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OCR for page 6
Relating Food
Consumption Data and
Nutritional
Status Data
The value of food consumption data in relation to public health is realized
when the data are processed to yield estimates of nutrient intake and of
exposure to food additives and contaminants. It is not possible to assess
nutritional status from dietary data alone. It is possible, however, to provide
an estimate of the prevalence of individuals within a population group with
intakes below requirements, and, with current knowledge of human re-
quirements, it is possible to assess the individual's probability of inadequate
status based on intake of some (but not all) nutrients. It is also possible to
consider the potential risk of excessive intake of nutrients and of natural or
added food components, i.e., food ingredients and additives, if the associa-
tion between level of intake and risk of toxicity is known.
Health status indicators believed to be associated with food or nutrient
intake can be identified if current dietary data can be linked to existing
health data. Through appropriate studies of the food consumption patterns of
individuals in a group, it would then be feasible to predict the prevalence of
individuals who may have increased risk to their health from a particular
pattern of food or nutrient intake.
Environmental variables in addition to diet (such as endemic infectious
disease or unusual stress) may increase the prevalence of nutritional in-
adequacy from that indicated by nutrient intake. In the United States, these
other factors, with the possible exception of recurrent infections in children,
probably do not generally contribute to variability of requirements, and
therefore a valid estimate of risk can be generated by appropriate collection
and analysis of dietary data.
The following discussion of an approach to the data collection system
6
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Relating Food Consumption Data and Nutritional Status Data 7
focuses primarily on application of the information to a prediction of nutri-
tional status. The same system could be used for assessing toxicological risk
and, with some modifications that will be discussed later, for relating food
consumption to various aspects of health (growth, longevity, chronic dis-
eases, etc.~.
The linkage between dietary intake and nutritional status is portrayed in
Figure 1. Interest is in the usual situation on a particular day. Thus, the data
essential to this system are usual intake of food, usual composition of foods,
usual bioavailability of nutrients, and usual nutrient requirement of the
individual. There is no precise definition of the time span represented by
"usual," but for purpose of discussion it may be taken as the average value
persisting over a period of weeks rather than a day or two.
The statistical approach to analysis and to the prediction of the prevalence
of individuals with usual intakes below their actual requirements has been
identified and discussed by Lorstad (1971~. It has been applied in a predic-
tion of the effects of iron fortification on the prevalence of inadequate
intakes among menstruating women (Swiss and Beaton, 1974) and in the
identification of protein:energy ratios associated with predetermined pre-
valences of individuals having inadequate intakes (Beaton and Swiss,
1974~. This statistical approach is based on the bivariate distribution of
intakes and of requirements among individuals in the population. It is
applicable to a dietary analysis system designed to monitor conditions in the
United States.
Analyses based upon the bivariate distribution require knowledge of (1)
the distribution of usual intakes (mean, variability), (2) the distribution of
usual requirements (mean, variability), and (3) the correlation between in-
take and requirement among individuals (FAD/WHO, 1973~. For many nutri-
ents, requirements are at first-order approximations; and for most nutrients,
but not energy, the correlation between intake and requirement appears to be
very low and may be ignored provided that care is taken to express the data in a
manner that avoids the effect of common variables such as body size, energy
intake, etc. In the case of energy, intake and requirement of the individual are
generally matched relatively closely, probably through physiologic regula-
tory mechanisms. Thus, a high correlation between intake and requirement
would be expected. For various micronutrients, there is little or no reason to
expect a correlation between intake and requirement unless both relate to a
third variable. For example, thiamin intake is likely to relate to total energy
intake and thiamin requirement is believed to be related to energy utilization.
If intake and requirement are both expressed as milligrams per day, a spurious
correlation may appear. This error can be avoided by expressing intake in a
manner (such as mg/l,OOO kcal), which controls for the confounding vari-
able.
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Relating Food Consumption Data and Nutritional Status Data 9
Attention must focus on the need for information about the distribution of
intakes and of requirements. This can be considered in the framework of the
relationship portrayed in Figure 1. Data on the observed usual intakes of
food by the population may be translated to nutrient intake with the use of
existing food composition data. A data bank, an appropriate coding system,
and a computer program are required and could possibly be based upon one
or more of the several systems now existing in the United States. However,
for some nutrients, the nature of the food ingested and the total diet affect
bioavailability. Reasonable information about the relationship between na-
ture of the diet and relative bioavailability is available for a number of these
nutrients.
