Modelling the national economic burden of non-surgical periodontal management in specialist clinics in Malaysia using a markov model.

BACKGROUND: Non-surgical periodontal treatment is the mainstay of periodontal treatment. In Malaysia, the prevalence of periodontal disease is substantial among adults with almost half of them having periodontitis. Therefore, we estimated the economic burden of non-surgical periodontal treatment in specialist clinics in Malaysia. METHODS: Relevant data from multiple data sources which include national oral health and health surveys, national census, extensive systematic literature reviews, as well as discussion with experts, were used to estimate the economic burden of non-surgical periodontal management in specialist clinics in Malaysia in 2020. This estimation was done from the oral healthcare provider's perspective in both public and private sectors using an irreducible Markov model of 3-month cycle length over a time horizon of one year. RESULTS: In 2020, the national economic burden of non-surgical periodontal treatment during the first year of periodontal management in specialist clinics in Malaysia was MYR 696 million (USD 166 million), ranging from MYR 471 million (USD 112 million) to MYR 922 million (USD 220 million). Of these, a total of MYR 485 million (USD 115 million) and MYR 211 million (USD 50 million) were the direct oral healthcare cost in public and private dental clinics, respectively. CONCLUSION: The findings of this study demonstrated substantial economic burden of non-surgical periodontal management in specialist clinics in Malaysia. Being a life-long disease, these findings highlight the importance of enforcing primary and secondary preventive measures. On the strength and reliability of this economic evidence, this study provides vital information to inform policy- and decision-making regarding the future direction of managing periodontitis in Malaysia.

Utilizing decision tree machine model to map dental students' preferred learning styles with suitable instructional strategies.

BACKGROUND: Growing demand for student-centered learning (SCL) has been observed in higher education settings including dentistry. However, application of SCL in dental education is limited. Hence, this study aimed to facilitate SCL application in dentistry utilising a decision tree machine learning (ML) technique to map dental students' preferred learning styles (LS) with suitable instructional strategies (IS) as a promising approach to develop an IS recommender tool for dental students. METHODS: A total of 255 dental students in Universiti Malaya completed the modified Index of Learning Styles (m-ILS) questionnaire containing 44 items which classified them into their respective LS. The collected data, referred to as dataset, was used in a decision tree supervised learning to automate the mapping of students' learning styles with the most suitable IS. The accuracy of the ML-empowered IS recommender tool was then evaluated. RESULTS: The application of a decision tree model in the automation process of the mapping between LS (input) and IS (target output) was able to instantly generate the list of suitable instructional strategies for each dental student. The IS recommender tool demonstrated perfect precision and recall for overall model accuracy, suggesting a good sensitivity and specificity in mapping LS with IS. CONCLUSION: The decision tree ML empowered IS recommender tool was proven to be accurate at matching dental students' learning styles with the relevant instructional strategies. This tool provides a workable path to planning student-centered lessons or modules that potentially will enhance the learning experience of the students.