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      Effects of ambient temperature on mortality among elderly residents of Chengdu city in Southwest China, 2016–2020: a distributed-lag non-linear time series analysis

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          Abstract

          Background

          With complex changes in the global climate, it is critical to understand how ambient temperature affects health, especially in China. We aimed to assess the effects of temperature on daily mortality, including total non-accidental, cardiovascular disease (CVD), respiratory disease, cerebrovascular disease, and ischemic heart disease (IHD) mortality between 2016 and 2020 in Chengdu, China.

          Methods

          We obtained daily temperature and mortality data for the period 2016–2020. A Poisson regression model combined with a distributed-lag nonlinear model was used to examine the association between temperature and daily mortality. We investigated the effects of individual characteristics by sex, age, education level, and marital status.

          Results

          We found significant non-linear effects of temperature on total non-accidental, CVD, respiratory, cerebrovascular, and IHD mortality. Heat effects were immediate and lasted for 0–3 days, whereas cold effects persisted for 7–10 days. The relative risks associated with extreme high temperatures (99th percentile of temperature, 28 °C) over lags of 0–3 days were 1.22 (95% confidence interval [CI]: 1.17, 1.28) for total non-accidental mortality, 1.40 (95% CI: 1.30, 1.50) for CVD morality, 1.34 (95% CI: 1.24, 1.46) for respiratory morality, 1.33 (95% CI: 1.20, 1.47) for cerebrovascular mortality, and 1.38 (95% CI: 1.20, 1.58) for IHD mortality. The relative risks associated with extreme cold temperature (1st percentile of temperature, 3.0 °C) over lags of 0–14 days were 1.32 (95% CI: 1.19, 1.46) for total mortality, 1.45 (95% CI: 1.24, 1.68) for CVD morality, 1.28 (95% CI: 1.09, 1.50) for respiratory morality, 1.36 (95% CI: 1.09, 1.70) for cerebrovascular mortality, and 1.26 (95% CI: 0.95, 1.68) for IHD morality. We found that hot and cold affects were greater in those over 85 years of age, and that women, individuals with low education levels, and those who were widowed, divorced, or never married, were more vulnerable.

          Conclusions

          This study showed that exposure to hot and cold temperatures in Chengdu was associated with increased mortality, with people over 85 years old, women, those with low education levels, and unmarried individuals being more affected by hot and cold temperatures.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-022-14931-x.

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          Most cited references56

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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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            Distributed Lag Linear and Non-Linear Models in R: The Package dlnm.

            Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.
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              International study of temperature, heat and urban mortality: the 'ISOTHURM' project.

              This study describes heat- and cold-related mortality in 12 urban populations in low- and middle-income countries, thereby extending knowledge of how diverse populations, in non-OECD countries, respond to temperature extremes. The cities were: Delhi, Monterrey, Mexico City, Chiang Mai, Bangkok, Salvador, São Paulo, Santiago, Cape Town, Ljubljana, Bucharest and Sofia. For each city, daily mortality was examined in relation to ambient temperature using autoregressive Poisson models (2- to 5-year series) adjusted for season, relative humidity, air pollution, day of week and public holidays. Most cities showed a U-shaped temperature-mortality relationship, with clear evidence of increasing death rates at colder temperatures in all cities except Ljubljana, Salvador and Delhi and with increasing heat in all cities except Chiang Mai and Cape Town. Estimates of the temperature threshold below which cold-related mortality began to increase ranged from 15 degrees C to 29 degrees C; the threshold for heat-related deaths ranged from 16 degrees C to 31 degrees C. Heat thresholds were generally higher in cities with warmer climates, while cold thresholds were unrelated to climate. Urban populations, in diverse geographic settings, experience increases in mortality due to both high and low temperatures. The effects of heat and cold vary depending on climate and non-climate factors such as the population disease profile and age structure. Although such populations will undergo some adaptation to increasing temperatures, many are likely to have substantial vulnerability to climate change. Additional research is needed to elucidate vulnerability within populations.
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                Author and article information

                Contributors
                summer_1z@163.com
                18382032023@163.com
                380129089@qq.com
                4218011642@qq.com
                2530886006@qq.com
                15688415@qq.com
                460364510@qq.com
                24176113@qq.com
                1348924454@qq.com
                948295493@qq.com
                757951992@qq.com
                1040282504@qq.com
                aculacjy@163.net
                657096242@qq.com
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                21 January 2023
                21 January 2023
                2023
                : 23
                : 149
                Affiliations
                [1 ]GRID grid.419221.d, ISNI 0000 0004 7648 0872, Sichuan Provincial Center for Disease Control and Prevention, ; No.6, Zhongxue Road, Wuhou District, Chengdu, 610041 China
                [2 ]GRID grid.413856.d, ISNI 0000 0004 1799 3643, School of Public Health, , Chengdu Medical College, ; No.783, Xindu Road, Xindu District, Chengdu, 610500 China
                [3 ]GRID grid.507966.b, Chengdu Center for Disease Control and Prevention, ; No.6, Longxiang Road, Wuhou District, Chengdu, 610041 China
                [4 ]Zigong Center for Disease Control and Prevention, No.826, Huichuan Road, Ziliujing District, Zigong, 643000 China
                [5 ]Panzhi Hua Center for Disease Control and Prevention, Dong District, No.996, Jichang Road617067, Panzhi Hua, China
                [6 ]Guangyuan Center for Disease Control and Prevention, No.996, Binhebei RoadLizhou District, Guangyuan, 628017 China
                [7 ]Mianyang Center for Disease Control and Prevention, Gaoxin District, No.50, Mianxingdong Road, Mianyang, 621000 China
                [8 ]Yaan Center for Disease Control and Prevention, No.9, Fangcao Road, Yucheng District, Yaan, 625000 China
                Article
                14931
                10.1186/s12889-022-14931-x
                9863161
                36681785
                3a7b3697-3071-4502-aef7-075413dd99e7
                © 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
                : 3 August 2022
                : 22 December 2022
                Funding
                Funded by: Sichuan Provincial Cadre Health Care Research Project
                Award ID: 2021-1801
                Award ID: 2021-1801
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2023

                Public health
                temperature,mortality,distributed-lag nonlinear model
                Public health
                temperature, mortality, distributed-lag nonlinear model

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