Sex differences in the impact of extreme heat on cardiovascular disease outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Climate change is intensifying extreme heat events, posing significant risks to cardiovascular health. While sex differences in heat vulnerability have been observed, the evidence remains inconsistent. This systematic review and meta-analysis examined sex-specific associations between extreme heat exposure and cardiovascular disease (CVD) outcomes over the past decade. METHODS: We searched PubMed, Embase, and Scopus for studies published between 2004 and 2024 that reported sex-stratified cardiovascular outcomes associated with heat exposure following the PRISMA guidelines. The quality of the evidence was evaluated following the Navigation Guide Criteria. Random-effects meta-analysis was conducted to calculate pooled relative risk ratios (RRR) comparing males to females for studies addressing incremental temperature increase. Heat wave studies were synthesized narratively due to methodological heterogeneity. RESULTS: Of 6126 articles, 79 met inclusion criteria (62 in meta-analysis, 17 in narrative synthesis), primarily from East Asia, Europe, and North America. A 1 °C temperature increase was associated with elevated cardiovascular risks for both sexes. The pooled relative risk ratio (RRR) comparing males to females was 1.008 [1.002-1.014] for mortality, suggesting slightly higher female vulnerability, but not for morbidity (RRR 0.996 [0.987-1.004]). Significant heterogeneity was noted (Mortality I² = 50.3%, Morbidity I² = 70.3%). Heat wave studies showed inconsistent sex-specific impacts across populations. CONCLUSIONS: Females showed marginally higher vulnerability to heat-related cardiovascular mortality compared to males, while no significant sex differences were observed for morbidity outcomes. Future research should focus on understanding these mechanisms and developing sex-specific interventions.
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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) | 0.009 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".