ArticleInternational journal of health geographics2006
A simulation study of three methods for detecting disease clusters.
Article in International journal of health geographics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review.BMC medicine · 2018Pooled it
- Geographical patterns of malaria transmission based on serological markers for falciparum and vivax malaria in Ratanakiri, Cambodia.Malaria journal · 2016Trial
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- Spatial and spatio-temporal analysis of malaria cases in Zimbabwe.Infectious diseases of poverty · 2020Article
- Spatiotemporal Tools for Emerging and Endemic Disease Hotspots in Small Areas: An Analysis of Dengue and Chikungunya in Barbados, 2013-2016.The American journal of tropical medicine and hygiene · 2020Article
- Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry.Scientific reports · 2020Article
- Utility of Spatial Point-Pattern Analysis Using Residential and Workplace Geospatial Information to Localize Potential Outbreak Sources.American journal of epidemiology · 2019Article
- Analyzing spatial and space-time clustering of facility-based deliveries in Bangladesh.Tropical medicine and health · 2019Article
- Spatial overlaps in the distribution of HIV/AIDS and malaria in Zimbabwe.BMC infectious diseases · 2018Article
- Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005-2016.BMC infectious diseases · 2018Article
- Childhood and Adolescence Cancers in the Palermo Province (Southern Italy): Ten Years (2003⁻2012) of Epidemiological Surveillance.International journal of environmental research and public health · 2018Article
- Spatio-temporal analysis of malaria incidence in the Peruvian Amazon Region between 2002 and 2013.Scientific reports · 2017Article
- Spatiotemporal epidemic characteristics and risk factor analysis of malaria in Yunnan Province, China.BMC public health · 2017Article
2 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
backgroundCluster detection is an important part of spatial epidemiology because it can help identifying environmental factors associated with disease and thus guide investigation of the aetiology of diseases. In this article we study three methods suitable for detecting local spatial clusters: (1) a spatial scan statistic (SaTScan), (2) generalized additive models (GAM) and (3) Bayesian disease mapping (BYM). We conducted a simulation study to compare the methods. Seven geographic clusters with different shapes were initially chosen as high-risk areas. Different scenarios for the magnitude of the relative risk of these areas as compared to the normal risk areas were considered. For each scenario the performance of the methods were assessed in terms of the sensitivity, specificity, and percentage correctly classified for each cluster.
resultsThe performance depends on the relative risk, but all methods are in general suitable for identifying clusters with a relative risk larger than 1.5. However, it is difficult to detect clusters with lower relative risks. The GAM approach had the highest sensitivity, but relatively low specificity leading to an overestimation of the cluster area. Both the BYM and the SaTScan methods work well. Clusters with irregular shapes are more difficult to detect than more circular clusters.
conclusionBased on our simulations we conclude that the methods differ in their ability to detect spatial clusters. Different aspects should be considered for appropriate choice of method such as size and shape of the assumed spatial clusters and the relative importance of sensitivity and specificity. In general, the BYM method seems preferable for local cluster detection with relatively high relative risks whereas the SaTScan method appears preferable for lower relative risks. The GAM method needs to be tuned (using cross-validation) to get satisfactory results.
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