Long-term symptom profiles after COVID-19 versus other acute respiratory infections: an analysis of data from the COVIDENCE UK study
Bibliographic record
Abstract
Background Long COVID is a well recognised, if heterogeneous, entity. Acute respiratory infections (ARIs) due to other pathogens may cause long-term symptoms, but few studies compare post-acute sequelae between SARS-CoV-2 and other ARIs. We aimed to compare symptom profiles between people with previous SARS-CoV-2 infection, people with previous non-COVID-19 ARIs, and contemporaneous controls, and to identify clusters of long-term symptoms. Methods COVIDENCE UK is a prospective, population-based UK study of ARIs in adults. We analysed data for 16 potential long COVID symptoms and health-related quality of life (HRQoL), reported between January 21 and February 15, 2021, by participants unvaccinated against SARS-CoV-2. We classified participants as having previous SARS-CoV-2 infection or previous non-COVID-19 ARI (≥4 weeks prior) or no reported ARI. We compared symptoms by infection status using logistic and fractional regression, and identified symptom clusters using latent class analysis (LCA). This study is registered with ClinicalTrials.gov, NCT04330599. Findings We included 10,171 participants (1311 [12.9%] with SARS-CoV-2 infection, 472 [4.6%] with non-COVID-19 ARI). Both types of infection were associated with increased prevalence/severity of most symptoms and decreased HRQoL compared with no infection. Participants with SARS-CoV-2 infection had increased odds of problems with taste/smell (odds ratio 19.74, 95% CI 10.53–37.00) and lightheadedness or dizziness (1.74, 1.18–2.56) compared with participants with non-COVID-19 ARIs. Separate LCA models identified three symptom severity groups for each infection type. In the most severe groups (representing 22% of participants for both SARS-CoV-2 and non-COVID-19 ARI), SARS-CoV-2 infection presented with a higher probability of problems with taste/smell (probability 0.41 vs 0.04), hair loss (0.25 vs 0.16), unusual sweating (0.38 vs 0.25), unusual racing of the heart (0.43 vs 0.33), and memory problems (0.70 vs 0.55) than non-COVID-19 ARI. Interpretation Both SARS-CoV-2 and non-COVID-19 ARIs are associated with a wide range of symptoms more than 4 weeks after the acute infection. Research on post-acute sequelae of ARIs should extend from SARS-CoV-2 to include other pathogens. Funding Barts Charity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".