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Researchers attempting to identify and quantify risk factors have not paid adequate attention to whether or not they actually are identifying risk factors and prognostic factors. 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 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. 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. [ABSTRACT FROM AUTHOR]

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 time[subx] 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. [ABSTRACT FROM AUTHOR]

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 (DMFS[SUBadj]) when compared with the predicted prevalence (paired 1-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 DMFS[SUBadj] resulted in patterns of estimates similar to the predicted prevalence, while the DFS and the DMFS were likely to result in different findings. The DM FS 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 DES indices. This study showed that predicted caries prevalence could be estimated by adjusting the NI component of the DMFS. [ABSTRACT FROM AUTHOR]

Two types of reversals occur in longitudinal caries studies, remineralization (true reversals) and reversals due to examiner misclassification (D[sub3] to S; F to S). Since 1966, the standard practice has been to subtract examiner reversals from the crude increment (CCI), 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, particularly 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=y[sub2](1-(y[sub3]/(y[sub3]+y[sub4]))), where y[sub2]=S to D or F; y[sub3]=D or F to S; y[sub4]=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 ADJCI 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. [ABSTRACT FROM AUTHOR]