BIOGAS: A Bright Idea for Africa

Background: In the context of climate change, an efficient alert system to prevent the risk associated with summer heat is necessary. The authors' objective was to describe the temperature-mortality relationship in France over a 29-year period and to define and validate a combination of temperature factors enabling optimum prediction of the daily fluctuations in summer mortality. Methods: The study addressed the daily mortality rates of subjects aged over 55 years, in France as a whole, from 1975 to 2003. The daily minimum and maximum temperatures consisted in the average values recorded by 97 meteorological stations. For each day, a cumulative variable for the maximum temperature over the preceding 10 days was defined. The mortality rate was modelled using a Poisson regression with over-dispersion and a first-order autoregressive structure and with control for long-term and within-summer seasonal trends. The lag effects of temperature were accounted for by including the preceding 5 days. A "backward" method was used to select the most significant climatic variables. The predictive performance of the model was assessed by comparing the observed and predicted daily mortality rates on a validation period (summer 2003), which was distinct from the calibration period (1975–2002) used to estimate the model. Results: The temperature indicators explained 76% of the total over-dispersion. The greater part of the daily fluctuations in mortality was explained by the interaction between minimum and maximum temperatures, for a day t and the day preceding it. The prediction of mortality during extreme events was greatly improved by including the cumulative variables for maximum temperature, in interaction with the maximum temperatures. The correlation between the observed and estimated mortality ratios was 0.88 in the final model. Conclusion: Although France is a large country with geographic heterogeneity in both mortality and temperatures, a strong correlation between the daily fluctuations in mortality and the temperatures in summer on a national scale was observed. The model provided a satisfactory quantitative prediction of the daily mortality both for the days with usual temperatures and for the days during intense heat episodes. The results may contribute to enhancing the alert system for intense heat waves. Published: 19 June 2007 BMC Public Health 2007, 7:114 doi:10.1186/1471-2458-7-114 Received: 12 March 2007 Accepted: 19 June 2007 This article is available from: http://www.biomedcentral.com/1471-2458/7/114 © 2007 Fouillet et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Page 1 of 11 (page number not for citation purposes) BMC Public Health 2007, 7:114 http://www.biomedcentral.com/1471-2458/7/114 Background A weather-mortality relationship in summer and marked excess mortality during extremely hot periods have been clearly established [1,2]. Some studies have described the main epidemiologic and environmental characteristics of specific heat waves [3-14]. Others have focused on a timeseries approach in order to model the general temperature-mortality shape or the lag time between a climatic event and its impact on mortality [15-24]. The weather component has mainly been considered on the basis of the temperatures recorded on various lag days. Other climatic parameters, such as humidity, wind speed and pressure, have also been considered as independent variables [17,18,22,24,25] or by constructing combined indices [21] or synoptic patterns [26]. Air pollution has also been included in some studies focusing on specific urban areas [18,20,22,23,25]. Although all the studies have concluded that long and intense heat episodes are responsible for major excess mortality, quantitative indicators that take into account both the intensity and duration of heat episodes have seldom been proposed and formally validated [7,8,21,27,28]. In August 2003, Western Europe experienced a heat wave that was exceptional in terms of its duration, intensity and geographic extent [7,8,27,29,30]. Unlike prior lessmarked heat waves, its health impact attracted considerable public interest and drew attention to the need for efficient alert systems. Moreover, the Intergovernmental Panel on Climate Change predicts an increase in extreme climatic events in the twenty-first century, [31] and several scenario studies, e.g. Beniston's study, [32] predict that heat waves like that in 2003 may occur every two or three years on average, by the third part of this century. In that context, the objective of this paper is to describe and model the relationship between mortality and temperature in France over a 29-year period (from 1975 to 2003) and, more generally, to propose an approach for the selection of the most predictive combination of temperature factors with a view to predicting the risk of shortterm mortality in summer (June to September). Methods The study analysed the relationship between daily fluctuations in mortality and temperature for the whole of France, over the 122 summer days, from 1st June to 30th September of each year, from 1975 to 2003, i.e. 3,538 summer days in all. Mortality data The mortality data were provided by the French National Institute for Medical Research (Inserm). The daily counts of all-cause mortality (Ot) for people aged 55 years and over were analysed. The use of this mortality data in the frame of epidemiological studies has been authorised by the French National Commission