Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • 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 178–185 (2017)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Australian Bureau of Statistics. National Health Survey: first results 2014–15, 2015. Available at http://www.abs.gov.au/ausstats/abs@.nsf/mf/4364.0.55.001.

  2. Australian Bureau of Statistics. Information Paper: National Nutrition Survey 1995 (Cat. No. 4805.0).

  3. Colagiuri S, Lee CM, Colagiuri R, Magliano D, Shaw JE, Zimmet PZ et al. The cost of overweight and obesity in Australia. Med J Aust 2010; 192: 260–264.

    PubMed  Google Scholar 

  4. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 2011; 377: 557–567.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Flegal KM, Carroll MD, Ogden CL, Curtin LR . Prevalence and trends in obesity among US adults, 1999–2008. JAMA 2010; 303: 235–241.

    Article  CAS  PubMed  Google Scholar 

  6. Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B et al. Obesity and severe obesity forecasts through 2030. Am J Prev Med 2012; 42: 563–570.

    Article  PubMed  Google Scholar 

  7. Thomas DM, Weedermann M, Fuemmeler BF, Martin CK, Dhurandhar NV, Bredlau C et al. Dynamic model predicting overweight, obesity, and extreme obesity prevalence trends. Obesity 2014; 22: 590–597.

    Article  PubMed  Google Scholar 

  8. Taylor R, Lewis M, Powles J . The Australian mortality decline: all-cause mortality 1788–1990. Aust N Z J Public Health 1998; 22: 27–36.

    Article  CAS  PubMed  Google Scholar 

  9. Kesteloot H, Sans S, Kromhout D . Dynamics of cardiovascular and all-cause mortality in Western and Eastern Europe between 1970 and 2000. Eur Heart J 2006; 27: 107–113.

    Article  PubMed  Google Scholar 

  10. Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M . Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 2011; 378: 815–825.

    Article  PubMed  Google Scholar 

  11. Walls HL, Wolfe R, Haby MM, Magliano DJ, de Courten M, Reid CM et al. Trends in BMI of urban Australian adults, 1980–2000. Public Health Nutr 2010; 13: 631–638.

    Article  PubMed  Google Scholar 

  12. Haby MM, Markwick A, Peeters A, Shaw J, Vos T . Future predictions of body mass index and overweight prevalence in Australia, 2005–2025. Health Promot Intl 2012; 27: 250–260.

    Article  Google Scholar 

  13. van Baal PH, van den Berg M, Hoogenveen RT, Vijgen SM, Engelfriet PM . Cost-effectiveness of a low-calorie diet and orlistat for obese persons: modeling long-term health gains through prevention of obesity-related chronic diseases. Value Health 2008; 11: 1033–1040.

    Article  PubMed  Google Scholar 

  14. Veerman JL, Barendregt JJ, Forster M, Vos T . Cost-effectiveness of pharmacotherapy to reduce obesity. PLoS ONE 2011; 6: e26051.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. van Baal PH, Polder JJ, de Wit GA, Hoogenveen RT, Feenstra TL, Boshuizen HC et al. Lifetime medical costs of obesity: prevention no cure for increasing health expenditure. PLoS Med 2008; 5: e29.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sacks G, Veerman JL, Moodie M, Swinburn B . 'Traffic-light' nutrition labelling and 'junk-food' tax: a modelled comparison of cost-effectiveness for obesity prevention. Int J Obes 2011; 35: 1001–1009.

    Article  CAS  Google Scholar 

  17. Prospective Studies Collaboration Prospective Studies Collaboration Whitlock G Prospective Studies Collaboration Lewington S Prospective Studies Collaboration Sherliker P Prospective Studies Collaboration Clarke R Prospective Studies Collaboration Emberson J et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009; 373: 1083–1096.

    Article  PubMed Central  Google Scholar 

  18. Cawley J, Meyerhoefer C, Biener A, Hammer M, Wintfeld N . Savings in medical expenditures associated with reductions in body mass index among US adults with obesity, by diabetes status. Pharmacoeconomics 2015; 33: 707–722.

    Article  PubMed  Google Scholar 

  19. Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB et al. Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task force-7. Value Health 2012; 15: 843–850.

    Article  PubMed  Google Scholar 

  20. Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD . Model parameter estimation and uncertainty: a report of the ISPOR-SMDM modeling good research practices task force-6. Value Health 2012; 15: 835–842.

    Article  PubMed  Google Scholar 

  21. Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD . Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group-6. Med Decision Making 2012; 32: 722–732.

    Article  Google Scholar 

  22. Caro JJ, Briggs AH, Siebert U, Kuntz KM . Modeling good research practices–overview: a report of the ISPOR-SMDM modeling good research practices task force-1. Value Health 2012; 15: 796–803.

