Outcomes for people experiencing homelessness with COVID-19 presenting to emergency departments in Canada, compared with housed patients
Bibliographic record
Abstract
BACKGROUND: Whether people experiencing homelessness (PEH) have different COVID-19 outcomes than housed patients in Canada remains unclear. We sought to ascertain whether rates of in-hospital mortality, hospital admission, critical care admission, and mechanical ventilation differed between PEH and housed people with symptomatic SARS-CoV-2 infection. METHODS: We conducted a propensity score-matched cohort study to compare the outcomes of PEH and housed patients presenting to emergency departments for acute symptomatic COVID-19. We used data from the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry. Covariates in our propensity score model included age, sex, comorbidities, substance use, vaccination status, previous do-not-resuscitate documentation, hospital type, province and calendar quarter of presentation to the emergency department, symptom duration, and severity of illness on presentation. RESULTS: We found no difference in mortality for PEH (3%) compared with a propensity score-matched cohort of housed patients (3%) (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.43-1.74). We also found no difference in admission rates for PEH (44%) versus housed patients (45%). There was a reduced rate of critical care admission for PEH compared with housed patients (OR 0.66, 95% CI 0.44-1.00), and a trend toward decreased use of mechanical ventilation for PEH versus housed patients, which was not significant (OR 0.60, 95% CI 0.35-1.02). INTERPRETATION: We found no difference in mortality for PEH with COVID-19 compared with those who were housed. A signal for reduced critical care admission among PEH may reflect differential treatment unrelated to clinical characteristics that we matched for. Future research on resource allocation during pandemics could shed light on potential inequities for vulnerable populations and how best to address them.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".