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Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases

  • Anna Ostropolets
  • , Xintong Li
  • , Rupa Makadia
  • , Gowtham Rao
  • , Peter R. Rijnbeek
  • , Talita Duarte-Salles
  • , Anthony G. Sena
  • , Azza Shaoibi
  • , Marc A. Suchard
  • , Patrick B. Ryan
  • , Daniel Prieto-Alhambra
  • , George Hripcsak*
  • *Corresponding author for this work
  • Columbia University
  • University of Oxford
  • Janssen Research & Development (Titusville)
  • Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol)
  • University of California at Los Angeles
  • David Geffen School of Medicine
  • New York Presbyterian Hospital

Research output: Contribution to journal › Article › Academic › peer-review

18 Citations (Scopus)
93 Downloads (Pure)

Abstract

Objective: Background incidence rates are routinely used in safety studies to evaluate an association of an exposure and outcome. Systematic research on sensitivity of rates to the choice of the study parameters is lacking. Materials and Methods: We used 12 data sources to systematically examine the influence of age, race, sex, database, time-at-risk, season and year, prior observation and clean window on incidence rates using 15 adverse events of special interest for COVID-19 vaccines as an example. For binary comparisons we calculated incidence rate ratios and performed random-effect meta-analysis. Results: We observed a wide variation of background rates that goes well beyond age and database effects previously observed. While rates vary up to a factor of 1,000 across age groups, even after adjusting for age and sex, the study showed residual bias due to the other parameters. Rates were highly influenced by the choice of anchoring (e.g., health visit, vaccination, or arbitrary date) for the time-at-risk start. Anchoring on a healthcare encounter yielded higher incidence comparing to a random date, especially for short time-at-risk. Incidence rates were highly influenced by the choice of the database (varying by up to a factor of 100), clean window choice and time-at-risk duration, and less so by secular or seasonal trends. Conclusion: Comparing background to observed rates requires appropriate adjustment and careful time-at-risk start and duration choice. Results should be interpreted in the context of study parameter choices.

Original languageEnglish
Article number814198
JournalFrontiers in Pharmacology
Volume13
DOIs
Publication statusPublished - 26 Apr 2022

Bibliographical note

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

Publisher Copyright:
Copyright © 2022 Ostropolets, Li, Makadia, Rao, Rijnbeek, Duarte-Salles, Sena, Shaoibi, Suchard, Ryan, Prieto-Alhambra and Hripcsak.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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