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Niu Y, Li Z, Gao Y, Liu X, Xu L, Vardoulakis S, Yue Y, Wang J, Liu Q. A Systematic Review of the Development and Validation of the Heat Vulnerability Index: Major Factors, Methods, and Spatial Units. CURRENT CLIMATE CHANGE REPORTS 2021; 7:87-97. [PMID: 34745843 PMCID: PMC8531084 DOI: 10.1007/s40641-021-00173-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/29/2021] [Indexed: 06/13/2023]
Abstract
PURPOSE OF REVIEW This review aims to identify the key factors, methods, and spatial units used in the development and validation of the heat vulnerability index (HVI) and discuss the underlying limitations of the data and methods by evaluating the performance of the HVI. RECENT FINDINGS Thirteen studies characterizing the factors of the HVI development and relating the index with validation data were identified. Five types of factors (i.e., hazard exposure, demographic characteristics, socioeconomic conditions, built environment, and underlying health) of the HVI development were identified, and the top five were social cohesion, race, and/or ethnicity, landscape, age, and economic status. The principal component analysis/factor analysis (PCA/FA) was often used in index development, and four types of spatial units (i.e., census tracts, administrative area, postal code, grid) were used for establishing the relationship between factors and the HVI. Moreover, although most studies showed that a higher HVI was often associated with the increase in health risk, the strength of the relationship was weak. SUMMARY This review provides a retrospect of the major factors, methods, and spatial units used in development and validation of the HVI and helps to define the framework for future studies. In the future, more information on the hazard exposure, underlying health, governance, and protection awareness should be considered in the HVI development, and the duration and location of validation data should be strengthened to verify the reliability of HVI. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s40641-021-00173-3.
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Affiliation(s)
- Yanlin Niu
- 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, 155 Changbai Road, Changping, Beijing, 102206 China
- Beijing Center for Disease Prevention and Control, Institute for Nutrition and Food Hygiene, Beijing, China
- Research Center for Preventive Medicine of Beijing, Beijing, China
- University College London, London, UK
| | - Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Yuan Gao
- 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, 155 Changbai Road, Changping, Beijing, 102206 China
| | - Xiaobo 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, 155 Changbai Road, Changping, Beijing, 102206 China
| | - Lei Xu
- 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, 155 Changbai Road, Changping, Beijing, 102206 China
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
- Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing, China
| | - Sotiris Vardoulakis
- National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, Canberra, Australia
| | - Yujuan Yue
- 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, 155 Changbai Road, Changping, Beijing, 102206 China
| | - Jun Wang
- 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, 155 Changbai Road, Changping, Beijing, 102206 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, 155 Changbai Road, Changping, Beijing, 102206 China
- University College London, London, UK
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Statistical Modelling of Temperature-Attributable Deaths in Portuguese Metropolitan Areas under Climate Change: Who Is at Risk? ATMOSPHERE 2020. [DOI: 10.3390/atmos11020159] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Several studies emphasize that temperature-related mortality can be expected to have differential effects on different subpopulations, particularly in the context of climate change. This study aims to evaluate and quantify the future temperature-attributable mortality due to circulatory system diseases by age groups (under 65 and 65+ years), in Lisbon metropolitan area (LMA) and Porto metropolitan area (PMA), over the 2051–2065 and 2085–2099 time horizons, considering the greenhouse gas emissions scenario RCP8.5, in relation to a historical period (1991–2005). We found a decrease in extreme cold-related deaths of 0.55% and 0.45% in LMA, for 2051–2065 and 2085–2099, respectively. In PMA, there was a decrease in cold-related deaths of 0.31% and 0.49% for 2051–2065 and 2085–2099, respectively, compared to 1991–2005. In LMA, the burden of extreme heat-related mortality in age group 65+ years is slightly higher than in age group <65 years, at 2.22% vs. 1.38%, for 2085–2099. In PMA, only people aged 65+ years showed significant temperature-related burden of deaths that can be attributable to hot temperatures. The heat-related excess deaths increased from 0.23% for 2051–2065 to 1.37% for 2085–2099, compared to the historical period.
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