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Leonora Kona, Eleftherios Meletis, Georgios Mavraganis, Georgios Georgiopoulos, Olympia Lioupi, Evangelia Anifanti, Polychronis Kostoulas, & Konstantinos Pateras. Estimating the True Prevalence of Diabetes in Patients with Stroke. Journal of Cardiovascular and Metabolic Disease Epidemiology. 2025. doi: Retrieved from https://w3.sciltp.com/journals/jcmde/article/view/2505000597
Volume 1, Issue 1, 2025
Copyright (c) 2025 by the authors.
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Article

Estimating the True Prevalence of Diabetes in Patients with Stroke

Leonora Kona 1,*, Eleftherios Meletis 1, Georgios Mavraganis 2, Georgios Georgiopoulos 2,3,4,5, Olympia Lioupi 1, Evangelia Anifanti 1, Polychronis Kostoulas 1,† and Konstantinos Pateras 1,†

1 Laboratory of Epidemiology, Applied Artificial Intelligence & Biostatistics, Faculty of Public and One Health,
University of Thessaly, 431 00 Karditsa, Greece

2 Department of Clinical Therapeutics, National and Kapodistrian University of Athens, 157 72 Athens, Greece

3 Department of Physiology, School of Medicine, University of Patras, 265 04 Patras, Greece

4 Institute of Cardiovascular Sciences, University College London (UCL), London WC1E 6BT, UK

5 School of Biomedical Engineering and Imaging Sciences, St Thomas Hospital, King’s College London,
London WC2R 2LS, UK

* Correspondence: leonorakona@gmail.com

† These authors contributed equally to this work.

Received: 9 December 2024; Revised: 4 April 2025; Accepted: 29 April 2025; Published: 6 May 2025

Abstract: Aims: This study aims to investigate challenges associated with diabetes prevalence estimates in stroke survivors, focusing on the issue of misclassification bias in diagnostic tests, and to propose measures for improving the accuracy of these estimates. Methods: The study examines the inherent misclassification biases associated with the diagnostic tests, including Fasting Blood Glucose (FBG), Oral Glucose Tolerance Test (OGTT), and Hemoglobin A1c (HbA1c), commonly used to identify diabetes in stroke survivors. To address misclassification biases, three parameter Bayesian latent class models are applied to delineate true prevalence from the apparent prevalence reported in studies, using FBG, OGTT, HbA1c as standard diagnostic tests for diabetes. Results: The results revealed discrepancies between apparent and true prevalence of diabetes in stroke patients, highlighting the influence of the sensitivity and specificity of each diagnostic test on prevalence estimates. Conclusions: Correcting misclassification biases in diabetes diagnostic tests is crucial for accurate prevalence estimates in stroke survivors, which is necessary for proper diagnosis and patient care. The study underscores the need for future research to address data biases and uncertainties in diagnostic test measures, which will optimize the accuracy of diabetes diagnosis in this vulnerable population.

Keywords:

diabetes stroke apparent prevalence true prevalence diagnostic test

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