schenk et al., 2006).

Geographic characteristics were defined using data from the Federal Institute for Research on Building, Urban Affairs and Spatial Development (referred to as INKAR data, “Indicators and Maps for Spatial and Urban Development”). Urban residential environments included medium-sized cities (more than 20,000 inhabitants) and large cities (over 100,000 inhabitants), while rural areas were classified as small towns (fewer than 20,000 inhabitants) and rural communities (Milbert & Porsche, 2022).

ADHD symptom severity was assessed using the parent-reported German ADHD Rating Scale (FBB-ADHS) (Döpfner & Görtz-Dorten, 2017). The scale consists of 20 items that correspond to the ADHD symptom criteria of the ICD-10 and DSM-V. Responses reflect the severity of ADHD symptoms and range from “not true” (0) to “especially true” (3). Erhart et al. (2008) found that the FBB-ADHS demonstrated good to excellent internal consistency (Cronbach’s α = .73–.90) as well as factorial validity (RMSEA = 0.06).

Parental psychopathology was measured using the short version of the multidimensional Symptom-Checklist 90-R (SCL-90-R) (Derogatis, 1977, 1992; Derogatis & Savitz, 1999; Franke, 1995, 2002)—the SCL-K-9 (Klaghofer & Brähler, 2001). Items measuring parental psychopathological symptoms were measured on a five-point Likert scale, ranging from “not at all” (0) to “very strongly” (4). The internal consistency of the short form has been reported with a Cronbach’s alpha of .87, indicating good reliability (Petrowski et al., 2019).

Statistical Analyses

To investigate current mental health care utilization among children and adolescents with ADHD, parental treatment satisfaction, parental belief in treatment efficacy as well as factors influencing mental health care utilization, descriptive analyses including the calculation of absolute and relative frequencies were conducted. To examine whether sociodemographic factors, geographic characteristics, and health-related factors are associated with (1) current mental health care utilization and (2) parental satisfaction with treatment, two multivariate logistic regression analyses were performed. In the second regression model, the 4-point Likert scale treatment satisfaction variable was dichotomized, with response options of 1 or 2 indicating no treatment satisfaction and 3 or 4 indicating treatment satisfaction. For the logistic regression models, odds ratios (OR) and confidence intervals (CI) were calculated. Significant effects were considered at a significance level of p < .05. All analyses were conducted using SPSS Version 29.

Results

Descriptive Statistics

An analysis of the sociodemographic characteristics of the sample revealed that the average age of the children and adolescents was 12 years. Among them, 75% were boys. The majority of the parents (60%) had a medium level of education and did not have a migration background (91%). Additionally, 63% of the children and adolescents resided in an urban area. Further details on the sociodemographic and psychosocial characteristics of the analyzed sample are presented in Table 1.

Table 1.

Description of the Analyzed Sample of Children and Adolescents (N = 4,984).

INTEGRATE-ADHD sample
Variable n (%) M (SD)
Age (7–17 years) 4,948 12.36 (2.71)
Gender
 Male 3,707 (74.9)
 Female 1,241 (25.1)
Parental education
 Low 517 (10.4)
 Medium 2,968 (60.0)
 High 1,238 (25.0)
 No information 225 (4.5)
Migration background
 No 4,490 (90.7)
 Yes 302 (6.1)
 No information 156 (3.2)
Geographic region
 Urban 3,122 (63.1)
 Rural 1,756 (35.5)
 No information 70 (1.4)
Mental health care utilization
 Yes 1,994 (40.3)
 No 2,954 (59.7)
Treatment satisfaction a
 Not at all satisfied 82 (4.1)
 A little satisfied 395 (19.8)
 Quite satisfied 916 (45.9)
 Very satisfied 600 (30.1)
Parental belief in treatment efficacy a
 Not at all effective 46 (2.3)
 Barely effective 225 (11.3)
 Somewhat effective 988 (49.6)
 Very effective 732 (36.8)
ADHD symptom severity 4,870 1.29 (0.68)
 No information 78 (1.6)
Parental psychopathology 4,825 8.59 (6.81)
 No information 123 (2.5)
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Note. M = mean; SD = standard deviation.

a

This question was only presented to those parents who had previously indicated that their child had an ADHD diagnosis and was utilizing mental health care (n = 1,994).

Current Mental Health Care Utilization

Approximately 40% of the children and adolescents with an administrative ADHD diagnosis were receiving psychological, psychotherapeutic, or psychiatric treatment for ADHD (see Table 1).

Parental Treatment Satisfaction and Belief in Treatment Efficacy

Among the children and adolescents currently utilizing mental health care, 76% of parents reported being somewhat or very satisfied with their child’s treatment (see Table 1). Further, nearly half (49.6%) of them considered the treatment to be somewhat effective, while 36.8% rated it as very effective, 11.3% as barely effective, and 2.3% as not effective at all.

