Abstract Number:
2358
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Alissa O'Halloran (1), Angelle Naquin (2), Svetlana Masalovich (3), Kameela Noah (2), Shikha Garg (2), Catherine Bozio (2)
Institutions:
(1) Centers for Disease Control and Prevention, N/A, (2) CDC, N/A, (3) N/A, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Influenza vaccination can attenuate severe disease in hospitalized patients, but vaccination status can be inconsistently captured in the medical record or immunization registry, necessitating provider or patient interview to verify a patient's vaccination status. In the Influenza Hospitalization Surveillance Network (FluSurv-NET), vaccination status was unknown for 15-30% of patients hospitalized with laboratory-confirmed influenza in recent seasons, even after attempting patient interviews. Implementing an imputation procedure for vaccination status could be beneficial, particularly if medical records and registries continue to yield missing vaccination status, interviews yield fewer responses, or self-reported status remains less reliable. We evaluated several individual-level factors available in the medical record that could be used as predictors in a multiple imputation model using 2022-2023 FluSurv-NET data. Race/ethnicity and state of residence were each associated with having a known influenza vaccination status based on data from the medical record, registry, or interviews. Sex, race/ethnicity, the presence of underlying medical conditions, and state were each associated
Keywords:
Influenza vaccination|Multiple imputation| | | |
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Miscellaneous
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