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      Projecting heat-related excess mortality under climate change scenarios in China

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          Abstract

          Recent studies have reported a variety of health consequences of climate change. However, the vulnerability of individuals and cities to climate change remains to be evaluated. We project the excess cause-, age-, region-, and education-specific mortality attributable to future high temperatures in 161 Chinese districts/counties using 28 global climate models (GCMs) under two representative concentration pathways (RCPs). To assess the influence of population ageing on the projection of future heat-related mortality, we further project the age-specific effect estimates under five shared socioeconomic pathways (SSPs). Heat-related excess mortality is projected to increase from 1.9% (95% eCI: 0.2–3.3%) in the 2010s to 2.4% (0.4–4.1%) in the 2030 s and 5.5% (0.5–9.9%) in the 2090 s under RCP8.5, with corresponding relative changes of 0.5% (0.0–1.2%) and 3.6% (−0.5–7.5%). The projected slopes are steeper in southern, eastern, central and northern China. People with cardiorespiratory diseases, females, the elderly and those with low educational attainment could be more affected. Population ageing amplifies future heat-related excess deaths 2.3- to 5.8-fold under different SSPs, particularly for the northeast region. Our findings can help guide public health responses to ameliorate the risk of climate change.

          Abstract

          Global warming is expected to increase mortality due to heat stress in many regions. Here, the authors asses how mortality due to high temperatures changes in China changes for different demographic groups and show that heat-related excess mortality is increasing under climate change, a process that is strongly amplified by population ageing.

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          Double-slit photoelectron interference in strong-field ionization of the neon dimer

          Wave-particle duality is an inherent peculiarity of the quantum world. The double-slit experiment has been frequently used for understanding different aspects of this fundamental concept. The occurrence of interference rests on the lack of which-way information and on the absence of decoherence mechanisms, which could scramble the wave fronts. Here, we report on the observation of two-center interference in the molecular-frame photoelectron momentum distribution upon ionization of the neon dimer by a strong laser field. Postselection of ions, which are measured in coincidence with electrons, allows choosing the symmetry of the residual ion, leading to observation of both, gerade and ungerade, types of interference.
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            Mortality risk attributable to high and low ambient temperature: a multicountry observational study

            Summary Background Although studies have provided estimates of premature deaths attributable to either heat or cold in selected countries, none has so far offered a systematic assessment across the whole temperature range in populations exposed to different climates. We aimed to quantify the total mortality burden attributable to non-optimum ambient temperature, and the relative contributions from heat and cold and from moderate and extreme temperatures. Methods We collected data for 384 locations in Australia, Brazil, Canada, China, Italy, Japan, South Korea, Spain, Sweden, Taiwan, Thailand, UK, and USA. We fitted a standard time-series Poisson model for each location, controlling for trends and day of the week. We estimated temperature–mortality associations with a distributed lag non-linear model with 21 days of lag, and then pooled them in a multivariate metaregression that included country indicators and temperature average and range. We calculated attributable deaths for heat and cold, defined as temperatures above and below the optimum temperature, which corresponded to the point of minimum mortality, and for moderate and extreme temperatures, defined using cutoffs at the 2·5th and 97·5th temperature percentiles. Findings We analysed 74 225 200 deaths in various periods between 1985 and 2012. In total, 7·71% (95% empirical CI 7·43–7·91) of mortality was attributable to non-optimum temperature in the selected countries within the study period, with substantial differences between countries, ranging from 3·37% (3·06 to 3·63) in Thailand to 11·00% (9·29 to 12·47) in China. The temperature percentile of minimum mortality varied from roughly the 60th percentile in tropical areas to about the 80–90th percentile in temperate regions. More temperature-attributable deaths were caused by cold (7·29%, 7·02–7·49) than by heat (0·42%, 0·39–0·44). Extreme cold and hot temperatures were responsible for 0·86% (0·84–0·87) of total mortality. Interpretation Most of the temperature-related mortality burden was attributable to the contribution of cold. The effect of days of extreme temperature was substantially less than that attributable to milder but non-optimum weather. This evidence has important implications for the planning of public-health interventions to minimise the health consequences of adverse temperatures, and for predictions of future effect in climate-change scenarios. Funding UK Medical Research Council.
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              Distributed lag non-linear models

              Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                yangjun_eci@jnu.edu.cn
                liuqiyong@icdc.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                15 February 2021
                15 February 2021
                2021
                : 12
                : 1039
                Affiliations
                [1 ]GRID grid.258164.c, ISNI 0000 0004 1790 3548, Institute for Environmental and Climate Research, , Jinan University, ; Guangzhou, China
                [2 ]Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou, China
                [3 ]GRID grid.258164.c, ISNI 0000 0004 1790 3548, JNU-QUT Joint Laboratory for Air Quality Science and Management, , Jinan University, ; Guangzhou, China
                [4 ]GRID grid.508400.9, National Center for Chronic and Noncommunicable Disease Control and Prevention, ; Beijing, China
                [5 ]GRID grid.9227.e, ISNI 0000000119573309, State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Nature Resources Research, , Chinese Academy of Sciences, ; Beijing, China
                [6 ]GRID grid.488530.2, ISNI 0000 0004 1803 6191, State Key Laboratory of Oncology in Southern China, Department of Epidemiology, Cancer Prevention Center, , Sun Yat-Sen University Cancer Center, ; Guangzhou, China
                [7 ]GRID grid.1680.f, ISNI 0000 0004 0559 5189, NSW Department of Primary Industries, , Wagga Wagga Agricultural Institute, ; Wagga Wagga, NSW Australia
                [8 ]GRID grid.1005.4, ISNI 0000 0004 4902 0432, Climate Change Research Centre, , University of New South Wales, ; Sydney, NSW Australia
                [9 ]GRID grid.284723.8, ISNI 0000 0000 8877 7471, State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, , Southern Medical University, ; Guangzhou, China
                [10 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, , Chinese Center for Disease Control and Prevention, ; Beijing, China
                [11 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Shanghai Children’s Medical Center, , Shanghai Jiao Tong University, ; Shanghai, China
                [12 ]GRID grid.186775.a, ISNI 0000 0000 9490 772X, School of Public Health and Institute of Environment and Population Health, , Anhui Medical University, ; Hefei, China
                [13 ]GRID grid.1024.7, ISNI 0000000089150953, School of Public Health and Institute of Health and Biomedical Innovation, , Queensland University of Technology, ; Brisbane, Australia
                [14 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, , Monash University, ; Melbourne, Australia
                Author information
                http://orcid.org/0000-0002-8049-4746
                http://orcid.org/0000-0003-1766-2820
                http://orcid.org/0000-0003-2574-1908
                http://orcid.org/0000-0002-6687-9420
                http://orcid.org/0000-0002-1766-6592
                Article
                21305
                10.1038/s41467-021-21305-1
                7884743
                33589602
                0c1e25de-fbb5-441a-8e24-e7dd29984596
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 January 2020
                : 21 January 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 82003552
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003453, Natural Science Foundation of Guangdong Province (Guangdong Natural Science Foundation);
                Award ID: 2018A030310655
                Award Recipient :
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                © The Author(s) 2021

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                climate-change impacts,environmental health,environmental impact,public health,risk factors

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