Factors Influencing Mental Health Care Utilization

The reasons for not currently utilizing mental health services despite the child having an ADHD diagnosis are illustrated in Figure 1. The most common reason was that the mental health treatment had already ended (37.7%), followed by the child being in treatment with a pediatrician or general practitioner (23.5%). Other frequent barriers included that the child was unwilling to participate in treatment (15.2%) and the impact of the COVID-19 pandemic (11.6%). Additionally, 7.8% of families reported that no health care provider was available in their area, while another 7.8% cited excessively long waiting times for appointments.

Associations of Sociodemographic Factors, Geographic Characteristics, and Health-Related Factors With Current Mental Health Care Utilization and Treatment Satisfaction

Logistic regression analyses revealed that families with a migration background had a 0.64 lower likelihood of utilizing mental health care services. Children with more severe ADHD symptoms were nearly three times more likely to use mental health services (OR = 2.82) (see Table 2). In terms of treatment satisfaction, logistic regression analyses further revealed that parents of children with more severe ADHD symptoms were less likely to be satisfied with the mental health care their children received (OR = 0.57). Additionally, parents with higher levels of psychopathology were less likely to be satisfied with their child’s treatment; however, the OR was nearly 1 (0.96), indicating that this effect can be considered negligible (see Table 3).

Table 2.

Results of Multivariate Logistic Regression Analyses on Factors Predicting Current Mental Health Care Utilization Among Children and Adolescents With ADHD (n = 4,582).

Variable Estimate SE OR [CI] p
Constant −1.75 0.21 0.17 <.001
Predictor variables
 Gender (reference: male) −0.05 0.07 0.95 [0.82, 1.10] .507
 Age 0.00 0.01 1.00 [0.98, 1.03] .873
 Parental education (reference: low)
  Medium −0.01 0.11 0.99 [0.80, 1.21] .904
  High 0.10 <0.12 1.11 [0.89, 1.40] .350
 Migration background −0.44 0.14 0.64 [0.49, 0.84] .001
 Geographic region (reference: rural) 0.70 0.07 1.07 [0.94, 1.22] .291
 ADHD symptom severity 1.04 0.05 2.82 [2.54, 3.13] <.001
 Parental psychopathology 0.00 0.01 1.00 [0.99, 1.01] .938
Model fit χ 2 (8) = 40.72, p < .001
Nagelkerke’s R 2  = .14
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Note. SE = standard error; OR = odds ratio; CI = confidence interval.

Table 3.

Results of Multivariate Logistic Regression Analyses on Factors Predicting Treatment Satisfaction (Subsample Currently Receiving Mental Health Care Services; n = 1,907).

Variable Estimate SE OR [CI] p
Constant 2.22 0.39 9.21 <.001
Predictor variables
 Gender (reference: male) 0.01 0.13 1.01 [0.78, 1.31] .920
 Age 0.01 0.02 1.01 [0.97, 1.06] .559
 Parental education (reference: low)
  Medium 0.16 0.18 1.17 [0.83, 1.66] .369
  High −0.20 0.20 0.82 [0.56, 1.20] .306
 Migration background −0.06 0.25 0.95 [0.56, 1.53] .818
 Geographic region (reference: rural) 0.30 0.12 1.03 [0.82, 1.30] .782
 ADHD symptom severity −0.56 0.10 0.57 [0.47, 0.69] <.001
 Parental psychopathology −0.04 0.01 0.96 [0.95, 0.98] <.001
Model fit χ 2 (8) = 9.43, p = .308
Nagelkerke’s R 2  = .07
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Note. SE = standard error; OR = odds ratio; CI = confidence interval.

Discussion

The aim of the present study was to examine the utilization of mental health care services in a sample of children and adolescents with an administrative ADHD diagnosis, using data from the consortium project INTEGRATE-ADHD. In addition to treatment satisfaction and beliefs about treatment efficacy, the study also investigated factors associated with mental health care utilization and treatment satisfaction.

The study found that mental health care utilization among children and adolescents with ADHD was strongly associated with the severity of ADHD symptoms. Children with more severe symptoms were three times more likely to receive psychological, psychotherapeutic, or psychiatric treatment. This finding is intuitively understandable, as more severe ADHD symptoms are often linked to disruptive behaviors in the family or at school (e.g., attention shifts, impulsivity, and hyperactivity), which can lead to increased stress and burden for parents (Peasgood et al., 2021; Peñuelas-Calvo et al., 2021). Consequently, parents may seek additional support from mental health services. Additionally, more severe ADHD symptoms may be more readily recognized by pediatricians compared to less obvious or covert symptoms, potentially resulting in more referrals to mental health specialists. Furthermore, our findings indicated that families with a migration background were less likely to utilize mental health care. This may be due to various factors, such as language barriers, unfamiliarity with the German health care system, or discrimination against migrant families within that system. Although language proficiency was not assessed directly, limited German skills may have contributed to lower treatment engagement among some families. Future research should examine this factor more systematically, given the language demands of navigating mental health services. Our findings align with previous literature highlighting racial disparities in mental health service use and access to care among families with a migration background (Coker et al., 2016; Miller et al., 2009; Morgan et al., 2013; Shi et al., 2021).

