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      The spatial distribution of health vulnerability to heat waves in Guangdong Province, China

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          Abstract

          Background

          International literature has illustrated that the health impacts of heat waves vary according to differences in the spatial variability of high temperatures and the social and economic characteristics of populations and communities. However, to date there have been few studies that quantitatively assess the health vulnerability to heat waves in China.

          Objectives

          To assess the spatial distribution of health vulnerability to heat waves in Guangdong Province, China.

          Methods

          A vulnerability framework including dimensions of exposure, sensitivity, and adaptive capacity was employed. The last two dimensions were called social vulnerability. An indicator pool was proposed with reference to relevant literatures, local context provided by relevant local stakeholder experts, and data availability. An analytic hierarchy process (AHP) and a principal component analysis were used to determine the weight of indicators. A multiplicative vulnerability index (VI) was constructed for each district/county of Guangdong province, China.

          Results

          A total of 13 items (two for exposure, six for sensitivity, and five for adaptive capacity) were proposed to assess vulnerability. The results of an AHP revealed that the average VI in Guangdong Province was 0.26 with the highest in the Lianzhou and Liannan counties of Qingyuan (VI=0.50) and the lowest in the Yantian district of Shenzhen (VI=0.08). Vulnerability was gradiently distributed with higher levels in northern inland regions and lower levels in southern coastal regions. In the principal component analysis, three components were isolated from the 11 social vulnerability indicators. The estimated vulnerability had a similar distribution pattern with that estimated by AHP (Intraclass correlation coefficient (ICC)=0.98, p<0.01).

          Conclusions

          Health vulnerability to heat waves in Guangdong Province had a distinct spatial distribution, with higher levels in northern inland regions than that in the southern coastal regions.

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          Most cited references51

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          Heat stress and public health: a critical review.

          R. Sari Kovats, Shakoor Hajat (2008)
          Heat is an environmental and occupational hazard. The prevention of deaths in the community caused by extreme high temperatures (heat waves) is now an issue of public health concern. The risk of heat-related mortality increases with natural aging, but persons with particular social and/or physical vulnerability are also at risk. Important differences in vulnerability exist between populations, depending on climate, culture, infrastructure (housing), and other factors. Public health measures include health promotion and heat wave warning systems, but the effectiveness of acute measures in response to heat waves has not yet been formally evaluated. Climate change will increase the frequency and the intensity of heat waves, and a range of measures, including improvements to housing, management of chronic diseases, and institutional care of the elderly and the vulnerable, will need to be developed to reduce health impacts.
          Article 0 comments Cited 496 times – based on 0 reviews     Review now
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            Mapping Community Determinants of Heat Vulnerability

            Colleen E. Reid, Marie S. O'Neill, Carina Gronlund … (2009)
            Background The evidence that heat waves can result in both increased deaths and illness is substantial, and concern over this issue is rising because of climate change. Adverse health impacts from heat waves can be avoided, and epidemiologic studies have identified specific population and community characteristics that mark vulnerability to heat waves. Objectives We situated vulnerability to heat in geographic space and identified potential areas for intervention and further research. Methods We mapped and analyzed 10 vulnerability factors for heat-related morbidity/mortality in the United States: six demographic characteristics and two household air conditioning variables from the U.S. Census Bureau, vegetation cover from satellite images, and diabetes prevalence from a national survey. We performed a factor analysis of these 10 variables and assigned values of increasing vulnerability for the four resulting factors to each of 39,794 census tracts. We added the four factor scores to obtain a cumulative heat vulnerability index value. Results Four factors explained > 75% of the total variance in the original 10 vulnerability variables: a) social/environmental vulnerability (combined education/poverty/race/green space), b) social isolation, c) air conditioning prevalence, and d) proportion elderly/diabetes. We found substantial spatial variability of heat vulnerability nationally, with generally higher vulnerability in the Northeast and Pacific Coast and the lowest in the Southeast. In urban areas, inner cities showed the highest vulnerability to heat. Conclusions These methods provide a template for making local and regional heat vulnerability maps. After validation using health outcome data, interventions can be targeted at the most vulnerable populations.
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              The impact of heat waves on mortality.

              Peter Armstrong, Antonio Gasparrini (2010)
              Heat waves have been linked with an increase in mortality, but the associated risk has been only partly characterized. We examined this association by decomposing the risk for temperature into a "main effect" due to independent effects of daily high temperatures, and an "added" effect due to sustained duration of heat during waves, using data from 108 communities in the United States during 1987-2000. We adopted different definitions of heat-wave days on the basis of combinations of temperature thresholds and days of duration. The main effect was estimated through distributed lag nonlinear functions of temperature, which account for nonlinear delayed effects and short-time harvesting. We defined the main effect as the relative risk between the median city-specific temperature during heat-wave days and the 75th percentile of the year-round distribution. The added effect was defined first using a simple indicator, and then a function of consecutive heat-wave days. City-specific main and added effects were pooled through univariate and multivariate meta-analytic techniques. The added wave effect was small (0.2%-2.8% excess relative risk, depending on wave definition) compared with the main effect (4.9%-8.0%), and was apparent only after 4 consecutive heat-wave days. Most of the excess risk with heat waves in the United States can be simply summarized as the independent effects of individual days' temperatures. A smaller added effect arises in heat waves lasting more than 4 days.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Glob Health Action
                Journal ID (iso-abbrev): Glob Health Action
                Journal ID (publisher-id): GHA
                Title: Global Health Action
                Publisher: Co-Action Publishing
                ISSN (Print): 1654-9716
                ISSN (Electronic): 1654-9880
                Publication date (Electronic): 21 October 2014
                Publication date Collection: 2014
                Volume: 7
                Electronic Location Identifier: 10.3402/gha.v7.25051
                Affiliations
                [1 ]Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
                [2 ]Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
                [3 ]Environment and Health, Guangdong Provincial Key Medical Discipline of Twelfth Five-Year Plan, Guangzhou, China
                [4 ]Griffith School of Environment, Griffith University, Brisbane, Australia
                [5 ]Guangdong Provincial Climate Center, Guangzhou, China
                Author notes
                [* ]Correspondence to: Wenjun Ma, No. 160, Qunxian Road, Panyu District, Guangzhou, 511430, China, Email: mwj68@ 123456vip.tom.com
                [† ]These authors contributed equally to this work.

                Responsible Editor: Nawi Ng, Umeå University, Sweden.

                Article
                Publisher ID: 25051
                DOI: 10.3402/gha.v7.25051
                PMC ID: 4212080
                PubMed ID: 25361724
                SO-VID: df9467a0-6972-4924-bf38-986821236c87
                Copyright © © 2014 Qi Zhu et al.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 28 May 2014
                Date revision received : 18 September 2014
                Date accepted : 25 September 2014
                Categories
                Subject: Original Article

                ScienceOpen disciplines: Health & Social care
                Keywords: vulnerability assessment,heat waves,climate change,analytic hierarchy process,principal component analysis
                Data availability:
                ScienceOpen disciplines: Health & Social care
                Keywords: vulnerability assessment, heat waves, climate change, analytic hierarchy process, principal component analysis

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