Worldwide malaria incidence and cancer mortality are inversely associated
Bibliographic record
Abstract
Investigations on the effects of malaria infection on cancer mortality are limited except for the incidence of Burkitt’s lymphoma (BL) in African children. Our previous murine lung cancer model study demonstrated that malaria infection significantly inhibited tumor growth and prolonged the life span of tumor-bearing mice. This study aims to assess the possible associations between malaria incidence and human cancer mortality. We compiled data on worldwide malaria incidence and age-standardized mortality related to 30 types of cancer in 56 countries for the period 1955–2008, and analyzed their longitudinal correlations by a generalized additive mixed model (GAMM), adjusted for a nonlinear year effect and potential confounders such as country’s income levels, life expectancies and geographical locations. Malaria incidence was negatively correlated with all-cause cancer mortality, yielding regression coefficients (log scale) of −0.020 (95%CI: −0.027,-0.014) for men ( P < 0.001) and-0.020 (95%CI: −0.025,-0.014) for women ( P < 0.001). Among the 29 individual types of cancer studied, malaria incidence was negatively correlated with colorectum and anus (men and women), colon (men and women), lung (men), stomach (men), and breast (women) cancer. Our analysis revealed a possible inverse association between malaria incidence and the mortalities of all-cause and some types of solid cancers, which is opposite to the known effect of malaria on the pathogenesis of Burkitt’s lymphoma. Activation of the whole immune system, inhibition of tumor angiogenesis by Plasmodium infection may partially explain why endemic malaria might reduce cancer mortality at the population level.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".