Among families utilizing mental health care services, 76% of parents reported being satisfied with their child’s treatment. However, parents of children with more severe ADHD symptoms were less likely to be satisfied with the psychological treatment their child received. This may be related to parents having excessively high expectations for a rapid decrease in symptoms during treatment or to the possibility that the treatment was not yet effective, resulting in their child continuing to display severe symptoms. In this context, a study by Görtz-Dorten et al. (2011) found that ADHD symptoms are negatively correlated with treatment satisfaction, while symptom reduction is positively correlated with parental satisfaction with their child’s ADHD treatment.

Over 85% of parents believe that the ADHD treatment their child received was somewhat or very effective, highlighting the perceived benefits of the treatment among most parents. Generally, evidence indicates that treatment for ADHD—especially the combination of behavioral therapy and medication—is effective in improving various outcomes for children with ADHD (Brown et al., 2005). Beneficial treatment not only alleviates the suffering of children but also reduces stress in affected families, as the mental health of children and parents is closely intertwined (Theule et al., 2018).

In terms of factors influencing mental health care utilization, most families whose children were not currently receiving mental health care despite having an ADHD diagnosis indicated that treatment had already ended. This may be attributed to the timing of the consortium project INTEGRATE-ADHD, as there was a lag between the administrative data (indicating that the children had an administratively documented ADHD diagnosis in 2020) and the online survey data (collected between October 2021 and August 2022). Therefore, it can be assumed that some children had already completed treatment before the study began. Further, this raises the question of whether the treatment they received was sufficient or whether the appropriate resources for a necessary longer treatment were not available in the care system. Additionally, more severe ADHD symptoms may be associated with longer treatment durations, leading to higher rates of current (vs. completed) service utilization. Moreover, our findings show that about a quarter of children and adolescents were treated by a pediatrician or family doctor, rather than by ADHD specialists such as child and adolescent psychotherapists. Given that pediatricians in Germany are authorized to prescribe ADHD medication, it is likely that they are playing a key role in ADHD care—especially in pharmacological treatment. These results underline the importance of considering pediatric and general practitioners as an integral part of the ADHD care pathway in Germany. Other frequently reported barriers to seeking mental health care included a lack of providers in their area and excessive waiting times for appointments. This finding underscores the urgent need to expand mental health care availability in communities, such as by funding more mental health therapists specializing in ADHD for children. Additionally, 15% of families did not utilize mental health services due to low motivation among the youth, aligning with previous findings (Sibley, Link, et al., 2022). Sibley, Ortiz, et al. (2022) examined how patient and parent motivation and engagement in ADHD treatment, along with other barriers, could be addressed to improve the implementation and delivery of ADHD treatment. They concluded that training and educating professionals, increasing psychoeducation, digitizing treatment materials, applying motivational interviewing, and fostering relationships with children and parents could enhance motivation and implementation of ADHD treatment. Further recommendations for increasing engagement with ADHD care include integrating behavioral services into general medical settings and utilizing telehealth, which has been shown to reduce attitudinal and motivational barriers (Baweja et al., 2021). These measures appear worth applying to the German health care system. Moreover, our findings highlight that, especially during times of crisis (such as the COVID-19 pandemic), it is crucial to maintain mental health care for children who may suffer the most from crisis intervention measures. A recent meta-analysis found that children with ADHD (and their families) particularly suffered from pandemic restrictions and experienced symptom deterioration (Rogers & MacLean, 2023). This could be, among other factors, due to the restrictive pandemic measures (such as the closure of playgrounds) hindering children with ADHD from spending time outdoors, which may have helped alleviate stress and attention deficits.

