Gout and Excess Risk of Severe <scp>SARS</scp>–<scp>CoV</scp>‐2 Infection Among Vaccinated Individuals: A General Population Study
Bibliographic record
Abstract
OBJECTIVE: Gout patients often have multiple comorbidities, making them susceptible to SARS-CoV-2 infection and poor outcomes. This study was undertaken to examine the association between gout and the risk of SARS-CoV-2 infection and severe outcomes, especially in patients who have received a SARS-CoV-2 vaccine. METHODS: We conducted 2 cohort studies using The Health Improvement Network in the UK. Individuals with gout and those without gout from the general population were followed up from December 8, 2020 to October 31, 2021. We estimated the rate difference (RD) and hazard ratio (HR) of SARS-CoV-2 infection and severe outcomes (i.e., hospitalization and death within 30 days after SARS-CoV-2 infection) for individuals with gout versus those without gout using a Cox proportional hazards model according to SARS-CoV-2 vaccination status. We adjusted for potential confounders by using overlap weighting of exposure scores. RESULTS: Among the vaccinated cohort, 1,955 cases of breakthrough COVID-19 infection occurred in 54,576 individuals with gout (4.68 cases per 1,000 person-months), and 52,468 cases occurred in 1,336,377 individuals without gout (3.76 cases per 1,000 person-months). The partially adjusted RD of breakthrough infection was 0.91 cases per 1,000 person-months (95% confidence interval [95% CI] 0.62-1.20 cases per 1,000 person-months), and the partially adjusted HR was 1.24 (95% CI 1.19-1.30). Gout was also associated with an increased risk of hospitalization (adjusted HR 1.30 [95% CI 1.10-1.53]) and death (adjusted HR 1.36 [95% CI 0.87-2.13]). Women with gout had an increased risk of hospitalization (adjusted HR 1.55 [95% CI 1.15-2.10]) and death (adjusted HR 2.46 [95% CI 1.12-5.41]). Similar associations with gout were observed in the unvaccinated cohort. CONCLUSION: These general population data suggest that individuals with gout, especially women, have higher risks of SARS-CoV-2 infection and severe outcomes, even when vaccinated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".