Future attributable deaths of heatwaves in Italian cities using high resolution climate change scenarios
Bibliographic record
Abstract
OPS 12: Morbidity effects of high and low temperatures, Room 315, Floor 3, August 28, 2019, 10:30 AM - 12:00 PM Background: Future climate change poses a significant health threat and in particular for the Mediterranean where projected increase in heat waves are projected to increase in frequency and intensity. The aim of the study is to estimate the heat wave attributable deaths in 21 Italian cities for the periods 2021-2050 and 2051-2080 considering two RCP scenarios. Heat waves (HW) were defined as 3 or more days with maximum temperature above the 90th percentile during the summer season (June-August). Methods: The optimized configuration of the COSMO-CLM model developed by CMCC over Italy with a spatial resolution of 8km was used to estimate the variation in the number of HW days in the two future periods compared to 1981-2010. Two scenarios were considered: RCP4.5 (medium range scenario) and RCP8.5 (business as usual). We estimated city-specific attributable deaths on HW days compared to non-HW days using Poisson regression models. We calculated variations in the impact of HW on mortality by multiplying the change in HW days predicted for each scenario and period to the number of heat attributable deaths. We also considered a demographic scenario (ageing of the population) and an adaptation scenario (reduction due to heat prevention plans). Results: For 2021-2050 an extra 5.5 to 8.4 HW days/year were projected, while for 2051-2080 number rose even more, with an average of +10 to +18 HWdays/year. By 2021-2050 an average annual increase in HW attributable deaths of about 140% (880 deaths/year) for both RCP scenarios, taking into account population aging. While for 2051-2080 the impact is greater and differs by scenario; with 1230(+190%) and 1855(290%) annual HW attributable deaths respectively 1230-709/ for RCP4.5 and RCP8.5. Taking into account the adaptation scenario the impact is reduced by around 50%. Conclusion: Adaptation measures need to be strengthened to contrast the mortality burden of climate change.
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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) | 0.004 | 0.001 |
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".