The Impact of Social Stress and Healthy Lifestyle on the Mortality of Chinese Older Adults: Prospective Cohort Study
Bibliographic record
Abstract
Background: With social progress, social stress (SS) has become a key factor affecting health. Unhealthy lifestyles may exacerbate these effects. However, the relationship between SS, lifestyle, and older adults' mortality rate still needs to be studied. Objective: This study aimed to explore the relationship between SS and all-cause mortality in Chinese older adults, as well as the influence of healthy lifestyle factors. Methods: Three groups of SS were defined through latent class analysis: low, medium, and high. We created a healthy lifestyle index based on smoking, alcohol consumption, physical activity, and diet. Multivariable Cox proportional hazards models, interaction analyses, and mediation analyses were conducted. Results: The Chinese Longitudinal Healthy Longevity Survey (CLHLS) datasets included participants from 806 cities and counties across 23 provinces in China from 1998 to 2018. In this study, participants were recruited from 4 waves of the CLHLS (2005, 2008, 2011, and 2014). Finally, 19,236 participants were included in this study, of which 6891 (35.8%) had low SS, 11,662 (60.6%) had medium SS, and 683 (3.6%) had high SS. In the fully adjusted model, the hazard ratio (HR) for medium SS was 1.16 (95% CI 1.11-1.20; P<.001), and for high SS, it was 1.28 (95% CI 1.18-1.40; P<.001) compared to the low SS group. For individuals aged ≥80 years, the medium SS group had a 28% (HR 1.28, 95% CI 1.22-1.34; P<.001) increased mortality risk, and the high SS group had a 38% (HR 1.38, 95%CI 1.26-1.52; P<.001) increased risk compared to the low SS group. Approximately 7% of the association between SS and mortality was mediated through the healthy lifestyle. Under different SS, the lower the healthy lifestyle score, the higher the risk of mortality. Conclusions: SS was an independent predictor of all-cause mortality in Chinese older adults. The healthy lifestyle mediated this effect to some extent. Unhealthy lifestyle behaviors were associated with a higher risk of mortality at all SS levels.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".