Fine particulate matter air pollution and the mortality of children under five: a multilevel analysis of the Ethiopian Demographic and Health Survey of 2016
Bibliographic record
Abstract
Background Every year, polluted air is costing the globe 543,000 deaths of children under five. The particulate matter below 2.5 μm diameter (PM 2.5 ) is a part of air pollution that has adverse effects on children’s health. In Ethiopia, the effect of ambient PM 2.5 is least explored. This study aimed to assess the association between PM 2.5 and under-five mortality in Ethiopia. Methods The study used the data from the Ethiopian Demographic Health Surveys conducted in 2016, collected between January 18 and June 27. All children under five who had data on child mortality and location coordinates were included in the study. Exposure to ambient PM 2.5 concentration was a satellite-based estimate by the Atmospheric Composition Analysis Group at Washington and Dalhousie University, in the United States and Canada, respectively. Annual mean pollution levels and mortality datasets were matched by children’s geographical location and dates of birth, death, and interview. The relationship between ambient PM 2.5 and under-five mortality was determined by a multilevel multivariable logistic regression on R software. The statistical analyses were two-sided at a 95% confidence interval. Results The study addressed 10,452 children with the proportion of under-five mortality being 5.4% (95% CI 5.0–6.8%). The estimated lifetime annual mean exposure of ambient total PM 2.5 was 20.1 ± 3.3 μgm −3 . A 10-unit increase in the lifetime annual mean ambient total PM 2.5 was associated with 2.29 [95% CI 1.44, 3.65] times more odds of under-five mortality after adjusting for other variables. Conclusion Children under five are exposed to higher levels of ambient PM 2.5 concentration, exceeding the limit set by the World Health Organization. Ambient PM 2.5 is significantly associated with under-five mortality, adjusting for other variables. Strong measures need to be taken to reduce air pollution.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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, 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".