The Effects of Precipitation and Temperature on Birth Weight: A Cross-Sectional Study from the Republic of Benin
Bibliographic record
Abstract
Climate change, particularly changes in temperature and precipitation, prevents the improvement of maternal and neonatal health (MNH) in low-and middle-income countries (LMICs). Pregnant women and newborns in LMICs are considered the most vulnerable to adverse climate conditions, including extreme heat, floods, and droughts. This study examined the effects of precipitation and temperature on birth weight in the Republic of Benin, emphasizing climatic differences between the southern and northern regions. As a cross-sectional study, we pooled four rounds of Benin Demographic and Health Survey (BDHS) data, identifying 19 646 live births. We investigated the effects of precipitation and temperature on birth weight and the likelihood of low birth weight (LBW) newborns using multivariate multilevel linear and logistic regression models. We found that the average precipitation amount during the nine months before birth was positively correlated with higher birth weight in the south and was associated with a lower likelihood of LBW in the north. During the nine months before birth, a heat wave reduced birth weight by 57.0 g in the north. Furthermore, women and newborns in the north were more susceptible to precipitation and temperature, possibly due to food insecurity. Evaluating the MNH consequences of climate change is imperative for many developing countries facing severe climate change threats. Our findings provide an essential benchmark for future policies in Benin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".