Increasing Dental Students' Understanding of Population Surveillance Through Data Mining

Abstract

An understanding of population surveillance is important for predoctoral dental students, so they will comprehend the health needs of the population in order to provide needed care and promote overall health. The aim of this study was to teach data mining and surveillance methodologies to dental students and to assess the association between systemic health factors and tooth loss in patients visiting the University of Colorado School of Dental Medicine clinics. The students were calibrated to using the data mining methodologies from the Electronic Health Record (EHR) using a rubric and presentation by a faculty member. The EHR was reviewed for age, gender, race/ethnicity, number of natural teeth present, systemic diseases reported including cardiovascular disease and diabetes, and history of any form of tobacco use. A total of 1,338 patients who visited the clinics in spring 2017 were included in the study; of those, 354 (27%) had <20 teeth. The results showed greater odds of having <20 teeth for those who reported cardiovascular disease (OR=2.1, 95% CI 1.6, 2.7), diabetes (OR=1.7, 95% CI 1.2, 2.3), tobacco use (OR=1.4, 95% CI 1.0, 1.9), and being Hispanic (OR=1.4, 95% CI 1.0, 1.9). After adjusting for age, gender, and ethnicity, the odds of having <20 teeth for patients with tobacco use were found to be twice that of patients with no tobacco use (OR=2.1, 95% CI 1.5, 3.0). Understanding population surveillance could be beneficial in designing evidence-based interventions at the dental school and community levels.