Global burden of tuberculosis attributable to cancer in 2019: Global, regional, and national estimates
Bibliographic record
Abstract
BACKGROUND: Cancer is an independent risk factor for tuberculosis (TB). The global burden of incident TB attributable to cancer has never been explored. We aimed to evaluate the cancer-attributable burden of TB. METHODS: We estimated the population attributable fraction (PAF) by Levin's formula. The cancer prevalence rates were derived from the Institute for Health Metrics and Evaluation. The relative risk of TB in cancer patients was estimated by using the National Health Insurance Research Database in Taiwan. The global burden of incidence TB attributable to cancer was the weighted sum of PAFs multiplied by the incidence of TB retrieved from the World Health Organization. RESULTS: Worldwide, the total of incident TB cases attributable to cancer was 115,478 cases with a 95% confidence interval (CI), 110,482-123,007, in 2019. The global PAF of TB due to cancer was 1.85% (95% CI, 1.77-1.97%). The three countries with the highest PAFs were Greenland (7.77%), Canada (7.75%), and the United States of America (6.79%), while the three countries with the highest attributable TB cases due to cancer were China (25,240), India (21,629), and Indonesia (13,917). Cancer of respiratory system contributed to 60,257 of TB cases. CONCLUSIONS: This study comprehensively explored the impact of cancer on the global burden of TB. Efforts to reduce cancer risk, delay the occurrence of cancer, or treat latent TB infection in the cancer population could potentially reduce the burden of TB and rely on formulating integrated strategies.
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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 | 0.001 |
| 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".