Childhood Cancer Mortality in India: Direct Estimates From a Nationally Representative Survey of Childhood Deaths
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Although most children with cancer live in low- and middle-income countries, measurements of childhood cancer burden in such countries have been restricted to incidence rates from a few subnational cancer registries and mortality rates from vital statistics. We aimed to provide alternative burden estimates by using nationally representative longitudinal survey-derived mortality rates. METHODS: We examined cancer deaths in childhood (1 month to 14 years of age) in the Million Death Study, a cohort of > 27,000 pediatric deaths in India on the basis of enhanced verbal autopsies. All deaths potentially due to childhood cancer were identified. Two pediatric specialists independently categorized deaths as definite, probable, possible, or unlikely cancer related. From definite and probable deaths, we estimated national and regional mortality rates attributable to childhood malignancies. Data on symptoms and health care-seeking behavior were abstracted from closed-ended questions and caregiver narratives. RESULTS: Of 700 included deaths, 189 were classified as definite or possibly cancer related. The κ-statistic between reviewers was 0.75 (95% CI, 0.71 to 0.78). From these deaths, we estimated that in 2010, 13,700 were a result of childhood cancer in India, which led to a mortality rate of 37 (95% CI, 31 to 42) per million population per year, which exceeds many prior estimates of mortality and even some estimates of incidence. Disparities between mortality estimates were widest in northeast India and for brain tumors. A preponderance of male deaths was seen (male:female ratio, 1.6:1). CONCLUSION: The burden of childhood cancer in India is substantially higher than previously suggested. This information will aid advocacy for national strategies aimed at improving outcomes for Indian children with cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it