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      Spatial Epidemiology: Current Approaches and Future Challenges


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          Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health.

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          Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies

          There is currently much interest in conducting spatial analyses of health outcomes at the small-area scale. This requires sophisticated statistical techniques, usually involving Bayesian models, to smooth the underlying risk estimates because the data are typically sparse. However, questions have been raised about the performance of these models for recovering the “true” risk surface, about the influence of the prior structure specified, and about the amount of smoothing of the risks that is actually performed. We describe a comprehensive simulation study designed to address these questions. Our results show that Bayesian disease-mapping models are essentially conservative, with high specificity even in situations with very sparse data but low sensitivity if the raised-risk areas have only a moderate ( 50 per area). Semiparametric spatial mixture models typically produce less smoothing than their conditional autoregressive counterpart when there is sufficient information in the data (moderate-size expected count and/or high true excess risk). Sensitivity may be improved by exploiting the whole posterior distribution to try to detect true raised-risk areas rather than just reporting and mapping the mean posterior relative risk. For the widely used conditional autoregressive model, we show that a decision rule based on computing the probability that the relative risk is above 1 with a cutoff between 70 and 80% gives a specific rule with reasonable sensitivity for a range of scenarios having moderate expected counts (~ 20) and excess risks (~1.5- to 2-fold). Larger (3-fold) excess risks are detected almost certainly using this rule, even when based on small expected counts, although the mean of the posterior distribution is typically smoothed to about half the true value.
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            Prospective time periodic geographical disease surveillance using a scan statistic

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              The ecological fallacy.


                Author and article information

                Environ Health Perspect
                Environmental Health Perspectives
                National Institue of Environmental Health Sciences
                June 2004
                15 April 2004
                : 112
                : 9
                : 998-1006
                1Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom
                2Environmental and Occupational Health Sciences Institute and The Cancer Institute of New Jersey, University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, Piscataway, New Jersey, USA
                Author notes
                Address correspondence to P. Elliott, Small Area Health Statistics Unit, Department of Epidemiology and Public Health, Imperial College London, Faculty of Medicine, St. Mary’s Campus, Norfolk Place, London W2 1PG, United Kingdom. Telephone: 44 0 20 75943328. Fax: 44 0 20 7262 1034. E-mail: p.elliott@imperial.ac.uk

                The Small Area Health Statistics Unit is funded by a grant from the Department of Health, Department of the Environment, Food and Rural Affairs, Environment Agency, Health and Safety Executive, Scottish Executive, Welsh Assembly Government, and Northern Ireland Department of Health, Social Services and Public Safety. This research was also supported by grants R01 CA92693 from the National Cancer Institute and U61/ATU272387 from the Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, to D.W.

                The views expressed in this publication are those of the authors and not necessarily those of the funding bodies.

                The authors declare they have no competing financial interests.

                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.
                Mini-Monograph: Information Systems

                Public health
                epidemiology,geographic studies,methods,environmental pollution,disease mapping,disease clusters


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