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Predicting small-area health-related behaviour: a comparison of smoking and drinking indicators

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  • Twigg, Liz
  • Moon, Graham
  • Jones, Kelvyn

Abstract

Health-related behaviours are of central importance to health promotion and to the promotion of enhanced population health. In the UK, localised knowledge of the quantitative dimensions of health-related behaviours is traditionally attained by conducting a costly sample survey. Such surveys seldom generate reliable data at scales more local than that of the health authority, they also need to be repeated regularly. This paper outlines an alternative framework for generating statistics on small-area health related behaviours using routinely available data from the annual Health Survey for England (N=17,000) and the decennial Population Census. Using a multilevel modelling approach nesting individuals within postcode sectors within health authorities, and focusing on the prevalence of smoking and 'problem' drinking, the paper comprises four sections: a consideration of the modelling strategy, a comparison of the smoking and drinking models, an outline of the estimation strategy, and the presentation and discussion of ward-level estimates of smoking and drinking behaviour for England. The paper concludes that the method is better at estimating smoking than drinking but that it offers a feasible, cheap and more informative alternative to the survey approach to the generation of information on smoking and drinking behaviour.

Suggested Citation

  • Twigg, Liz & Moon, Graham & Jones, Kelvyn, 2000. "Predicting small-area health-related behaviour: a comparison of smoking and drinking indicators," Social Science & Medicine, Elsevier, vol. 50(7-8), pages 1109-1120, April.
    • Handle: RePEc:eee:socmed:v:50:y:2000:i:7-8:p:1109-1120
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    Citations

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    Cited by:

    1. Gill Rowlands & David Whitney & Graham Moon, 2018. "Developing and Applying Geographical Synthetic Estimates of Health Literacy in GP Clinical Systems," IJERPH, MDPI, vol. 15(8), pages 1-8, August.
    2. Jan Pablo Burgard & Joscha Krause & Ralf Münnich, 2019. "Penalized Small Area Models for the Combination of Unit- and Area-level Data," Research Papers in Economics 2019-05, University of Trier, Department of Economics.
    3. Liz Twigg & Steve Barnard & John Mohan & Kelvyn Jones, 2006. "Developing and Evaluating Small-Area Indicators of the Neighbourhood Social Environment," Environment and Planning A, , vol. 38(11), pages 2173-2192, November.
    4. Giancarlo Manzi & David J. Spiegelhalter & Rebecca M. Turner & Julian Flowers & Simon G. Thompson, 2011. "Modelling bias in combining small area prevalence estimates from multiple surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 31-50, January.
    5. A. Singleton & A. Wilson & O. O’Brien, 2012. "Geodemographics and spatial interaction: an integrated model for higher education," Journal of Geographical Systems, Springer, vol. 14(2), pages 223-241, April.
    6. Moon, Graham & Twigg, Liz & Jones, Kelvyn & Aitken, Grant & Taylor, Joanna, 2019. "The utility of geodemographic indicators in small area estimates of limiting long-term illness," Social Science & Medicine, Elsevier, vol. 227(C), pages 47-55.
    7. Merlo, Juan & Viciana-Fernández, Francisco J. & Ramiro-Fariñas, Diego, 2012. "Bringing the individual back to small-area variation studies: A multilevel analysis of all-cause mortality in Andalusia, Spain," Social Science & Medicine, Elsevier, vol. 75(8), pages 1477-1487.
    8. Moon, Graham & Quarendon, Gemma & Barnard, Steve & Twigg, Liz & Blyth, Bill, 2007. "Fat nation: Deciphering the distinctive geographies of obesity in England," Social Science & Medicine, Elsevier, vol. 65(1), pages 20-31, July.
    9. Twigg, Liz & Moon, Graham & Szatkowski, Lisa & Iggulden, Paul, 2009. "Smoking cessation in England: Intentionality, anticipated ease of quitting and advice provision," Social Science & Medicine, Elsevier, vol. 68(4), pages 610-619, February.
    10. David Manley & Kelvyn Jones & Ron Johnston, 2017. "The geography of Brexit – What geography? Modelling and predicting the outcome across 380 local authorities," Local Economy, London South Bank University, vol. 32(3), pages 183-203, May.

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