Expanding Coverage of Oncology Drugs in an Aging, Upper-Middle-Income Country: Analyses of Public and Private Expenditures in Chile
Bibliographic record
Abstract
PURPOSE: The population of Chile has aged, and in 2017, cancer became the leading cause of death. Since 2005, a national health program has expanded coverage of drugs for 13 types of cancer and related palliative care. We describe the trends in public and private oncology drug expenditures in Chile and consider how increasing expenditures might be addressed. METHODS: We analyzed total quarterly drug expenditures for 131 oncology drugs from quarter (Q)3 2012 until Q1 2017, including public and private insurance payments and patient out-of-pocket spending. The data were analyzed by drug-mix, sources of funding, growth, and intellectual property status. The Laspeyres Price Index was used to analyze expenditure growth. RESULTS: We found 131 oncology drugs associated with 87,129 observations. Spending on drugs rose 120% from the first period, spanning from the first 3 quarters (Q3, Q4, Q1 2012-2013) to the last period (Q3, Q4, Q1 2016-2017), corresponding to an annualized rate of 19.2% and totaling US$398 million (in 2017 dollars). The public sector accounted for 84.2% of spending, which included 50 drugs in the official treatment protocols, whereas private insurance accounted for 7.3% in on-protocol drugs. The remaining 8.5% was paid out of pocket. In the public sector, more than 90% of growth resulted from increased use. Seven drugs, including 3 with nonexpired patents, accounted for 50% of total expenditures. CONCLUSION: Increased use and access enabled by expanded public expenditures drove most of the growth in oncology drug expenditures. However, the rate of public expenditure growth may be fiscally unsustainable. Policies are urgently needed to promote the use of generic drugs, the appropriate mix of on-protocol versus off-protocol drugs, and the curbing of off-label prescribing.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".