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Qi X, Guo X, Han S, Xia X, Wang L, Li X. The effects of ambient temperature on non-accidental mortality in the elderly hypertensive subjects, a cohort-based study. BMC Geriatr 2024; 24:746. [PMID: 39251913 PMCID: PMC11382412 DOI: 10.1186/s12877-024-05333-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 08/26/2024] [Indexed: 09/11/2024] Open
Abstract
BACKGROUND The association between ambient temperature and mortality has yielded inconclusive results with previous studies relying on in-patient data to assess the health effects of temperature. Therefore, we aimed to estimate the effect of ambient temperature on non-accidental mortality among elderly hypertensive patients through a prospective cohort study conducted in northeastern China. METHODS A total of 9634 elderly hypertensive patients from the Kailuan research who participated in the baseline survey and follow-up from January 1, 2006 to December 31, 2017, were included in the study. We employed a Poisson generalized linear regression model to estimate the effects of monthly ambient temperature and temperature variations on non-accidental mortality. RESULTS After adjusting for meteorological parameters, the monthly mean temperature (RR = 0.989, 95% CI: 0.984-0.993, p < 0.001), minimum temperature (RR = 0.987, 95% CI: 0.983-0.992, p < 0.001) and maximum temperature (RR = 0.989, 95% CI: 0.985-0.994, p < 0.001) exhibited a negative association with an increased risk of non-accidental mortality. The presence of higher monthly temperature variation was significantly associated with an elevated risk of mortality (RR = 1.097, 95% CI:1.051-1.146, p < 0.001). Further stratified analysis revealed that these associations were more pronounced during colder months as well as among male and older individuals. CONCLUSIONS Decreased temperature and greater variations in ambient temperature were observed to be linked with non-accidental mortality among elderly hypertensive patients, particularly notable within aging populations and males. These understanding regarding the effects of ambient temperature on mortality holds clinical significance for appropriate treatment strategies targeting these individuals while also serving as an indicator for heightened risk of death.
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Affiliation(s)
- Xuemei Qi
- Department of Neurology, The Second Hospital of Tianjin Medical University, No. 23, Pingjiang Road, Tianjin, 300211, China
| | - Xiaobin Guo
- Department of Neurology, The Second Hospital of Tianjin Medical University, No. 23, Pingjiang Road, Tianjin, 300211, China
- Department of General Medicine, The Second Hospital of Tangshan, No. 21 Jianshe North Road, Tangshan, 063015, China
| | - Suqin Han
- Tianjin Environmental Meteorology Center, No. 100, Qixiang Tai Road, Tianjin, 300074, China
| | - Xiaoshuang Xia
- Department of Neurology, The Second Hospital of Tianjin Medical University, No. 23, Pingjiang Road, Tianjin, 300211, China
| | - Lin Wang
- Department of Geriatrics, The Second Hospital of Tianjin Medical University, No. 23, Pingjiang Road, Tianjin, 300211, China
| | - Xin Li
- Department of Neurology, The Second Hospital of Tianjin Medical University, No. 23, Pingjiang Road, Tianjin, 300211, China.
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Wen B, Wu Y, Guo Y, Gasparrini A, Tong S, Overcenco A, Urban A, Schneider A, Entezari A, Vicedo-Cabrera AM, Zanobetti A, Analitis A, Zeka A, Tobias A, Nunes B, Alahmad B, Armstrong B, Forsberg B, Pan SC, Íñiguez C, Ameling C, Valencia CDLC, Åström C, Houthuijs D, Van Dung D, Royé D, Indermitte E, Lavigne E, Mayvaneh F, Acquaotta F, de'Donato F, Rao S, Sera F, Carrasco-Escobar G, Kan H, Orru H, Kim H, Holobaca IH, Kyselý J, Madureira J, Schwartz J, Jaakkola JJK, Katsouyanni K, Diaz MH, Ragettli MS, Hashizume M, Pascal M, Coélho MDSZS, Ortega NV, Ryti N, Scovronick N, Michelozzi P, Matus Correa P, Goodman P, Saldiva PHN, Raz R, Abrutzky R, Osorio S, Dang TN, Colistro V, Huber V, Lee W, Seposo X, Honda Y, Kim Y, Guo YL, Bell ML, Li S. Comparison for the effects of different components of temperature variability on mortality: A multi-country time-series study. ENVIRONMENT INTERNATIONAL 2024; 187:108712. [PMID: 38714028 DOI: 10.1016/j.envint.2024.108712] [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: 11/23/2023] [Revised: 04/27/2024] [Accepted: 04/28/2024] [Indexed: 05/09/2024]
Abstract
BACKGROUND Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. OBJECTIVES We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. METHODS We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. RESULTS Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0-7 (0.9 °C). An IQR increase in inter-day TV0-7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0-7 and inter-day TV0-7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. CONCLUSIONS Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health.
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Affiliation(s)
- Bo Wen
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yao Wu
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
| | - Antonio Gasparrini
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change & Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Shilu Tong
- Shanghai Children's Medical Centre, Shanghai Jiao Tong University, Shanghai, China; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China; Center for Global Health, Nanjing Medical University, Nanjing, China; School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Ala Overcenco
- National Agency for Public Health of the Ministry of Health, Labour and Social Protection of the Republic of Moldova, Republic of Moldova
| | - Aleš Urban
- Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Alireza Entezari
- Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
| | - Ana Maria Vicedo-Cabrera
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Antonis Analitis
- Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, Greece
| | - Ariana Zeka
- Institute for Environment, Health and Societies, Brunel University London, London, UK
| | - Aurelio Tobias
- Institute of Environmental Assessment and Water Research, Spanish Council for Scientific Research, Barcelona, Spain; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Baltazar Nunes
- Department of Epidemiology, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal; Centro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Barrak Alahmad
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Ben Armstrong
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Bertil Forsberg
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Shih-Chun Pan
- National Institute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan
| | - Carmen Íñiguez
- Department of Statistics and Computational Research, Universitat de València, València, Spain; CIBER of Epidemiology and Public Health, Madrid, Spain
| | - Caroline Ameling
- National Institute for Public Health and the Environment (RIVM), Centre for Sustainability and Environmental Health, Bilthoven, Netherlands
| | | | - Christofer Åström
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Danny Houthuijs
- National Institute for Public Health and the Environment (RIVM), Centre for Sustainability and Environmental Health, Bilthoven, Netherlands
| | - Do Van Dung
- Department of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam
| | - Dominic Royé
- CIBER of Epidemiology and Public Health, Madrid, Spain; Department of Geography, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Ene Indermitte
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Eric Lavigne
- School of Epidemiology & Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada; Air Health Science Division, Health Canada, Ottawa, ON, Canada
| | - Fatemeh Mayvaneh
- Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
| | | | | | - Shilpa Rao
- Norwegian Institute of Public Health, Oslo, Norway
| | - Francesco Sera
- Department of Statistics, Computer Science and Applications "G. Parenti", University of Florence, Florence, Italy
| | - Gabriel Carrasco-Escobar
- Health Innovation Lab, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Haidong Kan
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China
| | - Hans Orru
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Ho Kim
- Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | | | - Jan Kyselý
- Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Joana Madureira
- Environmental Health Department, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal; EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jouni J K Jaakkola
- Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Klea Katsouyanni
- Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, Greece; School of Population Health and Environmental Sciences, King's College London, London, UK
| | - Magali Hurtado Diaz
- Department of Environmental Health, National Institute of Public Health, Cuernavaca Morelos, Mexico
| | - Martina S Ragettli
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Masahiro Hashizume
- Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mathilde Pascal
- Santé Publique France, Department of Environmental and Occupational Health, French National Public Health Agency, Saint Maurice, France
| | | | | | - Niilo Ryti
- Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Noah Scovronick
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | | | - Patrick Goodman
- School of Physics, Technological University Dublin, Dublin, Ireland
| | | | - Raanan Raz
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Israel
| | - Rosana Abrutzky
- Universidad de Buenos Aires, Facultad de Ciencias Sociales, Instituto de Investigaciones Gino Germani, Buenos Aires, Argentina
| | - Samuel Osorio
- Department of Environmental Health, University of São Paulo, São Paulo, Brazil
| | - Tran Ngoc Dang
- Department of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Viet Nam
| | - Valentina Colistro
- Department of Quantitative Methods, School of Medicine, University of the Republic, Montevideo, Uruguay
| | - Veronika Huber
- IBE-Chair of Epidemiology, LMU Munich, Munich, Germany; Department of Physical, Chemical and Natural Systems, Universidad Pablo de Olavide, Sevilla, Spain
| | - Whanhee Lee
- School of the Environment, Yale University, New Haven, CT, USA; Department of Occupational and Environmental Medicine, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Xerxes Seposo
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Yasushi Honda
- Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan
| | - Yoonhee Kim
- Department of Global Environmental Health, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Yue Leon Guo
- National Institute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan; Environmental and Occupational Medicine, National Taiwan University College of Medicine and NTU Hospital, National Taiwan University, Taipei, Taiwan; Graduate Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
| | - Shanshan Li
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
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Zhang F, Zhu S, Zhao D, Tang H, Ruan L, Zhu W. Ambient temperature variations and AIDS-related mortality: A time-stratified case-crossover study in 103 counties, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169474. [PMID: 38135089 DOI: 10.1016/j.scitotenv.2023.169474] [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: 09/30/2023] [Revised: 12/16/2023] [Accepted: 12/16/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND Climate change, characterized by the steady ascent of global temperatures and the escalating unpredictability of climate patterns, poses multifaceted challenges to public health worldwide. However, vulnerable groups, particularly the population affected by HIV/AIDS, have received little attention. OBJECTIVES We aimed to examine the impacts of temperature variations on AIDS-related mortality. METHODS Data on individuals with HIV/AIDS were collected from the HIV/AIDS Comprehensive Response Information Management System between 2013 and 2019. Temperature variation metrics were constructed by diurnal temperature range (DTR), temperature changes between neighboring days (TCN), and temperature variability (TV0-t). Time-stratified case-crossover design with conditional logistic regression models was used to investigate the associations between ambient temperature variations and AIDS-related mortality. RESULTS Each 1 °C elevated in DTR was linked with a 5.28 % [95 % confidence intervals (CIs): 1.61, 9.08] increment in AIDS-related mortality at a lag of 0-6 days. Stronger associations between DTR and AIDS-related mortality were observed in the married than in single, with corresponding excess ORs (%) of 5.33 (95 % CIs: 0.29, 10.62) versus 4.79 (95 % CIs: -0.50, 10.36) for 1 °C increased in DTR at lag 0-6 days. Additionally, we noticed the impact of DTR was more pronounced in the warm season, leading to a 7.32 % (95 % CIs: 0.57, 14.51) elevation in the risks of AIDS-related mortality for 1 °C increase in DTR at lag 0-6 days, while the effect value decreased to 5.16 % (95 % CIs: 0.71, 9.81) in the cold season. CONCLUSIONS Our findings indicated that DTR might be a significant risk factor for AIDS-related deaths among ambient temperature variation indicators, and underscored the importance of considering temperature variability in public health interventions aimed at mitigating this risk of AIDS-related mortality.
