Tsai Emily W, Perng Wei, Mora-Plazas Mercedes, Marín Constanza, Baylin Ana, Villamor Eduardo
Department of Environmental Health Sciences, University of Michigan School of Public Health , Ann Arbor, MI , USA .
Ann Hum Biol. 2014 Sep-Oct;41(5):473-6. doi: 10.3109/03014460.2013.856939. Epub 2013 Nov 25.
To identify correlates of bias in self-reported anthropometry among reproductive-aged Colombian women and to correct overweight/obesity and obesity prevalence based on self-reported data using two calibration techniques.
Self-reported and objectively measured anthropometry were obtained from 597 women aged 21-55 years from Bogotá, Colombia. This study identified correlates of reporting bias (self-reported minus measured anthropometry) by examining its distribution across categories of sociodemographic characteristics, objectively measured anthropometry and body shape perception using linear regression. Next, weight status misclassification was assessed using self-reported anthropometry. Finally, multivariable linear regression and ROC curves were used to calibrate weight status misclassification from self-reported data; these techniques were applied in half of the study population and validated in the remaining half.
Women under-estimated weight by 2.0 ± 5.0 kg and over-estimated height by 0.6 ± 4.0 cm. Correlates of bias included objectively measured anthropometry and marital status. Self-reported BMI yielded spuriously low prevalences of overweight/obesity and obesity. The ROC approach effectively corrected overweight/obesity prevalence, while the regression method provided a more accurate estimate of obesity prevalence.
Bias in self-reported anthropometry varied with respect to objectively measured anthropometry and sociodemographic characteristics. BMI from self-reported anthropometry under-estimates overweight/obesity and obesity prevalence; calibration methods can effectively correct reporting bias.
确定哥伦比亚育龄妇女自我报告人体测量数据偏差的相关因素,并使用两种校准技术根据自我报告数据校正超重/肥胖及肥胖患病率。
从哥伦比亚波哥大的597名21至55岁女性中获取自我报告和客观测量的人体测量数据。本研究通过线性回归分析报告偏差(自我报告减去测量的人体测量数据)在社会人口统计学特征、客观测量的人体测量数据和体型感知类别中的分布,确定报告偏差的相关因素。接下来,使用自我报告的人体测量数据评估体重状况错误分类。最后,使用多变量线性回归和ROC曲线校准自我报告数据中的体重状况错误分类;这些技术应用于一半的研究人群,并在另一半人群中进行验证。
女性低估体重2.0±5.0千克,高估身高0.6±4.0厘米。偏差的相关因素包括客观测量的人体测量数据和婚姻状况。自我报告的BMI得出的超重/肥胖及肥胖患病率虚假偏低。ROC方法有效校正了超重/肥胖患病率,而回归方法对肥胖患病率的估计更准确。
自我报告人体测量数据的偏差因客观测量的人体测量数据和社会人口统计学特征而异。自我报告人体测量数据得出的BMI低估了超重/肥胖及肥胖患病率;校准方法可有效校正报告偏差。