Improving environmental exposure analysis using cumulative distribution functions and individual geocoding

BACKGROUND: Assessments of environmental exposure and health risks that utilize Geographic Information Systems (GIS) often make simplifying assumptions when using: (a) one or more discrete buffer distances to define the spatial extent of impacted regions, and (b) aggregated demographic data at the level of census enumeration units to derive the characteristics of the potentially exposed population. A case-study of school children in Orange County, Florida, is used to demonstrate how these limitations can be overcome by the application of cumulative distribution functions (CDFs) and individual geocoded locations. Exposure potential for 159,923 school children was determined at the childrens' home residences and at school locations by determining the distance to the nearest gasoline station, stationary air pollution source, and industrial facility listed in the Toxic Release Inventory (TRI). Errors and biases introduced by the use of discrete buffer distances and data aggregation were examined. RESULTS: The use of discrete buffers distances in proximity-based exposure analysis introduced substantial bias in terms of determining the potentially exposed population, and the results are strongly dependent on the choice of buffer distance(s). Comparisons of exposure potential between home and school locations indicated that different buffer distances yield different results and contradictory conclusions. The use of a CDF provided a much more meaningful representation and is not based on the a-priori assumption that any particular distance is more relevant than another. The use of individual geocoded locations also provided a more accurate characterization of the exposed population and allowed for more reliable comparisons among sub-groups. In the comparison of children's home residences and school locations, the use of data aggregated at the census block group and tract level introduced variability as well as bias, leading to incorrect conclusions as to whether exposure potential was higher at school or at home. CONCLUSION: The use of CDFs in distance-based environmental exposure assessment provides more robust results than the use of discrete buffer distances. Unless specific circumstances warrant the use of discrete buffer distances, their applcation should be discouraged in favor of CDFs. The use of aggregated data at the census tract or block group level introduces substantial bias in environmental exposure assessment, which can be reduced through individual geocoding. The use of aggregation should be minimized when individual-level data are available. Existing GIS analysis techniques are well suited to determine CDFs as well as reliably geocode large datasets, and computational issues do not present a barrier for their more widespread use in environmental exposure and risk assessment

A protocol for investigation of the effects of outdoor air pollution on stroke incidence, phenotypes and survival using the South London Stroke Register.

Stroke is a major cause of death and disability. About 5.3 million people die every year from stroke worldwide with over 9 million people surviving at any one time after suffering a stroke. About 1 in 4 men and 1 in 5 women aged 45 years will suffer a stroke if they live to their 85th year. It is estimated that by 2023 there will be an absolute increase in the number of people experiencing a first ever stroke of about 30% compared with 1983. In the UK, stroke is the third commonest cause of death and the most common cause of adult physical disability and consumes 5% of the health and social services budget. Stroke is assuming strategic public health importance because of increased awareness in society, an ageing population and emerging new treatments. It is an NHS health service and research priority, being identified as a target in Our Healthier Nation and the NSF for Older People for prevention and risk factor control and in the NHS Plan as a disease requiring intermediate care planning and reduction in inequalities of care. Whilst a number of risk factors for stroke are well known (e.g. increasing age, ethnicity, socioeconomic deprivation, hypertension), the potential importance of outdoor air pollution as a modifiable risk factor is much less well recognised. This is because studies to date are inconclusive or have methodological limitations. In Sheffield, we estimated that 11% of stroke deaths may be linked to current levels of outdoor air pollution and this high figure is explained by the fact that so many people are exposed to air pollution.We plan to study the effects of outdoor air pollution on stroke using a series of epidemiological (i.e. population based) studies. The purpose of this project is: to examine if short term increases in pollution can trigger a stroke in susceptible individuals, to investigate if the occurrence of stroke is higher amongst people living in more polluted areas (which would be explained by a combination of exposure to short term increases and longer term exposure to higher pollution levels), and to see if people living in more polluted areas have reduced survival following their stroke. We will use geographical information systems, robust statistical methods and powerful grid computing facilities to link and analyse the data. The datasets we will use are the South London Stroke Register database, daily monitored pollution data from national monitoring networks and modelled pollution data for London from the Greater London Authority. The South London Stroke Register records information on all patients who suffer a stroke ("incident" cases) living within a defined area. This stroke incidence dataset offers major advantages over previous studies examining the effects of pollution on hospital admissions and mortality, as not all patients with stroke are admitted or die and there may be a delay between the onset of stroke and admission or death. In addition, it contains other useful information, particularly the type of stroke people have suffered. Air pollution is a potentially modifiable risk factor for stroke. This study will provide robust population level evidence regarding the effects of outdoor air pollution on stroke. If it confirms the link, it will suggest to policy-makers at national and international levels that targeting policy interventions at high pollution areas may be a feasible option for stroke prevention