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Affiliation(s)
- Faxue Zhang
- Department of Occupational and Environmental Health, School of Public Health, Wuhan University, Wuhan 430071, China
| | - Shijie Zhu
- Department of Occupational and Environmental Health, School of Public Health, Wuhan University, Wuhan 430071, China
| | - Dingyuan Zhao
- Institute for the Prevention and Control of HIV/AIDS, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
| | - Heng Tang
- Institute for the Prevention and Control of HIV/AIDS, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
| | - Lianguo Ruan
- Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China; Hubei Clinical Research Center for Infectious Diseases, Wuhan 430023, China; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences, Wuhan 430023, China; Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan 430023, China
| | - Wei Zhu
- Department of Occupational and Environmental Health, School of Public Health, Wuhan University, Wuhan 430071, China.
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Tang S, Fu J, Liu Y, Zhao Y, Chen Y, Han Y, Zhao X, Liu Y, Jin X, Fan Z. Temperature fluctuation and acute myocardial infarction in Beijing: an extended analysis of temperature ranges and differences. Front Public Health 2023; 11:1287821. [PMID: 38146477 PMCID: PMC10749349 DOI: 10.3389/fpubh.2023.1287821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 11/17/2023] [Indexed: 12/27/2023] Open
Abstract
Purpose Few studies examined the relationship between temperature fluctuation metrics and acute myocardial infarction (AMI) hospitalizations within a single cohort. We aimed to expand knowledge on two basic measures: temperature range and difference. Methods We conducted a time-series analysis on the correlations between temperature range (TR), daily mean temperature differences (DTDmean), and daily mean-maximum/minimum temperature differences (TDmax/min) and AMI hospitalizations, using data between 2013 and 2016 in Beijing, China. The effects of TRn and DTDmeann over n-day intervals were compared, respectively. Subgroup analysis by age and sex was performed. Results A total of 81,029 AMI hospitalizations were included. TR1, TDmax, and TDmin were associated with AMI in J-shaped patterns. DTDmean1 was related to AMI in a U-shaped pattern. These correlations weakened for TR and DTDmean with longer exposure intervals. Extremely low (1st percentile) and high (5°C) DTDmean1 generated cumulative relative risk (CRR) of 2.73 (95% CI: 1.56-4.79) and 2.15 (95% CI: 1.54-3.01). Extremely high TR1, TDmax, and TDmin (99th percentile) correlated with CRR of 2.00 (95% CI: 1.73-2.85), 1.71 (95% CI: 1.40-2.09), and 2.73 (95% CI: 2.04-3.66), respectively. Those aged 20-64 had higher risks with large TR1, TDmax, and TDmin, while older individuals were more affected by negative DTDmean1. DTDmean1 was associated with a higher AMI risk in females. Conclusion Temperature fluctuations were linked to increased AMI hospitalizations, with low-temperature extremes having a more pronounced effect. Females and the older adult were more susceptible to daily mean temperature variations, while younger individuals were more affected by larger temperature ranges.
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Affiliation(s)
- Siqi Tang
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Jia Fu
- Department of Cardiology, Fuwai Yunnan Cardiovascular Hospital, Kunming, Yunnan, China
| | - Yanbo Liu
- Department of International Medical Services, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yakun Zhao
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yuxiong Chen
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yitao Han
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xinlong Zhao
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yijie Liu
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaofeng Jin
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhongjie Fan
- Department of Cardiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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Liu J, Du X, Yin P, Kan H, Zhou M, Chen R. Cause-specific mortality and burden attributable to temperature variability in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 896:165267. [PMID: 37406687 DOI: 10.1016/j.scitotenv.2023.165267] [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: 02/01/2023] [Revised: 06/23/2023] [Accepted: 06/30/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Few large-scale, nationwide studies have assessed cause-specific mortality risks and burdens associated with temperature variability (TV). OBJECTIVE To estimate associations between TV and cause-specific mortality and quantify the mortality burden in China. METHODS Data on daily total and cause-specific mortality in 272 Chinese cities between 2013 and 2015 were recorded. TVs were computed as the standard deviations of daily minimum and maximum temperatures over a duration of 2 to 7 days. The time-series quasi-Poisson regression model with adjustment of the cumulative effects of daily mean temperature over the same duration was applied to evaluate the city-specific associations of TV and mortality. Then, we pooled the effect estimates using a random-effects meta-analysis and calculated the mortality burdens. RESULTS Overall, TV showed significant and positive associations with total and cause-specific mortality. The TV-mortality associations were generally stronger when using longer durations. A 1 °C increase in TV at 0-7 days (TV0-7) was associated with a 0.79 % [95 % confidence interval (CI): 0.55 %, 0.96 %] increase in total mortality. Mortality fractions attributable to TV0-7 were 4.37 % for total causes, 4.75 % for overall cardiovascular disease, 4.37 % for coronary heart disease, 5.05 % for stroke, 8.28 % for ischaemic stroke, 1.08 % for haemorrhagic stroke, 6.93 % for respiratory disease, and 6.81 % for COPD, respectively. The mortality risk and burden were generally higher in the temperate monsoon zone, females, and elders. CONCLUSION This nationwide study indicated that TV was an independent risk factor of mortality, and could result in significant burden for main cardiorespiratory diseases.
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Affiliation(s)
- Jiangmei Liu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xihao Du
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National Health Commission (NHC) Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Peng Yin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National Health Commission (NHC) Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Maigeng Zhou
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National Health Commission (NHC) Key Lab of Health Technology Assessment, Fudan University, Shanghai, China; Shanghai Typhoon Institute/CMA, Shanghai Key Laboratory of Meteorology and Health, Shanghai, China.
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6
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Zheng S, Zhang X, Zhu W, Nie Y, Ke X, Liu S, Wang X, You J, Kang F, Bai Y, Wang M. A study of temperature variability on admissions and deaths for cardiovascular diseases in Northwestern China. BMC Public Health 2023; 23:1751. [PMID: 37684635 PMCID: PMC10486070 DOI: 10.1186/s12889-023-16650-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
OBJECTIVE To explore the effect of temperature variability (TV) on admissions and deaths for cardiovascular diseases (CVDs). METHOD The admissions data of CVDs were collected in 4 general hospitals in Jinchang City, Gansu Province from 2013 to 2016. The monitoring data of death for CVDs from 2013 to 2017 were collected through the Jinchang City Center for Disease Control and Prevention. Distributed lag nonlinear model (DLNM) was combined to analyze the effects of TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) on the admissions and deaths for CVDs after adjusting confounding effects. Stratified analysis was conducted by age and gender. Then the attribution risk of TV was evaluated. RESULTS There was a broadly linear correlation between TV and the admissions and deaths for CVDs, but only the association between TV and outpatient and emergency room (O&ER) visits for CVDs have statistically significant. DTV and HTV have similar lag effect. Every 1 ℃ increase in DTV and HTV was associated with a 3.61% (95% CI: 1.19% ~ 6.08%), 3.03% (95% CI: 0.27% ~ 5.86%) increase in O&ER visits for CVDs, respectively. There were 22.75% and 14.15% O&ER visits for CVDs can attribute to DTV and HTV exposure during 2013-2016. Males and the elderly may be more sensitive to the changes of TV. Greater effect of TV was observed in non-heating season than in heating season. CONCLUSION TV was an independent risk factor for the increase of O&ER visits for CVDs, suggesting effective guidance such as strengthening the timely prevention for vulnerable groups before or after exposure, which has important implications for risk management of CVDs.
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Affiliation(s)
- Shan Zheng
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
| | - Xiaofei Zhang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China
| | - Wenzhi Zhu
- Center for Immunological and Metabolic Diseases (CIMD), MED-X Institute, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yonghong Nie
- Jinchang Center for Disease Control and Prevention, Jinchang, 737100, China
| | - Ximeng Ke
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China
| | - Shaodong Liu
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China
| | - Xue Wang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China
| | - Jinlong You
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China
| | - Feng Kang
- Workers' Hospital of Jinchuan Group Co., Ltd, Jinchang, 737103, China
| | - Yana Bai
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China
| | - Minzhen Wang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 730000, China.
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Zhu W, Liu Y, Zhang L, Shi G, Zhang X, Wang M, Nie Y, Zhang D, Yin C, Bai Y, Zheng S. Ambient temperature variability and blood pressure in a prospective cohort of 50,000 Chinese adults. J Hum Hypertens 2023; 37:818-827. [PMID: 36257970 DOI: 10.1038/s41371-022-00768-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 09/19/2022] [Accepted: 10/05/2022] [Indexed: 11/09/2022]
Abstract
Blood pressure has been shown to change by outdoor temperature, but whether intra- and inter-day temperature variability (TV) will bring higher effect on BP is not clear. Based on a prospective cohort study, the mixed effect model was selected to estimate the relationship between TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) and BP (systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP)) after adjusting for confounding variables. We found that there was a positive linear correlation between TV and BP. The results of DTV and HTV were basically consistent, but the effect estimates of HTV seemed to be larger. Gender, age, BMI, education level and BP status may modify the relationship between TV and BP. The effect of TV on BP was greater in non-heating season than in heating season. Our work contributes to a further macro mechanism evidence for the TV-CVDs association.
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Affiliation(s)
- Wenzhi Zhu
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 73000, China
| | - Yanli Liu
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 73000, China
| | - Li Zhang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 73000, China
| | - Guoxiu Shi
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 73000, China
| | - Xiaofei Zhang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 73000, China
| | - Minzhen Wang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 73000, China
| | - Yonghong Nie
- Jinchang Center for Disease Prevention and Control, Jinchang, 737100, China
| | - Desheng Zhang
- Workers' Hospital of Jinchuan Group Co., Ltd, Jinchang, 737103, China
| | - Chun Yin
- Workers' Hospital of Jinchuan Group Co., Ltd, Jinchang, 737103, China
| | - Yana Bai
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 73000, China
| | - Shan Zheng
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou, 73000, China.
