{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T18:48:57Z","timestamp":1776451737333,"version":"3.51.2"},"reference-count":27,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Recent advances in the spatial epidemiology literature have extended traditional approaches by including determinant disease factors that allow for non-local smoothing and\/or non-spatial smoothing. In this article, two of those approaches are compared and are further extended to areas of high interest from the public health perspective. These are a conditionally specified Gaussian random field model, using a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping; and a spatially adaptive conditional autoregressive prior model. The methods are specially design to handle cases when there is no evidence of positive spatial correlation or the appropriate mix between local and global smoothing is not constant across the region being study. Both approaches proposed in this article are producing results consistent with the published knowledge, and are increasing the accuracy to clearly determine areas of high- or low-risk.<\/jats:p>","DOI":"10.1515\/em-2019-0025","type":"journal-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T09:30:26Z","timestamp":1608629426000},"source":"Crossref","is-referenced-by-count":1,"title":["Disease mapping models for data with weak\u00a0spatial dependence or spatial discontinuities"],"prefix":"10.1515","volume":"9","author":[{"given":"Helena","family":"Baptista","sequence":"first","affiliation":[{"name":"NOVA Information Management School, Universidade Nova de Lisboa , Lisboa , Portugal"}]},{"given":"Peter","family":"Congdon","sequence":"additional","affiliation":[{"name":"School of Geography and Life Sciences Institute, Queen Mary , University of London , London , United Kingdom of Great Britain"}]},{"given":"Jorge M.","family":"Mendes","sequence":"additional","affiliation":[{"name":"NOVA Information Management School, Universidade Nova de Lisboa , Lisboa , Portugal"}]},{"given":"Ana M.","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Nova Medical School , Lisbon , Portugal"},{"name":"Comprehensive Health Research Center , Lisbon , Portugal"}]},{"given":"Helena","family":"Canh\u00e3o","sequence":"additional","affiliation":[{"name":"Comprehensive Health Research Center , Lisbon , Portugal"},{"name":"EpiDoC Unit, CEDOC, NOVA Medical School, Universidade Nova de Lisboa , Lisboa , Portugal"}]},{"given":"Sara S.","family":"Dias","sequence":"additional","affiliation":[{"name":"Comprehensive Health Research Center , Lisbon , Portugal"},{"name":"EpiDoC Unit, CEDOC, NOVA Medical School, Universidade Nova de Lisboa , Lisboa , Portugal"},{"name":"Center for Innovative Care and Health Technology (ciTechCare), School of Health Sciences, Polytechnic of Leiria , Leiria , Portugal"}]}],"member":"374","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"key":"2026041718052437332_j_em-2019-0025_ref_001_w2aab3b7d577b1b6b1ab2b3b1Aa","doi-asserted-by":"crossref","unstructured":"Banerjee, S., B. P. Carlin, and A. E. Gelfand. 2014. Hierarchical Modeling and Analysis for Spatial Data. , 2nd ed. Boca Raton: Chapman&Hall\/CRC.","DOI":"10.1201\/b17115"},{"key":"2026041718052437332_j_em-2019-0025_ref_002_w2aab3b7d577b1b6b1ab2b3b2Aa","doi-asserted-by":"crossref","unstructured":"Baptista, H., J.\u00a0M. Mendes, Y. C. MacNab, M. Xavier, and J.\u00a0M. C. de Almeida. 2016. \u201cA Guassian Random Field Model for Similarity-Based Smoothing in Bayesian Disease Mapping.