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Objective: The aim of this study is to evaluate the hygiene methods, attitudes, and habits with regard to the use of removable dentures, whether patients have been instructed by the dentist on how to take care of their dentures, and the interest of the patients about the guidelines given to them after the placement of removable dentures. Methods: This was a cross-sectional hospital-based study carried out using a questionnaire given to 100 patients who were wearing partial and/ or complete removable dentures at that time. The questionnaire was designed by the investigators to collect data on socio-demographic characteristics of patients, and hygiene, attitudes, and habits with regard to using removable dentures. Results: It was showed that 31% of patients continuously wore their dentures when sleeping at night. Of the patients, 63% mentioned that they had not been advised about how to clean their dentures by their dentists. There is no statistically significant difference regarding the frequency of cleaning dentures and the rate of removal of the dentures at night before the sleep for age and gender (p>0.05). Of the patients, 91% stated that they would be interested in a written guideline explaining how to care for their dentures. Conclusion: It was found that dentists could neglect to inform patients. Dentists should pay attention to instructing patients regarding how to care for their removable dentures after treatment. Giving a guideline to patients might help them use their removable dentures for a longer time and in a healthier way.

This paper proposes an alternative predictor for the total claim amount of individuals that can be used for any type of non-life insurance products in which individuals may have multiple claims within one policy period. The impact of heterogeneity on expected total claim amount is investigated focusing on marginal predictions. Generalized linear mixed model (GLMM) is used for the amounts of loss per claim. Closedform expression of the predictor is derived using marginal mean under GLMM and claim count distribution. Empirical studies are performed using a private health insurance data set of a Turkish insurance company. Proposed predictive model provides the lowest prediction errors among competing models according to the mean absolute error criterion.