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8
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Healy JP, Danesh Yazdi M, Wei Y, Qiu X, Shtein A, Dominici F, Shi L, Schwartz JD. Seasonal Temperature Variability and Mortality in the Medicare Population. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:77002. [PMID: 37404028 PMCID: PMC10321237 DOI: 10.1289/ehp11588] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 04/17/2023] [Accepted: 06/12/2023] [Indexed: 07/06/2023]
Abstract
BACKGROUND Seasonal temperature variability remains understudied and may be modified by climate change. Most temperature-mortality studies examine short-term exposures using time-series data. These studies are limited by regional adaptation, short-term mortality displacement, and an inability to observe longer-term relationships in temperature and mortality. Seasonal temperature and cohort analyses allow the long-term effects of regional climatic change on mortality to be analyzed. OBJECTIVES We aimed to carry out one of the first investigations of seasonal temperature variability and mortality across the contiguous United States. We also investigated factors that modify this association. Using adapted quasi-experimental methods, we hoped to account for unobserved confounding and to investigate regional adaptation and acclimatization at the ZIP code level. METHODS We examined the mean and standard deviation (SD) of daily temperature in the warm (April-September) and cold (October-March) season in the Medicare cohort from 2000 to 2016. This cohort comprised 622,427,230 y of person-time in all adults over the age of 65 y from 2000 to 2016. We used daily mean temperature obtained from gridMET to develop yearly seasonal temperature variables for each ZIP code. We used an adapted difference-in-difference approach model with a three-tiered clustering approach and meta-analysis to observe the relationship between temperature variability and mortality within ZIP codes. Effect modification was assessed with stratified analyses by race and population density. RESULTS For every 1°C increase in the SD of warm and cold season temperature, the mortality rate increased by 1.54% [95% confidence interval (CI): 0.73%, 2.15%] and 0.69% (95% CI: 0.22%, 1.15%) respectively. We did not see significant effects for seasonal mean temperatures. Participants who were classified by Medicare into an "other" race group had smaller effects than those classified as White for Cold and Cold SD and areas with lower population density had larger effects for Warm SD. DISCUSSION Warm and cold season temperature variability were significantly associated with increased mortality rates in U.S. individuals over the age of 65 y, even after controlling for seasonal temperature averages. Warm and cold season mean temperatures showed null effects on mortality. Cold SD had a larger effect size for those who were in the racial subgroup other, whereas Warm SD was more harmful for those living in lower population density areas. This study adds to the growing calls for urgent climate mitigation and environmental health adaptation and resiliency. https://doi.org/10.1289/EHP11588.
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Affiliation(s)
- James P. Healy
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mahdieh Danesh Yazdi
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Yaguang Wei
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Xinye Qiu
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Alexandra Shtein
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Liuhua Shi
- Gangarosa Department of Environmental Health, Emory Rollins School of Public Health, Atlanta, Georgia, USA
| | - Joel D. Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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Wen B, Su BB, Xue J, Xie J, Wu Y, Chen L, Dong Y, Wu X, Wang M, Song Y, Ma J, Zheng X. Temperature variability and common diseases of the elderly in China: a national cross-sectional study. Environ Health 2023; 22:4. [PMID: 36609287 PMCID: PMC9824998 DOI: 10.1186/s12940-023-00959-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
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.
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Affiliation(s)
- Bo Wen
- 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
- 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
| | - Bin Bin Su
- 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
| | - Jiahui Xue
- First Clinical Medical College of Shanxi Medical University, No. 56 Xinjian South Road, Yingze District, Taiyuan City, 030001, Shanxi Province, China
| | - Junqing Xie
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Yao Wu
- 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
| | - Li Chen
- 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
| | - Yanhui Dong
- 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.
| | - Xiaolan Wu
- China Research Center on Ageing, 48 Guang 'anmen South Street, Xicheng District, Beijing, 100054, China
| | - Mengfan Wang
- University of Toronto, St.Geogre, 27 King's College Cir, Toronto, ON, M5S, Canada
| | - Yi Song
- 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
| | - Jun Ma
- 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
| | - Xiaoying Zheng
- 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.
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10
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Gong W, Li X, Zhou M, Zhou C, Xiao Y, Huang B, Lin L, Hu J, Xiao J, Zeng W, He G, Huang C, Liu T, Du Q, Ma W. Mortality burden attributable to temperature variability in China. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:118-124. [PMID: 35332279 PMCID: PMC8944404 DOI: 10.1038/s41370-022-00424-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 02/24/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Several studies have investigated the associations between temperature variability (TV) and death counts. However, evidence of TV-attributable years of life lost (YLL) is scarce. OBJECTIVES To investigate the associations between TV and YLL rates (/100,000 population), and quantify average life loss per death (LLD) caused by TV in China. METHODS We calculated daily YLL rates (/100,000 population) of non-accidental causes and cardiorespiratory diseases by using death data from 364 counties of China during 2006-2017, and collected meteorological data during the same period. A distributed lag non-linear model (DLNM) and multivariate meta-analysis were used to estimate the effects of TV at national or regional levels. Then, we calculated the LLD to quantify the mortality burden of TV. RESULTS U-shaped curves were observed in the associations of YLL rates with TV in China. The minimum YLL TV (MYTV) was 2.5 °C nationwide. An average of 0.89 LLD was attributable to TV in total, most of which was from high TV (0.86, 95% CI: 0.56, 1.16). However, TV caused more LLD in the young (<65 years old) (1.87, 95% CI: 1.03, 2.71) than 65-74 years old (0.85, 95% CI: 0.40-1.31) and ≥75 years old (0.40, 95% CI: 0.21-0.59), cerebrovascular disease (0.74, 95% CI: 0.36, 1.11) than respiratory disease (0.54, 95% CI: 0.21, 0.87), South (1.23, 95% CI: 0.77, 1.68) than North (0.41, 95% CI: -0.7, 1.52) and Central China (0.40, 95% CI: -0.02, 0.81). TV-attributed LLD was modified by annual mean temperature, annual mean relative humidity, altitude, latitude, longitude, and education attainment. SIGNIFICANCE Our findings indicate that high and low TVs are both associated with increases in premature death, however the majority of LLD was attributable to high TV. TV-related LLD was modified by county level characteristics. TV should be considered in planning adaptation to climate change or variability. IMPACT (1) We estimated the associations of TV with YLL rates, and quantified the life loss per death (LLD) caused by TV. (2) An average of 0.89 years of LLD were attributable to TV, most of which were from high TVs. (3) TV caused more LLD in the young, cerebrovascular disease, and southern China. (4) The mortality burdens were modified by county level characteristics.
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Affiliation(s)
- Weiwei Gong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, Zhejiang, China
| | - Xing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Maigeng Zhou
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, 100050, Beijing, China
| | - Chunliang Zhou
- Department of Environment and Health, Hunan Provincial Center for Disease Control and Prevention, Changsha, 450001, China
| | - Yize Xiao
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, China
| | - Biao Huang
- Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, China
| | - Lifeng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Weilin Zeng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Guanhao He
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Cunrui Huang
- School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Disease Control and Prevention Institute of Jinan University, Guangzhou, 510632, China.
| | - Qingfeng Du
- General Practice Center, The Seventh Affiliated Hospital, Southern Medical University, Foshan, 528200, China.
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, 510632, China
- Disease Control and Prevention Institute of Jinan University, Guangzhou, 510632, China
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11
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Liu H, Tong M, Guo F, Nie Q, Li J, Li P, Zhu T, Xue T. Deaths attributable to anomalous temperature: A generalizable metric for the health impact of global warming. ENVIRONMENT INTERNATIONAL 2022; 169:107520. [PMID: 36170754 DOI: 10.1016/j.envint.2022.107520] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 08/05/2022] [Accepted: 09/10/2022] [Indexed: 06/16/2023]
Abstract
The U-shaped association between health outcomes and ambient temperatures has been extensively investigated. However, such analyses cannot fully estimate the mortality burden of climate change because the features of the association (e.g., minimum mortality temperature) vary with human adaptation; thus, they are not generalizable to different locations. In this study, we assumed that humans could adapt to regular temperature variations; and thus examined the all-cause mortality attributable to temperature anomaly (TA), an indicator widely utilized in climate science to measure irregular temperature fluctuations, across 115 cities in the United States (US). We first used quasi-Poisson regressions to obtain the city-specific TA-mortality associations, then used meta-regression to pool these city-specific estimates. Finally, we calculated the number of TA-related deaths using the uniform pooled association, then compared it to the estimates from city-specific associations, which had been controlled for adaptation. Meta-regression showed a U-shaped TA-mortality association, centered at a TA near 0. According to the pooled association, 0.579 % (95 % confidence interval [CI]: 0.465-0.681 %), 0.394 % (95 % CI: 0.332-0.451 %), and 0.185 % (95 % CI: 0.107-0.254 %) of all-cause deaths were attributable to all anomalous temperatures (TA ≠ 0), anomalous heat (TA > 0), and anomalous cold (TA < 0), respectively. At the city level, heat-related deaths estimated from the pooled association were in good agreement with heat-related deaths estimated from the city-specific associations (R2 = 0.84). However, the cold-related deaths estimated from the two methods showed a weaker correlation (R2 = 0.07). Our findings suggest that TA constitutes a generalizable indicator that can uniformly evaluate deaths attributable to anomalous heat in distinct geographical locations.
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Affiliation(s)
- Hengyi Liu
- Institute of Reproductive and Child Health / National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Mingkun Tong
- Institute of Reproductive and Child Health / National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Fuyu Guo
- Institute of Reproductive and Child Health / National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Qiyue Nie
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Jiwei Li
- School of Computer Science, Zhejiang University, Hangzhou, China
| | - Pengfei Li
- Institute of Reproductive and Child Health / National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China; Advanced Institute of Information Technology, Peking University, Hangzhou, China
| | - Tong Zhu
- College of Environmental Sciences and Engineering, Peking University, Beijing, China
| | - Tao Xue
- Institute of Reproductive and Child Health / National Health Commission Key Laboratory of Reproductive Health and Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Centre, Beijing, China.
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12
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Huang K, Hu CY, Yang XY, Zhang Y, Wang XQ, Zhang KD, Li YQ, Wang J, Yu WJ, Cheng X, Cao JY, Zhang T, Kan XH, Zhang XJ. Contributions of ambient temperature and relative humidity to the risk of tuberculosis admissions: A multicity study in Central China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:156272. [PMID: 35644395 DOI: 10.1016/j.scitotenv.2022.156272] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/08/2022] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND As a communicable disease and major public health issue, many studies have quantified the associations between tuberculosis (TB) and meteorological factors with inconsistent results. The purpose of this multicenter study was to characterize the associations between ambient temperature, humidity and the risk of TB hospitalizations and to investigate potential heterogeneity. METHOD Data on daily hospitalizations for TB, meteorological factors and ambient air pollutants for 16 cities in Anhui Province were collected from 2015 to 2020. A distributed lag nonlinear model (DLNM) was performed to obtain the estimates of meteorological-TB relationships by cities. Then, we used the multivariate meta-regression model to pool the city-specific estimates with air pollution, demographic indicators, medical resource and latitude as potential modifiers to explore the sources of heterogeneity. Finally, we divided the whole province into three regions to validate the meteorological-TB relationships by regions. RESULTS The overall pooled temperature-TB association presented an approximate S-shaped curve, with relative risk (RR) peaking at 5 °C (RR = 1.536, 95% CI: 1.303-1.811) compared to the reference temperature (27 °C). Lag-response curve suggested that low temperature exposure increased the risk of TB hospitalizations at lag 0 and 1 day (lag0 day: RR = 1.136, 95% CI: 1.048-1.231, lag1 day: RR = 1.052, 95% CI: 1.023-1.082). However, the overall exposure-response curve between relative humidity and TB showed almost horizontal line with reference relative humidity to 78%. The residual heterogeneity ranged from 27.1% to 36.9%, with air pollution, latitude and medical resource explained the largest proportion. CONCLUSION We found that low temperature exposure is associated with an acute increased risk of TB hospitalizations in Anhui Province. The association between temperature and TB admission varies depending on air pollution, latitude, and medical resources. Since the effect of short-term exposure to humidity is not significant, further studies are supposed to focus on the long-term effect of humidity.
