Félix-Martínez Gerardo J, Godínez-Fernández J Rafael
Department of Electrical Engineering, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, Mexico; Department of Applied Mathematics and Computer Sciences, Universidad de Cantabria, Santander, Cantabria, Spain.
Department of Electrical Engineering, Universidad Autónoma Metropolitana, Iztapalapa, Ciudad de México, Mexico.
Endocrinol Diabetes Nutr (Engl Ed). 2018 Dec;65(10):603-610. doi: 10.1016/j.endinu.2018.04.004. Epub 2018 Jun 23.
Prevalence of diabetes in Mexico has constantly increased since 1993. Since type 2 diabetes may remain undiagnosed for many years, identification of subjects at high risk of diabetes is very important to reduce its impact and to prevent its associated complications.
To develop easily implementable screening models to identify subjects with undiagnosed diabetes based on the characteristics of Mexican adults.
Screening models were developed using datasets from the 2006 and 2012 National Health and Nutrition Surveys (NHNS). Variables used to develop the multivariate logistic regression models were selected using a backward stepwise procedure. Final models were validated using data from the 2000 National Health Survey (NHS).
The model based on the 2006 NHNS included age, waist circumference, and systolic blood pressure as explanatory variables, while the model based on the 2012 NHNS included age, waist circumference, height, and family history of diabetes. The sensitivity and specificity values obtained from the external validation procedure were 0.74 and 0.62 (2006 NHNS model) and 0.76 and 0.55 (2012 NHNS model) respectively.
Both models were equally capable of identifying subjects with undiagnosed diabetes (∼75%), and performed satisfactorily when compared to other models developed for other regions or countries.
自1993年以来，墨西哥糖尿病患病率持续上升。由于2型糖尿病可能多年未被诊断出来，识别糖尿病高危人群对于减轻其影响和预防相关并发症非常重要。
基于墨西哥成年人的特征，开发易于实施的筛查模型，以识别未被诊断出糖尿病的人群。
使用2006年和2012年全国健康与营养调查（NHNS）的数据集开发筛查模型。用于构建多变量逻辑回归模型的变量采用向后逐步法进行选择。最终模型使用2000年全国健康调查（NHS）的数据进行验证。
基于2006年NHNS的模型将年龄、腰围和收缩压作为解释变量，而基于2012年NHNS的模型将年龄、腰围、身高和糖尿病家族史作为解释变量。外部验证程序获得的灵敏度和特异度值分别为0.74和0.62（2006年NHNS模型）以及0.76和0.55（2012年NHNS模型）。
两种模型在识别未被诊断出糖尿病的人群方面能力相当（约75%），并且与为其他地区或国家开发的其他模型相比，表现令人满意。
