Pregnancy and extreme heat events: A rapid review of evidence related to health outcomes, risk factors and interventions
Bibliographic record
Abstract
BACKGROUND: Climate change is increasing the frequency and severity of extreme heat events (EHEs), resulting in increased morbidity and mortality for vulnerable populations. Pregnant people and fetuses are at risk for adverse pregnancy outcomes from EHEs. OBJECTIVE: To collate and synthesize existing evidence on the effects of EHE on pregnant people and fetuses and relevant mitigating factors and interventions to inform healthcare providers and other pregnancy-focused audiences. METHODS: A peer-reviewed search strategy was conducted in MEDLINE, EMBASE, Global Health, CAB Abstracts, SCOPUS, and ProQuest Public Health, for empirical studies and reviews published between 2009 and 2023 in English and French. The search strategy focused on terms related to EHEs, exposure, and pregnancy. Health outcomes, risk factors and interventions relating to EHEs (defined based on high ambient temperature thresholds) were reviewed and narratively reported. FINDINGS: Sixty-eight studies were included (n = 16 reviews; n = 52 empirical studies). Associations between both adverse fetal outcomes (e.g., pre-term birth) and maternal outcomes (e.g., severe maternal morbidities) and EHEs were identified. Pregnant people with low socioeconomic status were found to be more likely to have morbidities. Interventions such as improved clinician support have been proposed by researchers to reduce the risk of poor pregnancy outcomes. CONCLUSION: There is an association between EHEs and the development of pregnancy-related morbidity and mortality, mediated by environmental, social and intrinsic individual factors. There are remaining knowledge gaps that have been identified that should be addressed, but more importantly, the synthesis of this evidence highlights the urgent need for interventions such as improved healthcare provider education, and policy interventions to mitigate the health riskscaused by exposure to heat in pregnant populations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".