Hayes A, Gearon E, Backholer K, Bauman A, Peeters A
Sydney School of Public Health, University of Sydney, Sydney, New South Wales, Australia.
1] Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia [2] School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia.
Int J Obes (Lond). 2015 Aug;39(8):1209-16. doi: 10.1038/ijo.2015.50. Epub 2015 Apr 14.
Research efforts have focused mainly on trends in obesity among populations, or changes in mean body mass index (BMI), without consideration of changes in BMI across the BMI spectrum. Examination of age-specific changes in BMI distribution may reveal patterns that are relevant to targeting of interventions.
Using a synthetic cohort approach (which matches members of cross-sectional surveys by birth year) we estimated population representative annual BMI change across two time periods (1980 to 1989 and 1995 to 2008) by age, sex, socioeconomic position and quantiles of BMI. Our study population was a total of 27349 participants from four nationally representative Australian health surveys; Risk Factor Prevalence Study surveys (1980 and 1989), the 1995 National Nutrition Survey and the 2007/8 National Health Survey.
We found greater mean BMI increases in younger people, in those already overweight and in those with lower education. For men, age-specific mean annual BMI change was very similar in the 1980s and the early 2000s (P=0.39), but there was a recent slowing down of annual BMI gain for older women in the 2000s compared with their same-age counterparts in the 1980s (P<0.05). BMI change was not uniform across the BMI distribution, with different patterns by age and sex in different periods. Young adults had much greater BMI gain at higher BMI quantiles, thus adding to the increased right skew in BMI, whereas BMI gain for older populations was more even across the BMI distribution.
The synthetic cohort technique provided useful information from serial cross-sectional survey data. The quantification of annual BMI change has contributed to an understanding of the epidemiology of obesity progression and identified key target groups for policy attention-young adults, those who are already overweight and those of lower socioeconomic status.
研究工作主要集中在人群肥胖趋势或平均体重指数（BMI）的变化上，而未考虑BMI分布范围内的变化。对BMI分布的年龄特异性变化进行研究，可能会揭示与干预目标相关的模式。
我们采用合成队列方法（根据出生年份匹配横断面调查的成员），按年龄、性别、社会经济地位和BMI分位数，估算了两个时间段（1980年至1989年和1995年至2008年）具有人群代表性的年度BMI变化。我们的研究人群共有27349名来自四项具有全国代表性的澳大利亚健康调查的参与者；危险因素患病率研究调查（1980年和1989年）、1995年全国营养调查以及2007/8年全国健康调查。
我们发现年轻人、已超重者和受教育程度较低者的平均BMI增幅更大。对于男性，20世纪80年代和21世纪初年龄特异性的平均年度BMI变化非常相似（P=0.39），但与20世纪80年代同年龄段女性相比，21世纪初老年女性的年度BMI增幅最近有所放缓（P<0.05）。BMI变化在BMI分布中并不均匀，不同时期按年龄和性别呈现不同模式。年轻成年人在较高BMI分位数处的BMI增幅更大，从而增加了BMI右偏程度，而老年人群的BMI增幅在整个BMI分布中更为均匀。
合成队列技术从系列横断面调查数据中提供了有用信息。年度BMI变化的量化有助于理解肥胖进展的流行病学，并确定了政策关注的关键目标群体——年轻成年人、已超重者和社会经济地位较低者。