U.S. census unit population exposures to ambient air pollutants

<p>Abstract</p> <p>Background</p> <p>Progress has been made recently in estimating ambient PM_2.5(particulate matter with aerodynamic diameter < 2.5 μm) and ozone concentrations using various data sources and advanced modeling techniques, which resulted in gridded surfaces. However, epidemiologic and health impact studies often require population exposures to ambient air pollutants to be presented at an appropriate census geographic unit (CGU), where health data are usually available to maintain confidentiality of individual health data. We aim to generate estimates of population exposures to ambient PM_2.5and ozone for U.S. CGUs.</p> <p>Methods</p> <p>We converted 2001-2006 gridded data, generated by the U.S. Environmental Protection Agency (EPA) for CDC's (Centers for Disease Control and Prevention) Environmental Public Health Tracking Network (EPHTN), to census block group (BG) based on spatial proximities between BG and its four nearest grids. We used a bottom-up (fine to coarse) strategy to generate population exposure estimates for larger CGUs by aggregating BG estimates weighted by population distribution.</p> <p>Results</p> <p>The BG daily estimates were comparable to monitoring data. On average, the estimates deviated by 2 μg/m³(for PM_2.5) and 3 ppb (for ozone) from their corresponding observed values. Population exposures to ambient PM_2.5and ozone varied greatly across the U.S. In 2006, estimates for daily potential population exposure to ambient PM_2.5in west coast states, the northwest and a few areas in the east and estimates for daily potential population exposure to ambient ozone in most of California and a few areas in the east/southeast exceeded the National Ambient Air Quality Standards (NAAQS) for at least 7 days.</p> <p>Conclusions</p> <p>These estimates may be useful in assessing health impacts through linkage studies and in communicating with the public and policy makers for potential intervention.</p

Environmental risk assessment in the mediterranean region using artificial neural networks

Los mapas auto-organizados han demostrado ser una herramienta apropiada para la clasificación y visualización de grupos de datos complejos. Redes neuronales, como los mapas auto-organizados (SOM) o las redes difusas ARTMAP (FAM), se utilizan en este estudio para evaluar el impacto medioambiental acumulativo en diferentes medios (aguas subterráneas, aire y salud humana). Los SOMs también se utilizan para generar mapas de concentraciones de contaminantes en aguas subterráneas simulando las técnicas geostadísticas de interpolación como kriging y cokriging. Para evaluar la confiabilidad de las metodologías desarrolladas en esta tesis, se utilizan procedimientos de referencia como puntos de comparación: la metodología DRASTIC para el estudio de vulnerabilidad en aguas subterráneas y el método de interpolación espacio-temporal conocido como Bayesian Maximum Entropy (BME) para el análisis de calidad del aire. Esta tesis contribuye a demostrar las capacidades de las redes neuronales en el desarrollo de nuevas metodologías y modelos que explícitamente permiten evaluar las dimensiones temporales y espaciales de riesgos acumulativos

Combining spatial information sources while accounting for systematic errors in proxies

Environmental research increasingly uses high-dimensional remote sensing and numerical model output to help fill space-time gaps between traditional observations. Such output is often a noisy proxy for the process of interest. Thus one needs to separate and assess the signal and noise (often called discrepancy) in the proxy given complicated spatio-temporal dependencies. Here I extend a popular two-likelihood hierarchical model using a more flexible representation for the discrepancy. I employ the little-used Markov random field approximation to a thin plate spline, which can capture small-scale discrepancy in a computationally efficient manner while better modeling smooth processes than standard conditional auto-regressive models. The increased flexibility reduces identifiability, but the lack of identifiability is inherent in the scientific context. I model particulate matter air pollution using satellite aerosol and atmospheric model output proxies. The estimated discrepancies occur at a variety of spatial scales, with small-scale discrepancy particularly important. The examples indicate little predictive improvement over modeling the observations alone. Similarly, in simulations with an informative proxy, the presence of discrepancy and resulting identifiability issues prevent improvement in prediction. The results highlight but do not resolve the critical question of how best to use proxy information while minimizing the potential for proxy-induced error.Comment: 5 figures, 2 table

Spatio-temporal models for air pollution

Air pollution is the biggest environmental risk to global health and it is estimated that, globally, 7 million deaths can be attributed to air pollution each year \citep{WHO2018}. The World Bank estimates that, in 2016, the overall cost of ambient air pollution to the global economy was an estimated US \5.7 trillion or 4.4 per cent of global GDP \citep{worldbank}. A number of different air pollutants have been associated with adverse health effects, including fine particulate matter (PM_{2.5}$), nitrogen dioxide and ozone. In studies of the effects of air pollution, exposure information is often obtained from a fixed number of monitoring sites within the region of interest. However, an increasing number of models of air pollution are being used that provide estimates of concentrations. These are used to represent exposures at every location in a health study area, rather than just at a number of fixed measurement locations. Another use of modelling of air pollution is to provide short-term forecasts that can be used to inform the behaviour of vulnerable people. In this thesis, we develop statistical approaches to modelling, and forecasting, daily concentrations of$\mbox{PM}_{2.5}$in urban areas. We consider two different approaches, both in terms of model formulation and performing inference. The first approach is Dynamic Space-Time Models (DSTM). Under this framework, a \textit{data} model relates observations (measurements) to a \textit{process} model that specifies the dynamic evolution of the "true" underlying process. This approach is implemented using two different methods for estimation: methods of moments and expectation-maximisation. We also develop an approach using Bayesian Hierarchical Spatio-Temporal modelling (BHSTM). The inference is done using computational efficient methods for Bayesian inference (integrated nested Laplace approximations). This model allows predictions of daily PM$_{2.5}$over both space and time, which can be used to interpolate both past measurements and future predictions. Both approaches were implemented using data from Greater London, with their performance evaluated in terms of their ability to predict daily concentrations of PM$_{2.5}$over time at different measuring sites. Both methods were able to accurately predict future values of daily PM$_{2.5}$ at different locations, with one-day ahead predictions being more accurate than those used for longer periods, as might be expected. One of the major advantages of the BHSTM approach is that it provides a straightforward method for producing estimates of the uncertainty that is associated with predictions

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided