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      Temperature variability and common diseases of the elderly in China: a national cross-sectional study

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          Abstract

          Background

          In the context of climate change, it has been well observed that short-term temperature variability (TV) could increase the overall and cause-specific mortality and morbidity. However, the association between long-term TV and a broader spectrum of diseases is not yet well understood, especially in the elderly.

          Methods

          Our study used data from the fourth Urban and Rural Elderly Population (UREP) study. Long-term TV was calculated from the standard deviation (SD) of daily minimum and maximum temperatures within the study periods (2010–2014, 2011–2014, 2012–2014, 2013–2014, and 2014). Ten self-reported diseases and conditions were collected by questionnaire, including cataract, hypertension, diabetes, cardio-cerebrovascular diseases, stomach diseases, arthritis, chronic lung disease, asthma, cancer, and reproductive diseases. The province-stratified logistic regression model was used to quantify the association between long-term TV and the prevalence of each disease.

          Results

          A total of 184,047 participants were included in our study. In general, there were significant associations between TV and the prevalence of most diseases at the national level. Cardio-cerebrovascular disease (OR: 1.16, 95% CI: 1.13, 1.20) generated the highest estimates, followed by stomach diseases (OR: 1.15, 95% CI: 1.10, 1.19), asthma (OR: 1.14, 95% CI: 1.06, 1.22), chronic lung diseases (OR: 1.08, 95% CI: 1.03, 1.13), arthritis (OR: 1.08, 95% CI: 1.05, 1.11), and cataract (OR: 1.06, 95% CI: 1.02, 1.10). Moreover, the associations varied by geographical regions and across subgroups stratified by sex, household income, physical activity, and education.

          Conclusions

          Our study showed that long-term exposure to TV was associated with the prevalence of main diseases in the elderly. More attention should be paid to the elderly and targeted strategies should be implemented, such as an early warning system.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12940-023-00959-y.

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          Most cited references40

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          Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

          Summary Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding Bill & Melinda Gates Foundation.
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            The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate

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              A research agenda for aging in China in the 21st century

              Highlights • The elderly population in China is growing exponentially and this growth will last for decades. • The aging problem in China is expected to lead to a significant socioeconomic burden which will require a combined effort among gerontologists, healthcare professionals, policymakers, and social forces. • A research agenda on the collection of public health data, diet and food safety, physical exercise, pharmacological interventions in age associated diseases, the elderly and geriatric care, and policy dialogues are potential ways to relieve the aging problem. • Increased political and financial commitments from the Chinese government are critical for achieving a research agenda on aging in China for the 21st century.
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                Author and article information

                Contributors
                dongyanhui@bjmu.edu.cn
                zhengxiaoying@sph.pumc.edu.cn
                Journal
                Environ Health
                Environ Health
                Environmental Health
                BioMed Central (London )
                1476-069X
                7 January 2023
                7 January 2023
                2023
                : 22
                : 4
                Affiliations
                [1 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, Institute of Child and Adolescent Health, School of Public Health, , Peking University Health Science Center, ; No 38 Xue Yuan Road, Haidian District, Beijing, 100191 China
                [2 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Climate, Air Quality Research (CARE) Unit, School of Public Health and Preventive Medicine, , Monash University, ; Level 2, 553 St Kilda Road, Melbourne, VIC 3004 Australia
                [3 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, School of Population Medicine and Public Health, , Chinese Academy of Medical Sciences & Peking Union Medical College, ; No.31, Beijige-3, Dongcheng District, Beijing, 100730 China
                [4 ]GRID grid.452461.0, ISNI 0000 0004 1762 8478, First Clinical Medical College of Shanxi Medical University, ; No. 56 Xinjian South Road, Yingze District, Taiyuan City, 030001 Shanxi Province China
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, , University of Oxford, ; Oxford, UK
                [6 ]China Research Center on Ageing, 48 Guang ‘anmen South Street, Xicheng District, Beijing, 100054 China
                [7 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, University of Toronto, ; St.Geogre, 27 King’s College Cir, Toronto, ON M5S Canada
                Article
                959
                10.1186/s12940-023-00959-y
                9824998
                36609287
                4283fb7b-be37-4510-9fcf-f1bdc6f00db8
                © The Author(s) 2023

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 14 February 2022
                : 2 January 2023
                Categories
                Research
                Custom metadata
                © The Author(s) 2023

                Public health
                temperature variability,elderly,diseases,cardio-cerebrovascular
                Public health
                temperature variability, elderly, diseases, cardio-cerebrovascular

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