Associations between solid fuel use and early child development among 3 to 4 years old children in Bangladesh: Evidence from a nationally representative survey
Bibliographic record
Abstract
Abstract Background Household Air Pollution (HAP) from solid fuel use (SFU) may have impacts on children’s health in low-resources countries. Despite these potential health effects, SFU is still highly prevalent in Bangladesh. Objectives This study was conducted to assess the associations between SFU and early childhood development index (ECDI) among under-five children in Bangladesh and explore the potential effect modification by sex and urbanicity. Materials and methods This cross-sectional study used Bangladesh Multiple Indicator Cluster Survey (MICS) 2019, a nationally representative survey data collected by UNICEF from all 64 districts in Bangladesh. The ECDI consisted of ten different items across four developmental domains: literacy-numeracy, physical, social-emotional development, and learning skills in the early years of life (36 to 59 months). A total of 9,395 children aged 36 to 59 months were included in this analysis. We used multilevel Poisson regression models with a robust variance where SFU was a proxy indicator for HAP exposure. Results Children exposed to SFU were 1.47 times more likely to be not developmentally on track (95% CI: 1.25, 1.73; <0.001 ) compared to children with no SFU exposure. Two sub-domains explained these associations, SFU was significantly associated with socio-emotional development (prevalence ratio [PR]: 1.17; 95% CI: 1.01, 1.36; p=0.035), and learning-cognitive development (PR: 1.90; 95% CI: 1.39, 2.60; p<0.001). Associations between SFU and ECDI were not significantly different (p-difference=0.210) between girls (PR: 1.64; 95% CI: 1.31, 2.07) and boys (PR: 1.37; 95% CI: 1.13, 1.65). Likewise, urbanicity did not modify the associations between SFU and ECDI outcomes. Conclusion Bangladeshi children aged 36-59 months exposed to SFU exhibited delays in childhood development compared to unexposed children. Public health policies should promote a better early life environment for younger children to meet their developmental milestones by reducing the high burden of HAP exposure in low-resource settings where most disadvantaged kids struggle to reach their full developmental potentials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".