德国肥胖症生活方式干预开始时社会人口学特征和心血管危险因素的异质性:一项针对40,942名儿童和青少年的APV多中心研究

B. Bohn, R. Stachow, I. Gellhaus, Johannes Matthias, H. Lichtenstern, R. Holl
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引用次数: 2

摘要

摘要目的:本研究的目的是分析德国儿童和青少年在肥胖生活方式干预(LI)开始时的社会人口学特征或心血管风险因素是否存在差异。方法:纳入来自APV登记处的40942名居住在德国的儿童和青少年。受试者根据邮政编码被分配到德国的16个联邦州。比较了联邦各州在肥胖LI开始时的社会形态和心血管风险因素。对极端肥胖、高血压、血脂异常、碳水化合物代谢异常、非酒精性脂肪肝(NAFLD)和治疗方式(住院与门诊)的患病率进行了Logistic模型分析。结果:LI发病初期年龄为11.5~13.5岁。有移民背景的比例在5.8%至49.7%之间。在德国,引发LI的儿童和青少年的极端肥胖在35.6%至50.8%之间存在很大差异。肥胖相关风险因素也存在地区差异:高血压(39.0–68.1%)、血脂异常(24.9–44.6%)、NAFLD(9.4–20.4%),碳水化合物代谢异常(0.7-6.2%)(均<0.0001)。住院治疗在11.2%-88.2%之间。总体而言,没有观察到明显的区域模式。结论:个人社会经济地位、个人态度、治疗可及性或报销决定的地区差异等因素可能是造成差异的原因。
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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
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.
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