Over 4000 first and fifth grade children from the areas surrounding Aiken, South Carolina, and Portland, Maine, participated in a 4-yr study to develop caries risk assessment models. The predictors used at baseline included detailed clinical examinations, salivary microbiological tests, and sociodemographic and dental behavior data. Mean 3-yr caries increments in South Carolina were twice those in Maine. For the four risk assessment models (two grade cohorts at two sites) specificity values averaged 0.83 and sensitivity values averaged 0.60. Clinical predictors such as prior DMFS, pit and fissure morphology, and predicted caries risk status were the major contributors to the models.

No abstract

This article presents the rationale and content of a current study that seeks to improve methods to identify children at high risk to dental caries. It summarizes the results of the development of a 12-factor, preliminary caries prediction model based on data derived from the National Preventive Demonstration Program. Despite data limitations, the model produced a sensitivity of .5 and specificity of .8 for four-year caries increment prediction in first- and fifth-grade children. Data on a number of additional potential predictors are being collected in two sites to expand and improve the existing model. These factors are identified.

No abstract

The development and validation of a caries prediction model comprising 13 sociodemographic and dental examination variables on Grade 1 and Grade 5 children in the National Preventive Dentistry Demonstration Program are described. The objective was to derive a method of predicting children at high risk to caries early in order that preventive measures might be undertaken. True high risk children were defined in two ways: highest 25% of children based on their 4-yr DMFS increment, and their total DMFS score at the end of the study. In both cases, children predicted to be at high risk were defined as the 25% with the highest discriminant score. Discriminant function and logistic regression analyses were used to determine the extent to which the 13 variables collectively discriminated between true high risk and non-high risk children so defined. Sensitivity was approximately 0.50 and specificity around 0.82, using the 4-yr increment as the criterion for defining true high risk, and approximately 0.64 and 0.88, respectively, using the final DMFS score for defining true high risk.

In 1965, Warner developed an interviewing procedure designed to eliminate evasive answer bias when questions of a sensitive nature are asked. He called the procedure ‘randomized response.’ The authors have been studying the technique for several years and, in this paper, are reporting some of the estimates of induced abortion in urban North Carolina using randomized response. Estimates of the proportion of women having an abortion during the past year among women 18–44 years of age are reported. For the study population indices were developed relating induced abortion to total conceptions for whites and nonwhites. The illegal abortion rate per 100 conceptions was estimated to be 14.9 for whites and 32.9 for nonwhites. Estimates of the proportion of women having an abortion during their lifetime among women 18 years old or over are also shown. Among ever married women, the proportion having an abortion during their lifetime declined as education increased. Estimates were high for women with 5 or more pregnancies. Most of the respondents stated that they were satisfied that the randomized response approach would not reveal their personal situation. Furthermore, they did not think their friends would truthfully respond to adirect question regarding abortion.

No 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.