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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".