Predicting Caries in Permanent Teeth from Caries in Primary Teeth: An Eight-year Cohort Study

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1 RESEARCH REPORTS Clinical Y. Li 1 * and W. Wang 2 1 Department of Basic Science and Craniofacial Biology, New York University College of Dentistry, 345 E. 24th Street, New York, NY , USA; and 2 Department of Community and Preventive Dentistry, Peking University School of Stomatology, Beijing, China; *corresponding author, yihong.li@nyu.edu Predicting Caries in Permanent Teeth from Caries in Primary Teeth: An Eight-year Cohort Study J Dent Res 81(8): , 2002 ABSTRACT Several cross-sectional studies report that caries in primary teeth is correlated with caries in permanent teeth. This eight-year cohort study sought to determine if caries in the primary dentition can predict caries in the permanent dentition of the same individuals and, if so, with what degree of prediction accuracy. A total of 362 Chinese children, from 3 to 5 years old at the time of the 1992 baseline study, were re-examined in The study found statistically significant associations between caries prevalence in primary and permanent dentitions (p < 0.01). Children having caries in their primary teeth were three times more likely to develop caries in their permanent teeth (relative ratio = 2.6, 95% CI = ; p < 0.001). Caries on primary molars had the highest predictive value (85.4%). This study demonstrates that caries status in the primary teeth can be used as a risk indicator for predicting caries in the permanent teeth. KEY WORDS: dental caries, primary and permanent teeth, Chinese children. Received October 31, 2001; Last revision June 7, 2002; Accepted June 13, 2002 INTRODUCTION Dental caries is the most prevalent of all chronic diseases among US children (USDHHS, 2000). It affects 18% of all children ages 2-4, 52% of children ages 6-8, and 80% of adolescents age 17 (Kaste et al., 1996). In China, caries prevalence ranges from 67% to 86% among pre-school children (3 to 6 yrs old) and 32% to 48% among adolescents (12 yrs old) (PRCMPH, 1987, 1999; Petersen and Guang, 1994; Wang et al., 1994; Douglass et al., 1995; Peng et al., 1997; Wong et al., 1997, 2001; Petersen and Esheng, 1998). High caries prevalence still endures as one of the major oral health issues in children. Because dental caries is a disease that is both preventable and costly to treat, previous studies have focused on identifying caries risk predictors, including developmental tooth defects, mutans streptococci infection (time, source, and level), lactobacilli counts, salivary buffer capacity and flow rate, sucrose intake frequency, and past caries experience (Seppä et al., 1989; Alaluusua et al., 1990; Helfenstein et al., 1991; Disney et al., 1992; Steiner et al., 1992; Vehkalahti et al., 1996; van Palenstein Helderman et al., 2001). Most of these studies, however, consisted of a single or several cross-sectional surveys. Information validating the correlation between caries in primary and permanent dentitions of the same individuals is needed. The present eight-year cohort study addresses the following questions: Does the caries status of the primary teeth correlate with that of the permanent teeth in the same individual? If so, which teeth contribute most strength to the correlation? Finally, can caries in the permanent teeth be predicted from caries status in the primary teeth of the same individual? Here, we examined the specificity, sensitivity, predictive value, and efficiency of various risk predictors that might be used to predict future caries in this prospective study. MATERIALS & METHODS Study Cohort The study was approved by the University of Alabama at Birmingham Institutional Review Board for Human Use with English Informed Consent and Chinese translation. Parents consent was obtained prior to the study. The baseline dental examination for caries status in the primary dentition was conducted from July to October, 1992 (Li et al., 1996). A total of 504 children, 256 boys and 248 girls, participated in this eight-year cohort study. They were randomly selected from 11 villages and 4 kindergartens of two representative communities, outside the Beijing metropolitan area. The children were then 3 to 4 yrs old (mean = 3.5). Gender, age, and socioeconomic status (SES) were equally distributed. Forty-nine percent of the families had annual incomes less than 3000 RMB ($370 US dollars) and were categorized as the low-ses group. The average fluoride concentration was below 0.26 ppm in the drinking water. 561

