Sex differences in COVID-19 deaths in the early months of the pandemic in Canada: An examination with an immigration lens
Bibliographic record
Abstract
Background: At the onset of the COVID-19 pandemic, there was an overrepresentation of males in COVID-19 deaths worldwide, with Canada reporting more female COVID-19 deaths. This paper examines the overrepresentation of female COVID-19 deaths in Canada, with an immigration lens. Data and methods: Data were extracted from the COVID-19 Sex-Disaggregated Data Tracker to compare the sex distribution of COVID-19 deaths in Canada with that of other countries. A linkage of deaths to the Longitudinal Immigration Database (IMDB) allows for the comparison of sex-specific COVID-19 death rates by immigrant status for age and geography, as well as by major employment sector among immigrants, using the tax data from the IMDB. Results: While there were proportionately more female than male COVID-19 deaths in Canada in the early months of the pandemic, this trend was mainly a phenomenon among non-immigrants aged 85 and older. In addition, COVID-19-specific death rates for males were higher than those for females across age groups by immigrant status, except for those aged 85 and older among the non-immigrant population. Among immigrants, the death rate among health care and social assistance workers was higher among males than among females (10.7 vs. 2.9 per 100,000 population). The initially observed overrepresentation of female COVID-19 deaths to male COVID-19 deaths in Canada evened out in the summer of 2021. Interpretation: The higher proportion of female COVID-19 deaths was likely related to the high concentration of COVID-19 deaths in long-term care facilities, where a lower institutionalization rate for immigrants had been observed. Since the implementation of vaccination targeting long-term care facility residents in Canada, the overrepresentation of female COVID-19 deaths ceased.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| 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) | 0.003 | 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".