| Food Composition Data: A User's Perspective (1987) |
|Consideration of food composition variability: What is the variance of the estimate of one-day intakes? Implications for setting priorities|
Relevance to priorities for food composition data
The above considerations have import in considering priorities in improving food composition data bases. Some of these are outlined below.
First, if there is any suspicion of true bias in the food composition data, the error will carry through all calculations. Thus, if there is suspicion that a methodologic error gives consistent under- or overestimation of composition, correction of the error should have high priority.
Second, it should be apparent from the foregoing that the major contribution to the error term in estimated one-day intakes will be associated with the foods that make the greatest contribution to total nutrient intake. That is, greater benefit will accrue from improvement of the composition estimate of the major nutrient contributors than from improvement of minor contributors. Therefore, obtaining more replicates of composition for major contributors will be more cost-effective than addressing minor contributors.
Similarly, and particularly in the connotation of the INFOODS programme, which will have international implications, the effect of improving the reliability of food composition will be greater in diets that include only a few foods than in diets that are marked by great diversity. It follows then that increasing the composition replicates will be more cost-effective in major foods of limited diets than in the case of diverse diets.
Obviously, if data are missing for certain foods, either imputed values must be used or intake from that food will be taken as 0. Either way there is a potential error. In the latter instance, the error will always be a bias toward underestimation of total intake; in the former, the error could be in either direction, across foods it might even be random. If the food is a major contributor of total nutrient intake, then the error term could be quite important. It follows that filling in missing data in the food composition table must have a priority. If the food is expected to be a minor contributor to total intake, and if reasonable imputations can be made, imputation of missing data may be quite reasonable. Conversely, if the food is thought to be a major contributor, it will be cost-effective to undertake analyses. Unless there is an a priori reason to believe that varietal differences are great, it may not be cost effective to undertake composition determinations for each variety in use. The same consideration will apply to compositional differences attributable to soil composition and growing conditions (but note here that if the soil composition and growing conditions of a specific area affect all, or many, of the foods consumed in that area, a bias in the estimate of intake could be present). Perhaps the most cost-effective approach in this case would be research intended to determine whether major effects are likely to be present, and then a reconsideration of analytical priorities.
In all considerations, across nutrients, a scale of relative priorities must be based upon the perceived importance of examination of the nutrient in question as well as the relative cost of determinations.