2 562 Li & Wang J Dent Res 81(8) 2002 Eight years later, in December of 2000, the same group of children (N = 362, 46% boys and 54% girls) was re-examined for caries status in their permanent dentition. Fifty-seven of the children had passed their 13th birthday when the oral examination was conducted. Therefore, the average age of the children examined was 11.7 yrs, ranging from 11 to 13 yrs. The rate of loss to followup was 28%, in part the result of two entire villages relocating for regional irrigation projects. More than half (56%) of the families remained at annual income levels below the regional average. Dental Examination Both baseline and follow-up dental examinations were conducted under the same conditions, in classroom settings with natural light and standard mouth mirrors and explorers. Two calibrated dentists, trained at the WHO Collaborative Training Center in Beijing, performed the dental examinations according to WHO caries diagnostic criteria (WHO, 1987). Caries status, decayed (cavitated), missing, and filled primary (dmf) or permanent (DMF) teeth or tooth surfaces were assessed. Children s teeth were not air-dried or cleaned prior to the examinations. No radiographs were taken. Prior to the baseline study, a caries diagnostic standardization was performed between the examiners. The agreement in applying the diagnostic criteria was 92%. The intra-examiner reproducibility for applying the diagnosis criteria was 95%. The Kappa coefficients were 0.86 and 0.88, respectively. Statistical Analysis The data analyses were performed with SPSS software 10.0 (SPSS Inc., Chicago, IL, USA). Caries outcome in permanent teeth was defined as the dependent variable. Both descriptive and categoric statistics were applied for the prevalence and the mean of dental caries experience. Binary logistic regression analyses, as well as Spearman s correlation coefficient, logistic regression analysis, predictive value, and relative risk (RR) analysis, were also computed for caries risk estimations and for comparative analyses between caries in primary teeth and permanent teeth. RESULTS Caries Status Caries prevalence and the mean values are summarized in Table 1. Caries prevalence in the primary dentition was 83%; the mean dmft (dmfs) was 6.1 ± 4.7 (12.5 ± 12.4). The caries prevalence associated with increase in age was significant (p < 0.01). No significant difference was observed between genders. Caries prevalence in the permanent dentition among Chinese adolescents was 41%, and the means of both DMFT and DMFS were low. When caries status was compared with SES, higher caries prevalence was found among children with lower SES (86% vs. 81%, p < 0.05; dmft 6.7 vs. 5.7, p < 0.01) in primary dentitions. In the permanent dentition, however, a reverse correlation was observed (35% vs. 47%, p < 0.05). Adolescents in the high-ses group also experienced more caries than adolescents in the low- SES group (Independent t test, p < 0.05). Caries Correlation and Relative Risk Results from this study illustrated a significant correlation between caries experience in the primary and that in the permanent teeth (r = 0.38, p < 0.001). A greater correlation coefficient was found between caries in primary maxillary anterior teeth and caries in permanent molars (r = 0.49, p < 0.001). Of the children who developed dental caries in permanent teeth, 94% of them experienced caries in their primary teeth (Pearson 2 = 13.7; p < 0.001). Of the children who did not have any caries in their primary teeth, 83% remained caries-free by the age of 12. A relative risk value of 2.6 (95% CI of 1.4 to 4.7, p < 0.001) indicated that children who manifested caries in their primary teeth were three times more likely to develop caries in their permanent teeth than were those children previously free of caries. The study also revealed that the mean decay in the permanent teeth significantly correlated with the mean decay in the primary teeth (p < Table 1. Comparison of Dental Caries Status in the Primary and Permanent Dentitions of the Children Baseline Examination, 1992 Follow-up Examinations, 2000 Primary Dentition Permanent Dentition Prevalence dmft dmfs Prevalence DMFT DMFS (%) (mean ± SD) (mean ± SD) (%) (mean ± SD) (mean ± SD) Total (N = 504) ± ± 12.4 Total (N = 362) ± ± 1.8 By age 3 yrs (n = 252) 78.2 a 5.5 ± 4.5 b 10.5 ± 10.8 b 11 yrs (n = 154) ± ± yrs (n = 252) ± ± yrs (n = 151) ± ± yrs (n = 57) ± ± 2.5 By gender Boys (n = 256) ± ± 13.1 Boys (n = 196) ± ± 1.7 Girls (n = 248) ± ± 11.5 Girls (n = 166) ± ± 2.0 By SES Low (n = 247) 85.8 c 6.6 ± ± 12.0 Low (n = 201) 35.3 c 0.7 ± 1.2 d 0.9 ± 1.7 d High (n = 257) ± ± 12.6 High (n = 161) ± ± 2.1 a Pearson chi-square test for prevalence comparison; p < b Independent t test; p < c Pearson chi-square test for prevalence comparison; p < d Independent t test; p < 0.05.

