A new way to estimate disease prevalence from random partial-mouth samples

Standard partial-mouth estimators of chronic periodontitis (CP) that define an individual's disease status solely in terms of selected sites underestimate prevalence. This study proposes an improved prevalence estimator based on randomly sampled sites and evaluates its accuracy in a well-characterized population cohort.

Importantly, this method does not require determination of disease status at the individual level. Instead, it uses a statistical distributional approach to derive a prevalence formula from randomly selected periodontal sites. The approach applies the conditional linear family of distributions for correlated binary data (i.e. the presence or absence of disease at sites within a mouth) with two simple working assumptions: (i) the probability of having disease is the same across all sites; and (ii) the correlation of disease status is the same for all pairs of sites within the mouth.

Using oral examination data from 6793 participants in the Arteriolosclerosis Risk in Communities study, the new formula yields CP prevalence estimates that are much closer than standard partial mouth estimates to full mouth estimates.

Resampling of the cohort shows that the proposed estimators give good precision and accuracy for as few as six tooth sites sampled per individual.