Further clues concerning the aetiology of childhood central nervous system tumours
Introduction
The aetiology of childhood central nervous system (CNS) tumours remains uncertain. The only established causes are heritable syndromes, but these only account for a small number of cases [1]. We have recently reported space–time clustering among cases of CNS tumours included in the Manchester Children’s Tumour Registry (MCTR) [2]. This was due to space–time clustering among cases of astrocytoma and ependymoma, but there was no space–time clustering among cases of medulloblastoma and other primitive neuroectodermal tumours (PNETs), nor among cases of “miscellaneous glioma” [2]. We have also reported seasonal variation in certain CNS tumours, including astrocytoma and ependymoma [2]. These findings are consistent with a role for infections in aetiology.
Descriptive epidemiological studies, including those on disease clustering and the relationship between incidence rates and area-based demographic variables are very useful in formulating aetiological hypotheses. Whilst there have been numerous clustering and ecological studies on childhood leukaemia, there have been very few similar studies on CNS tumours [3]. Increasing incidence, space–time clustering, spatial clustering and ecological relationships have been found for leukaemia, an infectious aetiology has been postulated and several mechanistic hypotheses have been suggested [4], [5], [6]. There has been a lack of plausible aetiological hypotheses for childhood CNS tumours.
To provide further insight into aetiology, we have examined the geographical distribution of CNS tumours for the same study area. Specifically, we have tested for spatial clustering, for spatial autocorrelation (the adjacency of areas with similarly high or low rates) and for ecological gradients.
The Manchester Children’s Tumour Registry (MCTR) collects incidence data on all cancers in children, aged 0–14 years, from a defined geographical region of North-West England. Ascertainment has been estimated to be close to 100% [7]. The registry retains diagnostic specimens and re-review is undertaken periodically in line with improved knowledge about disease and technological advances. The MCTR thus provides a unique data-set for the investigation of incidence patterns over a wide geographical area and time-frame.
First, we have analysed the data for spatial clustering. Secondly, we have tested for spatial autocorrelation, i.e. we have tested for the adjacency of areas with similarly high or low rates. Finally, we have examined the data for ecological gradients by analysing the relationship between incidence rates and population density, measures of ethnic population composition and the level of deprivation, all at small-area (census ward) level. We have interpreted the geographical analyses in combination with the previously reported findings of space–time clustering and seasonal variation.
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Patients and methods
Cases, aged 0–14 years, diagnosed between 1 January 1976 and 31 December 2000, registered with the MCTR and resident in the counties of Lancashire and Greater Manchester were included in the study. This time period was used, because appropriate census population counts and socio-demographic data were only consistently available from the 1981 census onwards. The classification scheme based on the international classification of diseases for oncology (ICD-O) second edition and revised to take
Results
The observed numbers of cases by age group, time period, diagnostic group and gender are given in Table 1. The person–years at risk for the analyses are also presented.
Discussion
This analysis completes a set of analyses of childhood CNS tumours from North-West England and thus is important in interpreting the data and formulating aetiological hypotheses. This study has only been made possible by the availability of high-quality and consistent population-based diagnostic and residential address data. As ascertainment is close to 100%, there is no reason to suspect that there is any artifactual bias by the small-area of diagnosis.
There are two main types of clustering:
Conflict of interest statement
None declared.
Acknowledgements
The Manchester Children’s Tumour Registry is supported by Cancer Research UK. Jillian M. Birch is Cancer Research UK Professorial Fellow in Paediatric Oncology and Tim O.B. Eden is Cancer Research UK Professor of Paediatric Oncology at the University of Manchester. We thank Mr. D.P Cairns, Mrs. E.A. Dale, Mrs. D.A. Elliott, Mrs. J.F. Hogg and Mr. C. Nikolaisen for all their hard work on data processing and verification. The work is based on census data, which are copyright of The Crown. The
References (30)
- et al.
Geographical and ecological analyses of childhood Wilms’ tumours and soft-tissue sarcomas in North-West England
Eur J Cancer
(2003) - et al.
Temporal increase in the incidence of childhood peak precursor B-cell acute lymphoblastic leukaemia seen in North-West England
Lancet
(2000) - et al.
Genetic epidemiology of childhood brain tumors
Genet Epidemiol
(1991) - et al.
An infectious aetiology for childhood brain tumours? Evidence from space–time clustering and seasonality analyses
Br. J. Cancer
(2002) Epidemiology of childhood cancer. IARC Scientific Publications No. 140
(1999)Speculations on the cause of childhood acute lymphoblastic leukaemia
Leukemia
(1988)Epidemiological evidence for an infective basis in childhood leukaemia
Br J Cancer
(1995)Considerations on a possible viral etiology for B-precursor acute lymphoblastic leukemia of childhood
J Immunother
(1997)Manchester Children’s Tumour Registry 1954–1970 and 1971–1983
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