3 J Dent Res 81(8) 2002 Dental Caries in Primary and Permanent Dentitions ). The linear regression analysis demonstrated a trend in which the number of permanent teeth with caries rose during the study period in response to an increase of the mean caries score of the primary teeth (F DMFT = *dmft; r = 0.27; p < 0.001). In addition, a steady increase in the relative risk (RR) and predictive value for caries was observed as the number of decayed tooth surfaces increased (Table 2). Caries Prediction When the caries experience in the primary teeth was used to predict future caries of the same individual, the study found an overall sensitivity of 93.9%, an overall specificity of 20.0%, and an overall positive predictive value of 85.4%. To answer the question as to which teeth contribute the most to the positive predictive value, we repeated the statistical analyses on subsets of teeth, by dividing the primary teeth with caries into different groups: maxillary incisors (4 teeth), maxillary anterior teeth (6 teeth), maxillary first and second molars (4 teeth), mandibular first and second molars (4 teeth), and all primary molars (8 teeth). The sensitivity, specificity, predictive value, and efficiency were determined for different tooth combinations according to two categories: caries present in any one of these teeth, or caries present in all of the teeth. Table 3 shows that the highest sensitivity (93.9%) was observed in Table 2. Relative Risk (RR) of Developing Caries in the Permanent Teeth as Predicted by Different Caries Experiences in the Primary Teeth or Tooth Surfaces Caries in Primary Teeth Caries in Permanent Teeth Score N (%) RR 95% CI 2 p value a Caries-free 52 (14.4) dmfs 5 74 (20.4) (22.1) (21.8) < > (21.3) < dmft 3 70 (19.3) (19.6) (22.9) < (23.8) < a Pearson chi-square test computed by a series of 2x2 crosstab comparisons of caries-active vs. caries-free primary teeth to estimate caries risk in permanent teeth. An increase in the RR and significance levels was evidenced in the Table as the numbers of decayed teeth or tooth surfaces increased. caries on any of the primary molars with the highest predictive value (85.4%), and the highest specificity (91.6%) was observed in caries on all of the primary molars. An almostperfect specificity, 97.7% for caries, on all maxillary anterior teeth was paired with a low sensitivity. The highest efficiency (65.8%) was for caries on all mandibular primary molars. The positive predictive value decreased when the prevalence was low, even for high values of sensitivity or specificity. Table 3. Positive Predictive Value (PPV) of Caries in the Primary Teeth for Caries in the Permanent Teeth Caries in Primary Teeth Prediction of Caries in Permanent Teeth Tooth Type a Prevalence (%) Sensitivity (%) Specificity (%) Efficiency (%) PPV (%) Maxillary incisors (52, 51, 61, 62) Any one of them All of them Maxillary anterior teeth (53, 52, 51, 61, 62, 63) Any one of them All of them Maxillary 1st & 2nd molars (55, 54, 64, 65) Any one of them All of them Mandibular 1st & 2nd molars (85, 84, 74, 75) Any one of them All of them All molars (55, 54, 64, 65, 74, 75, 85, 84) Any of the molars All of the molars a Since only 6.4 of the children had caries on any of the mandibular incisors, statistical analyses on predictive values for this group were excluded.

4 564 Li & Wang J Dent Res 81(8) 2002 Figure. The overall relative risk (RR) for caries development in the permanent dentition was 2.6 (Pearson chi-square test, p < 0.001), indicating that children who had caries in their primary teeth were nearly three times more likely to develop caries in their permanent teeth than the children who were previously free of caries. This Fig. shows the specific value of RR for caries development in the permanent dentition, depending upon caries presence on different types of primary teeth. Caries on primary maxillary (Max) incisors alone did not have significant predictable value. Caries on mandibular (Man) molars had the highest RR value (1.89) with the highest predictive efficiency (65.8%). Another way to determine the predictive value was to compare relative risk values according to caries status of different tooth segments in the primary dentition. As shown in the Fig., caries presence on all upper incisors (4 teeth) did not have significant predictive value for caries development in the permanent dentition (RR = 1.1; 95% CI = ; p = 0.43). Caries presence on all upper anterior teeth (6 teeth) had marginally significant predictive value for caries development in the permanent dentition (RR = 1.6; 95% CI = ; p = 0.07). Caries presence either on all upper