Income inequality among American states and the incidence of major depression
Bibliographic record
Abstract
BACKGROUND: Although cross-sectional and ecological studies have shown that higher area-level income inequality is related to increased risk for depression, few longitudinal studies have been conducted. This investigation examines the relationship between state-level income inequality and major depression among adults participating in a population-based, representative longitudinal study. METHODS: We used data from the National Epidemiologic Survey on Alcohol and Related Conditions (n=34 653). Respondents completed structured diagnostic interviews at baseline (2001-2002) and follow-up (2004-2005). Weighted multilevel modelling was used to determine if U.S. state-level income inequality (measured by the Gini coefficient) was a significant predictor of depression at baseline and at follow-up, while controlling for individual-level and state-level covariates. We also repeated the longitudinal analyses, excluding those who had a history of depression or at baseline, in order to test whether income inequality was related to incident depression. RESULTS: State-level inequality was associated with increased incidence of depression among women but not men. In comparison to women residing in states belonging to the lowest quintile of income inequality, women were at increased risk for depression in the second (OR=1.18, 95% CI 0.86 to 1.62), third (OR=1.22, 95% CI 0.91 to 1.62), fourth (OR=1.37, 95% CI 1.03 to 1.82) and fifth (OR=1.50, 95% CI 1.14 to 1.96) quintiles at follow-up (p<0.05 for the linear trend). CONCLUSIONS: Living in a state with higher income inequality increases the risk for the development of depression among women.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".