Estimating Age Using Nationwide Survey Data on the Number of Residual Teeth

Abstract

Given that tooth loss is a degenerative change, the number of residual teeth may be used to specify a particular age range as a marker for age estimation. This study examined changes in the number of teeth with age using a nationwide oral survey database and derived the age distribution of the Korean population according to the number of teeth. Data on the number of teeth and age were extracted from the oral examination data of the Korean National Health and Nutrition Examination Survey (KNHANES) from 2016 to 2018. Statistical analyses of a complex sample survey were performed using weighted values. The distribution range of the number of teeth by age was broad. The proportion of young people decreased progressively as the number of remaining teeth decreased. In contrast, the proportion of those from the older age group decreased slightly as the number of teeth increased. The number of teeth was subdivided into groups of four, age was categorized into 5-year intervals, and the distribution of age groups by the number of teeth was analyzed. We attempted to determine the age group threshold at approximately 95th percentile for age. In summary, we found that if there were ≤4, 5-12, and 13-20 residual teeth, the estimated age was ≥60, ≥55, and ≥50 years, respectively, with an approximately 95% probability. When many teeth are lost and it is difficult to apply conventional dental age estimation methods, our method may assist in narrowing the age range, although it is not an accurate age determination method.

Risk attitudes and asset diversification: Evidence from Ghana

“…The children and adults denote the number of children in the household and the number of working adults respectively. The choice of covariates in the equations is influenced by existing research (Kim & Lee, 2012; Treibich, 2015; Krause, 2019). Because of the count nature of the outcome variables, equation was estimated using Poisson regression models and marginal effects were reported.…”

“…A separate regression is run for each measure of diversification using the following estimating equations: (Kim & Lee, 2012;Treibich, 2015;Krause, 2019). Because of the count nature of the outcome variables, equation 1 was estimated using Poisson regression models and marginal effects were reported.…”

The Power of Financial Incentives versus the Power of Suggestion for Individual Pension: Are Financial Incentives or Automatic Enrollment Policies More Effective?

“…We found that married participants are more sensitive to matching contributions than their single counterparts. The reason for this may be that married participants may be more risk averse than their single counterparts and they evaluate matching contributions as a saving instrument with guaranteed yields [47].…”