Design
The Burden of oral disease study was conducted using a 2-stage sampling design whereby dentists were randomly sampled from the South Australian Dental Register, randomised into one of seven equal-sized study groups (n = 100) and sent a mailed self-complete dentist questionnaire along with up to five self-complete patient questionnaires depending on the study group. The dentist questionnaire collected data on dentist and practice details and patient oral health details. A pilot study was conducted which collected five patients per dentist in order to establish the feasibility of the 2-stage methodology. Since the optimum number of patients to sample from dentists was not known, dentists in the main study were randomised into six groups in order to assess the sample size-related efficiency and response properties of recording data on from 1 to 5 patients and distributing between 0 to 5 patient questionnaires. Note that dentists in the group that had no patient questionnaires to distribute recorded details of 5 patients in their dentist questionnaire, while dentists in all other groups recorded the same number of patients in their dentists questionnaire as they distributed patient questionnaires. Within the questionnaire dentists were provided with a practitioner logbook in which to record for the first 1 to 5 adult patients (depending on study group assignment of dentist) of a random clinical day the diagnosis of the oral disease or condition treated and the treatment they performed. At the conclusion of treatment each practitioner (except dentists in the study group that had no patient questionnaires to distribute) passed on a survey kit to their sampled patient(s) containing a cover letter and explanation sheet, and a patient questionnaire. Sampled patients completing the patient questionnaire recorded basic socio-demographic characteristics and data concerning the nature, severity and duration of their symptoms. The patient questionnaires were identified using the practitioner identification number allowing linkage between the practitioner logbook data and patient questionnaire data, but maintaining the anonymity of each patient to the investigators. While the primary rationale for this 2-stage methodology was to allow linkage of dentist-assessed oral health status to patient perceptions of quality of life this paper reports solely on the patient perception data. The research project was reviewed and approved by the Human Research Ethics Committee of the University of Adelaide.
Sampling and data collection
Data were collected during 2001–2 with a primary approach letter sent initially to each dentist, followed a week later by the survey materials, with a reminder card two weeks later, and up to four follow-up mailings of survey materials to dentists who had not yet responded in order to ensure higher response rates [13].
Data items
Dentists recorded the details of the dental conditions that patients had, and patients recorded their experience of those dental conditions. Diagnosis of dental conditions was collected from dentists using an open-ended question in the dentist questionnaire. In the patient questionnaire, patients were asked if the dental conditions had caused problems in each of six health state dimensions. The six health state dimensions were: mobility (e.g, walking about), self-care (e.g, washing, dressing), usual activities (e.g., work, study, housework, family or leisure), pain/discomfort, anxiety/depression and cognition (e.g, memory, concentration, coherence, IQ). They were measured using the European Quality of Life indicator or EuroQol (EQ-5D+) instrument [6]. The EuroQol measures each of these six dimensions according to a 3-level response grading from 1 = no problems, 2 = some / moderate problems and 3 = extreme problems. Patients were also asked to rate their experience of dental problems in the last year using the OHIP-14 [5], which uses 14 items to capture measures of the seven dimensions of functional limitation, physical pain, psychological discomfort, physical disability, psychological disability, social disability and handicap. For each of the 14 OHIP questions subjects were asked how frequently they had experienced impact in the preceding 12 months using a 5-point scale coded 4 = very often, 3 = fairly often, 2 = occasionally, 1 = hardly ever and 0 = never.
Data analysis
The characteristics of responding patients were compared descriptively with published data on dental patients and the Australian population. The distributions of responses to the EQ-5D+ and OHIP items were examined, and the items were analysed by factor analysis and cluster analysis. Factor analysis was used to examine the battery of quality-of-life items for underlying component factors. Standard errors and confidence intervals were reported adjusted for the effect of clustering within the primary sampling unit of dentist.
Principal components factor analyses were performed using varimax rotation [14]. A range of n-factor solutions were performed and assessed. While selecting the number of factors involves the reasonableness of the solution and knowledge of the subject matter [15, 16], retaining factors with eigenvalues greater than 1.0 is commonly used, based on heuristic and practical grounds. Sampling adequacy, or the degree that the subset of variables used represents a potentially larger domain, was assessed by Kaiser's measure of sampling adequacy [15]. Communality measures the common factor variance of a variable. A communality of 0.3 or less indicates that a variable may be unreliable [17], while a value greater than 0.3 indicates a large percentage of the sample variance of each variable is accounted for by the factors [16].
Hierarchical clustering of variables was performed using SAS PROC VARCLUS [14], an oblique multiple-group component analysis. Associated with each cluster is a linear combination of the variables in the cluster, the first principal component. The first principal component is a weighted average of the variables that explains as much variance as possible. Clusters are chosen to maximise the variation accounted for by the first principal component of each cluster. SAS PROC TREE was used to construct a dendrogram to present the results of the hierarchical clustering as a tree structure [14].