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.

  • Article
  • Published:

Pediatrics

Microsimulation model of child and adolescent overweight: making use of what we already know

Abstract

Background

New Zealand has high rates of child overweight and obesity when compared with other countries. Despite an abundance of research documenting the problem, it is unclear what the most effective policy changes or interventions are, and how policy changes might unfold over time within complex systems.

Methods

We use estimates derived from meta-analyses to create a dynamic microsimulation model of child overweight (including obesity). Using census records we created a synthetic birth cohort of 10,000 children. Information on parental education, ethnicity and father’s socio-economic position at birth were taken from census records. We used the New Zealand Health Survey to estimate population base rates for the prevalence of overweight and obesity. Information on other modifiers (such as maternal smoking, breastfeeding, preterm birth, regular breakfast consumption and so forth) were taken from three birth cohorts: Christchurch Health and Development Study, The Dunedin Multidisciplinary Health and Development Study and the Pacific Islands Families Study. Published intervention studies were used to derive plausible estimates for changes to modifiers.

Results

Reducing the proportion of mothers classified as overweight and obesity (−3.31(95% CI −3.55; −3.07) percentage points), reducing the proportion of children watching two or more hours of TV (−3.78(95% CI −4.01; −3.54)), increasing the proportion of children eating breakfast regularly (−1.71(95% CI −1.96; −1.46)), and reducing the proportion of children born with high birth weights (−1.36(95% CI −1.61; −1.11)), lead to sizable decreases in the estimated prevalence of child overweight (including obesity). Reducing the proportion of mothers giving birth by caesarean (−0.23(95% CI −0.49; −0.23)) and increasing parental education (−0.07(95% CI −0.31; 0.18)) did not impact upon child overweight rates.

Conclusions

We created a working simulation model of New Zealand children that can be accessed by policy makers and researchers to determine known relationships between predictors and child overweight, as well as potential gains from targeting specific pathways.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Fig. 1

Similar content being viewed by others

References

  1. Spencer W, Devaux M, Cecchini M, Sassi F. OECD obesity update. Paris: OECD; 2014.

  2. Ministry of Health. Annual update of key results 2014/15: New Zealand Health Survey. Wellington: Ministry of Health; 2015.

  3. Reilly JJ, Methven E, McDowell ZC, Hacking B, Alexander D, Stewart L, et al. Health consequences of obesity. Arch Dis Child. 2003;88:748–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Dietz WH. Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics. 1998;101:518–25.

    CAS  PubMed  Google Scholar 

  5. Wang YC, McPherson K, Marsh T. Health and economic burden of the projected obesity trends in the USA and the UK (vol 378, pg 815, 2011). Lancet. 2011;378:1778–1778.

    Article  Google Scholar 

  6. Butland B, Jebb S, Kopelman P, McPherson K, Thomas S, Mardell J, et al. Foresight. In: Tackling obesities: future choices. Project report. London: Department of Innovation, Universities and Skills; 2007.

  7. Egger G, Swinburn B. An “ecological” approach to the obesity pandemic. BMJ. 1997;315:477.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Roberto CA, Swinburn B, Hawkes C, Huang TTK, Costa SA, Ashe M, et al. Patchy progress on obesity prevention: emerging examples, entrenched barriers, and new thinking. Lancet. 2015;385:2400–9.

    Article  PubMed  Google Scholar 

  9. Levy DT, Mabry PL, Wang YC, Gortmaker S, Huang TTK, Marsh T, et al. Simulation models of obesity: a review of the literature and implications for research and policy. Obes Rev. 2011;12:378–94.

    Article  CAS  PubMed  Google Scholar 

  10. Calder M, Craig C, Culley D, de Cani R, Donnelly CA, Douglas R, et al. Computational modelling for decision-making: where, why, what, who and how. R Soc Open Sci. 2018;5:172096.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Stoker G, Evans M Evidence-based policy making in the social sciences: Methods that matter, Policy Press, 2016.

  12. Gilbert G, Ahrweiler P, Barbrook-Johnson P, Narasimhan K, Wilkinson H. Computational modelling of public policy: reflections on practice. J Artif Soc Soc Simul. 2018;21:1–14.

    Article  Google Scholar 

  13. Chow CC, Hall KD. The dynamics of human body weight change. PLoS Comput Biol. 2008;4:e1000045.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Westerterp KR, Donkers JH, Fredrix EW. Energy intake, physical activity and body weight: a simulation model. Br J Nutr. 1995;73:337–47.

    Article  CAS  PubMed  Google Scholar 

  15. Bahr DB, Browning RC, Wyatt HR, Hill JO. Exploiting social networks to mitigate the obesity epidemic. Obesity. 2009;17:723–8.