INTEGRATE-ADHD is the first study in Germany to consolidate administrative, epidemiological, and clinical data. Its strengths include the large sample size of children and adolescents with an administrative ADHD diagnosis in Germany. Another strength is the use of established questionnaires and items to assess mental health care utilization, factors influencing utilization, ADHD symptom severity, and parental psychopathology. Limitations of the study include that INTEGRATE-ADHD is an observational study that only identifies associations and no cause-effect relationships. The variables included in our regression models explained 7% to 14% of the variance in current mental health care utilization and treatment satisfaction. This suggests that these outcomes may be associated with other important factors that were not considered in our models. Additionally, the present analyses focused on current mental health care utilization (including psychological, psychotherapeutic, and psychiatric treatment), so the results may not be comparable to the utilization of other health care services. Further, we did not differentiate between specific types of treatment (e.g., medication vs. psychotherapy), which limits conclusions about how particular modalities relate to treatment satisfaction or barriers to care. Moreover, the variables of interest were primarily assessed using single-item measures rather than validated multi-item scales, which may limit the reliability and depth of the assessments. In addition, the response rate of 21.5% may limit the generalizability of the findings due to potential selection bias. Although migration background was assessed and included in our analyses, no specific data on race, ethnicity, or primary language were collected. Future studies should consider including more detailed demographic variables to better examine potential disparities in ADHD care. Further, the sample was drawn exclusively from individuals insured with DAK-Gesundheit, a large statutory health insurance provider in Germany. Although DAK-Gesundheit covers a diverse and nationwide population, the findings may not be generalizable to individuals insured with other statutory or private health insurers, who may differ in terms of socioeconomic or regional characteristics. Lastly, the variable geographic region only categorizes areas into two groups: those with more or fewer than 20,000 inhabitants. This division does not allow for a nuanced understanding of the relationship between population size and mental health care utilization. A continuous metric variable could be beneficial in future studies to assess this relationship in a more detailed manner.

To conclude, our study provides important insights into specific treatment gaps and needs among children with ADHD and their families in Germany. Our findings indicate that symptom severity is a key driver of current mental health care utilization, yet also a risk factor for reduced parental treatment satisfaction. Additionally, families with a migration background showed lower service utilization, suggesting potential structural or cultural barriers to care. These results emphasize the need to better support families facing multiple challenges by expanding culturally sensitive services and systematically addressing barriers to access. Our recommendation to enhance interdisciplinary care structures is supported by Döpfner et al. (2010) and aligns with international evidence on the benefits of integrated service models for ADHD. To improve treatment effectiveness and satisfaction, care capacities must be increased, including more training for providers in culturally adapted and engagement-focused interventions. Better remuneration, especially for time-intensive diagnostic and therapeutic services, remains crucial to ensure sustainable improvements in ADHD care. Finally, ensuring access to evidence-based ADHD treatment is vital not only for immediate symptom management but also for preventing long-term risks such as psychiatric comorbidities, educational failure, and social exclusion. Our study thus highlights concrete starting points for improving ADHD care structures in Germany and contributes important epidemiological data to inform future service development and health policy.

Acknowledgments

The authors would like to thank all the children, adolescents, and their parents who participated in this study for their time and involvement.

Author Biographies

Anne Kaman, PhD, is a senior researcher at the University Medical Center Hamburg-Eppendorf in Germany.

Martha Gilbert is a research associate at the University Medical Center Hamburg-Eppendorf in Germany.

Janine Devine, PhD, is a senior researcher at the University Medical Center Hamburg-Eppendorf in Germany.

Sophie Möller is a research assistant at the University Medical Center Hamburg-Eppendorf in Germany.

Robert Schlack, PhD, is a senior researcher at the Robert Koch Institute in Germany.

Ann-Kristin Beyer, PhD, is a post-doctoral researcher at the Robert Koch Institute in Germany.

Marcel Romanos is a specialist in child and adolescent psychiatry and psychotherapy and a professor at the University Hospital Würzburg in Germany.

Thomas Jans is a leading psychologist and a professor at the University Hospital Würzburg in Germany.

Annalena Berner is a specialist in child and adolescent psychiatry and psychotherapy at the University Hospital Würzburg in Germany.

Sophia Weyrich is a research associate at the University Hospital Würzburg in Germany.

Ulrike Ravens-Sieberer is a professor at University Medical Center Hamburg-Eppendorf in Germany.

Footnotes

Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethical Approval and Informed Consent Statements: The study was reviewed and ethically approved by the Ethics Committee of the University of Würzburg (24 March 2021; reference number 249/20). The study subjects were informed about the objectives and content of the study as well as about data protection, and informed consent was obtained from the parents. Children and adolescents aged 14 and older, also had to give their informed consent for their parents to provide information about them in the online survey.

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Marcel Romanos is a board member of the national self-help organisation ADHS Deutschland e.V. All other authors declare that there is no conflict of interest.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The project “INTEGRATE-ADHD” was funded by the German Innovation Fund of the German Federal Joint Committee (Gemeinsamer Bundesausschuss) under the funding code 01VSF19014.

