Health service use and costs in the last 6 months of life in elderly decedents with a history of cancer: a comprehensive analysis from a health payer perspective
Bibliographic record
Abstract
BACKGROUND: There is growing interest in end-of-life care in cancer patients. We aim to characterise health service use and costs in decedents with cancer history and examine factors associated with resource use and costs at life's end. METHODS: We used routinely collected claims data to quantify health service use and associated costs in two cohorts of elderly Australians diagnosed with cancer: one cohort died from cancer (n=4271) and the other from non-cancer causes (n=3072). We used negative binomial regression to examine the factors associated with these outcomes. RESULTS: Those who died from cancer had significantly higher rates of hospitalisations and medicine use but lower rates of emergency department use than those who died from non-cancer causes. Overall health care costs were significantly higher in those who died from cancer than those dying from other causes; and 40% of costs were expended in the last month of life. CONCLUSIONS: We analysed health services use and costs from a payer perspective, and highlight important differences in patterns of care by cause of death in patients with a cancer history. In particular, there are growing numbers of highly complex patients approaching the end of life and the heterogeneity of these populations may present challenges for effective health service delivery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".