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This chapter is a discussion of the use of physical growth measurements (i.e., anthropometry) for evaluating the impact of nutrition interventions in developing countries. As resources of all types are likely to be limited in most developing countries, suggestions made here have a bias towards simple and inexpensive evaluation designs and techniques. Also, this chapter deals almost exclusively with the impact of nutrition interventions on populations. While some attention is paid to the use of anthropometric indicators for selecting individuals in need of better nutrition, little is said about the monitoring of nutritional status in individuals.
Mason and Habicht (see chapter 2) warn investigators not to embark on impact evaluations without first examining whether it is reasonable to expect any effects. For example, a feeding programme is unlikely to affect physical growth if only very insignificant amounts of food are actually distributed to the target population. Also, food-for-work programmes of very short duration (e.g., one month) are unlikely to improve the nutritional status of workers and their families. On the other hand, a long-term and major increase in protein and energy intakes in preschool children previously found to have poor diets and marked growth retardation is likely to result in significant changes in health and nutritional status.
Strategies for showing whether nutrition programmes have had an impact on physical growth will vary greatly, depending upon the programme's design and data characteristics. As is repeatedly pointed out by others in this volume, conditions are rarely ideal, as many programmes lack adequate built-in evaluation modules. The first step in assessing whether an impact on growth has occurred is to show that the programme is associated with improvements in physical growth. In the rare situations when baseline data are available, pre-test/post-test differences in the target population will provide a measure of the changes associated with the intervention. As there is often a wide range in the degree of programme participation (e.g.. attendance, time in the programme, amounts received, etc.), researchers should also examine whether these gradients are associated with physical growth.
A second step in the analyses is to show that the changes observed were due to the programme and not to other factors. Data collected for the same time interval on comparison groups (i.e., similar in all important characteristics but not participating in the programme) would be useful for this purpose but are rarely available. Instead, researchers often rely on multivariate analysis to test whether potentially confounding variables account for the observed associations between nutrition variables and physical growth.
In some nutrition interventions, particularly those involving supervised feeding, anthropometric data on those individuals attending the food distribution centres are often collected. These data can be very useful in evaluating whether the programme has improved nutritional status. For example, children just entering the programme can be compared to children the same age who have been in the programme for a longer time. Data showing that the latter are heavier and taller would suggest that the programme is effective. Moreover, if longitudinal data are available, growth rates of participating children can be related to programme characteristics (e.g., attendance).
While useful, as the above examples show, data on just the participants have important limitations. Coverage is often poor and serial measurements on individuals may be few and far between. Those individuals not participating in the programme will obviously not be represented in the sample and, because of variability in participation, there will be a tendency for more frequent attenders to be measured more often. The absence of representative data on the target population will make it very difficult for programme evaluators to ascertain whether the intervention reached those in greatest need and would also make interpretation of the results difficult. For example, an apparent improvement in physical growth when compared to surveys prior to the beginning of the programme might only reflect that attendance was greater for the better educated, wealthier, and better fed families.
Where possible, it is highly recommended that anthropometric data on participants be complemented by cross-sectional surveys in the target population (including participants and non-participants) and in similar populations not participating in the programme (control or comparison groups!.
Ideally, cross-sectional surveys, including at least one during the time before the initiation of the programme (baseline), should be carried out on a regular basis. Because the logistic problems involed in measuring the same individual through time are formidable, the longitudinal approach is not recommended. (In the clinic or feeding centre, where the aim might be to monitor the health and nutritional status of individual children, serial data would, of course, be desired.) In the simple design
TABLE 3.1. A Simple Design for Evaluating the Mean Effects of Nutritional Interventions on Physical Growth at Two Points in Time
Where A1 and A2 are mean values for samples collected at the baseline and intervention phases, respectively, in the comparison population. Similarly, B1 and B2 values are mean values for baseline and intervention phases, respectively, in the test population. No impact would be concluded when A2 - A1 = B2 - B1 Positive impact would be concluded to the degree that B2 - B1 > A2 - A1 Negative impact would be concluded to the degree that A2 - A1 > B2 - B1 shown in table 3.1., target and comparison populations would be measured prior to the intervention (baseline) as well as during it. Ideally, data on several points throughout the intervention phase are desirable in order to characterize the dose-response nature of the relationships. As explained in the footnote to the table, impact would be a function of changes taking place in the target sample relative to the comparison sample.
The use of a control population facilitates the interpretation of the results but raises ethical considerations. Ethical issues become most salient when food or nutrients are available for distribution but are withheld for research reasons. On the other hand, where resources do not allow for full coverage of the needy population, the selection of a control population from the untreated areas would seem to be justified. Ethical issues also need to be examined when collecting baseline data. While a rapid, cross-sectional survey before the program's initiation might be viewed as an ethically acceptable strategy, the seasonality aspects mentioned below would argue for a longer-term period of observation. There are no general answers to ethical questions, and each particular programme needs to be examined carefully. (There is always the possibility that well-meaning programmes will actually produce detrimental results. Hence, researchers would seem to be morally compelled to have adequate research designs and this would require the use of baseline information and of comparison groups.)