References

  1. The American Association for Public Opinion Research. (2020). Survey outcome rate calculator 4.1. [Google Scholar]
  2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (5th ed). [Google Scholar]
  3. Bachmann C. J., Philipsen A., Hoffmann F. (2017). ADHD in Germany: Trends in diagnosis and pharmacotherapy. Deutsches Ärzteblatt International, 114(9), 141–148. 10.3238/arztebl.2017.0141 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Baweja R., Soutullo C. A., Waxmonsky J. G. (2021). Review of barriers and interventions to promote treatment engagement for pediatric attention deficit hyperactivity disorder care. World journal of Psychiatry, 11(12), 1206. 10.5498/wjp.v11.i12.1206 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Beyer A.-K., Beck L., Pfeifer S., Kuhnert R., Hölling H., Jans T., Berner A., Hetzke L., Weyrich S., Scholz V., Emser T., Mager D., Ulsamer S., Wallau C., Romanos M., Gilbert M., Kaman A., Ravens-Sieberer U., Witte J., . . . Schlack R. (2025). The consortium project INTEGRATE-ADHD: Comparison and integration of administrative and epidemiological ADHD diagnostic data by clinical assessment: Study description and sample characteristics. 10.21203/rs.3.rs-4901197/v1 [DOI] [Google Scholar]
  6. Brauns H., Scherer S., Steinmann S. (2003). The CASMIN educational classification in international comparative research. In Hoffmeyer-Zlotnik J. H. P., Wolf C. (Eds.), Advances in cross-national comparison: A European working book for demographic and socio-economic variables (pp. 221–244). Springer US. 10.1007/978-1-4419-9186-7_11 [DOI] [Google Scholar]
  7. Brown R. T., Amler R. W., Freeman W. S., Perrin J. M., Stein M. T., Feldman H. M., Pierce K., Wolraich M. L., & Committee on Quality Improvement, Subcommittee on Attention-Deficit/Hyperactivity Disorder. (2005). Treatment of attention-deficit/hyperactivity disorder: Overview of the evidence. Pediatrics, 115(6), e749–e757. 10.1542/peds.2004-2560 [DOI] [PubMed] [Google Scholar]
  8. Brownell M. D., Mayer T., Chateau D. (2006). The incidence of methylphenidate use by Canadian children: What is the impact of socioeconomic status and urban or rural residence? The Canadian Journal of Psychiatry, 51(13), 847–854. 10.1177/070674370605101306 [DOI] [PubMed] [Google Scholar]
  9. Bussing R., Zima B. T., Perwien A. R., Belin T. R., Widawski M. (1998). Children in special education programs: Attention deficit hyperactivity disorder, use of services, and unmet needs. American Journal of Public Health, 88(6), 880–886. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chacko A., Jensen S. A., Lowry L. S., Cornwell M., Chimklis A., Chan E., Lee D., Pulgarin B. (2016). Engagement in behavioral parent training: Review of the literature and implications for practice. Clinical Child and Family Psychology Review, 19(3), 204–215. 10.1007/s10567-016-0205-2 [DOI] [PubMed] [Google Scholar]
  11. Coker T. R., Elliott M. N., Toomey S. L., Schwebel D. C., Cuccaro P., Tortolero Emery S., Davies S. L., Visser S. N., Schuster M. A. (2016). Racial and ethnic disparities in ADHD diagnosis and treatment. Pediatrics, 138(3), e20160407. 10.1542/peds.2016-0407 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cuffe S. P., Moore C. G., McKeown R. (2009). ADHD and health services utilization in the National Health Interview Survey. Journal of Attention Disorders, 12(4), 330–340. 10.1177/1087054708323248 [DOI] [PubMed] [Google Scholar]
  13. Danielson M. L., Bitsko R. H., Ghandour R. M., Holbrook J. R., Kogan M. D., Blumberg S. J. (2018). Prevalence of parent-reported ADHD diagnosis and associated treatment among U.S. children and adolescents, 2016. Journal of Clinical Child & Adolescent Psychology, 47(2), 199–212. 10.1080/15374416.2017.1417860 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Derogatis L. R. (1977). SCL-90-R, administration, scoring & procedures manual-I for the R(evised) version. John Hopkins University School of Medicine. [Google Scholar]
  15. Derogatis L. R. (1992). SCL-90-R: Administration, scoring & procedures manual-II for the (revised) version and other instruments of the psychopathology rating scale series (pp. 1–16). Clinical Psychometric Research. [Google Scholar]
  16. Derogatis L. R., Savitz K. L. (1999). The SCL-90-R, Brief Symptom Inventory, and Matching Clinical Rating Scales. In Maruish M. E. (Ed.), The use of psychological testing for treatment planning and outcomes assessment (2nd ed., pp. 679–724). Lawrence Erlbaum Associates Publishers. [Google Scholar]
