Exploring frailty in Brazil: an analysis of the ELSI-Brazil survey
Bibliographic record
Abstract
The Brazilian aging population will challenge publicly funded health services, on which most Brazilians rely. The country must prepare for aging-associated health challenges such as frailty. We used data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil) to generate a standardized Frailty Index (FI), assess frailty levels among this population, and supply reliable and nationwide information. In total, 9,901 adults aged 50 years or older were studied in the second wave of ELSI-Brazil. A 53-item FI was created according to a standardized protocol. Logistic regression was used to determine the association between frailty levels and disability/health status, whereas the relationship between frailty level, disabilities, and healthcare use was analyzed by a negative binomial regression. Frailty was high, with a 0.19 weighted mean FI score and 0.19 median. Frailty distribution was right-skewed, with higher levels in women and increased exponentially with age. Widow(er)s, black and mixed-race individuals, and those living in rural areas had higher levels of frailty. Regression models showed that higher frailty was associated with poorer self-assessment of health, higher disability, and greater use of healthcare services. This study shows a high prevalence of frailty in Brazilian middle-aged and older adults and its association with disability, health status, and healthcare service use. These relevant findings can inform healthcare policies and design services prioritizing this population's health, particularly for those using public healthcare.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".