RISK ASSESSMENT FOR ORAL DISEASES J.W. STAMM1'2 P.W. STEWART2
H.M. BOHANNAN1 J.A. DISNEY1 R.C. GRAVES12 J.R. ABERNATHY2
School of Dentistry and School of Public Health The University of North Carolina Chapel Hill, North Carolina 27599-7450 2
Adv Dent Res 5:4-17, December, 1991
Abstract—This paper seeks to achieve four goals, each of which forms the basis for a section in the presentation. First, the rationale of risk assessment is fully described. In this section, some of the necessary conditions are identified that make disease prediction worth pursuing. The second section discusses some essential background to the understanding of risk assessment in dentistry. In this segment, attention is focused on population-based and individual-based perspectives, alternative approaches to expressing health risk, and methods for comparing the predictive accuracy of alternative risk assessment models. The third section of the paper develops a conceptual framework for risk assessment in dentistry. Particular emphasis is devoted to the identification of risk factors and their incorporation into alternative statistical models. In the fourth section, empirical data are offered by which certain comparisons of the alternative risk models can be drawn. The paper concludes with a discussion that emphasizes data and technical limitations, speculates on future applications, and suggests new avenues for research.
D
uring the past decade, risk assessment and its application to oral diseases have marched to the front of the dental health care policy field. The impetus for this development has come from a number of quarters. First, with the inexorable rise in health care costs, the linking of health risk assessment to the "appropriateness of care movement" is seen as a potentially important strategy for cost containment. With US dental care expenditures at 29.4 billion dollars in 1988 (ADA News, 1990), risk assessment for oral diseases and the resultant impact on dental care are assumed to offer significant efficiencies in the future dental care market. The second factor derives from the dramatic decline in dental caries experience and the substantial alterations in caries incidence and severity (Brunelle and Carlos, 1990; National Caries Program, NIDR, 1981). In recent years, it has become more apparent that in industrialized or developed countries, approximately 60 to 70% of the caries burden falls on roughly 20% of schoolchildren. This development has made it obvious that extensive dental caries is no longer a ubiquitous oral health problem among the young. In turn, that reality has led to acceptance of the concept that individualized or targeted approaches to prevention and treatment, along with community fluoridation programs, represent a more effective and efficient strategy for the management of dental caries. A third factor springs out of the changing concept of periodontal disease initiation and progression. Since the early 1980's, periodontists have attempted to move away from defining periodontal disease on the basis of past periodontal attachment and bone loss and have re-focused their attention on determining the microbial, enzymatic, immunological, and other host factors that may be associated prospectively with periodontal disease. Finally, as the science of health risk assessment has continued to evolve in medicine, its potential utility in the dental field has grown concomitantly. Thus, for such important oral conditions as cleft lip and palate, as well as for oral cancers, risk assessment represents a promising technology for both research and patient care.
RATIONALE FOR THE PREDICTION OF FUTURE ORAL DISEASE OCCURRENCE
Presented during "Prevention Revisited", a Symposium at Eastman Dental Center's 75th Anniversary Celebration, Rochester, NY, September 13-14, 1990 This study was supported by a grant from the Robert Wood Johnson Foundation and by NIH grant SO7RR05333.
The rationale for attempting to predict the future occurrence of oral diseases or conditions is based on a number of considerations. First, the disease in question must have a relatively low incidence to justify the effort and expense of identifying individuals believed to be susceptible to a particular condition. Until the late 1970's, the incidence of extensive dental decay was sufficiently high as to be nearly ubiquitous in schoolchildren. Under such conditions, any attempt to predict the subset of children susceptible to substantial caries experience would have been inefficient. In contrast, as caries experience has declined and has become concentrated on approximately 20% of American schoolchildren, the logic of attempting to
VOL.
5
RISK ASSESSMENT FOR ORAL DISEASES
identify, a priori, the high-disease-formers becomes a useful and practical activity. Most other oral diseases and conditions— including periodontal diseases, craniofacial malformations, and oropharyngeal cancers—exhibit the low disease incidence required for effective risk management implementation. Second, the development of new and powerful diagnostic technologies will often permit the implementation of risk assessment activities where such efforts were largely useless before the introduction of the newer techniques. New techologies for the rapid and accurate identification of putative pathogens for periodontal diseases and dental caries represent such potentially useful, if insufficiently tested, diagnostic methods. Until recently, considerable hope was held out for viral markers, such as HPV 16 and 18, Herpesvirus, and Epstein-Barr virus, as risk factors for future oral cancer. In the future, genetic markers will almost certainly play a role in determining the likelihood that specific sets of parents would have a child born with cleft lip/palate. Third, risk assessment becomes more compelling if it is linked to an explicit strategy to target appropriate levels of preventive care or treatment to those individuals identified as being at high risk to future disease. Risk assessment exclusively as a screening activity, without appropriate follow-up, is usually not justified. Fourth, risk assessment is inherently attractive as a means for increasing the efficacy and efficiency of preventive interventions. By targeting the most susceptible subset of the population, one is able to justify a more intensive form of preventive care that stands a better chance of achieving a clinically significant impact. In the case of caries, weekly fluoride mouthrinsing may be too little prevention for everyone, while placement of pit and fissure sealants in a highly-cariessusceptible population can be demonstrably more efficacious. Matching prevention to an individual's risk of future disease also leads to an elimination of wasted preventive services to those at low or negligible risk to future disease. In short, and as has recently been demonstrated for routine medical therapy with long-term beta blockers, risk assessment helps practitioners to define appropriate levels of preventive care, contributing to a more rational and efficient use of resources (Goldman et al., 1988).
BACKGROUND TO UNDERSTANDING HEALTH RISK ASSESSMENT The science of health risk assessment has only recently entered the lexicon of dental practice. To understand fully the framework of risk assessment, to interpret accurately the data generated by risk assessment, and to be able to compare the predictive accuracy of alternative risk assessment models require a basic understanding of several concepts.
Two Perspectives on Risk Assessment Health risk assessment draws from two intellectual roots, each of which has provided a particular view on how risk assessment may be used to improve health (Jeffery, 1989). The first and most common of these is the population perspective, an approach developed almost entirely within the field of epidemiology and public health. The second view is the individual perspective, a
system that uses epidemiological findings but owes its real origins to clinical preventive medicine and the science of clinical decision theory. The population perspective does not attempt to predict which individuals in society are most likely to develop disease. Rather, the population perspective is concerned with the identification and quantification of risk factors that significantly compromise the population's health. This approach is also known as hazard appraisal, and in addition to its epidemiological roots, owes its development to occupational and environmental medicine (Gordis, 1988). Under the rubric of hazard appraisal, the National Research Council has defined risk assessment as consisting of four steps—hazard identification, dose-response assessment, exposure assessment, and risk characterization. Hazard identification usually begins with an acute clinical observation that a certain type of disease or condition appears to be occurring in erstwhile-inexplicable clusters of patients. It was precisely such insightful clinical observations that triggered subsequent epidemiological investigations linking, for example, methylmercury to Minymata disease, thalidomide as well as the retinoids to teratogenic effects, and diethylstilbestrol (DES) to vaginal adenocarcinoma. It can be seen from these examples that hazard identification includes consideration of the emergence of unforeseen side-effects from new pharmaceutical agents. Dose-response assessment is typically carried out by means of animal studies. The usual form of this research phase relies on classic toxicological studies, frequently using small-animal models. Such studies are essential because they cannot be conducted on humans, but they are difficult to interpret for two reasons (Crump, 1985): One, it is difficult to obtain reliable low-dose information in small (rodent) animal studies; and two, it is difficult to extrapolate projected low-dose effects in rodents to be meaningful for humans, because of the extremely wide confidence bands associated with such studies. Exposure assessment is simply the determination of the proportion of a population that may be at risk to a certain environmental or occupational hazard. For example, among the current issues being explored about radon contamination is the question of how many persons in the country might be affected. The fourth step, risk characterization, attempts to combine the information from the three prior activities. Typically, this will lead to estimates of the attributable risk associated with a given health hazard. Turning to the individual perspective, this approach is based on Robbins' early work in the 1930's and 1940's in which he developed methods to aid the physician in recording patient health risk factors, leading then to the identification of appropriate preventive procedures that would be effective in avoiding anticipated future health problems (Robbins and Hall, 1970). The objective was to create a prospective (preventive) rather than a retrospective (treatment) orientation for the physician. A distinctive characteristic of this perspective is its use of epidemiologic data to generate quantitative risk predictions based on the presence or absence of identified risk factors in the individual (Schoenbach, 1987). Information describing an
STAMM et
individual's biological characteristics, personal and family medical history, habits, lifestyle, and health-related practices are inserted into statistical decision models, developed from epidemiological data and vital statistics, to project that person's risk of mortality over some future period. These projections are used then in educational and motivational techniques to encourage the patient to adopt a healthier, prevention-oriented lifestyle. In this regard, one of this system's most notable contributions to preventive medicine has been its application to the prediction and prevention of coronary heart disease where stress, smoking, obesity, alcohol use, and lack of exercise were identified as strong predictors of heart disease in a series of epidemiological studies. Of course, dentistry, too, has been experimenting with efforts to predict the occurrence of future disease. Most of these attempts have focused on linear regression and correlation methods to predict dental caries in individual subjects, usually schoolchildren. Indeed, attempts to develop tests with predictive capability began even before 1900 (Black, 1899;Miller, 1890). Since that time, many distinguished investigators have studied a wide variety of demographic, dietary, physical, chemical, electrical, and microbiological factors, searching for methods to predict caries occurrence. Much of the previous prediction work prior to 1976 was summarized in a publication reporting the proceedings of an international symposium, "Methods of Caries Prediction", held in October of 1977 (Bibby and Shern, 1978). In short, that publication reported no caries prediction method sufficiently valid or reliable to warrant recommendation for use at that time. Interest in caries prediction was revived in the United States in the early 1980's, when the secular decline in caries among children was reported.
