Global spread of infectious disease
threatens the well-being of human, domestic, and wildlife health. A proper
understanding of global distribution of these diseases is an important part of
disease management and policy making. However, data are subject to complexities
by heterogeneity across host classes. The use of frequentist methods in
biostatistics and epidemiology is common and is therefore extensively utilized
in answering varied research questions. In this paper, we applied the hierarchical
Bayesian approach to study the spatial distribution of tuberculosis in Kenya.
The focus was to identify best fitting model for modeling TB relative risk in
Kenya. The Markov Chain Monte Carlo (MCMC) method via WinBUGS and R packages
was used for simulations. The Deviance Information Criterion (DIC) proposed by
[1] was used for models comparison and selection. Among the models considered,
unstructured heterogeneity model perfumes better in terms of modeling and
mapping TB RR in Kenya. Variation in TB risk is observed among Kenya counties
and clustering among counties with high TB Relative Risk (RR). HIV prevalence
is identified as the dominant determinant of TB. We find clustering and
heterogeneity of risk among high rate counties. Although the approaches are
less than ideal, we hope that our formulations provide a useful stepping stone
in the development of spatial methodology for the statistical analysis of risk
from TB in Kenya.
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http://dx.doi.org/10.1016/j.aap.2013.07.030
