Severe outcomes and length of stay among people with schizophrenia hospitalized for COVID-19: A population-based retrospective cohort study
Bibliographic record
Abstract
Abstract Background and Hypothesis Schizophrenia is associated with substantial physical and psychiatric comorbidities that increase the risk of severe outcomes in COVID-19 infection. However, few studies have examined the differences in care and outcomes among people with schizophrenia throughout the pandemic. We hypothesized that rates of in-hospital mortality, admission to the intensive care unit (ICU), and length of stay differed among people with and without schizophrenia. Study Design We conducted a population-based retrospective cohort study using administrative health data from Ontario, Canada that included individuals hospitalized for COVID-19 between February 2020 and October 2023. We compared mortality, ICU admission, and length of stay using regression models adjusted for age, sex, comorbidities, vaccination status, and sociodemographic characteristics. Study Results We evaluated 66,959 hospital admissions, 4.3% (2,884) of which involved people with schizophrenia. People with schizophrenia had a significantly decreased rate of ICU admission (adjusted OR: 0.74, [0.67, 0.82]), a longer length of stay (adjusted RR: 1.25, [1.21, 1.30]), but a similar risk of mortality (adjusted OR: 1.09, [0.98, 1.22]) as people without schizophrenia. Age modified the relationship between schizophrenia and ICU admission. People with schizophrenia aged 60-75 were substantially less likely to be admitted to the ICU relative to those without (18.4% vs. 26.5%, p < .001). Conclusions Our findings underscore disparities in care among people with and without schizophrenia. These disparities vary by age and suggest that people with schizophrenia may not be receiving the same level of care as people without schizophrenia hospitalized for COVID-19.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".