医学
肥胖
超重
体质指数
腰围
人口学
血压
横断面研究
混淆
逻辑回归
人口
风险因素
内科学
环境卫生
病理
社会学
作者
Yongze Li,Di Teng,Xiaoguang Shi,Xiaochun Teng,Weiping Teng,Zhongyan Shan,Yaxin Lai
标识
DOI:10.1016/j.lanwpc.2021.100227
摘要
Previous studies have shown increases in the prevalence of obesity and hypertension, but nationally representative data on recent changes in prevalence adjusted for population structure changes are lacking. Two nationwide surveys were conducted in 2007 and 2017 to assess the prevalence changes of these conditions in China.A multistage stratified random sampling method was used to obtain a nationally representative sample of adults aged 20 years and older in mainland China in 2007 and 2017. Temporal changes in the prevalence of hypertension and obesity were investigated. Changes in blood pressure, body mass index (BMI) and waist circumference were also assessed. Logistic regression models were constructed to assess the changes in prevalence over time.The weighted prevalence of hypertension (25.7% vs. 31.5%, P=0.04), high-normal blood pressure (11.7% vs. 14.3%, P<0.0001), general obesity (31.9% vs. 37.2%, P=0.008), and central obesity (25.9% vs. 35.4%, P=0.0002) was significantly higher in 2017 (n=72824) than in 2007 (n=45956) in the overall population. No significant changes in the prevalence of overweight and grade 1 or grade 2 hypertension were observed in the overall population, but a significantly higher prevalence was observed among participants aged 20-29 years for grade 1 hypertension (P=0.002) and among participants aged 70 years and older for grade 2 hypertension (P=0.046) in 2017.Compared with 2007, the prevalence of hypertension and obesity was significantly higher among adults in mainland China after adjusting for demographic confounding factors in 2017. More targeted interventions and prevention strategies are needed to offset the increasing risk of cardiovascular disease due to increases in the prevalence of hypertension and obesity.The Clinical Research Fund of the Chinese Medical Association (Grant No. 15010010589), the National Natural Science Foundation of China (Grant No. 82000753), and the Chinese Medical Association Foundation and Chinese Diabetes Society (Grant No. 07020470055).
科研通智能强力驱动
Strongly Powered by AbleSci AI