  17. Deutsche Gesellschaft für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie e.V. (DGKJP), Deutsche Gesellschaft für Psychiatrie und Psychotherapie, Psychosomatik und Nervenheilkunde (DGPPN), & Deutsche Gesellschaft für Sozialpädiatrie und Jugendmedizin (DGSPJ). (2017). Langfassung der interdisziplinären evidenz- und konsensbasierten (S3) Leitlinie „Aufmerksamkeitsdefizit- / Hyperaktivitätsstörung (ADHS) im Kindes-, Jugend- und Erwachsenenalter. AWMF-Registernummer 028-045, Stand 02.05.2017 (in Überarbeitung seit 01.05.2022). https://register.awmf.org/assets/guidelines/028-045l_S3_ADHS_2018-06-abgelaufen.pdf
  18. Döpfner M., Banaschewski T., Krause J., Skrodzki K. (2010). Management of children, adolescents and adults with attention deficit/hyperactivity disorder (ADHD) in Germany. Position of the central ADHD network for 2009 special expert assessment by the medical expert on evaluation of development in public health. Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie, 38(2), 131–136. 10.1024/1422-4917.a000020 [DOI] [PubMed] [Google Scholar]
  19. Döpfner M., Görtz-Dorten A. (2017). DISYPSIII, Diagnostik-System für psychische Störungen nach ICD-10 und DSM-5 für Kinder und Jugendliche – III. Hogrefe. [Google Scholar]
  20. Eiraldi R. B., Mazzuca L. B., Clarke A. T., Power T. J. (2006). Service utilization among ethnic minority children with ADHD: A model of help-seeking behavior. Administration and Policy in Mental Health and Mental Health Services Research, 33(5), 607–622. 10.1007/s10488-006-0063-1 [DOI] [PubMed] [Google Scholar]
  21. Erhart M., Döpfner M., Ravens-Sieberer U., & The BELLA Study Group. (2008). Psychometric properties of two ADHD questionnaires: Comparing the Conners’ scale and the FBB-HKS in the general population of German children and adolescents – results of the BELLA study. European Child & Adolescent Psychiatry, 17(1), 106–115. 10.1007/s00787-008-1012-1 [DOI] [PubMed] [Google Scholar]
  22. Erskine H. E., Norman R. E., Ferrari A. J., Chan G. C., Copeland W. E., Whiteford H. A., Scott J. G. (2016). Long-term outcomes of attention-deficit/hyperactivity disorder and conduct disorder: A systematic review and meta-analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 55(10), 841–850. 10.1016/j.jaac.2016.06.016 [DOI] [PubMed] [Google Scholar]
  23. Franke G. H. (1995). SCL-90-R. Die Symptom-Checkliste von Derogatis - Deutsche Version. Beltz Test. [Google Scholar]
  24. Franke G. H. (2002). Die Symptom-Checkliste von Derogatis (SCL-90-R) - Deutsche Version - Manual. Beltz Test. [Google Scholar]
  25. French B., Sayal K., Daley D. (2019). Barriers and facilitators to understanding of ADHD in primary care: A mixed-method systematic review. European Child & Adolescent Psychiatry, 28(8), 1037–1064. 10.1007/s00787-018-1256-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Froehlich T. E., Lanphear B. P., Epstein J. N., Barbaresi W. J., Katusic S. K., Kahn R. S. (2007). Prevalence, recognition, and treatment of attention-deficit/hyperactivity disorder in a national sample of US children. Archives of Pediatrics & Adolescent Medicine, 161(9), 857–864. 10.1001/archpedi.161.9.857 [DOI] [PubMed] [Google Scholar]
  27. Gamma A. X., Müller A., Candrian G., Eich D. (2017). Attention deficit / hyperactivity -disorder in Swiss primary care. Swiss Archives of Neurology, Psychiatry and Psychotherapy, 168(2), 41–47. 10.4414/sanp.2017.00480 [DOI] [Google Scholar]
  28. Görtz-Dorten A., Breuer D., Hautmann C., Rothenberger A., Döpfner M. (2011). What contributes to patient and parent satisfaction with medication in the treatment of children with ADHD? A report on the development of a new rating scale. European Child & Adolescent Psychiatry, 20, 297–307. 10.1007/s00787-011-0207-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Howell E., McFeeters J. (2008). Children’s mental health care: Differences by race/ethnicity in urban/rural areas. Journal of Health Care for the Poor and Underserved, 19(1), 237–247. 10.1353/hpu.2008.0008 [DOI] [PubMed] [Google Scholar]
  30. Janicke D. M., Davis A. M. (2011). Introduction to the special section: Rural health issues in pediatric psychology. Journal of Pediatric Psychology, 36(6), 647–651. 10.1093/jpepsy/jsr037 [DOI] [PubMed] [Google Scholar]
  31. Klaghofer R., Brähler E. (2001). Konstruktion und teststatistische Prüfung einer Kurzform der SCL-90-R. Zeitschrift für Klinische Psychologie, Psychiatrie und Psychotherapie, 49(2), 115–124. [Google Scholar]
  32. Klasen F., Reiß F., Otto C., Haller A.-C., Meyrose A.-K., Barthel D., Ravens-Sieberer U. (2017). The BELLA study–the mental health module of KIGGS Wave 2. Journal of Health Monitoring, 2(3), 52. 10.17886/RKI-GBE-2017-109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kurth B.-M., Kamtsiuris P., Hölling H., Schlaud M., Dölle R., Ellert U., Kahl H., Knopf H., Lange M., Mensink G. B. (2008). The challenge of comprehensively mapping children’s health in a nation-wide health survey: Design of the German KiGGS-Study. BMC Public health, 8, 1–8. 10.1186/1471-2458-8-196 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Libutzki B., Ludwig S., May M., Jacobsen R. H., Reif A., Hartman C. A. (2019). Direct medical costs of ADHD and its comorbid conditions on basis of a claims data analysis. European Psychiatry, 58, 38–44. 10.1016/j.eurpsy.2019.01.019 [DOI] [PubMed] [Google Scholar]
  35. Matza L. S., Paramore C., Prasad M. (2005). A review of the economic burden of ADHD. Cost Effectiveness and Resource Allocation, 3, 5. 10.1186/1478-7547-3-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Mauz E., Gößwald A., Kamtsiuris P., Hoffmann R., Lange M., von Schenck U., Allen J., Butschalowsky H., Frank L., Hölling H. (2017). New data for action. Data collection for KiGGS Wave 2 has been completed. Journal of Health Monitoring, 2(3), 2. 10.17886/RKI-GBE-2017-105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Mauz E., Lange M., Houben R., Hoffmann R., Allen J., Gößwald A., Hölling H., Lampert T., Lange C., Poethko-Müller C. (2020). Cohort profile: KiGGS cohort longitudinal study on the health of children, adolescents and young adults in Germany. International Journal of Epidemiology, 49(2), 375–375k. 10.1093/ije/dyz231 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Milbert A., Porsche L. (2022). Small towns in Germany. BBSR-Analysen KOMPAKT. [Google Scholar]
  39. Miller T. W., Nigg J. T., Miller R. L. (2009). Attention deficit hyperactivity disorder in African American children: What can be concluded from the past ten years? Clinical Psychology Review, 29(1), 77–86. 10.1016/j.cpr.2008.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Morgan P. L., Staff J., Hillemeier M. M., Farkas G., Maczuga S. (2013). Racial and ethnic disparities in ADHD diagnosis from kindergarten to eighth grade. Pediatrics, 132(1), 85–93. 10.1542/peds.2012-2390 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Otto C., Reiss F., Voss C., Wüstner A., Meyrose A.-K., Hölling H., Ravens-Sieberer U. (2021). Mental health and well-being from childhood to adulthood: Design, methods and results of the 11-year follow-up of the BELLA study. European Child & Adolescent Psychiatry, 30(10), 1559–1577. 10.1007/s00787-020-01630-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Peasgood T., Bhardwaj A., Brazier J. E., Biggs K., Coghill D., Daley D., Cooper C. L., De Silva C., Harpin V., Hodgkins P., Nadkarni A., Setyawan J., Sonuga-Barke E. J. S. (2021). What is the health and well-being burden for parents living with a child with ADHD in the United Kingdom? Journal of Attention Disorders, 25(14), 1962–1976. doi: 10.1177/1087054720925899 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Peñuelas-Calvo I., Palomar-Ciria N., Porras-Segovia A., Miguélez-Fernández C., Baltasar-Tello I., Colmenero S. P., Delgado-Gómez D., Carballo J. J., Baca-García E. (2021). Impact of ADHD symptoms on family functioning, family burden and parents’ quality of life in a hospital area in Spain. European Journal of Psychiatry, 35(3), 166–172. 10.1016/j.ejpsy.2020.10.003 [DOI] [Google Scholar]
  44. Petrowski K., Schmalbach B., Kliem S., Hinz A., Brahler E. (2019). Symptom-Checklist-K-9: Norm values and factorial structure in a representative German sample. PLoS ONE, 14(4), e0213490. 10.1371/journal.pone.0213490 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Philipsen A., Döpfner M. (2020). ADHD in the transition to adulthood: Prevalence, symptoms, risks, and care. Bundesgesundheitsblatt Gesundheitsforschung Gesund-heitsschutz, 63(7), 910–915. 10.1007/s00103-020-03175-y [DOI] [PubMed] [Google Scholar]
