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Potential biases arising from epidemic dynamics in observational seroprotection studies
The extent and duration of immunity following SARS-CoV-2 infection are critical outstanding questions about the epidemiology of this novel virus, and studies are needed to evaluate the effects of serostatus on reinfection. Understanding the potential sources of bias and methods to alleviate biases i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273253/ https://www.ncbi.nlm.nih.gov/pubmed/32511485 http://dx.doi.org/10.1101/2020.05.02.20088765 |
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author | Kahn, Rebecca Kennedy-Shaffer, Lee Grad, Yonatan H. Robins, James M. Lipsitch, Marc |
author_facet | Kahn, Rebecca Kennedy-Shaffer, Lee Grad, Yonatan H. Robins, James M. Lipsitch, Marc |
author_sort | Kahn, Rebecca |
collection | PubMed |
description | The extent and duration of immunity following SARS-CoV-2 infection are critical outstanding questions about the epidemiology of this novel virus, and studies are needed to evaluate the effects of serostatus on reinfection. Understanding the potential sources of bias and methods to alleviate biases in these studies is important for informing their design and analysis. Confounding by individual-level risk factors in observational studies like these is relatively well appreciated. Here, we show how geographic structure and the underlying, natural dynamics of epidemics can also induce noncausal associations. We take the approach of simulating serologic studies in the context of an uncontrolled or a controlled epidemic, under different assumptions about whether prior infection does or does not protect an individual against subsequent infection, and using various designs and analytic approaches to analyze the simulated data. We find that in studies assessing the efficacy of serostatus on future infection, comparing seropositive individuals to seronegative individuals with similar time-dependent patterns of exposure to infection, by stratifying or matching on geographic location and time of enrollment, is essential to prevent bias. |
format | Online Article Text |
id | pubmed-7273253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-72732532020-06-07 Potential biases arising from epidemic dynamics in observational seroprotection studies Kahn, Rebecca Kennedy-Shaffer, Lee Grad, Yonatan H. Robins, James M. Lipsitch, Marc medRxiv Article The extent and duration of immunity following SARS-CoV-2 infection are critical outstanding questions about the epidemiology of this novel virus, and studies are needed to evaluate the effects of serostatus on reinfection. Understanding the potential sources of bias and methods to alleviate biases in these studies is important for informing their design and analysis. Confounding by individual-level risk factors in observational studies like these is relatively well appreciated. Here, we show how geographic structure and the underlying, natural dynamics of epidemics can also induce noncausal associations. We take the approach of simulating serologic studies in the context of an uncontrolled or a controlled epidemic, under different assumptions about whether prior infection does or does not protect an individual against subsequent infection, and using various designs and analytic approaches to analyze the simulated data. We find that in studies assessing the efficacy of serostatus on future infection, comparing seropositive individuals to seronegative individuals with similar time-dependent patterns of exposure to infection, by stratifying or matching on geographic location and time of enrollment, is essential to prevent bias. Cold Spring Harbor Laboratory 2020-05-06 /pmc/articles/PMC7273253/ /pubmed/32511485 http://dx.doi.org/10.1101/2020.05.02.20088765 Text en https://creativecommons.org/licenses/by/4.0/It is made available under a CC-BY 4.0 International license (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Article Kahn, Rebecca Kennedy-Shaffer, Lee Grad, Yonatan H. Robins, James M. Lipsitch, Marc Potential biases arising from epidemic dynamics in observational seroprotection studies |
title | Potential biases arising from epidemic dynamics in observational seroprotection studies |
title_full | Potential biases arising from epidemic dynamics in observational seroprotection studies |
title_fullStr | Potential biases arising from epidemic dynamics in observational seroprotection studies |
title_full_unstemmed | Potential biases arising from epidemic dynamics in observational seroprotection studies |
title_short | Potential biases arising from epidemic dynamics in observational seroprotection studies |
title_sort | potential biases arising from epidemic dynamics in observational seroprotection studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273253/ https://www.ncbi.nlm.nih.gov/pubmed/32511485 http://dx.doi.org/10.1101/2020.05.02.20088765 |
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