Dokumentdetails
ID

doi:10.1038/s43856-024-00539-2...

Autor
Hadley, Emily Yoo, Yun Jae Patel, Saaya Zhou, Andrea Laraway, Bryan Wong, Rachel Preiss, Alexander Chew, Rob Davis, Hannah Brannock, M. Daniel Chute, Christopher G. Pfaff, Emily R. Loomba, Johanna Haendel, Melissa Hill, Elaine Moffitt, Richard N3C and RECOVER consortia
Langue
en
Editor

Nature

Kategorie

Medicine & Public Health

Jahr

2024

Auflistungsdatum

17.07.2024

Schlüsselwörter
findings electronic patients characterize following epoch million record study health initial individuals infection reinfection severe reinfections covid-19 covid
Metrisch

Zusammenfassung

More than three years after the start of the COVID-19 pandemic, individuals are frequently reporting multiple COVID-19 infections.

However, these reinfections remain poorly understood.

Here, we investigate COVID-19 reinfections in a large electronic health record cohort of over 3 million patients.

We use data summary techniques and statistical tests to characterize reinfections and their relationships with disease severity, biomarkers, and Long COVID.

We find that individuals with severe initial infection are more likely to experience severe reinfection, that some protein levels are lower, leading to reinfection, and that a lower proportion of individuals are diagnosed with Long COVID following reinfection than initial infection.

Our work highlights the prevalence and impact of reinfections and suggests the need for further research.

Hadley et al. characterize COVID-19 re-infections utilizing electronic health record study cohort data of over 3 million patients.

They find severe initial COVID-19 infection linked to severe reinfections and less frequent long COVID diagnosis after reinfection.

Background Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood.

We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection.

Methods We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative.

We calculate summary statistics, effect sizes, and Kaplan–Meier curves to better understand COVID-19 reinfections.

Results Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections.

We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch.

We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p -value: <0.0001) with a medium effect size (Cramer’s V : 0.20, DoF = 3).

Individuals who experienced severe initial and first reinfection were older in age and at a higher mortality risk than those who had mild initial infection and reinfection.

Conclusions In a large patient cohort, we find that the severity of reinfection appears to be associated with the severity of initial infection and that Long COVID diagnoses appear to occur more often following initial infection than reinfection in the same epoch.

Future research may build on these findings to better understand COVID-19 reinfections.

Hadley, Emily,Yoo, Yun Jae,Patel, Saaya,Zhou, Andrea,Laraway, Bryan,Wong, Rachel,Preiss, Alexander,Chew, Rob,Davis, Hannah,Brannock, M. Daniel,Chute, Christopher G.,Pfaff, Emily R.,Loomba, Johanna,Haendel, Melissa,Hill, Elaine,Moffitt, Richard,N3C and RECOVER consortia, 2024, Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID, Nature

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