144 research outputs found
Time-varying coefficient models for the analysis of air pollution and health outcome data
No full textIn this article a time-varying coefficient model is developed to examine the relationship between adverse health and short-term (acute) exposure to air pollution. This model allows the relative risk to evolve over time, which may be due to an interaction with temperature, or from a change in the composition of pollutants, such as particulate matter, over time. The model produces a smooth estimate of these time-varying effects, which are not constrained to follow a fixed parametric form set by the investigator. Instead, the shape is estimated from the data using penalized natural cubic splines. Poisson regression models, using both quasi-likelihood and Bayesian techniques, are developed, with estimation performed using an iteratively re-weighted least squares procedure and Markov chain Monte Carlo simulation, respectively. The efficacy of the methods to estimate different types of time-varying effects are assessed via a simulation study, and the models are then applied to data from four cities that were part of the National Morbidity, Mortality, and Air Pollution Study
Statistical methodological aspects of modelling relationships between air pollution, temperature and health
No full textOpen Acces
From Satellites to Burden: New Approaches to Assessing the Global Burden Associated with Ambient Air Pollution
No full textHighly Multivariate High-dimensionality Spatial Stochastic Processes-A Mixed Conditional Approach
No full textWe propose a hybrid mixed spatial graphical model framework and novel
concepts, e.g., cross-Markov Random Field (cross-MRF), to comprehensively
address all feature aspects of highly multivariate high-dimensionality (HMHD)
spatial data class when constructing the desired joint variance and precision
matrix (where both p and n are large). Specifically, the framework accommodates
any customized conditional independence (CI) among any number of p variate
fields at the first stage, alleviating dynamic memory burden. Meanwhile, it
facilitates parallel generation of covariance and precision matrix, with the
latter's generation order scaling only linearly in p. In the second stage, we
demonstrate the multivariate Hammersley-Clifford theorem from a column-wise
conditional perspective and unearth the existence of cross-MRF. The link of the
mixed spatial graphical framework and the cross-MRF allows for a mixed
conditional approach, resulting in the sparsest possible representation of the
precision matrix via accommodating the doubly CI among both p and n, with the
highest possible exact-zero-value percentage. We also explore the possibility
of the co-existence of geostatistical and MRF modelling approaches in one
unified framework, imparting a potential solution to an open problem. The
derived theories are illustrated with 1D simulation and 2D real-world spatial
data.Comment: 46 pages; 11 figure
Modelling daily multivariate pollutant data at multiple sites
No full textThis paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling. Copyright 2002 Royal Statistical Society.
A case study in preferential sampling: long term monitoring of air pollution in the UK
No full textThe effects of air pollution are a major concern both in terms of the environment and human health. The majority of information relating to concentrations of air pollution comes from monitoring networks, data from which are used to inform regulatory criteria and in assessing health effects. In the latter case, measurements from the network are interpreted as being representative of levels to which populations are exposed. However there is the possibility of selection bias if monitoring sites are located in only the most polluted areas, a concept referred to as preferential sampling. Here we examine long-term changes in levels of air pollution from a monitoring network in the UK which was operational from the 1960s until 2006. During this unique period in history, concentrations fell dramatically from levels which would be unrecognisable in the UK today, reflecting changes in the large scale use of fossil fuels. As levels fell the network itself was subject to considerable change. We use spatio-temporal models, set within a Bayesian framework using INLA for inference, to model declining concentrations in relation to changes in the network. The results support the hypothesis of preferential sampling that has largely been ignored in environmental risk analysis
Health-Exposure Modelling and the Ecological Fallacy
No full textRecently there has been increased interest in modelling the association between aggregate disease counts and environmental exposures measured, for example via air pollution monitors, at point locations. This paper has two aims: first we develop a model for such data in order to avoid ecological bias; second we illustrate that modelling the exposure surface and estimating exposures may lead to bias in estimation of health effects. Design issues are also briefly considered, in particular the loss of information in moving from individual to ecological data, and the at-risk populations to consider in relation to the pollution monitor locations. The approach is investigated initially through simulations, and is then applied to a study of the association between mortality in the over 65’s in the year 2000, and the previous year’s SO2, in London. We conclude that the use of the proposed model can provide valid inference, but the use of estimated exposures should be carried out with great caution
Modelling Nonstationary Processes Through Dimension Expansion
No full textIn this paper, we propose a novel approach to modeling nonstationary spa-tial fields. The proposed method works by expanding the geographic plane over which these processes evolve into higher dimensional spaces, transforming and clarifying complex patterns in the physical plane. By combining aspects of multi-dimensional scaling, group lasso, and latent variable models, a dimen-sionally sparse projection is found in which the originally nonstationary field exhibits stationarity. Following a comparison with existing methods in a simu-lated environment, dimension expansion is studied on a classic test-bed data set historically used to study nonstationary models. Following this, we explore the use of dimension expansion in modeling air pollution in the United Kingdom, a process known to be strongly influenced by rural/urban effects, amongst others, which gives rise to a nonstationary field.
Impacts of a reduction in 11 kV voltage settings in South Wales
No full textThis study investigates the impacts on the low-voltage distribution network of a reduction in 11 kV voltage control settings. Through the use of statistical modelling, the effects of a real-life reduction in voltage settings were measured and quantified. This highlighted statistically significant reductions in average real power demand, maximum real power demand and average reactive power demand. A reduction in demand of the magnitude observed would equate to an estimated saving of £14.9 million for customers if all substations in South Wales were changed
Spatio-Temporal Methods in Environmental Epidemiology
No full texteaches Students How to Perform Spatio-Temporal Analyses within Epidemiological StudiesSpatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and environmental epidemiologists, the book links recent developments in spatio-temporal methodology with epidemiological applications. Drawing on real-life problems, it provides the necessary tools to exploit advances in methodology when assessing the health risks associated with environmental hazards. The book’s clear guidelines enable the implementation of the methodology and estimation of risks in practice.
Designed for graduate students in both epidemiology and statistics, the text covers a wide range of topics, from an introduction to epidemiological principles and the foundations of spatio-temporal modeling to new research directions. It describes traditional and Bayesian approaches and presents the theory of spatial, temporal, and spatio-temporal modeling in the context of its application to environmental epidemiology. The text includes practical examples together with embedded R code, details of specific R packages, and the use of other software, such as WinBUGS/OpenBUGS and integrated nested Laplace approximations (INLA). A supplementary website provides additional code, data, examples, exercises, lab projects, and more.
Representing a major new direction in environmental epidemiology, this book―in full color throughout―underscores the increasing need to consider dependencies in both space and time when modeling epidemiological data. Students will learn how to identify and model patterns in spatio-temporal data as well as exploit dependencies over space and time to reduce bias and inefficiency
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