Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space

  • Benjamin G. Jacob | bjacob@uab.edu School of Medicine, Division of Infectious Disease, University of Alabama at Birmingham, Birmingham, AL, United States.
  • Fiorella Krapp Instituto de Medicina Tropical Alexander Von Humboldt-Universidad Peruana Cayetano Heredia, Lima, Peru.
  • Mario Ponce Instituto de Medicina Tropical Alexander Von Humboldt-Universidad Peruana Cayetano Heredia, Lima, Peru.
  • Eduardo Gotuzzo Instituto de Medicina Tropical Alexander Von Humboldt-Universidad Peruana Cayetano Heredia, Lima, Peru.
  • Daniel A. Griffith School of Social Sciences, The University of Texas at Dallas, Richardson, TX, United States.
  • Robert J. Novak School of Medicine, Division of Infectious Disease, University of Alabama at Birmingham, Birmingham, AL, United States.

Abstract

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multidrug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDR-TB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e. the Moran’s coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird (spatial resolution = 0.61 m) data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centres, using a 10 m2 grid-based algorithm. Geographical information system (GIS)- gridded measurements of each health centre were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDRTB covariates. Pearson’s correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS® module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases, using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centres and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran’s coefficient into uncorrelated, orthogonal map pattern components which revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

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Published
2010-05-01
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Original Articles
Keywords:
multi-drug resistant tuberculosis, geographical information system, digital elevation model, Poisson regression analyses, spatial eigenvector mapping, Peru.
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How to Cite
Jacob, B. G., Krapp, F., Ponce, M., Gotuzzo, E., Griffith, D. A., & Novak, R. J. (2010). Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space. Geospatial Health, 4(2), 201-217. https://doi.org/10.4081/gh.2010.201