Original Article | Published:

Epidemiology and Population Health

Modelling obesity trends in Australia: unravelling the past and predicting the future

International Journal of Obesity volume 41, pages 178185 (2017) | Download Citation

Abstract

Background/Objectives:

Modelling is increasingly being used to predict the epidemiology of obesity progression and its consequences. The aims of this study were: (a) to present and validate a model for prediction of obesity among Australian adults and (b) to use the model to project the prevalence of obesity and severe obesity by 2025.

Subjects/Methods:

Individual level simulation combined with survey estimation techniques to model changing population body mass index (BMI) distribution over time. The model input population was derived from a nationally representative survey in 1995, representing over 12 million adults. Simulations were run for 30 years. The model was validated retrospectively and then used to predict obesity and severe obesity by 2025 among different aged cohorts and at a whole population level.

Results:

The changing BMI distribution over time was well predicted by the model and projected prevalence of weight status groups agreed with population level data in 2008, 2012 and 2014.

The model predicts more growth in obesity among younger than older adult cohorts. Projections at a whole population level, were that healthy weight will decline, overweight will remain steady, but obesity and severe obesity prevalence will continue to increase beyond 2016. Adult obesity prevalence was projected to increase from 19% in 1995 to 35% by 2025. Severe obesity (BMI>35), which was only around 5% in 1995, was projected to be 13% by 2025, two to three times the 1995 levels.

Conclusions:

The projected rise in obesity severe obesity will have more substantial cost and healthcare system implications than in previous decades. Having a robust epidemiological model is key to predicting these long-term costs and health outcomes into the future.

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Acknowledgements

We thank the Australian Bureau of Statistics for access to National Nutrition Survey and Australian Health Survey data. We acknowledge funding support from NHMRC Project Grant 571372 ‘Using health economics to strengthen ties between evidence, policy and practice in chronic disease’.

Author information

Affiliations

  1. Sydney School of Public Health, University of Sydney, Sydney, New South Wales, Australia

    • A J Hayes
    • , A Bauman
    •  & K Howard
  2. Office of the Chief Scientist, The George Institute for Global Health, Sydney Medical School, University of Sydney, New South Wales, Australia

    • T W C Lung

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Competing interests

The authors declare no conflict of interest.

Corresponding author

Correspondence to A J Hayes.

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DOI

https://doi.org/10.1038/ijo.2016.165

Supplementary Information accompanies this paper on International Journal of Obesity website (http://www.nature.com/ijo)

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