Objective To investigate the annual growth rate of obesity prevalence of residents aged 18 and above in China and prevention keypoints for target populations from 2013 to 2018.Methods This was a cross-sectional study.Subjects from China Chronic Disease and Risk Factor Surveillance project in 2013 and 2018 were included.The prevalence of obesity and growth rate in 31 provinces(autonomous regions and municipalities)in China were collected through survey questionnaires and on-site measurements.Other demographic data such as the proportion of obesity control measures,diet,exercise and drug use was also analyzed.Obesity among adults was defined as body mass index≥28.0 kg/m2.Results A total of 174 736 residents,aged(51.5±14.2)years,which included 74 704(42.8％)males were recruited in 2013,and 179 125 residents,aged(55.1±13.8)years,which included 79 337(44.3％)males were included in 2018.The average annual increase rate of adult obesity prevalence in China from 2013 to 2018 was 3.2％(uncertainty interval(UI)2.7％-3.6％),and the average increase rate of obesity prevalence among men(5.2％(UI 4.6％-5.9％))was higher than that of women(0.9％(UI 0.5％-1.3％)).For subgroups analysis,the average increase rate of obesity prevalence among residents aged 18 to 29(7.4％(UI 6.9％-7.9％)),education level beyond college degree(6.3％(UI 5.5％-7.1％)),and unmarried population(11.2％(UI 10.2％-12.1％))were higher than that of other subgroups between 2013 and 2018.The residents in Hainan province showed the highest average annual growth rate of obesity.With the exception of Shanxi,Hunan,Gansu and Ningxia province,the annual growth rate of obesity prevalence among adults increased in all other provinces(autonomous regions and municipalities)from 2013 to 2018.For the obese population,the proportion of people who took weight control measures increased from 22.6％in 2013 to 32.7％in 2018.Conclusions The prevalence of obesity growth characteristics in subpopulations and regions in China are obviously different.Accordingly the focus points of obesity prevention and control in different regions should have their own emphasis.
NETL
NSTL
维普
万方数据