for Data protection and theLiberties(CNIL). The yearly population estimates were supplied by the French National Institute of Statistics and Economic Studies (INSEE). Mortality was expressed as the daily mortality rate per 100,000 subjects. Climatic data The daily minimum and maximum temperatures (Tmin and Tmax) and minimum and maximum relative humidities (Hmin and Hmax) were recorded by 97 weather stations considered representative of the climate affecting the populations of the 96 French départements by the national meteorological service (Météo-France). The national daily values of those climatic indicators were the average of those 97 values, weighted by the populations of the départements. A 10-day moving average of the mean temperature (average of the daily minimum and maximum temperatures) was also calculated. A cumulative temperature variable which was close to the total degree-days of excedance, was constructed [27]. For each day, the cumulative minimum/maximum temperature variable (CTmin and CTmax) was defined as the sum of the number of degrees above a cut-off point from the current day t to either day t-10 or the last day with a temperature higher than the cut-off point. This variable was equal to zero if the temperature was below the cut-off point on the day considered: in which: k is the lower of the value 10 and the value of the first previous day on which Tmaxt fell below the cut-off point; is equal to 1 if Tmaxt-d is higher than the cut-off and 0 otherwise. The cut-off points were selected by minimising the deviance of the model including the minimum/maximum temperatures and the minimum/maximum cumulative variables over a grid of cut-off values. The cut-off point for maximum temperatures was found to be equal to 27°C (80.6°F). The cut-off point for minimum temperature was so close to 0°C that the cumulative variable for minimum CT T cut off I t t d T cut off d k t d max max max = − × − > = − ∑ ( ) 0 IT cut off t d max − > Page 2 of 11 (page number not for citation purposes) BMC Public Health 2007, 7:114 http://www.biomedcentral.com/1471-2458/7/114 temperature was very strongly correlated with the moving average of the mean temperature (0.95). Therefore, CTmin was not included in the model. Statistical analysis The daily mortality rates were modelled using a generalized estimating equations (GEE) approach, with a Poisson distribution. This model enables both specification of an over-dispersion term and a first-order autoregressive structure that accounts for the autocorrelation of the daily numbers of deaths within each summer and assumes the independence of the summers. A log-linear long-term mortality trend (Trend) and the seasonality of mortality during summer, using a quadratic time function by day (Season), were included in the model. The model was also adjusted for a dummy variable (Summer) which differentiated the 122 summer days (from June to September inclusive) from the other days of the year. The non-summer days provided useful information on the long-term trend of the baseline mortality. The baseline model M0 was: (M0) Log [E (Ot)] = Log (PopJ) + μ + β Trend + Season In which,PopJ was the population estimate for the year considered. The temperature factors were added to the baseline model to yield the model M1: in which the temperature factors are the minimum and maximum temperatures (Tmin and Tmax), the moving average of the mean temperature (MA) and the cumulative variable for maximum temperatures (CTmax). In order to distinguish the specific impact of temperatures up to 5 days before death, the lagged minimum/maximum temperatures and cumulative maximum temperature were also included in the model. Some interactions between minimum/maximum temperatures and the cumulative indicator, recorded on the day considered and the preceding two days, were also added. The full model M1 thus contained 19 different temperature indicators and 10 interactions (Table 1). In a sensitivity analysis, the model was also adjusted for the daily minimum/maximum relative humidities, both as individual factors and as interactions with temperature, as confounder indicators. However, the results did not change. Definition of temperature variables In order to select the most predictive temperature indicators among the 29 variables used in the present paper, a "backward" method was applied on model M1. First, the decision was taken to divide the 29 temperature variables and interactions into 17 groups, in order to ensure that the interactions between two indicators were systematically included in the model with the main effects (Table 1). Most groups contained indicators of the same lag day (G1, GCum1). Four groups contained one temperature indicator recorded on the day considered and another indicator recorded on the preceding day (G2, G2', GCum2 and GCum2'). The 17 groups of indicators were divided in three categories. The first category contained the moving average of the mean temperatures, which reflects the climatic environment in which the subjects lived over the preceding ten days (GMA). The second category contained the minimum and maximum temperatures recorded on various lag days and thus reflected the specific exposure for each day (G1, G2 and G2'). The last category characterised the long periods of high temperatures and therefore included the cumulative indicators (GCum1, GCum2 and GCum2'). ( ) [ ( )] ( ) M Log E O Log PopJ Trend Season Summer Tempera t i 1 = + + + + × μ β θ ture factori t i , ∑ ⎡