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Statistical Methods for Quantifying Spatial Effects on Disease Incidence Using Individual-Level Data

  • Author(s): Bai, Lu
  • Advisor(s): Gillen, Daniel
  • et al.
Abstract

In epidemiologic studies, researchers are commonly interested in quantifying geospatial effects on the incidence of disease to illustrate health disparities potentially attributable to environmental, demographic, and/or socioeconomic factors. The current research we focus on the development of advanced statistical modeling methods to determine how the pattern of disease outcome changes over geographical location.

We first extend and illustrate the utility of MapGAM, a user-friendly R package that provides a unified framework for estimating, predicting and drawing inference on covariate-adjusted spatial effects using individual-level data. The package also facilitates visualization of spatial effects via automated mapping procedures. MapGAM estimates covariate-adjusted spatial associations with a univariate outcome using generalized additive models that include a non-parametric bivariate smooth term of geolocation parameters. Estimation and mapping methods are implemented for continuous, discrete, and right-censored survival data.

Next, we note that smoothing approaches commonly implemented in generalized additive models and used in spatial analyses assume the amount of smoothing to be equal across geographical regions. The result is that some regions tend to be under-smoothed, while others are over-smoothed. We extend the work of Yue, etc. (2010) in the context of brain imaging analysis and propose a hierarchical Bayesian adaptive thin-plate spline that allows for spatial smoothing of continuous, binary and count outcomes along with the ability to adjust for potential confounding factors. The proposed method allows for the amount of smoothing to flexibly vary depending on the local extent of spatial effect by using nonstationary spatial Gaussian Markov random fields. Performance of the approach is evaluated via simulation and the proposed methodology is applied to an epidemiologic study investigating spatial heterogeneity in the risk of preterm birth among Massachusetts residents.

Finally, we further extend the proposed adaptive smoothing techniques to the case of right-censored survival outcome data. Simulation is used to compare the performance of the proposed method to commonly used non-adaptive smoothing method, and the approach is applied to data from an epidemiology study seeking to quantify spatial variability in the survival times of advanced-stage ovarian cancer patients in California.

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