146Prevalence And Determinants Of Frailty And Associated Co-Morbidities Among Older People In Nepal
Bibliographic record
Abstract
Population ageing is increasing in low income countries. Despite this, there is distinct lack of knowledge about prevalence of co-morbidities and determinants of frailty among older people in these countries. We used data from “Health and Social Care Needs Assessment Survey of the Gurkha Welfare Pensioners” conducted in 2014. Participants were age ≥60 years from Gorakha, Lamjung and Tanahu districts of Nepal. Face to face interviews were conducted using validate questionnaires. Demographic, socio-economic, self-reported illnesses, and symptoms were collected. Frailty was assessed using Canadian Study of Health and Ageing (CSHA) scale. Univariable and multivariable regression models were constructed to identify the determinants of frailty defined as CSHA scale ≥4. A total of 253 participants (32.0% men) were included in this study. Majority (82.2%) of the participants were from Janajati ethnic background. Men who are Ex-serviceman had higher educational attainment than women, majority of whom (95.3%) are widows of ex-serviceman who no longer alive (p < 0.01). 48.5% of women lived with their sons whereas 43% of the male participants live with their wives. Women reported higher prevalence of mental health issues such as anxiety and insomnia compared with men. The prevalence of frailty was 46.2% (46.3% in men and 46.1% in women). In this population frailty was significantly associated with older age, smoking , living with son, breathing problems, unspecified pain and fatigue, poor dental health, history of falls and fracture (p < 0.001 for all) after controlling for potential confounders. Our study highlights the growing nature of co-morbidity burden and frailty and its determinants in low income setting. Concerted efforts should be made with regard to how best to tackle this in global scale.
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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 | 0.001 |
| 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 | 0.001 | 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".