Ghosh-Dastidar Madhumita Bonnie, Haas Ann C, Nicosia Nancy, Datar Ashlesha
RAND Corporation, Department of Economics and Statistics, 1776 Main Street, Santa Monica, CA 90401 USA.
RAND Corporation, Department of Economics and Statistics, 4570 Fifth Ave, Pittsburgh, PA 15213 USA.
BMC Obes. 2016 Sep 13;3(1):37. doi: 10.1186/s40608-016-0117-1. eCollection 2016.
Errors in reported height and weight raise concerns about body mass index (BMI) and obesity estimates obtained from self or proxy reports. Researchers have corrected BMI using linear statistical models, primarily with adult samples. We compared the accuracy of BMI correction in children for models that included child or parent reports versus both reports, and models that separately predicted height and weight compared to a single model for BMI.
Height and weight from child reports, parent reports, and objective measurements for 475 children participating in the Military Teenagers' Environment, Exercise and Nutrition Study were analyzed. Two approaches were evaluated: (1) separate linear correction models for height and weight versus (2) a single linear correction model for BMI. Each approach considered models for height, weight, or BMI with child reports, parent reports, or both reports, respectively, as predictors, stratified by gender. Prediction accuracy was computed using leave-one-out validation. Models were compared using root mean squared error for BMI, and sensitivity and specificity for overweight and obesity indicators.
Models that included both reports provided the best fit relative to a model using either set of reports, with adjusted R(2) of height, weight, and BMI models ranging from 67.1 to 87.6 % in males, and 69.2 to 88.3 % in females. Estimates of BMI from separate models for height and weight had the least prediction error, relative to those derived from a single model for BMI or from uncorrected (child or parent) reports. Cross-validated Root Mean Squared Error (RMSEs) preferred a model that included only parent reports among males and females, compared to models with only child reports or both reports. When assessing sensitivity (true positive) for obesity and overweight/obesity, the results varied by gender and outcomes. Specificity (true negative) was similarly high for all models.
Objective measurements are more accurate than self- or proxy-reports of BMI. In situations where objective measurement is infeasible, an approach that combines collecting a validation sub-sample including multiple reports of children's height and weight, with estimation of BMI correction models maybe a cost-effective and practical solution. Correction models generate BMI estimates that are closer to objective measurements than reports.
报告的身高和体重误差引发了对通过自我报告或他人代报得出的体重指数（BMI）及肥胖估计值的担忧。研究人员主要使用线性统计模型对成人样本校正BMI。我们比较了儿童BMI校正模型的准确性，这些模型包括儿童报告或家长报告以及两者兼有的情况，还比较了分别预测身高和体重的模型与单一BMI模型。
分析了参与军事青少年环境、运动与营养研究的475名儿童的儿童报告身高和体重、家长报告身高和体重以及客观测量值。评估了两种方法：（1）身高和体重的单独线性校正模型与（2）BMI的单一线性校正模型。每种方法分别考虑以儿童报告、家长报告或两者报告作为预测指标的身高、体重或BMI模型，并按性别分层。使用留一法交叉验证计算预测准确性。使用BMI的均方根误差以及超重和肥胖指标的敏感性和特异性对模型进行比较。
相对于使用任何一组报告的模型，同时包含两种报告的模型拟合度最佳，男性身高、体重和BMI模型的调整R²范围为67.1%至87.6%，女性为69.2%至88.3%。相对于从单一BMI模型或未校正（儿童或家长）报告得出的BMI估计值，身高和体重单独模型得出的BMI估计值预测误差最小。交叉验证均方根误差（RMSE）显示，与仅包含儿童报告或两者报告的模型相比，男性和女性中仅包含家长报告的模型更优。在评估肥胖和超重/肥胖的敏感性（真阳性）时，结果因性别和结果而异。所有模型的特异性（真阴性）同样较高。
BMI的客观测量比自我报告或他人代报更准确。在客观测量不可行的情况下，一种将收集包括儿童身高和体重多份报告的验证子样本与BMI校正模型估计相结合的方法可能是一种经济有效的实用解决方案。校正模型得出的BMI估计值比报告更接近客观测量值。