    Article  PubMed  Google Scholar 

  16. Edwards KL, Clarke GP. The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: simobesity. Soc Sci Med. 2009;69:1127–34.

    Article  PubMed  Google Scholar 

  17. Spielauer M. What is social science microsimulation? Soc Sci Comput Rev. 2011;29:9–20.

    Article  Google Scholar 

  18. Abowd JM, Lane J. New approaches to confidentiality protection: synthetic data, remote access and research data centers. In: Domingo-Ferrer J, Torra V, editors. Privacy in statistical databases: CASC project final conference, PSD 2004, Barcelona, Spain, June 9–11, 2004. Proceedings. Springer Berlin Heidelberg: Berlin, Heidelberg; 2004. p. 282–9.

  19. Statistics New Zealand. 2006 Census data and reports; 2006. Available at http://archive.stats.govt.nz/Census/2006-census.aspx.

  20. Statistics New Zealand. 2006 Census birth cohort SURF; 2014. Available at http://archive.stats.govt.nz/tools_and_services/university-students/2006-census-birth-cohort.aspx.

  21. Ang YN, Wee BS, Poh BK, Ismail MN. Multifactorial influences of childhood obesity. Curr Obes Rep. 2013;2:10–22.

    Article  Google Scholar 

  22. Park SH, Kim MJ, Park CG, McCreary L, Patil C, Norr KF. Family factors and body mass index among Korean-American preschoolers. J Pediatr Nurs. 2015;30:e101–e111.

    Article  PubMed  Google Scholar 

  23. Rosenkranz RR, Dzewaltowski DA. Model of the home food environment pertaining to childhood obesity. Nutr Rev. 2008;66:123–40.

    Article  PubMed  Google Scholar 

  24. WHO. Factsheet: Obesity and overweight; 2016. Available at https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.

  25. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320:1240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fergusson DM, Horwood LJ. The Christchurch Health and Development Study: review of findings on child and adolescent mental health. Aust N Z J Psychiatry. 2001;35:287–96.

    Article  CAS  PubMed  Google Scholar 

  27. Silva PA, Stanton WR. From child to adult: the Dunedin multidisciplinary health and development study. Auckland; Oxford: Oxford University Press; 1996.

    Google Scholar 

  28. Paterson J, Percival T, Schluter P, Sundborn G, Abbott M, Carter S, et al. Cohort profile: the Pacific Islands Families (PIF) Study. Int J Epidemiol. 2008;37:273–9.

    Article  PubMed  Google Scholar 

  29. Fyfe EM, Anderson NH, North RA, Chan EHY, Taylor RS, Dekker GA, et al. Risk of first-stage and second-stage cesarean delivery by maternal body mass index among Nulliparous women in labor at term. Obstet Gynecol. 2011;117:1315–22.

    Article  PubMed  Google Scholar 

  30. Utter J, Scragg R, Mhurchu CN, Schaaf D. At-home breakfast consumption among New Zealand children: associations with body mass index and related nutrition behaviors. J Am Diet Assoc. 2007;107:570–6.

    Article  PubMed  Google Scholar 

  31. Carter PJ, Taylor BJ, Williams SM, Taylor RW. Longitudinal analysis of sleep in relation to BMI and body fat in children: the FLAME study. BMJ. 2011;342:d2712.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Dotter D. Breakfast at the desk: the impact of universal breakfast programs on academic performance. Working Paper, University of California San Diego Department of Economics; 2012. Retrieved from http://econweb.ucsd.edu/~ddotter/pdfs/Dotter_JMP_Manuscript.pdf.

  33. Lumley J, Chamberlain C, Dowswell T, Oliver S, Oakley L, Watson L. Interventions for promoting smoking cessation during pregnancy. Cochrane Database Syst Rev. 2009;3:CD001055.

    Google Scholar 

  34. Haroon S, Das JK, Salam RA, Imdad A, Bhutta ZA. Breastfeeding promotion interventions and breastfeeding practices: a systematic review. BMC Public Health. 2013;13(Suppl 3):S20–S20.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Quach J, Hiscock H, Ukoumunne OC, Wake M. A brief sleep intervention improves outcomes in the school entry year: a randomized controlled trial. Pediatrics. 2011;128:2011–0409. peds

    Article  Google Scholar 

  36. Wu L, Sun S, He Y, Jiang B. The effect of interventions targeting screen time reduction: A systematic review and meta-analysis. Medicine. 2016;95:e4029.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Chaillet N, Dumont A. Evidence‐based strategies for reducing cesarean section rates: a meta‐analysis. Birth. 2007;34:53–64.

    Article  PubMed  Google Scholar 

  38. Tobias M, Paul S, Turley M. Tracking the obesity epidemic: New Zealand 1977–2003. Public Health Intelligence Occasional Bulletin; 2004.

