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Background Airborne particles are a complex mix of organic and inorganic compounds, with a range of physical and chemical properties. Estimation of how simultaneous exposure to air particles affects the risk of adverse health response represents a challenge for scientific research and air quality management. In this paper, we present a Bayesian approach that can tackle this problem within the framework of time series analysis. Methods We used Dirichlet process mixture models to cluster time points with similar multipollutant and response profiles, while adjusting for seasonal cycles, trends and temporal components. Inference was carried out via Markov Chain Monte Carlo methods. We illustrated our approach using daily data of a range of particle metrics and respiratory mortality for London (UK) 2002–2005. To better quantify the average health impact of these particles, we measured the same set of metrics in 2012, and we computed and compared the posterior predictive distributions of mortality under the exposure scenario in 2012 vs 2005. Results The model resulted in a partition of the days into three clusters. We found a relative risk of 1.02 (95% credible intervals (CI): 1.00, 1.04) for respiratory mortality associated with days characterised by high posterior estimates of non-primary particles, especially nitrate and sulphate. We found a consistent reduction in the airborne particles in 2012 vs 2005 and the analysis of the posterior predictive distributions of respiratory mortality suggested an average annual decrease of − 3.5% (95% CI: − 0.12%, − 5.74%). Conclusions We proposed an effective approach that enabled the better understanding of hidden structures in multipollutant health effects within time series analysis. It allowed the identification of exposure metrics associated with respiratory mortality and provided a tool to assess the changes in health effects from various policies to control the ambient particle matter mixtures. [ABSTRACT FROM AUTHOR]

Abstract: analysed the effect of outdoor modelled NO x levels, classified into quintiles, on stroke mortality using a Poisson Bayesian hierarchical model with spatial random effects. An association was observed between higher levels of NO x and stroke mortality at the small area (enumeration district) level. As this model is framed in an ecological perspective, the relative risk estimates suffer from ecological bias. In this paper we use a different model specification based on , modelling the number of cases of mortality due to stroke as a binomial random variable where p(i) is the probability of dying from stroke in area i. The within-area variation in outdoor modelled NO x levels is used to determine the proportion of the population in area i falling into each of the five exposure categories in order to estimate the probability of an individual dying from stroke given the kth level of NO x exposure assuming a homogeneous effect across the study region. The inclusion of within-area variability in an ecological regression model has been demonstrated to help reduce the ecological bias (). Revised estimates of relative risk are obtained and compared with previous estimates. [Copyright &y& Elsevier]

Abstract: It is well established that there exist substantial area-level socio-demographic variations in population health. However, area-level associations between deprivation and health cannot necessarily be interpreted as place effects on individual health. We demonstrate how recently developed statistical models for combining individual and aggregate data can help to separate the effects of place of residence and personal circumstances. We apply these to two health outcomes: risk of hospitalisation for cardiovascular disease (CVD) and risk of self-reported limiting long-term illness (LLTI). A combination of small-area data from UK hospital episode statistics and the UK census and individual data from the Health Survey for England are analysed, using a new multilevel modelling method termed hierarchical related regression (HRR). The standard multilevel model for place and health explains outcomes from individual data in terms of individual and area-level characteristics. HRR models increase precision by also explaining population aggregate outcomes, in terms of the same predictors. Aggregate outcomes are modelled by averaging the individual-level exposure–outcome relationship over the area, which can alleviate the ecological bias associated with interpreting the relationship between aggregate quantities as an individual-level relationship. We find that there are associations between area-level deprivation indicators and both area-level rates of hospital admission for CVD and area-level rates of LLTI. Multilevel models fitted to the individual data alone had insufficient power to determine whether these associations were due to compositional or contextual effects. Using HRR models which incorporate area-level outcomes in addition to individual outcomes, we found that for CVD, the area-level differences were mostly explained by individual-level effects, in particular the increased risk for individuals from non-white ethnic backgrounds. In contrast, there remained a significant association between LLTI and area-level deprivation even after adjusting for the significant increased risk associated with individual-level ethnicity and income. Our study illustrates that extending multilevel models to incorporate both individual and area-level outcomes increases power to distinguish between contextual and compositional effects. [Copyright &y& Elsevier]

Abstract: Objective: The wide spectrum of intracardiac anatomy and reparative surgery available for adults with congenital heart disease (ACHD) makes uniform measurement of cardiac size and disease severity challenging. The aim of this study was to assess the prognostic potential of cardiothoracic ratio, a simple marker of cardiomegaly, in a large cohort of ACHD. Patients and setting: Chest radiographs from 3033 ACHD patients attending our institution between 1998 and 2007 and 113 normal controls of similar age were analyzed blindly. Design: Cardiothoracic ratio derived from plain postero-anterior chest radiographs, was compared between ACHD patients and controls, different diagnostic subgroups and different functional classes. Relationship between cardiothoracic ratio and survival was assessed using Cox regression. Results: Average cardiothoracic ratio in ACHD was 52.0±7.6% (over 50% in 56.4%), significantly higher in all ACHD diagnostic subgroups compared to controls (42.3±4.0%, p<0.0001) and highest in the “complex” cardiac anatomy, Ebstein''s anomaly and Eisenmenger subgroups. Cardiothoracic ratio related to functional class, but was high even in asymptomatic patients. During a median follow-up of 4.2years, 164 patients died. Patients with a cardiothoracic ratio >55% had an 8-fold increased risk of death compared to those in the lowest tertile (<48%). Even patients with mildly increased cardiothoracic ratio (48–55%) had an adjusted 3.6-fold increased mortality compared to the lowest tertile. Conclusions: Cardiothoracic ratio derived from postero-anterior chest radiographs is a simple, and reproducible marker, which relates to functional class and predicts independently mortality risk in ACHD patients. [Copyright &y& Elsevier]