| Food Composition Data: A User's Perspective (1987) |
|The uses of food composition data|
|Epidemiological uses of food composition data in the European context|
The difficulties of nutritional epidemiology as they relate to the nutritional side begin with problems in dietary assessment methodology  (see chap. 10). This includes the questions asked, how they are asked, who asks them, built-in validation attempts, the degree of specificity strived for, the forms, booklets, and guidelines used, the conditions surrounding the information exchange, and motivational measures, among other factors. In addition to these difficulties, quantification of amounts [8,13,21], translational difficulties, coding of consumed foods into computer-readable form , differences between tables  and between foods, and what to do about missing values present trying and complex obstacles. Some of these issues are related directly, others indirectly, to food composition tables.
These problems are more clearly seen in perspective against the background of a typical dietary assessment. Taking, for example, the often-used 24-hour recall method to compare intakes in high-risk individuals and a control group, the following steps, listed with their possible shortcomings, are necessary:
1. Question the individual about what was eaten the previous day. (Memory failure; interviewer-related bias.)
2. Question the individual about the amounts of the foods eaten. (Memory failure; estimation difficulties; non-edible portions - bones, pits, skin. )
3. Code foods into machine-readable form, usually numbers. (Few codes leading to compromised information; errors in reading or writing codes.)
4. Convert portion sizes into gram amounts. (Plate waste, refuse deduction, portion size calculation from recipes.)
5. Enter subject identification, date, meals, foods, and amounts. (Transcription errors. )
6. Check entered data for correctness. (Oversight; difficult forms; no printout of food names. )
7. Correct the errors. (Renewed typing errors.)
8. Recheck the entered corrections (Oversight or elimination of this step altogether.)
9. Merge this information into a common data base with food-nutrient information and calculate the average nutrient intake for the day. (Non-standard algorithms; program errors.)
10. Group foods for comparison of frequency and amounts consumed of basic food groups between individuals. (Double counting; mistakes in grouping.)
11. Compare intake of nutrients between case and control groups. (Missing values in nutrient tables resulting in artifactual differences.)
12. Test for significant differences between groups. (Invalid methods selected.)
Every step in this process is fraught with potential errors. Questioning about previous intake varies from interviewer to interviewer  unless, as is done in some cases, the subject is asked to reconstruct on paper his meals of the previous day  or a computer program prompts questions . The subject may inadvertently adjust responses to cues from the interviewer. Subjectivity in recording responses is also a danger. Estimation of the amount eaten is subject to memory failure , estimation difficulties, and misquantification or incorrect subtraction of non-edible portions . The coding of foods usually involves a compromising of the available information to fit the length and breadth of the coding system used (see chap. 12). The coding process itself, unless computerized , generally involves searching for the correct number from code books, transcribing it onto paper, and having these numbers entered into a terminal. This process allows for many possible reading, transcribing, and keyboarding errors. Different individuals may code the same information differently .
The calculation of food intakes into nutrients depends on the availability of information on the foods as consumed (cooked, fried, etc.) for the nutrients of interest. Missing values in food tables are often handled interchangeably as zero values for these nutrients, which can result in false or artifactual results . Inaccuracies in the calculation of nutrients from foods can also result from faulty programming, although this is seldom a major difficulty. Differences in estimating nutrient intakes have been documented between systems with identical sources of nutrient information, for a number of reasons .
Regrouping the tens of thousands of food items on the market in a particular country is also generally non-standardized. Comparisons are therefore subject to differences in the systems used . An example of this is butter being included in either the dairy group or the fats and oils group, and egg-rich products such as quiches and egg noodles being grouped together with cereal and grain products.
Mention should also be made of the misuse of methods for the testing of hypotheses as a major problem in nutritional epidemiology. For example, the results of a single 24-hour recall or a frequency questionnaire of food consumption are often used to determine interrelationships and the interpretation made that no underlying relationship exists. Strong interactions may be statistically insignificant due to low subject number or days of observation and great intraindividual day-to-day variability [5, 7].