  46. Polanczyk G. V., Willcutt E. G., Salum G. A., Kieling C., Rohde L. A. (2014). ADHD prevalence estimates across three decades: An updated systematic review and meta-regression analysis. International Journal of Epidemiology, 43(2), 434–442. doi: 10.1093/ije/dyt261 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Rivas-Vazquez R. A., Diaz S. G., Visser M. M., Rivas-Vazquez A. A. (2023). Adult ADHD: Underdiagnosis of a treatable condition. Journal of Health Service Psychology, 49(1), 11–19. 10.1007/s42843-023-00077-w [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Rogers M. A., MacLean J. (2023). ADHD symptoms increased during the Covid-19 pandemic: A meta-analysis. Journal of Attention Disorders, 27(8), 800–811. 10.1177/10870547231158750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Russell A. E., Ford T., Russell G. (2019). Barriers and predictors of medication use for childhood ADHD: Findings from a UK population-representative cohort. Social Psychiatry and Psychiatric Epidemiology, 54(12), 1555–1564. 10.1007/s00127-019-01720-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sayal K., Mills J., White K., Merrell C., Tymms P. (2015). Predictors of and barriers to service use for children at risk of ADHD: Longitudinal study. European Child & Adolescent Psychiatry, 24(5), 545–552. 10.1007/s00787-014-0606-z [DOI] [PubMed] [Google Scholar]
  51. Schenk L., Bau A. M., Borde T., Butler J., Lampert T., Neuhauser H., Razum O., Weilandt C. (2006). Mindestindikatorensatz zur Erfassung des Migrationsstatus. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundh-eitsschutz, 49(9), 853–860. 10.1007/s00103-006-0018-4 [DOI] [PubMed] [Google Scholar]
  52. Schlack R., Beyer A. K., Beck L., Hölling H., Pfeifer S., Romanos M., Jans T., Hetzke L., Berner A., Weyrich S., Scholz V., Ravens-Sieberer U., Kaman A., Gilbert M., Reiß F., Greiner W., Witte J., Hasemann L., Heuschmann P., Fiessler C., . . . Riederer C. (2024). INTEGRATE-ADHD: Comparison and integration of administrative and epidemiological ADHD diagnosis data through clinical assessment - Presentation of the project. Gesundheitswesen, 86(S 03), S231–S237. 10.1055/a-2340-1474 [DOI] [PubMed] [Google Scholar]
  53. Schlack R., Hölling H., Kurth B. M., Huss M. (2007). Die Prävalenz der Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) bei Kindern und Jugendlichen in Deutschland. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 50(5), 827–835. 10.1007/s00103-007-0246-2 [DOI] [PubMed] [Google Scholar]
  54. Shi Y., Hunter Guevara L. R., Dykhoff H. J., Sangaralingham L. R., Phelan S., Zaccariello M. J., Warner D. O. (2021). Racial disparities in diagnosis of attention-deficit/hyperactivity disorder in a US national birth cohort. JAMA Network Open, 4(3), e210321. 10.1001/jamanetworkopen.2021.0321 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Sibley M. H., Link K., Torres Antunez G., Greenwood L. (2022). Engagement barriers to behavior therapy for adolescent ADHD. Journal of Clinical Child & Adolescent Psychology, 52(6), 834–849. 10.1080/15374416.2022.2025597 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Sibley M. H., Ortiz M., Rios-Davis A., Zulauf-McCurdy C. A., Graziano P. A., Bickman L. (2022). Stakeholder-generated implementation strategies to promote evidence-based ADHD treatment in community mental health. Administration and Policy in Mental Health, 49(1), 44–58. 10.1007/s10488-021-01143-5 [DOI] [PubMed] [Google Scholar]
  57. Theule J., Cheung K., Aberdeen K. (2018). Children’s ADHD interventions and parenting stress: A meta-analysis. Journal of Child and Family Studies, 27(9), 2744–2756. 10.1007/s10826-018-1137-x [DOI] [Google Scholar]
  58. World Health Organization. (2004). ICD-10: International statistical classification of diseases and related health problems: Tenth revision. https://apps.who.int/iris/handle/10665/42980 [PubMed]
  59. Wright N., Moldavsky M., Schneider J., Chakrabarti I., Coates J., Daley D., Kochhar P., Mills J., Sorour W., Sayal K. (2015). Practitioner review: Pathways to care for ADHD - A systematic review of barriers and facilitators. Journal of Child Psychology and Psychiatry, 56(6), 598–617. 10.1111/jcpp.12398 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Yang K. G., Flores M. W., Carson N. J., Cook B. L. (2022). Racial and ethnic disparities in childhood ADHD treatment access and utilization: Results from a national study. Psychiatric Services, 73(12), 1338–1345. 10.1176/appi.ps.202100578 [DOI] [PMC free article] [PubMed] [Google Scholar]

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