primary molars or on lower primary molars had similar predictive values (p < 0.001). A relative risk value for caries presence on all first and second primary molars was 1.8 (95% CI = , p < 0.001). The relative risk value was even greater for caries presence on any of the first and second primary molars (RR = 3.4; 95% CI = , p < 0.001), with the positive prediction value 94%. DISCUSSION When caries experience in primary teeth is used as a risk indicator to predict caries in permanent teeth, the commonly used and most pragmatic characteristics of tests are sensitivity, specificity, and predictive value. Previous studies reported ranges of sensitivities from 28% to 76%, specificities from 63% to 93%, and predictive values from 61% to 77% for overall positive prediction (Abernathy et al., 1987; Seppä et al., 1989; Wilson and Ashley, 1989; Helfenstein et al., 1991; Disney et al., 1992). Results of this longitudinal study demonstrated that the best indicator of sensitivity (94%) was caries on any of the primary molars. The best indicator of specificity (98%) was caries on all maxillary anterior teeth. There was no single indicator with both high sensitivity and specificity for the identification of individuals at high risk before they develop caries. In addition to the sensitivity, specificity, and positive predictive values, the study also systematically examined the prediction efficiency, calculated from true caries-active and caries-free status, for each of the tooth groups over the grand total. The predictive value of 85.4% suggests that nine out of ten children who had caries in their primary molars will develop caries in their permanent teeth. Accordingly, a combination of caries present on primary molars (the highest sensitivity) and caries-free primary maxillary anterior teeth (the highest specificity) would be the best predictor for distinguishing children as high- or low-risk for caries. Analysis of the baseline data showed that 78% of children at age 3 and 88.5% at age 4 had at least one decayed tooth. According to the new definition of early childhood caries (ECC) (Drury et al., 1999), 90% of this cohort manifested ECC. But not all of them developed caries in his/her permanent teeth. Since the caries scores ranged from 1 to 20 for dmft and from 1 to 60 for dmfs, a wide spread in positive predictive values was also observed. Based on caries prediction analyses as a function of different caries scores, the study discovered that caries on primary maxillary incisors alone or fewer decayed teeth (dmft 6) might not necessarily be a sufficient predictive risk indicator for future caries. Children with severe ECC (24%) who had dmft scores equal to or greater than 10 were 3.5 times at greater risk for developing caries in their permanent dentition. Therefore, the current definition for ECC or severe ECC may lack specific and sufficient power to distinguish between those individuals at high risk and those at low risk for caries. Since 1990, several cross-sectional epidemiological studies on caries in Chinese children have been reported (PRCMPH, 1987, 1999; Petersen and Guang, 1994; Wang et al., 1994; Douglass et al., 1995; Peng et al., 1997; Wong et al., 1997, 2001; Petersen and Esheng, 1998). The caries prevalence in primary dentitions was 76% to 83%, with an average of 4.3 to 4.8 decayed teeth. Caries prevalence in permanent dentitions was 32% to 45%; the average ranged from 0.7 to 1.0. This study found similar results. Interestingly, high caries prevalence and more decayed teeth were observed in the primary dentition of children with lower SES. Eight years later, the caries prevalence in the same cohort was higher among children with higher SES. This discordance of caries status between the primary and the permanent dentitions in Chinese children is remarkable compared with that in populations in developed countries (Kaste et al., 1996). One hypothesis was that the primary teeth, compared with the permanent teeth, are more susceptible to developmental defects (Li et al., 1995), such as enamel hypoplasia, as the result of maternal chronic nutritional deficiency, low birth weight, and prenatal infectious diseases. The second hypothesis was that enamel hypoplastic lesions could foster earlier colonization of cariogenic bacteria, mainly mutans streptococci, in the oral cavity colonization that could accelerate caries development and progression (Li et al., 1994, 1996). As expected, the socio-economic status may not indicate a direct etiologic risk factor for dental caries, but it can significantly undermine tooth susceptibility, especially in the primary teeth, for caries.

