Understanding Type 1 Diabetes in Alberta by applying a
case definition to administrative data
Diabetes and Obesity
Ever wonder if we're accurately recognizing Type 1 diabetes in administrative health databases? This project uses a new algorithm to find the answer, aiming to give people with this condition the targeted care and resources they need.
Project Partners
Dr. Rose Yeung
Dr. Peter Senior
Dr. Padma Kaul
PLP (Partnered research initiative; full name not provided but assumed to be a healthcare or research organization)
Background
Type 1 and type 2 diabetes are distinct conditions with different diagnostic and treatment needs. Despite making up only 5–10% of diabetes cases, type 1 diabetes is underrepresented in population-level surveillance and healthcare planning in Canada. Unlike type 2 diabetes, type 1 is an autoimmune condition requiring lifelong insulin therapy. Current administrative data lacks the accuracy to differentiate between the two, making it difficult to determine the true prevalence and healthcare needs of people living with type 1 diabetes.
Aims/Objectives
Identify individuals with type 1 diabetes in Alberta using a validated algorithm.
Differentiate between type 1 and type 2 diabetes in administrative and electronic health records.
Assess the algorithm’s effectiveness, sensitivity, and predictive accuracy.
Support better healthcare planning and quality improvement for people living with type 1 diabetes.
Findings/Summary
This study, led by Dr. Rose Yeung in partnership with Drs. Peter Senior and Padma Kaul, utilizes a previously published algorithm to identify and distinguish people with type 1 diabetes from those with type 2 using multiple administrative and electronic health datasets in Alberta. The study tests the algorithm’s validity and reliability across these datasets, focusing on metrics such as sensitivity and predictive accuracy. By confirming the effectiveness of the algorithm, the research seeks to close a critical gap in diabetes data in Canada. Improved identification of type 1 diabetes will enable policymakers and clinicians to more effectively address the unique clinical needs and resource requirements of this underrepresented group.
Conclusions/Outcomes/Impact/Implications
If successful, this study will enable accurate identification of type 1 diabetes cases at the population level in Alberta. This could transform healthcare resource allocation, inform clinical guidelines, and ensure targeted support for people living with type 1 diabetes. The results may also serve as a model for other provinces and countries seeking to address similar data gaps.