Clustering Tooth Surfaces into Biologically Informative Caries Outcomes

Shaffer, John R.; Feingold, Eleanor; Wang, X.; Weeks, Daniel E.; Weyant, Robert J.; Crout, Richard J.; McNeil, Daniel W.; Marazita, Mary L.

doi:10.1177/0022034512463241

Cited by 30 publications

(34 citation statements)

References 15 publications

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“…This study has shown a substantial difference between the estimated power of multilevel models and classic regression analysis. Traditional regression analysis is known to overestimate the beneficial effects of interventions where clustering is present (Shaffer et al 2013;Masood and Reidpath 2014). In this analysis, we used fluoridation and frequency of brushing as examples of preventive measures: simple regression analysis ascribes a nearly 6 times greater beneficial effect to such interventions than 2-or 3-level multilevel analysis.…”

Section: Discussionmentioning

confidence: 99%

The Clustering Effects of Surfaces within the Tooth and Teeth within Individuals

Masood

Newton

2014

J Dent Res

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The objectives of this study were 1) to provide an estimate of the value of the intraclass correlation coefficient (ICC) for dental caries data at tooth and surface level, 2) to provide an estimate of the design effect (DE) to be used in the determination of sample size estimates for future dental surveys, and 3) to explore the usefulness of multilevel modeling of cross-sectional survey data by comparing the model estimates derived from multilevel and single-level models. Using data from the United Kingdom Adult Dental Health Survey 2009, the ICC and DE were calculated for surfaces within a tooth, teeth within the individual, and surfaces within the individual. Simple and multilevel logistic regression analysis was performed with the outcome variables carious tooth or surface. ICC estimated that 10% of the variance in surface caries is attributable to the individual level and 30% of the variance in surfaces caries is attributable to variation between teeth within individuals. When comparing multilevel with simple logistic models, β values were 4 to 5 times lower and the standard error 2 to 3 times lower in multilevel models. All the fit indices showed multilevel models were a better fit than simple models. The DE was 1.4 for the clustering of carious surfaces within teeth, 6.0 for carious teeth within an individual, and 38.0 for carious surfaces within the individual. The ICC for dental caries data was 0.21 (95% confidence interval [CI], 0.204-0.220) at the tooth level and 0.30 (95% CI, 0.284-0.305) at the surface level. The DE used for sample size calculation for future dental surveys will vary on the level of clustering, which is important in the analysis-the DE is greatest when exploring the clustering of surfaces within individuals. Failure to consider the effect of clustering on the design and analysis of epidemiological trials leads to an overestimation of the impact of interventions and the importance of risk factors in predicting caries outcome.

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Section: Discussionmentioning

confidence: 99%

The Clustering Effects of Surfaces within the Tooth and Teeth within Individuals

Masood

Newton

2014

J Dent Res

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“…Previously, the authors have shown that relevant categories were obtained by grouping surfaces based on caries co-occurrence. 27 This suggests that new models of phenotypes may capture more biologically informative patterns of tooth decay. This method indicated that bacteriolytic LYZL2 polymorphism was implicated in reducing caries on mandibular anterior tooth surfaces (incisors, canines, and first premolars).…”

Section: Compilation Of Reported Associations and Outcomesmentioning

confidence: 99%

Salivary protein polymorphisms and risk of dental caries: a systematic review

et al. 2017

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Dental caries is an oral pathology associated with both lifestyle and genetic factors. The caries process can be influenced by salivary composition, which includes ions and proteins. Studies have described associations between salivary protein polymorphisms and dental caries experience, while others have shown no association with salivary proteins genetic variability. The aim of this study is to assess the influence of salivary protein polymorphisms on the risk of dental caries by means of a systematic review of the current literature. An electronic search was performed in PubMed, Scopus, and Virtual Health Library. The following search terms were used: "dental caries susceptibility," "dental caries," "polymorphism, genetics," "saliva," "proteins," and "peptides." Related MeSH headings and free terms were included. The inclusion criteria comprised clinical investigations of subjects with and without caries. After application of these eligibility criteria, the selected articles were qualified by assessing their methodological quality. Initially, 338 articles were identified from the electronic databases after exclusion of duplicates. Exclusion criteria eliminated 322 articles, and 16 remained for evaluation. Eleven articles found a consistent association between salivary protein polymorphisms and risk of dental caries, for proteins related to antimicrobial activity (beta defensin 1 and lysozyme-like protein), pH control (carbonic anhydrase VI), and bacterial colonization/adhesion (lactotransferrin, mucin, and proline-rich protein Db). This systematic review demonstrated an association between genetic polymorphisms and risk of dental caries for most of the salivary proteins.

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“…Schematic illustration of varying tooth surface-level susceptibilities within a population group estimated to have relatively homogeneous (e.g., moderate) early childhood caries (ECC) risk, within the context of proximal causes and distal determinants of ECC development. Susceptibilities of tooth surfaces vary individually and may be grouped in biologically informative clusters (as discussed by Batchelor and Sheiham 2004;Shaffer et al 2013): case A depicts the dentition of a child with highly susceptible upper anterior smooth surfaces, B reflects high pit and fissure susceptibility, and C an overall uniform moderate susceptibility. Panels D and E illustrate the potential of using proximal measures of disease activity and a systems biology approach (discussed by Nyvad et al 2013) to ascertain precise estimates of disease propensity, which, for example, are likely to vary between upper anterior facial (D) and lower proximal [E] surfaces.…”

Section: Quantification and Time: The Missing Dimensionsmentioning

confidence: 99%

“…To inform tooth surface-specific predictions and decision making, analytical approaches should take into consideration both the varying propensity of lesion development between surfaces, the potentially informative clustering of surface-type groups, and the multilevel correlation of tooth surface-specific outcomes (Batchelor and Sheiham 2004;Divaris et al 2013;Shaffer et al 2013). For example, facial surfaces of maxillary anterior teeth are usually the first teeth to show evidence of the disease (Slayton 2015), whereas posterior approximal surfaces are at higher risk after the development of posterior contacts.…”

Section: Utilization Of Informative Heterogeneity Clustering and Comentioning

confidence: 99%

Predicting Dental Caries Outcomes in Children

Divaris

2015

J Dent Res

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In recent years, unprecedented gains in the understanding of the biology and mechanisms underlying human health and disease have been made. In the domain of oral health, although much remains to be learned, the complex interactions between different systems in play have begun to unravel: host genome, oral microbiome with its transcriptome, proteome and metabolome, and more distal influences, including relevant behaviors and environmental exposures. A reasonable expectation is that this emerging body of knowledge can help improve the oral health and optimize care for individuals and populations. These goals are articulated by the National Institutes of Health as "precision medicine" and the elimination of health disparities. Key processes in these efforts are the discovery of causal factors or mechanistic pathways and the identification of individuals or population segments that are most likely to develop (any or severe forms of) oral disease. This article critically reviews the fundamental concepts of risk assessment and outcome prediction, as they relate to early childhood caries (ECC)-a common complex disease with significant negative impacts on children, their families, and the health system. The article highlights recent work and advances in methods available to estimate caries risk and derive person-level caries propensities. It further discusses the reasons for their limited utility in predicting individual ECC outcomes and informing clinical decision making. Critical issues identified include the misconception of defining dental caries as a tooth or surface-level condition versus a person-level disease; the fallacy of applying population-level parameters to individuals, termed privatization of risk; and the inadequacy of using frequentist versus Bayesian modeling approaches to derive individual disease propensity estimates. The article concludes with the notion that accurate caries risk assessment at the population level and "precision dentistry" at the person level are both desirable and achievable but must be based on high-quality longitudinal data and rigorous methodology.

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Clustering Tooth Surfaces into Biologically Informative Caries Outcomes

Cited by 30 publications

References 15 publications

The Clustering Effects of Surfaces within the Tooth and Teeth within Individuals

The Clustering Effects of Surfaces within the Tooth and Teeth within Individuals

Salivary protein polymorphisms and risk of dental caries: a systematic review

Predicting Dental Caries Outcomes in Children

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