{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T22:33:23Z","timestamp":1790462003274,"version":"4.1.0"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2018,6,18]],"date-time":"2018-06-18T00:00:00Z","timestamp":1529280000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC)$^3$) algorithm. The effectiveness of the (MC)$^3$ algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxy024","type":"journal-article","created":{"date-parts":[[2018,5,25]],"date-time":"2018-05-25T19:10:41Z","timestamp":1527275441000},"page":"681-697","source":"Crossref","is-referenced-by-count":21,"title":["A Bayesian space\u2013time model for clustering areal units based on their disease trends"],"prefix":"10.1093","volume":"20","author":[{"given":"Gary","family":"Napier","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, University of Glasgow, University Place, Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duncan","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, University of Glasgow, University Place, Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Robertson","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of Strathclyde, 26 Richmond Street, Glasgow, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Lawson","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences, Medical University of South Carolina, South Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,6,18]]},"reference":[{"key":"2019101711390299300_B1","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1093\/bioinformatics\/btg427","article-title":"Parallel Metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference.","volume":"20","author":"Altekar,","year":"2004","journal-title":"Bioinformatics"},{"key":"2019101711390299300_B2","article-title":"Spatial clustering of average 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