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Mapping heatwave health risk at the community level for public health action.
Buscail C., Upegui E., Viel J.-F.
International Journal of Health Geographics 11, 1 (2012) 38 - http://www.hal.inserm.fr/inserm-00762303
Mapping heatwave health risk at the community level for public health action.
Camille Buscail1, Erika Upegui2, Jean-François Viel () 1, 3
1 :  Service de santé publique et d'épidémiologie
CHU Rennes – Hôpital Pontchaillou
2 :  Laboratoire Chrono-environnement
CNRS : UMR6249 – Université de Franche-Comté
UFR Sciences et Techniques 16, route de Gray 25030 BESANCON Cedex
3 :  Irset - Institut de recherche, santé, environnement et travail
INSERM : U1085 – Université de Rennes 1 – École Nationale de la Santé Publique – Université des Antilles et de la Guyane – Biosit
263 avenue Général Leclerc 35042 Rennes Cedex
ABSTRACT: BACKGROUND: Climate change poses unprecedented challenges, ranging from global and local policy challenges to personal and social action. Heat-related deaths are largely preventable, but interventions for the most vulnerable populations need improvement. Therefore, the prior identification of high risk areas at the community level is required to better inform planning and prevention. We aimed to demonstrate a simple and flexible conceptual framework relying upon satellite thermal data and other digital data with the goal of easily reproducing this framework in a variety of urban configurations. RESULTS: The study area encompasses Rennes, a medium-sized French city. A Landsat ETM + image (60 m resolution) acquired during a localized heatwave (June 2001) was used to estimate land surface temperature (LST) and derive a hazard index. A land-use regression model was performed to predict the LST. Vulnerability was assessed through census data describing four dimensions (socio-economic status, extreme age, population density and building obsolescence). Then, hazard and vulnerability indices were combined to deliver a heatwave health risk index. The LST patterns were quite heterogeneous, reflecting the land cover mosaic inside the city boundary, with hotspots of elevated temperature mainly observed in the city center. A spatial error regression model was highly predictive of the spatial variation in the LST (R2 = 0.87) and was parsimonious. Three land cover descriptors (NDVI, vegetation and water fractions) were negatively linked with the LST. A sensitivity analysis (based on an image acquired on July 2000) yielded similar results. Southern areas exhibited the most vulnerability, although some pockets of higher vulnerability were observed northeast and west of the city. The heatwave health risk map showed evidence of infra-city spatial clustering, with the highest risks observed in a north--south central band. Another sensitivity analysis gave a very high correlation between 2000 and 2001 risk indices (r = 0.98, p < 10-12). CONCLUSIONS: Building on previous work, we developed a reproducible method that can provide guidance for local planners in developing more efficient climate impact adaptations. We recommend, however, using the health risk index together with hazard and vulnerability indices to implement tailored programs because exposure to heat and vulnerability do not require the same prevention strategies.
Sciences du Vivant/Santé publique et épidémiologie

Articles dans des revues avec comité de lecture
International Journal of Health Geographics
Publisher BioMed Central
ISSN 1476-072X 

Heatwave health risk – Urban heat island – Vulnerable populations – Spatial risk assessment – Remote sensing – Land surface temperature – Land cover – Public health
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