Hematologic cancers in the SAARC region: current burden in 2022 and projections to 2050
Bibliographic record
Abstract
1 Hematologic malignancies in the SAARC region pose a growing burden, with incidence and mortality varying across countries 2 Disparities in healthcare infrastructure contribute to high mortality-to-incidence ratios, and require regionally tailored strategies South Asia, comprising the SAARC nations, bears a disproportionately high cancer mortality relative to incidence. Hematologic malignancies, though potentially curable with timely diagnosis and treatment, remain understudied in this region. We examined the burden, distribution, and future projections of hematologic cancers across SAARC countries to inform equitable cancer control strategies. We used GLOBOCAN 2022 data from the Global Cancer Observatory and UN population estimates to assess incidence and mortality for non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), leukemia, and multiple myeloma across SAARC countries. We report age-standardised incidence and mortality rates (ASIR/ASMR) per 100,000, and mortality-to-incidence ratios (MIRs). Future projections to 2045 were estimated under constant rate assumptions. In 2022, 148,312 hematologic cancers were diagnosed in the SAARC region, including 63,448 cases of leukemia, 52,363 cases of NHLs, 19,922 cases of multiple myeloma, and 12,579 cases of Hodgkin lymphoma. India accounted for over three-quarters of all cases, followed by Pakistan. In terms of age-standardized incidence rates, leukemia was the most common subtype, with highest ASIR in Maldives (6.7 per 100,000 males) and highest ASMR in Pakistan (3.4 per 100,000). In 2022, 30582 deaths from NHL, 4645 deaths from Hodgkin’s lymphoma, 17199 deaths from multiple myeloma, and 46671 deaths from leukemia occurred in SAARC. MIRs ranged widely, from 0.52 (Sri Lanka, NHL) to 1.0 (Bhutan, multiple myeloma). By 2045, 236,000 new cases and 163,149 deaths are projected, with India bearing the greatest burden. Substantial disparities in diagnostic access and healthcare infrastructure persist.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".