Developing and validating efficient and practical algorithms using administrative healthcare data to distinguish type 1 diabetes from type 2 diabetes among younger people with diabetes in Australia
The overall aim of the project is to develop and validate efficient and practical algorithms using administrative healthcare data to distinguish type 1 diabetes from type 2 diabetes among younger people with diabetes (diagnosed at age 15–40 years) in Australia.
The prevalence of type 2 diabetes in children, adolescents and young adults is increasing in parallel with the rise in the prevalence of childhood obesity in many countries. Individuals with young-onset type 2 diabetes have a greater risk of complications compared with young people with type 1 diabetes, suggesting a more aggressive disease phenotype.
The emergence and availability of large diabetes registry and administrative databases over the last two decades has provided a potentially powerful tool for population-based diabetes research. However, specific diagnostic codes for type 1 and type 2 diabetes may not be available or may be poorly documented in both administrative and clinical databases, and the absence of critical laboratory data, such as diabetes-related autoantibodies or C-peptide levels may lead to misclassification of diabetes type. The difficulties in the classification of diabetes type in large population-based databases have been major barriers to understanding the epidemiology and outcomes in younger people with type 1 diabetes and type 2 diabetes, respectively.
Our group has pioneered linkage of the NDSS to a variety of other databases to describe population-level incidence, mortality and complications across the Australian diabetes population. We have applied informative clinical characteristics (e.g. age of diabetes onset) and medication patterns to better differentiate type 2 diabetes from type 1 diabetes in the full population with diabetes. In order to extend this work to focus on young-adult-onset diabetes (diagnosed at age 15–40 years), it is critical that algorithms are developed and tested to adequately distinguish diabetes type in this age group. Therefore, we propose to take advantage of the NDSS, to develop and test algorithms from data typically available in administrative datasets to classify diabetes type defined by autoantibodies and C-peptide among people with onset of diabetes at a younger age (15–40 years). The outputs from this research will be important for populations globally with young-onset diabetes, allowing the answering of more complex and diabetes type-specific research questions and will also ensure that those with diabetes receive more appropriate management according to diabetes type.