The research project was reviewed and approved by the Human Research Ethics Committee of the University of Adelaide.

The Burden of Oral Disease Study was conducted as a cross-sectional study using a mailed survey approach. Dentists were provided with a practitioner logbook in the dentist questionnaire to record for the first 1 to 5 consecutive adult patients (depending on study group assignment of dentist) of a random clinical day the diagnosis of the oral disease or condition treated and treatment they performed. At the conclusion of treatment each practitioner (except those in the study group that had no patient questionnaires to distribute) passed on a survey kit to their sampled patient(s) containing a patient questionnaire, cover letter and explanation sheet. Sampled patients completing the patient questionnaire recorded basic socio-demographic characteristics and data concerning the nature, severity and duration of their symptoms. The primary rationale for this 2-stage sampling methodology was to allow linkage of dentist-assessed oral health status to patient perceptions of quality of life. 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.

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 equal-sized 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.

A 2-stage sampling design was used where stage 1 involved sampling dentists and stage 2 involved sampling of patients within selected dentists. Dentists were randomly sampled from the South Australian Dental Register. The Dental Register was used as a sampling frame as it provides a comprehensive listing of all persons registered to work as dentists in the State of South Australia, and is therefore representative of the target population of dentists. Sampled dentists were randomised into one of seven equal-sized study groups to assess the optimum number of patients to sample from dentists and sent a mailed self-complete dentist questionnaire along with up to five self-complete patient questionnaires depending on the study group. 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 dentist questionnaire as they distributed patient questionnaires.

Data were collected during 2001–02 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 16.

The emphasis of the project was to obtain precise estimates of the component measures of the burden of oral disease. These are typically expressed as percentages, such as the percentage of persons or percentage of time experiencing symptoms of a given degree of severity. Taking a parameter size of 10% as a reference estimate for any given measure, in order to achieve a level of precision of 20% or less relative standard error, a minimum target sample of n = 225 patients was required. This would provide an acceptable level of precision for estimates as low as 10% in size, and better precision for any estimates larger than 10% in size.

Dentists recorded the details of the dental conditions that patients had, and patients recorded their experience of those dental conditions. In the patient questionnaire, patients were asked if the dental conditions had caused problems in each of six health state dimensions using the European Quality of Life indicator or EuroQol (EQ-5D+) instrument 8. 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). 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 7, 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 Likert-type response scale re-coded as a Guttman scale 4 = very often, 3 = fairly often, 2 = occasionally, 1 = hardly ever and 0 = never.

The main output measure was calculated by converting EuroQol item responses to health state values, where each set of responses on the standard 5-item instrument was matched to a health state value where 0 = death and 1.0 = perfect health by an algorithm derived from modelling values 17 using health state preferences elicited from a general population 8. The responses to the OHIP-14 instrument were coded into categories of 'Never', 'Hardly ever' and 'Occasionally/Fairly often/Very often' with each category converted into indicator variables with values of one if there was a response in that category or with values of zero if there was no response in that category. Age was entered in years and sex was coded as one for males and zero for females.

The characteristics of responding patients were compared descriptively with published data on dental patients and the Australian population. A random sample of two thirds of the respondents was used to construct models of health state values (see Figure 1 for an outline of the sampling and analysis). Two tobit regression models were constructed using the EQ-5D based health state value as the dependent variable with the independent variables of OHIP-14 items, sex and age. Tobit regression was used to account for censoring of the characteristically bounded nature of health state values that can result in biased and inconsistent estimates using ordinary least-squares regression 181920. One model used the OHIP-14 items as categorical variables with the category 'Never' as the reference category, while the other model used the OHIP-14 item responses as continuous variables that were coded from 0 to 4. Cases with missing data on the dependent or independent variables were excluded from the analysis. Non-significant terms were retained for comparability across models, and their potential value in controlling for confounding 21. The remaining one third sample of the respondents was used to test the models by comparing fitted versus actual values using forecast errors. Forecast errors were calculated by subtracting fitted values from actual health state values and dividing by the mean actual health state value to convert absolute forecast errors into relative forecast errors as a percentage of the actual sample mean health state value. Model building strategies also included testing model fit after inclusion of additional terms such as age-squared to model non-constant age effects and interactions between OHIP-14 items, and examining correlations of independent variables for possible collinearity. The design effect of clustering of patients within sampled dentists was calculated and used as a weight to adjust the reported confidence intervals.

A total of 378 dentists responded to the survey (response rate = 60%). Response rates between study groups varied from 49 to 70% and tended to be higher in study groups that required dentists to sample less patients, but the effect was not monotonic (Table 1). Data were available for 375 patients from the patient questionnaire, comprising a response rate of 72% of patients sampled, with response rates between study groups varying from 69 to 92%.

The characteristics of patients are presented in Table 2 where data from private general practice 22 and Australian population estimates 2324 are presented for comparison. The majority of patients were female (59.5%), born in Australia (75.5%), had dental insurance (64.8%) and had visited a dentist in the last 12 months (65.3%). The main reason for dental visiting was for other dental problems not involving relief of pain (46.7%), followed by check-ups (35.2%) and emergency visits involving relief of pain (18.1%).

The mean health state value was 0.852 (95% confidence interval 0.840 to 0.964). Table 3 shows that a minority of patients reported symptoms in the 'Occasionally/Fairly often/Very often' category, ranging from 3.6% for 'unable to function' to 46.0% for 'uncomfortable eating'. Responses in the 'hardly ever' category ranged from 6.9% for 'unable to function' to 29.0% for 'painful aching in the mouth'. Mean values for the OHIP-14 items ranged from 0.140 for 'unable to function' up to 1.355 for 'uncomfortable eating'.

The OHIP-14 items and sociodemographic covariates had relatively high pseudo R-squared values (Table 4). OHIP items relating to 'painful aching in mouth' were significantly related to health state values in both models while 'life less satisfying' was also significant in the continuous model. In each case, the regression coefficients were negative indicating that responses to the OHIP-14 items were associated with lower health state values.

Table 5 shows the mean forecast errors for both the categorical and continuous variable models. Mean forecast errors relative to the mean observed health state value were higher when fitted to the categorical model (17.4%) compared to the continuous model (15.2%), (P < 0.05). However, the forecast errors were not consistently higher for the categorical model at each level of observed health state value.

Table 6 demonstrates the relationship between observed and fitted values within selected ranges of observed health state values using the categorical model. At each level of observed health state values the fitted values slightly overestimated the observed health state value.