Health care use and costs at the end of life: a comparison of elderly Australian decedents with and without a cancer history
Bibliographic record
Abstract
BACKGROUND: There is limited population-level research on end-of-life care in Australia that considers health care use and costs across hospital and community sectors. The aim of this study was to quantify health care use and costs in the last 6 months of life in a cohort of elderly Australian decedents and to examine the factors associated with end-of-life resource use and costs. METHODS: A retrospective cohort study using routinely collected health data from Australian Government Department of Veterans' Affairs clients. The study included two cohorts of elderly Australians who died between 2005 and 2009; one cohort with a recorded cancer diagnosis and a comparison cohort with no evidence of a cancer history. We examined hospitalisations, emergency department (ED) visits, prescription drugs, clinician visits, pathology, and procedures and associated costs in the last 6 months of life. We used negative binominal regression to explore factors associated with health service use and costs. RESULTS: The cancer cohort had significantly higher rates of health service use and 27% higher total health care costs than the comparison cohort; in both cohorts, costs were driven primarily by hospitalisations. Older age was associated with lower costs and those who died in residential aged care incurred half the costs of those who died in hospital. CONCLUSIONS: The results suggest differences in end-of-life care pathways dependent on patient factors, with younger, community-dwelling patients and those with a history of cancer incurring significantly greater costs. There is a need to examine whether the investment in end-of-life care meets patient and societal needs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".