Social inequalities in COVID-19 mortality by area and individual-level characteristics in Canada, January to July/August 2020: Results from two national data integrations
Bibliographic record
Abstract
Background: Despite early reports of social determinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) burden, national Canadian reporting on COVID-19 inequalities has been limited. The objective of this study is to describe inequalities in COVID-19 mortality in Canada using preliminary data, as part of the Pan-Canadian Health Inequalities Reporting Initiative. Methods: Two provisional Canadian Vital Statistics Death Database integrations were used. Data concerning deaths between January 1 and July 4, 2020, among private-dwelling residents were linked to individual-level data from the 2016 short-form Census, and disaggregated by sex and low-income status, dwelling type, household type and size. Data concerning deaths between January 1 and August 31, 2020 linked to 2016 Census area data were disaggregated by sex and neighbourhood ethno-cultural composition quintiles (based on the proportion of residents who are recent immigrants, visible minorities, born outside of Canada, with no knowledge of English or French), income quintiles and urban residence. The COVID-19 age-standardized mortality rate (per 100,000 population) differences and ratios between groups were estimated. Results: As of July/August 2020, apartment dwellers, residents of urban centres, neighbourhoods with the highest ethno-cultural composition or lowest income experienced 14 to 30 more COVID-19-related deaths/100,000 compared with reference groups (residents of single-detached homes, outside of urban centres, with lowest ethno-cultural concentration or highest income, respectively). Per 100,000 population, sex/gender inequalities were also larger in these four groups (11 to 18 more male than female deaths) than in the reference groups (two to four more male than female deaths). Conclusion: These findings highlight how populations facing socioeconomic disadvantage have experienced a higher overall burden of deaths. Areas for future research are discussed to guide health equity-informed pandemic response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".