Space‐time clustering of childhood cancer in great Britain: A national study, 1969–1993

Abstract

Previous studies have provided evidence that infections may play a part in the aetiology of certain childhood cancers. The finding of space-time clustering indicates the presence of an environmental component to aetiology and is especially supportive of a role for infections. Space-time clustering occurs when excess numbers of cases of a disease are observed within small geographical locations at limited periods of time and this cannot be explained in terms of general excesses in those locations or at those times. To investigate whether infections may be involved in the aetiology of childhood cancer, we have analysed for space-time clustering using a large set of national population-based data from Great Britain for the period 1969-1993. Data were examined by a second-order procedure based on K-functions, with fixed thresholds of closeness in space (0.5-7.5 km) and closeness in time (0.1-1.5 years). Locations were addresses at diagnosis. Tests were repeated, replacing geographical distances with distances to the 19th-33rd nearest neighbours and this provided the primary result for each analysis. There were a total of 32,295 cases of childhood cancer. The analyses showed statistically significant evidence of space-time clustering for acute lymphoblastic leukaemia over the whole age range (p 5 0.04), but especially for ages 1-4 years (p 5 0.03). There was less statistically significant evidence for total leukaemia (p 5 0.048). Significant space-time clustering was also evident for soft tissue sarcomas (p 5 0.03) and osteosarcomas (p 5 0.02). Results support other evidence suggesting a role for infections in the aetiology of these particular diagnostic groups. ' 2005 Wiley-Liss, Inc.

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

“…This study has the following merits: (i) it is much more up to date than the previous analyses from the whole of GB; (ii) two distance metrics were used: fixed geographical distance thresholds and variable nearest neighbour (NN) thresholds, allowing an assessment to be made to determine whether clustering is more likely to have arisen from a geostationary or an infective process (this was not done in the previous studies from GB [12,13]); (iii) the full set of diagnostic groups were analyzed; and (iv) full diagnostic case review was performed consistently by a single experienced neuropathologist. The analyses were performed using rigorous statistical methods on high-quality population-based incidence data.…”

“…These methods have been used in previous work related to space-time clustering of childhood cancer, type 1 diabetes and congenital anomalies [12,13,25,26]. The Knox test regards a pair of cases as being in "close proximity" if diagnosis time and addresses of residence at this time are close.…”

“…One study from Japan identified a peak for all central nervous system tumours who were born in the winter [9], a study from northern England found an excess of astrocytoma cases born in October [10], and a study from the USA showed an excess of PNET (medulloblastoma, NOS) cases born in October [11]. There have also been several recent studies of space-time clustering of national (GB) childhood cancer data [12,13], which found statistically significant evidence of clustering overall and marginally significant evidence for cases of astrocytoma diagnosed during the period 1969-1993. A study from North West England found evidence of space-time clustering and seasonal variation amongst cases of childhood CNS tumours, particularly astrocytoma and ependymoma [14].…”

Are transient environmental agents involved in the cause of primary biliary cirrhosis? Evidence from space-time clustering analysis

“…15 These methods have been used extensively for analyzing spatiotemporal patterning in the distribution of childhood cancer, type 1 diabetes, and congenital anomalies. [16][17][18] The Knox test considers a pair of cases to be in "close proximity" if dates of diagnosis and residential addresses at time of diagnosis are close. The number of pairs of cases observed to be in close proximity is obtained (denoted O), and the number of pairs of cases expected to be in close proximity is calculated (denoted E).…”

The Epidemiology of Childhood Cancers

“…[1][2][3][4][26][27][28][29][30][31][32][33][34][35][36][37][38][39] Also, significant space-time clustering has been found for childhood solid tumours, including HL, NHL, CNS tumours, soft-tissue sarcomas, osteosarcoma and Wilms' tumours. 36,37,40,41 In addition, one study found cross-space-time clustering between cases of leukaemia and CNS tumours. 42 The positive space-time clustering found for leukaemia is consistent with current hypotheses relating to infections.…”