24
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Feasibility and utility of mapping disease risk at the neighbourhood level within a Canadian public health unit: an ecological study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          We conducted spatial analyses to determine the geographic variation of cancer at the neighbourhood level (dissemination areas or DAs) within the area of a single Ontario public health unit, Wellington-Dufferin-Guelph, covering a population of 238,326 inhabitants. Cancer incidence data between 1999 and 2003 were obtained from the Ontario Cancer Registry and were geocoded down to the level of DA using the enhanced Postal Code Conversion File. The 2001 Census of Canada provided information on the size and age-sex structure of the population at the DA level, in addition to information about selected census covariates, such as average neighbourhood income.

          Results

          Age standardized incidence ratios for cancer and the prevalence of census covariates were calculated for each of 331 dissemination areas in Wellington-Dufferin-Guelph. The standardized incidence ratios (SIR) for cancer varied dramatically across the dissemination areas. However, application of the Moran's I statistic, a popular index of spatial autocorrelation, suggested significant spatial patterns for only two cancers, lung and prostate, both in males (p < 0.001 and p = 0.002, respectively). Employing Bayesian hierarchical models, areas in the urban core of the City of Guelph had significantly higher SIRs for male lung cancer than the remainder of Wellington-Dufferin-Guelph; and, neighbourhoods in the urban and surrounding rural areas of Orangeville exhibited significantly higher SIRs for prostate cancer. After adjustment for age and spatial dependence, average household income attenuated much of the spatial pattern of lung cancer, but not of prostate cancer.

          Conclusion

          This paper demonstrates the feasibility and utility of a systematic approach to identifying neighbourhoods, within the area served by a public health unit, that have significantly higher risks of cancer. This exploratory, ecologic study suggests several hypotheses for these spatial patterns that warrant further investigations. To the best of our knowledge, this is the first Canadian study published in the peer-reviewed literature estimating the risk of relatively rare public health outcomes at a very small areal level, namely dissemination areas.

          Related collections

          Most cited references54

          • Record: found
          • Abstract: not found
          • Article: not found

          Bayesian image restoration, with two applications in spatial statistics

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              An elliptic spatial scan statistic.

              The spatial scan statistic is commonly used for geographical disease cluster detection, cluster evaluation and disease surveillance. The most commonly used shape of the scanning window is circular. In this paper we explore an elliptic version of the spatial scan statistic, using a scanning window of variable location, shape (eccentricity), angle and size, and with and without an eccentricity penalty. The method is applied to breast cancer mortality data from Northeastern United States and female oral cancer mortality in the United States. Power comparisons are made with the circular scan statistic. Copyright (c) 2006 John Wiley & Sons, Ltd.
                Bookmark

                Author and article information

                Journal
                Int J Health Geogr
                International Journal of Health Geographics
                BioMed Central
                1476-072X
                2010
                10 May 2010
                : 9
                : 21
                Affiliations
                [1 ]Population Studies and Surveillance, Cancer Care Ontario, 620 University Avenue, Toronto, Ontario, Canada
                [2 ]Dalla Lana School of Public Health, University of Toronto, 155 College St., Toronto, Ontario, Canada
                [3 ]CIBER Epidemiología y Salud Pública (CIBERESP) and Centre for Public Health Research (CSISP), Valencia, Spain
                [4 ]Small Area Health Statistics Unit, MRC-HPA Centre for Environment and Health, Imperial College London, UK
                Article
                1476-072X-9-21
                10.1186/1476-072X-9-21
                2887786
                20459738
                02b14f81-6a3b-40ea-a17b-e556677fbb2d
                Copyright ©2010 Holowaty et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 March 2010
                : 10 May 2010
                Categories
                Research

                Public health
                Public health

                Comments

                Comment on this article