Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
BMC Public Health. 2024 Oct 12;24(1):2797. 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.
塔比阿特莫达勒斯大学医学科学系人群中 2 型糖尿病（T2DM）的患病率尚不清楚，但对医疗保健计划至关重要。本研究旨在验证四个非实验室风险评分模型，即美国糖尿病协会（ADA）风险评分、澳大利亚 2 型糖尿病风险评估工具（AUSDRISK）、芬兰糖尿病风险评分（FINDRISC）和 TOPICS 糖尿病筛查评分，以识别未诊断的糖尿病，并使用选定的模型间接估计 Tehranian 人群亚组中 T2DM 的患病率。
本研究由两部分组成。在第一部分中，使用 2016 年 STEPS 调查的伊朗数据验证了用于识别未诊断 T2DM 的非实验室风险评分模型。通过曲线下面积（AUC）和观测与预期（O/E）比值来评估模型性能。利用伊朗 STEPS 2011 调查的额外独立数据，通过比较间接患病率估计值与观察值来检验模型结果。在第二部分中，通过将选定的模型应用于 2023 年进行的德黑兰人口电话调查的代表性随机样本，间接估计 T2DM 的患病率。
在使用的不同模型中，AUSDRISK 在区分度（AUC（95%置信区间（CI））：0.80（0.78，0.81））和校准（O/E 比值=1.01）方面表现最佳。在更新原始模型后，AUC 值或校准没有变化。此外，我们的研究结果表明，间接估计值与 STEPS 2011 调查中的观察值非常接近。在研究的第二部分，通过将重新校准的模型应用于子样本，估计未诊断糖尿病和 T2DM 的间接患病率（95%CI）分别为 4.18%（3.87，4.49）和 11.1%（9.34，13.1）。
鉴于该模型的出色表现，间接方法似乎可以提供一种具有成本效益且简单的方法来评估疾病患病率和干预效果。
