Predictors of mortality among elderly people living in a south Indian urban community; a 10/66 Dementia Research Group prospective population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Eighty percent of deaths occur in low and middle income countries (LMIC), where chronic diseases are the leading cause. Most of these deaths are of older people, but there is little information on the extent, pattern and predictors of their mortality. We studied these among people aged 65 years and over living in urban catchment areas in Chennai, south India. METHODS: In a prospective population cohort study, 1005 participants were followed-up after three years. Baseline assessment included sociodemographic and socioeconomic characteristics, health behaviours, physical, mental and cognitive disorders, disability and subjective global health. RESULTS: At follow-up, 257 (25.6%) were not traced. Baseline characteristics were similar to the 748 whose vital status was ascertained; 154 (20.6%) had died. The mortality rate was 92.5/1,000 per annum for men and 51.0/1,000 per annum for women. Adjusting for age and sex, mortality was associated with older age, male sex, having no friends, physical inactivity, smaller arm circumference, dementia, depression, poor self-rated health and disability. A parsimonious model included, in order of aetiologic force, male sex, smaller arm circumference, age, disability, and dementia. The total population attributable risk fraction was 0.90. CONCLUSION: A balanced approach to prevention of chronic disease deaths requires some attention to proximal risk factors in older people. Smoking and obesity seem much less relevant than in younger people. Undernutrition is preventable. While dementia makes the largest contribution to disability and dependency, comorbidity is the rule, and more attention should be given to the chronic care needs of those affected, and their carers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".