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 27 349 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.
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We thank the Australian Bureau of Statistics for access to the National Nutrition and National Health Surveys and the National Heart Foundation's Risk Factor Prevalence Study Committee and the Australian Social Science Data Archive for access to the Risk Factor Prevalence Studies. AH was supported by National Health and Medical Research Council grant (571372). The research was supported in part by the Victorian Government’s OIS Program. EG and KB were supported by the ARC Linkage grant (LP120100418) and an Australian National Preventive Health Agency grant (188PEE2011), EG was supported by an Australian Postgraduate Award, KB was supported by a Post doctoral Research Fellowship from the National Heart Foundation of Australia (PH 12M6824), AP was supported by a National Health and Medical Research Council Career Development Fellowship (1045456) and the Baker IDI.
About this article
Supplementary Information accompanies this paper on International Journal of Obesity website (http://www.nature.com/ijo)
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