\u201d Statistical Methods in Medical Research 25: 1166\u20131184, https:\/\/doi.org\/10.1177\/0962280216660407.","DOI":"10.1177\/0962280216660407"},{"key":"2026041718052437332_j_em-2019-0025_ref_003_w2aab3b7d577b1b6b1ab2b3b3Aa","doi-asserted-by":"crossref","unstructured":"Barnett, K., S. W. Mercer, M. Norbury, G. Watt, S. Wyke, and B. Guthrie. 2012. \u201cEpidemiology of Multimorbidity and Implications for Health Care, Research, and Medical Education: A Cross-Sectional Study.\u201d The Lancet 380: 37\u201343, https:\/\/doi.org\/10.1016\/s0140-6736(12)60240-2.","DOI":"10.1016\/S0140-6736(12)60240-2"},{"key":"2026041718052437332_j_em-2019-0025_ref_004_w2aab3b7d577b1b6b1ab2b3b4Aa","doi-asserted-by":"crossref","unstructured":"Besag, J. 1974. \u201cSpatial Interaction and the Statistical Analysis of Lattice Systems.\u201d Journal of the Royal Statistical Society 36: 192\u2013236, https:\/\/doi.org\/10.1111\/j.2517-6161.1974.tb00999.x.","DOI":"10.1111\/j.2517-6161.1974.tb00999.x"},{"key":"2026041718052437332_j_em-2019-0025_ref_005_w2aab3b7d577b1b6b1ab2b3b5Aa","doi-asserted-by":"crossref","unstructured":"Besag, J., and C. Kooperberg. 1995. \u201cOn Conditional and Intrinsic Autoregressions.\u201d Biometrika 82: 733\u2013746, https:\/\/doi.org\/10.2307\/2337341.","DOI":"10.1093\/biomet\/82.4.733"},{"key":"2026041718052437332_j_em-2019-0025_ref_006_w2aab3b7d577b1b6b1ab2b3b6Aa","doi-asserted-by":"crossref","unstructured":"Besag, J., J. York, and A. Molli\u00e9. 1991. \u201cBayesian Image Restoration, with Two Applications in Spatial Statistics (With Discussion).\u201d Annals of the Institute of Statistical Mathematics 43: 1\u201359, https:\/\/doi.org\/10.1007\/bf00116466.","DOI":"10.1007\/BF00116466"},{"key":"2026041718052437332_j_em-2019-0025_ref_007_w2aab3b7d577b1b6b1ab2b3b7Aa","doi-asserted-by":"crossref","unstructured":"Best, N., R. Arnold, A. Thomas, L. Waller, and E. Conlon. 1999. \u201cBayesiann Models for Spatially Correlated Disease and Exposure Data.\u201d In Bayesian Statistics 6, edited by Bernardo, J., Berger, J., Dawid, A. and Smith, A., pp.\u00a0131\u2013147. Oxford: Oxford Science Publications.","DOI":"10.1093\/oso\/9780198504856.003.0006"},{"key":"2026041718052437332_j_em-2019-0025_ref_008_w2aab3b7d577b1b6b1ab2b3b8Aa","doi-asserted-by":"crossref","unstructured":"Best, N., S. Richardson, and A. Thomson. 2005. \u201cA Comparison of Bayesian Spatial Models for Disease Mapping.\u201d Statistical Methods in Medical Research 14: 35\u201359, https:\/\/doi.org\/10.1191\/0962280205sm388oa.","DOI":"10.1191\/0962280205sm388oa"},{"key":"2026041718052437332_j_em-2019-0025_ref_009_w2aab3b7d577b1b6b1ab2b3b9Aa","doi-asserted-by":"crossref","unstructured":"Brook, D. 1964. \u201cOn the Distinction between the Conditional Probability and the Joint Probability Approaches in the Specification of Nearest-Neighbour Systems.\u201d Biometrika 51: 481\u2013483, https:\/\/doi.org\/10.1093\/biomet\/51.3-4.481.","DOI":"10.1093\/biomet\/51.3-4.481"},{"key":"2026041718052437332_j_em-2019-0025_ref_010_w2aab3b7d577b1b6b1ab2b3c10Aa","doi-asserted-by":"crossref","unstructured":"Brooks, S., and A. Gelman. 1998. \u201cGeneral Methods for Monitoring Convergence of Iterative Simulations.\u201d Journal of Computational & Graphical Statistics 7: 434\u2013455, https:\/\/doi.org\/10.1080\/10618600.1998.10474787.","DOI":"10.1080\/10618600.1998.10474787"},{"key":"2026041718052437332_j_em-2019-0025_ref_011_w2aab3b7d577b1b6b1ab2b3c11Aa","unstructured":"Cancer Research UK and National Cancer Intelligence Network. 2014. Cancer by Deprivation in England: Incidence, 1996-2010, Mortality, 1997-2011. Technical report. London, UK: NCIN."