457. Low Rates of COVID-19 in a Vulnerable Population: Learning from Early and Decisive Public Health Policies
Bibliographic record
Abstract
Abstract Background Disasters, including pandemics, disproportionately affect vulnerable populations. The Downtown Eastside (DTES) neighborhood of Vancouver has high prevalence of mental illness, substance use, infectious disease and homelessness. While studies have described clinical characteristics of COVID-19 patients in other centres worldwide, data is lacking on marginalized groups. We describe the clinical characteristics and outcomes of COVID-19 patients seen at two urban hospitals who care for the vulnerable population in the DTES of Vancouver, British Columbia (BC), Canada. Methods A retrospective chart review was conducted on all COVID-19 patients ≥19 years seen at either centre from January 1 to June 10, 2020. Descriptive statistics assessed demographics, comorbidities, presenting symptoms, laboratory values and outcomes, and were compared between subjects managed as inpatients (died vs. discharged) and outpatients. Results Of 71 COVID-19 subjects, mean age was 57y (SD 20); 36 (51%) were male. Time to presentation, symptoms and laboratory values were similar to other reports. 58 (82%) presented from the community, 3 (4%) from long-term care/rehabilitation centres, and 8 (11%) had no fixed address (NFA) or lived in the DTES. 45 (64%) had a known exposure, 20 (28%) were healthcare workers, 85% involved in direct patient care; 0/20 were admitted to hospital. Of the 8 NFA/DTES subjects, mean age was 46y (SD 13), 50% were male, 5 (63%) were admitted to hospital and all survived. Admitted subjects (n=34) were older (mean age 69 vs 46y, p< 0.001), 62% were male, and had more comorbidities (mean [SD] 3 [3] vs. 1 [2], p< 0.001). Eight (24%) died, 26 (76%) were discharged, 29% developed acute respiratory distress syndrome, 21% secondary infection, 18% renal failure, and 15% cardiac dysfunction. Of patients admitted to intensive care, 5/10 died. Conclusion Our results concur with other studies showing older age and comorbidities contribute to more severe COVID-19 disease. 64% of subjects had a known exposure, and only 11% had NFA/DTES residence. Given that there is no financial barrier to access healthcare in Canada and these hospitals serve our most vulnerable populations, our results may indicate that BC Public Health has done an effective job of tracking and limiting community spread of COVID-19. Disclosures All Authors: No reported disclosures
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".