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Affiliation(s)
- Kai Huang
- Department of Hospital Infection Prevention and Control, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Cheng-Yang Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Xi-Yao Yang
- Department of Hospital Infection Prevention and Control, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Xin-Qiang Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Kang-Di Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Ying-Qing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Jie Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Wen-Jie Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Xin Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Ji-Yu Cao
- Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, Anhui, China
| | - Tao Zhang
- Anhui Chest Hospital, 397 Jixi Road, Hefei 230022, China
| | - Xiao-Hong Kan
- Anhui Chest Hospital, 397 Jixi Road, Hefei 230022, China; Anhui Medical University Clinical College of Chest, 397 Jixi Road, Hefei 230022, China.
| | - Xiu-Jun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
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13
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Zhou CL, Lv LS, Jin DH, Xie YJ, Ma WJ, Hu JX, Wang CE, Xu YQ, Zhang XE, Lu C. Temperature Change between Neighboring Days Contributes to Years of Life Lost per Death from Respiratory Disease: A Multicounty Analysis in Central China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105871. [PMID: 35627408 PMCID: PMC9141323 DOI: 10.3390/ijerph19105871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/06/2022] [Accepted: 05/07/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Many epidemiological studies have recently assessed respiratory mortality attributable to ambient temperatures. However, the associations between temperature change between neighboring days and years of life lost are insufficiently studied. Therefore, we assessed the attributable risk of temperature change between neighboring days on life loss due to respiratory disease. METHODS We obtained daily mortality and weather data and calculated crude rates of years of life lost for 70 counties in Hunan Province, Central China, from 2013 to 2017. A time-series design with distributed lag nonlinear model and multivariate meta-regression was used to pool the relationships between temperature change between neighboring days and rates of years of life lost. Then, we calculated the temperature change between neighboring days related to average life loss per death from respiratory disease. RESULTS The total respiratory disease death was 173,252 during the study period. The association between temperature change and years of life lost rates showed a w-shape. The life loss per death attributable to temperature change between neighboring days was 2.29 (95% CI: 0.46-4.11) years, out of which 1.16 (95% CI: 0.31-2.01) years were attributable to moderately high-temperature change between neighboring days, and 0.99 (95% CI: 0.19-1.79) years were attributable to moderately low-temperature change between neighboring days. The temperature change between neighboring days related to life loss per respiratory disease death for females (2.58 years, 95% CI: 0.22-4.93) and the younger group (2.97 years, 95% CI: -1.51-7.44) was higher than that for males (2.21 years, 95% CI: 0.26-4.16) and the elderly group (1.96 years, 95% CI: 0.85-3.08). An average of 1.79 (95% CI: 0.18-3.41) life loss per respiratory disease death was related to non-optimal ambient temperature. CONCLUSIONS The results indicated that more attention should be given to temperature change, and more public health policies should be implemented to protect public health.
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Affiliation(s)
- Chun-Liang Zhou
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Ling-Shuang Lv
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
- Correspondence: (L.-S.L.); (C.L.)
| | - Dong-Hui Jin
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Yi-Jun Xie
- Hunan Provincial Climate Center, Changsha 410007, China;
| | - Wen-Jun Ma
- School of Medicine, Jinan University, Guangzhou 510632, China;
| | - Jian-Xiong Hu
- Guangdong Provincial Institute of Public Health, Guangzhou 511430, China;
| | - Chun-E Wang
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Yi-Qing Xu
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Xing-E Zhang
- Hunan Provincial Center for Disease Control and Prevention, Changsha 410005, China; (C.-L.Z.); (D.-H.J.); (C.-E.W.); (Y.-Q.X.); (X.-E.Z.)
| | - Chan Lu
- XiangYa School of Public Health, Central South University, Changsha 410078, China
- Correspondence: (L.-S.L.); (C.L.)
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Yan S, Wang X, Yao Z, Cheng J, Ni H, Xu Z, Wei Q, Pan R, Yi W, Jin X, Tang C, Liu X, He Y, Wu Y, Li Y, Sun X, Liang Y, Mei L, Su H. Seasonal characteristics of temperature variability impacts on childhood asthma hospitalization in Hefei, China: Does PM 2.5 modify the association? ENVIRONMENTAL RESEARCH 2022; 207:112078. [PMID: 34599899 DOI: 10.1016/j.envres.2021.112078] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 09/06/2021] [Accepted: 09/14/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES Evidence of childhood asthma hospitalizations associated with temperature variability (TV) and the attributable risk are limited in China. We aim to use a comprehensive index that reflected both intra- and inter-day TV to assess the TV-childhood asthma relationship and disease burden, further to identify seasonality vulnerable populations, and to explore the effect modification of PM2.5. METHODS A quasi-distributed lagged nonlinear model (DLNM) combined with a linear threshold function was applied to estimate the association between TV and childhood asthma hospitalizations during 2013-2016 in Hefei, China. Subgroup analysis was conducted by age and sex. Disease burden is reflected by the attributable fraction and attributable number. Besides, modifications of PM2.5 were tested by introducing the cross-basis of TV and binary PM2.5 as an interaction term. RESULTS The risk estimates peaked at TV0-3 and TV0-4 in the cool and the warm season separately, with RR of 1.051 (95%CI: 1.021-1.081) and 1.072 (95%CI: 1.008-1.125), and the effects lasted longer in the cool season. The school-age children in the warm season and all subgroups except pre-school children in the cool season were vulnerable to TV. It is estimated that the disease burden related to TV account for 6.2% (95% CI: 2.7%-9.4%) and 4% (95% CI: 0.6%-7.1%) during the cool and warm seasons in TV0-3. In addition, the risks of TV were higher under the high PM2.5 level compared with the low PM2.5 level in the cool season, although no significant differences between them. CONCLUSIONS TV exposure significantly increases the risk and disease burden of childhood asthma hospitalizations, especially in the cool season. More medical resources should be allocated to school-age children. Giving priority to pay attention to TV in the cool season in practice could obtain the greatest public health benefits and those days with high TV and high PM2.5 need more attention.
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Affiliation(s)
- Shuangshuang Yan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Xu Wang
- Anhui Provincial Children's Hospital, China
| | - Zhenhai Yao
- Anhui Public Meteorological Service Center, Hefei, Anhui, 230011, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Hong Ni
- Anhui Provincial Children's Hospital, China
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Australia
| | - Qiannan Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Xiaoyu Jin
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Chao Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Xiangguo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Yangyang He
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Yudong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Yuxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Xiaoni Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Yunfeng Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Lu Mei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China; Anhui Province Key Laboratory of Major Autoimmune Disease, China.
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15
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Wu Y, Li S, Zhao Q, Wen B, Gasparrini A, Tong S, Overcenco A, Urban A, Schneider A, Entezari A, Vicedo-Cabrera AM, Zanobetti A, Analitis A, Zeka A, Tobias A, Nunes B, Alahmad B, Armstrong B, Forsberg B, Pan SC, Íñiguez C, Ameling C, De la Cruz Valencia C, Åström C, Houthuijs D, Van Dung D, Royé D, Indermitte E, Lavigne E, Mayvaneh F, Acquaotta F, de'Donato F, Rao S, Sera F, Carrasco-Escobar G, Kan H, Orru H, Kim H, Holobaca IH, Kyselý J, Madureira J, Schwartz J, Jaakkola JJK, Katsouyanni K, Hurtado Diaz M, Ragettli MS, Hashizume M, Pascal M, de Sousa Zanotti Stagliorio Coélho M, Ortega NV, Ryti N, Scovronick N, Michelozzi P, Correa PM, Goodman P, Nascimento Saldiva PH, Abrutzky R, Osorio S, Dang TN, Colistro V, Huber V, Lee W, Seposo X, Honda Y, Guo YL, Bell ML, Guo Y. Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study. Lancet Planet Health 2022; 6:e410-e421. [PMID: 35550080 PMCID: PMC9177161 DOI: 10.1016/s2542-5196(22)00073-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 05/08/2023]
Abstract
BACKGROUND Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5° × 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19. METHODS In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5° × 0·5° from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. FINDINGS An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2-4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7-5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3-10·4), followed by Europe (4·4%, 2·2-5·6) and Africa (3·3, 1·9-4·6). INTERPRETATION Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability. FUNDING Australian Research Council, Australian National Health & Medical Research Council.
