Excess Risk of Mortality Due to Heatwaves in Dezful city, Southwest of Iran
Bibliographic record
Abstract
Abstract Background: Recent studies show that heatwaves pose risks to human health. Iran is exposed to heatwaves, but the evidence for the health impact of heatwaves is scarce. We aimed to evaluate the impact of heatwaves on daily deaths from non-accidental, cardiovascular, and respiratory in the city of Dezful in Iran from 2013 to 2019.Method: We collected daily ambient temperature and mortality and defined two types of heatwaves by combining daily temperature ³90thin each month of the study period or since 30 years with duration ³2 and 3 days. We used a distributed lag non-linear model to investigate the association between each type of heatwave definition and deaths from non-accidental, cardiovascular, and respiratory with lags up to 13 days.Results: Heat waves of both definitions were associated with a higher risk of non-accidental mortality. The association between heat waves and mortality appeared acutely and lasted for 3 and 4 days. The main effect and added effect are more pronounced among male and older adults than their counterparts. We found no evidence of an association of cardiovascular and respiratory deaths with heat waves.Conclusion: Dezful is a city with a hot climate. However, the results showed that heatwaves could have detrimental effects on health, even in populations accustomed to the extreme heat. Therefore, early warning systems which monitor heat waves should provide the necessary warnings to all exposed groups, especially the elderly and the male groups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".