Prevalence and factors associated with frailty in older adults from the Kenyan coast
Bibliographic record
Abstract
Abstract Background: The burden and determinants of frailty in sub-Saharan Africa (SSA) are largely unknown despite a rapidly aging population in the region. To address the gap in Kenya, we: a) determined the prevalence of frailty among older adults living with HIV (OALWH) and compared to their uninfected peers; b) investigated HIV status as an independent predictor of frailty in the older adults; c) investigated the correlates of frailty in these adults. Methods: This cross-sectional study was conducted between 2020 and 2021 at the Kenyan coast among 440 older adults aged ≥50 years (257 OALWH). Frailty was assessed using the Reported Edmonton Frail Scale, which comprises nine domains of frailty, including cognition, general health status, functional independence, social support, medication use, nutrition, mood, continence, and self-reported functional performance. We also collected biopsychosocial information from the respondents. Logistic regression was used to examine the correlates of frailty. Results: The prevalence of frailty was significantly higher among OALWH (24%) compared to their uninfected peers (13%), p<0.01. However, after adjusting for biopsychosocial factors, HIV seropositivity was not independently associated with frailty. Among OALWH, sleeping difficulties, ageism, higher waist/hip ratio, visiting traditional healers, a history of combination antiretroviral therapy (cART) regimen change/interruption and prolonged illness following HIV diagnosis were significantly associated with higher odds of frailty. Residing in larger households, higher household income, having a social network of friends, and light physical activities were significantly associated with reduced odds of frailty. Among HIV uninfected older adults, sleeping difficulties, self-reported diabetes and light physical activities were significantly associated with elevated odds of frailty, while being male was significantly associated with reduced odds of frailty. Conclusions: The prevalence of frailty is elevated among older adults living with HIV when compared to their uninfected peers; however, factors other than HIV are predominant, particularly psychosocial factors. Our findings provide the foundation for further research on frailty and developing culturally appropriate interventions and healthcare strategies to prevent, delay and manage frailty at the Kenyan Coast and other similar settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".