4: Measurement Error in Dietary assessment (Chapter 5)
- Page ID
- 116755
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Data on food consumption are typically collected using self-report methods. The resulting data are affected by measurement errors that can have serious consequences for study findings and interpretation. Measurement error refers to the difference between true and observed intake and may be random or systematic. Errors arise due to the interaction of the participant with the assessment method and can also be generateded by interviewers and coders, as well as by limitations in food composition databases. Accordingly, the type and extent of the errors vary with the method used and how it is implemented, the target population of interest, and the nutrients and foods investigated.
Both unaddressed random and systematic measurement error can introduce substantial bias into results. Pertinent to surveillance and monitoring, measurement error can lead to erroneous inferences about the proportions of a population with inadequate or excessive intakes relative to nutrient requirement estimates and food group recommendations. In epidemiologic research, measurement error distorts observed associations between diet and disease, as well as reducing statistical power to detect such associations. In intervention research, measurement error can mask the effects of the intervention, particularly if the error is differential between intervention and control groups. Strategies to minimize and/or mitigate error are therefore fundamental to research making use of dietary intake data and should be considered early in study design through to reporting results and implications.
- 4.1: Measurement error in dietary intake data
- This page discusses random and systematic errors in dietary intake measurement methods. Random errors lead to imprecise data from within-person variation, affecting reproducibility, while systematic errors introduce bias in self-reported data, distorting dietary estimates. Correcting systematic errors is more difficult and can undermine the validity of epidemiological studies. Although statistical modeling may help adjust for some errors, understanding their implications is critical in research.
- 4.2: Sources of measurement error
- This page discusses the complexities and biases involved in reporting dietary intake, including recall, social desirability, and interviewer biases, which impact data reliability. It highlights strategies to improve accuracy, such as using automated reporting tools, shorter recall periods, and standardized coding methods. The importance of thorough data processing and adherence to guidelines is emphasized, alongside studies illustrating the effects of various biases on dietary assessments.
- 4.3: Implications of measurement error for estimated dietary intakes
- This page addresses the challenges of accurately estimating energy intake and portion sizes in dietary assessments, particularly in self-reported data which often leads to underreporting. It discusses the use of aids for portion size estimation, the need for training in accurate reporting, and the impact of dietary supplements on nutrient assessments. children's dietary reporting is also examined, focusing on their difficulties with recall and the effectiveness of prompts.
- 4.4: Minimizing measurement error through data collection procedures
- This page discusses strategies to reduce measurement error in dietary assessments, including quality-control techniques like standardization, interviewer training, and pilot studies. Continuous compliance checks and technology-enabled methods are highlighted, alongside the importance of validating methods for specific target populations. Reducing random error is achievable with more observations, while systematic error can be minimized through method tailoring and calibration.
- 4.5: Summary
- This page examines systematic and random errors in food consumption data collection that affect nutrient intake accuracy. Key issues include quantification problems, biases, and memory lapses, which can be addressed with quality-control measures, standardized interviews, and technology. It highlights the importance of using portion-size aids and accurate dietary supplement data, along with a computerized coding system.