Mapping Geospatial Access to Comprehensive Cancer Care in Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE To address the increasing burden of cancer in Nigeria, the National Cancer Control Plan outlines the development of 8 public comprehensive cancer centers. We map population-level geospatial access to these eight centers and explore equity of access and the impact of future development. METHODS Geospatial methods were used to estimate population-level travel times to the 8 cancer centers. A cost distance model was built using open source road infrastructure data with verified speed limits. Geolocated population estimates were amalgamated with this model to calculate travel times to cancer centers at a national and regional level for both the entire population and the population living on < US$2 per day. RESULTS Overall, 68.9% of Nigerians have access to a comprehensive cancer center at 4 hours of continuous vehicular travel. However, there is significant variability in access between geopolitical zones ( P < .001). The North East has the lowest access at 4 hours (31.4%) and the highest mean travel times (268 minutes); this is significantly lower than the proportion with 4-hour access in the South East (31.4% v 85.0%, respectively; P < .001). The addition of a second comprehensive cancer center in the North East, in either Bauchi or Gombe, would significantly improve access to this underserved region. CONCLUSION The Federal Ministry of Health endorses investment in 8 public comprehensive cancer centers. Strengthening these centers will allow the majority of Nigerians to access the full complement of multidisciplinary care within a reasonable time frame. However, geospatial access remains inequitable, and the impact on outcomes is unclear. This must be considered as the cancer control system matures and expands.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it