Health Care Use at the End of Life Among Older Adults: Does It Vary by Age?
Bibliographic record
Abstract
BACKGROUND: Issues around end-of-life health care have attracted increasing attention in the last decade. One question that has arisen is whether very elderly individuals receive overly aggressive treatment at the end of life. The purpose of this study was to address this issue by examining whether health care use at the end life varies by age. METHODS: The study included all adults 65 years old or older who died in Manitoba, Canada in 2000 (N = 7678). Measures were derived from administrative data files and included location of death, hospitalizations, intensive care unit (ICU) admission, long-term care (LTC) use, physician visits, and prescription drug use in the last 30 days versus 180 days before death, respectively. RESULTS: Individuals 85 years old or older had increased odds of being in a LTC institution and also dying there than did individuals 65-74 years old. They had, correspondingly, lower odds of being hospitalized and being admitted to an ICU. Although some statistically significant age differences emerged for physician visits, the effects were small. Prescription drug use did not vary by age. CONCLUSIONS: These findings indicate that very elderly individuals tended to receive care within LTC settings, with care that might be considered aggressive declining with increasing age. However, health care use among all age groups was substantial. A critical issue that needs to be examined in future research is how to ensure quality end-of-life care in a variety of clinical contexts and care settings for individuals of all ages.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".