Sex differences in COVID-19 mortality: A large US-based cohort study (2020–2022)
Bibliographic record
Abstract
Background: In the present study, we aim to assess the trend in mortality in COVID-19 by time and sex in a large cohort using Datavant's Death Index database. The main objectives of this study are to analyze mortality cases over time, which are categorized by sex and age, and to identify potential reasons for the observed differences. Methods: This is a retrospective cohort containing information on deceased individuals in the United States and Canada (n = 4,384,265). We included adult male and female patients with a clinical diagnosis of COVID-19 (January-December 2022) (ICD-10 code: U07.1). Mortality cases for males and females were presented over a three-year period of COVID-19 pandemic. Sex ratios presenting the change of mortality cases over time was also computed as the number of diagnosed males over female patients. Sex-differences in the mortality rates were illustrated by age groups. Results: In 2020, mortality cases increased to reach up to 200,000 cases per day and fluctuated due to social and/or cultural events in the US. In 2021, mortality cases reached the highest peak over the time period despite the US vaccine rollout due to holiday gatherings during November and December 2021, as well as the spread of a more contagious strain of the virus. In 2022, mortality cases decreased due to widespread vaccinations and a rise in natural immunity following the first Omicron surge. Furthermore, the proportion of COVID-19 cases in males and females remained stable during the pandemic; however, the number of diagnosed male patients markedly increased during the first months of 2022. Gender discrepancies suggest the role of various factors such as occupation, underlying comorbidities, and behavioral and immunological factors. Conclusion: Our study highlights higher mortality rates observed among males, suggesting that several factors may contribute to such differences, including social, behavioral, and biological factors. Our findings highlight the importance of implementing sex-specific treatment approaches in COVID-19 patients.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".