Heterogeneity in sociodemographic characteristics and cardiovascular risk factors at the initiation of a lifestyle intervention for obesity within Germany: an APV multicenter study on 40, 942 children and adolescents. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Bohn, Barbara; Stachow, Rainer; Gellhaus, Ines; Matthias, Johannes; Lichtenstern, Hans; Holl, Reinhard W.
• Is Part Of: Child and adolescent obesity. Volume 1:Issue 1(2018)
• Page Start: 5
• Page End: 17
• Publication Date: 2018-01-01
• Subjects: Regional differences -- obesity -- cardiovascular risk factors -- lifestyle intervention -- children and adolescents; 618.92398
• DOI: 10.1080/2574254X.2018.1547070
• Languages: English
• ISSNs: 2574-254X

Abstract

ABSTRACT: Objective : The objective of this study is to analyze whether sociodemographic characteristics or cardiovascular risk factors differ in children and adolescents at the beginning of a lifestyle intervention (LI) for obesity within Germany. Methods : A total of 40, 942 children and adolescents with German residence from the APV-registry were included. Subjects were assigned to the 16 federal states of Germany according to their postal code. Sociodemographic and cardiovascular risk factors at the beginning of a LI for obesity were compared between the federal states. Logistic models were implemented for the prevalence of extreme obesity, hypertension, dyslipidemia, abnormal carbohydrate metabolism, nonalcoholic fatty liver disease (NAFLD), and treatment modality (inpatient vs. outpatient). Results : Age at the beginning of a LI ranged from 11.5 to 13.5 years. Proportion with a migration background was between 5.8% and 49.7%. Within Germany, extreme obesity in children and adolescents initiating a LI strongly differed between 35.6% and 50.8%. Regional differences were also found for obesity-related risk factors: hypertension (39.0–68.1%), dyslipidemia (24.9–44.6%), NAFLD (9.4–20.4%), abnormal carbohydrate metabolism (0.7–6.2%) (all p < 0.0001). Inpatient treatment varied between 11.2% and 88.2%. Overall, no clear regional pattern was observed. Conclusion : Several factors as individual socioeconomic status, personal attitude, treatment accessibility, or regional differences in reimbursement decisions might have contributed to the disparities.