Cancer control in developing countries: using health data and health services research to measure and improve access, quality and efficiency
Bibliographic record
Abstract
BACKGROUND: Cancer is a rapidly increasing problem in developing countries. Access, quality and efficiency of cancer services in developing countries must be understood to advance effective cancer control programs. Health services research can provide insights into these areas. DISCUSSION: This article provides an overview of oncology health services in developing countries. We use selected examples from peer-reviewed literature in health services research and relevant publicly available documents. In spite of significant limitations in the available data, it is clear there are substantial barriers to access to cancer control in developing countries. This includes prevention, early detection, diagnosis/treatment and palliation. There are also substantial limitations in the quality of cancer control and a great need to improve economic efficiency. We describe how the application of health data may assist in optimizing (1) Structure: strengthening planning, collaboration, transparency, research development, education and capacity building. (2) PROCESS: enabling follow-up, knowledge translation, patient safety and quality assurance. (3) OUTCOME: facilitating evaluation, monitoring and improvement of national cancer control efforts. There is currently limited data and capacity to use this data in developing countries for these purposes. SUMMARY: There is an urgent need to improve health services for cancer control in developing countries. Current resources and much-needed investments must be optimally managed. To achieve this, we would recommend investment in four key priorities: (1) Capacity building in oncology health services research, policy and planning relevant to developing countries. (2) Development of high-quality health data sources. (3) More oncology-related economic evaluations in developing countries. (4) Exploration of high-quality models of cancer control in developing countries. Meeting these needs will require national, regional and international collaboration as well as political leadership. Horizontal integration with programs for other diseases will be important.
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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 | 0.203 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.039 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".