C C Patterson1, E Gyürüs2, J Rosenbauer3, O Cinek4, A Neu5, E Schober6, R C Parslow7, G Joner8, J Svensson9, C Castell10, P J Bingley11, E Schoenle12, P Jarosz-Chobot13, B Urbonaité14, U Rothe15, C Kržišnik16, C Ionescu-Tirgoviste17, I Weets18, M Kocova19, G Stipancic20, M Samardzic21, C E de Beaufort22, A Green23, G Soltész2, G G Dahlquist24. 1. Centre of Excellence for Public Health Northern Ireland, Queen's University Belfast, Belfast, UK. 2. Department of Paediatrics, Pécs University, Pecs, Hungary. 3. Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany. 4. Department of Pediatrics, 2nd Faculty of Medicine, Charles University in Prague and University Hospital Motol, Prague, Czech Republic. 5. Pediatric Endocrinology & Diabetes, University Children's Hospital, Tübingen, Germany. 6. Department of Pediatric and Adolescent Medicine, Medical University of Vienna, Vienna, Austria. 7. Leeds Institute of Genetics, Health and Therapeutics, University of Leeds, Leeds, UK. 8. Department of Pediatrics, Ullevål University Hospital, Oslo, Norway. 9. Department of Paediatrics, Herlev University Hospital, Copenhagen, Denmark. 10. Public Health Agency, Department of Health, Government of Catalonia, Barcelona, Spain. 11. School of Clinical Sciences, University of Bristol, Bristol, UK. 12. Department of Endocrinology and Diabetology, University Children's Hospital, Zurich, Switzerland. 13. Department of Pediatrics, Endocrinology and Diabetes, Medical University of Silesia, Katowice, Poland. 14. Institute of Endocrinology, Lithuanian University of Health Science, Kaunas, Lithuania. 15. Department for Epidemiology and Health Care Research, Technical University of Dresden, Dresden, Germany. 16. Department of Pediatrics, University Children's Hospital, Ljubljana, Slovenia. 17. Nutrition and Metabolic Diseases Clinic, N Paulescu Institute of Diabetes and Metabolic Diseases, Bucharest, Romania. 18. Diabetes Research Center, Brussels Free University, Vrije Universiteit Brussel, Brussels, Belgium. 19. Department of Endocrinology and Genetics, University Children's Hospital, Skopje, Macedonia. 20. Department of Paediatrics, University Hospital Sestre Milosrdnice, Zagreb, Croatia. 21. Department of Endocrinology and Diabetes, University Children's Hospital, Podgorica, Montenegro. 22. Department of Paediatric Diabetes and Endocrinology, Centre Hospitalier de Luxembourg, Luxembourg. 23. Odense Patient data Exploratory Network, University of Southern Denmark, Odense, Denmark. 24. Department of Clinical Science, University of Umeå, Umeå, Sweden.
Abstract
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.
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.
Authors: Christopher C Patterson; Valma Harjutsalo; Joachim Rosenbauer; Andreas Neu; Ondrej Cinek; Torild Skrivarhaug; Birgit Rami-Merhar; Gyula Soltesz; Jannet Svensson; Roger C Parslow; Conxa Castell; Eugen J Schoenle; Polly J Bingley; Gisela Dahlquist; Przemysława K Jarosz-Chobot; Dalė Marčiulionytė; Edna F Roche; Ulrike Rothe; Natasa Bratina; Constantin Ionescu-Tirgoviste; Ilse Weets; Mirjana Kocova; Valentino Cherubini; Natasa Rojnic Putarek; Carine E deBeaufort; Mira Samardzic; Anders Green Journal: Diabetologia Date: 2018-11-28 Impact factor: 10.122
Authors: James Rafferty; Jeffery W Stephens; Mark D Atkinson; Stephen D Luzio; Ashley Akbari; John W Gregory; Stephen Bain; David R Owens; Rebecca L Thomas Journal: Int J Popul Data Sci Date: 2021-04-15