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Residual spatial correlation between geographically referenced observations: a Bayesian hierarchical modeling

Heather A Boyd1, W Dana Flanders, David G Addiss

  • 1Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA. hoy@ssi.dk

Epidemiology (Cambridge, Mass.)
|June 14, 2005
PubMed
Summary

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Accounting for spatial correlation in Wuchereria bancrofti infection data is crucial. Bayesian hierarchical models (BHMs) offer a flexible approach, but spatial smoothing requires careful application to avoid biased estimates.

Area of Science:

  • Epidemiology
  • Spatial statistics
  • Biostatistics

Background:

  • Traditional epidemiological methods often overlook spatial correlation, potentially biasing results.
  • This can lead to inaccurate parameter and standard error estimates in regression analyses.
  • Wuchereria bancrofti infection data presents a case where spatial patterns are significant.

Purpose of the Study:

  • To introduce and evaluate a Bayesian hierarchical model (BHM) for analyzing spatially correlated epidemiological data.
  • To compare the performance of BHMs with traditional methods like logistic regression (GLM) and generalized linear mixed models (GLMMs).
  • To assess the impact of accounting for spatial correlation on Wuchereria bancrofti infection prevalence data in Haiti.

Main Methods:

  • Data on Wuchereria bancrofti infection prevalence from 57 schools in Haiti were analyzed.

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  • Spatial patterns were examined using semi-variograms and correlograms.
  • Data were modeled using GLM, non-Bayesian GLMMs, non-spatial BHMs, and spatial BHMs.

    • Main Results:

    • An exponential semi-variogram indicated a spatial correlation with an effective range of 2.15 km.
    • Point estimates from GLMM and non-spatial BHM were similar to GLM estimates.
    • Spatial BHM point estimates were significantly attenuated compared to non-spatial models, highlighting the impact of spatial correlation.

    Conclusions:

    • The study underscores the importance of addressing spatial correlation in Wuchereria bancrofti infection data analysis.
    • Bayesian hierarchical models are presented as a flexible and implementable tool for spatially correlated data.
    • Careful application of spatial smoothing is essential, as demonstrated by the results.