Prevalence and determinants of persistent symptoms after infection with SARS-CoV-2: protocol for an observational cohort study (LongCOVID-study)

Lower prevalence of post-Covid-19 Condition following Omicron SARS-CoV-2 infection

Objectives

Different SARS-CoV-2 variants can differentially affect the prevalence of Post Covid-19 Condition (PCC). This prospective study assesses prevalence and severity of symptoms three months after an Omicron infection, compared to Delta, test-negative and population controls. This study also assesses symptomology after reinfection and breakthrough infections.

Methods

After a positive SARS-CoV-2 test, cases were classified as Omicron or Delta based on ≥ 85% surveillance prevalence. Three months after enrolment, participants indicated point prevalence for 41 symptoms and severity, using validated questionnaires for four symptoms. PCC prevalence was estimated as the difference in prevalence of at least one significantly elevated symptom, identified by permutation test, in cases compared to population controls.

Results

At three months follow-up, five symptoms and severe dyspnea were significantly elevated in Omicron cases (n = 4138) compared to test-negative (n = 1672) and population controls (n = 2762). PCC prevalence was 10·4% for Omicron cases and 17·7% for Delta cases (n = 6855). In Omicron cases, severe fatigue and dyspnea were more prevalent in reinfected than primary infected, while severity of symptoms did not significantly differ between cases with a booster or primary vaccination course.

Conclusions

Prevalence of PCC is 41% lower after Omicron than Delta at three months. Reinfection seems associated with more severe long-term symptoms compared to first infection.

Contribution of SARS-CoV-2 infection preceding COVID-19 mRNA vaccination to generation of cellular and humoral immune responses in children

Primary COVID-19 vaccination for children, 5-17 years of age, was offered in the Netherlands at a time when a substantial part of this population had already experienced a SARS-CoV-2 infection. While vaccination has been shown effective, underlying immune responses have not been extensively studied. We studied immune responsiveness to one and/or two doses of primary BNT162b2 mRNA vaccination and compared the humoral and cellular immune response in children with and without a preceding infection. Antibodies targeting the original SARS-CoV-2 Spike or Omicron Spike were measured by multiplex immunoassay. B-cell and T-cell responses were investigated using enzyme-linked immunosorbent spot (ELISpot) assays. The activation of CD4+ and CD8+ T cells was studied by flowcytometry. Primary vaccination induced both a humoral and cellular adaptive response in naive children. These responses were stronger in those with a history of infection prior to vaccination. A second vaccine dose did not further boost antibody levels in those who previously experienced an infection. Infection-induced responsiveness prior to vaccination was mainly detected in CD8+ T cells, while vaccine-induced T-cell responses were mostly by CD4+ T cells. Thus, SARS-CoV-2 infection prior to vaccination enhances adaptive cellular and humoral immune responses to primary COVID-19 vaccination in children. As most children are now expected to contract infection before the age of five, the impact of infection-induced immunity in children is of high relevance. Therefore, considering natural infection as a priming immunogen that enhances subsequent vaccine-responsiveness may help decision-making on the number and timing of vaccine doses.

Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Background

Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning.

Methods

We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes.

Findings

Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively.

Interpretation

We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems.

Funding

Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.