Epidemiological insights into neonatal deaths: The role of cooking fuel pollution in Colombia
Bibliographic record
Abstract
PURPOSE: Household air pollution is one of the leading causes of death and disease globally. Emerging evidence of elevated risk of neonatal death has been reported in Africa and South Asia. However, evidence on the extent of the problem in Latin America is limited despite the persistent use of highly polluting cooking fuels. We assessed whether the use of high-polluting household cooking fuels increases the risk of neonatal death compared to low-polluting fuels in Colombia. METHODS: We used cross-sectional data from the 2005-2015 Colombian Demographic Health Survey and performed a survey-featured multivariate logistic regression. We selected adjustment covariates based on a causal diagram, addressed missing data through multiple imputation, and conducted several sensitivity analysis, such as propensity score matching. RESULT: We found evidence suggesting an increased risk of neonatal death in households using high-polluting fuels (OR: 1.48; 95% CI: 0.91, 2.39). The sensitivity analyses were consistent with the main analysis. CONCLUSION: We observed increased odds of neonatal death associated with using high-polluting household cooking fuels compared to low-polluting fuels, although this association was not statistically significant. This study contributes evidence to a region where the issue is not yet a priority and should be included in national-level discussions and interventions that impact cooking fuel use patterns.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".