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How dental patients are affected by oral conditions can be described with the concept of oral health-related quality of life ( OHRQoL). This concept intends to make the patient experience measurable. OHRQoL is multidimensional, and Oral Function, Oro-facial Pain, Oro-facial Appearance and Psychosocial Impact were suggested as its four dimensions and consequently four scores are needed for comprehensive OHRQoL assessment. When only the presence of dimensional impact is measured, a pattern of affected OHRQoL dimensions would describe in a simple way how oral conditions influence the individual. By determining which patterns of impact on OHRQoL dimensions exist in prosthodontic patients and general population subjects, we aimed to identify in which combinations oral conditions' functional, painful, aesthetical and psychosocial impact occurs. Data came from the Dimensions of OHRQoL Project with Oral Health Impact Profile ( OHIP)-49 data from 6349 general population subjects and 2999 prosthodontic patients in the Learning Sample ( N = 5173) and the Validation Sample ( N = 5022). We hypothesised that all 16 patterns of OHRQoL dimensions should occur in these individuals who suffered mainly from tooth loss, its causes and consequences. A dimension was considered impaired when at least one item in the dimension was affected frequently. The 16 possible patterns of impaired OHRQoL dimensions were found in patients and general population subjects in both Learning and Validation Samples. In a four-dimensional OHRQoL model consisting Oral Function, Oro-facial Pain, Oro-facial Appearance and Psychosocial Impact, oral conditions' impact can occur in any combination of the OHRQoL dimensions. [ABSTRACT FROM AUTHOR]

Although oral health-related quality of life ( OHRQoL) as measured by the Oral Health Impact Profile ( OHIP) is thought to be multidimensional, the nature of these dimensions is not known. The aim of this report was to explore the dimensionality of the OHIP using the Dimensions of OHRQoL ( DOQ) Project, an international study of general population subjects and prosthodontic patients. Using the project's Learning Sample ( n = 5173), we conducted an exploratory factor analysis on the 46 OHIP items not specifically referring to dentures for 5146 subjects with sufficiently complete data. The first eigenvalue (27·0) of the polychoric correlation matrix was more than ten times larger than the second eigenvalue (2·6), suggesting the presence of a dominant, higher-order general factor. Follow-up analyses with Horn's parallel analysis revealed a viable second-order, four-factor solution. An oblique rotation of this solution revealed four highly correlated factors that we named Oral Function, Oro-facial Pain, Oro-facial Appearance and Psychosocial Impact. These four dimensions and the strong general factor are two viable hypotheses for the factor structure of the OHIP. [ABSTRACT FROM AUTHOR]

Previous exploratory analyses suggest that the Oral Health Impact Profile ( OHIP) consists of four correlated dimensions and that individual differences in OHIP total scores reflect an underlying higher-order factor. The aim of this report is to corroborate these findings in the Dimensions of Oral Health-Related Quality of Life ( DOQ) Project, an international study of general population subjects and prosthodontic patients. Using the project's Validation Sample ( n = 5022), we conducted confirmatory factor analyses in a sample of 4993 subjects with sufficiently complete data. In particular, we compared the psychometric performance of three models: a unidimensional model, a four-factor model and a bifactor model that included one general factor and four group factors. Using model-fit criteria and factor interpretability as guides, the four-factor model was deemed best in terms of strong item loadings, model fit ( RMSEA = 0·05, CFI = 0·99) and interpretability. These results corroborate our previous findings that four highly correlated factors - which we have named Oral Function, Oro-facial Pain, Oro-facial Appearance and Psychosocial Impact - can be reliably extracted from the OHIP item pool. However, the good fit of the unidimensional model and the high interfactor correlations in the four-factor solution suggest that OHRQoL can also be sufficiently described with one score. [ABSTRACT FROM AUTHOR]