COVID-19 Cases Among Facility-Staff by Neighbourhood of Residence and Social and Structural Determinants: An Observational Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Disproportionate risks of COVID-19 in congregate settings including long-term care homes, retirement homes, and shelters both affect and are affected by SARS-CoV-2 infections among facility-staff. In cities across Canada, there has been a consistent trend of geographic clustering of COVID-19 cases. However, there remain limited data on how COVID-19 among facility-staff reflect urban neighbourhood disparities, particularly stratified by the social and structural determinants of community-level transmission. OBJECTIVE To compare the concentration of cumulative cases by geography and social/structural determinants across three mutually exclusive subgroups in the Greater Toronto Area (population 7.1 million): community, facility-staff, and healthcare workers (HCW) in other settings. METHODS We conducted a retrospective, observational study using surveillance data on laboratory-confirmed COVID-19 cases (January 23 to December 13, 2020; prior to vaccination roll-out). We derived neighbourhood-level social/structural determinants from census data, and generated Lorenz curves and Gini coefficients to visualize and quantify inequalities in cases. RESULTS The hardest-hit neighbourhoods (comprising 20% of the population) accounted for 53.4% of community cases, 48.6% of facility-staff cases, and 42.3% of other HCW cases. Compared with other HCW, cases in facility-staff more closely reflected the distribution of community cases. Cases in facility-staff reflected greater social and structural inequalities (larger Gini coefficients) than other HCW across all determinants. Facility-staff cases were also more likely than community cases to be concentrated in lower income neighbourhoods (Gini 0.24[0.15-0.38] vs 0.14[0.08-0.21] with lower household density (Gini 0.23[0.17-0.29] vs 0.17[0.12-0.22]) and with a greater proportion working in other essential services (Gini 0.29 [0.21-0.40], 0.22[0.17-0.28]). CONCLUSIONS COVID-19 cases among facility-staff largely reflects neighbourhood-level heterogeneity and disparities; even more so than cases in other HCW. Findings signal the importance of interventions prioritized and tailored to home geographies of facility-staff in addition to workplace measures, including prioritization and reach of vaccination at home (neighbourhood-level) and at work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".