Expressing Health Risk There are a number of ways health risk may be expressed. The three key concepts are absolute risk, relative risk, and population- attributable risk (Fleiss, 1981; Fletcher et ah, 1982). Absolute risk is simply the risk that an individual will get a certain disease over a defined period of time. Absolute risk is frequently estimated from the incidence (I) determined for a specific disease in a population. Relative risk is a measure that compares health risks among two populations. Algebraically, it is the ratio of the absolute risks of disease in two individuals or the ratio of disease incidences in two populations. Most commonly, the risk estimate for the individual or population exposed to the risk factor forms the numerator of the risk ratio, while the risk estimate for the unexposed individual or population is the denominator. Algebraically, relative risk (RR) may be expressed as
where IE is the incidence rate in the exposed population, and IE is the incidence rate in the unexposed or reference population. In prospective or cohort studies, higher relative risk implies stronger evidence for causation. Related to relative risk is the odds ratio, which is used to estimate relative risk in retrospective or case-control studies, particularly when the base disease incidence is low (i.e., 2 from grade 1 to grade 3; more exactly, HIGHRATE = 1 if the rate (DMFS3 - DMFS^ x
(2 x 365.25 davs^ (elapsed days at risk)
is greater than or equal to 2 when rounded to an integer. Otherwise, HIGHRATE = 0. Table 7 describes each of the 32 candidate predictor variables selected for consideration. Only children with complete data for the 33 outcome and predictor variables were included in the prediction efforts reported here. It is assumed that the reader is familiar with the essentials of LDA and LRA. Details about the CART can be found in Breiman et al (1984), and the application of CART to caries risk assessment has been explored by Stewart and Stamm (1991). The CART analysis, as implemented through the software developed by Breiman et al, uses recursive partitioning and regression tree pruning procedures to construct an optimal decision or classification rule. The CART method, it should be re-
al
ADV DENT RES DECEMBER
1991
TABLE 8 LINEAR DISCRIMINANT ANALYSIS STEP-WISE, 32 VARIABLES P-VAL. AIKEN P-VAL. PORTLAND pdmfs 0.0001 0.0001 PREDCAR 0.0001 MORPH 0.0001 MORPH 0.0044 0.0001 DMFS pdmfs 0.0001 dssmall 0.0201 FSARS 0.0008 EDHHH 0.0335 fslarge DSSMALL 0.0266 WHITE 0.0777 LACTOBAC 0.0221 REFCAR 0.1008 N=1024 0.0316 MALE 0.0626 FL-TABS 0.0834 WSLS 0.1010 CHILDREN 0.0957 EDHHH N = 914 emphasized, provides for step-wise variable selection, honest control of model complexity, and estimation of the prediction error rate as automated features of the algorithm. Further, the method is extremely robust with respect to the outliers in the predictor variables and with respect to the presence of variables having little or no predictive value. Table 8 displays the key results from step-wise LDA models for the caries data from Portland and Aiken Grade 1 children. The P-value to enter and to remove variables from the models was set at 0.15. It is apparent that the caries risk prediction model for Portland was more parsimonious than the one for Aiken, containing seven against 12 predictor variables. The LDA variables providing useful caries risk assessment information for Portland were: examiner's baseline subjective prediction of caries increment (PREDCAR), sum of morphology scores on fissured sound surfaces of permanent molars (MORPH), deciduous posterior dmfs (pdmfs), baseline fissured sound permanent surfaces erupted and at risk for caries (FSARS), educational level of head of household (EDHHH), number of permanent surfaces with minimal decay (DSSMALL), and lactobacillus growth index on dip slide (LACTOBAC). For Aiken, the variables relevant for risk classification by LDA were: pdmfs, MORPH, baseline permanent DMFS score (DMFS), number of deciduous tooth surfaces with minimal decay (dssmall), number of deciduous surfaces with extensive fillings (fslarge), indicator variable for race (WHITE), examiner's baseline referral score reflecting need for restorative care (REFCAR), indicator variable for gender (MALE), indicator variable for prior fluoride supplement use (FL-TABS), number of white-spot lesions on deciduous and permanent teeth (WSLS), number of other children in the household (CHILDREN), and EDHHH. It is interesting to note that, although these two models are substantially different, there are commonalities—reflected by the variables MORPH, pdmfs, and EDHHH—that contribute to both models. For Portland, LDA generates a sensitivity of 0.58 and specificity of 0.80. For Aiken, LDA sensitivity is
VOL.
5
13
RISK ASSESSMENT FOR ORAL DISEASES
TABLE 9 LOGISTIC REGRESSION ANALYSIS STEP-WISE, 32 VARIABLES PORTLAND P-VAL. AIKEN P-VAL. PREDCAR
0.0001
pdmfs
0.0001
MORPH
0.0001
MORPH
0.0001
EDHHH
0.0266
DMFS
0.0001
SEALANTS
0.0285
dssmall
0.0001
fslarge
0.0011
DSLARGE
0.0024
MALE
0.0105
ANTIBIOT
0.0390
N = 1024
N = 914
0.69, and specificity is 0.78. Table 9 gives analogous caries risk assessment models for Portland and Aiken grade 1 children using the method of stepwise logistic regression (LRA). Variable entry and removal criteria were maintained at 0.15. Once again, the caries risk models for the two groups of children are somewhat different, with the Portland model containing only four prediction variables against eight for the Aiken model. The LRA model variables significant for caries risk classification in Portland were: PREDCAR, MORPH, EDHHH, and the baseline number of surfaces with sealants present (SEALANTS). In Aiken, the variables useful for caries risk assessment by LRA were: pdmfs, MORPH, DMFS, dssmall, fslarge, baseline permanent tooth surface with extensive decay or with both filling and caries (DSLARGE), MALE, and reported prioruse of antibiotics at baseline (ANTIBIOT). The only commonality in LRA models across the two different grade 1 population groups resided in the variable MORPH. Considering the different numbers of variables in these two models, and the fact that only MORPH entered and remained in both models, suggests that the caries pattern and approaches to caries prediction may be quite dissimilar from population to population.
Fig. 2 shows the results from the third modeling technique, classification and regression tree analysis, applied to the Portland grade 1 sample. The regression tree generated by CART suggests that only two variables, posterior deciduous dmfs (pdmfs) and examiner subjective prediction of caries increment (PREDCAR), were substantially useful in differentiating between high- and low-caries-risk children in this population. For the Portland children, the regression tree model generated three terminal nodes or branches, and classified 289 (183 + 71 + 23+12) children as predicted high-risk candidates and 735 (684 + 51) as predicted low-risk children. Notice also that 83 (71 + 12) of 134 (71 + 12 + 51) observed or true high-risk children were correctly predicted to be at high risk, for a model sensitivity of 0.62. A total of 684 of 890 (684 + 23 + 183) observed or true low-risk children were predicted to be at low risk, for a model specificity of 0.77. Fig. 3 gives the regression tree model for the Aiken grade 1 children. As is evident, this tree is vastly more complex than the one for Portland, and features 12 terminal nodes and nine predictor variables. The predictor variables used in the CART model include, in order of importance: deciduous posterior,
split-1pctafs > 3
spli t-2—| n=770 PREDCAR > 2
183 l o | 71 hi
yes
23 lo 12 hi
TERMINAL NIXES
10
11
12
TERMINAL NODES
Fig. 2—Classification tree diagram. Final two-variable classification tree based on N = 1024 children studied in Portland, Maine. Each terminal node is designated as high-risk (HI) or low-risk (LO).
Fig. 3—Classification tree diagram. Final nine-variable classification tree based on N = 914 children studied in Aiken, South Carolina. Each terminal node is designated as high-risk (HI) or low-risk (LO).
14
TABLE 10 CARIES RISK ASSESSMENT MODELS: COMPARISON OF LINEAR DISCRIMINANT ANALYSIS, LOGISTIC REGRESSION ANALYSIS, AND CLASSIFICATION ANALYSIS AND REGRESSION TREE PORTLAND. N = 1024 AIKEN. N = 914 MODEL
ADV DENT RES DECEMBER
STAMM et al.
