A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London

Abstract

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices.

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

“…Short term and long term effects have been documented, with a scientific literature increasing exponentially since the London great smog in 1952 and the following Clean Air Act of 1955 in the US and 1956 in the UK (Schwartz and Marcus, 1990;Dockery et al, 1993;Atkinson et al, 2014;Rushworth et al, 2014), with particular focus on cardiovascular and respiratory diseases (Brunekreef and Holgate, 2002;Künzli, 2012;Schikowski et al, 2005;Lanki et al, 2006;Brook et al, 2010).…”

“…An additional feature of our modelling framework is that we move away from the standard public health approach, where the exposure estimated at the first stage is plugged in the second stage without taking its uncertainty into account (Rushworth et al, 2014;Lee and Sarran, 2015;Huang et al, 2015); instead we consider the entire NO 2 posterior distribution from the first stage and feed it forward into the health model. We compare the results from this model with the naive one, that plugs in the mean from the NO 2 distribution into the second stage model.…”

Air pollution and hospital admissions for cardiorespiratory diseases in Iran: artificial neural network versus conditional logistic regression

“…Over the past two decades, a number of studies have evaluated the associations between outdoor air pollution and adverse health effects such as cardiovascular and respiratory diseases in different parts of the world including North America (Li et al 2011;Lin et al 2008;Rodopoulou et al 2014;Wilson et al 2004), Europe (Kalantzi et al 2011;Di Ciaula 2012;Rushworth et al 2014), Australia (Hansen et al 2012), and some Asian countries including China, Taiwan, and India (Cao et al 2009;Chang et al 2005;Chen et al 2010;Ge et al 2011;Gurjar et al 2010;Lin et al 2013;Tao et al 2014). These studies have shown an increase in mortality and/or hospital admissions for cardiovascular and respiratory diseases due to short-and long-term exposure to air pollutants.…”

Modeling pediatric tumor risks in Florida with conditional autoregressive structures and identifying hot-spots

“…When ρ = 0, t becomes independent of each time t. A larger value of ρ implies stronger temporal autocorrelation. This model is considered in Rushworth et al (2014).…”