Estimating Periodontitis Susceptibility Cases for Epidemiological Studies with Multiple Imputation

Abstract

Approximately half of the US noninstitutionalized population ≥30 y old has periodontitis. An accurate measure for the assessment of periodontitis burden is crucial to assess population-level treatment needs. Nonetheless, because periodontitis ultimately leads to tooth loss, missing periodontal measurements due to tooth loss may pose a critical challenge to accurate periodontitis surveillance in public health. In this study, we aim to propose a novel estimate, the prevalence of periodontitis susceptibility cases, which incorporates both observed values and plausible values at missing teeth generated using multivariate imputation by chained equations (MICE). Using data from the 2009–2012 National Health and Nutrition Examination Survey (NHANES) (n = 7,006), preimputation, 48.4% participants were defined as moderate or severe periodontitis cases, and the number increased to 55.1% after considering the periodontitis susceptibility cases via imputation. The imputed periodontitis susceptibility status was validated by linking it to 2 well-established risk factors: age and smoking. Before imputation, the prevalence of severe periodontitis first increased with age and then declined drastically in older age groups (60+ y), reflecting an underestimation due to tooth loss. Postimputation, the prevalence demonstrated a remarkable increase in these groups, who are most affected by tooth loss, and then stabilized, consistent with known trends. The relative risk of periodontitis in smokers to that in nonsmokers was 1.04 (0.82–1.32) among the subpopulation with 15 to 28 teeth lost prior to imputation, while after imputation, the association considerably strengthened (relative risk = 1.16 [1.04–1.30]). Our results elucidated that leveraging the missing data due to tooth loss via MICE is informative for improving the assessment of total periodontitis burden, including active periodontitis cases and periodontitis susceptibility cases, and may have favorable impact in enhancing the validity of periodontitis associations in epidemiological studies. Knowledge Transfer Statement: Our proposed estimate of periodontitis susceptibility cases addresses the issue of missing teeth, offering an innovative solution through a generative missing data imputation model. The implications of our findings extend to fostering more robust investigations into the relationships between periodontal health and systemic diseases, thereby offering valuable insights to clinicians for informed decision-making. Moreover, the study’s capacity to shape clinical practices and interventions in public health will further fortify health policy strategies.