Indoor air pollution and diet mediates the effects of social inequality on birth outcomes
Bibliographic record
Abstract
BACKGROUND AND AIM: The detrimental effect of social inequality on birth outcomes in the low to middle income countries (LMICs) can only be partially explained by lack of access to care and biomass fuel use. We aim to investigate other possible factors that can mediate the effects of SES on birth outcomes. METHODS: The analyses were based on a pregnancy cohort of 723 mother-infant pairs with single live births between 2016-2017 in rural Bangladesh. Multivariable linear and logistic regressions, and mediation analysis were performed adjusting for demographic characteristics, maternal and infant-related factors, and air pollution from cooking sources. As potential mediating factors of SES (Lowest three quintiles of calculated Wealth Index were considered low SES), we considered other sources of indoor pollution (i.e., traditional lamp fuel and mosquito repellents), maternal diet, and stress during pregnancy. RESULTS: Compared to households with high SES, adjusted mean birth weight was 64.2 g (95% confidence interval, CI: -121.6, -6.8) less and the odds of small for gestational age (SGA) was 40% (RR:1.40, 95% CI: 1.01, 1.99) higher among households with low SES. Exposure to other sources of indoor air pollution and lower amount of protein- and dairy-rich maternal diet during pregnancy mediated 50% and 25% of the effect of SES on birth weight and SGA, respectively. Indoor air pollution from non-cooking sources alone mediated 33% of the SES effect on birthweight and 18% on SGA. CONCLUSIONS: This study highlights that non-cooking related household sources of air pollution and maternal diet are potential modifying factors in LMICs that can be addressed to reduce the effect of social inequity on birth outcomes. KEYWORDS: Indoor air pollution, socioeconomic status, birth weight, low- and middle-income countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".