title: Using scan statistics for congenital anomalies surveillance: the EUROCAT methodology.
abstract: Scan statistics have been used extensively to identify temporal clusters of health events. We describe the temporal cluster detection methodology adopted by the EUROCAT (European Surveillance of Congenital Anomalies) monitoring system. Since 2001, EUROCAT has implemented variable window width scan statistic for detecting unusual temporal aggregations of congenital anomaly cases. The scan windows are based on numbers of cases rather than being defined by time. The methodology is imbedded in the EUROCAT Central Database for annual application to centrally held registry data. The methodology was incrementally adapted to improve the utility and to address statistical issues. Simulation exercises were used to determine the power of the methodology to identify periods of raised risk (of 1-18 months). In order to operationalize the scan methodology, a number of adaptations were needed, including: estimating date of conception as unit of time; deciding the maximum length (in time) and recency of clusters of interest; reporting of multiple and overlapping significant clusters; replacing the Monte Carlo simulation with a lookup table to reduce computation time; and placing a threshold on underlying population change and estimating the false positive rate by simulation. Exploration of power found that raised risk periods lasting 1 month are unlikely to be detected except when the relative risk and case counts are high. The variable window width scan statistic is a useful tool for the surveillance of congenital anomalies. Numerous adaptations have improved the utility of the original methodology in the context of temporal cluster detection in congenital anomalies.
authors.affiliation: Department of Public Health and Primary Care, Trinity College, Dublin, Ireland.
authors.forename: Conor
authors.initials: C
authors.lastname: Teljeur
authors.affiliation: Department of Public Health and Primary Care, Trinity College, Dublin, Ireland.
authors.forename: Alan
authors.initials: A
authors.lastname: Kelly
authors.affiliation: EUROCAT Central Registry, WHO Collaborating Centre for Surveillance of Congenital Anomalies, Institute for Nursing and Health Research, University of Ulster, Shore Rd, Newtownabbey, BT370QB, UK.
authors.forename: Maria
authors.initials: M
authors.lastname: Loane
authors.affiliation: Biomedical Computing Ltd, East Sussex, UK.
authors.forename: James
authors.initials: J
authors.lastname: Densem
authors.affiliation: EUROCAT Central Registry, WHO Collaborating Centre for Surveillance of Congenital Anomalies, Institute for Nursing and Health Research, University of Ulster, Shore Rd, Newtownabbey, BT370QB, UK. h.dolk@ulster.ac.uk.
authors.forename: Helen
authors.initials: H
authors.lastname: Dolk
mesh_terms: D000013:Congenital Abnormalities / Q000453:epidemiology*; D004812:Epidemiologic Methods; D005060:Europe / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011159:Population Surveillance / Q000379:methods*; D011247:Pregnancy; D012042:Registries / Q000706:statistics & numerical data*; D012107:Research Design; D012306:Risk; D013029:Space-Time Clustering
keywords: Clusters; Congenital anomalies; Surveillance