Early-Life War Exposure and Later-Life Frailty Among Older Adults in Vietnam: Does War Hasten Aging?
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess the nature and degree of association between exposure to potentially traumatic wartime experiences in early life, such as living in a heavily bombed region or witnessing death firsthand, and later-life frailty. METHOD: The Vietnam Health and Aging Study included war survivors in Vietnam, 60+, who completed a survey and health exam between May and August 2018. Latent class analysis (LCA) is used to construct classes exposed to similar numbers and types of wartime experiences. Frailty is measured using a deficit accumulation approach that proxies biological aging. Fractional logit regression associates latent classes with frailty scores. Coefficients are used to calculate predicted frailty scores and expected age at which specific levels of frailty are reached across wartime exposure classes. RESULTS: LCA yields 9 unique wartime exposure classes, ranging from extreme exposure to nonexposed. Higher frailty is found among those with more heavy/severe exposures with a combination of certain types of experiences, including intense bombing, witnessing death firsthand, having experienced sleep disruptions during wartime, and having feared for one's life during war. The difference in frailty-associated aging between the most and least affected individuals is more than 18 years. DISCUSSION: War trauma hastens aging and warrants greater attention toward long-term implications of war on health among vast postconflict populations across the globe.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".