State-level association between income inequality and mortality in the USA, 1989–2019: ecological study
Bibliographic record
Abstract
BACKGROUND: Prior studies have shown a positive relationship between income inequality and population-level mortality. This study investigates whether the relationship between US state-level income inequality and all-cause mortality persisted from 1989 to 2019 and whether changes in income inequality were correlated with changes in mortality rates. METHODS: We perform repeated cross-sectional regressions of mortality on state-level inequality measures (Gini coefficients) at 10-year intervals. We also estimate the correlation between within-state changes in income inequality and changes in mortality rates using two time-series models, one with state- and year-fixed effects and one with a lagged dependent variable. Our primary regressions control for median income and are weighted by population. MAIN OUTCOME MEASURES: The two primary outcomes are male and female age-adjusted mortality rates for the working-age (25-64) population in each state. The secondary outcome is all-age mortality. RESULTS: There is a strong positive correlation between Gini and mortality in 1989. A 0.01 increase in Gini is associated with more deaths: 9.6/100 000 (95% CI 5.7, 13.5, p<0.01) for working-age females and 29.1 (21.2, 36.9, p<0.01) for working-age males. This correlation disappears or reverses by 2019 when a 0.01 increase in Gini is associated with fewer deaths: -6.7 (-12.2, -1.2, p<0.05) for working-age females and -6.2 (-15.5, 3.1, p>0.1) for working-age males. The correlation between the change in Gini and change in mortality is also negative for all outcomes using either time-series method. These results are generally robust for a range of income inequality measures. CONCLUSION: The absence or reversal of correlation after 1989 and the presence of an inverse correlation between change in inequality and change in all-cause mortality represents a significant reversal from the findings of a number of other studies. It also raises questions about the conditions under which income inequality may be an important policy target for improving population health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".