Differences in Health Care Expenditures by Cancer Patients During Their Last Year of Life: A Registry-Based Study
Bibliographic record
Abstract
Background. During the last year of life, persons with cancer should probably have similar care needs and costs, but studies suggest otherwise. Methods. A study of direct medical costs (excluding costs for expensive prescription drugs) was performed based on registry data in Stockholm County, which covers 2.4 million inhabitants, for all deceased persons with cancer during 2015–2021. The data were mainly analyzed with the aid of multiple regression models, including Generalized Linear Models (GLMs). Results. In a population of 20,431 deceased persons with cancer, the costs increased month by month (p < 0.0001). Higher costs were mainly associated with lower age (p < 0.0001), higher risk of frailty, as measured by the Hospital Frailty Risk Scale (p < 0.0001), and having a hematological malignancy. In a separate model, where those 5% with the highest costs were identified, these variables were strengthened. Sex and socio-economic groups on an area level had little or no significance. Systemic cancer treatments during the last month of life and acute hospitals as place of death had only a moderate impact on costs in adjusted models. Conclusions. Higher costs are mainly related to lower age, higher frailty risk and having a hematological malignancy, and the effects are both statistically and clinically significant despite the fact that expensive drugs were not included. On the other hand, the costs were mainly comparable in regard to sex or socio-economic factors, indicating equal care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".