UZH-Logo

Maintenance Infos

Dental predictors of high caries increment in children


Steiner, M; Helfenstein, U; Marthaler, T M (1992). Dental predictors of high caries increment in children. Journal of Dental Research, 71(12):1926-33.

Abstract

A comprehensive set of dental variables was investigated to find the "best" combination of predictors for high caries increment in 7/8-year-old and 10/11-year-old children. Four populations with widely different caries prevalence were studied. Logistic regression analysis supplied multiple-input models by stepwise selection of predictors. A "low number of sound primary molars" was the best and most consistent predictor of high caries increment. The second best predictors were "high numbers of pre-cavity lesions on permanent first molars" (discolored pits and fissures in the younger age group and white spots on the smooth parts of buccolingual surfaces in the older age group). Inclusion of radiological variables did not substantially increase the quality of prediction. For practical application, models with various multiple inputs selected by stepwise procedures were compared with "fixed" three-input models. These three-input models resulted in predictive quality nearly equal to those of the multiple models. Traditional one-input models, containing DMFT or dmft, were inferior to the three-input models, particularly in the older age class. The lower the caries prevalence of the source data, the better was the prediction. As a summary measure characterizing the predictive performance of a model, we used the index "area under the receiver operating characteristic curve" A. For the 1984 data and the three-input models, the area was approximately 80%, and for the 1972 data, the area was 65-70%.

A comprehensive set of dental variables was investigated to find the "best" combination of predictors for high caries increment in 7/8-year-old and 10/11-year-old children. Four populations with widely different caries prevalence were studied. Logistic regression analysis supplied multiple-input models by stepwise selection of predictors. A "low number of sound primary molars" was the best and most consistent predictor of high caries increment. The second best predictors were "high numbers of pre-cavity lesions on permanent first molars" (discolored pits and fissures in the younger age group and white spots on the smooth parts of buccolingual surfaces in the older age group). Inclusion of radiological variables did not substantially increase the quality of prediction. For practical application, models with various multiple inputs selected by stepwise procedures were compared with "fixed" three-input models. These three-input models resulted in predictive quality nearly equal to those of the multiple models. Traditional one-input models, containing DMFT or dmft, were inferior to the three-input models, particularly in the older age class. The lower the caries prevalence of the source data, the better was the prediction. As a summary measure characterizing the predictive performance of a model, we used the index "area under the receiver operating characteristic curve" A. For the 1984 data and the three-input models, the area was approximately 80%, and for the 1972 data, the area was 65-70%.

Citations

54 citations in Web of Science®
33 citations in Scopus®
Google Scholar™

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:December 1992
Deposited On:09 Jun 2015 10:38
Last Modified:05 Apr 2016 19:16
Publisher:Sage Publications Ltd.
ISSN:0022-0345
Publisher DOI:https://doi.org/10.1177/00220345920710121401
PubMed ID:1452896

Download

Full text not available from this repository.View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations