Description: This publication examines methods to address information biases in electronic health record (EHR) data for epidemiologic research on diabetes in young adults. It focuses on improving the accuracy of associations between risk factors, such as race, ethnicity, and asthma, and diabetes prevalence by applying missing data and causal inference frameworks.

Citation: Conderino, S., Anthopolos, R., Albrecht, S. S., Farley, S. M., Divers, J., Titus, A. R., & Thorpe, L. E. (2024). Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study. JMIR medical informatics, 12, e58085. https://doi.org/10.2196/58085

Copyright: Copyright © Sarah Conderino, Rebecca Anthopolos, Sandra S Albrecht, Shannon M Farley, Jasmin Divers, Andrea R Titus, Lorna E Thorpe. Originally published in JMIR Medical Informatics (https://medinform.jmir.org)

Creative Commons license: https://creativecommons.org/licenses/by/4.0/