Impact of extreme ambient temperatures on low birth weight: Insights from empirical findings in Pakistan
Bibliographic record
Abstract
BACKGROUND: Exposure to extreme ambient temperatures during pregnancy, including both heat and cold, can lead to complications such as preterm births, low birth weight (LBW), and developmental anomalies. These exposures pose immediate health risks to both mother and child and may exacerbate health disparities across future generations. OBJECTIVE: Pakistan, with limited health resources, is particularly vulnerable to the impacts of extreme temperatures. This study aimed to quantify the association between heat and cold exposure and LBW in Pakistan. DESIGN: Space-time-series study design. METHODS: We analysed 17,077 birth records from 10 datasets from the Multi-Indicator Cluster Surveys and 1 from the Pakistan Demographic and Health Surveys, covering monthly LBW cases from January 2008 to December 2017. These data were linked to monthly heat index estimates, derived from temperature and humidity, from Copernicus ERA5-Land, aggregated at the provincial level. We used a space-time-series study design with quasi-Poisson distributed lag nonlinear regression. Models were adjusted for long-term trends, seasonality, and socio-economic factors, including maternal education, wealth index and rural residence. We estimated the cumulative risk of LBW associated with heat and cold, individual lag effects and the attributable fraction of LBW cases due to temperature exposure. RESULTS: = 4444) of total birth records. The overall exposure-response relationship indicated a positive association between LBW and extreme heat; however, the estimates were imprecise and included the null. At lag 0 (month of conception), there was evidence of increased risk during periods of moderate heat (90th percentile: relative risk (RR) 1.70; 95% confidence interval (CI): 1.01, 2.87) and extreme heat (99th percentile: RR 1.93; 95% CI: 1.00, 3.71). The heat-related attributable fraction for LBW ranged from 0.34 to 0.42 across provinces. In contrast, no association was found between LBW and cold exposure. CONCLUSIONS: This study contributes to the existing body of evidence of the association between extreme temperatures and LBW, particularly from a low-resource, highly vulnerable country. Notably, we found a positive association between heat exposure and LBW during the first month of pregnancy (lag 0), suggesting that early gestation may be a critical period of vulnerability.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".