Abstracts: ISEE 22nd Annual Conference, Seoul, Korea, 28 August-1 September 2010: Climate Change and Environmental Health
1Institut de Veille Sanitaire, Saint-Maurice, France; and 2Laboratoire Géomer CNRS, Institut Universitaire Européen de la Mer, Plouzane, France.
Abstracts published in Epidemiology have been reviewed by the societies at whose meetings the abstracts have been accepted for presentation. These abstracts have not undergone review by the Editorial Board of Epidemiology.
Heat waves are particularly deadly in large cities where the distribution of surface heat fluxes is altered compared with natural areas. This could be reinforced by climate change. The objectives were to analyze the urban surface temperatures, and to build a new risk indicator of exposure according to the residence location.
The study is based on 61 thermal images at 1-km resolution, sensed by the NOAA-AVRR satellites during the 1–13 August 2003 heat wave, and a case-control study concerning 482 persons aged 65 or more, living in the Paris region (France) at that time. For each person, minimal, maximal, and mean temperature indices were built for different periods, and integrated into a conditional logistic regression model to test their use as exposure indicator and their effect on mortality. The model was adjusted on other risk factors such as age, sex, socioeconomic conditions, autonomy, behavior of heat adaptation, health problems, housing, and geographical district.
The observed surface temperature amplitude ranged from 12.18°C to 45.41°C, with a median at 21.4°C at night and 34.2°C during day. The differences of surface temperatures between cases and controls ranged from −6.1°C to 8.4°C. The results of the analysis are statistically significant for minimal temperatures computed from 1st to 13th August, and for minimal temperature averaged on the period going from the day of the death to the sixth preceding day (OR of 2.57 and 2.22, respectively).
The results confirm the significant health effect of night-time high temperature, and point out the location and time of heat islands. Such data could be used for long-term prevention, by targeting the districts where intervention is a priority. Studying the links between night temperatures and urban characteristics should help public health authorities and planning agencies to determine the better actions for reducing heat islands.