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Damtew YT, Varghese BM, Anikeeva O, Tong M, Hansen A, Dear K, Zhang Y, Morgan G, Driscoll T, Capon T, Gourley M, Prescott V, Bi P. Current and future burden of Ross River virus infection attributable to increasing temperature in Australia: a population-based study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 48:101124. [PMID: 39040035 PMCID: PMC11260579 DOI: 10.1016/j.lanwpc.2024.101124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 05/28/2024] [Accepted: 06/05/2024] [Indexed: 07/24/2024]
Abstract
Background Ross River virus (RRV), Australia's most notifiable vector-borne disease transmitted through mosquito bites, has seen increased transmission due to rising temperatures. Quantifying the burden of RRV infection attributable to increasing temperatures (both current and future) is pivotal to inform prevention strategies in the context of climate change. Methods As RRV-related deaths are rare in Australia, we utilised years lived with disability (YLDs) associated with RRV infection data from the Australian Institute of Health and Welfare (AIHW) Burden of Disease database between 2003 and 2018. We obtained relative risks per 1 °C temperature increase in RRV infection from a previous meta-analysis. Exposure distributions for each Köppen-Geiger climate zone were calculated separately and compared with the theoretical-minimum-risk exposure distribution to calculate RRV burden attributable to increasing temperatures during the baseline period (2003-2018), and projected future burdens for the 2030s and 2050s under two greenhouse gas emission scenarios (Representative Concentration Pathways, RCP 4.5 and RCP 8.5), two adaptation scenarios, and different population growth series. Findings During the baseline period (2003-2018), increasing mean temperatures contributed to 35.8 (±0.5) YLDs (19.1%) of the observed RRV burden in Australia. The mean temperature attributable RRV burden varied across climate zones and jurisdictions. Under both RCP scenarios, the projected RRV burden is estimated to increase in the future despite adaptation scenarios. By the 2050s, without adaptation, the RRV burden could reach 45.8 YLDs under RCP4.5 and 51.1 YLDs under RCP8.5. Implementing a 10% adaptation strategy could reduce RRV burden to 41.8 and 46.4 YLDs, respectively. Interpretation These findings provide scientific evidence for informing policy decisions and guiding resource allocation for mitigating the future RRV burden. The current findings underscore the need to develop location-specific adaptation strategies for climate-sensitive disease control and prevention. Funding Australian Research Council Discovery Program.
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Affiliation(s)
- Yohannes Tefera Damtew
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia
- College of Health and Medical Sciences, Haramaya University, P.O.BOX 138, Dire Dawa, Ethiopia
| | - Blesson Mathew Varghese
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | - Olga Anikeeva
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | - Michael Tong
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Canberra, ACT 2601, Australia
| | - Alana Hansen
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | - Keith Dear
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia
| | - Ying Zhang
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia
| | - Geoffrey Morgan
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia
| | - Tim Driscoll
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia
| | - Tony Capon
- Monash Sustainable Development Institute, Monash University, Melbourne, Victoria, Australia
| | - Michelle Gourley
- Burden of Disease and Mortality Unit, Australian Institute of Health and Welfare, Canberra, ACT 2601, Australia
| | - Vanessa Prescott
- Burden of Disease and Mortality Unit, Australian Institute of Health and Welfare, Canberra, ACT 2601, Australia
| | - Peng Bi
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia
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Ueta H, Kodera S, Sugimoto S, Hirata A. Projection of future heat-related morbidity in three metropolitan prefectures of Japan based on large ensemble simulations of climate change under 2 °C global warming scenarios. ENVIRONMENTAL RESEARCH 2024; 247:118202. [PMID: 38224937 DOI: 10.1016/j.envres.2024.118202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/08/2024] [Accepted: 01/11/2024] [Indexed: 01/17/2024]
Abstract
Recently, global warming has become a prominent topic, including its impacts on human health. The number of heat illness cases requiring ambulance transport has been strongly linked to increasing temperature and the frequency of heat waves. Thus, a potential increase in the number of cases in the future is a concern for medical resource management. In this study, we estimated the number of heat illness cases in three prefectures of Japan under 2 °C global warming scenarios, approximately corresponding to the 2040s. Based on the population composition, a regression model was used to estimate the number of heat illness cases with an input parameter of time-dependent meteorological ambient temperature or computed thermophysiological response of test subjects in large-scale computation. We generated 504 weather patterns using 2 °C global warming scenarios. The large-scale computational results show that daily amount of sweating increased twice and the core temperature increased by maximum 0.168 °C, suggesting significant heat strain. According to the regression model, the estimated number of heat illness cases in the 2040s of the three prefectures was 1.90 (95%CI: 1.35-2.38) times higher than that in the 2010s. These computational results suggest the need to manage ambulance services and medical resource allocation, including intervention for public awareness of heat illnesses. This issue will be important in other aging societies in near future.
