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Community risk indicators for dental caries in schoolchildren: an ecologic study
Author(s) -
Amstutz Richard D.,
Rozier R. Gary
Publication year - 1995
Publication title -
community dentistry and oral epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.061
H-Index - 101
eISSN - 1600-0528
pISSN - 0301-5661
DOI - 10.1111/j.1600-0528.1995.tb00216.x
Subject(s) - medicine , residence , bivariate analysis , demography , environmental health , medicaid , population , multivariate analysis , logistic regression , regression analysis , multivariate statistics , statistics , health care , mathematics , sociology , economics , economic growth
A statewide survey of NC schoolchildren found wide variation in dental caries prevalence among sampled classrooms. This study examined factors associated with this variation using classrooms as a surrogate for the larger community, in order to identify community risk indicators (CRI). In all, 172 classrooms (3400 students) in Grades K‐6 were available for analysis. Initially, 56 sociodemographic, environmental, health system, and clinical factors were evaluated for their association with caries prevalence (K‐3: average dfs‐f DMF'S; 4–6: average DMFS) using univariate and bivariate analyses. Of these, 21 factors met our criteria for evaluation using WLS multivariate regression. For Grades K‐3 (w=108), population density, parental education, and coastal residence were negatively associated with caries scores, while age, and medical and dental Medicaid expenditures were positive. For Grades 4–6 (n=64), age and fs:dfs ratio were positively associated with caries scores, while population density, population: dentist ratio, and years of natural fluoride exposure were negative. CRIs for both models, when compared to individual models, explained a substantial portion of the variation in caries prevalence, 31% for Grades K‐3 and 51% for Grades 4–6. Results suggest that a risk assessment model based on community rather than individual variables is feasible and further refinement may reveal factors useful in identifying high risk communities.