 |
 |
.gif)
Domenick T. Zero, D.D.S., M.S., Margherita Fontana, D.D.S., Ph.D., and Aine M. Lennon, B.Dent.Sc., Ph.D.:
Other papers at this conference have discussed individual risk indicators of caries. This review focuses on studies of the predictive validity of various combinations of risk indicators. Such indicators may be useful in the clinical management of dental caries by helping dental professionals determine if additional diagnostic procedures are required, identify patients who require caries control measures, assess the impact of caries control measures, make treatment planning decisions, and determine the timing of recall appointments. Although there is a high level of interest in identifying risk indicators, only a few studies have attempted to determine how the application of risk indicators affects dental health outcomes (Brambilla, Gagliani, Felloni, et al., 1999; Hausen, Karkkinena, Seppa, et al., 2000).
Multifactorial modeling has proved its value in longitudinal caries prediction studies by showing the interrelations and interactions of risk factors. Beck and colleagues (1988) indicated that one or more social, behavioral, microbiologic, environmental, and clinical variables should be included in such a model, given the many factors that influence dental caries. Modeling has usually been based on a dichotomized dependent variable, either as "no" versus "some" caries increment (Beck, Weintraub, Disney, et al., 1992) or with specified cut-off points in populations with high caries incidence (Abernathy, Graves, Bohannon, et al., 1987). The accuracy of models has rarely been 80 percent, which is considered to be the minimum level for screening purposes. "To be useful, a working model should produce a sensitivity of 0.75 or higher and specificity level of at least 0.85 or higher" (Stamm, Disney, Graves, et al., 1988). It has therefore been suggested that a risk model should have a combined sensitivity and specificity of at least 160 percent (Kingman, 1990).
References were systematically assessed for their validity. Since valid evidence is considered best obtained from randomized, controlled longitudinal (prospective) studies, those were given the highest scores in our review. Studies were graded as "good," "fair," or "poor," depending on the amount of information they provided to support the methodology used. The main variables assessed for this purpose (other than the inclusion criteria) were: (1) whether the study reported how samples were obtained, (2) whether the examiners were trained/calibrated, (3) whether examiner reliability was reported, and (4) whether examiners were blinded during the study. Tables 1, 2, and 3 include the longitudinal prospective studies considered to be good sources of evidence for predictions in primary teeth, permanent teeth in children and adolescents, and permanent teeth in adults. None of the root caries studies reviewed met these criteria.
Of all the models reviewed, none of those graded as "good" had a combined sensitivity and specificity in excess of 160 percent, although the model reported by Demers and colleagues (1992) comes very close (159 percent). These authors concluded that previous caries experience was the strongest predictor in their model, followed by parents¹ education. For primary teeth there was one "fair" study in which combined sensitivities and specificities totaled 170 percent (Holst, Martensson, Lavrin, et al., 1997). That study used infants 1 year old, for 2 years, and all categories of risk assessment factors. Visible plaque, deep fissures, and oral hygiene were the strongest predictors.
Table 1. Primary teeth-prospective studies (good level of evidence) Researcher N Age at onset Study Design Variables: Variables: Microflora Variables: Variables: Outcome = Sensitivity Specificity 297 (3-4 year olds) 3-4 Prospective Caries, Predicted caries Not used Not used Sociodemographic < 45% 92% 302 5 year olds Prospective (1 year) Caries experience: SM, LB (Bactotest) Buffer capacity Age, sex, parent’s education, family structure, fluoride consumption, oral hygiene (debris index) > 81.8% 77.4%
Table 1. Primary teeth-prospective studies (good level of evidence) (continued) Researcher Baseline Scores True High Risk Criteria Used Method of modeling Country Sampling method Training of examiners reported Reliability of examiners Blinding of examiners Blinding of patients Subject Attrition Authors conclusion [Isokangas et al., 1993] NR High risk: Any caries increment Not used Finland (Ylivieska) All 3-16 year olds in public dental care were included 15 clinicians participated. No training reported. NR (dentists examined different children) Not possible for ethical reasons NR NR Clinicians can predict risk using only caries and socio-demographic variables available at annual examinations [Demers et al., 1992] NR At least one new carious lesion in primary teeth: high risk (LRA; 9 variables studied) Canada (Montreal) Non-fluoridated community Random selection of schools Calibrated (2 examiners) For caries: NR NR 126 Previous caries experience was the best predictor, followed by parent’s education.
