ArticleInternational journal of health geographics2014
Spatial heterogeneity of type I error for local cluster detection tests.
Article in International journal of health geographics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Spatio-Temporal Variability of Malaria Incidence in the Health District of Kati, Mali, 2015-2019.International journal of environmental research and public health · 2022Article
- Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012.BMC infectious diseases · 2020Article
- Article
- Spatio-temporal dynamic of malaria in Ouagadougou, Burkina Faso, 2011-2015.Malaria journal · 2018Article
- Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping.BMC medical research methodology · 2016Article
- Evolution of research in health geographics through the International Journal of Health Geographics (2002-2015).International journal of health geographics · 2016Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundJust as power, type I error of cluster detection tests (CDTs) should be spatially assessed. Indeed, CDTs' type I error and power have both a spatial component as CDTs both detect and locate clusters. In the case of type I error, the spatial distribution of wrongly detected clusters (WDCs) can be particularly affected by edge effect. This simulation study aims to describe the spatial distribution of WDCs and to confirm and quantify the presence of edge effect.
methodsA simulation of 40 000 datasets has been performed under the null hypothesis of risk homogeneity. The simulation design used realistic parameters from survey data on birth defects, and in particular, two baseline risks. The simulated datasets were analyzed using the Kulldorff's spatial scan as a commonly used test whose behavior is otherwise well known. To describe the spatial distribution of type I error, we defined the participation rate for each spatial unit of the region. We used this indicator in a new statistical test proposed to confirm, as well as quantify, the edge effect.
resultsThe predefined type I error of 5% was respected for both baseline risks. Results showed strong edge effect in participation rates, with a descending gradient from center to edge, and WDCs more often centrally situated.
conclusionsIn routine analysis of real data, clusters on the edge of the region should be carefully considered as they rarely occur when there is no cluster. Further work is needed to combine results from power studies with this work in order to optimize CDTs performance.
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