},{"key":"2026041718052437332_j_em-2019-0025_ref_012_w2aab3b7d577b1b6b1ab2b3c12Aa","doi-asserted-by":"crossref","unstructured":"Congdon, P. 2008. \u201cA Spatially Adaptive Conditional Autoregressive Prior for Area Health Data.\u201d Statistical Methodology 5: 552\u2013563, https:\/\/doi.org\/10.1016\/j.stamet.2008.02.005.","DOI":"10.1016\/j.stamet.2008.02.005"},{"key":"2026041718052437332_j_em-2019-0025_ref_013_w2aab3b7d577b1b6b1ab2b3c13Aa","doi-asserted-by":"crossref","unstructured":"Etxeberria, J., T. Goicoa, and M. D. Ugarte. 2018. \u201cJoint Modelling of Brain Cancer Incidence and Mortality Using Bayesian Age- and Gender-specific Shared Component Models.\u201d Stochastic Environmental Research and Risk Assessment 32: 2951\u20131969, https:\/\/doi.org\/10.1007\/s00477-018-1567-4.","DOI":"10.1007\/s00477-018-1567-4"},{"key":"2026041718052437332_j_em-2019-0025_ref_014_w2aab3b7d577b1b6b1ab2b3c14Aa","doi-asserted-by":"crossref","unstructured":"Gathani, T., R. Ali, A. Balkwill, J. Green, G. Reeves, V. Beral, and K. A. Moser. 2014. \u201cEthnic Differences in Breast Cancer Incidence in England Are Due to Differences in Known Risk Factors for the Disease: Prospective Study.\u201d British Journal of Cancer 110: 224\u2013229. URL, https:\/\/doi.org\/10.1038\/bjc.2013.632.","DOI":"10.1038\/bjc.2013.632"},{"key":"2026041718052437332_j_em-2019-0025_ref_015_w2aab3b7d577b1b6b1ab2b3c15Aa","doi-asserted-by":"crossref","unstructured":"Griffith, D. A. 1996. \u201cSome Guidelines for Specifying the Geographic Weights Matrix Contained in Spatial Statistical Models.\u201d In Practical Handbook of Spatial Statistics, edited by Arlinghaus, S. L., pp.\u00a065\u201382. Boca Raton: CRC Press.","DOI":"10.1201\/9781003067689-4"},{"key":"2026041718052437332_j_em-2019-0025_ref_016_w2aab3b7d577b1b6b1ab2b3c16Aa","unstructured":"INE. 2013. Estudo sobre o Poder de Compra Concelhio. Technical report. Lisbon: INE."},{"key":"2026041718052437332_j_em-2019-0025_ref_017_w2aab3b7d577b1b6b1ab2b3c17Aa","doi-asserted-by":"crossref","unstructured":"Lee, D., and R. Mitchell. 2013. \u201cLocally Adaptive Spatial Smoothing Using Conditional Auto-Regressive Models.\u201d Journal of the Royal Statistical Society: Series C (Applied Statistics) 62: 593\u2013608, https:\/\/doi.org\/10.1111\/rssc.12009.","DOI":"10.1111\/rssc.12009"},{"key":"2026041718052437332_j_em-2019-0025_ref_018_w2aab3b7d577b1b6b1ab2b3c18Aa","doi-asserted-by":"crossref","unstructured":"Leroux, B. G., X. Lei, and N. Breslow (2000): \u2018\u2018Estimation of Disease Rates in Small Areas: A New Mixed Model for Spatial Dependence,\u2019\u2019 In Statistical Models in Epidemiology, the Environment, and Clinical Trials, The IMA Volumes in Mathematics and its Applications, edited by M. E. Halloran and D. Berry, Vol.\u00a0116, New York, NY: Springer New York, pp.\u00a0179\u2013191.","DOI":"10.1007\/978-1-4612-1284-3_4"},{"key":"2026041718052437332_j_em-2019-0025_ref_019_w2aab3b7d577b1b6b1ab2b3c19Aa","doi-asserted-by":"crossref","unstructured":"MacNab, Y. C. 2018. \u201cSome Recent Work on Multivariate Gaussian Markov Random Fields.\u201d Test 27: 554\u2013569, https:\/\/doi.org\/10.1007\/s11749-018-0608-0.","DOI":"10.1007\/s11749-018-0608-0"},{"key":"2026041718052437332_j_em-2019-0025_ref_020_w2aab3b7d577b1b6b1ab2b3c20Aa","unstructured":"Rodrigues, A., A. Sepriano, S. P. Gon\u00e7alves, A. M. Rodrigues, N. Gouveia, L. Pereira, M. Eus\u00e9bio, and S. Ramiro. 2015. \u201cEpiReumaPt- the Study of Rheumatic and Musculoskeletal Diseases in Portugal : A Detailed View of the Methodology EpiReumaPt\u00a0\u2013 the Study of Rheumatic and Musculoskeletal Diseases in Portugal : a Detailed View of the Methodology,\u201d Acta reumatologica portuguesa 40: 110\u2013124."