METHODS: Secondary analysis of data extracted from the British Household Panel Survey, a national longitudinal survey (n=5547). Analysis to ascertain whether patterns of attendance for dental check-ups for a period of 10 years (1991-2001) were associated with risk factors for oral cancer such as age, sex, education, social class, smoking status and smoking intensity.
RESULTS: Males, aged over 40 years, less educated manual workers and smokers were significantly less likely to attend for dental check-ups compared with females and younger, higher educated, higher socio-economic class non-smokers (p < 0.05). Throughout the 10-year period, young people, more than older people, had progressively lower odds ratios of attending. Those with more education used dental services more. Heavy smokers were infrequent attendees.
CONCLUSIONS: This study suggests that opportunistic oral cancer screening by dentists is not feasible to include high-risk groups as they are not regular attendees over 10 years. Those who would be screened would be the low-risk groups. However, dentists should continue screening all patients as oral precancers are also found in regular attendees. More should be done to encourage the high-risk groups to visit their dentists.
METHODS: Published literature on multicriteria decision analysis (MCDA) were studied and five sessions of expert group discussions were conducted to build the MAST framework and to review the evidence. The attributes identified and selected for analysis were efficacy (clinical efficacy, clinical endpoints), safety (drug interactions, serious side effects and documentation), drug applicability (drug strength/formulation, indications, dose frequency, side effects, food-drug interactions, and dose adjustments), and cost. The average weights assigned by the members for efficacy, safety, drug applicability and cost were 32.6%, 26.2%, 24.1%, and 17.1%, respectively. The utility values of the attributes were scored based on the published evidence or/and agreements during the group discussions. The attribute scores were added up to provide the total utility score.
RESULTS: Using the MAST, the six statins under review were successfully scored and ranked. Atorvastatin scored the highest total utility score (TUS) of 84.48, followed by simvastatin (83.11). Atorvastatin and simvastatin scored consistently high, even before drug costs were included. The low scores on the side effects for atorvastatin were compensated for by the higher scores on the clinical endpoints resulting in a higher TUS for atorvastatin. Fluvastatin recorded the lowest TUS.
CONCLUSION: The multiattribute scoring tool was successfully applied to organize decision variables in reviewing statins for the formulary. Based on the TUS, atorvastatin is recommended to remain in the formulary and be considered as first-line in the treatment of hypercholesterolemia.