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

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...

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Autores principales: Ostropolets, Anna, Li, Xintong, Makadia, Rupa, Rao, Gowtham, Rijnbeek, Peter R., Duarte-Salles, Talita, Sena, Anthony G., Shaoibi, Azza, Suchard, Marc A., Ryan, Patrick B., Prieto-Alhambra, Daniel, Hripcsak, George
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9087898/
https://www.ncbi.nlm.nih.gov/pubmed/35559254
http://dx.doi.org/10.3389/fphar.2022.814198
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author Ostropolets, Anna
Li, Xintong
Makadia, Rupa
Rao, Gowtham
Rijnbeek, Peter R.
Duarte-Salles, Talita
Sena, Anthony G.
Shaoibi, Azza
Suchard, Marc A.
Ryan, Patrick B.
Prieto-Alhambra, Daniel
Hripcsak, George
author_facet Ostropolets, Anna
Li, Xintong
Makadia, Rupa
Rao, Gowtham
Rijnbeek, Peter R.
Duarte-Salles, Talita
Sena, Anthony G.
Shaoibi, Azza
Suchard, Marc A.
Ryan, Patrick B.
Prieto-Alhambra, Daniel
Hripcsak, George
author_sort Ostropolets, Anna
collection PubMed
description 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.
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spelling pubmed-90878982022-05-11 Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases Ostropolets, Anna Li, Xintong Makadia, Rupa Rao, Gowtham Rijnbeek, Peter R. Duarte-Salles, Talita Sena, Anthony G. Shaoibi, Azza Suchard, Marc A. Ryan, Patrick B. Prieto-Alhambra, Daniel Hripcsak, George Front Pharmacol Pharmacology 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. Frontiers Media S.A. 2022-04-26 /pmc/articles/PMC9087898/ /pubmed/35559254 http://dx.doi.org/10.3389/fphar.2022.814198 Text en Copyright © 2022 Ostropolets, Li, Makadia, Rao, Rijnbeek, Duarte-Salles, Sena, Shaoibi, Suchard, Ryan, Prieto-Alhambra and Hripcsak. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Ostropolets, Anna
Li, Xintong
Makadia, Rupa
Rao, Gowtham
Rijnbeek, Peter R.
Duarte-Salles, Talita
Sena, Anthony G.
Shaoibi, Azza
Suchard, Marc A.
Ryan, Patrick B.
Prieto-Alhambra, Daniel
Hripcsak, George
Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases
title Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases
title_full Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases
title_fullStr Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases
title_full_unstemmed Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases
title_short Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases
title_sort factors influencing background incidence rate calculation: systematic empirical evaluation across an international network of observational databases
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9087898/
https://www.ncbi.nlm.nih.gov/pubmed/35559254
http://dx.doi.org/10.3389/fphar.2022.814198
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