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A spatial structural equation modelling framework for health count responses.

Peter Congdon1, Michael Almog, Sarah Curtis

  • 1Department of Geography, Queen Mary University of London, Mile End Rd, London E1 4NS, UK. p.congdon@qmul.ac.uk

Statistics in Medicine
|May 12, 2007
PubMed
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This study introduces a novel structural equation model to analyze how spatial factors influence health referral counts. The model incorporates both health needs and service-specific factors, offering a more comprehensive understanding of referral patterns.

Area of Science:

  • Spatial statistics
  • Health services research
  • Econometrics

Background:

  • Area health referral counts are influenced by complex spatial factors.
  • Existing models often fail to capture both health needs and service-specific spatial influences.
  • A comprehensive model is needed to understand referral count determinants.

Purpose of the Study:

  • To propose a novel structural equation model for analyzing area health referral counts.
  • To incorporate both indicator-based (morbidity) and residual (service configuration/risk) spatially correlated latent constructs.
  • To model spatial heterogeneity and nonlinearity in the impact of health need constructs.

Main Methods:

  • Development of a structural equation model.
  • Utilizing a normal errors measurement model for indicator-based constructs from socio-economic data.

Related Experiment Videos

  • Employing Poisson regression to relate referral outcomes to latent constructs.
  • Incorporating residual constructs for unobserved spatial factors.

    • Main Results:

    • The proposed model effectively integrates indicator-based and residual spatial constructs.
    • Spatially structured heterogeneity and nonlinearity in construct impacts were represented.
    • A case study in New York counties demonstrated the model's application to psychiatric referrals.

    Conclusions:

    • The novel structural equation model provides a robust framework for analyzing health referral counts.
    • The inclusion of both indicator-based and residual constructs enhances the understanding of spatial influences.
    • The model's ability to capture heterogeneity and nonlinearity offers deeper insights into healthcare utilization patterns.