Information about the individual's actual requirement is needed to deter-
mine the adequacy of intake relative to requirement. Clearly, such informa-
tion is not available. With respect to the individual, the best estimate that
can be made is the relative probability that the intake does or does not meet
the individual's actual requirement. At a population level, using the
bivariate distribution approach, an estimate can be made of the number of
individuals (but not which individuals) with intakes below their actual re-
quirements. The required data base for such estimates includes both the
mean requirement for that category of individual and the variability of
requirements among similar individuals. The Recommended Dietary A llow-
ances (Food and Nutrition Board, 1980) is not an appropriate reference
criteria* for these determinations, but, with current information, reasonable
assumptions can be made about the distribution pattern for the requirements
of a number of nutrients. Simplified analyses based on dietary scores or the
use of indicator nutrients warrant further investigation to enhance the use-
fulness of the data for various purposes.
The preceding discussion has been related to the prediction of the preva-
lence of intakes below actual requirements of healthy individuals. The pre-
sent information base relating "requirement" and level of nutritional status
is fragmentary. It is germane to recognize that neither the meaning of "re-
quirement," definition of the level of nutritional health to which the re-
quirement applies, nor consideration of the degree of inadequacy of intake
have been addressed in this discussion.
Information about the levels of intake required to maintain different levels
of nutritional status is necessary if intakes are to be linked with observed
severity or frequency of inadequate nutritional status. Lorstad (1974) has
*By definition, the "recommended dietary allowances" (RDA) as published by the Food and
Nutrition Board are intended to be sufficiently high to cover the known nutritional needs of
practically all healthy people. Therefore, RDA (except for energy) are estimated to exceed the
requirements of most individuals. Intakes below the RDA are not necessarily inadequate, but the risk
of inadequacy increases to the extent that intake is less than the level recommended as safe.
10 ASSESSING CHANGING FOOD CONSUMPTION PATTERNS
pointed out that with such information the same format of bivariate distribu-
tion analysis, with different requirement figures, could offer a series of
predictions of the prevalence of different levels of nutritional status.
It would seem that, for many nutrients, there is now sufficient informa-
tion to apply the bivariate system approach to the interpretation of dietary
data. The information currently available may be imprecise and could cer-
tainly be improved with additional research. Nevertheless, for many nu-
trients, the quality of data available seems adequate to justify the assump-
tions about bioavailability, average requirements, distribution of require-
ments, and even requirements for different levels of nutritional status that
are necessary for the statistical approach to analysis. Unfortunately, for
some other nutrients there are major gaps in the information base that place
analogous limitations on the above and other approaches to dietary data
interpretation (Beaton et al., 19791.
It is emphasized again that this approach does not make provision for
effects of recurrent infection, of unusual environmental stress, of genetic
abnormalities, of adaptabilities of host to excesses or deficiencies, or even
of nutrient imbalances other than as they are built into estimates of
bioavailability or requirement. This is a limitation of the model but one that
should not preclude its use in the United States in a nutrition monitoring
system.
Dietary data may also be used in the assessment of the probable risk
associated with excessive, rather than inadequate, intakes of natural or
added constituents of foods. The approach and data requirements are
analogous. In this case, what will be needed are data pertaining to the
average level of intake at which manifestations of harm are suspected
(analogous to average requirements of a nutrient) and the possible variability
of this sensitivity among individuals. As before, the probable prevalence of
various grades/severities of toxicity (analogous to various levels of nutri-
tional status) can be estimated if information is available on the relationship
between level of intake and severity of the detrimental effect.
The bivariate analysis of dietary data, then, permits an examination of
the population risks associated with both low and high intakes. It also per-
mits an examination of the probable effects of a change in food consump-
tion, such as a proposed food fortification policy or a proposed limit for a
food additive, on intake and population risk. Implementation of this ap-
proach to analysis poses certain study design requirements and necessitates
the compilation of certain data bases. These are discussed in some detail in
later sections of the report.
As previously mentioned, predicting health status of individuals from
dietary data alone is not possible. The role of certain foods or food compo-
nents in the etiology of such chronic diseases as diabetes, coronary heart
Relating Food Consumption Data and Nutritional Status Data 11
disease, atherosclerosis, hypertension, and cancer is not well documented
(Ahrens and Conner, 1979~. In addition, there appears to be a wide variation
in the susceptibility of individuals within a population to these diseases.
Knowledge concerning the relationship of food consumption patterns to
susceptibility to chronic disease can better be determined from a careful
examination of the food consumption patterns of population groups with
known unusual incidences of these chronic diseases. Care must be taken to
control adequately for environmental and genetic factors.
A number of the health-data bases in existence could be used in conjunc-
tion with food intake data to develop an appropriate monitoring system.
Data from current health-data bases would need to be identified, sum-
marized, and evaluated in those population subgroups used for food intake
data analysis. Details are discussed in a later section of the report.
.
