Education and income-based inequality in tooth loss among Brazilian adults: does the place you live make a difference?

Ferreira, Raquel Conceição; Senna, Maria Inês Barreiros; Rodrigues, Lorrany Gabriela; Campos, Fernanda Lamounier; Martins, Andrea Eleuterio Barros Lima; Kawachi, Ichiro

doi:10.1186/s12903-020-01238-9

Cited by 22 publications

(33 citation statements)

References 56 publications

(72 reference statements)

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“…Importantly in the current study, more missing teeth were related to low level of education and weak oral hygiene practices. These results were in line with previous findings, as there was a social gradient in tooth loss by education and their results showed that living in disadvantaged municipalities cannot overcome the risk associated with low schooling [38]. Moreover, it has been reported that experience of dental caries was associated with refugee behaviors, including poor dental hygiene methods and level of education, where higher education levels provided a better understanding of oral and general health [2] Previous studies assessing awareness of dental implants have shown high levels (77.0% and 70.1% in American and a Norwegian samples, respectively [39,40]), similar to the present study (83.4%); however, this is subjective awareness, and when the actual knowledge was tested (objective awareness), only 1.9% were extremely knowledgeable and 10.4% had moderate knowledge about dental implants.…”

Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 93%

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Knowledge and awareness of dental implants among Syrian refugees: a cross sectional study in Zaatari camp

et al. 2021

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Background The popularity of implant dentistry is increasing dramatically, but the success of implant treatment depends on a patient’s knowledge and expectations. Methods This study aimed to assess dental implant knowledge among refugees as a treatment option (n = 565), using face-to-face interviews. The frequency distribution of the responses in general and according to age, gender and education was calculated. Results 81.8% of the interviewees had missing teeth, however, only 26.2% replaced them. 16.6% of participants had never/hardly heard about implants. Females who never heard of implants were more than males (P < 0.001). 51.3% of participants described dental implants as a screw, and only 35.4% knew implants were placed in jawbones. 6.2% of respondents reported that implants required more care than a natural teeth, with 47.6% believing that diseases affect implant success. Friends were the main source of information (61.4%). Highly educated participants showed higher knowledge about implants. Conclusions The surveyed sample revealed limited knowledge about dental implants with high cost being a major obstacle, warranting more strategies to increase awareness and to make implants more affordable for this population.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 93%

Knowledge and awareness of dental implants among Syrian refugees: a cross sectional study in Zaatari camp

et al. 2021

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“…Our findings are consistent with those of previous studies to have identified age and socioeconomic conditions as risk factors for tooth loss [11,12,28]. Aging populations have accumulated oral and non-communicable health conditions, and so remain susceptible to ongoing tooth loss.…”

Section: Plos Onesupporting

confidence: 92%

“…While previous research has identified the determinants of tooth loss, most of this literature examined only edentulism and did not examine incremental tooth loss, which is a far more prevalent oral state [11]. Moreover, those studies were mostly based on cross-sectional data or examined a few variables using classical statistical modeling rather than predictive modeling [11,12,24]. We found that machine-learning models performed better than conventional statistical methods (logistic regression) for predicting edentulism and the absence of a functional dentition.…”

Section: Plos Onementioning

confidence: 99%

“…While prior research indicates that dental caries remains (by far) the greatest contributor to tooth loss [9], followed by periodontal disease [10], the role of socioeconomic conditions and other health characteristics is less clear. This is primarily because most prior analyses were based on descriptive studies with a limited number of variables [11,12]. Machine-learning algorithms comprise an approach that utilizes information on a large number of characteristics to identify variables that predict an outcome.…”

Section: Introductionmentioning

confidence: 99%

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Predictors of tooth loss: A machine learning approach

et al. 2021

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Introduction Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models. Methods We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values. Results The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone. Conclusions Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.

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Identifying predictors of tooth loss using a rule‐based machine learning approach: A retrospective study at university‐setting clinics

Lee

Zhang

et al. 2023

Journal of Periodontology

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BackgroundThis study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach.MethodsInformation on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two‐step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root‐mean‐squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model.ResultsIn total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models.ConclusionThe two‐step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule‐based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.

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Education and income-based inequality in tooth loss among Brazilian adults: does the place you live make a difference?

Cited by 22 publications

References 56 publications

Knowledge and awareness of dental implants among Syrian refugees: a cross sectional study in Zaatari camp

Knowledge and awareness of dental implants among Syrian refugees: a cross sectional study in Zaatari camp

Predictors of tooth loss: A machine learning approach

Identifying predictors of tooth loss using a rule‐based machine learning approach: A retrospective study at university‐setting clinics

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