The economic benefits of increased sugar-free chewing gum in China: a budget impact analysis.

BACKGROUND: To analyze the potential cost savings in dental care associated with increased sugar-free gum (SFG) use among Chinese teenagers and adults. METHODS: The amount of SFG chewed per year and decayed, missing and filled teeth (DMFT) was collected from a cross-sectional survey to create a dose-response curve assumption. A cost analysis of dental restoration costs was carried out. A budget impact analysis was performed to model the decrease in DMFT and the subsequent cost savings for dental care. Three different scenarios for the increase in the number of SFG were calculated. RESULTS: The average cost savings per person in the Chinese population due to increasing SFG use ranged from 45.95 RMB (6.94 USD) per year to 67.41 RMB (10.19 USD) per year. It was estimated that 21.51-31.55 billion RMB (3.25-4.77 billion USD) could be saved annually if all SFG chewers among Chinese teenagers and adults chewed SFG regularly. CONCLUSION: This study suggests that dental care costs could be significantly reduced if SFG use increased in the Chinese population.

Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma.

Background: Recurrence remains one of the key reasons of relapse after the radical radiation for locally advanced nasopharyngeal carcinoma (NPC). Here, the multiple molecular and clinical variables integrated decision tree algorithms were designed to predict individual recurrence patterns (with VS without recurrence) for locally advanced NPC. Methods: A total of 136 locally advanced NPC patients retrieved from a randomized controlled phase III trial, were included. For each patient, the expression levels of 33 clinicopathological biomarkers in tumor specimen, 3 Epstein-Barr virus related serological antibody titer and 5 clinicopathological variables, were detected and collected to construct the decision tree algorithm. The expression level of 33 clinicopathological biomarkers in tumor specimen was evaluated by immunohistochemistry staining. Results: Three algorithm classifiers, augmented by the adaptive boosting algorithm for variable selection and classification, were designed to predict individual recurrence pattern. The classifiers were trained in the training subset and further tested using a 10-fold cross-validation scheme in the validation subset. In total, 13 molecules expression level in tumor specimen, including AKT1, Aurora-A, Bax, Bcl-2, N-Cadherin, CENP-H, HIF-1α, LMP-1, C-Met, MMP-2, MMP-9, Pontin and Stathmin, and N stage were selected to construct three 10-fold cross-validation decision tree classifiers. These classifiers showed high predictive sensitivity (87.2-93.3%), specificity (69.0-100.0%), and overall accuracy (84.5-95.2%) to predict recurrence pattern individually. Multivariate analyses confirmed the decision tree classifier was an independent prognostic factor to predict individual recurrence (algorithm 1: hazard ration (HR) 0.07, 95% confidence interval (CI) 0.03-0.16, P < 0.01; algorithm 2: HR 0.13, 95% CI 0.04-0.44, P < 0.01; algorithm 3: HR 0.13, 95% CI 0.03-0.68, P = 0.02). Conclusion: Multiple molecular and clinicopathological variables integrated decision tree algorithms may individually predict the recurrence pattern for locally advanced NPC. This decision tree algorism provides a potential tool to select patients with high recurrence risk for intensive follow-up, and to diagnose recurrence at an earlier stage for salvage treatment in the NPC endemic region.