Disparities in COVID-19 infection, hospitalisation and death in people with schizophrenia, bipolar disorder, and major depressive disorder: a cohort study of the UK Biobank
Bibliographic record
Abstract
People with severe mental illness (SMI; including schizophrenia/psychosis, bipolar disorder (BD), major depressive disorder (MDD)) experience large disparities in physical health. Emerging evidence suggests this group experiences higher risks of infection and death from COVID-19, although the full extent of these disparities are not yet established. We investigated COVID-19 related infection, hospitalisation and mortality among people with SMI in the UK Biobank (UKB) cohort study. Overall, 447,296 participants from UKB (schizophrenia/psychosis = 1925, BD = 1483 and MDD = 41,448, non-SMI = 402,440) were linked with healthcare and death records. Multivariable logistic regression analysis was used to examine differences in COVID-19 outcomes by diagnosis, controlling for sociodemographic factors and comorbidities. In unadjusted analyses, higher odds of COVID-19 mortality were seen among people with schizophrenia/psychosis (odds ratio [OR] 4.84, 95% confidence interval [CI] 3.00-7.34), BD (OR 3.76, 95% CI 2.00-6.35), and MDD (OR 1.99, 95% CI 1.69-2.33) compared to people with no SMI. Higher odds of infection and hospitalisation were also seen across all SMI groups, particularly among people with schizophrenia/psychosis (OR 1.61, 95% CI 1.32-1.96; OR 3.47, 95% CI 2.47-4.72) and BD (OR 1.48, 95% CI 1.16-1.85; OR 3.31, 95% CI 2.22-4.73). In fully adjusted models, mortality and hospitalisation odds remained significantly higher among all SMI groups, though infection odds remained significantly higher only for MDD. People with schizophrenia/psychosis, BD and MDD have higher risks of COVID-19 infection, hospitalisation and mortality. Only a proportion of these disparities were accounted for by pre-existing demographic characteristics or comorbidities. Vaccination and preventive measures should be prioritised in these particularly vulnerable groups.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".