5 J Dent Res 81(8) 2002 Dental Caries in Primary and Permanent Dentitions 565 Since dental caries is a dietary carbohydrate-modified bacterial infectious disease (van Houte, 1994), one would expect a similar caries development pattern in both the primary and the permanent dentitions in the same individual. However, the study observed reversed caries prevalence in the permanent dentition for high-ses children. One explanation could be that the permanent teeth have a longer developmental and maturational period than the primary teeth. They are less affected by prenatal disturbances. The impact of developmental defects on tooth susceptibility to caries might be diminished in children with low SES. Second, there was a substantial difference in the levels of consumption of sugar-containing beverages and sweetness between children of low SES (rural area) and those of high SES (urban area), as a result of overall economic improvement in China (ISO, 2001). An increase in caries prevalence has been reported among high-ses children (Lo et al., 1999; PRCMPH, 1999). In comparison, in children of low SES with well-developed permanent teeth and a more traditional diet, the caries prevalence remained relatively low. Although the study presents several strengths such as a very homogeneous study cohort with known traditional dietary habits, limited exposure to dental restorative treatments, fluoride, and antibiotics the main drawback was that caries diagnostic criteria in the study were based on exclusion of noncavitation (enamel caries lesion) for caries risk prediction. The caries status in primary teeth, the predisposing variable, could have been underestimated. It might have affected the measurement of association between the predisposing variable and the outcome variable, the caries status in permanent teeth, and contribute to the low prediction efficiency observed in the study. Clearly, more sensitive caries diagnostic and detecting techniques are needed for the future study of caries prediction. Overall, this eight-year cohort study demonstrated a significant positive association between caries in primary and caries in permanent dentitions. Future caries development could be predicted based on the overall risk estimation and caries status of specific groups of primary teeth. As evident in this study, assessment of caries status in primary teeth constitutes a valuable prognostic tool with a high level of confidence in predicting future caries, making it possible for preventive regimens to be initiated in anticipation of future caries. ACKNOWLEDGMENTS We express our grateful appreciation to Dr. Page W. Caufield for his critical comments on this manuscript. We also thank Drs. Xiang-Yong Pan and Hua Wu and Ms. Shu-Yuan Wang for their technical assistance, and Dr. Howard Sage for his English editing. This study was supported, in part, by the John J. Sparkman Center for International Public Health Education (SCIPHE) of the University of Alabama at Birmingham and by NIH/NIDCR Grant DERR REFERENCES Abernathy JR, Graves RC, Bohannan HM, Stamm JW, Greenberg BG, Disney JA (1987). Development and application of a prediction model for dental caries. Community Dent Oral Epidemiol 15: Alaluusua S, Kleemola-Kujala E, Gronroos L, Evalahti M (1990). Salivary caries-related tests as predictors of future caries increment in teenagers. A three-year longitudinal study. Oral Microbiol Immunol 5: Disney JA, Graves RC, Stamm JW, Bohannan HM, Abernathy JR, Zack DD (1992). The University of North Carolina Caries Risk Assessment study: further developments in caries risk prediction. Community Dent Oral Epidemiol 20: Douglass JM, Wei Y, Zhang BX, Tinanoff N (1995). Caries prevalence and patterns in 3-6-year-old Beijing children. Community Dent Oral Epidemiol 23: Drury TF, Horowitz AM, Ismail AI, Maertens MP, Rozier RG, Selwitz RH (1999). Diagnosing and reporting early childhood caries for research purposes. A report of a workshop sponsored by the National Institute of Dental and Craniofacial Research, the Health Resources and Services Administration, and the Health Care Financing Administration. J Public Health Dent 59: Helfenstein U, Steiner M, Marthaler TM (1991). Caries prediction on the basis of past caries including precavity lesions. Caries Res 25: ISO (2001). Sugar year book London, England: International Sugar Organization. Kaste LM, Selwitz RH, Oldakowski RJ, Brunelle JA, Winn DM, Brown LJ (1996). Coronal caries in the primary and permanent dentition of children and adolescents 1-17 years of age: United States, J Dent Res 75(Spec Iss): Li Y, Navia JM, Caufield PW (1994). Colonization by mutans streptococci in the mouths of 3- and 4-year-old Chinese children with or without enamel hypoplasia. Arch Oral Biol 39: Li Y, Navia JM, Bian JY (1995). Prevalence and distribution of developmental enamel defects in primary dentition of Chinese children 3-5 years old. Community Dent Oral Epidemiol 23: Li Y, Navia JM, Bian JY (1996). Caries experience in deciduous dentition of rural Chinese children 3-5 years old in relation to the presence or absence of enamel hypoplasia. Caries Res 30:8-15. Lo EC, Holmgren CJ, Hu DY, Wan HC (1999). Dental caries status and treatment needs of year-old children in Sichuan Province, southwestern China. Community Dent Health 16: Peng B, Petersen PE, Fan MW, Tai BJ (1997). Oral health status and oral health behaviour of 12-year-old urban schoolchildren in the People s Republic of China. Community Dent Health 