THE ASSOCIATION OF SOCIODEMOGRAPHIC CHARACTERISTICS AND ORAL HEALTH SERVICE UTILIZATION AMONG THAI OVER TIME

Abstract

Background: This study aimed to explore associations among sociodemographic characteristics and oral health service utilization (DU for dental utilization) among Thai over time. The outcome will be helpful for the next projection of the oral health workforce, and also for designing an oral health service system that is appropriate for the population in the future. Method: Retrospective data analysis, study of cohort behavior regarding oral health service utilization. Micro data from a series of the Health and Welfare Surveys (HWS) of Thailand were used. Descriptive analysis and binary logistic regression were used for exploring the outcome, applying three matrices of Age-Period-Cohort analysis for alternative perspectives on time. Sociodemographic characteristics of population were divided into predisposing – individual factors, predisposing – family factors, and enable factors. Three different compositions of those factors were used for exploring appropriate models for predicting dental health care demand. Result: All independent variables had significant association to DU. By the way, education of individual and role in family showed remarkably change of associations to DU over time. A large difference among age groups were seen from larger gaps of DU after controlled for all independent variables. In model which controlled only predisposing – individual factor, gender showed more remarkably impact to DU than other variables. While after controlled for both predisposing – individual factor and family factor, education of individual showed remarkably impact to DU instead. Then, after controlled for all three factors, variable which showed remarkably impact to DU was shifted to region of residence, and predicted power of this model was also the highest. Anyway, in all models, education of family head showed impact to DU independently from all other control variables. In term of predicted power of model, there were not much difference among all three models and also base model. Conclusion: Information on gender, education of individual, education of family head, region of residence, and health insurance were recommended to include in forecasting of demand for dental health care. All models included this set of variables were more appropriate for forecasting dental care demand than considering only differences among age group. These sets of variable will help to clarify existing inequality.