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Affiliation(s)
- Yao Wu
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Qi Zhao
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bo Wen
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Antonio Gasparrini
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London, UK; Centre on Climate Change & Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Shilu Tong
- Shanghai Children's Medical Centre, Shanghai Jiao Tong University, Shanghai, China; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China; Center for Global Health, Nanjing Medical University, Nanjing, China; School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ala Overcenco
- National Agency for Public Health of the Ministry of Health, Labour and Social Protection of the Republic of Moldova, Chișinău, Moldova
| | - Aleš Urban
- Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Alireza Entezari
- Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
| | - Ana Maria Vicedo-Cabrera
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Oeschger Center for Climate Change Research, University of Bern, Bern, Switzerland
| | - Antonella Zanobetti
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Antonis Analitis
- Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, Greece
| | - Ariana Zeka
- Institute for Environment, Health and Societies, Brunel University London, London, UK
| | - Aurelio Tobias
- Institute of Environmental Assessment and Water Research, Spanish Council for Scientific Research, Barcelona, Spain; School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Baltazar Nunes
- Department of Epidemiology, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal; Centro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Barrak Alahmad
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Ben Armstrong
- Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London, UK
| | - Bertil Forsberg
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Shih-Chun Pan
- NationalInstitute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan
| | - Carmen Íñiguez
- Department of Statistics and Computational Research, Universitat de València, València, Spain; CIBER of Epidemiology and Public Health, Madrid, Spain
| | - Caroline Ameling
- National Institute for Public Health and the Environment (RIVM), Centre for Sustainability and Environmental Health, Bilthoven, Netherlands
| | | | - Christofer Åström
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Danny Houthuijs
- National Institute for Public Health and the Environment (RIVM), Centre for Sustainability and Environmental Health, Bilthoven, Netherlands
| | - Do Van Dung
- Department of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Dominic Royé
- CIBER of Epidemiology and Public Health, Madrid, Spain; Department of Geography, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Ene Indermitte
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Eric Lavigne
- School of Epidemiology & Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada; Air Health Science Division, Health Canada, Ottawa, ON, Canada
| | - Fatemeh Mayvaneh
- Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
| | | | | | - Shilpa Rao
- Norwegian Institute of Public Health, Oslo, Norway
| | - Francesco Sera
- Department of Statistics, Computer Science and Applications "G Parenti", University of Florence, Florence, Italy
| | - Gabriel Carrasco-Escobar
- Health Innovation Lab, Institute of Tropical Medicine "Alexander von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Haidong Kan
- Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China
| | - Hans Orru
- Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia
| | - Ho Kim
- Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | | | - Jan Kyselý
- Institute of Atmospheric Physics, Czech Academy of Sciences, Prague, Czech Republic; Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech Republic
| | - Joana Madureira
- Environmental Health Department, Instituto Nacional de Saúde Dr Ricardo Jorge, Porto, Portugal; EPIUnit-Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal; Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), Porto, Portugal
| | - Joel Schwartz
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jouni J K Jaakkola
- Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Klea Katsouyanni
- Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, Greece; School of Population Health and Environmental Sciences, King's College London, London, UK
| | - Magali Hurtado Diaz
- Department of Environmental Health, National Institute of Public Health, Cuernavaca Morelos, Mexico
| | - Martina S Ragettli
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Masahiro Hashizume
- Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mathilde Pascal
- Santé Publique France, Department of Environmental and Occupational Health, French National Public Health Agency, Saint Maurice, France
| | | | | | - Niilo Ryti
- Center for Environmental and Respiratory Health Research (CERH), University of Oulu, Oulu, Finland; Medical Research Center Oulu (MRC Oulu), Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Noah Scovronick
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | | | - Patrick Goodman
- School of Physics, Technological University Dublin, Dublin, Ireland
| | | | - Rosana Abrutzky
- Universidad de Buenos Aires, Facultad de Ciencias Sociales, Instituto de Investigaciones Gino Germani, Buenos Aires, Argentina
| | - Samuel Osorio
- Department of Environmental Health, University of São Paulo, São Paulo, Brazil
| | - Tran Ngoc Dang
- Department of Environmental Health, Faculty of Public Health, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Valentina Colistro
- Department of Quantitative Methods, School of Medicine, University of the Republic, Montevideo, Uruguay
| | - Veronika Huber
- IBE-Chair of Epidemiology, LMU Munich, Munich, Germany; Department of Physical, Chemical and Natural Systems, Universidad Pablo de Olavide, Sevilla, Spain
| | - Whanhee Lee
- School of the Environment, Yale University, New Haven, CT, USA; Department of Occupational and Environmental Medicine, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Xerxes Seposo
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Yasushi Honda
- Center for Climate Change Adaptation, National Institute for Environmental Studies, Tsukuba, Japan
| | - Yue Leon Guo
- NationalInstitute of Environmental Health Science, National Health Research Institutes, Zhunan, Taiwan; Environmental and Occupational Medicine, National Taiwan University College of Medicine and NTU Hospital, National Taiwan University, Taipei, Taiwan; Graduate Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Michelle L Bell
- School of the Environment, Yale University, New Haven, CT, USA
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
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16
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Yu Y, Luo S, Zhang Y, Liu L, Wang K, Hong L, Wang Q. Comparative analysis of daily and hourly temperature variability in association with all-cause and cardiorespiratory mortality in 45 US cities. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:11625-11633. [PMID: 34537946 DOI: 10.1007/s11356-021-16476-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/05/2021] [Indexed: 06/13/2023]
Abstract
Temperature variability (TV) has been widely associated with increased mortality risk and burden. Extensive researches have used the standard deviations of several days' daily maximum and minimum temperatures or hourly mean temperatures as daily and hourly TV measures (TVdaily and TVhourly). However, comparative analysis of daily and hourly TV related to cardiorespiratory mortality is still limited. We collected daily mortality and meteorological data in 45 US metropolises, 1987-2000. A three-stage analysis was adopted to investigate TV-mortality associations using TVdaily and TVhourly as exposure metrics. We first applied a time-series quasi-Poisson regression to estimate location-specific TV-mortality relationships, which were then pooled using random-effects meta-analysis with maximum likelihood estimation. We additionally calculated attributable fraction (AF) as a reflection of mortality burden associated with TV. Stratified analyses by age were also performed to identify the susceptible group to TV-related risks. There were a total of 15.4 million all-cause deaths, of which 6.1 million were from cardiovascular causes and 1.2 million were from respiratory causes. Per 1 °C increase in TVdaily and TVhourly was associated with an increase of 0.53% (95% confidence interval: 0.31-0.76%) and 0.52% (0.26-0.79%) in cardiovascular mortality risks, 0.62% (0.26-0.98%) and 0.53% (0.13-0.94%) in respiratory mortality risks. Estimates of cardiovascular AF for TVdaily and TVhourly were 2.43% (1.42-3.43%) vs. 1.63% (0.82-2.43%), whereas estimates of respiratory AF were 3.07% (1.11-4.99%) vs. 1.89% (0.43-3.34%). Both daily and hourly TV indexes showed approximately linear relationships with different mortality categories and similar lag patterns, but greater fractions were estimated using TVdaily than those using TVhourly. People over 75 years old were relatively more vulnerable to TV-induced risks of mortality. In conclusion, both TVdaily and TVhourly significantly increased all-cause and cardiorespiratory mortality risks and burden. Daily and hourly TV metrics exhibited comparable effects of mortality risk, while greater mortality burden was estimated using TVdaily than TVhourly. Our findings may add significance to TV-mortality research and help to promote optimal health management strategies to better mitigate TV-related health effects.
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Affiliation(s)
- Yong Yu
- School of Public Health, Hubei University of Medicine, Shiyan, 442000, China
| | - Siqi Luo
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Linjiong Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Ke Wang
- Department of Nursing, Medical College, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Le Hong
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Qun Wang
- School of Public Health, Hubei University of Medicine, Shiyan, 442000, China.
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17
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Tang C, Ji Y, Li Q, Yao Z, Cheng J, He Y, Liu X, Pan R, Wei Q, Yi W, Su H. Effects of different heat exposure patterns (accumulated and transient) and schizophrenia hospitalizations: a time-series analysis on hourly temperature basis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:69160-69170. [PMID: 34286435 DOI: 10.1007/s11356-021-15371-7] [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: 05/21/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
Growing studies have shown that high temperature is a potential risk factor of schizophrenia occurrence. Therefore, elaborate analysis of different temperature exposure patterns, such as cumulative heat exposure within a time period and transient exposure at a particular time point, is of important public health significance. This study aims to utilize hourly temperature data to better capture the effects of cumulative and transient heat exposures on schizophrenia during the warm season in Hefei, China. We included the daily mean temperature and daily schizophrenia hospitalizations into the distributed lag non-linear model (DLNM) to simulate the exposure-response curve and determine the heat threshold (19.4 °C). We calculated and applied a novel indicator-daily excess hourly heat (DEHH)-to examine the effects of cumulative heat exposure over a day on schizophrenia hospitalizations. Temperature measurements at each time point were also incorporated in the DLNM as independent exposure indicators to analyze the impact of transient heat exposure on schizophrenia. Each increment of interquartile range (IQR) in DEHH was associated with elevated risk of schizophrenia hospitalizations from lag 1 (RR = 1.036, 95% confidence interval (CI): 1.016, 1.057) to lag 4 (RR = 1.025, 95% CI: 1.005, 1.046). Men and people over 40 years old were more susceptible to DEHH. Besides, we found a greater risk of heat-related schizophrenia hospitalizations between 0 a.m. and 6 a.m. This study revealed the adverse effects of accumulated and transient heat exposures on schizophrenia hospitalizations. Our findings need to be further tested in other regions with distinct regional features.
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Affiliation(s)
- Chao Tang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Yifu Ji
- Anhui Mental Health Center, Hefei, 230032, Anhui, China
| | - Qingru Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Zhenhai Yao
- Anhui Public Meteorological Service Center, Hefei, 230011, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Yangyang He
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Xiangguo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Qiannan Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
- Anhui Provincial Laboratory of Inflammatory and Immune Diseases, Hefei, 230032, Anhui, China.
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18
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Yang Z, Yang J, Zhou M, Yin P, Chen Z, Zhao Q, Hu K, Liu Q, Ou CQ. Hourly temperature variability and mortality in 31 major Chinese cities: Effect modification by individual characteristics, season and temperature zone. ENVIRONMENT INTERNATIONAL 2021; 156:106746. [PMID: 34247007 DOI: 10.1016/j.envint.2021.106746] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 06/27/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND In the context of ongoing climate change, temperature variability (TV) has been considered as an important trigger of death. However, evidence of association between mortality and hourly temperature variability (HTV) is scarce at the multi-city level, and the time window of health effects of HTV is lack of investigation. This study aims at quantifying the mortality risk and burden of HTV and exploring subpopulations susceptible to HTV from a large-scale multi-city perspective. METHODS Data on daily number of deaths and meteorology were collected for 31 Chinese major cities during 2007-2013. HTV was calculated as the standard deviation of hourly temperature within a few days. The optimal exposure period of HTV was chosen according to multiple scientific criteria. A quasi-Poisson regression combined with distributed lag nonlinear model was used to assess the city-specific HTV-mortality associations. Then, meta-analysis was further applied to pool city-specific effect estimates. Finally, we calculated the fraction of mortality attributable to HTV. Stratification analyses were conducted by individual characteristics (i.e. age, sex, and educational attainment), season, and region. RESULTS HTV calculated in a relatively long-time window like 18 d (HTV0-17) could capture the impact of HTV adequately. Per 1 °C raise of HTV0-17 associated with 1.38% (95%CI: 0.77, 1.99) increase of non-accidental mortality. During the study period, 5.47% (95%CI: 1.06, 9.64) of non-accidental mortality could be attributed to HTV. The females, the elderly, and individuals with low education level were more susceptible to HTV than their counterparts, respectively. Moreover, a stronger HTV-mortality association was observed in individuals who live in warmer season and temperature zone. CONCLUSION HTV is associated with a considerable mortality burden, which may be modified by season, geographic and individual-level factors. Our findings highlight the practical importance of establishing early warning systems and promoting health education to mitigate the impacts of temperature variability.
