Cancer burden across the South Asian Association for Regional Cooperation in 2022
Bibliographic record
Abstract
Objective: The objective of this study is to present a cross-sectional analysis of cancer burden in the South Asian Association for Regional Cooperation (SAARC) region and explain unique characteristics of its cancer burden as compared with the rest of the world. Methods and analysis: Using publicly available data from the Global Cancer Observatory (GCO) and the World Bank, we collected cancer statistics and population statistics for Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka from 2017 to 2022. Results: The number of newly diagnosed cases in the region was 1 846 963, representing 9.3% of the incidence worldwide. As defined by the GCO, the crude incidence rate (CIR) (per 100 000) of cancer in SAARC was 97.3 compared with the worldwide rate of 235.5. The crude mortality rate (per 100 000) in SAARC was 63.4, compared with 123.6 globally. However, the mortality to incidence ratio (MIR) (per 100 000) was 0.65, compared with 0.49 globally. Conclusion: Our research highlights SAARC's unique cancer landscape with low incidence (CIR) and mortality (CMR) but elevated MIR compared with global figures. These findings underscore the need for a united, contextually relevant approach to addressing the burden of cancer in SAARC. In particular, investment in collaborative, tailored cancer care programmes will build the SAARC region's capacity to address the growing cancer challenge.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".