    Article  PubMed  Google Scholar 

  23. Caro JJ, Briggs AH, Siebert U, Kuntz KM . Modeling good research practices–overview: a report of the ISPOR-SMDM modeling good research practices task force-1. Med Decision Making 2012; 32: 667–677.

    Article  Google Scholar 

  24. WHO Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013–2020, 2013 report no World Health Organization: Geneva, Switzerland, 2013.

  25. Levy DT, Mabry PL, Wang YC, Gortmaker S, Huang TT, Marsh T et al. Simulation models of obesity: a review of the literature and implications for research and policy. Obes Rev 2011; 12: 378–394.

    Article  CAS  PubMed  Google Scholar 

  26. Australian Bureau of Statistics. Australian Health Survey: First Results, 2011–12 Canberra: Australian Bureau of Statistics 2012 Contract No.: 4364.0.55.001.

  27. Australian Bureau of Statistics. Births Australia Cat. No. 33010.0, Canberra 2006. Canberra2005. Available at http://www.ausstats.abs.gov.au/Ausstats/subscriber.nsf/0/9977BFDD2273D1BCCA257209001A97CD/$File/33010_2005.pdf.

  28. Hayes A, Gearon E, Backholer K, Bauman A, Peeters A . Age-specific changes in BMI and BMI distribution among Australian adults using cross-sectional surveys from 1980 to 2008. Int J Obes 2015; 39: 1209–1216.

    Article  CAS  Google Scholar 

  29. Cameron AJ, Welborn TA, Zimmet PZ, Dunstan DW, Owen N, Salmon J et al. Overweight and obesity in Australia: the 1999–2000 Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Med J Aust 2003; 178: 427–432.

    PubMed  Google Scholar 

  30. Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009; 373: 1083–1096.

    Article  PubMed  Google Scholar 

  31. Australian Life Tables 2010–12. Australian Government, Commonwealth of Australia. Available at http://www.aga.gov.au/publications/life_table_2010-12/.

  32. Australian Bureau of Statistics. National Health Survey: Users' Guide, 2007–08. 2009 (Cat. No. 4363.0.55.001).

  33. Peeters A, Gearon E, Backholer K, Carstensen B . Trends in the skewness of the body mass index distribution among urban Australian adults, 1980 to 2007. Ann Epidemiol 2015; 25: 26–33.

    Article  PubMed  Google Scholar 

  34. Flegal KM, Carroll MD, Kit BK, Ogden CL . Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA 2012; 307: 491–497.

    Article  PubMed  Google Scholar 

  35. Green MA, Subramanian SV, Razak F . Population-level trends in the distribution of body mass index in England, 1992-2013. J Epidemiol Community Health 2016; 70: 832–835.

    Article  CAS  PubMed  Google Scholar 

  36. NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population based measurement studies on 19.2 million participants. Lancet 2016; 387: 2144–2162.

    Google Scholar 

  37. Zaninotto P, Head J, Stamatakis E, Wardle H, Mindell J . Trends in obesity among adults in England from 1993 to 2004 by age and social class and projections of prevalence to 2012. J Epidemiol Community Health 2009; 63: 140–146.

    Article  CAS  PubMed  Google Scholar 

  38. Peeters A, Magliano DJ, Backholer K, Zimmet P, Shaw JE . Changes in the rates of weight and waist circumference gain in Australian adults over time: a longitudinal cohort study. BMJ Open 2014; 4: e003667.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Schmidt Morgen C, Rokholm B, Sjoberg Brixval C, Schou Andersen C, Geisler Andersen L, Rasmussen M et al. Trends in prevalence of overweight and obesity in Danish infants, children and adolescents—are we still on a plateau? PLoS ONE 2013; 8: e69860.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Lissner L, Sohlstrom A, Sundblom E, Sjoberg A . Trends in overweight and obesity in Swedish schoolchildren 1999-2005: has the epidemic reached a plateau? Obesity Rev 2010; 11: 553–559.

    Article  CAS  Google Scholar 

  41. O'Dea JA, Nguyen Hoang TD, Dibley MJ . Plateau in obesity and overweight in a cross sectional study of low, middle and high socioeconomic status schoolchildren between 2004 and 2009. Int J Public Health 2011; 56: 663–667.

    Article  PubMed  Google Scholar 

Download references

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

Authors and 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

Authors
  1. A J Hayes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T W C Lung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A Bauman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K Howard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A J Hayes.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on International Journal of Obesity website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hayes, A., Lung, T., Bauman, A. et al. Modelling obesity trends in Australia: unravelling the past and predicting the future. Int J Obes 41, 178–185 (2017). https://doi.org/10.1038/ijo.2016.165

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

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

This article is cited by

Search

Advanced search

Quick links