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Affiliation(s)
- Zhou Yang
- 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 510515, China
| | - Jun Yang
- Institute for Environmental and Climate Research, Jinan University, Guangzhou 511443, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Guangzhou 511443, China; JNU-QUT Joint Laboratory for Air Quality Science and Management, Jinan University, Guangzhou 511443, China.
| | - Maigeng Zhou
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing 100050, China
| | - Peng Yin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing 100050, China
| | - Zhaoyue Chen
- 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 510515, China
| | - Qi Zhao
- Department of Epidemiology, Shandong University, Jinan, China
| | - Kejia Hu
- Institute of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, 310058, China
| | - Qiyong Liu
- 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 102206, China.
| | - Chun-Quan Ou
- 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 510515, China.
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McDermott-Levy R, Scolio M, Shakya KM, Moore CH. Factors That Influence Climate Change-Related Mortality in the United States: An Integrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18158220. [PMID: 34360518 PMCID: PMC8345936 DOI: 10.3390/ijerph18158220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/23/2021] [Accepted: 07/29/2021] [Indexed: 12/02/2022]
Abstract
Global atmospheric warming leads to climate change that results in a cascade of events affecting human mortality directly and indirectly. The factors that influence climate change-related mortality within the peer-reviewed literature were examined using Whittemore and Knafl’s framework for an integrative review. Ninety-eight articles were included in the review from three databases—PubMed, Web of Science, and Scopus—with literature filtered by date, country, and keywords. Articles included in the review address human mortality related to climate change. The review yielded two broad themes in the literature that addressed the factors that influence climate change-related mortality. The broad themes are environmental changes, and social and demographic factors. The meteorological impacts of climate change yield a complex cascade of environmental and weather events that affect ambient temperatures, air quality, drought, wildfires, precipitation, and vector-, food-, and water-borne pathogens. The identified social and demographic factors were related to the social determinants of health. The environmental changes from climate change amplify the existing health determinants that influence mortality within the United States. Mortality data, national weather and natural disaster data, electronic medical records, and health care provider use of International Classification of Disease (ICD) 10 codes must be linked to identify climate change events to capture the full extent of climate change upon population health.
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Affiliation(s)
- Ruth McDermott-Levy
- M. Louise Fitzpatrick College of Nursing, Villanova University, Villanova, PA 19085, USA
- Correspondence:
| | - Madeline Scolio
- Department of Geography and the Environment, Villanova University, Villanova, PA 19085, USA; (M.S.); (K.M.S.)
| | - Kabindra M. Shakya
- Department of Geography and the Environment, Villanova University, Villanova, PA 19085, USA; (M.S.); (K.M.S.)
| | - Caroline H. Moore
- Georgia Baptist College of Nursing, Mercer University, Atlanta, GA 30341, USA;
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Ma P, Zhang Y, Wang X, Fan X, Chen L, Hu Q, Wang S, Li T. Effect of diurnal temperature change on cardiovascular risks differed under opposite temperature trends. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:39882-39891. [PMID: 33768454 DOI: 10.1007/s11356-021-13583-5] [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: 12/08/2020] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
Temperature change between neighboring days (TCN) is an important trigger for cardiovascular diseases, but the modulated effects by seasonal temperature trends have been barely taken into account. A quantified comparison between impacts of positive TCNs (temperature rise) and negative situations (temperature drop) is also needed. We evaluated the associations of TCNs with emergency room (ER) visits for coronary heart disease (CHD) and cerebral infarction (CI) in Beijing, China, from 2008 to 2012. A year was divided into two segments dominated by opposite temperature trends, quasi-Poisson regression with distributed lag nonlinear models estimating TCN-morbidity relations were employed, separately for each period. High morbidities of CHD and CI both occurred in transitional seasons accompanied by large TCNs. Under warming backgrounds, positive TCNs increased CHD risk in patients younger than 65 years, and old people showed limited sensitivity. In the cooling periods, negative TCNs induced CHD risk in females and the elderly; the highest RR showed on lag 6 d. In particular, a same diurnal temperature decrease (e.g., - 2°C) induced greater RR (RR = 1.113, 95% CIs: 1.033-1.198) on old people during warming periods than cooling counterparts (RR = 1.055, 95% CIs: 1.011-1.100). Moreover, positive TCNs elevated CI risk regardless of background temperatures, and males were particularly vulnerable. Seasonal temperature trends modify TCN-cardiovascular morbidity associations significantly, which may provide new insights into the health impact of unstable weathers.
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Affiliation(s)
- Pan Ma
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China.
| | - Ying Zhang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Xinzi Wang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Xingang Fan
- Department of Geography and Geology, Western Kentucky University, Bowling Green, KY, 42101, USA
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Lei Chen
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Qin Hu
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Shigong Wang
- Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Science, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China
| | - Tanshi Li
- Chinese PLA General Hospital, Beijing, 100000, China
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Wu W, Chen B, Wu G, Wan Y, Zhou Q, Zhang H, Zhang J. Increased susceptibility to temperature variation for non-accidental emergency ambulance dispatches in Shenzhen, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:32046-32056. [PMID: 33624238 DOI: 10.1007/s11356-021-12942-6] [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: 10/30/2020] [Accepted: 02/09/2021] [Indexed: 06/12/2023]
Abstract
Most studies focused on the temporal trend of mortality risk associated with temperature exposure. The relative role of heat, cold, and temperature variation (TV) on morbidity and its temporal trends are explored insufficiently. This study aims to investigate the temporal trends of emergency ambulance dispatch (EAD) risk and the attributable burden of heat, cold, and hourly temperature variation (HTV). We collected time-series data of daily EAD and ambient temperature in Shenzhen from 2010 to 2017. HTV was calculated as the standard deviation of the hourly temperatures between 2 consecutive days. Quasi-Poisson generalized additive models (GAM) with a time-varying distributed lag nonlinear model (DLNM) were applied to examine temporal trends of the HTV-, heat-, and cold-EAD association. The temporal variation of the attributable fraction (AF%) and attributable number (AN) for different temperature exposures was also calculated. The largest RR was observed in extreme cold [1.30 (95% CI: 1.18, 1.43)] and moderate cold [1.25 (95% CI: 1.17, 1.34)]. Significant increasing trends in HTV-related effects and burden were observed, especially for the extreme HTV effects (P for interaction < 0.05). Decreasing trends were observed in the heat-related effect and burden, though it showed no significance (P for interaction = 0.46). There was no clear change pattern of cold-related effects and burdens. Overall, the three temperature exposure caused 13.7% of EAD, of which 4.1%, 4.3%, and 5.3% were attributed to HTV, heat, and cold, respectively. All the temperature indexes in this study, especially the cold effect, are responsible for the increased risk of EAD. People have become more susceptible to HTV over the recent decade. However, there is no clear evidence to support the temporal change of the population's susceptibility to heat and cold. Thus, in addition to heat and cold, the emergency ambulance service department should pay more attention to HTV under climate change.
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Affiliation(s)
- Wenjing Wu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Section 3, Renmin South Road, Chengdu, 610044, China
| | - Bo Chen
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Section 3, Renmin South Road, Chengdu, 610044, China
| | - Gonghua Wu
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Yunying Wan
- Shenzhen Emergency Medical Center, Shenzhen, 518035, China
| | - Qiang Zhou
- Shenzhen Emergency Medical Center, Shenzhen, 518035, China
| | - Hua Zhang
- Shenzhen Emergency Medical Center, Shenzhen, 518035, China
| | - Juying Zhang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Section 3, Renmin South Road, Chengdu, 610044, China.
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22
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Rowland ST, Parks RM, Boehme AK, Goldsmith J, Rush J, Just AC, Kioumourtzoglou MA. The association between ambient temperature variability and myocardial infarction in a New York-State-based case-crossover study: An examination of different variability metrics. ENVIRONMENTAL RESEARCH 2021; 197:111207. [PMID: 33932478 PMCID: PMC8609500 DOI: 10.1016/j.envres.2021.111207] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/13/2021] [Accepted: 04/17/2021] [Indexed: 05/31/2023]
Abstract
BACKGROUND Short-term temperature variability has been consistently associated with mortality, with limited evidence for cardiovascular outcomes. Previous studies have used multiple metrics to measure temperature variability; however, those metrics do not capture hour-to-hour changes in temperature. OBJECTIVES We assessed the correlation between sub-daily temperature-change-over-time metrics and previously-used metrics, and estimated associations with myocardial infarction (MI) hospitalizations. METHODS Hour-to-hour change-over-time was measured via three metrics: 24-hr mean absolute hourly first difference, 24-hr maximum absolute hourly first difference, and 24-hr mean hourly first difference. We first assessed the Spearman correlations between these metrics and four previously-used metrics (24-hr standard deviation of hourly temperature, 24-hr diurnal temperature range, 48-hr standard deviation of daily minimal and maximal temperatures, and 48-hr difference of daily mean temperature), using hourly data from the North America Land Data Assimilation System-2 Model. Subsequently, we estimated the association between these metrics and primary MI hospitalization in adult residents of New York State for 2000-2015 using a time-stratified case-crossover design. RESULTS The hour-to-hour change-over-time metrics were correlated, but not synonymous, with previously-used metrics. We observed 809,259 MI, 45% of which were among females and the mean (standard deviation) age was 70 (15). An increase from mean to 90th percentile in mean absolute first difference of temperature was associated with a 2.04% (95% Confidence Interval [CI]: 1.30-2.78%) increase in MI rate. An increase from mean to 90th percentile in mean first difference also yielded a positive association (1.86%; 95%CI: 1.09-2.64%). We observed smaller- or similar-in-magnitude positive associations for previously-used metrics. DISCUSSION First, short-term hour-to-hour temperature change was positively associated with MI risk. Second, all other variability metrics yielded positive associations with MI, with varying magnitude. In future research on temperature variability, researchers should define their research question, including which aspects of variability they intend to measure, and apply the appropriate metric. ALTERNATIVE All metrics of temperature variability, including short-term hour-to-hour temperature changes, were positively associated with MI risk, though the magnitude of effect estimates varied by metric.
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Affiliation(s)
- Sebastian T Rowland
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA.
| | - Robbie M Parks
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Amelia K Boehme
- Departments of Neurology, Columbia University Medical School and Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Jeff Goldsmith
- Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Johnathan Rush
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Liu T, Zhou C, Zhang H, Huang B, Xu Y, Lin L, Wang L, Hu R, Hou Z, Xiao Y, Li J, Xu X, Jin D, Qin M, Zhao Q, Gong W, Yin P, Xu Y, Hu J, Xiao J, Zeng W, Li X, Chen S, Guo L, Rong Z, Zhang Y, Huang C, Du Y, Guo Y, Rutherford S, Yu M, Zhou M, Ma W. Ambient Temperature and Years of Life Lost: A National Study in China. Innovation (N Y) 2021; 2:100072. [PMID: 34557729 PMCID: PMC8454660 DOI: 10.1016/j.xinn.2020.100072] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/12/2020] [Indexed: 12/27/2022] Open
Abstract
Although numerous studies have investigated premature deaths attributable to temperature, effects of temperature on years of life lost (YLL) remain unclear. We estimated the relationship between temperatures and YLL, and quantified the YLL per death caused by temperature in China. We collected daily meteorological and mortality data, and calculated the daily YLL values for 364 locations (2013–2017 in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces, and 2006–2011 in other locations) in China. A time-series design with a distributed lag nonlinear model was first employed to estimate the location-specific associations between temperature and YLL rates (YLL/100,000 population), and a multivariate meta-analysis model was used to pool location-specific associations. Then, YLL per death caused by temperatures was calculated. The temperature and YLL rates consistently showed U-shaped associations. A mean of 1.02 (95% confidence interval: 0.67, 1.37) YLL per death was attributable to temperature. Cold temperature caused 0.98 YLL per death with most from moderate cold (0.84). The mean YLL per death was higher in those with cardiovascular diseases (1.14), males (1.15), younger age categories (1.31 in people aged 65–74 years), and in central China (1.34) than in those with respiratory diseases (0.47), females (0.87), older people (0.85 in people ≥75 years old), and northern China (0.64) or southern China (1.19). The mortality burden was modified by annual temperature and temperature variability, relative humidity, latitude, longitude, altitude, education attainment, and central heating use. Temperatures caused substantial YLL per death in China, which was modified by demographic and regional characteristics. Years of life lost (YLL) is used to estimate the effects of temperature Both low and high temperatures can increase the YLLs Average 1.02 YLL per death is attributed to temperature exposure Temperature causes larger YLLs per death in males, younger people, and central China
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Affiliation(s)
- Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Chunliang Zhou
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Haoming Zhang
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, China
| | - Biao Huang
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, China
| | - Yanjun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Lifeng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Lijun Wang
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, 100050, China
| | - Ruying Hu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Zhulin Hou
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, China
| | - Yize Xiao
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, China
| | - Junhua Li
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Xiaojun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Donghui Jin
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Mingfang Qin
- Yunnan Center for Disease Control and Prevention, Kunming, 650022, China
| | - Qinglong Zhao
- Health Hazard Factors Control Department, Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062, China
| | - Weiwei Gong
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Peng Yin
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, 100050, China
| | - Yiqing Xu
- Hunan Provincial Center for Disease Control and Prevention, Changsha, 410005, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Weilin Zeng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Xing Li
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Siqi Chen
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Lingchuan Guo
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Zuhua Rong
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Yonghui Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Cunrui Huang
- School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yaodong Du
- Guangdong Provincial Climate Center, Guangzhou, 510080, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3800, Australia
| | | | - Min Yu
- Zhejiang Center for Disease Control and Prevention, Hangzhou, 310051, China
| | - Maigeng Zhou
- The National Center for Chronic and Noncommunicable Disease Control and Prevention, Beijing, 100050, China
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
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Ho HC, Fong KNK, Chan TC, Shi Y. The associations between social, built and geophysical environment and age-specific dementia mortality among older adults in a high-density Asian city. Int J Health Geogr 2020; 19:53. [PMID: 33276778 PMCID: PMC7716506 DOI: 10.1186/s12942-020-00252-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 11/25/2020] [Indexed: 12/26/2022] Open
Abstract
Background Although socio-environmental factors which may affect dementia have widely been studied, the mortality of dementia and socio-environmental relationships among older adults have seldom been discussed. Method A retrospective, observational study based on territory-wide register-based data was conducted to evaluate the relationships of four individual-level social measures, two community-level social measures, six short-term (temporally varying) environmental measures, and four long-term (spatially varying) environmental measures with dementia mortality among older adults in a high-density Asian city (Hong Kong), for the following decedents: (1) all deaths: age >= 65, (2) “old-old”: age > = 85, (3) “mid-old”: aged 75–84, and (4) “young-old”: aged 65–74. Results This study identified 5438 deaths (3771 old-old; 1439 mid-old; 228 young-old) from dementia out of 228,600 all-cause deaths among older adults in Hong Kong between 2007 and 2014. Generally, regional air pollution, being unmarried or female, older age, and daily O3 were associated with higher dementia mortality, while more urban compactness and greenness were linked to lower dementia mortality among older adults. Specifically, being unmarried and the age effect were associated with higher dementia mortality among the “old-old”, “mid-old” and “young-old”. Regional air pollution was linked to increased dementia mortality, while urban compactness and greenness were associated with lower dementia mortality among the “old-old” and “mid-old”. Higher daily O3 had higher dementia mortality, while districts with a greater percentage of residents whose native language is not Cantonese were linked to lower dementia mortality among the “old-old”. Economic inactivity was associated with increased dementia mortality among the “young-old”. Gender effect varied by age. Conclusion The difference in strengths of association of various factors with dementia mortality among different age groups implies the need for a comprehensive framework for community health planning. In particular, strategies for air quality control, usage of greenspace and social space, and activity engagement to reduce vulnerability at all ages are warranted.
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Affiliation(s)
- Hung Chak Ho
- Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China.
| | - Kenneth N K Fong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan.
| | - Yuan Shi
- Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China
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Xu R, Zhao Q, Coelho MSZS, Saldiva PHN, Abramson MJ, Li S, Guo Y. Socioeconomic inequality in vulnerability to all-cause and cause-specific hospitalisation associated with temperature variability: a time-series study in 1814 Brazilian cities. Lancet Planet Health 2020; 4:e566-e576. [PMID: 33278374 DOI: 10.1016/s2542-5196(20)30251-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 09/06/2020] [Accepted: 10/02/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND Exposure to temperature variability has been associated with increased risk of mortality and morbidity. We aimed to evaluate whether the association between short-term temperature variability and hospitalisation was affected by local socioeconomic level in Brazil. METHODS In this time-series study, we collected city-level socioeconomic data, and daily hospitalisation and weather data from 1814 Brazilian cities between Jan 1, 2000, and Dec 31, 2015. All-cause and cause-specific hospitalisation data was from the Hospital Information System of the Unified Health System in Brazil. City-specific daily minimum and maximum temperatures came from a 0·25° × 0·25° Brazilian meteorological dataset. We represented city-specific socioeconomic level using literacy rate, urbanisation rate, average monthly household income per capita (using the 2000 and 2010 Brazilian census), and GDP per capita (using statistics from the Brazilian Institute of Geography and Statistics for 2000-15), and cities were categorised according to the 2015 World Bank standard. We used quasi-Poisson regression to do time-series analyses and obtain city-specific associations between temperature variability and hospitalisation. We pooled city-specific estimates according to different socioeconomic quartiles or levels using random-effect meta-analyses. Meta-regressions adjusting for demographic and climatic characteristics were used to evaluate the modification effect of city-level socioeconomic indicators on the association between temperature variability and hospitalisation. FINDINGS We included a total of 147 959 243 hospitalisations (59·0% female) during the study period. Overall, we estimated that the hospitalisation risk due to every 1°C increase in the temperature variability in the current and previous day (TV0-1) increased by 0·52% (95% CI 0·50-0·55). For lower-middle-income cities, this risk was 0·63% (95% CI 0·58-0·69), for upper-middle-income cities it was 0·50% (0·47-0·53), and for high-income cities it was 0·39% (0·33-0·46). The socioeconomic inequality in vulnerability to TV0-1 was especially evident for people aged 0-19 years (effect estimate 1·21% [1·11-1·31] for lower-middle income vs 0·52% [0·41-0·63] for high income) and people aged 60 years or older (0·60% [0·50-0·70] vs 0·43% [0·31-0·56]), and for hospitalisation due to infectious diseases (1·62% [1·46-1·78] vs 0·56% [0·30-0·82]), respiratory diseases (1·32% [1·20-1·44] vs 0·55% [0·37-0·74]), and endocrine diseases (1·21% [0·99-1·43] vs 0·32% [0·02-0·62]). INTERPRETATION People living in less developed cities in Brazil were more vulnerable to hospitalisation related to temperature variability. This disparity could exacerbate existing health and socioeconomic inequalities in Brazil, and it suggests that more attention should be paid to less developed areas to mitigate the adverse health effects of short-term temperature fluctuations. FUNDING None.
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Affiliation(s)
- Rongbin Xu
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Qi Zhao
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Micheline S Z S Coelho
- Department of Pathology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Paulo H N Saldiva
- Department of Pathology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Michael J Abramson
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; College of Public Health, Zhengzhou University, Zhengzhou, Henan, China.
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Huang K, Yang XJ, Hu CY, Ding K, Jiang W, Hua XG, Liu J, Cao JY, Sun CY, Zhang T, Kan XH, Zhang XJ. Short-term effect of ambient temperature change on the risk of tuberculosis admissions: Assessments of two exposure metrics. ENVIRONMENTAL RESEARCH 2020; 189:109900. [PMID: 32980000 DOI: 10.1016/j.envres.2020.109900] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 06/20/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Although the effects of seasonal variations and ambient temperature on the incidence of tuberculosis (TB) have been well documented, it is still unknown whether ambient temperature change is an independent risk factor for TB. The aim of this study was to assess the association between ambient temperature change and the risk of TB admissions. METHOD A distributed lag non-linear model (DLNM) combined with Poisson generalized linear regression model was performed to assess the association between ambient temperature change and the risk of TB admissions from 2014 to 2018 in Hefei, China. Two temperature change metrics including temperature change between neighboring days (TCN) and diurnal temperature range (DTR) were used to assess the effects of temperature change exposure. Subgroup analyses were performed by gender, age and season. Besides, the attributable risk was calculated to evaluated the public health significance. RESULTS The overall exposure-response curves suggested that there were statistically significant associations between two temperature change metrics and the risk of TB admissions. The maximum lag-specific relative risk (RR) of TB admissions was 1.088 (95%CI: 1.012-1.171, lag 4 day) for exposing to large temperature drop (TCN= -4 °C) in winter. Besides, the overall cumulative risk of TB admissions increased continuously and peaked at a lag of 7 days (RR=1.350, 95%CI: 1.120-1.628). Subgroup analysis suggested that exposure to large temperature drop had an adverse effect on TB admissions among males, females and adults. Similarly, large level of DTR exposure (DTR=15 °C) in spring also increased the risk of TB admissions on lag 0 day (RR=1.039, 95%CI: 1.016-1.063), and the cumulative RRs peaked at a lag of 1 days (RR=1.029, 95%CI: 1.012-1.047). We also found that females and elderly people were more vulnerable to the large level of DTR exposure. Additionally, the assessment of attributable risk suggested that taking target measures for the upcoming large temperature drop (b-AF = 4.17%, 95% eCI: 1.24%, 7.22%, b-AN = 1195) may achieve great public health benefits for TB prevention. CONCLUSION This study suggests that ambient temperature change is associated with the risk of TB admissions. Besides, TCN may be a better predictor for the TB prevention and public health.
