IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i8p3047-3056.html
   My bibliography  Save this article

Modelling the impact of socioeconomic structure on spatial health outcomes

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00394-5
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Congdon, P., 2007. "Bayesian modelling strategies for spatially varying regression coefficients: A multivariate perspective for multiple outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2586-2601, February.
    2. Alexandra Schmidt & Ajax Moreira & Steven Helfand & Thais Fonseca, 2009. "Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 31(2), pages 101-112, April.
    3. Peter Congdon, 2011. "The Spatial Pattern of Suicide in the US in Relation to Deprivation, Fragmentation and Rurality," Urban Studies, Urban Studies Journal Limited, vol. 48(10), pages 2101-2122, August.
    4. Terrance Savitsky & Daniel McCaffrey, 2014. "Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 275-302, April.
    5. Maura Mezzetti, 2012. "Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 49-74, March.
    6. Cindy Xin Feng, 2015. "Bayesian joint modeling of correlated counts data with application to adverse birth outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1206-1222, June.
    7. Terry Elrod & Gerald Häubl & Steven Tipps, 2012. "Parsimonious Structural Equation Models for Repeated Measures Data, with Application to the Study of Consumer Preferences," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 358-387, April.
    8. F. S. Nathoo & C. B. Dean, 2008. "Spatial Multistate Transitional Models for Longitudinal Event Data," Biometrics, The International Biometric Society, vol. 64(1), pages 271-279, March.
    9. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    10. Peter Congdon, 2010. "A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US," IJERPH, MDPI, vol. 7(2), pages 1-20, January.
    11. Peter Congdon, 2010. "Estimating Prevalence of Coronary Heart Disease for Small Areas Using Collateral Indicators of Morbidity," IJERPH, MDPI, vol. 7(1), pages 1-14, January.
    12. Wheeler, David C. & Hickson, DeMarc A. & Waller, Lance A., 2010. "Assessing local model adequacy in Bayesian hierarchical models using the partitioned deviance information criterion," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1657-1671, June.
    13. Gamerman, Dani & Moreira, Ajax R. B., 2004. "Multivariate spatial regression models," Journal of Multivariate Analysis, Elsevier, vol. 91(2), pages 262-281, November.
    14. Koji Miyawaki, 2013. "Space-varying Coefficient Simultaneous Autoregressive Models for the Structural Analysis of Residential Water Demand," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(4), pages 498-518, May.
    15. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    16. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    17. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    18. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    19. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    20. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:3047-3056. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.