Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA
VDA
1 other identifier
observational
17,271
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jan 2025
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
August 23, 2024
CompletedFirst Posted
Study publicly available on registry
August 27, 2024
CompletedStudy Start
First participant enrolled
January 1, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 30, 2025
CompletedStudy Completion
Last participant's last visit for all outcomes
June 30, 2025
CompletedJanuary 1, 2025
December 1, 2024
6 months
August 23, 2024
December 30, 2024
Conditions
Keywords
Outcome Measures
Primary Outcomes (3)
Diagnostic Accuracy Measures (Percentages)
The primary outcome measure is the accuracy of the medico-administrative algorithms in correctly classifying participants into one of the following diabetes phenotypes: Type 1, Type 2, LADA, or Other Phenotypes, compared to clinical or self-reported diagnoses. 1.1. Diagnostic Accuracy Measures (Percentages) * Sensitivity (Se) * Specificity (Sp) * Positive Predictive Value (PPV) * Negative Predictive Value (NPV) All reported as proportions or percentages. These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Retrospective data from 1997 to 2024
Classification Counts (Number of Cases)
The primary outcome measure is the accuracy of the medico-administrative algorithms in correctly classifying participants into one of the following diabetes phenotypes: Type 1, Type 2, LADA, or Other Phenotypes, compared to clinical or self-reported diagnoses. 1.2. Classification Counts (Number of Cases) * True Positives (TP) * True Negatives (TN) * False Positives (FP) * False Negatives (FN) All reported as counts of participants. These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Retrospective data from 1997 to 2024
Likelihood Ratios (Unitless)
The primary outcome measure is the accuracy of the medico-administrative algorithms in correctly classifying participants into one of the following diabetes phenotypes: Type 1, Type 2, LADA, or Other Phenotypes, compared to clinical or self-reported diagnoses. 1.3. Likelihood Ratios (Unitless) * Positive Likelihood Ratio (LR+) * Negative Likelihood Ratio (LR-) Reported as unitless ratios. These indicators will not be aggregated into a single value, but will be presented separately to respect their distinct units of measurement.
Retrospective data from 1997 to 2024
Secondary Outcomes (2)
Prevalence of Each Diabetes Phenotype (Proportion/Percentage)
Retrospective data from 1997 to 2024
Incidence of Each Diabetes Phenotype
Retrospective data from 1997 to 2024
Study Arms (5)
type 1 diabetes
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.
type 2 diabetes
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.
Latent autoimmune diabete in adults
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.
Non-diabetic
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
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.
Interventions
no intervention. this is observational study.
Eligibility Criteria
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.
You may qualify if:
- 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.
You may not qualify if:
- Non-residents of Quebec during the study period.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Universite du Quebec en Outaouaislead
- McGill Universitycollaborator
- Centre de Recherche du Centre Hospitalier de l'Université de Montréalcollaborator
- University Hospital, Angerscollaborator
Study Sites (1)
Philippe Corsenac
Montreal, Quebec, J8X 3X7, Canada
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
philippe C corsenac, Ph.D
UQO
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Dr in epidemiology and immunology
Study Record Dates
First Submitted
August 23, 2024
First Posted
August 27, 2024
Study Start
January 1, 2025
Primary Completion
June 30, 2025
Study Completion
June 30, 2025
Last Updated
January 1, 2025
Record last verified: 2024-12
Data Sharing
- IPD Sharing
- Will not share
no planned