Predictive power of the severity measure of attachment loss for periodontal care need

Abstract

Background: The prevalence of periodontal diseases is high, and >15% of adults have severe gum disease. Clinical attachment loss (AL) is one of the most important measures for periodontal disease severity. With AL, one could measure the worst scenario, the average, or the cumulative sum of AL among all teeth. The objective of this study is to evaluate which of the 15 measures of periodontal problems (e.g., maximum, mean, and cumulative AL) best predict the need for periodontal treatment.

Methods: Using detailed periodontal data obtained through clinical examination from the National Health and Nutrition Examination Survey 1999 to 2002, weighted logistic regression was used to model the periodontal treatment need of 15 different periodontal disease measures. The outcome measure is the clinically determined periodontal need.

Results: After adjustment for the covariates of age, sex, ethnicity, education, smoking status, and diabetes, the three most predictive measures were identified as: 1) the sum of the maximum mid-buccal (B) and mesio-buccal (MB) measures, which reflects the worst case of both B and MB measures; 2) the sum of the maximum MB measure or the worst case of the MB measure; and 3) the sum of all B and MB measures, or the cumulative AL measures.

Conclusions: Cumulative periodontal morbidity, particularly the worst case of B and MB measures, has the strongest impact on the need for periodontal care. All the demographic variables and covariates follow the classic pattern of association with periodontal disease.