14: Petersen PE, Esheng Z (1998). Dental caries and oral health behaviour situation of children, mothers and schoolteachers in Wuhan, People s Republic of China. Int Dent J 48: Petersen PE, Guang LX (1994). Dental caries prevalence in a group of schoolchildren in Wuhan City, PR China, Community Dent Oral Epidemiol 22: PRCMPH (1987). National epidemiological survey of dental caries and periodontal disease among school children. Beijing: PRC Ministry of Public Health, People s Health Publishing Bureau. PRCMPH (1999). Second national epidemiological survey of oral health. Beijing: PRC Ministry of Public Health, People s Health Publishing Bureau. Seppä L, Hausen H, Pollanen L, Helasharju K, Karkkainen S (1989). Past caries recordings made in public dental clinics as predictors of caries prevalence in early adolescence. Community Dent Oral Epidemiol 17: Steiner M, Helfenstein U, Marthaler TM (1992). Dental predictors of high caries increment in children. J Dent Res 71: USDHHS (2000). A report of the Surgeon General. Rockville, MD: Department of Health and Human Services, US Public Health Service. van Houte J (1994). Role of micro-organisms in caries etiology. J Dent Res 73:

6 566 Li & Wang J Dent Res 81(8) 2002 van Palenstein Helderman WH, van t Hof MA, van Loveren C (2001). Prognosis of caries increment with past caries experience variables. Caries Res 35: Vehkalahti M, Nikula-Sarakorpi E, Paunio I (1996). Evaluation of salivary tests and dental status in the prediction of caries increment in caries-susceptible teenagers. Caries Res 30: Wang ZJ, Shen Y, Schwartz E (1994). Dental caries prevalence of 6-14-year-old children in Guangdong, China. Community Dent Oral Epidemiol 22: WHO (1987). Oral health surveys basic methods. Geneva: World Health Organization. Wilson RF, Ashley FP (1989). Identification of caries risk in schoolchildren: salivary buffering capacity and bacterial counts, sugar intake and caries experience as predictors of 2-year and 3- year caries increment. Br Dent J 167: Wong MC, Schwarz E, Lo EC (1997). Patterns of dental caries severity in Chinese kindergarten children. Community Dent Oral Epidemiol 25: Wong MC, Lo EC, Schwarz E, Zhang HG (2001). Oral health status and oral health behaviors in Chinese children. J Dent Res 80:

7 LETTERS TO THE EDITOR TO THE EDITOR: From an eight-year longitudinal study, Li and Wang (2002) analyzed the relationship between baseline caries on primary teeth at the age of 3 to 5 years and follow-up caries on permanent teeth in a cohort of 362 children. They found these variables to be statistically associated and the presence of caries in primary teeth to be predictive of caries in permanent teeth. Since the numbers of true-positive, false-positive, truenegative, and false-negative subjects were not directly available from the text, I have extrapolated them to build a 2 x 2 table and re-calculate the caries-predictive power of caries on primary teeth. The data I have used were caries prevalence on permanent teeth (40.6%), proportion of children with baseline dmf = 0 (14.4%), proportion of children with baseline dmf > 0 and DMF > 0 (94%), and the proportion of children with baseline dmf = 0 and DMF = 0 (83%) (Table). To be sure that my extrapolations were exact, I re-calculated the Relative Risk (RR = 2.57; 95% Confidence Interval, ). Finally, I re-calculated Sensitivity, Specificity, Positive, and Negative Predictive Values to be 0.939, 0.200, 0.445, and 0.827, respectively. While the RR, Sensitivity, and Specificity I calculated were equal to the values reported by Li and Wang, the PPV that I found was lower. If the true overall PPV was 44% and not 85%, I suspect that the other PPVs of baseline caries on subsets of primary teeth reported by the authors were also not correct. The numbers of tp, fp, fn, and tn subjects for all the possible combinations tested must be reported and analyzed. If not, the authors' error leads to inverse conclusions. Even if the reported PPVs were exact, there are other aspects that are not agreeable. First, the predictive power cannot be estimated by the correlation coefficient, RR, or by the PPV without the NPV, as in the paper of Li and Wang. There are many methods to assess predictive power, including the value of Sensitivity plus Specificity (Hausen, 1997). The highest value for this measure deducible from the paper was 119.5, far from the minimum required for an effective test (i.e., 160, with both parameters > 80%). There is a final important methodological limitation in the paper. Caries prediction is modeled as a high-risk preventive strategy. When caries incidence and the fraction of children at 804 high risk are extremely low, it is possible to save money and resources by identifying children at high risk and offering them efficacious individual protection (Rose, 1992). However, for a high-risk strategy to be justifiable, the fraction of children classified at high risk should not exceed 30% (Hausen, 1997). With the high caries prevalence in primary teeth reported by the authors of the paper, more than 80% of children would be classified as high risk and would receive intensive preventive care, whereas the remaining 20% would not. In this case, the time and the resources required to apply the test would be greater than those saved by leaving such a small fraction of children without preventive care. My opinion is that the paper by Li and Wang demonstrates that caries status of primary teeth is not predictive of caries on permanent teeth in their cohort. Study populations with a high proportion of subjects classified to be at high risk require population-based programs. REFERENCES Stefano Petti Department of Public Health Sciences G. Sanarelli University La Sapienza P. le Aldo Moro Rome, Italy Hausen H (1997). Caries prediction-state of the art. Community Dent Oral Epidemiol 25: Li Y, Wang W (2002). Predicting caries in permanent teeth from caries in primary teeth: an eight-year cohort study. J Dent Res 81: Rose G (1992). The strategy of preventive medicine. Oxford: Oxford University Press. Table. 