TY - JOUR
T1 - Economic burden of premature deaths attributable to non-optimum temperatures in Italy
T2 - A nationwide time-series analysis from 2015 to 2019
AU - Wu, Yao
AU - Xu, Rongbin
AU - Yu, Wenhua
AU - Wen, Bo
AU - Li, Shanshan
AU - Guo, Yuming
N1 - Funding Information:
YW, RX, BW were supported by China Scholarship Council [grant number 202006010044 to YW, 201806010405 to RX, 202006010043 to BW] ( https://www.csc.edu.cn/chuguo/s/1844 , https://www.csc.edu.cn/chuguo/s/1267 ). SL was supported by an Emerging Leader Fellowship ( GNT2009866 ) of the Australian National Health and Medical Research Council . YG was supported by Career Development Fellowship ( GNT1163693 ) and Leader Fellowship ( GNT2008813 ) of the Australian National Health and Medical Research Council . WY was supported by Monash Graduate Scholarship , Monash International Tuition Scholarship , and the CAR PhD Top-up scholarship . The funding bodies did not play any role in the study design, data collection, data analyses, results interpretation, and writing of this manuscript.
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/9
Y1 - 2022/9
N2 - Background: Human beings and society are experiencing substantial consequences caused by non-optimum temperatures. However, limited studies have assessed the economic burden of premature deaths attributable to non-optimum temperatures. Objectives: To characterize the association between daily mean temperature and the economic burden of premature deaths. Methods: A total of 3 228 098 deaths were identified from a national mortality dataset in Italy during 2015 and 2019. We used the value of statistical life to quantify the economic losses of premature death. A two-stage time-series analysis was performed to evaluate the economic losses of premature deaths associated with non-optimum temperatures. Attributable burden for non-optimum temperatures compared with minimum risk temperature were estimated. Potential effect modifiers were further explored. Results: From 2015 to 2019, the economic loss of premature deaths due to non-optimum temperatures was $525.52 billion (95% CI: $461.84–$580.80 billion), with the attributable fraction of 5.74% (95% CI: 5.04%–6.34%). Attributable economic burden was largely due to moderate cold temperatures ($309.54 billion, 95% CI: $249.49–$357.34 billion). A higher economic burden was observed for people above the age of 65, accounting for 75.97% ($452.42, 95%CI: $406.97–$488.76 billion) of the total economic burden. In particular, higher fractions attributable to heat temperatures were observed for provinces with the lowest level of GDP per capita but the highest level of urbanization. Discussion: This study shows a considerable economic burden of premature deaths attributed to non-optimum temperatures. These figures can help inform tailored prevention to tackle the large economic burden imposed by non-optimum temperatures.
AB - Background: Human beings and society are experiencing substantial consequences caused by non-optimum temperatures. However, limited studies have assessed the economic burden of premature deaths attributable to non-optimum temperatures. Objectives: To characterize the association between daily mean temperature and the economic burden of premature deaths. Methods: A total of 3 228 098 deaths were identified from a national mortality dataset in Italy during 2015 and 2019. We used the value of statistical life to quantify the economic losses of premature death. A two-stage time-series analysis was performed to evaluate the economic losses of premature deaths associated with non-optimum temperatures. Attributable burden for non-optimum temperatures compared with minimum risk temperature were estimated. Potential effect modifiers were further explored. Results: From 2015 to 2019, the economic loss of premature deaths due to non-optimum temperatures was $525.52 billion (95% CI: $461.84–$580.80 billion), with the attributable fraction of 5.74% (95% CI: 5.04%–6.34%). Attributable economic burden was largely due to moderate cold temperatures ($309.54 billion, 95% CI: $249.49–$357.34 billion). A higher economic burden was observed for people above the age of 65, accounting for 75.97% ($452.42, 95%CI: $406.97–$488.76 billion) of the total economic burden. In particular, higher fractions attributable to heat temperatures were observed for provinces with the lowest level of GDP per capita but the highest level of urbanization. Discussion: This study shows a considerable economic burden of premature deaths attributed to non-optimum temperatures. These figures can help inform tailored prevention to tackle the large economic burden imposed by non-optimum temperatures.
KW - Attributable burden
KW - Economic loss
KW - Non-optimum temperature
UR - https://www.scopus.com/pages/publications/85128301853
U2 - 10.1016/j.envres.2022.113313
DO - 10.1016/j.envres.2022.113313
M3 - Article
C2 - 35436452
AN - SCOPUS:85128301853
SN - 0013-9351
VL - 212
JO - Environmental Research
JF - Environmental Research
IS - Part B
M1 - 113313
ER -