Cumulative incidence of SARS-CoV-2 infection within the homeless population: insights from a city-wide longitudinal study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to determine the risk factors associated with SARS-CoV-2 infection in a cohort of homeless people using survival analysis. Seroprevalence in the homeless community was also compared with that of the general population. DESIGN: Cohort study. SETTING: Data were collected across two testing sessions, 3 months apart, during which each participant was tested for anti-SARS-CoV-2 antibodies and completed a face-to-face survey. PARTICIPANTS: All homeless adults sleeping rough, in slums or squats, in emergency shelters or transitional accommodation in Marseille were eligible. PRIMARY OUTCOME MEASURES: Occurrence of a seroconversion event defined as a biologically confirmed SARS-CoV-2 infection. Local data from a national seroprevalence survey were used for comparison between homeless people and the general population. RESULTS: A total of 1249 people were included. SARS-CoV-2 seroprevalence increased from 6.0% (4.7-7.3) during the first session to 18.9% (16.0-21.7) during the second one, compared with 3.0% (1.9-4.2) and 6.5% (4.5-8.7) in the general population. Factors significantly associated with an increased risk of COVID-19 infection were: having stayed in emergency shelters (1.93 (1.18-3.15)), being an isolated parent (1.64 (1.07-2.52)) and having contact with more than 5-15 people per day (1.84 (1.27-2.67)). By contrast, smoking (0.46 (0.32-0.65)), having financial resources (0.70 (0.51-0.97)) and psychiatric or addictive comorbidities (0.52 (0.32-0.85)) were associated with a lower risk. CONCLUSION: We confirm that homeless people have higher infection rates than the general population, with increased risk in emergency shelters. There is growing evidence that, in addition to usual preventive measures, public policies should pay attention to adapt the type of accommodation and overall approach of precariousness. TRIAL REGISTRATION NUMBER: NCT04408131.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".