Investigating spatio-temporal similarities in the epidemiology of childhood leukaemia and diabetes.
Childhood acute lymphoblastic leukaemia (ALL) and Type 1 diabetes (T1D) share some common epidemiological features, including rising incidence rates and links with an infectious aetiology. Previous work has shown a significant positive correlation in incidence between the two conditions both at the international and small-area level. The aim was to extend the methodology by including shared spatial and temporal trends using a more extensive dataset among individuals diagnosed with ALL and T1D in Yorkshire (UK) aged 0-14 years from 1978-2003. Cases with ALL and T1D were ascertained from 2 high quality population-based disease registers covering the Yorkshire region of the UK and linked to an electoral ward from the 1991 UK census. A Bayesian model was fitted where similarities and differences in risk profiles of the two diseases were captured by the shared and disease-specific components using a shared-component model, with space-time interactions. The extended model revealed a positive correlation of at least 0.70 between diseases across all time periods, and an increasing risk across time for both diseases, which was more evident for T1D. Furthermore, both diseases exhibited lower rates in the more urban county of West Yorkshire and higher rates in the more rural northern and eastern part of the region. A differential effect of T1D over ALL was found in the south-eastern part of the region, which had a more pronounced association with population mixing than with population density or deprivation. Our approach has demonstrated the utility in modelling temporally and spatially varying disease incidence patterns across small geographical areas. The findings suggest searching for environmental factors that exhibit similar geographical-temporal variation in prevalence may help in the development and testing of plausible aetiological hypotheses. Furthermore, identifying environmental exposures specific to the south-eastern part of the region, especially locally varying risk factors which may differentially affect the development of T1D and ALL, may also be fruitful.
Biostatistics Unit, South African Medical Research Council, 1 Soutpansberg Road, Pretoria, South Africa. samuel.manda@mrc.ac.za
Samuel O M
SO
Manda
Richard G
RG
Feltbower
Mark S
MS
Gilthorpe
D000293:Adolescent; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D003710:Demography; D003922:Diabetes Mellitus, Type 1 / Q000453:epidemiology*; D016021:Epidemiologic Studies; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008962:Models, Theoretical; D010372:Pediatrics; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000453:epidemiology*; D006113:United Kingdom / Q000453:epidemiology