End-of-life healthcare use and associated costs for First Nations Australians diagnosed with cancer in Queensland, Australia
Bibliographic record
Abstract
PURPOSE: Cancer significantly impacts First Nations Australians, with higher incidence and lower survival rates. However, understanding of end-of-life (EOL) service use and costs in this population is limited. We aimed to assess EOL healthcare utilisation and costs for First Nations cancer patients in Queensland, Australia. METHODS: Retrospective data from CancerCostMod, a linked administrative dataset of all cancer diagnoses in Queensland, were used. This dataset includes records from the Queensland Cancer Registry (QCR) from July 1, 2011, to June 30, 2015, linked to Queensland Health Admitted Patient Data Collection (QHAPDC), Emergency Department (ED) Information Systems, Medicare Benefits Schedule (MBS), and Pharmaceutical Benefits Scheme (PBS) data from July 2011 to June 30, 2018. All diagnosed cancer patients who had died during the study period (N = 467) were included. Health service usage and costs during the last 6 months of life were described and compared across care type, comorbidity status, age group, and residential remoteness using Mann-Whitney and Kruskal-Wallis tests. RESULTS: Individuals had at least one hospital episode (100%), ED visit (83%), MBS claim (96%), and PBS claim (96%). The median overall cost per person for hospital episodes was AUD$40,996, with higher costs for those receiving palliative care (AUD$43,521) and chemotherapy (AUD$50,437) compared to those who did not receive these services (palliative: AUD$34,208, chemotherapy: AUD$38,557). Having comorbidities and living in regional and remote areas were associated with higher hospital costs. CONCLUSION: The study findings may guide the re-design and delivery of optimal and culturally appropriate EOL care for First Nations Australians diagnosed with cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".