Optimism, lifestyle, and longevity in a racially diverse cohort of women
Bibliographic record
Abstract
BACKGROUND: Research has suggested optimism is associated with healthy aging and exceptional longevity, but most studies were conducted among non-Hispanic White populations. We examined associations of optimism to longevity across racial and ethnic groups and assessed healthy lifestyle as a possible mediating pathway. METHODS: Participants from the Women's Health Initiative (N = 159,255) completed a validated measure of optimism and provided other demographic and health data at baseline. We evaluated associations of optimism with increments in lifespan using accelerated failure time models, and with likelihood of exceptional longevity (survival to age ≥90) using Poisson regression models. Causal mediation analysis explored whether lifestyle-related factors mediated optimism-lifespan associations. RESULTS: After covariate adjustment, the highest versus lowest optimism quartile was associated with 5.4% (95% confidence interval [CI] = 4.5, 6.4%) longer lifespan. Within racial and ethnic subgroups, these estimates were 5.1% (95%CI = 4.0, 6.1%) in non-Hispanic White, 7.6% (95%CI = 3.6, 11.7%) in Black, 5.4% (95%CI = -0.1, 11.2%) in Hispanic/Latina, and 1.5% (95% CI = -5.0, 8.5) in Asian women. A high proportion (53%) of the women achieved exceptional longevity. Participants in the highest versus lowest optimism quartile had greater likelihood of achieving exceptional longevity (e.g., full sample risk ratio = 1.1, 95%CI = 1.1, 1.1). Lifestyle mediated 24% of the optimism-lifespan association in the full sample, 25% in non-Hispanic White, 10% in Black, 24% in Hispanic/Latina, and 43% in Asian women. CONCLUSIONS: Higher optimism was associated with longer lifespan and a greater likelihood of achieving exceptional longevity overall and across racial and ethnic groups. The contribution of lifestyle to these associations was modest. Optimism may promote health and longevity in diverse racial and ethnic groups. Future research should investigate these associations in less long-lived populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".