Long-Term Effects of Wealth on Mortality and Self-rated Health Status
Bibliographic record
Abstract
Epidemiologic studies seldom include wealth as a component of socioeconomic status. The authors investigated the associations between wealth and 2 broad outcome measures: mortality and self-rated general health status. Data from the longitudinal Panel Study of Income Dynamics, collected in a US population between 1984 and 2005, were used to fit marginal structural models and to estimate relative and absolute measures of effect. Wealth was specified as a 6-category variable: those with ≤0 wealth and quintiles of positive wealth. There were a 16%-44% higher risk and 6-18 excess cases of poor/fair health (per 1,000 persons) among the less wealthy relative to the wealthiest quintile. Less wealthy men, women, and whites had higher risk of poor/fair health relative to their wealthy counterparts. The overall wealth-mortality association revealed a 62% increased risk and 4 excess deaths (per 1,000 persons) among the least wealthy. Less wealthy women had between a 24% and a 90% higher risk of death, and the least wealthy men had 6 excess deaths compared with the wealthiest quintile. Overall, there was a strong inverse association between wealth and poor health status and between wealth and mortality.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".