TY - JOUR

T1 - Age-period-cohort modelling of type 1 diabetes incidence rates among children included in the EURODIAB 25-year follow-up study

AU - Svensson, J.

AU - Ibfelt, E. H.

AU - Carstensen, B.

AU - Neu, A.

AU - Cinek, O.

AU - Skrivarhaug, T.

AU - Rami-Merhar, B.

AU - Feltbower, R. G.

AU - Castell, C.

AU - Konrad, D.

AU - Gillespie, K.

AU - Jarosz-Chobot, P.

AU - Marčiulionytė, D.

AU - Rosenbauer, J.

AU - Bratina, N.

AU - Ionescu-Tirgoviste, C.

AU - Gorus, F.

AU - Kocova, M.

AU - de Beaufort, C.

AU - Patterson, C. C.

N1 - Funding Information: The authors acknowledge the Austrian Diabetes Incidence Study Group, the Belgian Diabetes Registry, the Czech Childhood Diabetes Register and the Czech Association of Parents and Friends of Diabetic Children, the Danish Study Group of Diabetes in Childhood and contributors to DanDiabKids, the Chronic Disease Prevention Unit of the Finnish National Institute for Health and Welfare, the Baden-Württemberg Diabetes Incidence Registry (DIARY), the German Paediatric Surveillance Unit, DPV Science Initiative, Ulm University, the German Competence Network Diabetes Mellitus and the German Center for Diabetes Research (DZD), the Saxonian Childhood Diabetes Register Group, the Hungarian Childhood Diabetes Epidemiology Group, the Irish Childhood Diabetes National Register, the Registry for Incidence of Diabetes in Italy (RIDI), the Lithuanian Childhood Diabetes Registry Study Group, the Norwegian Childhood Diabetes Study Group, the Catalan Epidemiology Type 1 Diabetes Study Group, the Swedish Childhood Diabetes Study Group, the Swiss Society for Paediatric Endocrinology and Diabetology, the Managed Clinical Network for Paediatric Diabetes in Northern Ireland, the Bart’s–Oxford Study Group and the Yorkshire Register of Diabetes in Children and Young People.

PY - 2023

Y1 - 2023

N2 - Aims: Specific patterns in incidence may reveal environmental explanations for type 1 diabetes incidence. We aimed to study type 1 diabetes incidence in European childhood populations to assess whether an increase could be attributed to either period or cohort effects. Methods: Nineteen EURODIAB centres provided single year incidence data for ages 0–14 in the 25-year period 1989–2013. Case counts and person years were classified by age, period and cohort (APC) in 1-year classes. APC Poisson regression models of rates were fitted using restricted cubic splines for age, period and cohort per centre and sex. Joint models were fitted for all centres and sexes, to find a parsimonious model. Results: A total of 57,487 cases were included. In ten and seven of the 19 centres the APC models showed evidence of nonlinear cohort effects or period effects, respectively, in one or both sexes and indications of sex-specific age effects. Models showed a positive linear increase ranging from approximately 0.6 to 6.6%/year. Centres with low incidence rates showed the highest overall increase. A final joint model showed incidence peak at age 11.6 and 12.6 for girls and boys, respectively, and the rate-ratio was according to sex below 1 in ages 5–12. Conclusion: There was reasonable evidence for similar age-specific type 1 diabetes incidence rates across the EURODIAB population and peaks at a younger age for girls than boys. Cohort effects showed nonlinearity but varied between centres and the model did not contribute convincingly to identification of environmental causes of the increase.

AB - Aims: Specific patterns in incidence may reveal environmental explanations for type 1 diabetes incidence. We aimed to study type 1 diabetes incidence in European childhood populations to assess whether an increase could be attributed to either period or cohort effects. Methods: Nineteen EURODIAB centres provided single year incidence data for ages 0–14 in the 25-year period 1989–2013. Case counts and person years were classified by age, period and cohort (APC) in 1-year classes. APC Poisson regression models of rates were fitted using restricted cubic splines for age, period and cohort per centre and sex. Joint models were fitted for all centres and sexes, to find a parsimonious model. Results: A total of 57,487 cases were included. In ten and seven of the 19 centres the APC models showed evidence of nonlinear cohort effects or period effects, respectively, in one or both sexes and indications of sex-specific age effects. Models showed a positive linear increase ranging from approximately 0.6 to 6.6%/year. Centres with low incidence rates showed the highest overall increase. A final joint model showed incidence peak at age 11.6 and 12.6 for girls and boys, respectively, and the rate-ratio was according to sex below 1 in ages 5–12. Conclusion: There was reasonable evidence for similar age-specific type 1 diabetes incidence rates across the EURODIAB population and peaks at a younger age for girls than boys. Cohort effects showed nonlinearity but varied between centres and the model did not contribute convincingly to identification of environmental causes of the increase.

KW - Age-period-and cohort model

KW - Children

KW - Diabetes type 1

KW - Europe

KW - Incidence

U2 - 10.1007/s00592-022-01977-x

DO - 10.1007/s00592-022-01977-x

M3 - Journal article

C2 - 36205797

AN - SCOPUS:85139619147

VL - 60

SP - 73

EP - 82

JO - Acta Diabetologica

JF - Acta Diabetologica

SN - 0940-5429

ER -