Objective: To (1) develop a framework for forecasting future dental expenditures, using currently available information, and (2) identify relevant research and data gaps such that dental expenditure predictions can continuously be improved in the future.
Methods: Our analyses focused on 32 OECD countries. Dependent on the number of predictors, we employed dynamic univariate and multivariate modelling approaches with various model specifications. For univariate modelling, an auto-regressive (AR) dynamic model was employed to incorporate historical trends in dental expenditures. Multivariate modelling took account of historical trends, as well as of relationships between dental expenditures, dental morbidity, economic growth in terms of gross domestic product and demographic changes.
Results: Estimates of dental expenditures varied substantially across different model specifications. Models relying on dental morbidity as one of the predictors performed worst regardless of their specification. Using the best-fitted model specification, that is the univariate second-order autoregression [AR(2)], the forecasted dental expenditures across 32 OECD countries amounted to US$316bn (95% forecasted interval, FI: 258-387) in 2020, US$434bn (95%FI: 354-532) in 2030 and US$594bn (95%FI: 485-728) in 2040. Per capita spending in 2040 was forecasted to be highest in Germany (US$889, 95%FI: 726-1090) and lowest in Mexico (US$52, 95%FI: 42-64).
Conclusions: The present study demonstrates the feasibility and challenges in predicting dental expenditures and can serve as a basis for improvement towards more sustainable and resilient health policy and resource planning. Within the limitations of available data sources, our findings suggest that dental expenditures in OECD countries could increase substantially over the next two decades and vary considerably across countries. For more accurate estimation and a better understanding of determinants of dental expenditures, more comprehensive data on dental spending and dental morbidity are urgently needed.
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http://dx.doi.org/10.1111/cdoe.12597 | DOI Listing |
Fam Community Health
January 2025
Author Affiliation: Behavioral Sciences and Social Medicine Department, College of Medicine, Florida State University, Tallahassee, Florida (Dr Ghaffari, Bradbury, and Dr Harman).
Background And Objectives: Though preventive measures are available to alleviate the burden of dental caries, there remain racial disparities in the utilization of preventative dental care. Our objectives were to determine whether racial disparities persisted in receiving preventive oral procedures between (1) black children and white children; and (2) Hispanic children and white children.
Methods: We used pooled Medical Expenditure Panel Survey data in the United States from 2018 to 2021.
Life (Basel)
December 2024
Department of Plastic Surgery, Meir Medical Center, Kfar Saba 4428164, Israel.
Biofilm formation on prostheses and implanted devices can lead to serious complications and increased healthcare expenditures. Once formed, biofilm management is difficult and may involve a long course of antibiotics, additional surgery, and, occasionally, implant removal. This study evaluated the antibacterial properties of medical-grade silicone samples integrated with novel, non-leaching, antibacterial, quaternary ammonium silica (QASi) particles.
View Article and Find Full Text PDFDiabetes Obes Metab
January 2025
State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Engineering Research Center for Dental Materials and Advanced Manufacture, Department of Anesthesiology, School of Stomatology, The Fourth Military Medical University, Xi'an, China.
Background: Given the potential role of brown adipose tissue (BAT) in stimulating energy expenditure, activating BAT can be an effective anti-obesity treatment. Here, we aimed to use adenoviruses to establish the effect of the inducible degrader of the low density lipoprotein receptor (IDOL) in the formation of BAT.
Methods: IDOL or green fluorescent protein was overexpressed by adenovirus and injected into the scapula of C57BL/6J mice and fed with high-fat diet for 12 weeks.
Sci Rep
December 2024
School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong, 723001, China.
This study aims to improve the detection of dental burs, which are often undetected due to their minuscule size, slender profile, and substantial manufacturing output. The present study introduces You Only Look Once-Dental bur (YOLO-DB), an innovative deep learning-driven methodology for the accurate detection and counting of dental burs. A Lightweight Asymmetric Dual Convolution module (LADC) was devised to diminish the detrimental effects of extraneous features on the model's precision, thereby enhancing the feature extraction network.
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