Indirect estimation of the prevalence of type 2 diabetes mellitus in the sub-population of Tehran: using non-laboratory risk-score models in Iran

• Published in: BMC public health 2024-10, Vol.24 (1), p.2797-13, Article 2797
• Main Authors: Azizpour, Yosra, Asgari, Samaneh, Yaseri, Mehdi, Fotouhi, Akbar, Akbarpour, Samaneh
• Format: Article
• Language: English
• Subjects: Adult; Aged; Analysis; Body mass index; Calibration; Care and treatment; Cholesterol; Complications and side effects; Confidence intervals; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - epidemiology; Effectiveness; Epidemiology; Estimates; Examinations; Exercise; Family medical history; Female; Health risk assessment; Health risks; Humans; Indirect estimation; Iran - epidemiology; Laboratories; Low income groups; Machine learning; Male; Medical science; Methods; Middle Aged; Non_laboratory; Performance evaluation; Polls & surveys; Population studies; Prevalence; Public health; Risk assessment; Risk Assessment - methods; Risk factors; Sample size; Surveillance; Surveys; Tehran; Type 2; Type 2 diabetes; Validity; Variables; Womens health; Young Adult
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/s12889-024-20278-2

The prevalence of type 2 diabetes mellitus (T2DM) in the population covered by the Tehran University of Medical Sciences is unclear but crucial for healthcare programs. This study aims to validate four non-laboratory risk-score models, the American Diabetes Association (ADA) Risk Score, Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK), Finnish Diabetes Risk Score (FINDRISC), and TOPICS Diabetes Screening Score, for identifying undiagnosed diabetes and indirectly estimate the prevalence of T2DM in a subset of the Tehranian population using the selected model. This research consisted of two main parts. In the first part, non-laboratory risk-score models to identify undiagnosed T2DM were validated using Iranian data from STEPs 2016 survey. The model performance was evaluated through the Area Under the Curve (AUC) and calibration via the observed-to-expected (O/E) ratio. Additional independent data from STEPs 2011 survey in Iran were utilized to test the model results by comparing indirect prevalence estimates with observed estimates. In the second part, the prevalence of T2DM was estimated indirectly by applying the selected model to a representative random sample from a Tehranian population telephone survey conducted in 2023. Among the different models used, AUSDRISK showed the best performance in both discrimination (AUC (95% confidence interval (CI)): 0.80 (0.78, 0.81)) and calibration (O/E ratio = 1.01). After updating the original model, there was no change in the AUC value or calibration. Additionally, our findings indicate that the indirect estimates are nearly identical to the observed values in STEPs 2011 survey. In the second part of the study, by applying the recalibrated model to a subsample, the indirect prevalence of undiagnosed diabetes and T2DM (95% CI) were estimated at 4.18% (3.87, 4.49) and 11.1% (9.34, 13.1), respectively. Given the strong performance of the model, it appears that indirect method can provide a cost-effective and simple approach to assess disease prevalence and intervention effectiveness.