Space-time clustering of childhood cancers: a systematic review and pooled analysis

Eur J Epidemiol. 2019 Jan;34(1):9-21. doi: 10.1007/s10654-018-0456-y. Epub 2018 Nov 16.


The aetiology of childhood cancers remains largely unknown. Space-time clustering of cases might imply an aetiological role of infections. We aimed to review the evidence of space-time clustering of specific childhood cancers. We searched Medline and Embase for population-based studies that covered a pre-defined study area, included cases under 20 years of age and were published before July 2016. We extracted all space-time clustering tests and calculated the proportion of positive tests per diagnostic group. In a pooled analysis, we performed a Knox test of the number of pairs of cases close to each other in time and space pooled across studies. 70 studies met our eligibility criteria, 32 of which reported Knox tests. For leukaemia, the proportion of positive tests was higher than expected by chance at both time of diagnosis (26%) and birth (11%). The pooled analysis showed strong evidence of clustering at diagnosis for children aged 0-5 years for a spatial and temporal lag of 5 km and 6 months, respectively (p < 0.001). The evidence was mixed for lymphoma and tumours of the central nervous system. The current study suggests that leukaemia cases cluster in space-time due to an aetiological factor affecting children under 5 years of age. The observed pattern of clustering of young children close to time of diagnosis is compatible with Greaves' delayed-infections-hypothesis.

Keywords: Aetiology; Cancer registry; Childhood cancer; Childhood leukaemia; Cluster analysis; Meta-analysis.

Publication types

  • Systematic Review

MeSH terms

  • Adolescent
  • Central Nervous System Neoplasms / epidemiology
  • Child
  • Child, Preschool
  • Female
  • Geographic Information Systems
  • Humans
  • Infant
  • Leukemia / epidemiology*
  • Lymphoma / epidemiology*
  • Male
  • Neoplasms / epidemiology
  • Neuroblastoma / epidemiology*
  • Registries
  • Space-Time Clustering*
  • United Kingdom / epidemiology
  • United States / epidemiology