Income inequality and COVID-19 mortality: Age-stratified analysis of 22 OECD countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it