  39. Schellong K, Schulz S, Harder T, Plagemann A. Birth weight and long-term overweight risk: systematic review and a meta-analysis including 643,902 persons from 66 studies and 26 countries globally. PLoS ONE. 2012;7:e47776.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Yu Z, Han S, Zhu J, Sun X, Ji C, Guo X. Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity: a systematic review and meta-analysis. PLoS ONE. 2013;8:e61627.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Li H, Zhou Y, Liu J. The impact of cesarean section on offspring overweight and obesity: a systematic review and meta-analysis. Int J Obes. 2013;37:893–9.

    Article  Google Scholar 

  42. Horikawa C, Kodama S, Yachi Y, Heianza Y, Hirasawa R, Ibe Y, et al. Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: a meta-analysis. Prev Med. 2011;53:260–7.

    Article  PubMed  Google Scholar 

  43. Wu S, Ding Y, Wu F, Li R, Hu Y, Hou J, et al. Socio-economic position as an intervention against overweight and obesity in children: a systematic review and meta-analysis. Sci Rep. 2015;5:11354.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Weng SF, Redsell SA, Swift JA, Yang M, Glazebrook CP. Systematic review and meta-analyses of risk factors for childhood overweight identifiable during infancy. Arch Dis Child. 2012;97:1019–26.

    Article  PubMed  Google Scholar 

  45. Yan J, Liu L, Zhu Y, Huang G, Wang PP. The association between breastfeeding and childhood obesity: a meta-analysis. BMC Public Health. 2014;14:1267.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Ino T. Maternal smoking during pregnancy and offspring obesity: meta‐analysis. Pediatr Int. 2010;52:94–99.

    Article  PubMed  Google Scholar 

  47. Schofer E, Meyer JW. The worldwide expansion of higher education in the twentieth century. Am Sociol Rev. 2005;70:898–920.

    Article  Google Scholar 

  48. Massion S, Wickham S, Pearce A, Barr B, Law C, Taylor-Robinson D. Exploring the impact of early life factors on inequalities in risk of overweight in UK children: findings from the UK Millennium Cohort Study. Arch Dis Child. 2016;10:724–30.

    Article  Google Scholar 

  49. Von Kries R, Toschke AM, Koletzko B, Slikker W Jr. Maternal smoking during pregnancy and childhood obesity. Am J Epidemiol. 2002;156:954–61.

    Article  Google Scholar 

  50. Modrek S, Basu S, Harding M, White J, Bartick M, Rodriguez E, et al. Does breastfeeding duration decrease child obesity? An instrumental variables analysis. Pediatr Obes. 2017;12:304–11.

    Article  CAS  PubMed  Google Scholar 

  51. Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act. 2011;8:98.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Tully L, Milne BJ. Interaction between Māori ethnicity and risk factors for health outcomes. Auckland, New Zealand: COMPASS Research Centre, University of Auckland.

  53. Yu Z, Han S, Zhu G, Zhu C, Wang X, Cao X, et al. Birth weight and subsequent risk of obesity: a systematic review and meta‐analysis. Obes Rev. 2011;12:525–42.

    Article  CAS  PubMed  Google Scholar 

  54. Druet C, Stettler N, Sharp S, Simmons RK, Cooper C, Davey Smith G, et al. Prediction of childhood obesity by infancy weight gain: an individual‐level meta‐analysis. Paediatr Perinat Epidemiol. 2012;26:19–26.

    Article  PubMed  Google Scholar 

  55. Cappuccio FP, Taggart FM, Kandala N-B, Currie A, Peile E, Stranges S, et al. Meta-analysis of short sleep duration and obesity in children and adults. Sleep. 2008;31:619–26.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Fatima Y, Doi SAR, Mamun AA. Longitudinal impact of sleep on overweight and obesity in children and adolescents: a systematic review and bias-adjusted meta-analysis. Obes Rev. 2015;16:137–49.

    Article  CAS  PubMed  Google Scholar 

  57. Chen X, Beydoun MA, Wang Y. Is sleep duration associated with childhood obesity? a systematic review and meta-analysis. Obesity. 2008;16:265–74.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We are sincerely grateful to Statistics New Zealand for their support and cooperation in our ongoing work with the census data. We would also like to thank Prof. Allen Lee, for his help with defining the intercept in our microsimulation model.

Funding

The study was funded by New Zealand’s Ministry of Business, Industry and Employment (ref. CONT-33892- HASTR-UOA).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nichola Shackleton.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shackleton, N., Chang, K., Lay-yee, R. et al. Microsimulation model of child and adolescent overweight: making use of what we already know. Int J Obes 43, 2322–2332 (2019). https://doi.org/10.1038/s41366-019-0426-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-019-0426-9

Search

Quick links