Analytic perspective

Geographic variation and localised clustering of congenital anomalies in Great Britain

Ben G Armstrong¹ , Helen Dolk² , Sam Pattenden¹ , Martine Vrijheid³ , Maria Loane² , Judith Rankin⁴ , Chris E Dunn⁵ , Chris Grundy¹ , Lenore Abramsky⁶ , Patricia A Boyd⁷ , David Stone⁸ and Diana Wellesley⁹

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

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

³  International Agency for Research on Cancer, 150 Cour Albert Thomas, 69372 Lyon Cedex, France

⁴  University of Newcastle, School of Population and health Studies, Newcastle upon Tyne NE24HH, UK

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

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

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

⁸  Paediatric Epidemiology and Community Health (PEACH), Yorkhill Hospital, Glasgow G38SJ, UK

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

author email corresponding author email

Emerging Themes in Epidemiology 2007, 4:14doi:10.1186/1742-7622-4-14

Published:	6 July 2007

Abstract

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.