scholarly journals Prevalence of multimodal treatment in children and adolescents with ADHD in Germany: a nationwide study based on health insurance data

Author(s):  
Oliver Riedel ◽  
Simon Klau ◽  
Ingo Langner ◽  
Christian Bachmann ◽  
Oliver Scholle

Abstract Background Attention-deficit hyperactivity disorder (ADHD) ranks top among neurodevelopmental disorders in children and adolescents. Due to a large number of unfavorable outcomes including psychiatric comorbidities, school problems, and lower socioeconomic status, early and effective treatment of ADHD is essential. Multimodal treatment has become the gold standard in ADHD management, comprising pharmacotherapy and psychosocial interventions, e.g., psychotherapy. Yet, little is known about the prevalence of multimodal treatment in routine care. Methods Based on German health claims data for the years 2009–2017, we identified children and adolescents aged 3–17 years diagnosed with ADHD and characterized them cross-sectionally (per calendar year) in terms of treatment status and psychiatric comorbidities. The detection of pharmacotherapy was based on dispensations of drugs to treat ADHD (e.g., methylphenidate); psychotherapeutic treatment was based on corresponding billing codes. Multimodal treatment was assumed if ADHD medication and psychotherapeutic treatment were coded within the same calendar year. Psychiatric comorbidities were based on outpatient and inpatient diagnoses. Prevalences of ADHD and proportions of different treatment options were calculated and standardized by age and sex. Results In 2017, 91,118 children met the study criteria for ADHD (prevalence: 42.8/1000). Of these, 25.2% had no psychiatric comorbidity, 28.8% had one, 21.6% had two, and 24.5% had three or more. Regarding overall treatment status, 36.2% were treated only pharmacologically, 6.5% received multimodal treatment, and 6.8% were treated with psychotherapy only (neither treatment: 50.2%). With increasing numbers of psychiatric comorbidities, the proportions of patients with multimodal treatment increased from 2.2% (no psychiatric comorbidities) to 11.1% (three or more psychiatric comorbidities) while the proportions of untreated (from 56.8% to 42.7%) or only pharmacologically treated patients (38.4% to 35.0%) decreased. From 2009 to 2017, prevalences were stable and the proportion of patients with only pharmacotherapy decreased from 48% to 36.5%. Concurrently, the proportion of patients with neither pharmacotherapy nor psychotherapy increased from 40.5% to 50.2%. The fraction of patients with multimodal treatment ranged between 6.5% (2017) and 7.4% (2013). Conclusions Multimodal treatment, although recommended as the standard of treatment, is rather the exception than the rule. It is, however, increasingly common in ADHD patients with psychiatric comorbidities.

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1782
Author(s):  
Monika Grabia ◽  
Renata Markiewicz-Żukowska ◽  
Katarzyna Socha

Overweight and obesity are an increasingly common problem, not only among the healthy population, but also in adolescents with type 1 diabetes (T1DM). Excess body weight is related to many cardiometabolic complications as well as a high risk of metabolic syndrome (MetS). The purpose of this systematic review is to provide a concise and critical overview of the prevalence of MetS in children and adolescents with T1DM and, ultimately, to discuss prevention and treatment options. The study was conducted in accordance with PRISMA guidelines. This review shows that, apart from the growing percentage of overweight and obese children and adolescents with T1DM (on average 20.1% and 9.5%, respectively), the problem of the increasing incidence of MetS (range from 3.2 to 29.9%, depending on the criteria used) is one of the most important phenomena of our time. One of the methods of prevention and treatment is a combined approach: changing eating habits and lifestyle, but there are also reports about the beneficial effects of the gut microflora.


2021 ◽  
Vol 104 ◽  
pp. 398-406
Author(s):  
Felix C. Ringshausen ◽  
Raphael Ewen ◽  
Jan Multmeier ◽  
Bondo Monga ◽  
Marko Obradovic ◽  
...  

2014 ◽  
Vol 05 (03) ◽  
pp. 621-629 ◽  
Author(s):  
S.K. Sauter ◽  
C. Rinner ◽  
L.M. Neuhofer ◽  
M. Wolzt ◽  
W. Grossmann ◽  
...  

SummaryObjective: The objective of our project was to create a tool for physicians to explore health claims data with regard to adverse drug reactions. The Java Adverse Drug Event (JADE) tool should enable the analysis of prescribed drugs in connection with diagnoses from hospital stays.Methods: We calculated the number of days drugs were taken by using the defined daily doses and estimated possible interactions between dispensed drugs using the Austria Codex, a database including drug-drug interactions. The JADE tool was implemented using Java, R and a PostgreSQL database.Results: Beside an overview of the study cohort which includes selection of gender and age groups, selected statistical methods like association rule learning, logistic regression model and the number needed to harm have been implemented.Conclusion: The JADE tool can support physicians during their planning of clinical trials by showing the occurrences of adverse drug events with population based information.Citation: Edlinger D, Sauter SK, Rinner C, Neuhofer LM, Wolzt M, Grossmann W, Endel G, Gall W. JADE: A tool for medical researchers to explore adverse drug events using health claims data. Appl Clin Inf 2014; 5: 621–629http://dx.doi.org/10.4338/ACI-2014-04-RA-0036


2001 ◽  
Vol 14 (3) ◽  
pp. 192-197 ◽  
Author(s):  
Mary A. Fristad ◽  
Amy E. Shaver

2020 ◽  
Author(s):  
Thomas Linden ◽  
Johann de Jong ◽  
Chao Lu ◽  
Victor Kiri ◽  
Kathrin Haeffs ◽  
...  

1AbstractEpilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological co-morbidities (e.g. anxiety, migraine, stroke, etc.). While general comorbidity prevalences and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting risks of future comorbidities for the individual epilepsy patient.In this work we use inpatient and outpatient administrative health claims data of around 19,500 US epilepsy patients. We suggest a dedicated multi-modal neural network architecture (Deep personalized LOngitudinal convolutional RIsk model - DeepLORI) to predict the time dependent risk of six common comorbidities of epilepsy patients. We demonstrate superior performance of DeepLORI in a comparison with several existing methods Moreover, we show that DeepLORI based predictions can be interpreted on the level of individual patients. Using a game theoretic approach, we identify relevant features in DeepLORI models and demonstrate that model predictions are explainable in the light of existing knowledge about the disease. Finally, we validate the model on independent data from around 97,000 patients, showing good generalization and stable prediction performance over time.


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