Spatial clustering of childhood cancers in Switzerland

Transcription

1 Spatial clustering of childhood cancers in Switzerland Garyfallos Konstantinoudis; Christian Kreis; Roland A. Ammann; Felix Niggli; Claudia E. Kuehni; Ben D. Spycher Institute of Social and Preventive Medicine ISPM University of Bern 22 November 2017

2 Background > 200 children diagnosed with cancer per year in Switzerland > High doses of ionising radiation can induce leukaemia and CNS tumours 1,2 > Possible environmental risk factors Low dose radiation (e.g. background radiation) Air pollution Pesticide exposure Electromagnetic fields Infections > Spatial variation of childhood cancer incidence 1 Wakeford J Radiol Prot 2013; 2 Wrensch Neuro Oncol

3 Background: Global clustering vs Cluster detection > Two type of tests are used: Global clustering test: An overall tendency of cases to occur closer to each other Cluster detection test: A single local area of high risk (local cluster) Can you guess which is the non-clustered process? Spatial randomness Global clustering Local cluster 3

4 Background: Previous studies Cancer type Number of studies (studies that found clustering) Lymphoma 7 (1) Hodgkin Lymphoma 5 (4) Non-Hodgkin Lymphoma 9 (3) CNS tumours 11 (1) Astrocytoma 3 (0) Medulloblastoma 2 (2) Other CNS 4 (0) Nephroblastoma 1 (1) Malignant bone tumours 2 (0) Soft tissue sarcomas 1 (1) > Mixed results > Methodological limitations Low resolution Modifiable areal unit problem Statistical power 4

5 Aims > To investigate if childhood cancer cases tend to occur closer to each other than expected (global clustering) > To examine if there are any local clusters of childhood cancers in Switzerland (cluster detection) 5

6 Methods > Cases > Outcome Children aged 0-15 years at diagnosis Registered in the Swiss Childhood Cancer Registry (SCCR) Diagnosed in Switzerland during All cancers, childhood lymphoma (HL and NHL), CNS tumours (Astrocytoma, embryonal CNS, other CNS), Neuroblastoma, Nephroblastoma, Malignant bone tumours and Soft tissue sarcomas > Control selection Case-control ratio 1:10 Randomly sampled geocodes from the Swiss censuses (1990, 2000, 2010 and onwards) Matching variables: time at diagnosis, sex and age at diagnosis 6

7 Methods > Spatial resolution > Birth and diagnosis Precise geocodes of residence for both cases and controls > Statistical methods K-functions Cuzick Edwards test Kulldorff circular scan Global clustering tests Cluster detection test > Multiple testing adjustment for tests and diagnostic groups 7

8 Results at birth ICCC3 code Number of cases k-functions P-value Cuzick- Edwards P-value Kulldorff s Scan P-value All cancers I-XII 4, Lymphoma II HL IIa NHL IIb-IIe CNS tumours III Astrocytoma IIIb Embryonal CNS IIIc Other CNS tumours IIIa, IIId, IIIe, IIIf Neuroblastoma IV Nephroblastoma VIa Malignant bone tumours VII Soft tissue sarcomas IX Overall P-value =

9 Results at diagnosis ICCC3 code Number of cases k-functions P-value Cuzick- Edwards P-value Kulldorff s Scan P-value All cancers I-XII 6,034 < Lymphoma II HL IIa NHL IIb-IIe CNS tumours III 1, Astrocytoma IIIb Embryonal CNS IIIc Other CNS tumours IIIa, IIId, IIIe, IIIf Neuroblastoma IV Nephroblastoma VIa Malignant bone tumours VII Soft tissue sarcomas IX Overall P-value =

10 A closer look 10

11 Embryonal CNS tumours 11

12 Discussion > Adjustment for multiple testing indicate chance > However results in agreement with the literature > Hodgkin lymphoma and embryonal CNS tumours might have environmental or infectious causes Take home message Future studies should stratify by tumour subtypes and pool data to maximise power. Cancer type Number of studies (studies that found clustering) Lymphoma 7 (1) Hodgkin Lymphoma 5 (4) Non-Hodgkin Lymphoma 9 (3) CNS tumours 11 (1) Astrocytoma 3 (0) Medulloblastoma 2 (2) Other CNS 4 (0) Nephroblastoma 1 (1) Malignant bone tumours 2 (0) Soft tissue sarcomas 1 (1) 12

13 Thank you for your attention! Funded by: Swiss cancer research ( , ) Swiss national science foundation (PZ00P3_147987)