},{"key":"2026041718052437332_j_em-2019-0025_ref_021_w2aab3b7d577b1b6b1ab2b3c21Aa","doi-asserted-by":"crossref","unstructured":"Salmasi, L., and M. Celidoni. 2017. \u201cInvestigating the Poverty-Obesity Paradox in Europe.\u201d Economics and Human Biology 26: 70\u201385, https:\/\/doi.org\/10.1016\/j.ehb.2017.02.005.","DOI":"10.1016\/j.ehb.2017.02.005"},{"key":"2026041718052437332_j_em-2019-0025_ref_022_w2aab3b7d577b1b6b1ab2b3c22Aa","doi-asserted-by":"crossref","unstructured":"Smith, T., J. Wakefield, and A. Dobra. 2015. \u201cRestricted Covariance Priors with Applications in Spatial Statistics.\u201d Bayesian Analysis 10: 965, https:\/\/doi.org\/10.1214\/14-ba927.","DOI":"10.1214\/14-BA927"},{"key":"2026041718052437332_j_em-2019-0025_ref_023_w2aab3b7d577b1b6b1ab2b3c23Aa","doi-asserted-by":"crossref","unstructured":"Tosetti, E., R. Santos, F. Moscone, and G. Arbia. 2018. \u201cThe Spatial Dimension of Health Systems.\u201d In Oxford Research Encyclopedia of Economics and Finance. Oxford: Oxford University Press.","DOI":"10.1093\/acrefore\/9780190625979.013.287"},{"key":"2026041718052437332_j_em-2019-0025_ref_024_w2aab3b7d577b1b6b1ab2b3c24Aa","doi-asserted-by":"crossref","unstructured":"Vehtari, A., A. Gelman, and J. Gabry. 2016. \u201cPractical Bayesian Model Evaluation Using Leave-One-Out Cross-Validation and WAIC.\u201d Statistics and Computing: 1\u201320.","DOI":"10.32614\/CRAN.package.loo"},{"key":"2026041718052437332_j_em-2019-0025_ref_025_w2aab3b7d577b1b6b1ab2b3c25Aa","unstructured":"Wakefield, J., and H. Lyons. 2010. \u201cSpatial Aggregation and the Ecological Fallacy.\u201d In Handbook of Spatial Statistics, edited by Gelfand, A. E., Diggle, P. J., Fuentes, M. and Guttorp, P., pp.\u00a0541\u201358. Boca Raton: Taylor & Francis Group. chapter 30."},{"key":"2026041718052437332_j_em-2019-0025_ref_026_w2aab3b7d577b1b6b1ab2b3c26Aa","doi-asserted-by":"crossref","unstructured":"Wang, Y. C., K. McPherson, T. Marsh, S. L. Gortmaker, and M. Brown. 2011. \u201cHealth and Economic Burden of the Projected Obesity Trends in the USA and the UK.\u201d The Lancet 378: 815\u2013825, https:\/\/doi.org\/10.1016\/s0140-6736(11)60814-3.","DOI":"10.1016\/S0140-6736(11)60814-3"},{"key":"2026041718052437332_j_em-2019-0025_ref_027_w2aab3b7d577b1b6b1ab2b3c27Aa","doi-asserted-by":"crossref","unstructured":"\u017bukiewicz-Sobczak, W., P. Wr\u00f3blewska, J. Zwoli\u0144ski, J. Chmielewska-Badora, P. Adamczuk, E. Krasowska, J. Zag\u00f3rski, A. Oniszczuk, J. Pia\u0327tek, and W. Silny. 2014. \u201cObesity and Poverty Paradox in Developed Countries.\u201d Annals of Agricultural and Environmental Medicine 21: 590\u2013594, https:\/\/doi.org\/10.5604\/12321966.1120608.","DOI":"10.5604\/12321966.1120608"}],"container-title":["Epidemiologic Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/view\/journals\/em\/9\/1\/article-20190025.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/em-2019-0025\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/em-2019-0025\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T18:05:31Z","timestamp":1776449131000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/em-2019-0025\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,1]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,11,11]]},"published-print":{"date-parts":[[2020,1,28]]}},"alternative-id":["10.1515\/em-2019-0025"],"URL":"https:\/\/doi.org\/10.1515\/em-2019-0025","relation":{},"ISSN":["2161-962X","2194-9263"],"issn-type":[{"value":"2161-962X","type":"electronic"},{"value":"2194-9263","type":"print"}],"subject":[],"published":{"date-parts":[[2020,1,1]]},"article-number":"20190025"}}