Scoping review of climate drivers on maternal health: current evidence and clinical implications
Bibliographic record
Abstract
Objective: To systematically review the literature on associations between climate drivers and health outcomes among pregnant people. This review fills a gap by synthesizing evidence for a clinician audience. Data Sources: Systematic scoping review of articles published in PubMed and clinicaltrials.gov from January 2010 through December 2023. Study Eligibility Criteria: Empirical studies published in English-language peer-reviewed journals, assessing associations between select climate drivers and adverse maternal and birth outcomes. The review included studies examining heat, storms, sea level rise, flooding, drought, wildfires, and other climate-related factors. Health outcomes included preterm birth, low birthweight, small for gestational age, gestational diabetes, pre-eclampsia/eclampsia, miscarriage/stillbirth and maternal mortality. Study Appraisal and Synthesis Methods: The scoping review protocol was registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202410004, January 3, 2024) and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Data were extracted by 2 authors; quality and risk of bias was assessed independently. Results: =158) met inclusion criteria. The majority of studies (146/158; 92.4%) documented statistically significant and clinically meaningful associations between climate drivers and adverse perinatal health outcomes, including risk of preterm birth, low birthweight, and stillbirth as well as preeclampsia, gestational diabetes, miscarriage, and maternal death. Among the most durable findings: extreme heat exposure in early and late pregnancy were associated with increased risk of preterm birth and stillbirth. Driven in part by large (often population-based) studies and objective outcomes from surveillance data or medical record reviews, studies in this scoping review were evaluated as high quality (scoring 7-9 on the Newcastle-Ottawa Scale). Risk of bias was generally low. Conclusions: Climate drivers are consistently associated with adverse health outcomes for pregnant people. Continuing education for clinicians, and clinician-patient communications should be expanded to address risks of climate change and extreme weather exposure, especially risks of extreme heat in late-pregnancy. Results from this review should inform multilevel interventions to address adverse health effects of climate during pregnancy as well as practice advisories, protocols, checklists, and clinical guidelines in obstetrics.
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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 | 0.061 | 0.257 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".