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Multiple Imputation for Partial Recording Periodontal Examination Protocols.

J S Preisser1, T Shing1, B F Qaqish1

  • 1Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

JDR Clinical and Translational Research
|January 16, 2023
PubMed
Summary
This summary is machine-generated.

Partial-mouth sampling underestimates periodontitis. Multiple imputation of missing data for periodontal sites can reduce bias in prevalence and extent estimates, offering a more accurate assessment of oral health.

Keywords:
biostatisticschronic disease surveillancecomputer simulationdental public healthepidemiologyperiodontal disease(s)/periodontitis

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Area of Science:

  • Periodontology
  • Biostatistics
  • Dental Public Health

Background:

  • Partial-mouth recording protocols are resource-efficient but often underestimate periodontitis prevalence and extent.
  • Accurate estimation of periodontitis is crucial for public health surveillance and clinical decision-making.

Purpose of the Study:

  • To evaluate the effectiveness of multiple imputation in reducing bias in periodontitis estimates derived from partial-mouth samples.
  • To assess the impact of multiple imputation on periodontitis prevalence and extent using a large national dataset.

Main Methods:

  • Utilized a generalized estimating equations framework for multiple imputation of clinical attachment loss data at non-selected tooth sites.
  • Applied periodontitis case definitions to imputed "complete" dentitions.
  • Employed a multiple imputation-bootstrap procedure for point and variance estimation.
  • Evaluated the method using pseudo-partial-mouth samples from the 2013-2014 National Health and Nutrition Examination Survey (NHANES).

Main Results:

  • Multiple imputation, when applied to partial-mouth samples, showed a tendency to overestimate periodontitis mean extent (9.5% for 84 sites) and prevalence (5-10%).
  • Despite overestimation in specific scenarios, the method provided less biased estimates compared to direct application of full-mouth definitions to partial-mouth samples.

Conclusions:

  • Multiple imputation offers a promising statistical approach to mitigate estimation bias inherent in partial-mouth recording for periodontitis.
  • This method has broad applicability for various periodontitis measures and sampling protocols, enhancing the accuracy of oral disease surveillance.