Social Vulnerability, Frailty, and Their Association With Mortality in Older Adults Living in Rural Tanzania
Bibliographic record
Abstract
BACKGROUND: Social vulnerability correlates with frailty and is associated with mortality and disability. However, few studies have investigated this relationship outside of high-income country settings. This study aimed to produce and analyze a culturally adapted social vulnerability index (SVI) to investigate the relationship between social vulnerability, frailty, and mortality in older adults in Tanzania. METHODS: An SVI was produced using data from a cohort study investigating frailty in older adults in Tanzania. Variables were selected based on previous SVI studies using the categories established by Andrew et al. from the Canadian Study of Health and Aging, and National Population Health Survey. The SVI distribution was examined and compared with a frailty index (FI) produced from the same sample, using mutually exclusive variables. Cox regression survival analysis was used to investigate the association between social vulnerability, frailty, and mortality. RESULTS: A stratified cohort of 235 individuals were included in the study at baseline, with a mean age of 75.2 (SD 11.5). Twenty-six participants died within the follow-up period, with a mean of 503 days (range: 405-568) following the initial assessment. The SVI had a median score of 0.47 (interquartile range: 0.23, range: 0.14-0.86). Social vulnerability significantly predicted mortality when adjusting for age and gender, but not when also adjusting for frailty. CONCLUSIONS: Social vulnerability can be successfully operationalized and culturally adapted in Tanzania. Social vulnerability is associated with mortality in Tanzania, but not independently of frailty.
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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 | 0.003 |
| 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) | 0.001 | 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".