Forecasting future dental health expenditures: development of a framework using data from 32 OECD countries
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 countr...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
June 2021
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| In: |
Community dentistry and oral epidemiology
Year: 2021, Jahrgang: 49, Heft: 3, Pages: 256-266 |
| ISSN: | 1600-0528 |
| DOI: | 10.1111/cdoe.12597 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1111/cdoe.12597 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/cdoe.12597 
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| Verfasserangaben: | Milica Jevdjevic, Stefan Listl, Morgan Beeson, Maroeska Rovers, Yusuke Matsuyama |
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| 245 | 1 | 0 | |a Forecasting future dental health expenditures |b development of a framework using data from 32 OECD countries |c Milica Jevdjevic, Stefan Listl, Morgan Beeson, Maroeska Rovers, Yusuke Matsuyama |
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| 520 | |a 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. | ||
| 650 | 4 | |a dental diseases | |
| 650 | 4 | |a dental healthcare costs | |
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| 700 | 1 | |a Rovers, Maroeska |e VerfasserIn |4 aut | |
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