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Patterson, CC; Gyuerues, E; Rosenbauer, J; Cinek, O; Neu, A; Schober, E; Parslow, RC; Joner, G; Svensson, J; Castell, C; Bingley, PJ; Schoenle, E; Jarosz-Chobot, P; Urbonaite, B; Rothe, U; Krzisnik, C; Ionescu-Tirgoviste, C; Weets, I; Kocova, M; Stipancic, G; Samardzic, M; de Beaufort, CE; Green, A; Soltesz, G; Dahlquist, GG (2015)
Publisher: Wiley
Languages: English
Types: Article
Background: The month of diagnosis in childhood type 1 diabetes shows seasonal variation. Objective: We describe the pattern and investigate if year-to-year irregularities are associated with meteorological factors using data from 50 000 children diagnosed under the age of 15 yr in 23 population-based European registries during 1989–2008. Methods: Tests for seasonal variation in monthly counts aggregated over the 20 yr period were performed. Time series regression was used to investigate if sunshine hour and average temperature data were predictive of the 240 monthly diagnosis counts after taking account of seasonality and long term trends. Results: Significant sinusoidal pattern was evident in all but two small centers with peaks in November to February and relative amplitudes ranging from ±11 to ±38% (median ±17%). However, most centers showed significant departures from a sinusoidal pattern. Pooling results over centers, there was significant seasonal variation in each age-group at diagnosis, with least seasonal variation in those under 5 yr. Boys showed greater seasonal variation than girls, particularly those aged 10–14 yr. There were no differences in seasonal pattern between four 5-yr sub-periods. Departures from the sinusoidal trend in monthly diagnoses in the period were significantly associated with deviations from the norm in average temperature (0.8% reduction in diagnoses per 1 °C excess) but not with sunshine hours. Conclusions: Seasonality was consistently apparent throughout the period in all age-groups and both sexes, but girls and the under 5 s showed less marked variation. Neither sunshine hour nor average temperature data contributed in any substantial way to explaining departures from the sinusoidal pattern.

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