Changes in essential cancer medicines and association with cancer outcomes: an observational study of 158 countries
Bibliographic record
Abstract
BACKGROUND: Cancer is a major cause of mortality worldwide, and differences in cancer mortality rates between countries are, in part, due to differences in access to cancer care, including medicines. National essential medicines lists (NEMLs) play a role in prioritization of healthcare expenditure and access to medicines. We examined the association between amenable cancer mortality and listing medicines used in the management of eight cancers (non-melanoma skin, uterine, breast, Hodgkin lymphoma, colon, leukemia, cervical, and testicular) in national essential medicines lists of 158 countries and summarized changes to the inclusion of cancer treatments in NEMLs. METHODS: We conducted a cross-sectional examination of NEMLs for 158 countries, which were obtained in May 2023. We identified medicines used to treat each of the eight cancers and determined the number of medicines listed by NEMLs for each cancer. We conducted multiple linear regressions to examine the association between the number of medicines listed on the NEMLs and cancer mortality. RESULTS: We found associations between cancer medicine listing and outcomes for six of the eight examined cancers (non-melanoma skin cancer (p = 0.001), uterine cancer (p = 0.006), breast cancer (p = 0.001), Hodgkin lymphoma (p = 0.021), colon cancer (p = 0.006), and leukemia (p = 0.002)), when adjusting for healthcare expenditure and population size. CONCLUSION: There was an association between listing cancer medicines on NEMLs and cancer mortality. Further research is required to explore how cancer mortality may be impacted by other cancer interventions, as well as policies to improve equitable access to cancer 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".