Characterizing the clinical and economic burden of COVID-19 among individuals with immunocompromising conditions in Ontario, Canada – a matched, population-based observational study
Bibliographic record
Abstract
Aims Despite high vaccination rates, COVID-19 continues to be associated with substantial burden among immunocompromised patients (IC). This study aimed to describe and compare outcomes during and following COVID-19 hospitalizations among immunocompromised IC and non-immunocompromised patients (non-IC).Methods Patients hospitalized with COVID-19 (01/2020-03/2023) were identified in Ontario health administrative claims databases. All eligible IC (≥1 of solid organ or stem cell transplant; hematological malignancy; rheumatoid arthritis; multiple sclerosis; or primary immunodeficiency) were matched (1:4) to eligible non-IC. Clinical burden, healthcare resource use, and costs were assessed during hospitalization and post-discharge. Multivariate regression models were used to estimate relative risks (RRi), rates (RRa), and corresponding 95% confidence intervals (CIs), adjusting for neighborhood deprivation, long-term care residency, baseline comorbidities, and COVID-19 vaccination status.Results 9,283 IC hospitalized with COVID-19 (mean age 68.7 years; 52.1% female) were matched to 37,127 non-IC. During index hospitalization, IC had greater risks of intensive care unit admission (RRi = 1.06[1.01-1.12]), receipt of ventilation (RRi = 1.27[1.19-1.36]), and all-cause mortality (RRi = 1.34[1.27-1.41]) compared to non-IC. Within 30-days post-discharge, IC had greater rates of all-cause readmission to hospital (RRa = 1.33[1.26-1.40]), admission to emergency departments (RRa = 1.13[1.08-1.18]), home oxygen use (RRi = 1.35[1.15-1.58]), and COVID-19-related rehabilitation (RRa = 1.52[1.22-1.89]), resulting in 21%(16%-25%) and 51%(45%-58%) greater costs in hospital and post-discharge, respectively. All-cause mortality remained approximately 5% higher for IC compared to non-IC at 30- and 60- days post-discharge (p < 0.001). Resource use rates remained elevated among IC with 57%(50%-64%) greater costs within 180 days post-discharge.Limitations Unmeasured confounding remain; the use of treatments for COVID-19 were not adjusted due to a lack of in-hospital prescription data. Attribution of post-discharge resource use and costs to COVID-19 hospitalizations was subject to greater uncertainty further from the index hospitalization.Conclusion IC experienced more severe COVID-19 outcomes in hospital and post-discharge compared to non-IC. COVID-mitigating policies and prophylactic treatments are needed to continue to protect immunocompromised populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".