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An imputation method for calculating and comparing autoimmune disease incidence using partial case review.

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Area of Science:

  • Immunology
  • Vaccinology
  • Epidemiology

Background:

  • Assessing autoimmune condition incidence after HPV4 vaccination is crucial for vaccine safety monitoring.
  • Electronic health record (EHR) data presents challenges due to potential inaccuracies and incomplete coding.
  • Manual chart review for all potential cases is often infeasible in large populations.

Purpose of the Study:

  • To develop and validate a method for estimating autoimmune condition incidence in vaccinated and unvaccinated populations using EHR data and targeted chart review.
  • To address limitations of relying solely on EHR data for vaccine safety surveillance.
  • To identify potential safety signals that might be missed with standard EHR data analysis.

Main Methods:

  • Incident cases were identified using ICD-9 codes, laboratory results, and medication data.
  • A random sample of charts was manually reviewed to confirm diagnoses and determine onset dates.
  • Multiple imputation using a Monte Carlo model estimated the case status of non-reviewed charts, calculating incidence rates across imputed datasets.

Main Results:

  • The proposed method successfully estimated incidence rates, providing a distribution of results.
  • The model accounted for varying confirmation rates based on age and case identification methods.
  • A potential safety signal was identified that would have been missed using only EHR data.

Conclusions:

  • This imputation-based method offers a feasible approach for calculating disease incidence when comprehensive chart review is not practical.
  • The technique enhances the reliability of vaccine safety surveillance by integrating EHR data with sampled manual review.
  • Accurate incidence estimation is vital for understanding vaccine safety profiles and informing public health decisions.