BACKGROUND: There is debate on how the effect of air pollution should be assessed. We propose an approach to estimate its impact on adult and infant mortality that integrates data from long-term epidemiological studies and studies of interventions to reduce pollution. We use the method to estimate the number of years of life lost (YLLs) attributable to air pollution during 1 year in Switzerland. METHODS: A dynamic exposure-response model was implemented, which uses an exponential function (exp(-kt)) to model the change in mortality after cessation of air pollution. The model was populated with relative risk estimates and estimates of time constant k from the literature. Air pollution exposure in Switzerland was modelled using data from emission inventories. YLLs attributable to air pollution were calculated by taking the difference between observed survival probabilities in Switzerland in 2000 and modified survival probabilities, assuming no air pollution during the year 2000. RESULTS: Meta-analyses of three studies of adult mortality and five studies of infant mortality gave relative risks of 1.059 (95% confidence interval (CI) 1.031-1.088) and 1.056 (95% CI 1.026-1.088) per 10 mug/m(3) increase in PM10 concentration. Time constants k derived from two studies of the effects of the closing down of a steel mill in the Utah Valley and of the coal ban in Dublin were 0.88 and 0.11. Assuming a time constant k of 0.5 resulted in 42 400 (95% CI 22 600-63 600) YLLs, with 4.0% being ascribed to infant deaths. A total of 39% of the effect occurred in the same year and 80% within 5 years. The estimated number of YLLs was little affected by the choice of the time constant. CONCLUSIONS: In contrast to traditional steady-state models the dynamic model allows changes in mortality following short-term increases or decreases in air pollution levels to be quantified. This type of information is of obvious interest to policy makers.
BACKGROUND: There is debate on how the effect of air pollution should be assessed. We propose an approach to estimate its impact on adult and infant mortality that integrates data from long-term epidemiological studies and studies of interventions to reduce pollution. We use the method to estimate the number of years of life lost (YLLs) attributable to air pollution during 1 year in Switzerland. METHODS: A dynamic exposure-response model was implemented, which uses an exponential function (exp(-kt)) to model the change in mortality after cessation of air pollution. The model was populated with relative risk estimates and estimates of time constant k from the literature. Air pollution exposure in Switzerland was modelled using data from emission inventories. YLLs attributable to air pollution were calculated by taking the difference between observed survival probabilities in Switzerland in 2000 and modified survival probabilities, assuming no air pollution during the year 2000. RESULTS: Meta-analyses of three studies of adult mortality and five studies of infant mortality gave relative risks of 1.059 (95% confidence interval (CI) 1.031-1.088) and 1.056 (95% CI 1.026-1.088) per 10 mug/m(3) increase in PM10 concentration. Time constants k derived from two studies of the effects of the closing down of a steel mill in the Utah Valley and of the coal ban in Dublin were 0.88 and 0.11. Assuming a time constant k of 0.5 resulted in 42 400 (95% CI 22 600-63 600) YLLs, with 4.0% being ascribed to infant deaths. A total of 39% of the effect occurred in the same year and 80% within 5 years. The estimated number of YLLs was little affected by the choice of the time constant. CONCLUSIONS: In contrast to traditional steady-state models the dynamic model allows changes in mortality following short-term increases or decreases in air pollution levels to be quantified. This type of information is of obvious interest to policy makers.
Authors: Marko Tainio; Jouni T Tuomisto; Otto Hänninen; Juhani Ruuskanen; Matti J Jantunen; Juha Pekkanen Journal: Environ Health Date: 2007-08-23 Impact factor: 5.984