Modelling the effects of air pollution on health using Bayesian dynamic generalised linear models

The relationship between short-term exposure to air pollution and mortality or morbidity has been the subject of much recent research, in which the standard method of analysis uses Poisson linear or additive models. In this paper, we use a Bayesian dynamic generalised linear model (DGLM) to estimate this relationship, which allows the standard linear or additive model to be extended in two ways: (i) the long-term trend and temporal correlation present in the health data can be modelled by an autoregressive process rather than a smooth function of calendar time; (ii) the effects of air pollution are allowed to evolve over time. The efficacy of these two extensions are investigated by applying a series of dynamic and non-dynamic models to air pollution and mortality data from Greater London. A Bayesian approach is taken throughout, and a Markov chain monte carlo simulation algorithm is presented for inference. An alternative likelihood based analysis is also presented, in order to allow a direct comparison with the only previous analysis of air pollution and health data using a DGLM

A general theory for preferential sampling in environmental networks

This is the final version. Available from the Institute of Mathematical Statistics via the DOI in this recordThis paper presents a general model framework for detecting the preferential sampling of environmental monitors recording an environmental process across space and/or time. This is achieved by considering the joint distribution of an environmental process with a site–selection process that considers where and when sites are placed to measure the process. The environmental process may be spatial, temporal or spatio–temporal in nature. By sharing random effects between the two processes, the joint model is able to establish whether site placement was stochastically dependent of the environmental process under study. Furthermore, if stochastic dependence is identified between the two processes, then inferences about the probability distribution of the spatio–temporal process will change, as will predictions made of the process across space and time. The embedding into a spatio–temporal framework also allows for the modelling of the dynamic site—selection process itself. Real–world factors affecting both the size and location of the network can be easily modelled and quantified. Depending upon the choice of population of locations to consider for selection across space and time under the site– selection process, different insights about the precise nature of preferential sampling can be obtained. The general framework developed in the paper is designed to be easily and quickly fit using the R-INLA package. We apply this framework to a case study involving particulate air pollution over the UK where a major reduction in the size of a monitoring network through time occurred. It is demonstrated that a significant response–biased reduction in the air quality monitoring network occurred, namely the relocation of monitoring sites to locations with the highest pollution levels, and the routine removal of sites at locations with the lowest. We also show that the network was consistently unrepresentative of the levels of particulate matter seen across much of GB throughout the operating life of the network. Finally we show that this may have led to a severe over-reporting of the population–average exposure levels experienced across GB. This could have great impacts on estimates of the health effects of black smoke levels.Natural Science and Engineering Research Council of Canad

Small-area study of the incidence of neoplasms of the brain and central nervous system among adults in the West Midlands region, 1974-86. Small Area Health Statistics Unit.

This small-area study of incidence of cancers of the brain and central nervous system found evidence of trend (P = 0.02) of cancer risk with deprivation (8% higher risk in affluent areas), but no significant association with urban-rural status. Results were not indicative of a strong geographically determined risk at small-area level

Cancer incidence near municipal solid waste incinerators in Great Britain. Part 2: histopathological and case-note review of primary liver cancer cases

We reported previously a 37% excess risk of liver cancer within 1 km of municipal incinerators. Of 119/235 (51%) cases reviewed, primary liver cancer was confirmed in 66 (55%) with 21 (18%) definite secondary cancers. The proportions of true primaries ranging between 55% and 82% (i.e. excluding secondary cancers) give revised estimates of between 0.53 and 0.78 excess cases per 105per year within 1 km. © 2000 Cancer Research Campaig

Advances in spatiotemporal models for non-communicable disease surveillance

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public

No one knows which city has the highest concentration of fine particulate matter

This is the final version. Available from the publisher via the DOI in this record.Exposure to ambient fine particulate matter (PM2.5) is the leading global environmental risk factor for mortality and disease burden, with associated annual global welfare costs of trillions of dollars. Examined within is the ability of current data to answer a basic question about PM2.5, namely the location of the city with the highest PM2.5 concentration. The ability to answer this basic question serves as an indicator of scientific progress to assess global human exposure to air pollution and as an important component of efforts to reduce its impacts. Despite the importance of PM2.5, we find that insufficient monitoring data exist to answer this basic question about the spatial pattern of PM2.5 at the global scale. Only 24 of 234 countries have more than 3 monitors per million inhabitants, while density is an order of magnitude lower in the vast majority of the world's countries, with 141 having no regular PM2.5 monitoring at all. The global mean population distance to nearest PM2.5 monitor is 220 km, too large for exposure assessment. Efforts to fill in monitoring gaps with estimates from satellite remote sensing, chemical transport modeling, and statistical models have biases at individual monitor locations that can exceed 50 μg m−3. Progress in advancing knowledge about the global distribution of PM2.5 will require a harmonized network that integrates different types of monitoring equipment (regulatory networks, low-cost monitors, satellite remote sensing, and research-grade instrumentation) with atmospheric and statistical models. Realization of such an integrated framework will facilitate accurate identification of the location of the city with the highest PM2.5 concentration and play a key role in tracking the progress of efforts to reduce the global impacts of air pollution.Natural Sciences and Engineering Research Council of CanadaDepartment of Biotechnology on ‘Air Pollution and Human Health

Multivariate Hierarchical Modelling of Household Air Pollution

This is the author accepted manuscript. The final version is available from the Statistical Modelling Society via the link in this recordExposure to household air pollution has been attributed to an estimated 3.8 million deaths per year. A major contributor to this exposure is the reliance on various polluting fuels for cooking by almost half of all households in low and middle-income countries. We present a multivariate hierarchical model for surveys of the proportion of people relying on each fuel type, for the period 1990-2017, addressing several challenges with modelling the data including incomplete surveys and sampling bias.Natural Environment Research Council (NERC)World Health Organizatio

Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA

This is the final version. Available on open access from the International Society for Bayesian Analysis via the DOI in this recordExposure to air pollution in the form of fine particulate matter (PM2.5) is known to cause diseases and cancers. Consequently, the public are increasingly seeking health warnings associated with levels of PM2.5 using mobile phone applications and websites. Often, these existing platforms provide one-size-fits-all guidance, not incorporating user specific personal preferences. This study demonstrates an innovative approach using Bayesian methods to support personalised decision making for air quality. We present a novel hierarchical spatio-temporal model for city air quality that includes buildings as barriers and captures covariate information. Detailed high resolution PM2.5 data from a single mobile air quality sensor is used to train the model, which is fit using R-INLA to facilitate computation at operational timescales. A method for eliciting multi-attribute utility for individual journeys within a city is then given, providing the user with Bayes-optimal journey decision support. As a proof-of-concept, the methodology is demonstrated using a set of journeys and air quality data collected in Brisbane city centre, Australia