VAR SENS SPEC
VAR SENS SPEC
LDA
7
58
80
12
69
78
LRA
4
66
77
8
68
80
9
61
86
77 2 62 CART VAR = Number of variables in the model SENS = sensitivity SPEC = specificity
dmfs (pdmfs), sum of morphology scores on fissured sound surfaces of permanent molars (MORPH), baseline permanent DMFS (DMFS), baseline permanent fissured sound surfaces erupted and at risk for caries (FSARS), age (AGE), number of deciduous tooth surfaces with more than minimal decay or with both filling and decay (dslarge), number of deciduous tooth surfaces with minimal decay (dssmall), examiner's baseline referral score reflecting need for restorative care (REFCAR), and number of deciduous filled surfaces (fs). Notice, incidentally, that the CART method permits variables to appear in more than one place in the model, e.g., MORPH and AGE. Once again, inside each terminal node the observed or true caries-risk status is given for the children classified there. The CART model generated a sensitivity of 0.61 and a specificity of 0.86. Remarkably, use of the CART modeling approach produced models for Aiken and Portland grade 1 children that have only one variable in common, namely, deciduous posterior dmfs. The three modeling approaches used above—LDA, LRA, and CART—generate results that are similar in some ways, but fairly divergent in others. Table 10 presents a comparison that tends to emphasize the similarities. When the identical data set from Portland was used, the three modeling strategies generated sensitivities of from 0.58 to 0.66 and specificities of from 0.77 to 0.80. For Aiken, the model sensitivities range from 0.61 to 0.69, with specificities between 0.78 and 0.86. These results suggest rather similar model performance, although the large range for number of included predictor variables hints at model disparities.
TAUT 17 11 X/TLAJX^XI*
XX
This is confirmed in Tables 11 and 12, which demonstrate the potential heterogeneity that can occur in caries risk assessment models based solely on the choice among alternative statistical techniques. Table 11 indicates that the Portland models incorporated anywhere from two variables (for the CART approach) to eight variables (when the LDA method was used). For Portland, the only variable common to all three models was PREDCAR, the clinician's own subjective baseline estimate of future caries increment. Caries in the deciduous dentition and fissure morphology on molars both entered two models at a high level. Four variables appeared in only one of the three models (FSARS, DSSMALL, LACTOBAC, and SEALANTS). Table 12 presents the models developed from the Aiken data set. Here, there is complete commonality among the three top variables (pdmfs, MORPH, and DMFS). The variables dssmall and fslarge were next in importance in the LDA and LRA models. Thereafter, the models diverge markedly. For example, race and prior use of fluoride supplements are considered significant only by LDA, antibiotic use only by LRA, and age of subject only by CART. These results indicate that choice of analytical method will quite strongly influence the model structure, even if the models' risk classification performances, as determined by sensitivity and specificity, are roughly comparable. This is not surprising considering the extensive inter-relations (correlations) among the predictor variables: For example, FSARS and DMFS convey partially redundant information.
DISCUSSION This paper has provided a four-point rationale as well as technological background relevant to risk assessment for oral diseases. Emphasis was placed on the need to distinguish the population perspective from the individual perspective of risk assessment. Key methods for expressing health risk were reviewed. Indices were described with which to compare the predictive accuracies of alternative risk models. This was followed by a detailed discussion of risk factor identification which, in turn, led to an elaboration of alternative modeling TABLE 12 VARIABLE SELECTION, IN ORDER OF IMPORTANCE, BY LDA, LRA, AND CART (AIKEN, GRADE 1, N = 914) CART LDA LRA pdmfs MORPH
pdmfs MORPH
pdmfs MORPH
DMFS
DMFS
DMFS
dssmall
dssmall
FSARS
VARIABLE SELECTION, IN ORDER OF IMPORTANCE, BY LDA, LRA, AND CART (PORTLAND, GRADE I, N = 1024) LDA LRA CART PREDCAR PREDCAR pdmfs
fslarge
fslarge
AGE
WHITE
DSLARGE
dslarge
MORPH
MORPH
REFCAR
MALE
dssmall
pdmfs
EDHHH
MALE
ANTIBIOT
REFCAR
FSARS
SEALANTS
FL-TABS
PREDCAR
1991
EDHHH
WSLS
DSSMALL
CHILDREN
LACTOBAC
EDHHH
fs
VOL.
5
RISK ASSESSMENT FOR ORAL DISEASES
strategies with which to link the risk factors or predictor variables with future disease outcome. Finally, recent data from the UNCCRA Study were used to compare the performances of three different statistical modeling approaches—linear discriminant analysis, logistic regression analysis, and classification and regression tree analysis. It is evident that the science of risk assessment has application to most of the major oral diseases and conditions. In the fields of oral cancer and orofacial malformations, risk assessment will be most successfully pursued by use of the population perspective. That is, risk assessment models will show clearly what genetic, environmental/behavioral, and occupational factors may be associated with elevated disease risk, and should therefore be avoided as a matter of better public health. Nevertheless, in spite of clear-cut evidence of etiological links to certain risk factors, the very low incidence of oral cancer and craniofacial malformations results in low positive-predictive values for these models. Hence, prevention and health promotion programs for these types of health problems will be most successful if a population perspective is pursued. The field of periodontology will also benefit by more explicit adoption of the risk assessment concept. Periodontology, however, highlights another dichotomy in the application of risk assessment methods. Whereas risk assessment for dental caries, oral cancers, and craniofacial malformations can be used to enhance current clinical or public health practice, in periodontology risk assessment models have their greatest current application in furthering research. One of the most significant current challenges in periodontology is to define a measure of contemporaneous periodontal disease and to clarify the etiological factors and chain of events that lead to periodontal breakdown. Although much microbiological, immunological, enzyme, and host factor data have been and are continuing to be generated, treating these and periodontal disease activity data in appropriate multi-factorial risk assessment models may permit a fuller evaluation of the role played by putative risk factors. Much of the recent oral-health-oriented risk assessment research has focused on dental caries. A great deal of the latest work has been directed at predicting future caries occurrence in individuals—hence, the individual perspective has been applied. Results from the UNCCRA Study in South Carolina and in Maine suggest that the current ability to identify prospecti vely the 20-25 % of the highest-caries-forming children in the population is limited to a sensitivity of 0.6 and a specificity of 0.8-0.85 (Disney etai, 1990). This is short of the prior goals of 0.75 sensitivity and 0.85 specificity. Versions of developed models are available with 0.75 sensitivity, but the corresponding specificities then fall into the 0.5-0.7 range. Several possible reasons may exist for the current 0.6/0.8 sensitivity/specificity limit encountered. One, the whole category of biochemical predictors, mostly related to salivary components, was not assessed in the UNCCRA Study. This was due, in large measure, to the absence of practical field procedures that could be used in the baseline examination of 5000 children. Two, the placement of dental sealants during the study, undoubtedly interfered with the natural biological conditions that would be needed for a successful caries
15
prediction study. Sealants were heavily used in the Portland group, which may account for the weakening of tooth pit and fissure morphology as a caries risk predictor in the Portland sample. Three, it is also possible that the microbiological dipslide test used, particularly for Mutans streptococci, was too general. Though the Mutans streptococci (MS) results were strongly associated with caries increment on a bivariate basis, the failure of MS to be a significant predictor in the larger multivariate models may be due to the fact that only a subset of the genetically diverse strains of MS may be related to significant caries initiation (Kuramitsu, 1987). And four, operating on the outcome variable side, a fairly common observation of small, possibly preventive, restorations contributed to the DMFS increment in excess of the actual occurrence of various lesions. To the degree this phenomenon was real, it would contribute to lower model sensitivity. A particularly interesting use of caries risk assessment modeling is its application as a research tool at the Eastman Dental Center. The Eastman approach uses a series of frequent, six-month examinations, uses intensive microbial and biochemical assays at each examination, is limited to children who are completely caries-free at baseline, and eliminates from the study children who receive restoration without a prior caries diagnosis for that surface by the study team. Using such a study paradigm, the Eastman group will undoubtedly learn a great deal more about the risk factors that lead predictably to caries initiation. What about the future? Work accomplished to date in the area of caries risk assessment suggests some significant themes where more research might be fruitful. First, it is evident that more detailed studies need to be done on the biological and clinical factors that are associated with disease initiation. Whether for oral cancer, craniofacial malformations, periodontal diseases, or caries, the development of molecular biology, recombinant DNA technologies, and new diagnostic tools holds the greatest promise for refining our understanding about oral disease etiology. Second, in the case of periodontal disease, progress could be made by refining the outcome measure that defines the highrisk individual. For periodontal disease, what is currently lacking is a widely-agreed-upon measure of disease activity. Periodontal disease is still largely defined by post hoc evidence of past disease activity (i.e., attachment loss) rather than by a technology that permits the diagnosis of current disease. This limitation makes it difficult to classify patients unambiguously into those currently with periodontal disease and those without. Third, the continuing decline in caries incidence and severity is leading to a situation where the research question may change from trying to predict those individuals likely to form high caries increments to one where the challenge is to predict those individuals who experience any decay at all during a given time period. The latter concept of risk assessment is much more in keeping with current medical models. Further, the "no caries/caries" criterion would eliminate current debates about how much caries increment in a given time period actually defines the high-caries-risk individual. Fourth, although the more appropriate modeling techniques—such as linear discriminant analysis, logistic
16
STAMM et
regression, and classification and regression tree analysis— are relatively comparable in terms of their predictive potential, these methods are not necessarily consistent in the risk factors they identify as being of most significance for the disease or condition under investigation. Such heterogeneity in outcome is important, since it obscures the leads investigators might pursue in further etiological research. For this reason, additional well-controlled comparative studies of alternative risk assessment models appear to be well-warranted. Fifth, as the science of risk assessment for oral diseases matures, a new arena of dental health services research will develop. As prospective classification of low- and high-risk patients becomes more workable, a whole range of issues emerges under the rubric of appropriate care. Building upon the Federal Government's present concern with health outcomes, risk assessment and appropriateness of dental care procedures may well occupy an important position in the oral health research agenda for the year 2000 and beyond. In conclusion, the combination of changing oral disease epidemiology, greater understanding of risk factors, and more powerful diagnostic tools has focused new attention on risk assessment in dentistry. The prevalence of dental caries, the severity of periodontal disease, and the rate of edentulousness have all declined to an extent that early identification of the atrisk subpopulation could become an important aspect of dental practice in both the private and public health setting. Growing knowledge about the etiological factors linked to oral cancer and craniofacial malformations permits the application of risk assessment principles to these conditions as well. The development of risk assessment strategies relevant to dentistry has occurred only in recent years. From both patient-care and public-policy perspectives, the case for risk assessment is appealing. However, up to the present time, risk assessment in dentistry has had limited applications. Retarding the implementation of risk assessment principles are: (1) insufficient knowledge concerning biological factors of oral diseases; (2) incomplete development of statistical modeling techniques; and (3) resistance to put into practice significant partial knowledge that has been generated already. Continued research on the first two problems and more effective technology transfer for the third are needed before risk assessment can become a reality in dentistry.
REFERENCES ABERNATHY, J.R.; GRAVES, R.C.; BOHANNAN, N.M.; STAMM, J.W.; GREENBERG, B.G.; and DISNEY, J.A. (1987): Development and Application of a Prediction Model for Dental Caries, Community Dent Oral Epidemiol 15:24-28. ADA NEWS (1990): HCFA Reduces Dental Expenditure Estimate. 21(10): 1. Chicago: American Dental Association. BENNETT, B.M. (1972): On Comparisons of Sensitivity, Specificity and Predictive Value of a Number of Diagnostic Procedures, Biometrics 28:793-800. BIBBY, B.G. and SHERN, R.J., Eds. (1978): Methods of
Caries Prediction. In: Microbiology Abstracts— Bacteriology (Spec Suppl), Washington, DC: Information Retrieval, Inc.
ADV DENT RES DECEMBER
al.
1991
BINNIE, W.H. and RANKIN, K.V. (1988): Epidemiology of Oral Cancer. In: Oral Cancer: Clinical and Pathological Considerations, B.A. Wright, J.M. Wright, and W.H. Binnie, Eds., Boca Raton, FL: CRC Press, pp. 2-11. BLACK, G.V. (1899): Susceptibility and Immunity to Dental Caries, Dent Cosmos 41:826-836. BREIMAN, L.; FRIEDMAN, J.H.; OLSHEN, R.A.; and STONE, CJ. (1984): Classification and Regression Trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. BRUNELLE, J.A. and CARLOS, J.P. (1990): Recent Trends in Dental Caries in U.S. Children and the Effect of Water Fluoridation, J Dent Res 69 (Spec Iss):723-727. CRUMP, K.S. (1985): Methods for Carcinogenic Risk Assessment. In: Principles of Health Risk Assessment, P.F. Ricci, Ed., Englewood Cliffs, NJ: Prentice-Hall, Inc., pp. 279-319. DISNEY, J.A.; STAMM, J.W.; GRAVES, R.C.; ABERNATHY, J.R.; BOHANNAN, H.M.; and ZACK, D.D. (1990): Description of Preliminary Results of a Caries RiskAssessmentModel. In: Risk Assessment in Dentistry, J.D. Bader, Ed., Chapel Hill, NC: University of North Carolina, Dental Ecology. ERICSON, A.; KALLEN, B.; and WESTERHOLM, P. (1979): Cigarette Smoking as an Etiologic Factor in Cleft Lip and Palate, Am J Obstet Gynecol 135:348-351. EVANS, D.R.; NEWCOMBE, R.G.; and CAMPBELL, H. (1979): Maternal Smoking Habits and Congenital Malformations: A Population Study, BrMedJl: 171-173. FLEISS, J.L. (1981): Statistical Methods for Rates and Proportions, 2nd ed., New York: John Wiley and Sons. FLETCHER, R.H.; FLETCHER, S.W.; and WAGNER, E.H.
(1982):
Clinical Epidemiology—The Essentials,
Baltimore, MD: Williams & Wilkins. GOLDMAN, L.; SIA, B.; COOK, E.F.; RUTHERFORD, J.D.; and WEINSTEIN, M.C. (1988): Cost and Effectiveness of Routine Therapy with Long-term Beta-adrenergic Antagonists After Acute Myocardial Infarction, TV Eng J Med 319:152-157. GORDIS, L. (1988): Epidemiology and Health Risk Assessment, New York: Oxford University Press. GREGG, N.M. (1941): Congenital Cataract Following German Measles in the Mother, Trans Aust Coll Ophthalmol 3:3546. HERBST, A.L.; ULFELDER, H.; and POSKANZER, D.C. (1971): Adenocarcinoma of the Vagina. Association of Maternal Stilbestrol Therapy with Tumor Appearance in Young Women, N Eng J Med 284:878-881. HILL, L.; MURPHY, M ; McDOWALL, M.; and PAUL, A.H. (1988): Maternal Drug Histories and Congenital Malformations: Limb Reduction Defects and Oral Clefts, / Epidemiol Commun Hlth 42:1-1. JEFFERY, R.W. (1989): Risk Behaviors and H e a l t h Contrasting Individual and Population Perspectives, Am Psychologist 44:1194-1202. KEELS, M.A. (1990): A Research Proposal to Study the Role of Maternal Cigarette Smoking in the Etiology of Cleft Lip With or Without Cleft Palate. Ph.D. Dissertation, University
VOL.
5
RISK ASSESSMENT FOR ORAL DISEASES
of North Carolina, Chapel Hill. KELSEY, J.L.; DWYER, T.; and HOLFORD, T.R. (1978): Maternal Smoking and Congenital Malformations: An Epidemiological Study, J Epidemiol Commun Hlth 32:102107. KELSEY, J.L.;THOMPSON,W.D.;andEVANS,A.S.(1986): Methods on Observational Epidemiology, New York: Oxford University Press. KHOURY, M.J.; GOMEZ-FARIAS, M.; MULINARE, J.; andBORING, J. (1988): "Does Maternal Cigarette Smoking During Pregnancy Cause Cleft Lip and Palate in Offspring?" Presentation, Society of Epidemiologic Research, Vancouver, British Columbia, Canada (Abst. No. 72). KLEINBAUM, D.G.; KUPPER, L.L.; andMORGENSTERN, H. (1982): Epidemiologic Research: Principles and Quantitative Methods, Belmont, CA: Lifetime Learning Publications. KURAMITSU, H.K. (1987): Recent Advances in Defining the Cariogenicity of Mutans Streptococci: Molecular Genetic Approaches, Eur J Epidemiol 3:257-260. MILLER, W.D. (1890): Microorganisms of the Human Mouth, Philadelphia: S.S. White Dental Mfg. Co. NATIONAL CARIES PROGRAM, NATIONAL INSTITUTE OF DENTAL RESEARCH (1981): The Prevalence of Dental Caries in United States School Children, 197980, The National Dental Caries Prevalence Survey.
17
Washington, DC: DHHS, NIH Publ. No. 82:2245. ROBBINS, L. and HALL, J. (1970): How to Practice Prospective Medicine. Indianapolis: Methodist Hospital of Indiana. ROTHMAN, K.J. and KELLER, A.Z. (1972): The Effect of Joint Exposure to Alcohol and Tobacco on Risk of Cancer of the Mouth and Pharynx, J Chronic Diseases 25:711 -716. SAXEN, I. (1974): Cleft Lip and Palate in Finland: Parental Histories, Course of Pregnancy and Selected Environmental Factors, Int J Epidemiol 3:263-270. SCHOENBACH, V.J. (1987): Appraising Health Risk Appraisal, Am J Pub Health 77:409-411. STAMM, J.W.; DISNEY, J. A.; GRAVES, R.C.; BOH ANN AN, H.M.; and ABERNATHY, J.R. (1988): The University of North Carolina Caries Risk Assessment Study. I. Rationale and Content, J Pub Hlth Dent 48:225-232. STEWART, P.W. and STAMM, J.W. (1991): Classification Tree Prediction Models for Dental Caries Using Clinical, Microbiological and Interview Data, / Dent Res 70:12391251. VECCHIO,T.J. (1966): Predictive Value of a Single Diagnostic Test in Unselected Populations, N Eng J Med 274:11711173. WRIGHT, B.A.; WRIGHT, J.S.; and BINNIE, W.H. (1988): Oral Cancer: Clinical and Pathological Considerations, Boca Raton, FL: CRC Press.
H.M. BOHANNAN1 J.A. DISNEY1 R.C. GRAVES12 J.R. ABERNATHY2
School of Dentistry and School of Public Health The University of North Carolina Chapel Hill, North Carolina 27599-7450 2
Adv Dent Res 5:4-17, December, 1991
Abstract—This paper seeks to achieve four goals, each of which forms the basis for a section in the presentation. First, the rationale of risk assessment is fully described. In this section, some of the necessary conditions are identified that make disease prediction worth pursuing. The second section discusses some essential background to the understanding of risk assessment in dentistry. In this segment, attention is focused on population-based and individual-based perspectives, alternative approaches to expressing health risk, and methods for comparing the predictive accuracy of alternative risk assessment models. The third section of the paper develops a conceptual framework for risk assessment in dentistry. Particular emphasis is devoted to the identification of risk factors and their incorporation into alternative statistical models. In the fourth section, empirical data are offered by which certain comparisons of the alternative risk models can be drawn. The paper concludes with a discussion that emphasizes data and technical limitations, speculates on future applications, and suggests new avenues for research.
D
uring the past decade, risk assessment and its application to oral diseases have marched to the front of the dental health care policy field. The impetus for this development has come from a number of quarters. First, with the inexorable rise in health care costs, the linking of health risk assessment to the "appropriateness of care movement" is seen as a potentially important strategy for cost containment. With US dental care expenditures at 29.4 billion dollars in 1988 (ADA News, 1990), risk assessment for oral diseases and the resultant impact on dental care are assumed to offer significant efficiencies in the future dental care market. The second factor derives from the dramatic decline in dental caries experience and the substantial alterations in caries incidence and severity (Brunelle and Carlos, 1990; National Caries Program, NIDR, 1981). In recent years, it has become more apparent that in industrialized or developed countries, approximately 60 to 70% of the caries burden falls on roughly 20% of schoolchildren. This development has made it obvious that extensive dental caries is no longer a ubiquitous oral health problem among the young. In turn, that reality has led to acceptance of the concept that individualized or targeted approaches to prevention and treatment, along with community fluoridation programs, represent a more effective and efficient strategy for the management of dental caries. A third factor springs out of the changing concept of periodontal disease initiation and progression. Since the early 1980's, periodontists have attempted to move away from defining periodontal disease on the basis of past periodontal attachment and bone loss and have re-focused their attention on determining the microbial, enzymatic, immunological, and other host factors that may be associated prospectively with periodontal disease. Finally, as the science of health risk assessment has continued to evolve in medicine, its potential utility in the dental field has grown concomitantly. Thus, for such important oral conditions as cleft lip and palate, as well as for oral cancers, risk assessment represents a promising technology for both research and patient care.
RATIONALE FOR THE PREDICTION OF FUTURE ORAL DISEASE OCCURRENCE
Presented during "Prevention Revisited", a Symposium at Eastman Dental Center's 75th Anniversary Celebration, Rochester, NY, September 13-14, 1990 This study was supported by a grant from the Robert Wood Johnson Foundation and by NIH grant SO7RR05333.
The rationale for attempting to predict the future occurrence of oral diseases or conditions is based on a number of considerations. First, the disease in question must have a relatively low incidence to justify the effort and expense of identifying individuals believed to be susceptible to a particular condition. Until the late 1970's, the incidence of extensive dental decay was sufficiently high as to be nearly ubiquitous in schoolchildren. Under such conditions, any attempt to predict the subset of children susceptible to substantial caries experience would have been inefficient. In contrast, as caries experience has declined and has become concentrated on approximately 20% of American schoolchildren, the logic of attempting to
VOL.
5
RISK ASSESSMENT FOR ORAL DISEASES
identify, a priori, the high-disease-formers becomes a useful and practical activity. Most other oral diseases and conditions— including periodontal diseases, craniofacial malformations, and oropharyngeal cancers—exhibit the low disease incidence required for effective risk management implementation. Second, the development of new and powerful diagnostic technologies will often permit the implementation of risk assessment activities where such efforts were largely useless before the introduction of the newer techniques. New techologies for the rapid and accurate identification of putative pathogens for periodontal diseases and dental caries represent such potentially useful, if insufficiently tested, diagnostic methods. Until recently, considerable hope was held out for viral markers, such as HPV 16 and 18, Herpesvirus, and Epstein-Barr virus, as risk factors for future oral cancer. In the future, genetic markers will almost certainly play a role in determining the likelihood that specific sets of parents would have a child born with cleft lip/palate. Third, risk assessment becomes more compelling if it is linked to an explicit strategy to target appropriate levels of preventive care or treatment to those individuals identified as being at high risk to future disease. Risk assessment exclusively as a screening activity, without appropriate follow-up, is usually not justified. Fourth, risk assessment is inherently attractive as a means for increasing the efficacy and efficiency of preventive interventions. By targeting the most susceptible subset of the population, one is able to justify a more intensive form of preventive care that stands a better chance of achieving a clinically significant impact. In the case of caries, weekly fluoride mouthrinsing may be too little prevention for everyone, while placement of pit and fissure sealants in a highly-cariessusceptible population can be demonstrably more efficacious. Matching prevention to an individual's risk of future disease also leads to an elimination of wasted preventive services to those at low or negligible risk to future disease. In short, and as has recently been demonstrated for routine medical therapy with long-term beta blockers, risk assessment helps practitioners to define appropriate levels of preventive care, contributing to a more rational and efficient use of resources (Goldman et al., 1988).
BACKGROUND TO UNDERSTANDING HEALTH RISK ASSESSMENT The science of health risk assessment has only recently entered the lexicon of dental practice. To understand fully the framework of risk assessment, to interpret accurately the data generated by risk assessment, and to be able to compare the predictive accuracy of alternative risk assessment models require a basic understanding of several concepts.
Two Perspectives on Risk Assessment Health risk assessment draws from two intellectual roots, each of which has provided a particular view on how risk assessment may be used to improve health (Jeffery, 1989). The first and most common of these is the population perspective, an approach developed almost entirely within the field of epidemiology and public health. The second view is the individual perspective, a
system that uses epidemiological findings but owes its real origins to clinical preventive medicine and the science of clinical decision theory. The population perspective does not attempt to predict which individuals in society are most likely to develop disease. Rather, the population perspective is concerned with the identification and quantification of risk factors that significantly compromise the population's health. This approach is also known as hazard appraisal, and in addition to its epidemiological roots, owes its development to occupational and environmental medicine (Gordis, 1988). Under the rubric of hazard appraisal, the National Research Council has defined risk assessment as consisting of four steps—hazard identification, dose-response assessment, exposure assessment, and risk characterization. Hazard identification usually begins with an acute clinical observation that a certain type of disease or condition appears to be occurring in erstwhile-inexplicable clusters of patients. It was precisely such insightful clinical observations that triggered subsequent epidemiological investigations linking, for example, methylmercury to Minymata disease, thalidomide as well as the retinoids to teratogenic effects, and diethylstilbestrol (DES) to vaginal adenocarcinoma. It can be seen from these examples that hazard identification includes consideration of the emergence of unforeseen side-effects from new pharmaceutical agents. Dose-response assessment is typically carried out by means of animal studies. The usual form of this research phase relies on classic toxicological studies, frequently using small-animal models. Such studies are essential because they cannot be conducted on humans, but they are difficult to interpret for two reasons (Crump, 1985): One, it is difficult to obtain reliable low-dose information in small (rodent) animal studies; and two, it is difficult to extrapolate projected low-dose effects in rodents to be meaningful for humans, because of the extremely wide confidence bands associated with such studies. Exposure assessment is simply the determination of the proportion of a population that may be at risk to a certain environmental or occupational hazard. For example, among the current issues being explored about radon contamination is the question of how many persons in the country might be affected. The fourth step, risk characterization, attempts to combine the information from the three prior activities. Typically, this will lead to estimates of the attributable risk associated with a given health hazard. Turning to the individual perspective, this approach is based on Robbins' early work in the 1930's and 1940's in which he developed methods to aid the physician in recording patient health risk factors, leading then to the identification of appropriate preventive procedures that would be effective in avoiding anticipated future health problems (Robbins and Hall, 1970). The objective was to create a prospective (preventive) rather than a retrospective (treatment) orientation for the physician. A distinctive characteristic of this perspective is its use of epidemiologic data to generate quantitative risk predictions based on the presence or absence of identified risk factors in the individual (Schoenbach, 1987). Information describing an
STAMM et
individual's biological characteristics, personal and family medical history, habits, lifestyle, and health-related practices are inserted into statistical decision models, developed from epidemiological data and vital statistics, to project that person's risk of mortality over some future period. These projections are used then in educational and motivational techniques to encourage the patient to adopt a healthier, prevention-oriented lifestyle. In this regard, one of this system's most notable contributions to preventive medicine has been its application to the prediction and prevention of coronary heart disease where stress, smoking, obesity, alcohol use, and lack of exercise were identified as strong predictors of heart disease in a series of epidemiological studies. Of course, dentistry, too, has been experimenting with efforts to predict the occurrence of future disease. Most of these attempts have focused on linear regression and correlation methods to predict dental caries in individual subjects, usually schoolchildren. Indeed, attempts to develop tests with predictive capability began even before 1900 (Black, 1899;Miller, 1890). Since that time, many distinguished investigators have studied a wide variety of demographic, dietary, physical, chemical, electrical, and microbiological factors, searching for methods to predict caries occurrence. Much of the previous prediction work prior to 1976 was summarized in a publication reporting the proceedings of an international symposium, "Methods of Caries Prediction", held in October of 1977 (Bibby and Shern, 1978). In short, that publication reported no caries prediction method sufficiently valid or reliable to warrant recommendation for use at that time. Interest in caries prediction was revived in the United States in the early 1980's, when the secular decline in caries among children was reported.
Expressing Health Risk There are a number of ways health risk may be expressed. The three key concepts are absolute risk, relative risk, and population- attributable risk (Fleiss, 1981; Fletcher et ah, 1982). Absolute risk is simply the risk that an individual will get a certain disease over a defined period of time. Absolute risk is frequently estimated from the incidence (I) determined for a specific disease in a population. Relative risk is a measure that compares health risks among two populations. Algebraically, it is the ratio of the absolute risks of disease in two individuals or the ratio of disease incidences in two populations. Most commonly, the risk estimate for the individual or population exposed to the risk factor forms the numerator of the risk ratio, while the risk estimate for the unexposed individual or population is the denominator. Algebraically, relative risk (RR) may be expressed as
where IE is the incidence rate in the exposed population, and IE is the incidence rate in the unexposed or reference population. In prospective or cohort studies, higher relative risk implies stronger evidence for causation. Related to relative risk is the odds ratio, which is used to estimate relative risk in retrospective or case-control studies, particularly when the base disease incidence is low (i.e., 2 from grade 1 to grade 3; more exactly, HIGHRATE = 1 if the rate (DMFS3 - DMFS^ x
(2 x 365.25 davs^ (elapsed days at risk)
is greater than or equal to 2 when rounded to an integer. Otherwise, HIGHRATE = 0. Table 7 describes each of the 32 candidate predictor variables selected for consideration. Only children with complete data for the 33 outcome and predictor variables were included in the prediction efforts reported here. It is assumed that the reader is familiar with the essentials of LDA and LRA. Details about the CART can be found in Breiman et al (1984), and the application of CART to caries risk assessment has been explored by Stewart and Stamm (1991). The CART analysis, as implemented through the software developed by Breiman et al, uses recursive partitioning and regression tree pruning procedures to construct an optimal decision or classification rule. The CART method, it should be re-
al
ADV DENT RES DECEMBER
1991
TABLE 8 LINEAR DISCRIMINANT ANALYSIS STEP-WISE, 32 VARIABLES P-VAL. AIKEN P-VAL. PORTLAND pdmfs 0.0001 0.0001 PREDCAR 0.0001 MORPH 0.0001 MORPH 0.0044 0.0001 DMFS pdmfs 0.0001 dssmall 0.0201 FSARS 0.0008 EDHHH 0.0335 fslarge DSSMALL 0.0266 WHITE 0.0777 LACTOBAC 0.0221 REFCAR 0.1008 N=1024 0.0316 MALE 0.0626 FL-TABS 0.0834 WSLS 0.1010 CHILDREN 0.0957 EDHHH N = 914 emphasized, provides for step-wise variable selection, honest control of model complexity, and estimation of the prediction error rate as automated features of the algorithm. Further, the method is extremely robust with respect to the outliers in the predictor variables and with respect to the presence of variables having little or no predictive value. Table 8 displays the key results from step-wise LDA models for the caries data from Portland and Aiken Grade 1 children. The P-value to enter and to remove variables from the models was set at 0.15. It is apparent that the caries risk prediction model for Portland was more parsimonious than the one for Aiken, containing seven against 12 predictor variables. The LDA variables providing useful caries risk assessment information for Portland were: examiner's baseline subjective prediction of caries increment (PREDCAR), sum of morphology scores on fissured sound surfaces of permanent molars (MORPH), deciduous posterior dmfs (pdmfs), baseline fissured sound permanent surfaces erupted and at risk for caries (FSARS), educational level of head of household (EDHHH), number of permanent surfaces with minimal decay (DSSMALL), and lactobacillus growth index on dip slide (LACTOBAC). For Aiken, the variables relevant for risk classification by LDA were: pdmfs, MORPH, baseline permanent DMFS score (DMFS), number of deciduous tooth surfaces with minimal decay (dssmall), number of deciduous surfaces with extensive fillings (fslarge), indicator variable for race (WHITE), examiner's baseline referral score reflecting need for restorative care (REFCAR), indicator variable for gender (MALE), indicator variable for prior fluoride supplement use (FL-TABS), number of white-spot lesions on deciduous and permanent teeth (WSLS), number of other children in the household (CHILDREN), and EDHHH. It is interesting to note that, although these two models are substantially different, there are commonalities—reflected by the variables MORPH, pdmfs, and EDHHH—that contribute to both models. For Portland, LDA generates a sensitivity of 0.58 and specificity of 0.80. For Aiken, LDA sensitivity is
VOL.
5
13
RISK ASSESSMENT FOR ORAL DISEASES
TABLE 9 LOGISTIC REGRESSION ANALYSIS STEP-WISE, 32 VARIABLES PORTLAND P-VAL. AIKEN P-VAL. PREDCAR
0.0001
pdmfs
0.0001
MORPH
0.0001
MORPH
0.0001
EDHHH
0.0266
DMFS
0.0001
SEALANTS
0.0285
dssmall
0.0001
fslarge
0.0011
DSLARGE
0.0024
MALE
0.0105
ANTIBIOT
0.0390
N = 1024
N = 914
0.69, and specificity is 0.78. Table 9 gives analogous caries risk assessment models for Portland and Aiken grade 1 children using the method of stepwise logistic regression (LRA). Variable entry and removal criteria were maintained at 0.15. Once again, the caries risk models for the two groups of children are somewhat different, with the Portland model containing only four prediction variables against eight for the Aiken model. The LRA model variables significant for caries risk classification in Portland were: PREDCAR, MORPH, EDHHH, and the baseline number of surfaces with sealants present (SEALANTS). In Aiken, the variables useful for caries risk assessment by LRA were: pdmfs, MORPH, DMFS, dssmall, fslarge, baseline permanent tooth surface with extensive decay or with both filling and caries (DSLARGE), MALE, and reported prioruse of antibiotics at baseline (ANTIBIOT). The only commonality in LRA models across the two different grade 1 population groups resided in the variable MORPH. Considering the different numbers of variables in these two models, and the fact that only MORPH entered and remained in both models, suggests that the caries pattern and approaches to caries prediction may be quite dissimilar from population to population.
Fig. 2 shows the results from the third modeling technique, classification and regression tree analysis, applied to the Portland grade 1 sample. The regression tree generated by CART suggests that only two variables, posterior deciduous dmfs (pdmfs) and examiner subjective prediction of caries increment (PREDCAR), were substantially useful in differentiating between high- and low-caries-risk children in this population. For the Portland children, the regression tree model generated three terminal nodes or branches, and classified 289 (183 + 71 + 23+12) children as predicted high-risk candidates and 735 (684 + 51) as predicted low-risk children. Notice also that 83 (71 + 12) of 134 (71 + 12 + 51) observed or true high-risk children were correctly predicted to be at high risk, for a model sensitivity of 0.62. A total of 684 of 890 (684 + 23 + 183) observed or true low-risk children were predicted to be at low risk, for a model specificity of 0.77. Fig. 3 gives the regression tree model for the Aiken grade 1 children. As is evident, this tree is vastly more complex than the one for Portland, and features 12 terminal nodes and nine predictor variables. The predictor variables used in the CART model include, in order of importance: deciduous posterior,
split-1pctafs > 3
spli t-2—| n=770 PREDCAR > 2
183 l o | 71 hi
yes
23 lo 12 hi
TERMINAL NIXES
10
11
12
TERMINAL NODES
Fig. 2—Classification tree diagram. Final two-variable classification tree based on N = 1024 children studied in Portland, Maine. Each terminal node is designated as high-risk (HI) or low-risk (LO).
Fig. 3—Classification tree diagram. Final nine-variable classification tree based on N = 914 children studied in Aiken, South Carolina. Each terminal node is designated as high-risk (HI) or low-risk (LO).
14
TABLE 10 CARIES RISK ASSESSMENT MODELS: COMPARISON OF LINEAR DISCRIMINANT ANALYSIS, LOGISTIC REGRESSION ANALYSIS, AND CLASSIFICATION ANALYSIS AND REGRESSION TREE PORTLAND. N = 1024 AIKEN. N = 914 MODEL
ADV DENT RES DECEMBER
STAMM et al.
VAR SENS SPEC
VAR SENS SPEC
LDA
7
58
80
12
69
78
LRA
4
66
77
8
68
80
9
61
86
77 2 62 CART VAR = Number of variables in the model SENS = sensitivity SPEC = specificity
dmfs (pdmfs), sum of morphology scores on fissured sound surfaces of permanent molars (MORPH), baseline permanent DMFS (DMFS), baseline permanent fissured sound surfaces erupted and at risk for caries (FSARS), age (AGE), number of deciduous tooth surfaces with more than minimal decay or with both filling and decay (dslarge), number of deciduous tooth surfaces with minimal decay (dssmall), examiner's baseline referral score reflecting need for restorative care (REFCAR), and number of deciduous filled surfaces (fs). Notice, incidentally, that the CART method permits variables to appear in more than one place in the model, e.g., MORPH and AGE. Once again, inside each terminal node the observed or true caries-risk status is given for the children classified there. The CART model generated a sensitivity of 0.61 and a specificity of 0.86. Remarkably, use of the CART modeling approach produced models for Aiken and Portland grade 1 children that have only one variable in common, namely, deciduous posterior dmfs. The three modeling approaches used above—LDA, LRA, and CART—generate results that are similar in some ways, but fairly divergent in others. Table 10 presents a comparison that tends to emphasize the similarities. When the identical data set from Portland was used, the three modeling strategies generated sensitivities of from 0.58 to 0.66 and specificities of from 0.77 to 0.80. For Aiken, the model sensitivities range from 0.61 to 0.69, with specificities between 0.78 and 0.86. These results suggest rather similar model performance, although the large range for number of included predictor variables hints at model disparities.
TAUT 17 11 X/TLAJX^XI*
XX
This is confirmed in Tables 11 and 12, which demonstrate the potential heterogeneity that can occur in caries risk assessment models based solely on the choice among alternative statistical techniques. Table 11 indicates that the Portland models incorporated anywhere from two variables (for the CART approach) to eight variables (when the LDA method was used). For Portland, the only variable common to all three models was PREDCAR, the clinician's own subjective baseline estimate of future caries increment. Caries in the deciduous dentition and fissure morphology on molars both entered two models at a high level. Four variables appeared in only one of the three models (FSARS, DSSMALL, LACTOBAC, and SEALANTS). Table 12 presents the models developed from the Aiken data set. Here, there is complete commonality among the three top variables (pdmfs, MORPH, and DMFS). The variables dssmall and fslarge were next in importance in the LDA and LRA models. Thereafter, the models diverge markedly. For example, race and prior use of fluoride supplements are considered significant only by LDA, antibiotic use only by LRA, and age of subject only by CART. These results indicate that choice of analytical method will quite strongly influence the model structure, even if the models' risk classification performances, as determined by sensitivity and specificity, are roughly comparable. This is not surprising considering the extensive inter-relations (correlations) among the predictor variables: For example, FSARS and DMFS convey partially redundant information.
DISCUSSION This paper has provided a four-point rationale as well as technological background relevant to risk assessment for oral diseases. Emphasis was placed on the need to distinguish the population perspective from the individual perspective of risk assessment. Key methods for expressing health risk were reviewed. Indices were described with which to compare the predictive accuracies of alternative risk models. This was followed by a detailed discussion of risk factor identification which, in turn, led to an elaboration of alternative modeling TABLE 12 VARIABLE SELECTION, IN ORDER OF IMPORTANCE, BY LDA, LRA, AND CART (AIKEN, GRADE 1, N = 914) CART LDA LRA pdmfs MORPH
pdmfs MORPH
pdmfs MORPH
DMFS
DMFS
DMFS
dssmall
dssmall
FSARS
VARIABLE SELECTION, IN ORDER OF IMPORTANCE, BY LDA, LRA, AND CART (PORTLAND, GRADE I, N = 1024) LDA LRA CART PREDCAR PREDCAR pdmfs
fslarge
fslarge
AGE
WHITE
DSLARGE
dslarge
MORPH
MORPH
REFCAR
MALE
dssmall
pdmfs
EDHHH
MALE
ANTIBIOT
REFCAR
FSARS
SEALANTS
FL-TABS
PREDCAR
1991
EDHHH
WSLS
DSSMALL
CHILDREN
LACTOBAC
EDHHH
fs
VOL.
5
RISK ASSESSMENT FOR ORAL DISEASES
strategies with which to link the risk factors or predictor variables with future disease outcome. Finally, recent data from the UNCCRA Study were used to compare the performances of three different statistical modeling approaches—linear discriminant analysis, logistic regression analysis, and classification and regression tree analysis. It is evident that the science of risk assessment has application to most of the major oral diseases and conditions. In the fields of oral cancer and orofacial malformations, risk assessment will be most successfully pursued by use of the population perspective. That is, risk assessment models will show clearly what genetic, environmental/behavioral, and occupational factors may be associated with elevated disease risk, and should therefore be avoided as a matter of better public health. Nevertheless, in spite of clear-cut evidence of etiological links to certain risk factors, the very low incidence of oral cancer and craniofacial malformations results in low positive-predictive values for these models. Hence, prevention and health promotion programs for these types of health problems will be most successful if a population perspective is pursued. The field of periodontology will also benefit by more explicit adoption of the risk assessment concept. Periodontology, however, highlights another dichotomy in the application of risk assessment methods. Whereas risk assessment for dental caries, oral cancers, and craniofacial malformations can be used to enhance current clinical or public health practice, in periodontology risk assessment models have their greatest current application in furthering research. One of the most significant current challenges in periodontology is to define a measure of contemporaneous periodontal disease and to clarify the etiological factors and chain of events that lead to periodontal breakdown. Although much microbiological, immunological, enzyme, and host factor data have been and are continuing to be generated, treating these and periodontal disease activity data in appropriate multi-factorial risk assessment models may permit a fuller evaluation of the role played by putative risk factors. Much of the recent oral-health-oriented risk assessment research has focused on dental caries. A great deal of the latest work has been directed at predicting future caries occurrence in individuals—hence, the individual perspective has been applied. Results from the UNCCRA Study in South Carolina and in Maine suggest that the current ability to identify prospecti vely the 20-25 % of the highest-caries-forming children in the population is limited to a sensitivity of 0.6 and a specificity of 0.8-0.85 (Disney etai, 1990). This is short of the prior goals of 0.75 sensitivity and 0.85 specificity. Versions of developed models are available with 0.75 sensitivity, but the corresponding specificities then fall into the 0.5-0.7 range. Several possible reasons may exist for the current 0.6/0.8 sensitivity/specificity limit encountered. One, the whole category of biochemical predictors, mostly related to salivary components, was not assessed in the UNCCRA Study. This was due, in large measure, to the absence of practical field procedures that could be used in the baseline examination of 5000 children. Two, the placement of dental sealants during the study, undoubtedly interfered with the natural biological conditions that would be needed for a successful caries
15
prediction study. Sealants were heavily used in the Portland group, which may account for the weakening of tooth pit and fissure morphology as a caries risk predictor in the Portland sample. Three, it is also possible that the microbiological dipslide test used, particularly for Mutans streptococci, was too general. Though the Mutans streptococci (MS) results were strongly associated with caries increment on a bivariate basis, the failure of MS to be a significant predictor in the larger multivariate models may be due to the fact that only a subset of the genetically diverse strains of MS may be related to significant caries initiation (Kuramitsu, 1987). And four, operating on the outcome variable side, a fairly common observation of small, possibly preventive, restorations contributed to the DMFS increment in excess of the actual occurrence of various lesions. To the degree this phenomenon was real, it would contribute to lower model sensitivity. A particularly interesting use of caries risk assessment modeling is its application as a research tool at the Eastman Dental Center. The Eastman approach uses a series of frequent, six-month examinations, uses intensive microbial and biochemical assays at each examination, is limited to children who are completely caries-free at baseline, and eliminates from the study children who receive restoration without a prior caries diagnosis for that surface by the study team. Using such a study paradigm, the Eastman group will undoubtedly learn a great deal more about the risk factors that lead predictably to caries initiation. What about the future? Work accomplished to date in the area of caries risk assessment suggests some significant themes where more research might be fruitful. First, it is evident that more detailed studies need to be done on the biological and clinical factors that are associated with disease initiation. Whether for oral cancer, craniofacial malformations, periodontal diseases, or caries, the development of molecular biology, recombinant DNA technologies, and new diagnostic tools holds the greatest promise for refining our understanding about oral disease etiology. Second, in the case of periodontal disease, progress could be made by refining the outcome measure that defines the highrisk individual. For periodontal disease, what is currently lacking is a widely-agreed-upon measure of disease activity. Periodontal disease is still largely defined by post hoc evidence of past disease activity (i.e., attachment loss) rather than by a technology that permits the diagnosis of current disease. This limitation makes it difficult to classify patients unambiguously into those currently with periodontal disease and those without. Third, the continuing decline in caries incidence and severity is leading to a situation where the research question may change from trying to predict those individuals likely to form high caries increments to one where the challenge is to predict those individuals who experience any decay at all during a given time period. The latter concept of risk assessment is much more in keeping with current medical models. Further, the "no caries/caries" criterion would eliminate current debates about how much caries increment in a given time period actually defines the high-caries-risk individual. Fourth, although the more appropriate modeling techniques—such as linear discriminant analysis, logistic
16
STAMM et
regression, and classification and regression tree analysis— are relatively comparable in terms of their predictive potential, these methods are not necessarily consistent in the risk factors they identify as being of most significance for the disease or condition under investigation. Such heterogeneity in outcome is important, since it obscures the leads investigators might pursue in further etiological research. For this reason, additional well-controlled comparative studies of alternative risk assessment models appear to be well-warranted. Fifth, as the science of risk assessment for oral diseases matures, a new arena of dental health services research will develop. As prospective classification of low- and high-risk patients becomes more workable, a whole range of issues emerges under the rubric of appropriate care. Building upon the Federal Government's present concern with health outcomes, risk assessment and appropriateness of dental care procedures may well occupy an important position in the oral health research agenda for the year 2000 and beyond. In conclusion, the combination of changing oral disease epidemiology, greater understanding of risk factors, and more powerful diagnostic tools has focused new attention on risk assessment in dentistry. The prevalence of dental caries, the severity of periodontal disease, and the rate of edentulousness have all declined to an extent that early identification of the atrisk subpopulation could become an important aspect of dental practice in both the private and public health setting. Growing knowledge about the etiological factors linked to oral cancer and craniofacial malformations permits the application of risk assessment principles to these conditions as well. The development of risk assessment strategies relevant to dentistry has occurred only in recent years. From both patient-care and public-policy perspectives, the case for risk assessment is appealing. However, up to the present time, risk assessment in dentistry has had limited applications. Retarding the implementation of risk assessment principles are: (1) insufficient knowledge concerning biological factors of oral diseases; (2) incomplete development of statistical modeling techniques; and (3) resistance to put into practice significant partial knowledge that has been generated already. Continued research on the first two problems and more effective technology transfer for the third are needed before risk assessment can become a reality in dentistry.
REFERENCES ABERNATHY, J.R.; GRAVES, R.C.; BOHANNAN, N.M.; STAMM, J.W.; GREENBERG, B.G.; and DISNEY, J.A. (1987): Development and Application of a Prediction Model for Dental Caries, Community Dent Oral Epidemiol 15:24-28. ADA NEWS (1990): HCFA Reduces Dental Expenditure Estimate. 21(10): 1. Chicago: American Dental Association. BENNETT, B.M. (1972): On Comparisons of Sensitivity, Specificity and Predictive Value of a Number of Diagnostic Procedures, Biometrics 28:793-800. BIBBY, B.G. and SHERN, R.J., Eds. (1978): Methods of
Caries Prediction. In: Microbiology Abstracts— Bacteriology (Spec Suppl), Washington, DC: Information Retrieval, Inc.
ADV DENT RES DECEMBER
al.
1991
BINNIE, W.H. and RANKIN, K.V. (1988): Epidemiology of Oral Cancer. In: Oral Cancer: Clinical and Pathological Considerations, B.A. Wright, J.M. Wright, and W.H. Binnie, Eds., Boca Raton, FL: CRC Press, pp. 2-11. BLACK, G.V. (1899): Susceptibility and Immunity to Dental Caries, Dent Cosmos 41:826-836. BREIMAN, L.; FRIEDMAN, J.H.; OLSHEN, R.A.; and STONE, CJ. (1984): Classification and Regression Trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. BRUNELLE, J.A. and CARLOS, J.P. (1990): Recent Trends in Dental Caries in U.S. Children and the Effect of Water Fluoridation, J Dent Res 69 (Spec Iss):723-727. CRUMP, K.S. (1985): Methods for Carcinogenic Risk Assessment. In: Principles of Health Risk Assessment, P.F. Ricci, Ed., Englewood Cliffs, NJ: Prentice-Hall, Inc., pp. 279-319. DISNEY, J.A.; STAMM, J.W.; GRAVES, R.C.; ABERNATHY, J.R.; BOHANNAN, H.M.; and ZACK, D.D. (1990): Description of Preliminary Results of a Caries RiskAssessmentModel. In: Risk Assessment in Dentistry, J.D. Bader, Ed., Chapel Hill, NC: University of North Carolina, Dental Ecology. ERICSON, A.; KALLEN, B.; and WESTERHOLM, P. (1979): Cigarette Smoking as an Etiologic Factor in Cleft Lip and Palate, Am J Obstet Gynecol 135:348-351. EVANS, D.R.; NEWCOMBE, R.G.; and CAMPBELL, H. (1979): Maternal Smoking Habits and Congenital Malformations: A Population Study, BrMedJl: 171-173. FLEISS, J.L. (1981): Statistical Methods for Rates and Proportions, 2nd ed., New York: John Wiley and Sons. FLETCHER, R.H.; FLETCHER, S.W.; and WAGNER, E.H.
(1982):
Clinical Epidemiology—The Essentials,
Baltimore, MD: Williams & Wilkins. GOLDMAN, L.; SIA, B.; COOK, E.F.; RUTHERFORD, J.D.; and WEINSTEIN, M.C. (1988): Cost and Effectiveness of Routine Therapy with Long-term Beta-adrenergic Antagonists After Acute Myocardial Infarction, TV Eng J Med 319:152-157. GORDIS, L. (1988): Epidemiology and Health Risk Assessment, New York: Oxford University Press. GREGG, N.M. (1941): Congenital Cataract Following German Measles in the Mother, Trans Aust Coll Ophthalmol 3:3546. HERBST, A.L.; ULFELDER, H.; and POSKANZER, D.C. (1971): Adenocarcinoma of the Vagina. Association of Maternal Stilbestrol Therapy with Tumor Appearance in Young Women, N Eng J Med 284:878-881. HILL, L.; MURPHY, M ; McDOWALL, M.; and PAUL, A.H. (1988): Maternal Drug Histories and Congenital Malformations: Limb Reduction Defects and Oral Clefts, / Epidemiol Commun Hlth 42:1-1. JEFFERY, R.W. (1989): Risk Behaviors and H e a l t h Contrasting Individual and Population Perspectives, Am Psychologist 44:1194-1202. KEELS, M.A. (1990): A Research Proposal to Study the Role of Maternal Cigarette Smoking in the Etiology of Cleft Lip With or Without Cleft Palate. Ph.D. Dissertation, University
VOL.
5
RISK ASSESSMENT FOR ORAL DISEASES
of North Carolina, Chapel Hill. KELSEY, J.L.; DWYER, T.; and HOLFORD, T.R. (1978): Maternal Smoking and Congenital Malformations: An Epidemiological Study, J Epidemiol Commun Hlth 32:102107. KELSEY, J.L.;THOMPSON,W.D.;andEVANS,A.S.(1986): Methods on Observational Epidemiology, New York: Oxford University Press. KHOURY, M.J.; GOMEZ-FARIAS, M.; MULINARE, J.; andBORING, J. (1988): "Does Maternal Cigarette Smoking During Pregnancy Cause Cleft Lip and Palate in Offspring?" Presentation, Society of Epidemiologic Research, Vancouver, British Columbia, Canada (Abst. No. 72). KLEINBAUM, D.G.; KUPPER, L.L.; andMORGENSTERN, H. (1982): Epidemiologic Research: Principles and Quantitative Methods, Belmont, CA: Lifetime Learning Publications. KURAMITSU, H.K. (1987): Recent Advances in Defining the Cariogenicity of Mutans Streptococci: Molecular Genetic Approaches, Eur J Epidemiol 3:257-260. MILLER, W.D. (1890): Microorganisms of the Human Mouth, Philadelphia: S.S. White Dental Mfg. Co. NATIONAL CARIES PROGRAM, NATIONAL INSTITUTE OF DENTAL RESEARCH (1981): The Prevalence of Dental Caries in United States School Children, 197980, The National Dental Caries Prevalence Survey.
17
Washington, DC: DHHS, NIH Publ. No. 82:2245. ROBBINS, L. and HALL, J. (1970): How to Practice Prospective Medicine. Indianapolis: Methodist Hospital of Indiana. ROTHMAN, K.J. and KELLER, A.Z. (1972): The Effect of Joint Exposure to Alcohol and Tobacco on Risk of Cancer of the Mouth and Pharynx, J Chronic Diseases 25:711 -716. SAXEN, I. (1974): Cleft Lip and Palate in Finland: Parental Histories, Course of Pregnancy and Selected Environmental Factors, Int J Epidemiol 3:263-270. SCHOENBACH, V.J. (1987): Appraising Health Risk Appraisal, Am J Pub Health 77:409-411. STAMM, J.W.; DISNEY, J. A.; GRAVES, R.C.; BOH ANN AN, H.M.; and ABERNATHY, J.R. (1988): The University of North Carolina Caries Risk Assessment Study. I. Rationale and Content, J Pub Hlth Dent 48:225-232. STEWART, P.W. and STAMM, J.W. (1991): Classification Tree Prediction Models for Dental Caries Using Clinical, Microbiological and Interview Data, / Dent Res 70:12391251. VECCHIO,T.J. (1966): Predictive Value of a Single Diagnostic Test in Unselected Populations, N Eng J Med 274:11711173. WRIGHT, B.A.; WRIGHT, J.S.; and BINNIE, W.H. (1988): Oral Cancer: Clinical and Pathological Considerations, Boca Raton, FL: CRC Press.
