Environmental determinants of leukemia and lymphoma: lessons from African epidemiology and global transition
Bibliographic record
Abstract
Childhood leukemia and lymphoma display striking global heterogeneity that cannot be explained by genetic ancestry or diagnostic access alone. African populations, historically characterized by high infectious burden, nutritional stress, and poor sanitation, exhibit a markedly different spectrum of hematologic malignancies from high-income countries, including reduced incidence of common/pre-B acute lymphoblastic leukemia (c-ALL), absence of the early childhood ALL peak, and increased prevalence of Burkitt lymphoma and chloroma-associated acute myeloid leukemia. Drawing on African epidemiologic data and global comparative studies, this review examines how environmental factors across the life course-particularly maternal health, intrauterine exposures, early-life infection, immune programming, and socioeconomic transition-shape leukemogenic pathways. We place these observations in the context of contemporary models of leukemogenesis that recognize prenatal initiation of preleukemic clones with postnatal environmental modulation of disease progression. As low- and middle-income countries undergo rapid epidemiologic transition, understanding how improvements in sanitation, nutrition, and population mixing may alter leukemia incidence is increasingly relevant for prevention strategies. African experience thus provides a natural experiment for elucidating environmental contributions to leukemogenesis with implications extending well beyond the continent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".