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Open Access Research article

Space-time clustering of childhood central nervous system tumours in Yorkshire, UK

Richard JQ McNally14*, Peter W James1, Susan V Picton2, Patricia A McKinney3, Marlous van Laar3 and Richard G Feltbower3

Author affiliations

1 Institute of Health and Society, Newcastle University, Sir James Spence Institute, Royal Victoria Infirmary, Newcastle upon Tyne NE1 4LP, UK

2 Paediatric Oncology and Haematology, Leeds Teaching Hospitals NHS Trust, Beckett Street, Leeds LS9 7TF, UK

3 Paediatric Epidemiology Group, Division of Epidemiology, University of Leeds, Leeds LS2 9NL, UK

4 Dr Richard JQ McNally, Institute of Health and Society, Newcastle University, Sir James Spence Institute, Royal Victoria Infirmary, Queen Victoria Road, Newcastle upon Tyne NE1 4LP, UK

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Citation and License

BMC Cancer 2012, 12:13  doi:10.1186/1471-2407-12-13

Published: 13 January 2012

Abstract

Background

We specifically tested the aetiological hypothesis that a factor influencing geographical or temporal heterogeneity of childhood central nervous system (CNS) tumour incidence was related to exposure to a transient environmental agent.

Methods

Information was extracted on individuals aged 0-14 years, diagnosed with a CNS tumour between the 1st January 1974 and 31st December 2006 from the Yorkshire Specialist Register of Cancer in Children and Young People. Ordnance Survey eight-digit grid references were allocated to each case with respect to addresses at the time of birth and the time of diagnosis, locating each address to within 0.1 km. The following diagnostic groups were specified a priori for analysis: ependymoma; astrocytoma; primitive neuroectodermal tumours (PNETs); other gliomas; total CNS tumours. We applied the K-function method for testing global space-time clustering using fixed geographical distance thresholds. Tests were repeated using variable nearest neighbour (NN) thresholds.

Results

There was statistically significant global space-time clustering for PNETs only, based on time and place of diagnosis (P = 0.03 and 0.01 using the fixed geographical distance and the variable NN threshold versions of the K-function method respectively).

Conclusions

There was some evidence for a transient environmental component to the aetiology of PNETs. However, a possible role for chance cannot be excluded.