Fine-scale spatial variability of heat-related mortality in Philadelphia County, USA, from 1983-2008: a case-series analysis

Abstract

BackgroundHigh temperature and humidity conditions are associated with short-term elevations in the mortality rate in many United States cities. Previous research has quantified this relationship in an aggregate manner over large metropolitan areas, but within these areas the response may differ based on local-scale variability in climate, population characteristics, and socio-economic factors.MethodsWe compared the mortality response for 48 Zip Code Tabulation Areas (ZCTAs) comprising Philadelphia County, PA to determine if certain areas are associated with elevated risk during high heat stress conditions. A randomization test was used to identify mortality exceedances for various apparent temperature thresholds at both the city and local scale. We then sought to identify the environmental, demographic, and social factors associated with high-risk areas via principal components regression.ResultsCitywide mortality increases by 9.3% on days following those with apparent temperatures over 34°C observed at 7:00 p.m. local time. During these conditions, elevated mortality rates were found for 10 of the 48 ZCTAs concentrated in the west-central portion of the County. Factors related to high heat mortality risk included proximity to locally high surface temperatures, low socioeconomic status, high density residential zoning, and age.ConclusionsWithin the larger Philadelphia metropolitan area, there exists statistically significant fine-scale spatial variability in the mortality response to high apparent temperatures. Future heat warning systems and mitigation and intervention measures could target these high risk areas to reduce the burden of extreme weather on summertime morbidity and mortality.

Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA

“…Heat-related illnesses are associated with a suite of individual, social, and environmental factors that are themselves strongly interrelated (Reid et al 2009;Hondula et al 2012;Harlan et al 2013;Petitti et al 2015). The causal mechanisms leading to heat illnesses and deaths are not fully understood, although research is making progress in explaining linkages between spatial variations in living conditions and individual risks.…”

“…However, extensive scale-dependent variation in LST is expected throughout any given day due to different speeds of warming within the complex spatial structure of urban environments (Li et al 2011;Weng et al 2011). Furthermore, the human consequences of urban LST warming are not well characterized at very fine spatial resolutions, such as individual residential parcels, although several studies have found LST variation affects spatial variability in the risk of heatrelated mortality at neighborhood scales (Johnson et al 2009;Buscail et al 2012;Hondula et al 2012;Johnson et al 2012;Harlan et al 2013).…”

Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors

“…Specific linkages to human outcomes demonstrate that UHI impacts tend to be registered most highly among those parts of city that have dense occupation with low levels of shade, either from buildings or trees, and greenspaces. These conditions, at least in the U.S. cities examined, tend to be related to lower levels of income, often linked to neighborhoods dominated by certain ethnic groups (Buyantuyev & Wu, 2010;Harlan et al, 2006;Hondula et al, 2012;Jenerette et al, 2007Jenerette et al, , 2011. Finally, the land architecture of urban areas, from the parcel to larger levels of assessment, has been hypothesized to amplify or ameliorate ecosystem services, such as those related to SUHI effects (Turner et al, 2013).…”

“…Extensively examined, the UHI draws increasing attention owing to its effects on energy and water consumption, human health, environmental (ecosystem) services, especially in the context of global warming (e.g. Gober, Kirkwood, Balling, Ellis, & Deitrick, 2009;Harlan, Brazel, Prashad, Stefanov, & Larsen, 2006;Harlan, Declet-Barreto, Stefanov, & Petitti, 2013;Hondula, Vanos, & Gosling, 2013;Hondula et al, 2012). For these and other reasons, attention to the means to mitigate the UHI effect have garnered considerable attention, especially through model simulations (e.g.…”

Hot playgrounds and children's health: A multiscale analysis of surface temperatures in Arizona, USA

“…Microscale urban climate models and remotely sensed data (on the order of 1 m 2 to 1 km 2 ) are often employed for heat stress analysis (e.g., Harlan, Declet-Barreto, Stefanov, & Petitti, 2013;Hondula et al, 2012;Masson, Champeaux, Chauvin, Meriguet, & Lacaze, 2003;Mishra, Ganguly, Nijssen, & Lettenmaier, 2015;Stefanov, Prashad, Eisinger, Brazel, & Harlan, 2004), with airborne remotely sensed data providing surface temperatures at a resolution ranging from 7 to 140 m (Stefanov et al, 2004). These scales may possess insufficient spatial resolution to determine certain personal health impacts based on micro-environmental heat exposure.…”