Effect of Dust Storms on Non-Accidental, Cardiovascular, and Respiratory Mortality: A Case of Dezful City in Iran
Bibliographic record
Abstract
Background: Despite the fact that Iran has been exposed to severe dust storms during the past 2 decades, few studies have investigated the health effects of these events in Iran. This study was conducted to assess the association between dust storms and daily non-accidental, cardiovascular, and respiratory mortality in Dezful City (Khuzestan Province, Iran) during 2014 to 2019. Methods: In this study, mortality, meteorological, and climatological data were obtained from the Dezful University of Medical Sciences, Iranian Meteorological Organization, and Department of Environment in Khuzestan Province, respectively. Days of dust storm were identified based on the daily concentration threshold of particulate matter with an aerodynamic diameter of less than 10 μm (PM 10 ) according to Hoffmanns҆ definition, and then an ecological time-series was used to estimate the short-term effects of dust storms on daily mortality. Statistical analysis was performed using a distributed lag linear model (DLM) and a distributed lag non-linear model (DLNM) packages by R software and the study results were reported as excess mortality. Results: During the study period, 15 223 deaths were recorded, and 139 dust storms occurred in Dezful city. In addition, there was statistically significant excess risk of mortality due to dust storms in Dezful City (mortality in the group under 15 years of age, lag 4 : 34.17% and 15-64 years of age groups, lag 5 : 32.19%, lag 6 : 3.28%), also dust storms had statistically significant effects on respiratory mortality (lag 6 : 5.49%). Conclusion: The findings of the current study indicate that dust storms increase the risk of mortality with some lags. An evidence-based early warning system may be able to aware the people of the health effects of dust storms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".