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Affiliation(s)
- Kai Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Xiao-Jing Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Cheng-Yang Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Kun Ding
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Wen Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Xiao-Guo Hua
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China
| | - Jie Liu
- Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Ji-Yu Cao
- Department of Occupational Health and Environmental Health, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Chen-Yu Sun
- AMITA Health Saint Joseph Hospital Chicago, 2900 N. Lake Shore Drive, Chicago, 60657, Illinois, USA
| | - Tao Zhang
- Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, China
| | - Xiao-Hong Kan
- Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, China; Anhui Medical University Clinical College of Chest, 397 Jixi Road, Hefei, 230022, China.
| | - Xiu-Jun Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, China.
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Zhan ZY, Tian Q, Chen TT, Ye Y, Lin Q, Han D, Ou CQ. Temperature Variability and Hospital Admissions for Chronic Obstructive Pulmonary Disease: Analysis of Attributable Disease Burden and Vulnerable Subpopulation. Int J Chron Obstruct Pulmon Dis 2020; 15:2225-2235. [PMID: 33061340 PMCID: PMC7519840 DOI: 10.2147/copd.s260988] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/12/2020] [Indexed: 12/12/2022] Open
Abstract
Purpose Chronic obstructive pulmonary disease (COPD) is a major cause of chronic diseases causing considerable social and economic burden globally. Despite substantial evidence on temperature-COPD association, few studies have investigated the acute effect of temperature variability (TV), a potential trigger of exacerbation of COPD disease, and it remains unknown what fraction of the disease burden of COPD is attributable to TV. Patients and Methods Based on 71,070 COPD hospitalizations during 2013–2015 in Guangzhou, China, we conducted a time-series analysis using quasi-Poisson regression to assess the association between TV and hospital admission for COPD after adjusting for daily mean temperature. Short-term TV was captured by the standard deviation of hourly or daily temperatures across various exposure days. We also provided the fraction (total number) of COPD attributable to TV. Stratified analyses by admission route, sex, age, occupation, marital status and season were performed to identify vulnerable subpopulations. Results We found a linear relationship between TV and COPD hospitalization, with a 1°C increase in hourly TV and daily TV associated with 4.3% (95%CI: 2.2–6.4) and 4.0% (2.3–5.8) increases in COPD, respectively. The greater relative risks of TV identified males, people aged 0–64 years, blue collar, and divorced/widowed people as vulnerable population. There were 12.0% (8500 cases) of COPD hospitalization attributable to hourly TV during the study period. Daily TV produced similar estimates of relative effects (relative risk) but grater estimates of absolute effects (attributable fraction) than hourly TV. Conclusion We concluded that TV was an independent risk factor of COPD morbidity, especially among the susceptible subgroups. These findings would be helpful to guide the development of targeted public intervention.
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Affiliation(s)
- Zhi-Ying Zhan
- 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, People's Republic of China.,Department of Health Care Management and Social Medicine, School of Public Health, Fujian Medical University, Fuzhou, People's Republic of China
| | - Qi Tian
- Department of Information Resources, Guangzhou Health Information Center, Guangzhou, People's Republic of China
| | - Ting-Ting Chen
- 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, People's Republic of China
| | - Yunshao Ye
- Department of Information Resources, Guangzhou Health Information Center, Guangzhou, People's Republic of China
| | - Qiaoxuan Lin
- Department of Information Resources, Guangzhou Health Information Center, Guangzhou, People's Republic of China
| | - Dong Han
- 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, People's Republic of China
| | - Chun-Quan Ou
- 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, People's Republic of China
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Zheng S, Zhu W, Wang M, Shi Q, Luo Y, Miao Q, Nie Y, Kang F, Mi X, Bai Y. The effect of diurnal temperature range on blood pressure among 46,609 people in Northwestern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 730:138987. [PMID: 32428804 DOI: 10.1016/j.scitotenv.2020.138987] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2020] [Revised: 04/08/2020] [Accepted: 04/23/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND A large number of studies have found a positive association between diurnal temperature range (DTR) and cardiovascular diseases (CVDs) incidence and mortality. Few studies regarding the effects of DTR on blood pressure (BP) are available. OBJECTIVE To investigate the effects of DTR on BP in Jinchang, northwestern China. METHODS Based on a prospective cohort research, a total of 46,609 baseline survey data were collected from 2011 to 2015. The meteorological observation data and environmental monitoring data were collected in the same period. The generalized additive model (GAM) was used to estimate the relationship between DTR and BP after adjusting for confounding variables. RESULTS Our study found that there was a positive linear correlation between DTR and systolic blood pressure (SBP) and plus pressure (PP), and a negative linear correlation between DTR and diastolic blood pressure (DBP). With a 1 °C increase of DTR, SBP and PP increased 0.058 mmHg (95%CI: 0.018-0.097) and 0.114 mmHg (95%CI: 0.059-0.168) respectively, and DBP decreased 0.039 mmHg (95%CI:-0.065 ~ -0.014). There was a significant interaction between season and DTR on SBP and PP. DTR had the greatest impact on SBP and PP in hot season. The association between DTR and BP varied significantly by education level. CONCLUSION There was a significant association between DTR and BP in Jinchang, an area with large temperature change at high altitudes in northwestern China. These results provide new evidence that DTR is an independent risk factor for BP changes among general population. Therefore, effective control and management of BP in the face of temperature changes can help prevent CVDs.
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Affiliation(s)
- Shan Zheng
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China.
| | - Wenzhi Zhu
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Minzhen Wang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Qin Shi
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Yan Luo
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Qian Miao
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Yonghong Nie
- Jinchang Center for Disease Prevention and Control, Jinchang 737100, China
| | - Feng Kang
- Workers' Hospital of Jinchuan Group Co., Ltd., Jinchang 737103, China
| | - Xiuying Mi
- Jinchang Meteorological Service, Jinchang 737100, China
| | - Yana Bai
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
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Wang X, Yu C, Zhang Y, Shi F, Meng R, Yu Y. Attributable Risk and Economic Cost of Cardiovascular Hospital Admissions Due to Ambient Particulate Matter in Wuhan, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E5453. [PMID: 32751102 PMCID: PMC7432018 DOI: 10.3390/ijerph17155453] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 07/25/2020] [Accepted: 07/25/2020] [Indexed: 12/25/2022]
Abstract
Although the adverse effects of ambient particulate matter (PM) on cardiovascular disease (CVD) have been previously documented, information about their economic consequence was insufficient. This study aimed to evaluate the attributable risk and economic cost of cardiovascular hospitalizations due to ambient PM. Data of CVD hospitalizations and PM concentrations from 1 January 2015 to 31 December 2017 were collected in Wuhan, China. A generalized additive model was applied to quantify the PM-attributable CVD hospitalizations, and total attributable hospitalization costs were calculated via multiplying the total attributable cases by the case-average hospitalization costs. A total of 45,714 CVD hospitalizations were included in this study. The results showed that a 10 µg/m3 increase in PM2.5 and PM10 concentrations at lag7 day, respectively, contributed to a 1.01% (95% confidence interval: 0.67-1.34) and 0.48% (0.26-0.70) increase in CVD hospitalizations. During the study period, 1487 and 983 CVD hospitalizations were attributable to PM2.5 and PM10, equaling an economic cost of 29.27 and 19.34 million RMB (1 RMB = 0.1424 USD), respectively, and significant differences in PM-attributable hospitalizations and economic burden were found between gender and age groups. Our study added evidence in heavily polluted megacities regarding the increased health risk and economic cost of CVD hospitalizations associated with ambient particulate pollution.
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Affiliation(s)
- Xuyan Wang
- Department of Epidemiology and Biostatistics, School of Health Sciences, Wuhan University, Wuhan 430071, China; (X.W.); (F.S.)
| | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, School of Health Sciences, Wuhan University, Wuhan 430071, China; (X.W.); (F.S.)
- Global Health Institute, Wuhan University, Wuhan 430072, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College, Wuhan University of Science and Technology, Wuhan 430065, China;
- Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Fang Shi
- Department of Epidemiology and Biostatistics, School of Health Sciences, Wuhan University, Wuhan 430071, China; (X.W.); (F.S.)
| | - Runtang Meng
- Department of Preventive Medicine, School of Medicine, Hangzhou Normal University, Hangzhou 311121, China;
| | - Yong Yu
- School of Public Health and Management, Hubei University of Medicine, Shiyan 442000, China
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Chai KC, Zhang YB, Chang KC. Regional Disparity of Medical Resources and Its Effect on Mortality Rates in China. Front Public Health 2020; 8:8. [PMID: 32117848 PMCID: PMC7011092 DOI: 10.3389/fpubh.2020.00008] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 01/10/2020] [Indexed: 11/13/2022] Open
Abstract
Objectives: The purpose of this study was two-fold. First, to empirically study the effects that medical resources (i.e., hospital, doctors, beds) have on the mortality rate in China. Second, to divide China into east, middle, and west regions, and empirically study the regional disparity of medical resources and its effect on mortality rates in China. Methodology and Data: This study utilized a panel data regression model to explore the effect medical resources have on the age-standardized mortality rate in China. The data came from the 2003-2017 China Statistical Yearbook compiled by the National Bureau of Statistics of China. Results: Nationwide, hospitals, doctors, and beds had a significant negative correlation with the mortality rate. In the western region, hospitals, beds, and doctors had a significant negative correlation with the mortality rate. In China's middle and eastern regions, hospitals, beds, and doctors had no significant effect on the mortality rate. In China, increased hospitals, doctors, and beds significantly reduced the mortality rate. The distribution of medical resources in eastern, middle, and western China was unequal. More hospitals, beds, and doctors in the less developed western regions can more effectively alleviate the local mortality rate. In the middle and east regions, hospitals, beds, and doctors had no significant impact on the local mortality rate. Conclusion: First, China's overall medical resources are still inadequate and improving medical resources throughout the country could reduce the mortality rate. Second, due to the imbalanced distribution of medical resources in China, the Chinese government should implement more supportive policies for medical resources in the western region. At the same time, we should also actively develop the western region by improving local per capita GDP and reducing unemployment, so as to fundamentally reduce the local mortality rate.
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Affiliation(s)
- Kuang-Cheng Chai
- Business School, Guilin University of Electronic Technology, Guilin, China
| | - Ying-Bin Zhang
- Business School, Guilin University of Electronic Technology, Guilin, China
| | - Ke-Chiun Chang
- School of Economic and Management, Wuhan University, Wuhan, China
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