Impact of time of diagnosis on out-of-pocket costs of cancer treatment, a side effect of health insurance design in Australia.
Bibliographic record
Abstract
The Extended Medicare Safety Net (EMSN) in Australia was designed to provide financial assistance to patients with high out-of-pocket (OOP) costs for medical treatment. The EMSN works on a calendar year basis. Once a patient incurs a specified amount of OOP costs, the EMSN provides additional financial benefits for the remainder of the calendar year. Its design is similar to many types of insurance products that have large deductibles and are applied on a calendar year basis. This study examines if the annual quarter within which a patient is diagnosed with cancer has an impact on the OOP costs incurred for treatment. We use administrative linked data from the Sax Institute's 45 and Up Study. Our results indicate that the timing of cancer diagnosis has a significant impact on OOP costs. Specifically, patients diagnosed in the fourth quarter of the calendar year experience significantly higher OOP costs compared to those diagnosed in the first quarter of the year. This pattern persists after controlling for different types of cancer and different stages of cancer and robustness checks. These findings have important implications for the design of the EMSN, as well as other insurance products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".