Neighborhood-level Racial/Ethnic and Economic Inequities in COVID-19 Burden Within Urban Areas in the US and Canada
Bibliographic record
Abstract
ABSTRACT The COVID-19 pandemic exhibits stark social inequities in infection and mortality outcomes. We investigated neighborhood-level inequities across cities in the US and Canada for COVID-19 cumulative case rates (46 cities), death rates (12 cities), testing rates and test positivity (12 cities), using measures that characterize social gradients by race/ethnicity, socioeconomic composition, or both jointly. We found consistent evidence of social gradients for case, death and positivity rates, with the most privileged neighborhoods having the lowest rates, but no meaningful variation in the magnitude of inequities between cities. Gradients were not apparent in testing rates, suggesting inadequate testing in the most deprived neighborhoods. Health agencies should monitor and compare inequities as part of their COVID-19 reporting practices and to guide pandemic response efforts. HIGHLIGHTS Within urban regions with available data in the US and Canada, there were strong social gradients for case, death and positivity rates The most racially and/or economically privileged neighborhoods had the lowest rates Social gradients were similar for neighborhood-level measures of racial/ethnic composition, income, racialized economic segregation, and racialized occupational segregation Testing rates did not show consistent social gradients, which suggests that the most deprived neighborhoods have inadequate access to testing relative to their higher disease burden
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".