Table 2. Permanent teeth-children and adolescents; prospective studies (good level of evidence) Researcher N Age at onset Study Design Variables: Past Caries or Disease Experience Variables: Microflora Variables: Variables: Other Outcome=Validation criteria=true disease Sensitivity Specificity 4158: 6 years Prospective DMFS (Radike, no radiographs), SM (Cariescreen), LB (Bactotest), mean plaque score Pit and Fissure Morphology Sociodemographic (higher in Portland-exclusively white); examiner, age, brushing frequency, between meals snacks > 59% (grade 1); 62% 83% (grade 1); 81% (grade 5) 1464 (5—16 year olds) 3—16 Prospective Caries, Predicted caries Not used Not used Socio-demographic < 5-16 year olds; 58% 5-16 year olds:84%;
Table 2. Permanent teeth-children and adolescents; prospective studies (good level of evidence) (continued) Researcher Baseline Scores (Mean + SD) True High Risk Criteria Used Method of modeling Country Sampling method Training of examiners reported Reliability of examiners Blinding of examiners Blinding of patients Subject Attrition Authors conclusion Aiken: DMFS:0.3 (grade 1), 3.0 (grade 5) High risk:25% of the total sample size. (LRA, stepwise, 38—43 variables studied) USA NR Trained Examiner reliability; intraclass correlations above 90% for 10/12 comparisons. Reliability for noncaries data showed fair agreement among examiners. Yes NR Lost approx. 20% from baseline (more than N) Models had high specificity for children at low risk. Clinical predictors were the most important ones, while the other factors contributed little to the prediction. NR High risk: Any caries increment Not used Finland (Ylivieska) All 3-16 year olds in public dental care were included 15 clinicians participate. No training reported. NR (dentists examined different children) Not possible for ethical reasons NR NR Clinicians can predict risk using only caries and sociodemographic variables available at annual examinations
Table 3. Permanent teeth adults-prospective studies (good level of evidence) Researcher N (dentate) Age (t outset Study Design Variables: Past Caries or Disease Experience Variables: Microflora Variables: Variables: Other Outcome= Sensitivity % Specificity % [Hawkins et al., 1997;van Houte, 1993] 699 50+ Prospective 3 years No calculus removed no radiographs Not Used Not Used Educational level One or more net coronal DFS increments 80.2 46.2 Table 3. Permanent teeth adults-prospective studies (good level of evidence) (continued) Researcher Baseline Scores True High Risk Criteria Used Method of modeling Country Sampling method Training of examiners reported Reliability of examiners Blinding of examiners Blinding of patients Subject Attrition Authors conclusion [Hawkins et al., 1997;van Houte, 1993] Caries incidence 57% NR LRA Canada, Ontario Random Calibration reported 94%kappa 0.76 coefficient of reproducibility 0.97 (p<0.001) NR NR 206 Non-clinical factors, which showed significant effects were education and marital status, both of these factors may influence attitudes towards oral health.
Another recommendation follows from the consistent finding that past caries experience is a strong predictor of future disease. Most studies have used the DMFS (decayed, missing, filled surfaces) index to determine past caries experience. This approach does not necessarily separate out the D component from the F component. Furthermore, this approach does not establish whether decayed lesions are active (progressing) or inactive (arrested). The presence of caries activity should be a much stronger predictor of future carious lesions (frank cavitations) than the DMFS index. The development of technology to detect early caries lesions and to directly assess caries lesion status may prove to be the best way to identify patients who need aggressive preventive intervention.
Beck JD, Weintraub JA, Disney JA, Graves RC, Stamm JW, Kaste LM, et al., University of North Carolina Caries Risk Assessment Study: comparisons of high risk prediction, any risk prediction, and any risk etiologic models. Comm Dent Oral Epidemiol 1992;20:31321.
Brambilla E, Gagliani M, Felloni A, García-Godoy F, Strohmenger L. Caries-preventive effect of topical amine fluoride in children with high and low salivary levels of mutans streptococci. Caries Res 1999;33:4237.
Demers M, Brodeur JM, Mouton C, Simard PL, Trahan L, Veilleux G. A multivariate model to predict caries increment in Montreal children aged 5 years. Comm Dental Health 1992;9:27381.
Disney JA, Graves RS, Stamm JW, Bohannan HM, Abernathy JR, Zack DD. The University of North Carolina Caries Risk Assessment study: further developments in caries risk prediction. Comm Dent Oral Epidemiol 1992;20:6475.
Hausen H, Karkkainen S, Seppa L. Application of the high-risk strategy to control dental caries. Comm Dent Oral Epidemiol 2000;28:2634.
Hawkins RJ, Jutai DK, Brothwell DJ, Locker D: Three-year coronal caries incidence in older Canadian adults. Caries Res 1997;31:40510.
Holst A, Martensson I, Laurin M. Identification of caries risk children and prevention of caries in pre-school children. Swed Dent J 1997;21:18591.
Isokangas P, Alanen P, Tiekso J. The clinician¹s ability to identify caries risk subjects without saliva tests‹a pilot study. Comm Dent Oral Epidemiol 1993;21:810.
Kingman A, Little W, Gomez I, Heifetz SB, Driscoll WS, Sheats R, et al., Salivary levels of Streptococcus mutans and lactobacilli and dental caries experiences in a US adolescent population. Comm Dent Oral Epidemiol 1988;16:98103.
Moss ME, Zero DT. An overview of caries risk assessment and its potential utility. J Dent Educ 1995;59:93240.
Stamm JW, Disney JA, Graves RC, Bohannan H, Abernathy JR. The University of North Carolina Caries Risk Assessment Study. I: Rationale and content. J Public Health Dent 1988;48:22532.
The main page for this site is located at http://www.lib.umich.edu/dentlib/nihcdc/
and is maintained by the University of Michigan Dentistry Library (dentistry.library@umich.edu)
in partnership with the National Institutes of Health, National Institute of Dental and Craniofacial Research.
Please see also the Consensus Development Program pages, and in particular the NIH Consensus Development Conference on Caries.
Copyright © 2000 Regents of the University of Michigan. All rights reserved.
Past Caries
or Disease Experience
Host
Other
Validation criteria = true disease
[Isokangas et al., 1993]
(1 year)
(actual data NR)
[Demers et al., 1992]
dmfs=0 or dmfs>0 (WHO, no radiographs)
(mean dfs increment:
2.1 + 3.6)
78.3%
(for caries experience only)
77.4%
(for caries experience only)
* Bold: included in final models or strongest predictors
MS: mutans streptococci
LB: Lactobacilli
LRA: logistic regression analysis
LDA: logistic discriminant analysis
NR: Not reported
(Mean + SD)
Intraexaminer reliability: intraclass correlation coefficient >0.95.
The same true for interexaminer reliability
For micro test:
Intraexaminer reliability:0.80-1.00; interexaminer reliability: 0.79-0.87.
Host
[Disney et al., 1992b]|
North Carolina Study
"High Risk Prediction Model"
2079 (Aiken, GA)
2096 (Portland, ME)
Both: fluoride deficient, high caries experience
(1st grade)
and 10 years old (5th grade)
(3 years)
> 2 DMFS
(At 3 years-DMFS increment:
Aiken: 1.9 (grade 1), 3.1 (grade 5)
Portland: 0.8 (grade 1), 1.5 (grade 5)
grade 5
59% (grade 1); 62% (grade 5)
84% (grade 1); 84% (grade 5)
[Isokangas et al., 1993]
(1 year)
(actual data NR)
[Disney et al., 1992b]
North Carolina Study
"High Risk Prediction Model"
dmfs: 9.3 (grade 1), 4.4 (grade 5)
Portland;
DMFS:0.2 (grade 1), 1.7 (grade 5)
dmfs: 2.9 (grade 1), 2.4 (grade 5)
[Isokangas et al., 1993]
Host
Validation criteria=
true disease
Third molars excluded
Mean AL (baseline)
No of teeth (baseline)
Coronal DF
Marital status
Total household income
Dental visiting pattern
Born in Canada
Major life event in past 6 months
Wearing partial denture
(Mean + SD)
Mean net increment 1.91±2.60
The baseline no. of teeth and mean periodontal AL may measure the number of tooth surfaces at risk of decay.Conclusions
Future Research
Clearly, there is a need for further research to identify and validate caries risk assessment strategies that can be applied in dental practice. More importantly, studies are required to establish whether identification of high-risk individuals can lead to more effective long-term patient management that arrests or reverses the progression of carious lesions.
References
Abernathy JR, Graves RC, Bohannan HM, Stamm JW, Greenberg BG, Disney JA. Development and application of a prediction model for dental caries. Comm Dent Oral Epidemiol 1987:15:248.
Background | Agenda | Questions/Topics | Searches | Abstracts
URL of current page: http://www.lib.umich.edu/dentlib/nihcdc/abstracts/zero.html
Last Update: 10:32 AM EST on Monday, December 31, 2007
