How urban characteristics affect vulnerability to heat and cold: a multi-country analysis. (27th February 2019)

• Record Type: Journal Article
• Authors: Sera, Francesco; Armstrong, Ben; Tobias, Aurelio; Vicedo-Cabrera, Ana Maria; Åström, Christofer; Bell, Michelle L; Chen, Bing-Yu; de Sousa Zanotti Stagliorio Coelho, Micheline; Matus Correa, Patricia; Cruz, Julio Cesar; Dang, Tran Ngoc; Hurtado-Diaz, Magali; Do Van, Dung; Forsberg, Bertil; Guo, Yue Leon; Guo, Yuming; Hashizume, Masahiro; Honda, Yasushi; Iñiguez, Carmen; Jaakkola, Jouni J K; Kan, Haidong; Kim, Ho; Lavigne, Eric; Michelozzi, Paola; Ortega, Nicolas Valdes; Osorio, Samuel; Pascal, Mathilde; Ragettli, Martina S; Ryti, Niilo R I; Saldiva, Paulo Hilario Nascimento; Schwartz, Joel; Scortichini, Matteo; Seposo, Xerxes; Tong, Shilu; Zanobetti, Antonella; Gasparrini, Antonio;
• Is Part Of: International journal of epidemiology. Volume 48:Number 4(2019)
• Page Start: 1101
• Page End: 1112
• Publication Date: 2019-02-27
• Subjects: Temperature -- heat -- mortality -- epidemiology -- cities -- climate; Epidemiology -- Periodicals; 614.4
• DOI: 10.1093/ije/dyz008
• Languages: English
• ISSNs: 0300-5771

Abstract

Abstract: Background: The health burden associated with temperature is expected to increase due to a warming climate. Populations living in cities are likely to be particularly at risk, but the role of urban characteristics in modifying the direct effects of temperature on health is still unclear. In this contribution, we used a multi-country dataset to study effect modification of temperature–mortality relationships by a range of city-specific indicators. Methods: We collected ambient temperature and mortality daily time-series data for 340 cities in 22 countries, in periods between 1985 and 2014. Standardized measures of demographic, socio-economic, infrastructural and environmental indicators were derived from the Organisation for Economic Co-operation and Development (OECD) Regional and Metropolitan Database. We used distributed lag non-linear and multivariate meta-regression models to estimate fractions of mortality attributable to heat and cold (AF%) in each city, and to evaluate the effect modification of each indicator across cities. Results: Heat- and cold-related deaths amounted to 0.54% (95% confidence interval: 0.49 to 0.58%) and 6.05% (5.59 to 6.36%) of total deaths, respectively. Several city indicators modify the effect of heat, with a higher mortality impact associated with increases in population density, fine particles (PM2.5 ), gross domestic product (GDP) and Gini index (a measure of income inequality), whereas higher levels of green spaces were linked with a decreased effect of heat. Conclusions: This represents the largest study to date assessing the effect modification of temperature–mortality relationships. Evidence from this study can inform public-health interventions and urban planning under various climate-change and urban-development scenarios.