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The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity

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  • Edwards, Kimberley L.
  • Clarke, Graham P.

Abstract

Obesogenic environments are a major explanation for the rapidly increasing prevalence in obesity. Investigating the relationship between obesity and obesogenic variables at the micro-level will increase our understanding about local differences in risk factors for obesity. SimObesity is a spatial microsimulation model designed to create micro-level estimates of obesogenic environment variables in the city of Leeds in the UK: consisting of a plethora of health, environment, and socio-economic variables. It combines individual micro-data from two national surveys with a coarse geography, with geographically finer scaled data from the 2001 UK Census, using a reweighting deterministic algorithm. This creates a synthetic population of individuals/households in Leeds with attributes from both the survey and census datasets. Logistic regression analyses identify suitable constraint variables to use. The model is validated using linear regression and equal variance t-tests. Height, weight, age, gender, and residential postcode data were collected on children aged 3-13 years in the Leeds metropolitan area, and obesity described as above the 98th centile for the British reference dataset. Geographically weighted regression is used to investigate the relationship between different obesogenic environments and childhood obesity. Validation shows that the small-area estimates were robust. The different obesogenic environments, as well as the parameter estimates from the corresponding local regression analyses, are mapped, all of which demonstrate non-stationary relationships. These results show that social capital and poverty are strongly associated with childhood obesity. This paper demonstrates a methodology to estimate health variables at the small-area level. The key to this technique is the choice of the model's input variables, which must be predictors for the output variables; this factor has not been stressed in other spatial microsimulation work. It also provides further evidence for the existence of obesogenic environments for children.

Suggested Citation

  • Edwards, Kimberley L. & Clarke, Graham P., 2009. "The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity," Social Science & Medicine, Elsevier, vol. 69(7), pages 1127-1134, October.
    • Handle: RePEc:eee:socmed:v:69:y:2009:i:7:p:1127-1134
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    References listed on IDEAS

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    1. Giles-Corti, Billie & Donovan, Robert J., 2002. "The relative influence of individual, social and physical environment determinants of physical activity," Social Science & Medicine, Elsevier, vol. 54(12), pages 1793-1812, June.
    2. Mohan, John & Twigg, Liz & Barnard, Steve & Jones, Kelvyn, 2005. "Social capital, geography and health: a small-area analysis for England," Social Science & Medicine, Elsevier, vol. 60(6), pages 1267-1283, March.
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    2. Chyzheuskaya, Aksana & O’Donoghue, Cathal & O’Neill, Stephen & Hynes, Stephen, 2015. "Using A Spatialmicrosimulationmodel To Estimate The Potential Economic Impact On Agriculture Of Possible Freshwater Pearl Mussel Protection Strategies," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212669, European Association of Agricultural Economists.
    3. Robin Lovelace & Mark Birkin & Dimitris Ballas & Eveline van Leeuwen, 2015. "Evaluating the Performance of Iterative Proportional Fitting for Spatial Microsimulation: New Tests for an Established Technique," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-21.
    4. Malcolm Campbell & Dimitris Ballas, 2013. "A spatial microsimulation approach to economic policy analysis in Scotland," Regional Science Policy & Practice, Wiley Blackwell, vol. 5(3), pages 263-288, August.
    5. Deborah J Schofield & Melanie J B Zeppel & Owen Tan & Sharyn Lymer & Michelle M Cunich & Rupendra N Shrestha, 2018. "A Brief, Global History of Microsimulation Models in Health: Past Applications, Lessons Learned and Future Directions," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 97-142.
    6. Lovelace, Robin & Ballas, Dimitris & Watson, Matt, 2014. "A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels," Journal of Transport Geography, Elsevier, vol. 34(C), pages 282-296.
    7. Rahman, Azizur & Harding, Ann & Tanton, Robert & Liu, Shuangzhe, 2013. "Simulating the characteristics of populations at the small area level: New validation techniques for a spatial microsimulation model in Australia," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 149-165.
    8. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    9. Kate A Timmins & Kimberley L Edwards, 2016. "Validation of Spatial Microsimulation Models: a Proposal to Adopt the Bland-Altman Method," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 106-122.
    10. Mark Birkin & Graham Clarke, 2012. "The enhancement of spatial microsimulation models using geodemographics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 49(2), pages 515-532, October.
    11. Dianna M. Smith & Alison Heppenstall & Monique Campbell, 2021. "Estimating Health over Space and Time: A Review of Spatial Microsimulation Applied to Public Health," J, MDPI, vol. 4(2), pages 1-11, June.

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