Space–time clustering analyses of type 1 diabetes among 0- to 29-year-olds in Yorkshire, UK

Abstract

Aims/hypothesis: Infections have been suggested to play a role in the aetiology of type 1 diabetes. The presence of space-time clustering is consistent with the notion of an environmental component in disease aetiology, possibly linked to infections. We tested for evidence of space-time clustering among children and young adults under 30 years of age using data from a population-based register in Yorkshire, UK. Subjects and methods: Two data sets of children and young people diagnosed with type 1 diabetes were analysed: (1) applied. Addresses at diagnosis were geo-coded and used as the basis for the analyses. Results: The study analysed 3,019 type 1 diabetic patients in the 0-14 years age group and 989 patients in the 15-29 years group. Statistically significant space-time clustering based on place and time of diagnosis was detected both for the 10-14-year-olds (p=0.04) and for the 15-19-year-olds (p=0.01). Conclusions/interpretation: Previous studies of clustering of type 1 diabetes have generally been restricted to childhood. Our results from a data set that includes teenagers and young adults show that space-time clustering was limited to young people aged 10-19 years. This finding is consistent with an aetiology involving late exposure to infection. However, the question of whether this is directly diabetogenic or unmasks latent diabetes cannot be addressed by this methodology.

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

“…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.…”

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).…”

Neighborhood context and incidence of type 1 diabetes: The SEARCH for Diabetes in Youth Study

“…Based on migrant studies, twin studies, and biological plausibility, Knip and colleagues suggest that environmental factors may be involved in triggering T1DM (Knip et al, 2005). Additional evidence comes from studies that have shown geographic variation of T1DM, including spatial clustering (McNally et al, 2006; Rytkonen et al, 2001; Samuelsson and Lofman, 2004). …”