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      Evaluation of Spatial Relationships between Health and the Environment: The Rapid Inquiry Facility


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          The initiation of environmental public health tracking systems in the United States and the United Kingdom provided an opportunity to advance techniques and tools available for spatial epidemiological analysis integrating both health and environmental data.


          The Rapid Inquiry Facility (RIF) allows users to calculate adjusted and unadjusted standardized rates and risks. The RIF is embedded in ArcGIS so that further geographical information system (GIS) spatial functionality can be exploited or results can be exported to statistical packages for further tailored analyses where required. The RIF also links directly to several statistical packages and displays the results in the GIS.


          The value of the RIF is illustrated here with two case studies: risk of leukemia in areas surrounding oil refineries in the State of Utah (USA) and an analysis of the geographical variation of risk of esophageal cancer in relation to zinc cadmium sulfide exposure in Norwich (United Kingdom).


          The risk analysis study in Utah did not suggest any evidence of increased relative risk of leukemia, multiple myeloma, or Hodgkin’s lymphoma in the populations around the five oil-refining facilities but did reveal an excess risk of non-Hodgkin’s lymphoma that might warrant further investigation. The disease-mapping study in Norwich did not reveal any areas with higher relative risks of esophageal cancer common to both males and females, suggesting that a common geographically determined exposure was unlikely to be influencing cancer risk in the area.


          The RIF offers a tool that allows epidemiologists to quickly carry out ecological environmental epidemiological analysis such as risk assessment or disease mapping.

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          Spatial Epidemiology: Current Approaches and Future Challenges

          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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              Methodologic Issues and Approaches to Spatial Epidemiology

              Spatial epidemiology is increasingly being used to assess health risks associated with environmental hazards. Risk patterns tend to have both a temporal and a spatial component; thus, spatial epidemiology must combine methods from epidemiology, statistics, and geographic information science. Recent statistical advances in spatial epidemiology include the use of smoothing in risk maps to create an interpretable risk surface, the extension of spatial models to incorporate the time dimension, and the combination of individual- and area-level information. Advances in geographic information systems and the growing availability of modeling packages have led to an improvement in exposure assessment. Techniques drawn from geographic information science are being developed to enable the visualization of uncertainty and ensure more meaningful inferences are made from data. When public health concerns related to the environment arise, it is essential to address such anxieties appropriately and in a timely manner. Tools designed to facilitate the investigation process are being developed, although the availability of complete and clean health data, and appropriate exposure data often remain limiting factors.

                Author and article information

                Environ Health Perspect
                Environmental Health Perspectives
                National Institute of Environmental Health Sciences
                September 2010
                10 May 2010
                : 118
                : 9
                : 1306-1312
                [1 ] Small Area Health Statistics Unit, MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, United Kingdom
                [2 ] Institute of Health and Society, Newcastle University, Newcastle Upon Tyne, United Kingdom
                [3 ] CIBER Epidemiología y Salud Pública, Centre for Public Health Research, Valencia, Spain
                [4 ] Bureau of Epidemiology, Utah Department of Health, Salt Lake City, Utah, USA
                Author notes
                Address correspondence to L. Beale, Imperial College London, Department of Epidemiology and Biostatistics, St Mary’s Campus, London, W2 1PG, UK. Telephone: 44-20-7594-3348. Fax: 44-20-7594-3196. E-mail: l.beale@ 123456imperial.ac.uk

                The authors declare they have no actual or potential 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.
                : 18 December 2009
                : 10 May 2010

                Public health
                geographical information systems (gis),spatial epidemiology,environmental epidemiology,disease mapping,tool,risk analysis


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