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Appendix E: Analysis of Error in the Estimation of Nutrient Intake Using Three Sample Data Sets
Pages 129-146

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From page 129... ... Emphasis is placed on the ef feet of these errors on the estimated distribution of usual intakes across people rather than on actual intakes of particular individuals. These constructs are first illustrated using actual data, and then their theoretical implications are developed. Read the entire page →
From page 130... ... The CV expresses this variability in relation to the mean, and it is useful in this exercise for comparing error in estimating nutrient content between several foods and for considering the Impact of the error on the estimate of the daily intake of a nutrient, as used in dietary evaluation. Because the SD cannot be estimated from the reference tables for all food items, the available SDs were examined and used to make a judgment about the possible CV or range of CVs that might apply for foods with missing data. Read the entire page →
From page 131... ... The results show that the relative error is decreased for the total record of food intake in comparison to the individual food items. me exercise could be repeated by selecting new random values for the CVs of the food items and then obtaining composite error estimates, which would not be expected to differ markedly from those shown in Table E-2. Read the entire page →
From page 132... ... provided two food intake records for use in a second set of analyses. New USDA food composition data and variance estimates (reported standard errors and number of analyses) Read the entire page →
From page 133... ... TABLE E-3. Stratification of CV Ranges for Use in Assigning Variability of Food Composition in Nonvegetarian Food Intake Recordsa Nutrient Cutoff Point CV Range Assumed (%) Read the entire page →
From page 134... ... 02 12. 38 Vitals n A 3,798.4 281.24 7.40 5,142.0 603.61 11.74 =sylvania State University, personal communication, 1985. Read the entire page →
From page 135... ... of Nutrient Content of Individual Foods in Food Serving Record 10 20 30 40 50 2 7.1 14.1 21.2 28.3 35.4 3 5.8 11.6 17.3 23.1 28.9 4 5.0 10.0 15.0 20.0 25.0 5 4.5 8.9 13.4 17.9 22.4 10 3.2 6.3 9.5 12.7 15.8 15 2.6 5.2 7.8 10.3 12.9 20 2.2 4.5 6.7 8.9 11.2 25 2.0 4.0 6.0 8.0 10.0 30 1.8 3.7 5.5 7.3 9.1 aThese calculations assume that all foods make an equal contribution to the total intake and that all food servings have the same error terms. The values are based on a simulated distribution. Read the entire page →
From page 136... ... Unless the random error is very large , there will be a limited additional effect on the error term generated by food composition varia Read the entire page →
From page 137... ... &iciklas-Wright, Pennsylvania State University, personal communication, 1985. For composition of diets and food composition variability estimates, see Tables E-ll and E-12. Read the entire page →
From page 138... ... TABLE E-8. Impact of ~ Random Error in Intake and Food Composition Data on the CV Calculated for Nutrient Content of an Individual Serving of Fooda~b CV 2 0 10 20 30 40 0 0 10 20 30 40 10 10 14.2 22.4 31.8 41.4 20 20 22.4 28.6 36.6 45.4 30 30 31.8 36.6 43.4 51.4 40 40 41.4 45.4 51.4 58.8 l aData from NFCS. Read the entire page →
From page 139... ... and in the estimation of food intake introduces an element of variation in computed nutrient intake across days for 1-day records and that the relative impact, although not as large as might have been expected, is nevertheless real. These considerations suggest that part of the reported difference between calculated intake and chemically determined intake for duplicate meals or composite diets may arise from random error and that perfect agreement should not be expected. Read the entire page →
From page 140... ... Nevertheless, it is clear that Improvement of food composition data bases can improve the estimate of the prevalence of inadequate intake. True biological variation between individual samples of food will limit the improvement that can be gained. Read the entire page →
From page 142... ... Mayonnaise 14 16 11 10 11 9 13 9 20 14 11 Corn on the cob 4 5 2 5 6 6 4 4 5 3 3 Peanut butter 126 134 143 136 131 142 102 148 166 157 104 Kidney beans 110 73 167 178 127 183 95 87 230 100 274 Celery 2 2 2 2 3 3 2 1 1 3 2 Cantaloupe 24 24 30 34 17 20 38 32 16 24 33 Black currants 6 6 8 6 6 4 5 6 7 8 7 Total 814 784 907 942 746 801 706 933 980 822 820 abased on vegetarian diet described in Table E-9. Overall mean = 844. Read the entire page →
From page 143... ... d tD CD ~ tn ~ o 0 1` ~ 0 tD ~ r~ ~ _d ~ ~ ~ CD - 1 ~ ~ ~ ~ r~ O4 ~ ~ e4 0 0 ~ 0 ~ ~ ~ V S~ ~ 0 ~ ~ ~ X = O ~ O ~ Y O , - ~ ~ ~ _ ~ ~ 0 ~ ~ O O ~ 0 0 V 3 0 ~ ~ ~ S 0~ ~ ~ ~ ~ V O ~ ~ ~ o, O mE m~ ~ ~ ~ 0 ~ ~ Y ~ ~ ~ ~ V - ~ mm 0S Y ~ S ~ ~ ~ S OX h 0 ~ ~ 3 ~ ~ 3 ~ ~ ~ S ~ O ~ ~ S ~ - O O ~ ~ 0 cn v~ u~ ~ C) Read the entire page →
From page 145... ... 145 rat o o o ~4 o a s 3 o A 07 JJ at o JJ ~n o C, ~o r~ E~ o ~o aC ~ 3 ~ _ ~o \|R u ~ 91cn � a' F: o ~ C) Read the entire page →

From page 129...

... Emphasis is placed on the ef feet of these errors on the estimated distribution of usual intakes across people rather than on actual intakes of particular individuals. These constructs are first illustrated using actual data, and then their theoretical implications are developed.

Appendix E: Analysis of Error in the Estimation of Nutrient Intake Using Three Sample Data Sets Pages 129-146

Appendix E: Analysis of Error in the Estimation of Nutrient Intake Using Three Sample Data Sets
Pages 129-146