Identifying the Sociodemographic and Environmental Risk Factors of Childhood Pneumonia in South Asia
Bibliographic record
Abstract
Background: Pneumonia is the leading infectious cause of mortality in children under five years old. Despite global progress in reducing pneumonia cases, South Asia continues to experience disproportionately high incidence and mortality rates. Purpose: This study aims to identify the sociodemographic and environmental risk factors contributing to childhood pneumonia morbidity and mortality across eight South Asian countries: Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. Methods: Six databases were searched for relevant studies: MEDLINE (Ovid), Global Health (Ovid), Embase, Web of Science, Emcare, and the Cochrane Library. Primary research studies focusing on sociodemographic and environmental risk factors for childhood pneumonia morbidity and mortality in children under five from eight South Asian countries were included. The review followed PRISMA guidelines, and data were extracted and analyzed using a DerSimonian and Laird random-effects meta-analysis model. Studies were assessed for quality using the Newcastle-Ottawa Scale, and pooled effect estimates were calculated to quantify the strength of associations between risk factors and pneumonia outcomes. Results: Higher age was associated with a lower risk of pneumonia morbidity (OR: 0.89). Male sex (OR: 1.15), preterm birth (OR: 2.53), and rural residence (OR: 2.03) significantly increased pneumonia risk. For mortality due to pneumonia, children under six months (OR: 3.78), children under one year (OR: 2.34), and low-weight-for-age children (OR: 6.07) had significantly higher risks, while females had a lower risk compared to males (OR: 0.58). Conclusion: The findings highlight the need for targeted public health interventions, particularly in rural areas and for vulnerable groups like preterm infants. Addressing these risk factors is essential for making progress toward reducing the childhood pneumonia burden in South Asia and achieving global health goals aimed at lowering under-five mortality.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".