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This paper is in the following e-collection/theme issue:

Statistical Methods for Surveillance and Population Health (198) Clinical Information and Decision Making (2149) mHealth for Data Collection and Research (1288) Machine Learning (2306) Vaccination and Immunization in the Digital Age (469) Data Science (407)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73916, first published .
Diverse job applicants wearing masks wait in line for interviews.

Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems: Retrospective Cohort Study

Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems: Retrospective Cohort Study

Authors of this article:

Sara Brown1 Author Orcid Image ;   Ousswa Kudia1 Author Orcid Image ;   Kaye Kleine1 Author Orcid Image ;   Bryndan Kidd2 Author Orcid Image ;   Robert Wines2 Author Orcid Image ;   Nathanael Meckes1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Khan A, Tidman M. Burden of Infectious Diseases in Afghanistan in 1999 and 2023: Findings From the Global Burden of Disease 2023 Study. Cureus 2026 View
  2. Sun J, Wang T, Li M, Wang M. Prediction of atelectasis in Mycoplasma pneumoniae pneumonia using a SHapley Additive exPlanations-interpretable machine learning model. Frontiers in Pediatrics 2026;14 View