Weight Status and Dental Problems in Early Childhood: Classification Tree Analysis of a National Cohort

Protocol for developing a dashboard for interactive cohort analysis of oral health-related data

INTRODUCTION

A working knowledge of data analytics is becoming increasingly important in the digital health era. Interactive dashboards are a useful, accessible format for presenting and disseminating health-related information to a wide audience. However, many oral health researchers receive minimal data visualisation and programming skills.

OBJECTIVES

The objective of this protocols paper is to demonstrate the development of an analytical, interactive dashboard, using oral health-related data from multiple national cohort surveys.

METHODS

The flexdashboard package was used within the R Studio framework to create the structure-elements of the dashboard and interactivity was added with the Shiny package. Data sources derived from the national longitudinal study of children in Ireland and the national children's food survey. Variables for input were selected based on their known associations with oral health. The data were aggregated using tidyverse packages such as dplyr and summarised using ggplot2 and kableExtra with specific functions created to generate bar-plots and tables.

RESULTS

The dashboard layout is structured by the YAML (YAML Ain't Markup Language) metadata in the R Markdown document and the syntax from Flexdashboard. Survey type, wave of survey and variable selector were set as filter options. Shiny's render functions were used to change input to automatically render code and update output. The deployed dashboard is openly accessible at https://dduh.shinyapps.io/dduh/ . Examples of how to interact with the dashboard for selected oral health variables are illustrated.

CONCLUSION

Visualisation of national child cohort data in an interactive dashboard allows viewers to dynamically explore oral health data without requiring multiple plots and tables and sharing of extensive documentation. Dashboard development requires minimal non-standard R coding and can be quickly created with open-source software.

Evaluation of the Relationship between the BMI and the Sequence and Chronology of Eruption in Permanent Dentition in Spanish Population

The aim was to analyze the relationship between BMI (body mass index) and the sequence and chronology of the eruption of permanent teeth in a sample of Spanish children. Methods: The study design was descriptive, cross-sectional, observational, and epidemiological. Patients of pediatric age were included. Emerged teeth, and patient’s age, race, and sex were recorded. The nutritional status of the child was assessed by calculating the BMI, according to the WHO parameters. Statistical analysis was carried out with a confidence interval of 95%. A prediction model with logistic regression models was obtained. Results: A total of 725 pediatric patients between 4 and 14 years old were analyzed. BMI acts as a predictor variable for eruption symmetry, as it was most frequent in overweight children (p < 0.001). The probability of symmetry in dental eruption increases for South American children, for an extra month of age, and each meter of height. BMI had an influence in the first tooth to appear only in the fourth quadrant. BMI did not seem to influence present teeth, and the sequence of permanent dental eruption was not influenced by the BMI category. Conclusions: Age, weight, height, and BMI act as significant predictors for eruption symmetry. BMI does not produce alterations in the eruption sequence of the permanent dentition.

Early childhood caries risk assessment in 1-year-olds evaluated at 6-years of age

OBJECTIVES

The aim was to identify caries risk factors in 1-year-olds predicting dentine caries in 6-year-olds.

MATERIALS AND METHODS

Caries risk assessment was performed in 804 one-year-olds. Their parents answered a questionnaire, regarding family factors, general health, food habits and oral hygiene. Clinical examinations and caries risk assessments at 1, 3 and 6 years of age were performed. Simple and multiple regression analyses were used for identification of caries-associated factors.

RESULTS

Caries risk was found in 5% of the 1-year-olds, and 12% of the 3-year-olds. Dentine caries was found in 3% of the 3-year-olds and in 16% of the 6-year-olds. Caries risk assessment was associated with caries at 6 years of age (OR = 5.1, p < .001). Multiple logistic regression analysis found the following variables associated with caries at 6 years of age: Caries in sibling (OR = 2.1, p = .012), Beverage other than water (OR = 2.1, p < .001), Night meal (OR = 1.9, p = .002), Presence of mutans streptococci (MS) (OR = 1.6, p = .033) and Male gender (OR = 1.5, p = .053). An overall caries risk assessment was more reliable than any single caries risk factor.

CONCLUSIONS

Caries risk assessment for 1-year-olds in a region with low caries prevalence has limited accuracy to predict dental caries at 6 years of age. Caries risk often changes over time and should be reassessed on a regularly basis. The presence of MS in 1-year-olds did not increase the prognostic accuracy at 6 years of age.