David S Brennan1, A John Spencer. 1. AIHW Dental Statistics and Research Unit, Dental School, Faculty of Health Sciences, University of Adelaide, South Australia 5005. aihw.dsru@dentistry.adelaide.edu.au
Abstract
OBJECTIVES: Service provision should reflect the oral health of the patient. However, patient and visit factors may influence service patterns and the appropriateness of care delivered. The aim of this study was to examine factors associated with variation in dental services and to assess whether variation by patient and visit characteristics persisted after controlling for oral health status. METHODS: A random sample of Australian dentists was surveyed during 1997-98 (response rate = 60.3%). Private general practitioners (n = 345) provided data on service provision, as well as patient, visit and oral health variables from a log of a typical clinical day (n = 4,115 patients). Multivariate Poisson regression models were run for eight service areas (e.g., diagnostic, preventive, and restorative). RESULTS: Significant effects (P < .05) were observed for oral health factors in all eight models, visit factors in all eight models, patient demographics in four models, dental knowledge/behavior in one model, and area-based socioeconomic status in one model. CONCLUSIONS: After controlling for oral health, visit characteristics persisted as significant predictors of services, with nonemergency visits, insurance, and capital city location associated with more favorable service mix patterns. Higher socioeconomic status areas and payment scale ratings also were associated with a better service pattern in particular service areas. These findings show that a wide range of factors, in addition to oral health, contribute to variation in service provision.
OBJECTIVES: Service provision should reflect the oral health of the patient. However, patient and visit factors may influence service patterns and the appropriateness of care delivered. The aim of this study was to examine factors associated with variation in dental services and to assess whether variation by patient and visit characteristics persisted after controlling for oral health status. METHODS: A random sample of Australian dentists was surveyed during 1997-98 (response rate = 60.3%). Private general practitioners (n = 345) provided data on service provision, as well as patient, visit and oral health variables from a log of a typical clinical day (n = 4,115 patients). Multivariate Poisson regression models were run for eight service areas (e.g., diagnostic, preventive, and restorative). RESULTS: Significant effects (P < .05) were observed for oral health factors in all eight models, visit factors in all eight models, patient demographics in four models, dental knowledge/behavior in one model, and area-based socioeconomic status in one model. CONCLUSIONS: After controlling for oral health, visit characteristics persisted as significant predictors of services, with nonemergency visits, insurance, and capital city location associated with more favorable service mix patterns. Higher socioeconomic status areas and payment scale ratings also were associated with a better service pattern in particular service areas. These findings show that a wide range of factors, in addition to oral health, contribute to variation in service provision.
Authors: David S Brennan; Kiran A Singh; A John Spencer; Kaye F Roberts-Thomson Journal: Health Qual Life Outcomes Date: 2006-10-20 Impact factor: 3.186