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

Overview

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.

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.

Conditions