Income inequality and COVID-19 mortality: Age-stratified analysis of 22 OECD countries
Bibliographic record
Abstract
Our study builds on a growing body of research that demonstrates an association between income inequality and COVID-19 mortality. Using Poisson multivariate regression, we age-stratify our analysis by separately examining each of four age groups over a nine-month study period in 22 OECD countries. Our full regression model controls for national median income and relative poverty, and a set of pandemic-specific variables to capture exposure, susceptibility and treatment. We found that country-level income inequality, as measured by the disposable income Gini coefficient, is significantly and positively associated with COVID-19 mortality for all four age groups. Consistent with previous studies that analyzed all-cause mortality by age, our regression results found that the point estimate of the Gini coefficient generally declines with age. Our results suggest that inequality is possibly acting through generic and pandemic-specific processes to increase mortality via a more pronounced negative COVID-19 socio-economic status gradient in higher inequality countries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".