Geographic Accessibility and Availability of Radiotherapy in Ghana
Bibliographic record
Abstract
Importance: Radiotherapy is critical for comprehensive cancer care, but there are large gaps in access. Within Ghana, data on radiotherapy availability and on the relationship between distance and access are unknown. Objectives: To estimate the gaps in radiotherapy machine availability in Ghana and to describe the association between distance and access to care. Design, Setting, and Participants: This is a cross-sectional, population-based study of radiotherapy delivery in Ghana in 2020 and model-based analysis of radiotherapy demand and the radiotherapy utilization rate (RUR) using the Global Task Force on Radiotherapy for Cancer Control investment framework. Exposures: Receipt of radiotherapy and the number of radiotherapy courses delivered. Main Outcomes and Measures: Geocoded location of patients receiving external beam radiotherapy (EBRT); median Euclidean distance from the district centroids to the nearest radiotherapy centers; proportion of population living within geographic buffer zones of 100, 150, and 200 km; additional capacity required for optimal utilization; and geographic accessibility after strategic location of a radiotherapy facility in an underserviced region. Results: A total of 2883 patients underwent EBRT courses in 2020, with an actual RUR of 11%. Based on an optimal RUR of 48%, 11 524 patients had an indication for radiotherapy, indicating that only 23% of patients received treatment. An investment of 23 additional EBRT machines would be required to meet demand. The median Euclidean distance from the district centroids to the nearest radiotherapy facility was 110.6 km (range, 0.62-513.2 km). The proportion of the total population living within a radius of 100, 150 and 200 km of a radiotherapy facility was 47%, 61% and 70%, respectively. A new radiotherapy facility in the northern regional capital would reduce the median of Euclidean distance by 10% to 99.4 km (range, 0.62-267.7 km) and increase proportion of the total population living within a radius of 100, 150 and 200 km to 53%, 69% and 84%, respectively. The greatest benefit was seen in regions in the northern half of Ghana. Conclusions and Relevance: In this cross-sectional study of geographic accessibility and availability of radiotherapy, Ghana had major national deficits of radiotherapy capacity, with significant geographic disparities among regions. Well-planned infrastructure scale-up that accounts for the population distribution could improve radiotherapy accessibility.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".