Healthcare Cost Trajectories in the Last 2 Years of Life Among Patients With a Solid Metastatic Cancer: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Most studies describe the "average healthcare cost trend" among patients with cancer. We aimed to delineate heterogeneous trajectories of healthcare cost during the last 2 years of life of patients with a metastatic cancer and to assess the associated sociodemographic and clinical characteristics and healthcare use. PATIENTS AND METHODS: We analyzed a sample of 353 deceased patients from a cohort of 600 with a solid metastatic cancer in Singapore, and we used group-based trajectory modeling to identify trajectories of total healthcare cost during the last 2 years of life. RESULTS: The average cost trend showed that mean monthly healthcare cost increased from SGD $3,997 during the last 2 years of life to SGD $7,516 during the last month of life (USD $1 = SGD $1.35). Group-based trajectory modeling identified 4 distinct trajectories: (1) low and steadily decreasing cost (13%); (2) steeply increasing cost in the last year of life (14%); (3) high and steadily increasing cost (57%); and (4) steeply increasing cost before the last year of life (16%). Compared with the low and steadily decreasing cost trajectory, patients with private health insurance (β [SE], 0.75 [0.37]; P=.04) and a greater preference for life extension (β [SE], -0.14 [0.07]; P=.06) were more likely to follow the high and steadily increasing cost trajectory. Patients in the low and steadily decreasing cost trajectory were most likely to have used palliative care (62%) and to die in a hospice (27%), whereas those in the steeply increasing cost before the last year of life trajectory were least likely to have used palliative care (14%) and most likely to die in a hospital (75%). CONCLUSIONS: The study quantifies healthcare cost and shows the variability in healthcare cost trajectories during the last 2 years of life. Policymakers, clinicians, patients, and families can use this information to better anticipate, budget, and manage healthcare costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".