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      Using Geographic Information Systems for Exposure Assessment in Environmental Epidemiology Studies

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          Abstract

          Geographic information systems (GIS) are being used with increasing frequency in environmental epidemiology studies. Reported applications include locating the study population by geocoding addresses (assigning mapping coordinates), using proximity analysis of contaminant source as a surrogate for exposure, and integrating environmental monitoring data into the analysis of the health outcomes. Although most of these studies have been ecologic in design, some have used GIS in estimating environmental levels of a contaminant at the individual level and to design exposure metrics for use in epidemiologic studies. In this article we discuss fundamentals of three scientific disciplines instrumental to using GIS in exposure assessment for epidemiologic studies: geospatial science, environmental science, and epidemiology. We also explore how a GIS can be used to accomplish several steps in the exposure assessment process. These steps include defining the study population, identifying source and potential routes of exposure, estimating environmental levels of target contaminants, and estimating personal exposures. We present and discuss examples for the first three steps. We discuss potential use of GIS and global positioning systems (GPS) in the last step. On the basis of our findings, we conclude that the use of GIS in exposure assessment for environmental epidemiology studies is not only feasible but can enhance the understanding of the association between contaminants in our environment and disease.

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          Most cited references44

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          Exposure measurement error in time-series studies of air pollution: concepts and consequences.

          Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them. Images Figure 1 Figure 2 Figure 3
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            Bias due to misclassification in the estimation of relative risk.

            Lack of bias in the estimation of relative effect in epidemiologic studies depends on the internal validity of the study. This paper conveys in graphic and tabular form the direction and magnitude of bias due to misclassification of study subjects. A series of computer-generated graphs shows that the departure of the estimate of effect (relative risk or odds ratio) from its true value is a function of sensitivity and specificity (measures of classification validity), disease frequency, and exposure frequency. The discussion of bias emphasizes misclassification of the "outcome" variable; i.e., disease occurrence in a cohort study and exposure rate in a case-control study. Examples are used to illustrate that the magnitude of the bias can be large under circumstances which occur readily in epidemiologic research. When misclassification is equal for the two compared groups, the estimate is biased toward the null value, and in some instances beyond; when differential misclassification occurs (as in selective recall in case-control studies) the bias can be in either direction, and may be great. Formulas are derived to estimate the underlying true value of the relative risk or odds ratio using the investigator's observations together with the estimated sensitivity and specificity of the classification procedure.
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              Health effects of residence near hazardous waste landfill sites: a review of epidemiologic literature.

              M Vrijheid (2000)
              This review evaluates current epidemiologic literature on health effects in relation to residence near landfill sites. Increases in risk of adverse health effects (low birth weight, birth defects, certain types of cancers) have been reported near individual landfill sites and in some multisite studies, and although biases and confounding factors cannot be excluded as explanations for these findings, they may indicate real risks associated with residence near certain landfill sites. A general weakness in the reviewed studies is the lack of direct exposure measurement. An increased prevalence of self-reported health symptoms such as fatigue, sleepiness, and headaches among residents near waste sites has consistently been reported in more than 10 of the reviewed papers. It is difficult to conclude whether these symptoms are an effect of direct toxicologic action of chemicals present in waste sites, an effect of stress and fears related to the waste site, or an effect of reporting bias. Although a substantial number of studies have been conducted, risks to health from landfill sites are hard to quantify. There is insufficient exposure information and effects of low-level environmental exposure in the general population are by their nature difficult to establish. More interdisciplinary research can improve levels of knowledge on risks to human health of waste disposal in landfill sites. Research needs include epidemiologic and toxicologic studies on individual chemicals and chemical mixtures, well-designed single- and multisite landfill studies, development of biomarkers, and research on risk perception and sociologic determinants of ill health.
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                Author and article information

                Journal
                Environ Health Perspect
                Environmental Health Perspectives
                National Institue of Environmental Health Sciences
                0091-6765
                June 2004
                15 April 2004
                : 112
                : 9
                : 1007-1015
                Affiliations
                1Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA
                2Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA
                3Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom
                Author notes
                Address correspondence to J.R. Nuckols, 125 EHB, Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523 USA. Telephone: (970) 491-7295. Fax: (970) 491-2940. E-mail: jnuckols@colostate.edu

                Special acknowledgement is given to S. Weigel for her contribution to the text used in the section on geospatial sciences, and to P. Stewart (OEEB-NCI) for her input on the exposure assessment process.

                Preparation of this article was funded in part by an intergovernmental personnel agreement between OEEB-NCI-NIH-DHHS and Colorado State University.

                The authors declare they have no competing financial interests.

                Article
                ehp0112-001007
                10.1289/ehp.6738
                1247194
                15198921
                3af6e306-6868-4351-99a2-5e3fcef83451
                This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.
                History
                : 12 September 2003
                : 25 March 2004
                Categories
                Mini-Monograph: Information Systems

                Public health
                geographic information systems,exposure assessment,environmental epidemiology

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