Background: The traditional measure for assessing dental treatment needs and workforce requirements based solely on normative need (NN) has major shortcomings. The sociodental approach (SDA) to assess needs overcomes some of the shortcomings as it combines normative and subjective needs assessments and also incorporates behavioural propensity (Sheiham and Tsakos 2007). The objective of this study was to estimate and compare prosthodontic treatment needs and workforce requirements, using the normative and the sociodental approaches for different skill mix models.
Methods: A cross-sectional study was conducted on 732 university employees aged 30-54 years. Normative prosthodontic need was assessed using the WHO (1997) method. The SDA includes NN and also considers oral impacts, measured through the OIDP index, and behavioural propensity. Estimates of prosthodontic need and dental workforce requirements using the two methods were compared using McNemar and Wilcoxon Signed Rank test respectively. The dental workforce required for prosthodontic treatment based on NN and SDA approaches were then compared using different workforce skill mix models.
Results: The proportion of subjects needing prosthodontic treatment was lower by more than 90% when the SDA was used compared to NN. The number of dentists required for prosthodontic treatment per 100,000 people were 98.8 using NN compared to 2.49 using SDA. Using a skill mix approach, the requirements for dentists per 100,000 people decreased slightly when more denture procedures were delegated to dental therapists.
Conclusion: There were very much lower levels of prosthodontic treatment needs and workforce requirements when using the sociodental approach compared to normative methods.
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http://dx.doi.org/10.1186/s12903-015-0015-9 | DOI Listing |
Sci Rep
January 2025
Department of Human Nutrition and Dietetics, School of Health Care Sciences, Sefako Makgatho Health Sciences University, Pretoria, South Africa.
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January 2025
School of Medicine, University of California, San Diego, La Jolla, California.
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