Dental caries are a significant public health problem it is a disease with multifactorial causes in sub sahara africa ethiopia is one of the countries with a high record of dental caries this study was to determine the risk factors affecting dental caries using both bayesian and classical approaches the study design was a retrospective cohort study in the period of march 2009 to march . Dental research gives rise to data with potentially complex correlation structure assessments of dental caries yields a binary outcome indicating the presence or absence of caries experience for each surface of each tooth in a subjects mouth in addition to this nesting caries outcome exhibit spatial structure among neighboring teeth. A bayesian hierarchical spatial model for dental caries assessment using non gaussian markov random fields say the presence absence of caries and the potential explanatory covariates via logistic regression while accounting for spatial dependence via an auto regression one can also predict the outcome at some unsampled surface . Analysis of dental caries is traditionally based on aggregated scores which are summaries of caries experience for each individual a well known example of such scores is the decayed missing and filled teeth or tooth surfaces index introduced in the 1930s although these scores have improved our . Bayesian modeling of multivariate spatial binary data with application to dental caries dipankar bandyopadhyay dbandyopumnedu division of biostatistics school of public health random effects logistic regression model spatial association terms autologistic besag 1972 model
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