This present research showed two critical findings. First, as a mediator of the socioeconomic statuses, the last dental visit was associated with functional dentition in adults and severe tooth loss in older adults. Secondly, in the machine learning approach, the trained Xgboost algorithm had good metrics predicting the lack of functional dentition in adults, indicating that it should be used for FHS in Brazil.
It is of fundamental importance to identify adults without functional dentition to schedule an appointment in primary health care. This can help with the principle of equity, giving more attention to those who need it most, considering that the absence of functional dentition affects the quality of life of adults and older adults25–29. The algorithm trained for the Brazilian context is easy to use and with only a few input variables, generally collected in one visit by community health workers, an accuracy of 90% was achieved, that is, getting 90 out of 100 adults right. Moreover, for older adults, identifying those with a higher chance for severe tooth loss in the adscript area of FHS could improve the planning and management for Dental Specialties Centers in Brazil. They are responsible for secondary care (prosthesis, for example) and give agility and resolution for older adults to reestablish masticatory function and quality of life. For example, even without a dental consultation by a dentist in the PHC, the Family Health Strategy could use the trained algorithm, with a few variables inputted, that can be collected in the first visit by a community health worker in the house and target all individuals at higher risk for tooth loss.
Some structural barriers and contextual factors could be related to tooth loss, as recent evidence on the commercial determinants of health and its association with oral health30. Future research needs to explore the role of contextual characteristics at different levels. An important finding is that schooling is one of the three most crucial predictors of lack of functional dentition and severe tooth loss in the Brazilian context. In a multicountry study, there were significant education-related inequalities in using oral health services by older adults13, corroborating our findings. Monitoring these inequalities is critical to planning and delivering dental services, organising dental agenda, and setting priorities. Limitations
This study has some strengths and limitations that should be acknowledged. A key strength was using a nationally representative sample of Brazilian adults and older adults. However, the self-reported nature of tooth loss assessment may lead to information bias. Although clinical data regarding tooth loss might have strengthened our findings, previous research has shown a good concordance between self-reported tooth loss and clinical evaluation in national surveys31. Moreover, we used two definitions of tooth loss internationally to compare populations17,32−34. Another limitation of our study is its cross-sectional design, which may not establish a temporal relationship between exposures and outcomes. However, machine learning could be a potential path to set priorities on the dental agenda in the Unified health system in Brazil.
Policymakers should use implementation science frameworks 35–36 to train community health workers and measure organisational readiness37,38 for using the machine learning approaches. The algorithm could be retrained at local levels and improve current performance in each context of primary care in Brazil. Future works using tooth loss and machine learning approaches need to be further investigated. Although the demographic transition will occur, dental floss use for adults and dental visits for older adults could be modifiable factors that FHS should focus.
In conclusion, more than two years of last dental visit appears to be associated with a severe loss in older adults and lack of functional dentition in adults. The machine learning approach had a good performance to predict those individuals. The Family Health Strategy should use the trained algorithm to target those individuals in the Brazilian context and set priorities on dental agendas.