Joint effects of US area-level social mobility & income inequality on mortality: an ecological study
Bibliographic record
Abstract
Abstract Background Income inequality and its relationship with health has been extensively studied. Intergenerational income mobility (IGIM) has received less attention, but may be an important independent mechanism explaining health inequalities. Methods We conducted ecological analyses of the effects of IGIM and income inequality on mortality across commuting zones (CZ) in the US. IGIM was measured using rank-rank slope, i.e. the correlation between household income rank at age 15-19 and own income rank at age 29-30. We used CZ-level IGIM data from the Opportunity Insights project, Gini index of inequality from the American Community Survey, and mortality rates from CDC WONDER. Cross-sectional and longitudinal (CZ fixed effects) regression models were fitted adjusting for mean income and weighted by population. Results CZs with higher IGIM between 1995-2001 and 2011-12 had lower age-standardised mortality in 2009-2013, with 53.9 (95% CI: 48.9-58.9) fewer deaths per 100,000 for a standard deviation (SD) increase in IGIM (adjusting for mean income and Gini index in 2009-2013). This equates to 778 (773-783) deaths at the lower quartile vs. 710 (705-715) at the upper. Income inequality had a negligible association at 1.1 (-5.0, 7.3) deaths per SD increase. Longitudinal analysis across three 5-year periods confirmed that within-CZ changes in infant mortality were predicted by changes in IGIM (32.0 [12.7-51.2] fewer deaths per SD increase) but not income inequality (4.5 [-12.0, 20.9] deaths per SD increase). Conclusions Preliminary evidence suggests IGIM is a more important indicator for population health than income inequality. European researchers and public health bodies should further research and monitor IGIM, which may require improved availability of intergenerationally linked income data. Policy measures that improve social mobility, such as education and early-years interventions, may have greater health effects than policies only aiming at resource redistribution. Key messages • Differences in intergenerational income mobility accounted for substantial differences in mortality across US commuting zones, while income inequality appeared relatively unimportant. • European policymakers should put greater emphasis on social mobility as an economic determinant of health, and facilitate research using intergenerationally linked income data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".