2 x 2 contingency table, indicating Relative Risk (RR) and Predictive Power of caries on primary teeth using the data reported by Li and Wang (2002). DMF > 0 DMF = 0 dmf > 0 true positive (tp) false positive (fp) (classified as high risk) dmf = 0 false negative (fn) true negative (tn) (classified as low risk) (true high risk) (true low risk) Relative Risk RR = tp/(tp + fp)x(fn + tn)/fn = (138/310) x (52/9) = 2.57; ln RR = Standard Error (ln RR ) = [1/tp] - [1/(tp + fp)] + [1/fn] - [1/(fn + tn)] = [1/138]- [1/( )] + [1/9] - [1/(9 + 43)] = % Confidence Interval RR = t-ratio = (ln RR )/SE(ln RR ) = / = 3.05; p < Predictive Power Sensitivity = tp/(tp +f n) =138/147 = 0.939; Specificity = tn/(fp + tn) = 43/215 = Positive Predictive Value (PPV) = tp/(tp + fp) = 138/310 = Negative Predictive Value (NPV) = tn/(fn + tn) = 43/52 = Sensitivity+Specificity = =113.9

8 J Dent Res 81(12) 2002 Letters to the Editor 805 THE AUTHORS REPLY: Our study found that the caries status of primary teeth, especially the primary molars, can be used as a risk indicator for caries development in the permanent dentition of Chinese children. This conclusion was made based not upon the positive predictive value which depends on the prevalence of the disease, but, rather, upon the following findings from the study: (1) a significant correlation between caries in the primary and permanent teeth; (2) a high sensitivity (93.9%) in predicting caries in permanent teeth for children with caries in the primary teeth; (3) a significant relative risk associated with caries in permanent teeth for children who manifested caries compared with children caries-free in the primary teeth; and (4) an increasing pattern of the relative risk associated with caries in permanent teeth as the mean dmfs and mean dmft scores in the primary teeth increase. In fact, our findings were consistent with the work performed by Heller et al. (2000), who used a different research approach. Based on insurance claims data, they reported that primary posterior teeth treatment was significantly associated with future caries treatment in first permanent molars. Their study, in addition to others (Powell, 1998), has suggested that caries experiences in primary teeth should be considered as a risk predictor for future caries. We appreciate Dr. Petti's thoughtful reading of the article and agree that the overall positive predictive value should be lower than that given in the original article, based on the fact that high caries prevalence occurred in the primary teeth and low caries prevalence in the permanent teeth. It should be clarified that the 85.4% was calculated from the subgroup children (23.4% of the total) who had a mean dmft score greater or equal to 7 and who developed caries in their permanent molars. Dr. Petti's second point relates to the relatively low value (119.5) of sensitivity plus specificity used in the caries-risk screening examination. Precisely, the highest combination value was in Table 3. By using the combination to evaluate a diagnostic test, one would normally assume that the two values were equally important to a test outcome, which is true for many diseases. Dental caries, however, has unique characteristics. It is infectious but not life-threatening, no single causality has been reported for the disease, a considerable amount of time is required for caries to develop, and preventive measures are available at reasonable cost. Considering these unique characteristics, we strongly believe that a risk-screening test with a high sensitivity is more important than one of high specificity. Thus, we intentionally decided not to use the combination approach, but rather to examine the sensitivity and specificity separately. To date, studies have failed to show the ability of a simple clinical diagnostic test to predict future caries activity accurately in individuals (Powell, 1998). With a multitude of variables and complex statistical models, in which bacterial levels, dietary behavior, salivary factors, and other social variables were included, a combined sensitivity and specificity score could be as high as 173 (Steiner et al., 1992). In our study, we examined only past caries experience; therefore, a low combination value of sensitivity plus specificity was expected. Dr. Petti also raises an important point regarding caries prediction and caries-preventive strategy. We believe that children having one decayed tooth or caries on only 4 maxillary incisors should not be simply classified as high-risk individuals. If more than 80% of the children already affected by dental caries in their primary teeth were classified as a high-risk group, it would be pointless to develop and implement a cost-effective preventive measure to prevent dental caries in the permanent dentition, especially in China, which has a severe shortage of dental professionals at all levels (PRCMPH, 