Impact of ambient heat exposure on pregnancy outcomes in low- and middle-income countries: A systematic review
Bibliographic record
Abstract
Increasing global temperatures due to climate change have raised serious concerns regarding its potential impact on health outcomes. Pregnant women and their fetuses are among the most vulnerable groups being affected by these dramatic changes resulting in adverse outcomes for both the mother and the developing fetus. Evidence regarding heat-related pregnancy adversities in high-income countries is conclusive, however, such evidence is rare in low- and middle-income countries (LMICs). This review was conducted to bridge the knowledge gap by providing evidence-based insights into the specific repercussions of high heat exposures during pregnancy and its effect on birth outcomes in LMICs. A systematic review was conducted to assess the impact of high environmental or ambient temperatures on pregnancy outcomes (abortion, stillbirth, preterm birth, and low birthweight (LBW)) in LMICs. Electronic searches were conducted on MEDLINE, Embase, CINAHL, Web of Science, and Scopus. Observational studies published between 2010 and 2023 were included in the review. Screening of studies was done using Covidence software, data was extracted on Excel sheets and quality assessment was done using the National Institutes of Health's National Heart, Lung, and Blood Institute tool. We included 11 studies. Four of six studies that included preterm births showed an association between heat and preterm births. Four of five studies reported an association between heat exposure and LBW. Three of four studies on stillbirths showed a significant association between heat exposure and stillbirths. One of the two studies that reported spontaneous abortion revealed a significant association of heat with abortion. Meta-analysis could not be performed due to the lack of homogeneity in defining heat exposure. Amongst the included studies, seven were categorized as "good" and four were categorized as "fair" on methodological quality. This study concluded that ambient temperature and heat exposure during pregnancy can impact birth outcomes such as preterm births, LBWs, abortions, and stillbirths in LMICs. Urgent action is imperative on both national and global scales to facilitate a comprehensive and definitive assessment of heat exposure in LMICs, enabling a deeper understanding of its repercussions on pregnant women. Longitudinal studies are paramount for confirming these associations and devising targeted interventions and strategies aimed at enhancing maternal and child health within LMIC contexts. Registration: PROSPERO ID: CRD42023449173.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".