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Hierarchical related regression for combining aggregate and individual data in studies of socio‐economic disease risk factors

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  • Christopher Jackson
  • And Nicky Best
  • Sylvia Richardson

Abstract

Summary. To obtain information about the contribution of individual and area level factors to population health, it is desirable to use both data collected on areas, such as censuses, and on individuals, e.g. survey and cohort data. Recently developed models allow us to carry out simultaneous regressions on related data at the individual and aggregate levels. These can reduce ‘ecological bias’ that is caused by confounding, model misspecification or lack of information and increase power compared with analysing the data sets singly. We use these methods in an application investigating individual and area level sociodemographic predictors of the risk of hospital admissions for heart and circulatory disease in London. We discuss the practical issues that are encountered in this kind of data synthesis and demonstrate that this modelling framework is sufficiently flexible to incorporate a wide range of sources of data and to answer substantive questions. Our analysis shows that the variations that are observed are mainly attributable to individual level factors rather than the contextual effect of deprivation.

Suggested Citation

  • Christopher Jackson & And Nicky Best & Sylvia Richardson, 2008. "Hierarchical related regression for combining aggregate and individual data in studies of socio‐economic disease risk factors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 159-178, January.
    • Handle: RePEc:bla:jorssa:v:171:y:2008:i:1:p:159-178
    DOI: 10.1111/j.1467-985X.2007.00500.x
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    References listed on IDEAS

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    1. Nicky Best & Samantha Cockings & James Bennett & Jon Wakefield & Paul Elliott, 2001. "Ecological regression analysis of environmental benzene exposure and childhood leukaemia: sensitivity to data inaccuracies, geographical scale and ecological bias," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 155-174.
    2. Jon Wakefield, 2003. "Sensitivity Analyses for Ecological Regression," Biometrics, The International Biometric Society, vol. 59(1), pages 9-17, March.
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    4. W. J. Browne & S. V. Subramanian & K. Jones & H. Goldstein, 2005. "Variance partitioning in multilevel logistic models that exhibit overdispersion," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(3), pages 599-613, July.
    5. Mark Tranmer & Andrew Pickles & Ed Fieldhouse & Mark Elliot & Angela Dale & Mark Brown & David Martin & David Steel & Chris Gardiner, 2005. "The case for small area microdata," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 29-49, January.
    6. Macintyre, Sally & Ellaway, Anne & Cummins, Steven, 2002. "Place effects on health: how can we conceptualise, operationalise and measure them?," Social Science & Medicine, Elsevier, vol. 55(1), pages 125-139, July.
    7. Gillian Lancaster & Mick Green, 2002. "Deprivation, ill‐health and the ecological fallacy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 263-278, June.
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    Cited by:

    1. Irene L. Hudson & Linda Moore & Eric J. Beh & David G. Steel, 2010. "Ecological inference techniques: an empirical evaluation using data describing gender and voter turnout at New Zealand elections, 1893–1919," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 185-213, January.
    2. 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.
    3. Win Wah & Susannah Ahern & Arul Earnest, 2020. "A systematic review of Bayesian spatial–temporal models on cancer incidence and mortality," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 65(5), pages 673-682, June.
    4. Winston Chou & Kosuke Imai & Bryn Rosenfeld, 2020. "Sensitive Survey Questions with Auxiliary Information," Sociological Methods & Research, , vol. 49(2), pages 418-454, May.
    5. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.
    6. Win Wah & Susannah Ahern & Arul Earnest, 0. "A systematic review of Bayesian spatial–temporal models on cancer incidence and mortality," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 0, pages 1-10.
    7. Jackson, Christopher H. & Richardson, Sylvia & Best, Nicky G., 2008. "Studying place effects on health by synthesising individual and area-level outcomes," Social Science & Medicine, Elsevier, vol. 67(12), pages 1995-2006, December.

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