Extreme Heat and Pregnancy Outcomes: A Scoping Review of the Epidemiological Evidence
Bibliographic record
Abstract
BACKGROUND: Extreme heat caused by climate change is a major public health concern, disproportionately affecting poor and racialized communities. Gestational heat exposure is a well-established teratogen in animal studies, with a growing body of literature suggesting human pregnancies are similarly at risk. Characterization of extreme heat as a pregnancy risk is problematic due to nonstandard definitions of heat waves, and variable study designs. To better focus future research in this area, we conducted a scoping review to assess the effects of extreme heat on pregnancy outcomes. METHODS: A scoping review of epidemiological studies investigating gestational heat-exposure and published 2010 and 2020, was conducted with an emphasis on study design, gestational windows of sensitivity, adverse pregnancy outcomes and characterization of environmental temperatures. RESULTS: A sample of 84 studies was identified, predominantly set in high-income countries. Preterm birth, birthweight, congenital anomalies and stillbirth were the most common pregnancy outcome variables. Studies reported race/ethnicity and/or socioeconomic variables, however these were not always emphasized in the analysis. CONCLUSION: Use of precise temperature data by most studies avoided pitfalls of imprecise, regional definitions of heat waves, however inconsistent study design, and exposure windows are a significant challenge to systematic evaluation of this literature. Despite the high risk of extreme heat events and limited mitigation strategies in the global south, there is a significant gap in the epidemiological literature from these regions. Greater consistency in study design and exposure windows would enhance the rigor of this field.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 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".