Nutrient Adequacy Assessment Using Food Consumption Surveys (1986) / Chapter Skim
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7. Errors in Nutrient Intake Measurement
7. Errors in Nutrient Intake Measurement
Pages 48-65
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From page 48... ...
This chapter presents the results of its analysis of the impact of random error and systematic bias on the estimates of the prevalence of inadequate intake. SAMPLING VARIATION Random Error By chance, the persons randomly selected in dietary surveys may not be representative of the reference population.
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From page 49... ...
At a minimum, it is important to determine the probable direction of any such bias and, where possible, it is preferable to estimate the magnitude as well. ERRORS IN ESTIMATING USUAL NUTRIENT rNTAKE Errors in Reporting Usual Food Intake Day-to-Day Variation in Intake.
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From page 50... ...
For example, if all or most members of the lower socioeconomic groups overestimate intake of such status foods as meat, biased estimates of iron intake may result. · Systematic bias.
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From page 51... ...
For example, are there systematic errors in the dietary intakes reported by some subgroups of the population, such as young and old subjects, or in the reporting of particular foods or food groups -- biases that would influence the reported nutrient intakes? the results of several validation studies indicate that low intakes may be overreported and high intakes underreported, with a resultant flat-slope syndrome (Gersovitz
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From page 52... ...
However, the flat slope syndrome may be nothing more than the attenuation of the "lope that arises from random error in the independent variable, as has been described by many authors. Systematic biases may exist in dietary reporting by selected population subgroups but have not been sufficiently addressed to warrant conclusions.
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From page 53... ...
Retrospective diet histories have been compared with direct measurements of food intake by obese patients hospitalized in a metabolic unit (Bray et al., 1978)
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From page 54... ...
(1981) , studying the reported food intake of 62 men in a military dining hall for a 7-day period in comparison to observed and weighed intakes, found that 13% underestimated their caloric intake by more than 30% and 34% underestimated their caloric intake by 10% to 20~.
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From page 55... ...
The technical errors in food composition data that influence the interpretation of food consumption surveys fall into three broad categories: true random variability of the composition of individual food items, biased food composition data, and the differences in bioavailability of individual dietary nutrients.
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From page 56... ...
Postharvest physiological changes, storage, and processing also contribute to the true variability of the nutrient content of individual foods. Estimates of this normal variation were included in the current USDA food composition data tables, which were used for the sensitivity analysis of dietary intake information included later in this chapter.
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From page 57... ...
For example, wide variations in nutrient content could be found in vit~min-fortified fruit juices and breakfast cereals, the sugar content of breakfast cereals, the fat content of bakery products, and the sodium content of many different prepared foods and meals. When brand loyalty is considerable, that is, when consumers consistently use the same brand rather than randomly selecting similar products, and when the mean nutrient content of the brand item differs from that of the category mean, biases will occur in the calculated estimations of nutrient intake.
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From page 58... ...
Imputed and calculated values in the current USDA food composition tables can be identified because the entry for the number of samples is left blank. However, there is no way for the users of the tables to determine directly if the other nutrient values are imputed or calculated.
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From page 59... ...
. The assessment of the iron intake of individuals has several components: the iron requirement of different age and sex groups, the food composition data, and often an assessment of the bioavailability of iron in the meals.
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From page 60... ...
Currently the vitamin A contributions of all these compounds are lumped together in food composition data bases as retinal equivalents. The chemical assay of the carotenoids is very complex (particularly in fruits and vegetables)
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From page 61... ...
riven the difficulties with assays of the folacin isomers, the data on the folacin content of food items do not appear to be reliable and the subcommittee does not believe that accurate estimates of the dietary intake of folacin can be obtained from the current food composition data bases.
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From page 62... ...
In one study, Wolf (1981) estimated nutrient intakes from food records, from the 1963 USDA food composition data base (USDA, 1963)
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From page 63... ...
Depending on the study design, such comparisons may be affected by variation and bias either in the food composition table alone or in combination with the food intake record. When interpreting the results of such studies, one must recognize that the composition of individual food items vary and that the food composition table, at best, presents the average composition of a class of foods.
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From page 64... ...
Potential Impact of Systematic Bias in Either Food Composition Tables or Food Estimates on Estimates of the Prevalence of Inadequate Intakesa . Adjustment to Intake Distribution (Mock Systematic Bias in Intake Estimate)
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From page 65... ...
65 The magnitude of the effect on estimates of the prevalence of inadequate intake will depend on the relative positions of the distributions of intake and of requirement. Nevertheless, it is readily apparent that for any such estimate of adequacy, systematic bias in either food intake estimates or food composition data will result in erroneous estimates of prevalence.
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