Discrete versus continuous domain models for disease mapping

Garyfallos Konstantinoudis, Dominic Schuhmacher, Haavard Rue, Ben D. Spycher

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The main goal of disease mapping is to estimate disease risk and identify high-risk areas. Such analyses are hampered by the limited geographical resolution of the available data. Typically the available data are counts per spatial unit and the common approach is the Besag–York–Mollié (BYM) model. When precise geocodes are available, it is more natural to use Log-Gaussian Cox processes (LGCPs). In a simulation study mimicking childhood leukaemia incidence using actual residential locations of all children in the canton of Zürich, Switzerland, we compare the ability of these models to recover risk surfaces and identify high-risk areas. We then apply both approaches to actual data on childhood leukaemia incidence in the canton of Zürich during 1985-2015. We found that LGCPs outperform BYM models in almost all scenarios considered. Our findings suggest that there are important gains to be made from the use of LGCPs in spatial epidemiology.
Original languageEnglish (US)
Pages (from-to)100319
JournalSpatial and Spatio-temporal Epidemiology
StatePublished - Dec 11 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors thank Dr Alex Karagiannis-Voules and Dr Haakon Bakka for their valuable input and comments. This work was supported by Swiss Cancer Research (4012-08-2016, 3515-08-2014). The work of the Swiss Childhood Cancer Registry (SCCR) is supported by the Swiss Paediatric Oncology Group (www.spog.ch), Schweizerische Konferenz der kantonalen Gesundheitsdirektorinnen und -direktoren (www.gdk-cds.ch), Swiss Cancer Research (www.krebsforschung.ch), Kinderkrebshilfe Schweiz (www.kinderkrebshilfe.ch), Ernst-Göhner Stiftung, Stiftung Domarena and National Institute of Cancer Epidemiology and Registration (www.nicer.ch). We thank the Swiss Federal Statistical Office for providing mortality and census data and for the support, which made the Swiss National Cohort and this study possible. The work of the Swiss National Cohort was supported by the Swiss National Science Foundation (grant nos. 3347CO-108806, 33CS30_134273 and 33CS30_148415). The members of the Swiss Pediatric Oncology Group Scientific Committee: M Ansari (Geneva), M Beck-Popovic (Lausanne), P Brazzola (Bellinzona), J Greiner (St Gallen), M Grotzer (Zürich), H Hengartner (St Gallen), T. Kuehne (Basel), C Kuehni (Bern), F Niggli (Zürich), J Rössler (Bern), F Schilling (Lucerne), K Scheinemann (Aarau), N von der Weid (Basel). The members of the Swiss National Cohort Study Group: Matthias Egger (Chairman of the Executive Board), Adrian Spoerri and Marcel Zwahlen (all Bern), Milo Puhan (Chairman of the Scientific Board), Matthias Bopp (both Zürich), Nino Künzli (Basel), Fred Paccaud (Lausanne) and Michel Oris (Geneva).


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