ABSTRACT
This study analyzed degrees of demineralization in bovine enamel using synchrotron microcomputed tomography (SMCT) and hardness measurements (Knoop hardness number, KHN). For 5 days, 40 bovine enamel blocks were individually subjected to a pH cycling model and treatment with fluoride dentifrices (placebo, 275, 550 and 1,100 microg F/g) diluted in deionized water twice a day. Surface hardness number and cross-sectional profiles of hardness and mineral concentration (by SMCT) were determined. Integrated hardness (KHN x microm) for sound and demineralized specimens was calculated and subtracted to give the integrated loss of hardness (DeltaKHN) for the lesions. Increasing fluoride concentration in the dentifrices led to higher values for surface hardness after pH cycling and mineral concentration (g(HAp) cm(-3)), and lower values for DeltaKHN (p < 0.05). From the present results, it may be concluded that hardness measurements revealed demineralization in all groups, which was lower in groups treated with dentifrice with a higher F concentration. SMCT and hardness measurements gave similar results in areas with higher demineralization, but diverged in areas with lower demineralization.
Subject(s)
Dental Enamel/pathology , Hardness Tests/methods , Tooth Demineralization/pathology , X-Ray Microtomography/methods , Animals , Cattle , Disease Models, Animal , Fluorides, Topical/therapeutic use , Synchrotrons , Tooth Demineralization/prevention & control , Tooth Remineralization/methodsABSTRACT
The key analytical challenge presented by longitudinal data is that observations from one individual tend to be correlated. Although longitudinal data commonly occur in medicine and public health, the issue of correlation is sometimes ignored or avoided in the analysis. If longitudinal data are modelled using regression techniques that ignore correlation, biased estimates of regression parameter variances can occur. This bias can lead to invalid inferences regarding measures of effect such as odds ratios (OR) or risk ratios (RR). Using the example of a childhood health intervention in Brazil, we illustrate how ignoring correlation leads to incorrect conclusions about the effectiveness of the intervention.