Exposure to Outdoor Particles (PM2.5) and Associated Child Morbidity and Mortality in Socially Deprived Neighborhoods of Nairobi, Kenya
Bibliographic record
Abstract
Exposure to air pollution is associated with adverse health outcomes. However, the health burden related to ambient outdoor air pollution in sub-Saharan Africa remains unclear. This study examined the relationship between exposure to outdoor air pollution and child health in urban slums of Nairobi, Kenya. We conducted a semi-ecological study among children under 5 years of age from two slum areas and exposure measurements of particulate matter (PM2.5) at the village level were aligned to data from a retrospective cohort study design. We used logistic and Poisson regression models to ascertain the associations between PM2.5 exposure level and child morbidity and mortality. Compared to those in low-pollution areas (PM2.5 < 25 µg/m3), children in high-pollution areas (PM2.5 ≥ 25 µg/m3) were at significantly higher risk for morbidity in general (odds ratio (OR) = 1.25, 95% confidence interval (CI): 1.11–1.41) and, specifically, cough (OR = 1.38, 95% CI: 1.20–1.48). Exposure to high levels of pollution was associated with a high child mortality rate from all causes (IRR = 1.22, 95% CI: 1.08–1.39) and respiratory causes (IRR = 1.12, 95% CI: 0.88–1.42). The findings indicate that there are associated adverse health outcomes with air pollution in urban slums. Further research on air pollution health impact assessments in similar urban areas is required.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".