Connor Gorber Sarah, Shields Margot, Tremblay Mark S, McDowell Ian
Health Information and Research Division, Statistics Canada, Ottawa.
Health Rep. 2008 Sep;19(3):71-82.
This study examines the feasibility of developing correction factors to adjust self-reported measures of body mass index (BMI) to more closely approximate measured values.
Data are from the 2005 Canadian Community Health Survey (subsample 2), in which respondents were asked to report their height and weight, and were subsequently measured. Regression analyses were used to determine which socio-demographic and health characteristics were associated with the discrepancies between self-reported and measured values. The sample was then split into two groups. In the first, self-reported BMI and the predictors of the discrepancies were regressed on measured BMI. Correction equations were generated using all predictor variables that were significant at the p < 0.05 level. These correction equations were then tested in the second group to derive estimates of sensitivity, specificity and obesity prevalence. Logistic regression was used to examine relationships between self-reported, measured and corrected BMI and obesity-related health conditions.
Corrected estimates provide more accurate measures of obesity prevalence, mean BMI and sensitivity levels (percentage correctly classified). In almost all cases, associations between BMI and health conditions are more accurate when based on corrected versus self-reported values.
本研究探讨了开发校正因子的可行性，以调整自我报告的体重指数（BMI）测量值，使其更接近实测值。
数据来自2005年加拿大社区健康调查（子样本2），其中要求受访者报告其身高和体重，随后进行测量。回归分析用于确定哪些社会人口统计学和健康特征与自我报告值和测量值之间的差异相关。然后将样本分为两组。在第一组中，将自我报告的BMI和差异预测因子对实测BMI进行回归。使用所有在p<0.05水平上显著的预测变量生成校正方程。然后在第二组中测试这些校正方程，以得出敏感性、特异性和肥胖患病率的估计值。使用逻辑回归来检验自我报告、测量和校正后的BMI与肥胖相关健康状况之间的关系。
校正后的估计值能更准确地测量肥胖患病率、平均BMI和敏感性水平（正确分类的百分比）。在几乎所有情况下，基于校正值而非自我报告值时，BMI与健康状况之间的关联更准确。