1999). Since caries distribution was skewed in this study cohort and in many other populations (Hausen, 1997), various methods have been used to delineate low- and high-risk individuals. For example, Kaste et al. (1992) found that Native American children with a dmft of 5 or more were more likely develop caries in their permanent teeth (RR = 2.4, 95% CI = ). Heller et al. (2000) demonstrated that primary posterior tooth treatment at ages 4-8 was significantly associated with future caries treatment (RR = 2.5, 95% CI = ). Bratthall (2000) introduced the Significant Caries Index using DMFT > 3 as a cut-off for the evaluation of caries risk in the permanent dentition. In our study, we proposed to divide the decayed primary teeth in different risk groups. The main objectives were to make caries prediction more accurate and to introduce a different means of detecting those individuals most in need of enhanced caries prevention. A less significant correlation was found between caries in the primary anterior teeth and that in the permanent teeth. Statistical analyses for the different combinations of caries in the primary molars demonstrated that: (1) the positive predictive values ranged from 47.3% (caries on any of the mandibular molars) to 65.4% (caries on all primary molars); (2) the relative risk values were 1.8 (95% CI = ) to 3.4 (95% CI = ); and (3) all of the results were highly significant. Statistical analyses were also performed for children with high risk (dmft > 7, 46.7%) and very high risk (dmft > 10, 23.8%) (Table 2). If we focused our analyses on those children who had a dmft score greater or equal to 7 and had pit and fissure caries in their permanent molars, the positive predictive values would be as high as 94.6%. Those children were truly high-risk individuals. Because positive predictive value fluctuated according to disease prevalence, which is one of the drawbacks, and because of its uncertainty as a predictive indicator (Galen and Gambino, 1975), the study conclusion was not based merely on the positive predictive value; rather, it was strongly supported by other analytical results. As Dr. Petti pointed out, a population-based cariesprevention program would be cost-effective in controlling the high caries prevalence in young Chinese children. Community-based water fluoridation, for example, has been a very successful caries-preventive approach in the United States and is listed as one of the top ten public health achievements in the United States in the 20th century (CDC, 1999). However, it will have to be carefully reconsidered to benefit Chinese children, for several reasons: Chinese food culture and dietary practices are very different from those of the United States, and the optimal level of fluoride that needs to be added to the water supply is yet to be determined (Guo, 2000). One-sixth of the nation (242,885,400 of the entire population) live in areas where excess fluoride has been found in the drinking water, soil, and air (Chen, 1997; Chen, 2000). Lack of infrastructure and

9 806 Letters to the Editor J Dent Res 81(12) 2002 professional manpower prevents the implementation of community-based water fluoridation programs. Since China has established an outstanding nationwide primary healthcare system and network, we believe that dental public health strategies should utilize the existing system and focus on improving maternal oral health conditions and awareness plus intervention to reduce primary teeth susceptibility to caries and caries incidence in permanent teeth. Further studies of those strategies are critically important. Yihong Li 1 and Weijian Wang 2 1 Department of Basic Science and Craniofacial Biology, New York University College of Dentistry, New York, NY 10010, USA; yihong.li@nyu.edu. 2 Department of Community and Preventive Dentistry, Peking University School of Stomatology, Beijing, China 10081; ncoh@public.bta.net.cn REFERENCES Bratthall D (2000). Introducing the significant caries index together with a proposal for a new global oral health goal for 12-year-olds. Int Dent J 50: CDC (1999). Fluoridation of drinking water to prevent dental caries. MMWR 48: Chen C (2000). Endemic fluorosis in China. In: Fluoride and oral health. Guo Y, Lin J, Chen C, editors. Beijing: Scientific Technology Publishers, pp Chen Z (1997). Distribution of endemic fluorosis in China. China Public Health Report 13: Galen RS, Gambino SR (1975). Beyond normality: the predictive value and efficiency of medical diagnoses. New York: John Wiley & Sons, Inc. Guo Y (2000). Dental fluorosis in China. In: Fluoride and oral health. Guo Y, Lin J, Chen C, editors. Beijing: Scientific Technology Publishers, pp Hausen H (1997). Caries prediction-state of the art. Community Dent Oral Epidemiol 25: Heller KE, Eklund SA, Pittman J, Ismail AA (2000). Associations between dental treatment in the primary and permanent dentitions using insurance claims data. Pediatr Dent 22: Kaste LM, Marianos D, Chang R, Phipps KR (1992). The assessment of nursing caries and its relationship to high caries in the permanent dentition. J Public Health Dent 52: Powell LV (1998). Caries prediction: a review of the literature. Community Dent Oral Epidemiol 26: PRCMPH (1999). Second national epidemiological survey of oral health Beijing: P.R.C. Ministry of Public Health. People's Health Publishing Bureau. Steiner M, Helfenstein U, Marthaler TM (1992). Dental predictors of high caries increment in children. J Dent Res 71: