Notizen: Objectives: This study reports the prevalence of dental sealants derived from a survey of schoolchildren in North Carolina, and its variation according to several sociodemographic characteristics. Methods: A single-stage, stratified probability sample of grade K–12 classrooms in the state yielded a sample of 8,026 students. Clinical examinations were performed on 83 percent of the sample. Estimates for sealant prevalence and their variation according to seven sociodemographic variables were determined. Results: About 117,000 children in the state, or 12 percent of those 6–17 years of age, have sealants. The prevalence of sealants varied according to geographic region of residence and the socioeconomic status of whites. Prevalence was affected little by age, sex, degree of urbanism, or socioeconomic status of those with races other than white. Conclusions: While underutilized, there are indications in these data that sealant use is increasing, thus having the potential to contribute to further declines in the prevalence of dental caries in the state. Those factors included in this study and associated with the prevalence of sealants suggest that major gains in sealant use will need to address not only those specific barriers preventing their use by the profession or public, but broader issues preventing the utilization of dental services in general.

Notizen: In this longitudinal study of a random sample of North Carolinians over the age of 65 and living in their homes, 325 blacks and 280 whites were examined and interviewed 18 months after baseline examinations. Coronal caries incidence was greater among whites than blacks. The increment due to teeth becoming root fragments were similar for both races; however, there were more newly crowned teeth among whites. Newly crowned surfaces were not used as part of the caries increment in logistic regression models to investigate potential risk predictors. For blacks, caries development over the 18-month period was associated with a higher lactobacillus score and more coronal caries at baseline, more previously filled coronal surfaces, and lack of active membership in clubs or other groups. For whites, having no self-reported tooth sensitivity, having a lower socioeconomic index score, taking antihistamine medications at baseline, and having the perception of more problems after the age of 40 than before were all associated with the development of coronal caries.

Notizen: Abstract A greater variety of categorical data methods are used today than 15 years ago. This article surveys categorical data methods widely applied in public health research. Whereas large sample chi-square methods, logistic regression analysis, and weighted least squares modeling of repeated measures once comprised the primary analytic tools for categorical data problems, today's methodology is comprised of a much broader range of tools made available by increasing computational efficiency. These include computational algorithms for exact inference of small samples and sparsely distributed data, conditional logistic regression for modeling highly stratified data, and generalized estimating equations for cluster samples. The latter, in particular, has found wide use in modeling the marginal probabilities of correlated counted, binary, and multinomial outcomes. The various methods are illustrated with examples including a study of the prevalence of cerebral palsy in very low birthweight infants and a study of cancer screening in primary care settings.

Notizen: in dentistry, the vast majority of studies attempting to predict who is at high risk for getting a disease or condition, or attempting to identify risk factors for a specific disease or condition, have focused either on only one risk factor at a time, or have measured multiple potential risk factors, but analyzed their effects in isolation. Since researchers tend to agree that most dental conditions have a multiple etiology, it is necessary to develop models that consider simultaneously the effect of a number of potential risk factors on the disease or condition of interest if we are to have any understanding of the relative impact of potential risk factors. Many existing statistical techniques will aid dental researchers in identifying risk factors. However, the selection of an appropriate analytic technique depends on a number of conditions. The strategy for this paper is to discuss a wide range of possible statistical techniques that may be applied to the problem of deriving a model for identification of multiple risk factors for dental diseases and conditions. We have approached this task by presenting a number of dental research problems needing an appropriate analytic technique. Next, basic issues that must be considered in choosing an appropriate analytic strategy are discussed. These issues include features of the study design, the data structure of the variables being measured, and the types of assumptions that are applicable to provide valid inferences about the target population of interest. A matrix of possible analytic techniques is presented for various combinations of study-design and data-structure features. After a discussion of each of the techniques, the appropriate statistical techniques for each of the dental examples are described. The issues and examples presented in this paper should be of use to dental researchers who wish to investigate multiple risk factors for a disease or condition of interest.

Notizen: Abstract The objective of this paper is to consider current methods for analyzing longitudinal caries data in adults. To illustrate these methods, we used data from the Piedmont dental study, a prospective investigation of the oral health of older adults. Longitudinal dental data sets comprise repeated observations of an outcome (often clustered within randomly selected primary sampling units), and a set of covariates for each of many subjects, in whom clustering can occur as a result of measuring teeth, or surfaces, within people. One objective of statistical analysis is to predict the outcome variable as a function of the covariates, while accounting for the correlation among the repeated observations for a given subject and the effect of clustering within subjects, as well as between subjects within primary sampling units, such as communities, schools, hospitals, or other such units. We considered two statistical approaches: generalized estimating equations and survey regression models. We also examined the impact of varying diagnostic criteria for caries estimation between epidemiologists and clinicians. One approach is to perform the usual timex exam score minus time0 score analysis for the baseline and final examinations, while an alternative is to analyze trends among interim examinations. Finally, because caries studies in which the onset of the disease is the endpoint face the problem of censoring due to subject attrition and/or tooth loss, we recommend the incidence density (time-to-event) analytic strategy to address this problem. This approach was found to be most suitable for longitudinal studies of older adults since it accounts for the time each surface remains at risk for the event of interest, making use of interim exam data until the moment the subject and/or the tooth are no longer available for examination. We also included a discussion on biases that occur upon application of the usual methods of estimating caries experience in missing teeth and crowns, which often ignore the classification error in the estimation. We propose a method to adjust for misclassification of the M-component of the DMFS index. In the case where one can observe true reversals or remineralization of caries lesions, we recommend an adjustment formula to account for reversals that are most likely due to examiner misclassification. We provide examples to demonstrate the applicability of the methods for covariates subject to outcome misclassification.

Notizen: Abstract Two types of reversals occur in longitudinal caries studies, remineralization (true reversals) and reversals due to examiner misclassification (D3 to S; F to S). Since 1966, the standard practice has been to subtract examiner reversals from the crude increment (CC1), resulting in a net caries increment (NCI). The use of the NCI has been based on the assumption that examiners make an equal number of false positive and false negative errors both at baseline and follow-up examination. Consequently, the difference between the two caries prevalence scores would provide an unbiased estimate of caries incidence between examinations. The NCI considers all reversals to be true reversals which is an extreme strategy, particulary when the level of diagnosis is set at lower thresholds. In this study we compromised between the NCI and CCI by creating a simple formula to calculate the caries increment using a prevalence-based adjustment for reversals. The formula is ADJCI = y2(l-(y3/(y3+y4))), where y2=S to D or F; y3, = D or F to S; y4=D to D/F or F to F. The impact of this adjustment is illustrated using data from a random sample of 452 older black and white adults followed over a 3-yr period. The ADJC1 was more likely to result in higher caries increments and more observed intergroup differences than the NCI, a finding that has implications for clinical trials. For example, the crude 3-yr coronal DS increment for whites was 0.62, the NCI was 0.26 and the ADJCI was 0.56. There were significant black-white differences for the CCI and ADJCI, but not the NCI. We conclude that the NCI was too severe of an adjustment for reversals for this study population.

Notizen: Abstract Cross-sectional studies of caries in older adults report a substantial number of missing teeth, making it difficult to estimate caries experience accurately. The goal of this study was to improve the method of estimating caries experience as expressed by the DMFS index in population groups with missing teeth. The adjustment was demonstrated with reference to the Piedmont 65+ Dental Study conducted on a random sample of 363 community-dwelling older adults in North Carolina who were followed for 5 years. These older dentate adults had a mean (±SE) of 11.7±0.5 teeth missing at baseline, 56.1±2.5 missing surfaces and a DMFS of 86.7±2.0. A predicted caries prevalence was determined from the DFS at 5 yrs plus the 5-yr DFS incidence and the baseline DFS of teeth lost during the study period. Then a formula was developed that would estimate the predicted caries prevalence as a function of the observed 5-yr DMFS. This formula provided a good estimation of caries prevalence at 5 yrs (DMFSadj) when compared with the predicted prevalence (paired t-test, p〉0.05), while prevalence was underestimated by the DFS and greatly overestimated by the traditional DMFS index. Subgroup analyses by race, sex. and periodontal status also indicated that the DMFSadj resulted in patterns of estimates similar to the predicted prevalence, while the DFS and the DMFS were likely to result in different findings. The DMFS from the time of tooth eruption also was adjusted using this formula. The resulting analyses of subgroup differences in caries were not different from the previous estimates based on the 5-yr historical data, indicating that the adjustment of all M surfaces avoids the biases inherent in the traditional DMFS and DFS indices. This study showed that predicted caries prevalence could be estimated by adjusting the M component of the DMFS. It appears that this adjustment formula can be used without obvious bias, but additional studies are needed to provide adjustment figures for populations with different caries prevalences.

Notizen: Abstract Researchers attempting to identify and quantify risk factors have not paid adequate attention to whether or not they actually are identifying risk factors (exposures related to the onset of disease) and prognostic factors (characteristics related to the progression of established disease). The purpose of this paper is to present the incidence of attachment loss in people who have attachment loss in sites previously without disease and people who experience further progression of sites with disease, and to compare and contrast the characteristics of people with the two types of attachment loss. The subjects used for this study are a random sample of community-dwelling older adults residing in five contiguous North Carolina counties who were followed for 3 yr. The subjects were categorized into four groups according to the type of clinical attachment loss (change of 3 or more mm) experienced, those who only had attachment loss in previously undiseased sites, those with progression of attachment loss in previously diseased sites, those who experienced both types of attachment loss, and those who had no new sites of attachment loss. A bivariate logistic model was developed to identify the characteristics associated with “new” disease onset as compared to “progression of disease”. Just over 40% of the people had no change in their baseline attachment level, 27.5% of the people experienced only new lesions. 11.1% of the people only experienced clinical attachment loss in sites that had clinical attachment loss at baseline, and 20.1% experienced both kinds of clinical attachment loss. Low income, taking medications associated with soft tissue reactions, smokeless tobacco users and those who experience a history of oral pain were at greater risk for new lesions. People at higher risk for disease progression were low income, taking medications that may result in soft tissue reactions, cigarette smokers, BANA positive, P. gingivalis positive, and had worsening financial problems. The model indicates that the characteristics are different enough that periodontitis may be like other diseases in which risk factors and prognostic factors are not the same.