Another important issue in cross-sectional studies is seasonality. Because of swollen rivers, deteriorated roads, and other similar obstacles, survey research is more difficult to carry out during the wet season. These difficulties are undoubtedly the reason why data collection in tropical countries is greater during the dry season (1). This is unfortunate because there is a strong seasonal dimension to nutritional deficiencies and infectious diseases in developing countries, the wet season typically being the time when prevalence and severity of many problems are greatest. It is strongly recommended that seasonality be taken into account in drawing up the data collection plans. A satisfactory approach would be to extend data collection over a full-year cycle, or always to collect data at the same time (e.g., after the harvest).
Another issue facing evaluators is whom to measure: all members of the family or only the so-called vulnerable groups: pregnant and lactating women and small children. If limitations of personnel and time are overwhelming, data collection should, of course, be limited to women and children. On the other hand, the collection of anthropometric data on other members of the household may, in conjunction with dietary and energy expenditure data, shed light on how food was distributed within the family. By focusing only on the so-called vulnerable groups, researchers might miss the full picture of what happens in nutrition interventions.
Researchers will always find less-than-ideal situations when evaluating programmes. Nonetheless, it is often possible to derive useful conclusions about the benefits of the programme if appropriate analyses are carried out. At the same time, researchers should be prepared to admit that sometimes a minimally satisfactory assessment of impact simply cannot be carried out. To proceed with the evaluation in such instances could lead to serious errors. Researchers might conclude, for example, that no impact occurred, not because none took place, but because the study lacked the power to detect it.
Length and Weight
The two most important and most widely-used anthropometric measurements are total body length (height) and weight, and, as Garn (2) notes, "these two measures are to be included even in the minimal nutritional appraisal, and under any circumstances ..." Length and weight, in combination with age and sex yield indicators that allow one to measure two types of growth disturbances: deceleration or cessation of linear growth (e.g., length-for-age) and loss of fat and muscle reserves (e.g., weight-for-length). In the terminology introduced by Waterlow (3), these two growth effects are known as stunting and wasting, respectively.
Evaluations of nutrition interventions should always include indicators of stunting as well as wasting. Whether one or the other or both types of indicators are affected by nutritional interventions will depend upon many factors, including the nature and degree of the nutritional deficiencies. As an example, the hypothetical response of stunting and wasting indicators to the range of energy intake adequacy, from very deficient to excessive, is shown in figure 3.1. If, indeed, energy is the principal dietary problem in developing countries, the figure depicts a model of what might occur in most nutrition intervention studies.
As part A of figure 3.1. (see FIG. 3.1. Response of Anthropometric Indicators to Varying Levels of Energy Intake. A: Measures of Stunting. B: Measures of Wasting. ) shows, growth in linear measurement is normal when energy intake is either normal or excessive. (There is evidence suggesting obese children are slightly taller than normal children. These effects are, however, small.) As energy becomes limiting, linear growth decelerates, the slope becoming steeper as energy intake deficiencies become severe. Finally, at a point when energy needs are yew, deficient (i.e., when energy intake falls short of basal metabolic requirements), all linear growth ceases (4).
Changes in anthropometric indicators of mass/length relationships as a result of energy deficiencies are shown in part B of figure 3.1. If energy intake is excessive relative to expenditure, obesity will result. Normal mass/length relationships appear, however, to be maintained when intake is normal or moderately deficient. Only when the available energy, after allowing for physical activity, is less than required for basal metabolic needs does the mass/length relationship begin to decrease, as in successful weight reduction programmes for adults.
Yarbrough et al. (5) found that the weight-for-length ratio of chronically malnourished Guatemalan children was similar to that of well-nourished children from the United States for the age range of birth through six years.
One interpretation of these results is that when faced with chronic but moderate deficiencies of nutrients, Guatemalan children appear to reduce their rate of linear and mass growth without altering normal mass for length relationships. Wasting would be expected to begin only when malnutrition becomes so severe that body reserves are utilized for basal metabolic functions over extended periods. In some areas,. seasonal variations in food availability and in the prevalence of infection may, for similar reasons, lead to varying degrees of wasting. The responses depicted in figure 3.1. might be appropriate for populations whose diets are lacking in energy or in energy and protein (i.e., to the point of maximal protein sparing). If energy were plentiful and protein lacking, providing only energy and maintaining physical activity constant would increase weight for length without stimulating growth in length. Food intake patterns that provide enough energy but not protein are not as common in the world as those that are limited by energy.
