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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Informatics

Background:

  • Electronic health records (EHRs) frequently use height for diagnosis and BMI calculation.
  • Adults often overestimate their height, leading to inaccuracies in EHR data.
  • Existing statistical models for height prediction require extensive input and lack external validation.

Purpose of the Study:

  • To develop and validate sex-stratified predictive models for examiner-measured height.
  • To improve the accuracy of height data in population health studies.
  • To create a model with minimal input requirements and demonstrated external validity.

Main Methods:

  • Utilized the National Health and Nutrition Examination Survey (NHANES) for model development (90% sample).
  • Internally validated the model in a held-out 10% NHANES sample.
  • Externally validated the model in two independent cohorts: Add Health and HRS.
  • Assessed model performance using C-index for discrimination, calibration plots, and RMSE for accuracy.

Main Results:

  • Models trained on 62,032 NHANES subjects showed excellent discrimination (C-index 0.88-0.89) across validation cohorts.
  • Models were well-calibrated and demonstrated lower RMSE compared to self-reported height.
  • Height prediction accuracy improved significantly in participants aged 45 and over, across various demographics.

Conclusions:

  • A simple predictive model using self-reported height and age significantly improves height estimation.
  • This model offers improved accuracy over self-reported height with minimal input.
  • It is the first model to enhance height estimation with demonstrated external validity across diverse populations.