Sociodemographic determinants of edentulism in the elderly population: A systematic review and meta‐analysis

Roberto, Luana Leal; Crespo, Thaísa Soares; Martins, Andréa Maria Eleutério Barros de Lima; Paula, Alfredo Maurício Batista de; Ferreira, Efigênia Ferreira e; Haikal, Desirée Sant’Ana

doi:10.1111/ger.12430

Cited by 49 publications

(49 citation statements)

References 42 publications

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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: 93%

“…Depending on its severity, it can significantly impact the ability to chew, speak, socialize, and overall general health [2,3,23]. 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].…”

Section: Plos Onementioning

confidence: 99%

“…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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Section: Plos Onesupporting

confidence: 93%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictors of tooth loss: A machine learning approach

et al. 2021

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“…Secondary analysis of nationwide oral health surveys in different countries reported the influence of poor individual socioeconomic characteristics, such as family income, occupational history and educational level, on tooth loss among elders and adults [5][6][7][8]. These findings are in accordance with a recent systematic that concluded that lower levels of education and poor income were meaningful factors associated with edentulism among elderly people [9].…”

Section: Introductionsupporting

confidence: 74%

Individual- and City-Level Socioeconomic Factors and Tooth Loss among Elderly People: A Cross-Level Multilevel Analysis

Vettore

Vieira

Gomes

et al. 2020

IJERPH

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This study aimed to test the association of contextual and individual socioeconomic status with tooth loss among Brazilian elderly people aged 65–74 years. Data from 5435 elderly participants from the Brazilian National Oral Health Survey (2010) were linked to city-level data for 27 state capitals and the Federal District. Tooth loss was clinically assessed according to the number of missing natural teeth. Contextual social variables included Human Development Index income (HDI-income) and HDI-education. Individual socioeconomic measures were monthly family income and years of schooling. Covariates included sex, skin colour, number of residents per room and number of goods. Multilevel Negative Binomial regression models were used to estimate rate ratios (RR) and 95% confidence intervals between contextual and individual variables and tooth loss. Contextual and individual income and education measures were consistently associated with tooth loss. Elderly people living in cities with low HDI-income and low HDI-education were respectively 21% and 33% more likely to present tooth loss. Cross-level interaction suggested that the relationship of lower income and lower schooling with tooth loss is different across levels of city-level income and city-level education inequality, respectively. Public policies aiming to reduce the income and education gaps and preventive dental interventions are imperative to tackle tooth loss among elderly people.

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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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Sociodemographic determinants of edentulism in the elderly population: A systematic review and meta‐analysis

Cited by 49 publications

References 42 publications

Predictors of tooth loss: A machine learning approach

Predictors of tooth loss: A machine learning approach

Individual- and City-Level Socioeconomic Factors and Tooth Loss among Elderly People: A Cross-Level Multilevel Analysis

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

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