Missing portion sizes in FFQ – alternatives to use of standard portions

Public Health Nutrition

Niels de Fine Olivarius¹  Volkert Siersma¹  Jan E Henriksen¹  Thorhallur I Halldorsson¹  Berit L Heitmann¹  Rasmus Køster-Rasmussen¹ 

关键词: FFQ;    Portion sizes;    Missing values;    Multiple imputation;    Bias;   

DOI  :  10.1017/S1368980014002389

学科分类：卫生学

来源: Cambridge University Press

【 摘 要 】

AbstractObjectiveStandard portions or substitution of missing portion sizes with medians may generate bias when quantifying the dietary intake from FFQ. The present study compared four different methods to include portion sizes in FFQ.DesignWe evaluated three stochastic methods for imputation of portion sizes based on information about anthropometry, sex, physical activity and age. Energy intakes computed with standard portion sizes, defined as sex-specific medians (median), or with portion sizes estimated with multinomial logistic regression (MLR), ‘comparable categories’ (Coca) or k-nearest neighbours (KNN) were compared with a reference based on self-reported portion sizes (quantified by a photographic food atlas embedded in the FFQ).SettingThe Danish Health Examination Survey 2007–2008.SubjectsThe study included 3728 adults with complete portion size data.ResultsCompared with the reference, the root-mean-square errors of the mean daily total energy intake (in kJ) computed with portion sizes estimated by the four methods were (men; women): median (1118; 1061), MLR (1060; 1051), Coca (1230; 1146), KNN (1281; 1181). The equivalent biases (mean error) were (in kJ): median (579; 469), MLR (248; 178), Coca (234; 188), KNN (−340; 218).ConclusionsThe methods MLR and Coca provided the best agreement with the reference. The stochastic methods allowed for estimation of meaningful portion sizes by conditioning on information about physiology and they were suitable for multiple imputation. We propose to use MLR or Coca to substitute missing portion size values or when portion sizes needs to be included in FFQ without portion size data.

【 授权许可】

Unknown