Temperature variability and birthweight: Epidemiological evidence from Africa
Bibliographic record
Abstract
BACKGROUND: Mounting evidence supports an association between nonoptimal ambient temperatures (i.e., heat or cold) and risk of low birthweight (LBW) (<2500 g), while the effect of temperature variability (TV) is largely unknown. We aimed to quantify the association between TV and risk of LBW in Africa. METHODS: Data on birthweight in 37 countries during 1990-2020 were collected from the Demographic and Health Surveys program. We calculated overall, intraday, and interday TV during the entire pregnancy and each trimester using hourly temperatures at ∼ 9 km resolution from ERA5-Land. We employed generalized linear mixed logistic regression, with random effects for country and survey cluster, to quantify the association between LBW and three separate TV metrics. RESULTS: In total there were 33,863 (10.2%) LBW births out of 333,618 records. We found a J-shaped association between TV and LBW. Compared to the reference TV where the lowest risk was observed, extremely high (97.5th percentile) overall, intraday, and interday TV during the entire pregnancy increased the odds of LBW birth by 37.3% (26.7-48.8%), 24.1% (16.4-32.3%), and 15.1% (6.9-24.0%), respectively. In total, 7.3% of all LBW births in Africa were attributable to elevated overall TV. These associations were observed in dry climate zones, but not in tropical or temperate zones. CONCLUSIONS: Our study suggests an adverse impact of TV on the risk of LBW in Africa, according to three different TV definitions, underlining the significance of climate-health risk assessment in those most vulnerable to climate change.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".