Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. METHODS: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. RESULTS: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. INTERPRETATION: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| 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 | 0.000 |
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".