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The incidences of VVS and shigellosis were associated with meteorologic disasters, and disaster characteristics were associated with the disease incidence patterns postdisaster. These findings provide important comprehensive evidence to develop and support policies for managing and protecting public health after meteorologic disasters.

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PURPOSEThe purpose of this study was to evaluate the reliability of computer-aided replica technique (CART) by calculating its agreement with the replica technique (RT), using statistical agreement analysis.MATERIALS AND METHODSA prepared metal die and a metal crown were fabricated. The gap between the restoration and abutment was replicated using silicone indicator paste (n = 25). Gap measurements differed in the control (RT) and experimental (CART) groups. In the RT group, the silicone replica was manually sectioned, and the marginal and occlusal gaps were measured using a microscope. In the CART group, the gap was digitized using optical scanning and image superimposition, and the gaps were measured using a software program. The agreement between the measurement techniques was evaluated by using the 95% Bland-Altman limits of agreement and concordance correlation coefficients (CCC). The least acceptable CCC was 0.90.RESULTSThe RT and CART groups showed linear association, with a strong positive correlation in gap measurements, but without significant differences. The 95% limits of agreement between the paired gap measurements were 3.84% and 7.08% of the mean. The lower 95% confidence limits of CCC were 0.9676 and 0.9188 for the marginal and occlusal gap measurements, respectively, and the values were greater than the allowed limit.CONCLUSIONThe CART is a reliable digital approach for evaluating the fit accuracy of fixed dental prostheses.

Over the past decade much statistical research has been carried out to develop models for correlated survival data; however, methods for model selection are still very limited. A stochastic search variable selection (SSVS) approach under the proportional hazards mixed-effects model (PHMM) is developed. The SSVS method has previously been applied to linear and generalized linear mixed models, and to the proportional hazards model with high dimensional data. Because the method has mainly been developed for hierarchical normal mixture distributions, it operates on the linear predictor under the Cox type models. The PHMM naturally incorporates the normal distribution via the random effects, which enables SSVS to efficiently search through the candidate variable space. The approach was evaluated through simulation, and applied to a multi-center lung cancer clinical trial data set, for which the variable selection problem was previously debated upon in the literature.