Vaccine Safety Surveillance Using Routinely Collected Healthcare Data—An Empirical Evaluation of Epidemiological Designs

Martijn J. Schuemie*, Faaizah Arshad, Nicole Pratt, Fredrik Nyberg, Thamir M. Alshammari, George Hripcsak, Patrick Ryan, Daniel Prieto-Alhambra, Lana Y.H. Lai, Xintong Li, Stephen Fortin, Evan Minty, Marc A. Suchard

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
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Abstract

Background: Routinely collected healthcare data such as administrative claims and electronic health records (EHR) can complement clinical trials and spontaneous reports to detect previously unknown risks of vaccines, but uncertainty remains about the behavior of alternative epidemiologic designs to detect and declare a true risk early. Methods: Using three claims and one EHR database, we evaluate several variants of the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations using real negative control outcomes (outcomes with no evidence to suggest that they could be caused by the vaccines) and simulated positive control outcomes. Results: Most methods show large type 1 error, often identifying false positive signals. The cohort method appears either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using effect-size estimates for negative control outcomes can bring type 1 error closer to nominal, often at the cost of increasing type 2 error. After calibration, the self-controlled case series (SCCS) design most rapidly detects small true effect sizes, while the historical comparator performs well for strong effects. Conclusion: When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address differences between vaccinated and unvaccinated, and for the cohort method the choice of index date is important for the comparability of the groups. Analysis of negative control outcomes allows both quantification of the systematic error and, if desired, subsequent empirical calibration to restore type 1 error to its nominal value. In order to detect weaker signals, one may have to accept a higher type 1 error.

Original languageEnglish
Article number893484
JournalFrontiers in Pharmacology
Volume13
DOIs
Publication statusPublished - 6 Jul 2022

Bibliographical note

Funding Information:
United Kingdom National Institute of Health Research (NIHR), European Medicines Agency, Innovative Medicines Initiative 2 (806968), US Food and Drug Administration CBER BEST Initiative (75F40120D00039), and US National Library of Medicine (R01 LM006910).

Publisher Copyright:
Copyright © 2022 Schuemie, Arshad, Pratt, Nyberg, Alshammari, Hripcsak, Ryan, Prieto-Alhambra, Lai, Li, Fortin, Minty and Suchard.

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