Shaddick, G., Lee, D., Zidek, J. V. and Salway, R., 2008. Estimating exposure response functions using ambient pollution concentrations. Annals of Applied Statistics, 2 (4), pp. 1249-1270.
This paper presents an approach to estimating the health effects of an environmental hazard. The approach is general in nature, but is applied here to the case of air pollution. It uses a computer model involving ambient pollution and temperature input to simulate the exposures experienced by individuals in an urban area, while incorporating the mechanisms that determine exposures. The output from the model comprises a set of daily exposures for a sample of individuals from the population of interest. These daily exposures are approximated by parametric distributions so that the predictive exposure distribution of a randomly selected individual can be generated. These distributions are then incorporated into a hierarchical Bayesian framework (with inference using Markov chain Monte Carlo simulation) in order to examine the relationship between short-term changes in exposures and health outcomes, while making allowance for long-term trends, seasonality, the effect of potential confounders and the possibility of ecological bias. The paper applies this approach to particulate pollution (PM10) and respiratory mortality counts for seniors in greater London (>= 65 years) during 1997. Within this substantive epidemiological study, the effects on health of ambient concentrations and (estimated) personal exposures are compared. The proposed model incorporates within day (or between individual) variability in personal exposures, which is compared to the more traditional approach of assuming a single pollution level applies to the entire population for each day. Effects were estimated using single lags and distributed lag models, with the highest relative risk, RR = 1.02 (1.01-1.04), being associated with a lag of two days ambient concentrations of PM10. Individual exposures to PM10 for this group (seniors) were lower than the measured ambient concentrations with the corresponding risk, RR = 1.05 (1.01-1.09), being higher than would be suggested by the traditional approach using ambient concentrations.
|Item Type ||Articles|
|Creators||Shaddick, G., Lee, D., Zidek, J. V. and Salway, R.|
|Departments||Faculty of Science > Mathematical Sciences|
Faculty of Humanities & Social Sciences > Health
|Research Centres||Bath Institute for Complex Systems (BICS)|
|Additional Information||Environmental epidemiology, Air pollution, Personal exposure simulator, Bayesian hierarchical models|
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