THE ASSOCIATION BETWEEN ADULT MORTALITY RISK AND FAMILY HISTORY OF LONGEVITY: THE MODERATING EFFECTS OF SOCIOECONOMIC STATUS
Bibliographic record
Abstract
Studies consistently show that increasing levels of socioeconomic status (SES) and having a familial history of longevity reduce the risk of mortality. But do these two variables interact, such that individuals with lower levels of SES, for example, may experience an attenuated longevity penalty by virtue of having long-lived relatives? This article examines this interaction by analysing survival past age 40 based on data from the Utah Population Database on an extinct cohort of men born from the years 1840 to 1909. Cox proportional hazards regression and logistic regression are used to test for the main and interaction mortality effects of SES and familial excess longevity (FEL), a summary measure of an individual's history of longevity among his or her relatives. This research finds that the mortality hazard rate for men in the top 15th percentile of occupational status decreases more as FEL increases than it does among men in the bottom 15th percentile. In addition, the mortality hazard rate among farmers decreases more as FEL increases than it does for non-farmers. With a strong family history of longevity as a proxy for a genetic predisposition, this research suggests that a gene-environment interaction occurs whereby the benefits of familial excess longevity are more available to those who have occupations with more autonomy and greater economic resources and/or opportunities for physical activity.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".