Cancer burden across South Asia in 2020: A global cancer observatory analysis.
Bibliographic record
Abstract
e23292 Background: The cancer burden in South Asia is often associated with late diagnosis and poor outcomes because of barriers to accessing advanced treatment facilities and targeted therapies, financial sequelae of care, and cultural stigmas and misconceptions. We use recent estimates of the Global Cancer Observatory (GCO) to better understand the epidemiology of cancer in South Asia, which may inform national and regional cancer control. Methods: Using publicly available data from the GCO and the World Bank, we collected cancer statistics and population statistics for Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka (collectively, SAARC) from 2015-2020. Results: The crude incidence of cancer in this region was 1,733,573 cases, representing 9.0% of the incidence worldwide. The crude incidence rate (CIR, per 100,000) of cancer in SAARC was 93.4 compared to the worldwide rate of 247.5. The 5-year prevalence (in total cases) of cancer in South Asia was 3,474,184, compared to 50,550,287 globally. The crude mortality rate (CMR, per 100,000) in South Asia was 60.6, compared to 127.8 globally. However, the mortality-to-incidence ratio (MIR) (per 100,000) was 0.65, compared to 0.52 globally. Age-adjusted estimates are reported in the table. Conclusions: Our research highlights South Asia's unique cancer landscape with CIR and MIR, but elevated MIR compared to global figures. These findings underscore the need for a united, contextually relevant approach to addressing the burden of cancer in South Asia. Investment in collaborative, tailored cancer care programs will build the SAARC region's capacity to address the growing global cancer challenge. [Table: see text]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".