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Affiliation(s)
- Haruto Ueta
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan
| | - Sachiko Kodera
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan; Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, 466-8555, Japan
| | - Shiori Sugimoto
- Japan Agency for Marine-Earth Science and Technology, Yokohama, 236-0001, Japan
| | - Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan; Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, 466-8555, Japan.
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Borg MA, Xiang J, Anikeeva O, Ostendorf B, Varghese B, Dear K, Pisaniello D, Hansen A, Zander K, Sim MR, Bi P. Current and projected heatwave-attributable occupational injuries, illnesses, and associated economic burden in Australia. ENVIRONMENTAL RESEARCH 2023; 236:116852. [PMID: 37558113 DOI: 10.1016/j.envres.2023.116852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/21/2023] [Accepted: 08/06/2023] [Indexed: 08/11/2023]
Abstract
INTRODUCTION The costs of global warming are substantial. These include expenses from occupational illnesses and injuries (OIIs), which have been associated with increases during heatwaves. This study estimated retrospective and projected future heatwave-attributable OIIs and their costs in Australia. MATERIALS AND METHODS Climate and workers' compensation claims data were extracted from seven Australian capital cities representing OIIs from July 2005 to June 2018. Heatwaves were defined using the Excess Heat Factor. OIIs and associated costs were estimated separately per city and pooled to derive national estimates. Results were projected to 2030 (2016-2045) and 2050 (2036-2065). RESULTS The risk of OIIs and associated costs increased during heatwaves, with the risk increasing during severe and particularly extreme heatwaves. Of all OIIs, 0.13% (95% empirical confidence interval [eCI]: 0.11-0.16%) were heatwave-attributable, equivalent to 120 (95%eCI:70-181) OIIs annually. 0.25% of costs were heatwave-attributable (95%eCI: 0.18-0.34%), equal to $AU4.3 (95%eCI: 1.4-7.4) million annually. Estimates of heatwave-attributable OIIs by 2050, under Representative Concentration Pathway [RCP]4.5 and RCP8.5, were 0.17% (95%eCI: 0.10-0.27%) and 0.23% (95%eCI: 0.13-0.37%), respectively. National costs estimates for 2030 under RCP4.5 and RCP8.5 were 0.13% (95%eCI: 0.27-0.46%) and 0.04% (95%eCI: 0.66-0.60), respectively. These estimates for extreme heatwaves were 0.04% (95%eCI: 0.02-0.06%) and 0.04% (95%eCI: 0.01-0.07), respectively. Cost-AFs in 2050 were, under RCP4.5, 0.127% (95%eCI: 0.27-0.46) for all heatwaves and 0.04% (95%eCI: 0.01-0.09%) for extreme heatwaves. Attributable fractions were approximately similar to baseline when assuming theoretical climate adaptation. DISCUSSION Heatwaves represent notable and preventable portions of preventable OIIs and economic burden. OIIs are likely to increase in the future, and costs during extreme heatwaves in 2030. Workplace and public health policies aimed at heat adaptation can reduce heat-attributable morbidity and costs.
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Affiliation(s)
- Matthew A Borg
- School of Public Health, University of Adelaide, 50 Rundle Mall, Adelaide, SA 5000, Australia
| | - Jianjun Xiang
- School of Public Health, University of Adelaide, 50 Rundle Mall, Adelaide, SA 5000, Australia; School of Public Health, Fujian Medical University, 1 Xue Yuan Road, Minhou Campus, Fuzhou, Fujian Province, 350122, China
| | - Olga Anikeeva
- School of Public Health, University of Adelaide, 50 Rundle Mall, Adelaide, SA 5000, Australia
| | - Bertram Ostendorf
- Ecology and Evolutionary Biology, University of Adelaide, 57 North Terrace, Adelaide, SA 5000, Australia
| | - Blesson Varghese
- School of Public Health, University of Adelaide, 50 Rundle Mall, Adelaide, SA 5000, Australia
| | - Keith Dear
- School of Public Health, University of Adelaide, 50 Rundle Mall, Adelaide, SA 5000, Australia
| | - Dino Pisaniello
- School of Public Health, University of Adelaide, 50 Rundle Mall, Adelaide, SA 5000, Australia
| | - Alana Hansen
- School of Public Health, University of Adelaide, 50 Rundle Mall, Adelaide, SA 5000, Australia
| | - Kerstin Zander
- Northern Institute, Charles Darwin University, Ellengowan Drive, Darwin, NT 0909, Australia
| | - Malcolm R Sim
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, The Alfred Centre, Monash University, 553 St Kilda Road, Melbourne, VIC, 3004, Australia
| | - Peng Bi
- School of Public Health, University of Adelaide, 50 Rundle Mall, Adelaide, SA 5000, Australia.
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