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Risk Assessment Full Summary Description and Use:

Risk assessment is a strategy for improving the efficiency and effectiveness of

prevention procedures and programs. Risk assessment protocols that are based on statistical

models that include a collection of risk factors/indicators are used to collectively predict an

individualʼs risk of disease. These models can be used to determine high risk individuals/groups

to develop an appropriate treatment plans for intervention upon identified risk factors, determine

appropriate intervals for patient recall and identify high risk individuals for preventive

treatments1,2. While risk assessment models are generally used to predict dental caries risk at

the individual level, their utility may be greatest when applied at the population level. Dental

caries management by risk assessment is an evidence based dental caries management

protocol where by clinicians can identify the underlying cause of disease based on (risk

indicators – signs of disease, risk factors – biologic predisposing factors including clinical and

behavvior variables, and protective factors – a variety of clinical, behavioral and therapeutic

components)3,4.

Unfortunately there is no consensus in the literature concerning the use of the terms "risk

factor" and "risk indicator5. The types of variables used to predict risk of dental caries are

generally individual level risk factors such as socio-demographic, behavioral and clinical

variables which will vary in their predictability by the age and population under study. However,

community level risk factors such as for low income patients, percentage of children

participating in a free or reduced cost school lunch program and number of neighborhood

grocery stores have also been shown to be significantly associated with dental caries among

school children6-8.

Risk assessment models have primarily focused on coronal caries risk among young

children and to a lesser extent, the risk of root caries among older adults1,9. Past dental caries

experience has shown to be the single best predictor of future dental caries experience across

various prediction models, while other contributing risk factors vary depending on the particular

population under study.

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Several tools are available for dental caries risk assessment, two of these are fillable

forms the Caries-Risk Assessment Tool (CAT) from the American Academy of Pediatric

Dentistry2 and Caries Management by Risk Assessment (CAMBRA) which is available for

different age groups and suggests a bacterial culture for particular subgroups10. Cariogram is a

computer based program that produces a pie-chart illustrating the contribution of a patientʼs risk

factors within broad categories and his/her future susceptibility to disease based on a

predetermined algorithm11.

Effectiveness and Efficacy:

The effectiveness of the risk assessment model is most often based on measures of

sensitivity (SN), specificity (SP) and positive and negative predictive values (PPV and NPV,

respectively). Each of these parameters varies based on the age range under study and

variables in the prediction model. The index most predictive of future dental caries depends

highly upon the patientʼs dentition, and the presence of sealants and/or restorations9.

Toddlers to Preschool Among young children (toddlers to preschool) the predictive ability of risk assessment

models to correctly classify children using combination of risk factors has generally been shown

to be high with SN and SP greater than 0.80 9,12. However, once children reached 2.5 years

previous dental caries experience alone appears to be the single best predictor of dental caries.

Few studies have evaluated noncavitated lesions; however, these have repeatedly been shown

to be predictive of future dental caries for individuals and may be useful in identifying high risk

groups prior to dental caries initiatio13,14. Carious lesions in the deciduous teeth have been

assessed as a predictor of future dental caries in the permanent dentition. Although the quality

of studies is heterogeneous, dental caries in the primary teeth is a strong predictor of future

dental caries experience with SN=62% and SP=79%, on average1. While, mutans streptococci

and lactobacillus count in saliva are often assessed as potential predictors they have shown

poor accuracy (combination of sensitivity and specificity) when used as the sole predictor.

Similarly, the presence of visible plaque on the labial surfaces of anterior teeth shows poor

sensitivity with a high specificity (SN=26%, SP=88%), distinguishing children who will not

develop dental caries, however poorly identifying those who will1. These results corroborate

results from oral hygiene data suggesting that children (1-3 yrs) who brush at least once a day

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with fluoridated tooth paste, are less likely to develop dental caries by age 3 than those with

poor oral hygiene1. Dietary factors are consistently observed to be important predictors in risk

assessment models for young children. It is well known that sugar is a necessary cause for the

demineralization. However, the association between sugar and dental caries is much weaker

among children who have had consistent exposure to fluoride, whether intake of sugar is

measured as total amount or frequency of intake15.

Elementary to Adolescent Among school aged children and adolescents prior carious lesions are the single best

predictor of dental caries risk, with little to no additional benefit in model performance by the

inclusion of additional risk factors1,5,16-18. Beck et al.17 compared the selection and performance

of predictors between 3 risk assessment models (any dental caries risk, high dental caries risk

and an etiologic dental caries risk model) among a population of 4,117 children from two cities

followed for 3 years. The authors found substantial variation in the final selection of significant

predictors between each model based on city and school grade. For example, race and

education were significant predictors in one population but not another, while among

participants form Aiken, South Carolina in first grade race was a significant predictor in the “any

risk” model but not among those in Grade 5. Parentsʼ education has additionally been shown to

increase predictability in some studies19,20. The literature clearly highlights limitations in

generalizability of risk assessment models and the need for population specific risk factor

models. Furthermore, in the permanent teeth, risk of developing a carious lesion is highest

immediately following eruption1, which calls for the use of prevention to protect teeth vulnerable

to decay21,22.

Adults and the Elderly Studies among adults have focused on evaluating risk of root caries among older

adults9. Indicators of past dental caries experience remain the strongest predictors of risk of

dental caries among older adult populations, irrespective of additional predictors9. There have

been no reported studies evaluating dental caries risk assessment in young adults.

Recommendations for community-based protocol:

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One risk assessment model cannot be used across all populations. Rather, in order to

obtain accuracy, models should be developed based on age, population characteristics and

levels of disease16.Amstutz et al7. sampled children in classrooms across North Carolina, and

examined the relationship between various community level socio-economic indicators with dfs

+ DMFS score. The authors found wide variation, up to 36-fold, in df and DMF scores

(depending on grade level) across the state. Tellez et al8. evaluated the association between

neighborhood factors and carious lesions among a population of low-income African Americans.

The authors reported a decreased severity of dental caries with higher number of churches,

while observing an increased severity in dental caries associated with number of grocery

stories. While seemingly paradoxical, grocery stores in low income areas are generally small

stores with limited availability of fresh food, often offering an abundance of refined and

processed foods. Limited evidence for the added predictive power of additional variables in risk

assessment models within certain populations does not indicate that potential risk

indicators/factors should be ignored in individualized risk assessment treatment plans. The

failure of these risk factors to show predictive power in statistical models may simply highlight a

weakness of the data available to evaluate their role in the risk of dental caries.

Cost:

Badovinac et al23. evaluated a dental caries risk assessment model in identifying high

risk children in 1st grade for sealant application to avoid carious lesions in the first permanent

molars by 4th grade. Using a threshold of dmfs +DMFS>0 (any dental caries history) in a

simulation of 100 children, the authors found that 69.4% of dental caries in 4th grade could have

been prevented and 44.5% of children who would not have developed decay, would have had

sealants placed. The authorsʼ cost analysis showed an overall savings of $4,038.42 in program

costs versus applying sealants to all 100 children irrespective of their dental caries risk profile.

Similarly, Griffin et al. 24 conducted a cost analysis of 3 different sealant programs: seal all, seal

none or seal children assessed to be at high risk. The authors concluded that either sealing all

or those at high risk was less costly than sealing none. However, as the SN and SP of the risk

assessment model increased, sealing those at high risk became the most cost effective

program. Hence, dental caries risk assessment provides a method to identify high risk groups

to maximize true positives while minimizing false positives (incorrectly identifying high risk

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individuals and providing treatment to those who would not go on to develop dental caries) to

improve cost efficiency when resources are limited. Furthermore, the cost could further be

minimized by utilizing allied health professionals for sealant placement.

Safety:

Dental caries risk assessment provides a method to identify high risk groups to

maximize the allocation of limited resources such as application of sealants while ethically

withholding services from low risk populations. The use of a dental caries risk assessment

dental caries the potential for misclassification of individuals as false positives (classifying an

individual as high risk who would not go one to develop dental caries) and false negatives

(incorrectly classifying an individual as low risk who would go on to develop dental caries).

Dental caries risk assessment can be used to maximize true positives while minimizing false

negatives to mitigate potential safety concerns of withholding care from misclassified

individuals who will later develop carious lesions which may lead to tooth loss.

Summary and Recommendations:

Based on findings from the literature, dental caries risk assessment can be effective in

identifying individuals at high risk of developing dental caries who need preventive services and

the management of risk factors. The current literature has focused heavily on the development

of risk assessment models for dental caries among populations of young children and to a

lesser extent to root caries among older adults. Past dental caries experience has shown to be

the single best predictor of future dental caries experience irrespective of age across various

prediction models. Other variables vary in their predictability by the age and population under

study. Hence, one risk assessment model cannot be used across all populations. Dental caries

risk assessment provides a non-invasive and patient acceptable method to identify high risk

groups to maximize the allocation of limited resources in community prevention programs.

