Child mortality from solid-fuel use in India: a nationally-representative case-control study
Bibliographic record
Abstract
BACKGROUND: Most households in low and middle income countries, including in India, use solid fuels (coal/coke/lignite, firewood, dung, and crop residue) for cooking and heating. Such fuels increase child mortality, chiefly from acute respiratory infection. There are, however, few direct estimates of the impact of solid fuel on child mortality in India. METHODS: We compared household solid fuel use in 1998 between 6790 child deaths, from all causes, in the previous year and 609 601 living children living in 1.1 million nationally-representative homes in India. Analyses were stratified by child's gender, age (neonatal, post-neonatal, 1-4 years) and colder versus warmer states. We also examined the association of solid fuel to non-fatal pneumonias. RESULTS: Solid fuel use was very common (87% in households with child deaths and 77% in households with living children). After adjustment for demographic factors and living conditions, solid-fuel use significantly increase child deaths at ages 1-4 (prevalence ratio (PR) boys: 1.30, 95%CI 1.08-1.56; girls: 1.33, 95%CI 1.12-1.58). More girls than boys died from exposure to solid fuels. Solid fuel use was also associated with non-fatal pneumonia (boys: PR 1.54 95%CI 1.01-2.35; girls: PR 1.94 95%CI 1.13-3.33). CONCLUSIONS: Child mortality risks, from all causes, due to solid fuel exposure were lower than previously, but as exposure was common solid, fuel caused 6% of all deaths at ages 0-4, 20% of deaths at ages 1-4 or 128 000 child deaths in India in 2004. Solid fuel use has declined only modestly in the last decade. Aside from reducing exposure, complementary strategies such as immunization and treatment could also reduce child mortality from acute respiratory infections.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Case report | high |
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".