Clinical trial NCT06573905: Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA
Official title: Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Diabetes Mellitus, Type 1
Diabete Type 2
Diabetes;Adult Onset
Diabetes, Autoimmune
Keywords: medico-administrative algorithms
cohort study
observational study
diabetes
Lead sponsor: Universite du Quebec en Outaouais
Sponsor class: OTHER
Collaborators: name: McGill University
name: Centre de Recherche du Centre Hospitalier de l'Université de Montréal
name: University Hospital, Angers
First posted: 2024-08-27
Enrollment: 17271
Sex: ALL
Minimum age (years): 1
Maximum age (years): 40
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The study population consists of individuals diagnosed with Type 1, Type 2, or LADA diabetes, as well as other diabetes-related phenotypes, within the province of Quebec. The population includes children, adolescents, and young adults up to 40 years of age at the time of diagnosis. The cohort is drawn from a comprehensive dataset of medical, self-reported, and medico-administrative records spanning from 1997 to 2024. This diverse population allows for a robust validation of the diabetes classification algorithms across different age groups and phenotypes, providing valuable insights into the epidemiology of diabetes in Quebec.

Brief summary: 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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Diagnostic Accuracy Measures (Percentages)
timeFrame: Retrospective data from 1997 to 2024
measure: Classification Counts (Number of Cases)
timeFrame: Retrospective data from 1997 to 2024
measure: Likelihood Ratios (Unitless)
timeFrame: Retrospective data from 1997 to 2024
Secondary outcomes:
measure: Prevalence of Each Diabetes Phenotype (Proportion/Percentage)
timeFrame: Retrospective data from 1997 to 2024
measure: Incidence of Each Diabetes Phenotype
timeFrame: Retrospective data from 1997 to 2024
Other outcomes:
Locations:
city: Montréal
country: Canada
facility: Philippe Corsenac
state: Quebec
Study officials:
affiliation: UQO
name: philippe C corsenac, Ph.D
role: PRINCIPAL_INVESTIGATOR
References: