Daylight Saving Time Transitions and Risk of Heart Attack
Bibliographic record
Abstract
BACKGROUND: The health risks of daylight saving time transitions are intensely debated. Disturbed circadian rhythms and lack of sleep after transitions might increase the risk of acute myocardial infarction (AMI). The only meta-analysis on the risk of AMI has now been considerably expanded. METHODS: In this systematic review and meta-analysis (including meta-regressions and sensitivity analyses), we examine the frequency of AMI in the first few weeks after daylight saving time transitions (OSF registration www.doi.org/10.17605/OSF.IO/7CFKS). Eight databases were searched for pertinent literature up to September 2023. Authors were contacted for additional information. Study quality was rated using the Newcastle-Ottawa Scale. RESULTS: Twelve studies from ten countries were included in the meta-analysis. Nine were of adequate quality, and three were of good quality. The pooled relative risk (RR) of AMI after daylight saving time onset (spring) was 1.04 (95% confidence interval [1.02; 1.07], I2: 57.3%), and 1.02 ([0.99; 1.05], I2: 51.6%) after daylight saving time offset (autumn). Recalculation after the exclusion of one study with inconsistencies yielded pooled RR values of 1.04 [1.01; 1.06] and 1.00 [0.99; 1.02], with the spring results being heterogeneous (I2: 56.9%) and the autumn results homogeneous (I2: 17.1%). CONCLUSION: Current evidence suggests that there may be an increased risk of AMI after the spring transition, although there is moderate to marked heterogeneity among the studies that support this conclusion. More easily interpretable studies, such as those already conducted in the field of economics, should clarify associations with the aid of discontinuity regression and placebo tests. To this end, comparative risk analyses using years or places wherein daylight saving time was not practiced would be suitable.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".