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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

Modelling childhood caries using parametric competing risks survival analysis methods for clustered data.

J Stephenson1, B L Chadwick, R A Playle

  • 1School of Dentistry, Cardiff University, Cardiff, UK. J.Stephenson@hud.ac.uk

Caries Research
|February 5, 2010
PubMed
Summary
This summary is machine-generated.

Childhood caries in primary teeth is common. Poor socioeconomic status, non-fluoridated areas, and occlusal surfaces strongly predict caries, but exfoliation risk lessens these differences.

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Area of Science:

  • Pediatric Dentistry
  • Dental Public Health
  • Epidemiology

Background:

  • Caries in primary teeth presents challenges in quantification due to data clustering and exfoliation risk.
  • Understanding factors influencing caries development in young children is crucial for effective public health interventions.

Purpose of the Study:

  • To identify factors associated with caries occurrence in primary tooth surfaces.
  • To assess the impact of exfoliation risk on caries development in primary teeth.
  • To apply multilevel competing risks survival analysis to pediatric dental caries data.

Main Methods:

  • Analysis of 103,776 primary molar tooth surfaces from 2,654 British children aged 4-5 years.
  • Utilized multilevel competing risks survival analysis and multivariate multilevel parametric survival models.
  • Modeled transitions from sound to carious and sound to exfoliated states at the tooth surface level.

Main Results:

  • Socioeconomic class, fluoridation status, and tooth surface type were significant predictors of primary caries.
  • Occlusal surfaces in children from low socioeconomic backgrounds in non-fluoridated areas showed the highest caries rates and shortest survival times.
  • The risk of exfoliation mitigated the observed differences in caries development related to surface type, socioeconomic status, and fluoridation.

Conclusions:

  • Childhood caries is strongly influenced by socioeconomic factors, environmental fluoride exposure, and tooth surface characteristics.
  • Multilevel competing risks survival analysis is a suitable methodology for analyzing complex pediatric dental data with competing risks.
  • Public health strategies should address socioeconomic disparities and fluoridation access to reduce primary tooth caries effectively.