occasions and street food
3.6 measurement errors in dietary
assessment
Measurement error18 can be in the form of random error and/or systematic error. The former reduces precision and the latter results in incorrect estimates. Hence, it is important to quantify and correct for these effects. In general, the errors that affect the validity of a dietary assessment method are systematic and those associated with reproducibility are random (Gibson, 2005; Willett, 2013a).
• Random errors may occur across all respondents and all days, causing associations to be underestimated and even failure to detect associations in the first place. This type of error
can be minimized by increasing the number of measurements.
• Systematic errors may be respondent-, food- or interviewer-specific and can result in underestimated or overestimated associations;
these type or errors cannot be minimized by increasing the number of measurements.
Major measurement errors are due to:
nonresponse bias, respondent biases, interviewer biases, respondent memory lapses, incorrect estimation of portion size, supplement usage, coding errors, mistakes in the handling of mixed dishes, etc. (Gibson, 2005). Table 12 describes possible sources of error that should be considered in different dietary assessment methods. Depending on the population group being studied, it is important to employ appropriate strategies to optimize the information being retrieved and reported to the investigator, and to minimize errors. Considerable efforts have been made in developing statistical techniques to deal with these errors and to enhance the performance of various methods. Linear regression calibration, energy adjustment and analysis of variance can be used to correct for random and systematic errors during the data analyses stage (Slimani et al., 2015).
15 FAO INFOODS http://www.fao.org/docrep/017/ap805e/ap805e.pdf (Accessed 1 December 2016)
16 FAO INFOODS http://www.fao.org/infoods/infoods/en/ (Accessed 1 December 2016)
17 FAO INFOODS http://www.fao.org/infoods/infoods/standards-guidelines/en/ (Accessed 1 December 2016)
18 For an interactive way to learn more about the implications of measurement error, readers are also encouraged to visit the Measurement Error Webinar Series, organized by collaborators from the National Cancer Institute, the Office of
Dietary Supplements, the United States Department of Agriculture, the Gertner Institute, Texas A&M University, and Wake
table 12 - Sources of errors in direct dietary assessment methods for assessing food and nutrient intakes source of errorestimated food records*
weighed food records*24-hour recall*∞dietary historyffQBrief dietary instruments
duplicate meal method food composition table+++++-- food coding+++++++ incorrect weighing of food-+----+ reporting error++++++- diet variations with time and season+++++++ wrong frequency---+++- modified eating pattern±±+++++ respondent memory lapses--++++- portion size estimation+-+++**--
estimation+-+++**--source of errorestimated food records*
weighed food records*24-hour recall*∞dietary historyffQBrief dietary instruments
duplicate meal method respondent bias±±***++++- interview bias ±±±+±-- dapted from FAO (2002). ary assessment methods inherit the sources of errors of conventional dietary assessment methods ary diversity score inherits the sources of errors of the 24-hour recall, when data was collected by a 24-hR recall questionnaire y for the method to be affected by the source of error ative FFQ eld worker or nutritionist is weighing the food (happens in low resource countries)
19 In a state of energy balance, energy intake is assumed to be equal to TEE considering that the individual’s weight is stable.
3.6.1 misreporting energy intaKes
Misreporting is characterized by the reporting of implausibly low (underreporting) or high (overreporting) energy intakes19 at person or group level. Energy intakes are often used as proxies of dietary intakes, therefore, if energy intakes are underestimated then intakes of other nutrients are also underestimated. Underreporting is a widely acknowledged limitation of dietary assessment methods characterized by unlikely low reports of habitual energy intake. Underreporting can occur because of the presence of systematic bias and differential misreporting, and can lead to an overestimation of undernourishment prevalence.
The magnitude of underreporting varies with different dietary assessment methods (Burrows et al., 2010; Poslusna et al., 2009). A better understanding of other factors and mechanisms that are inter-related when dietary data are gathered will help enhance the quality and accuracy of dietary assessment surveys. Such factors include body weight and BMI, gender, socio-economic position, memory disturbances, motivation and social expectations (e.g. dieting), and the nature of the study environment. Specific social mechanisms involved in producing errors may include lack of motivation and deliberate or subconscious errors in recordings. These may be inevitable in some groups or linked to the nature of the activity itself. Memory disturbances can be especially prevalent in the elderly, while other social groups may demonstrate disturbed perceptions of body image, a preoccupation with weight, and therefore an unhealthy attitude towards food leading to the unintentional or intentional misreporting of dietary intakes (Hill et al., 2001).
Validating dietary surveys to identify the degree of misreporting can be carried out by comparing
reported energy intake with Total Energy Expenditure (TEE), often measured by using biomarkers for energy intake (DLW technique) (Subar et al., 2003). However, this method is expensive because it requires laboratory-based investigation. A review conducted in low resource countries found that only a few studies concerning underreporting have been conducted, and none of them have used DLW. Most of these studies have used an alternative approach to identify the degree of underreporting, using the standard equation for estimating basal metabolic rate (BMR) for different ages, gender and body weight, as proposed by (Schofield, 1984). On the other hand, the Goldberg cut-off method is also used, despite concerns about its accuracy (Tooze et al., 2012). It is based on the assumption that energy intake (EI) is equal to total energy expenditure (TEE) assuming weight stability and using a 95 percent confidence internal for statistical comparison with reported EI/BMR and physical activity level (PAL), given by TEE/BMR (Black, 2000; Coward, 1998). Information on PAL can be obtained by observing physical activity, applying lifestyle questionnaires to the target population, or generating information from very similar samples. An EI/BMR ratio below the calculated cut off indicates implausible reported energy intake, i.e. energy intake is too low to reflect the true habitual energy requirements. Increasing the number of days of dietary assessment may help to resolve this issue, because it increases precision and reduces within-person variability. However, it should also be noted that long recording periods reduce reporting accuracy because they increase fatigue and boredom. Rankin et al. (2011) assessed energy intakes by 24-hour recalls investigating a group of peri-urban African adolescents and compared the results with estimated energy expenditure. The latter was estimated using BMR equations and estimated physical activity factors
derived from the previous day’s physical activity recall. After calculation of energy expenditure, the relative validity of reported energy intake derived from multiple 24-hour recalls was tested using correlation analysis. It was reported that the 24-hour recalls collected at five different measurements over two years offered poor validity between energy intakes reported (EIrep) and energy expenditure estimated (EIest).
Goldberg’s formula identified cut off points for under and over reporting of energy intake. The ratio EIrep: EIest was calculated and compared with the Goldberg cut-offs, indicating that 87 percent of the boys and 95 percent of the girls underreported their dietary intakes, whereas only 2 percent of boys and girls over reported their energy intakes (Rankin et al., 2011).
Managing misreporting prior to data analysis has been carried out in different ways: for example, by excluding individuals who have an EI/BMR ratio below the calculated cut off (note that this may leave out individuals who do in fact have a true low intake level), or by performing energy adjustments. Misreporting cannot be completely avoided, because of the self-report nature of the dietary assessment methods.
Nevertheless it is important to attempt to deal with this, especially in regard to factors affecting a subject’s memory lapses – one of the main causes of under-reporting (Poslusna et al., 2009).
Solutions can include a more careful design of the study so that it uses appropriate methodologies and standardized procedures, along with the use of visual memory aids and effective interpersonal communication between respondents and field workers to ensure the quality of the reported data.