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

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.

Core Standards of the EUBIROD Project. Defining a European Diabetes Data Dictionary for Clinical Audit and Healthcare Delivery

BACKGROUND

A set of core diabetes indicators were identified in a clinical review of current evidence for the EUBIROD project. In order to allow accurate comparisons of diabetes indicators, a standardised currency for data storage and aggregation was required. We aimed to define a robust European data dictionary with appropriate clinical definitions that can be used to analyse diabetes outcomes and provide the foundation for data collection from existing electronic health records for diabetes.

METHODS

Existing clinical datasets used by 15 partner institutions across Europe were collated and common data items analysed for consistency in terms of recording, data definition and units of measurement. Where necessary, data mappings and algorithms were specified in order to allow partners to meet the standard definitions. A series of descriptive elements were created to document metadata for each data item, including recording, consistency, completeness and quality.

RESULTS

While datasets varied in terms of consistency, it was possible to create a common standard that could be used by all. The minimum dataset defined 53 data items that were classified according to their feasibility and validity. Mappings and standardised definitions were used to create an electronic directory for diabetes care, providing the foundation for the EUBIROD data analysis repository, also used to implement the diabetes registry and model of care for Cyprus.

CONCLUSIONS

The development of data dictionaries and standards can be used to improve the quality and comparability of health information. A data dictionary has been developed to be compatible with other existing data sources for diabetes, within and beyond Europe.

Paediatric screening for hypercholesterolaemia in Europe

Different screening strategies are currently recommended to identify children with (familial) hypercholesterolaemia in order to initiate early lipid management. However, these strategies are characterised to date by low adherence by the medical community and limited compliance by parents and children. In a literature review, the authors assess which children should undergo screening and which children are in effect identified through the currently recommended strategies. Furthermore, the authors discuss the different screening tools and strategies currently used in Europe and what is known about the negative aspects of screening. The authors conclude that currently recommended selective screening strategies, which are mainly based on family history, lack precision and that a large percentage of affected children who are at increased risk of future coronary artery disease are not being identified. The authors propose universal screening of children between 1 and 9 years of age, a strategy likely to be most effective in terms of sensitivity and specificity for the identification of children with familial hypercholesterolaemia. However, this concept has yet to be proven in clinical practice.

An influence of HLA-A, B, DR, DQ, and MICA on the occurrence of Celiac disease in patients with type 1 diabetes

Celiac disease (CD) is more common in individuals with insulin dependent diabetes mellitus (T1D) than in the general population. HLA class II molecules DQ8 (DQB1*0302-DQA1*0301) and DQ2 (DQB1*0201-DQA1*0501) have been identified as key genetic risk factors in both diseases. While DQ8 conveys a higher risk for T1D, DQ2 is more frequent in CD. Less is known about the contribution of HLA class I. The gut immune system has been implicated in the pathogenesis of both diseases. The MICA, which is mainly expressed in the gastrointestinal epithelium and recognized by gammadeltaT lymphocytes and natural killer (NK) cells via the NKG2D, might play a role. The aim of our study was to identify possible HLA class I and MICA alleles and conserved extended haplotypes as risk factors for the development of CD in T1D. Three groups consisting of 37 individuals with T1D and CD, 67 individuals with only T1D and 70 controls were analyzed. HLA class I and MICA alleles were determined using Luminex technology. An occurrence of CD in individuals with T1D was most significantly associated with B*08 (P = 7.3 x 10(-13)), contributing more than any of the HLA class II alleles (DRB1*0301, P = 5.00 x 10(-10); DQB1*0201, P = 7.65 x 10(-8)). Moreover, the association with CD became stronger when B*08(B*08-DQA*0501-DQB1*0201-DRB1*0301, P = 5.07 x 10(-12)) was present in the DRB1*0301-DQB1*0201-DQA1*0501 (P = 5.00 x 10(-10)) extended haplotype. We suggest a combined influence of alleles present in the MICA*008-B*08-A1-DR3-DQ2 extended haplotype on the development of CD in Slovenian individuals with T1D, where B*08 or/and a gene located close to it may play an important role, independently of HLA class II.