Back

Incidence of Long COVID Following Reinfection with COVID-19

Brannock, M. D.; Hadley, E.; Preiss, A.; Fitzgerald, M. L.; Jain, N.; Taylor, E.; Wylam, A.; Yoo, Y. J.; Hill, E.; Moffitt, R. A.; N3C Consortium, ; RECOVER Consortium,

2025-08-13 infectious diseases
10.1101/2025.08.12.25333155 medRxiv
Show abstract

BackgroundCOVID-19 reinfections have emerged as a critical concern, particularly in relation to post-acute sequelae of SARS-CoV-2 infection, commonly known as long COVID. Long COVID is known to manifest diverse, debilitating symptoms across all demographics. Limited studies have investigated the causal relationship of COVID-19 reinfections and long COVID. MethodsWe leveraged demographically diverse electronic health records from the COVID-19 enclave of the National Clinical Cohort Collaborative, part of the RECOVER initiative, to create a matched cohort of reinfected and control adults. All participants had at least one documented COVID-19 infection. We used one-to-one coarsened exact matching on sex, race/ethnicity, age, healthcare utilization, existing comorbidities, site of care, and the timing and severity of first infection. Index dates were assigned to each matched pair as the date of reinfection for the reinfected case. Long COVID was defined using a machine learning computable phenotype trained on clinically diagnosed long COVID cases. Cumulative incidence one year after index was calculated using an Aalen-Johansen estimator. Risk ratios were calculated by taking the ratio of long COVID incidence among reinfected and control cases. ResultsWe found that reinfection resulted in a significantly higher risk of long COVID compared to not being reinfected (risk ratio, 1.35, 95% CI, 1.32-1.39; risk difference, 0.029, 95% CI, 0.027-0.031). This effect was consistent across most stratifications. ConclusionsWe found that COVID-19 reinfection resulted in a roughly 35% increase in the incidence of long COVID in a matched cohort using observational electronic health records.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.