Geographic variation and localised clustering of congenital anomalies in Great Britain

被引：16

作者：

Armstrong B.G. ^{[1
]}

Dolk H. ^{[2
]}

Pattenden S. ^{[1
]}

Vrijheid M. ^{[3
]}

Loane M. ^{[2
]}

Rankin J. ^{[4
]}

Dunn C.E. ^{[5
]}

Grundy C. ^{[1
]}

Abramsky L. ^{[6
]}

Boyd P.A. ^{[7
]}

Stone D. ^{[8
]}

Wellesley D. ^{[9
]}

机构：

[1] Public and Environmental Health Research Unit, London School of Hygiene and Tropical Medicine, London WC1E7HT, Keppel St

[2] Faculty of Life and Health Sciences, University of Ulster, Newtownabbey BT370QB, Shore Rd

[3] International Agency for Research on Cancer, 69372 Lyon Cedex

[4] University of Newcastle, School of Population and Health Studies

[5] Department of Geography, University of Durham, Durham, DH1 3LE, South Road

[6] Congenital Malformations Register, Perinatal Public Health, Northwick Park Hospital, Harrow HA13UJ, Watford Rd

[7] CAROBB, National Perinatal Epidemiology Unit, Institute of Health Sciences, Headington, OX37LF, Old Rd

[8] Paediatric Epidemiology and Community Health (PEACH), Yorkhill Hospital

[9] Wessex Clinical Genetics Services, Princess Anne Hospital, Southampton S0165YA, Coxford Rd

来源：

Emerging Themes in Epidemiology | / 4卷 / 1期

关键词：

Down Syndrome; Negative Binomial Model; Gastroschisis; Negative Binomial Regression Model; Socioeconomic Deprivation;

D O I：

10.1186/1742-7622-4-14

中图分类号：

学科分类号：

摘要：

Background. Environmental pollution as a cause of congenital anomalies is sometimes suspected because of clustering of anomalies in areas of higher exposure. This highlights questions around spatial heterogeneity (clustering) in congenital anomaly rates. If spatial variation is endemic, then any one specific cluster is less remarkable, though the presence of uncontrolled geographically clustered risk factors is suggested. If rates are relatively homogeneous across space other than around specific hazards, then evidence for these hazards causing the clusters is strengthened. We sought to estimate the extent of spatial heterogeneity in congenital anomaly rates in the United Kingdom. Methods. The study population covered about one million births from five registers in Britain from 1991-1999. We estimated heterogeneity across four geographical levels: register area, hospital catchment, electoral ward, and enumeration district, using a negative binomial regression model. We also sought clusters using a circular scan statistic. Results. Congenital anomaly rates clearly varied across register areas and hospital catchments (p < 0.001), but not below this level (p > 0.2). Adjusting for socioeconomic deprivation and maternal age made little difference to the extent of geographical variation for most congenital anomaly subtypes. The two most significant circular clusters (of four ano-rectal atresias and six congenital heart diseases) contained two or more siblings. Conclusion. The variation in rates between registers and hospital catchment area may have resulted in part from differences in case ascertainment, and this should be taken into account in geographical epidemiological studies of environmental exposures. The absence of evidence for variation below this level should be interpreted cautiously in view of the low power of general heterogeneity tests. Nevertheless, the data suggest that strong localised clusters in congenital anomalies are uncommon, so clusters around specific putative environmental hazards are remarkable when observed. Negative binomial models applied at successive hierarchical levels provide an approach of intermediate complexity to characterising geographical heterogeneity. © 2007 Armstrong et al; licensee BioMed Central Ltd.

引用

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