Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA (VDA)

Universite du Quebec en Outaouais

Status: Not yet enrolling

Conditions: Diabetes Mellitus, Type 1; Diabetes;Adult Onset; Diabetes, Autoimmune; Diabete Type 2

Treatments: Other: no intervention

Study type: Observational

Funder types: Other

Identifiers

NCT06573905

F1-14464

Details and patient eligibility

About

The goal of this observational study is to validate medico-administrative algorithms that classify diabetes phenotypes (Type 1, Type 2, and Latent Autoimmune Diabetes in Adults - LADA) in a population-based cohort in Quebec, including children, adolescents, and young adults up to 40 years old with diagnosed diabetes. The main questions it aims to answer are:

Can these algorithms accurately distinguish between Type 1, Type 2, and LADA across different age groups? What is the prevalence and incidence of each diabetes phenotype in Quebec? Participants will have their medical and administrative data analyzed, including data on medication usage and healthcare visits, to validate the accuracy of the algorithms. The study will involve comparing these algorithm-based classifications with clinical diagnoses or self-reported data to ensure reliability.

Full description

The goal of this observational study is to validate the effectiveness of medico-administrative algorithms developed to classify diabetes phenotypes, specifically Type 1, Type 2, and Latent Autoimmune Diabetes in Adults (LADA), in a population-based cohort in Quebec. The study focuses on children, adolescents, and young adults up to 40 years old who have been diagnosed with diabetes.

The main questions it aims to answer are:

Can these algorithms accurately differentiate between Type 1, Type 2, and LADA across various age groups? What are the prevalence and incidence rates of these diabetes phenotypes in the Quebec population? Participants, who are already diagnosed with one of the three diabetes types and receiving standard medical care, will have their data collected from existing medical and administrative records. This data includes information on medication usage, healthcare visits, and self-reported health outcomes.

The study will involve a retrospective analysis where the classifications made by the algorithms will be compared with clinical diagnoses and self-reported data to determine the accuracy and reliability of the algorithms. This validation process is crucial for improving diabetes management and public health strategies by ensuring that these algorithms can be reliably used in broader epidemiological studies.

Enrollment: 17,271 estimated patients

Sex: All

Ages: 1 to 40 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Individuals diagnosed with Type 1, Type 2, or Latent Autoimmune Diabetes in Adults (LADA) based on clinical or self-reported data.
• Participants diagnosed between 1997 and 2024.
• Residents of Quebec with available medico-administrative records from 1997 to 2024.

Exclusion criteria

• Non-residents of Quebec during the study period.

Trial design

17,271 participants in 5 patient groups

type 1 diabetes

Description:

This group comprises participants diagnosed with Type 1 diabetes according to self-reported data. The primary goal of comparing this group with medico-administrative records is to validate the algorithm's ability to accurately classify individuals with Type 1 diabetes, ensuring that they are correctly identified as such without being misclassified into other categories.

Treatment:

Other: no intervention

type 2 diabetes

Description:

This group includes participants diagnosed with Type 2 diabetes based on clinical data. The validation process focuses on assessing the algorithm's accuracy in identifying individuals with Type 2 diabetes, ensuring correct classification and minimizing the risk of misclassification as other diabetes phenotypes or non-diabetic.

Treatment:

Other: no intervention

Latent autoimmune diabete in adults

Description:

This group consists of participants diagnosed with Latent Autoimmune Diabetes in Adults (LADA) according to self-reported data. The validation process for this group focuses on assessing the algorithm's ability to accurately identify individuals with LADA, which is often challenging due to its characteristics that overlap with both Type 1 and Type 2 diabetes. Accurate classification of LADA is crucial for improving treatment strategies and understanding its epidemiology.

Treatment:

Other: no intervention

Non-diabetic

Description:

This group includes participants who, according to self-reported data from individuals, do not have any phenotypes of diabetes. The comparison of this group's data with medico-administrative records is crucial for identifying false positives and ensuring that the algorithms accurately exclude non-diabetic individuals from being misclassified as having diabetes.

other phenotypes

Description:

This group contains participants diagnosed with diabetes-related phenotypes other than Type 1, Type 2, or LADA, as well as those with rarer forms of the disease (based on clinical data). The validation aims to determine the algorithm's effectiveness in correctly identifying and classifying these less common phenotypes, which is critical for ensuring comprehensive and accurate diabetes classification.

Treatment:

Other: no intervention