Prevalence and factors associated with frailty among community-dwelling older people in rural Thanjavur district of South India: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: There is sparse data on the prevalence of frailty from rural parts of India. Our aim was to estimate prevalence of frailty among community-dwelling older people in rural South Indian population and explore socio-demographic factors associated with frailty. We further explored the associations between frailty with fear of falling and falls. DESIGN: Community based cross-sectional study. SETTING: Four villages in Thanjavur district of Southern India. PARTICIPANTS: Random sample of adults aged 60 years and above from four villages. METHODS: We sampled community-dwelling older adults from the electoral list of four villages using stratified random sampling. We report prevalence of frailty as defined by physical definition (Fried's Phenotype), accumulation of deficits (Frailty Index) and multi-domain definition (Tilburg Frailty Indicator). We report proportion of agreement of frailty status between the frailty tools. We used logistic regressions with robust SEs to examine the associations between socio-demographic determinants with frailty and the association between frailty with fear of falling and falls. RESULTS: Among the 408 participants, the weighted (non-response and poststratification for sex) prevalence and 95% CI of frailty was 28% (18.9 to 28.1) for physical definition, 59% (53.9 to 64.3) for accumulation of deficits and 63% (57.4 to 67.6) for multi-domain definition. Frailty Index and Tilburg Frailty Indicator had good agreement (80%). Age, female, lower education, lower socioeconomic status, minimum physical activity in routine work were independently associated with frailty irrespective of the frailty definitions. Frail elderly had higher odds of falls as well as fear of falling compared with non-frail, irrespective of the definitions. CONCLUSION: Prevalence of frailty among older people in rural Thanjavur district of South India was high compared with low-income and middle-income countries. Understanding the modifiable determinants of frailty can provide a valuable reference for future prevention and intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".