Dental caries risk assessment can be used to maximize true positives while minimizing false

negatives to mitigate potential safety concerns (i.e. withholding care).

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References:

1. Report: Caries-Diagnosis, Risk Assessment and Non-Invasive Treatment: A Systematic Review. 2008, The Swedish Council on Technology Assessment in Health Care.

2. American Academy of Pediatric Dentistry, Policy on Use of a Caries-risk Assessment Tool (CAT) for Infants, Children, and Adolescents, in Oral Health Policies. 2006. p. 29-33.

3. Young, D.A., J.D. Featherstone, and J.R. Roth, Curing the silent epidemic: caries management in the 21st century and beyond. J Calif Dent Assoc, 2007. 35(10): p. 681-5.

4. Ramos-Gomez, F.J., et al., Caries risk assessment appropriate for the age 1 visit (infants and toddlers). J Calif Dent Assoc, 2007. 35(10): p. 687-702.

5. Twetman, S. and M. Fontana, Patient caries risk assessment. Monogr Oral Sci, 2009. 21: p. 91-101.

6. Gooch, B.F., et al., Preventing dental caries through school-based sealant programs: updated recommendations and reviews of evidence. J Am Dent Assoc, 2009. 140(11): p. 1356-65.

7. Amstutz, R.D. and R.G. Rozier, Community risk indicators for dental caries in school children: an ecologic study. Community Dent Oral Epidemiol, 1995. 23(3): p. 129-37.

8. Tellez, M., et al., Assessment of the relationship between neighborhood characteristics and dental caries severity among low-income African-Americans: a multilevel approach. J Public Health Dent, 2006. 66(1): p. 30-6.

9. Powell, L.V., Caries prediction: a review of the literature. Community Dent Oral Epidemiol, 1998. 26(6): p. 361-71.

10. Featherstone, J.D., et al., Caries risk assessment in practice for age 6 through adult. J Calif Dent Assoc, 2007. 35(10): p. 703-7, 710-3.

11. Bratthall, D. and G. Hansel Petersson, Cariogram--a multifactorial risk assessment model for a multifactorial disease. Community Dent Oral Epidemiol, 2005. 33(4): p. 256-64.

12. Grindefjord, M., et al., Prediction of dental caries development in 1-year-old children. Caries Res, 1995. 29(5): p. 343-8.

13. Klock, B. and B. Krasse, A comparison between different methods for prediction of caries activity. Scand J Dent Res, 1979. 87(2): p. 129-39.

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14. Steiner, M., U. Helfenstein, and T.M. Marthaler, Dental predictors of high caries increment in children. J Dent Res, 1992. 71(12): p. 1926-33.

15. Burt, B.A. and S. Pai, Sugar consumption and caries risk: a systematic review. J Dent Educ, 2001. 65(10): p. 1017-23.

16. Disney, J.A., et al., The University of North Carolina Caries Risk Assessment Study: Further developments in caries risk prediction. Community Dent Oral Epidemiol, 1992. 20(2): p. 64-75.

17. Beck, J.D., et al., University of North Carolina Caries Risk Assessment Study: comparisons of high risk prediction, any risk prediction, and any risk etiologic models. Community Dent Oral Epidemiol, 1992. 20(6): p. 313-21.

18. Stewart, P.W. and J.W. Stamm, Classification tree prediction models for dental caries from clinical, microbiological, and interview data. J Dent Res, 1991. 70(9): p. 1239-51.

19. Demers, M., et al., A multivariate model to predict caries increment in Montreal children aged 5 years. Community Dent Health, 1992. 9(3): p. 273-81.

20. Tagliaferro, E.P., et al., Risk indicators and risk predictors of dental caries in schoolchildren. J Appl Oral Sci, 2008. 16(6): p. 408-13.

21. Preventing Dental Caries in Children at High Caries Risk. 2000, Scottish Intercollegiate Guidelines Network.

22. Llodra, J.C., et al., Factors influencing the effectiveness of sealants--a meta-analysis. Community Dent Oral Epidemiol, 1993. 21(5): p. 261-8.

23. Badovinac, R.L., et al., Risk assessment criteria applied to a screening exam: implications for improving the efficiency of a sealant program. J Public Health Dent, 2005. 65(4): p. 203-8.

24. Griffin, S.O., et al., Comparing the costs of three sealant delivery strategies. J Dent Res, 2002. 81(9): p. 641-5.