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Insights from an N3C RECOVER EHR-based cohort study characterizing SARS-CoV-2 reinfections and Long COVID.
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.
Afiliación
  • Hadley E; RTI International, Durham, NC, USA. ehadley@rti.org.
  • Yoo YJ; Emory University, Atlanta, GA, USA.
  • Patel S; Stony Brook University, Stony Brook, NY, USA.
  • Zhou A; University of Virginia, Charlottesville, VA, USA.
  • Laraway B; University of North Carolina, Chapel Hill, NC, USA.
  • Wong R; Stony Brook University, Stony Brook, NY, USA.
  • Preiss A; RTI International, Durham, NC, USA.
  • Chew R; RTI International, Durham, NC, USA.
  • Davis H; Patient Led Research Collaborative (PLRC), Calabasas, CA, USA.
  • Brannock MD; RTI International, Durham, NC, USA.
  • Chute CG; Johns Hopkins University, Baltimore, MD, USA.
  • Pfaff ER; University of North Carolina, Chapel Hill, NC, USA.
  • Loomba J; University of Virginia, Charlottesville, VA, USA.
  • Haendel M; University of North Carolina, Chapel Hill, NC, USA.
  • Hill E; University of Rochester Medical Center, Rochester, NY, USA.
  • Moffitt R; Emory University, Atlanta, GA, USA.
Commun Med (Lond) ; 4(1): 129, 2024 Jul 11.
Article en En | MEDLINE | ID: mdl-38992084
ABSTRACT

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.
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.

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Commun Med (Lond) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Commun Med (Lond) Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos