Mortality Risk Related to Heatwaves in Dezful City, Southwest of Iran
Bibliographic record
Abstract
Background: We aimed to evaluate the impact of heatwaves on daily deaths due to non-accidental, cardiovascular and respiratory causes in the city of Dezful in Iran from 2013 to 2019. Method: We collected daily ambient temperature and mortality and defined 2 types of heatwaves by combining daily temperature ⩾90th in each month of the study period or since 30 years with duration ⩾2 and 3 days. We used a distributed lag non-linear model to study the association between each type of heatwave definition, and deaths due to non-accidental, cardiovascular and respiratory causes with lags up to 13 days. Results: There was no discernible correlation in this area, despite the fact that heatwaves raised the risk of death from cardiovascular causes and lowered the risk from respiratory causes. On the other hand, the risk of total non-accidental mortality on days with the heatwaves is significantly higher than normal days. In main effects, the heatwaves have a significant relationship with the risk of total non-accidental mortality (in the first heatwave definition, Cumulative Excess Risk (CER) in lag 0-2 was 10.4 and in second heatwave definition, CER values in lag 0, 0-2, and 0-6 were 12.4, 29.2, and 38.8 respectively). Also, in added effects, heatwaves have a significant relationship with the risk of total non-accidental mortality (in the first heatwave definition, CER in lag 0 and 0-2 were 1.79 and 4.11 and in the second heatwave definition, CER values in lag 0, 0-2, and 0-6 were 7.76, 18.35 and 24.87 respectively). In addition, heatwaves appeared to contribute to a cumulative excess risk of non-accidental death among the male group as well as the older adults. Conclusion: 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 heatwaves should provide the necessary warnings to the population, especially the most vulnerable groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".