Dental implants have become an increasingly popular treatment choice for replacing missing teeth. Yet, little is known about the prevalence and sociodemographic distribution of dental implant use in the United States. To address this knowledge gap, we analyzed data from 7 National Health and Nutrition Examination Surveys from 1999 to 2016. We estimated dental implant prevalence among adults missing any teeth for each survey period overall as stratified by sociodemographic characteristics. We calculated absolute and relative differences from 1999–2000 to 2015–2016 and fit logistic regression models to estimate changes over time. We also used multivariable logistic regression to estimate independent associations of sociodemographic covariates with the presence of any implant. We projected the proportion of patients treated with dental implants into the year 2026 under varying assumptions of how the temporal trend would continue. There has been a large increase in the prevalence of dental implants, from 0.7% in 1999 to 2000 to 5.7% in 2015 to 2016. The largest absolute increase in prevalence (12.9%) was among individuals 65 to 74 y old, whereas the largest relative increase was ~1,000% among those 55 to 64 y old. There was an average covariate-adjusted increase in dental implant prevalence of 14% per year (95% CI, 11% to 18%). Having private insurance (vs. none or public insurance) or more than a high school education (vs. high school or less) was each associated with a 2-fold increase in prevalence, with an almost 13-fold (95% CI, 8 to21) increase for older adults. Dental implant prevalence projected to 2026 ranged from 5.7% in the most conservative scenario to 23% in the least. This study demonstrates that dental implant prevalence among US adults with missing teeth has substantially increased since 1999. Yet access overall is still very low, and prevalence was consistently higher among more advantaged groups.

Socioeconomic inequalities are associated with oral health status, either subjectively (self-rated oral health) or objectively (clinically-diagnosed dental diseases). The aim of this study is to compare the magnitude of socioeconomic inequality in oral health and dental disease among adults in Australia, Canada, New Zealand and the United States (US). Nationally-representative survey examination data were used to calculate adjusted absolute differences (AD) in prevalence of untreated decay and fair/poor self-rated oral health (SROH) in income and education. We pooled age- and gender-adjusted inequality estimates using random effects meta-analysis. New Zealand demonstrated the highest adjusted estimate for untreated decay; the US showed the highest adjusted prevalence of fair/poor SROH. The meta-analysis showed little heterogeneity across countries for the prevalence of decayed teeth; the pooled ADs were 19.7 (95% CI = 16.7–22.7) and 12.0 (95% CI = 8.4–15.7) between highest and lowest education and income groups, respectively. There was heterogeneity in the mean number of decayed teeth and in fair/poor SROH. New Zealand had the widest inequality in decay (education AD = 0.8; 95% CI = 0.4–1.2; income AD = 1.0; 95% CI = 0.5–1.5) and the US the widest inequality in fair/poor SROH (education AD = 40.4; 95% CI = 35.2–45.5; income AD = 20.5; 95% CI = 13.0–27.9). The differences in estimates, and variation in the magnitude of inequality, suggest the need for further examining socio-cultural and contextual determinants of oral health and dental disease in both the included and other countries.

This paper describes and compares the magnitude of socio-economic inequalities in oral health among adults in Canada and the US over the past 35 years. We analyzed data from nationally representative examination surveys in Canada and the US: Nutrition Canada National Survey (1970-1972, N = 11,546), Canadian Health Measures Survey (2007-2009, N = 3,508), The First National Health and Nutrition Examination Survey (1971-1974, N = 13,131), and National Health and Nutrition Examination Survey (2007-2008, N = 5,707). Oral health outcomes examined were prevalence of edentulism, proportion of individuals having at least 1 untreated decayed tooth, and proportion of individuals having at least 1 filled tooth. Sociodemographic indicators included in our analysis were place of birth, education, and income. Data were age-adjusted, and survey weights were used to account for the complex survey design in making population inferences. Our findings demonstrate that oral health outcomes have improved for adults in both countries. In the 1970s, Canada had a higher prevalence of edentulism and dental decay and lower prevalence of filled teeth. This was also combined with a more pronounced social inequality gradient among place of birth, education, and income groups. Over time, both countries demonstrated a decline in absolute socio-economic inequalities in oral health.

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.