Beyond Limitations: Practical Strategies for Improving Cancer Care in Nigeria
Bibliographic record
Abstract
BACKGROUND: The burden due to cancers is an emerging public health concern especially in resource-limited countries like Nigeria. The WHO estimates that cancer kills more people than tuberculosis, HIV/AIDS and malaria combined. As people in Nigeria and other developing countries are beginning to survive infectious diseases, there is an observed epidemiologic transition to chronic diseases, such as cancers. In 2008, 75 out of 1,000 Nigerians died of cancer. Despite the rising incidence and public health importance, Nigeria lacks an organized and comprehensive strategy to deal with cancers. MATERIALS AND METHODS: This article reviewed 30 peer-reviewed manuscripts on cancer care in four countries. It highlights the limitations to cancer care in Nigeria; due to lack of awareness, low health literacy, absence of organized screening programs, inadequate manpower (in terms of quality and quantity) as well as limited treatment options. RESULTS: This review led to the formulation of a proposal for Nigerian National Cancer Policy, mainly drawn from effective strategies used in Canada, Brazil and Kenya. This is a vertical cancer program that is patient-centered with an emphasis on tobacco control and cancer disease screening (similar to Canada and Brazil). Additionally, it emphasizes primary cancer prevention (similar to Kenya). Its horizontal integration with other disease programs like HIV/AIDS will improve affordability in a poor resourced country like Nigeria. Capacity building for health professionals, hub-and-spoke implementation of screening services, as well as investment in effective treatment options and increased research in cancer care are essential. International 'twinning collaborations' between institutions in richer countries and Nigeria will enhance effective knowledge translation and improve the quality of patient care. CONCLUSIONS: A national cancer policy must be developed and implemented in Nigeria in order to overcome the present limitations which help contribute to the observed increases in cancer morbidity and mortality rates. Cancer control is feasible in Nigeria if the nation was to consider and employ some of the cost-effective strategies proposed here.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".