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Modelling the impact of socioeconomic structure on spatial health outcomes

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  • Congdon, Peter

Abstract

A factor analytic model is proposed for the impact of spatially defined latent social constructs on area health outcomes (e.g.mortality or hospitalisation counts). The model has two components or sub-models. The first component is a social indicator measurement model using socioeconomic variables (e.g.from population censuses) as indicators of latent social constructs. The other sub-model considers variations in spatial health outcomes in terms both of the latent social constructs and of residual common factors -- the latter have only the health variation component as their measurement model. The two sets of latent variables can be mutually correlated and latent scores can be correlated over areas, though the extent of the spatial dependence in the scores on any particular latent variable is determined by the data. A case study application considers the impact of two latent social constructs (denoted as social deprivation and social fragmentation) on four types of psychiatric hospitalisation in 33 local authorities in London, England.

Suggested Citation

  • Congdon, Peter, 2009. "Modelling the impact of socioeconomic structure on spatial health outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3047-3056, June.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:3047-3056
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    References listed on IDEAS

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    1. Congdon, Peter, 2007. "Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3197-3212, March.
    2. Hogan J.W. & Tchernis R., 2004. "Bayesian Factor Analysis for Spatially Correlated Data, With Application to Summarizing Area-Level Material Deprivation From Census Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 314-324, January.
    3. Gamerman, Dani & Moreira, Ajax R. B. & Rue, Havard, 2003. "Space-varying regression models: specifications and simulation," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 513-533, March.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. Gelfand, Alan E. & Banerjee, Sudipto & Sirmans, C.F. & Tu, Yong & Eng Ong, Seow, 2007. "Multilevel modeling using spatial processes: Application to the Singapore housing market," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3567-3579, April.
    6. Gerhard Arminger & Bengt Muthén, 1998. "A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm," Psychometrika, Springer;The Psychometric Society, vol. 63(3), pages 271-300, September.
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    Cited by:

    1. Decai Tang & Zhijiang Li & Brandon J. Bethel, 2019. "Relevance Analysis of Sustainable Development of China’s Yangtze River Economic Belt Based on Spatial Structure," IJERPH, MDPI, vol. 16(17), pages 1-16, August.
    2. LeSage, James & Banerjee, Sudipto & Fischer, Manfred M. & Congdon, Peter, 2009. "Spatial statistics: Methods, models & computation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2781-2785, June.

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