BackgroundClimate 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.ResultsThe 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).ConclusionsBuilding 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.

In France, haemorrhagic fever with renal syndrome (HFRS) is endemic along the Belgian border. However, this rodent-borne zoonosis caused by the Puumala virus has recently spread south to the Franche-Comté region. We investigated the space-time distribution of HFRS and evaluated the influence of environmental factors that drive the hantavirus reservoir abundance and/or the disease transmission in this area. A scan test clearly indicated space-time clustering, highlighting a single-year (2005) epidemic in the southern part of the region, preceded by a heat-wave 2 years earlier. A Bayesian regression approach showed an association between a variable reflecting biomass (normalized difference vegetation index) and HFRS incidence. The reasons why HFRS cases recently emerged remain largely unknown, and climate parameters alone do not reliably predict outbreaks. Concerted efforts that combine reservoir monitoring, surveillance, and investigation of human cases are warranted to better understand the epidemiological patterns of HFRS in this area.

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