Mapping Diabetes in Quebec: Validating Medico-administrative Algorithms for Type 1 Diabetes, Type 2 Diabetes and LADA
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 17,271
- 试验地点
- 1
- 主要终点
- Diagnostic Accuracy Measures (Percentages)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 1 Year 至 40 Years（Child, Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
- •Residents of Quebec with available medico-administrative records from 1997 to 2024.
排除标准
- •Non-residents of Quebec during the study period.
结局指标
主要结局
Diagnostic Accuracy Measures (Percentages)
时间窗: Retrospective data from 1997 to 2024
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.
Classification Counts (Number of Cases)
时间窗: Retrospective data from 1997 to 2024
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.
Likelihood Ratios (Unitless)
时间窗: Retrospective data from 1997 to 2024
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.
次要结局
- Prevalence of Each Diabetes Phenotype (Proportion/Percentage)(Retrospective data from 1997 to 2024)
- Incidence of Each Diabetes Phenotype(Retrospective data from 1997 to 2024)
研究者
Corsenac Philippe
Dr in epidemiology and immunology
Universite du Quebec en Outaouais