--- title: "Population-based retrospective cohorts to validate algorithms and estimate prevalence of T1D, T2D" id: "bmj-open-1-retrospective-population-based-cohorts-for-assessing-the-performance-of" canonical_url: "https://medichelpline.com/clinical-feed/bmj-open-1-retrospective-population-based-cohorts-for-assessing-the-performance-of" content_type: "clinical_feed_article" specialty: "Endocrinology" source_name: "BMJ Open" source_url: "http://bmjopen.bmj.com/cgi/content/short/16/8/e109209?rss=1" published_at: "2026-08-31T10:16:00.000Z" evidence_level: "Journal Feed" license: "CC-BY-NC-4.0 / Informational Use" --- # Population-based retrospective cohorts to validate algorithms and estimate prevalence of T1D, T2D ## Provenance & Clinical Metadata - **Canonical URL:** https://medichelpline.com/clinical-feed/bmj-open-1-retrospective-population-based-cohorts-for-assessing-the-performance-of - **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md) - **Primary Source:** BMJ Open - **Source URL:** [Original Journal Publication](http://bmjopen.bmj.com/cgi/content/short/16/8/e109209?rss=1) - **Published At:** 2026-08-31T10:16:00.000Z - **Evidence Rating:** Journal Feed ## Executive GIST (TL;DR) - The study is a retrospective, longitudinal population-based protocol in Quebec covering 1997–2027 to evaluate diabetes subtype classification and quantify phenotype burden. - It highlights that **type 2 diabetes (T2D)** comprises most global diabetes but **type 1 diabetes (T1D)**, **latent autoimmune diabetes in adults (LADA)** and other specific types are under-recognised. - No existing population-based prevalence or incidence estimates by diabetes phenotype are available for Quebec; current medico-administrative algorithms do not reliably distinguish subtypes. - Phase 1 will assess diagnostic performance of the Corsenac et al. (2022) medico-administrative algorithms across four phenotypes (T1D, T2D, LADA, others) using three independent reference subsamples within a cohort (A1–A2–A3; n = 5,200). - Subsamples: A1 = self-reported T1D and LADA; A2 = physician-confirmed T2D and other specific types; A3 = general population respondents reporting diabetes status and phenotype when applicable. - Records from A1–A3 will be probabilistically linked to medico-administrative and pharmaceutical claims from the **RAMQ** by the Institut de la statistique du Québec (ISQ). - Machine learning methods will be used to refine algorithmic definitions for the four phenotypes based on linked data and reference subsamples. - Phase 2 will apply refined algorithms to a second medico-administrative, population-based cohort C (n = 50,000) to produce simultaneous prevalence and incidence estimates for the four phenotypes. - Analyses are limited to individuals continuously covered by RAMQ's public drug insurance, which provides pharmaceutical data and covers about 46% of Quebec's population. - Statistical reweighting — calibration on population margins (phase 1) and standardised inverse probability weighting (phase 2) — will adjust performance metrics and frequency estimates to represent the general Quebec population. - Ethics approvals from partner institutions confirmed feasibility; the study is registered on ClinicalTrials.gov (NCT06573905). Results will be disseminated via scientific and public health channels. ## Clinical Analysis & Structured Key Points Introduction Diabetes affects 537 million people worldwide, with type 2 diabetes (T2D) estimated to account for most cases. Type 1 diabetes (T1D), latent autoimmune diabetes in adults (LADA) and other specific types due to other causes remain under-recognised, especially LADA, given the absence of a standardised definition. In the province of Quebec (Canada), no population-based prevalence and incidence estimates are available for all diabetes phenotypes. Current medico-administrative algorithms fail to distinguish among diabetes subtypes, limiting accurate surveillance and effective prevention and clinical strategies. Methods and analysis This retrospective, longitudinal study (1997-2027) will be conducted in Quebec. Phase 1 will assess classification performance of the Corsenac et al. (2022) medico-administrative algorithms. Diagnostic performance metrics will be calculated per phenotype (T1D, T2D, LADA and others), using three independent subsamples as references in a first cohort (A1-A2-A3; n5200). It composed respectively of: (A1) self-reported diagnoses of T1D and LADA; (A2) diagnoses of T2D and other specific types due to others causes, confirmed by a physician; and (A3) general population respondents reporting diabetes status and phenotypes (if applicable). Subsamples are structured to capture the diversity and relative proportions of diabetes phenotypes, ensuring sufficient statistical power for population-based and subgroup analyses. All records (A1, A2, A3) will be probabilistically linked with medico-administrative and pharmaceutical claims from the Régie de l'assurance maladie du Québec (RAMQ) by the Institut de la statistique du Québec (ISQ). Machine learning methods will be then applied to refine algorithmic definitions for the four phenotypes (T1D, T2D, LADA, others). In phase 2, refined algorithms will be applied to a second medico-administrative population-based cohort, named C (n=50 000) to produce the first simultaneous prevalence and incidence estimates for the four phenotypes. Analyses will be restricted to individuals continuously covered by RAMQ's public drug insurance, which provides pharmaceutical data and covers 46% of the Quebec population. Calibration on population margins (phase 1) and standardised inverse probability weighting (phase 2) will reweight estimates (performance metrics in phase 1 and frequencies in phase 2) to represent the general Quebec population. Ethics and dissemination The different ethics boards of partner institutions approved the feasibility of the study. The study was registered on ClinicalTrials.gov, NCT06573905. 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