Jessri Mahsa, Lou Wendy Y, L'Abbé Mary R
1Department of Nutritional Sciences, Faculty of Medicine,University of Toronto,Toronto,Ontario,Canada,M5S 3E2.
2Dalla Lana School of Public Health,Biostatistics Division,University of Toronto,Toronto,Ontario,Canada,M5S 3M7.
Br J Nutr. 2016 Jan 14;115(1):147-59. doi: 10.1017/S0007114515004237. Epub 2015 Nov 2.
The association of dietary exposures with health outcomes may be attenuated or reversed as a result of energy intake (EI) misreporting. This study evaluated several methods for dealing with implausible recalls when analysing the association between dietary factors and obesity. We examined data from 16,187 Canadians aged ≥12 years in the nationally representative Canadian Community Health Survey 2.2. Under- and over-reporting were defined as the ratio of EI:estimated energy requirement <0·7 and >1·42, respectively. Multinomial logistic regression-generalised logit model was conducted to test the utility of different methods for handling misreporting, including (a) adjusting for variables related to misreporting, (b) excluding misreported recalls, (c) adjusting for reporting groups (under-, plausible and over-reporters), (d) adjusting for propensity score and (e) stratifying the analyses by reporting groups. In the basic model, EI showed a negative association with overweight (OR 0·988; 95% CI 0·979, 0·998) and obesity (OR 0·989; 95% CI 0·977, 0·999). Similarly, the association between total energy density and overweight (OR 0·670; 95% CI 0·487, 0·923) and obesity (OR 0·709; 95% CI 0·495, 1·016) was inverse. Among all methods of handling misreporting, adjusting for the reporting status revealed the most satisfactory results, where a positive association between EI and overweight (OR 1·037; 95% CI 1·019, 1·055) and obesity (OR 1·109; 95% CI 1·082, 1·137) was observed (P<0·0001), as well as direct positive associations between energy density and percentage energy from solid fats and added sugars with obesity (P<0·05). The results of this study can help advance knowledge about the relationship between dietary variables and obesity and demonstrate to researchers and nutrition policy makers the importance of adjusting for recall plausibility in obesity research, which is highly relevant in light of global obesity epidemic.
由于能量摄入（EI）误报，饮食暴露与健康结果之间的关联可能会减弱或逆转。本研究评估了在分析饮食因素与肥胖之间的关联时处理不可信回忆的几种方法。我们在具有全国代表性的加拿大社区健康调查2.2中检查了16187名年龄≥12岁的加拿大人的数据。低报和高报分别定义为EI与估计能量需求的比值<0.7和>1.42。采用多项逻辑回归广义logit模型来测试处理误报的不同方法的效用，包括（a）对与误报相关的变量进行调整，（b）排除误报的回忆，（c）对报告组（低报者、合理报告者和高报者）进行调整，（d）对倾向得分进行调整，以及（e）按报告组对分析进行分层。在基本模型中，EI与超重（OR 0.988；95%CI 0.979，0.998）和肥胖（OR 0.989；95%CI 0.977，0.999）呈负相关。同样，总能量密度与超重（OR 0.670；95%CI 0.487，0.923）和肥胖（OR 0.709；95%CI 0.495，1.016）之间的关联呈负相关。在所有处理误报的方法中，对报告状态进行调整显示出最令人满意的结果，其中观察到EI与超重（OR 1.037；95%CI 1.019，1.055）和肥胖（OR 1.109；95%CI 1.082，1.137）之间呈正相关（P<0.0001），以及能量密度与来自固体脂肪和添加糖的能量百分比与肥胖之间呈直接正相关（P<0.05）。本研究结果有助于推进关于饮食变量与肥胖之间关系的知识，并向研究人员和营养政策制定者证明在肥胖研究中调整回忆可信度的重要性，鉴于全球肥胖流行，这一点高度相关。