From figure 3.1., part A, one would also predict that the response or sensitivity of linear growth would not be linear throughout the range of nutritional status. There comes a point, as in the privileged classes of the United States, when the seemingly inevitable secular changes in height grind to a halt. At this stage, measures of linear growth at the population level are no longer informative. Data from food supplementation experiments offer proof of the non-linearity of the relationship. When children are divided as to the degree of prior growth retardation (i.e., as a measure of nutritional status), the impact of food supplements is greatest on those with worst status (6). The impact of food supplementation on growth rates also varies by the degree to which growth rates are depressed at particular ages during the preschool period. Thus, at ages when children are growing poorly, the impact is large; whereas at ages where growth rates are normal or nearly so. the impact is minimal.
Examples of the various purposes for which length and weight are useful are shown in table 3.2. Indicators of wasting (i.e., weight for length) provide the simplest way of identifying individuals requiring immediate medical and nutritional attention. Longitudinal records of length and weight changes are valuable additional information in assessing the health and nutritional status of individual children. One the other hand, monitoring changes in populations is best done, not in terms of indicators of wasting, but of stunting, primarily height or length for age. The reasons for the inappropriateness of indicators of wasting for monitoring changes in populations has already been cited: most children in chronically malnourished populations are able to maintain normal weight/length relationships. Improvements over time in measurements of stunting can therefore take place without changes in measures of wasting as has occurred in rural Guatemalan children as a result of a food supplementation programme (8). The reverse is reported by WHO to have happened in a child population participating in a food aid programme (9).
Weight combines information about stunting and wasting and is hence a mixed indicator. Although the value of weight charts in monitoring the health of infants has been amply demonstrated, as Morley (10) points out, the charts are not always used properly:
But in so many countries which are making use of weight-for-age charts for the surveillance of individual children, a red line is included at the "60 per cent of the Harvard mean." As a result the health workers consider that children above this line must be satisfactory even if the child has not gained weight over a period of six months. (10).
TABLE 3.2. Uses of Total Body Length (Height) and Weight in the Evaluation Of Nutritional Status in Malnourished Populations.
|To select individuals in need of prompt medical attention||Weight/length (acute PEM)||Precise age is not required. Definition of high risk (e.g., 80 per cent weight for length) is necessary|
|To monitor the development of children||Growth velocities in length and weight||Childs's values are compared to age-sex specific norms (i.e., length and weight charts); frequent exams of the child are required|
|To identify high-risk pregnancies upon first examination||Height, weight, weight/height||Definition of high risk is required|
|To monitor nutritional status during pregnancy||Weight changes||Individual's values are compared
Mother must be examined periodically during pregnancy and gestational age must be known.
|To select zones or groups with greater nutritional problems||Length for age (chronic PEM) and weight/length (acute PEM)||Comparison of zones in terms of measures of central tendency and of the distribution of values|
|To monitor changes in nutritional status to evaluate the impact of specific interventions||Length for age (primarily), weight/length||Data through time required for test and control populations|
|To monitor secular changes||Length for age; weight/length
(monitoring of obesity)
|Data through time required for the population in question|
|To monitor seasonal changes in nutritional status in groups||Weight/length; growth velocities for various times of the year are appropriate but more difficult to obtain||Comparison of values collected in each season|
Inversely, an older child whose weight is low with respect to age but not to length may be incorrectly diagnosed as in need of immediate nutritional treatment when, in fact, he may have overcome his nutritional problems long ago. Overfeeding such a child will not make him appreciably taller.
Other Anthropometric Measures
If resources permit, length and weight should be complemented by anthropometric measures of body composition (i.e., muscle and fat). The simplest way to assess fatness is through the measurement of skinfolds, and the two most widely preferred sites are over the triceps muscle and over the subscapular region. The data on skinfolds will be particularly useful if changes in adults are being evaluated.
Arm circumference should also be measured. Arm circumference and triceps skinfold are the basis for estimating two useful measurements - arm muscle and arm fat areas. These two areas were used in a study of malnourished children in Guatemala to show that while arm muscle and arm fat areas were low in the study subjects in comparison to the norms relative to total body length, arm muscle was adequate but arm fat was notably deficient. On the basis of these and other data, the authors inferred that energy, and not protein, was more likely to be deficient in the diets of these children (11).
Arm circumference is sometimes recommended for evaluating the impact of nutrition interventions largely because it can be measured with very simple and inexpensive instruments. A simple string has sometimes been used, and by colour-coding regions of the string or tape, numerical reading is obviated. As attractive as these aspects might be, it would be folly to rely on arm circumference as a single criterion for evaluating nutrition interventions Arm circumference is highly related to weight for length, but its association with total body length and arm length is weak (12). This suggests that arm circumference measures wasting and not stunting. As already noted, one of INCAP's supplementation studies found that while there was a major impact on stunting indicators (e.g., total body length, arm length), no effects on wasting indicators (i.e., weight-forlength, arm circumference) were observed.
To reiterate, arm circumference should not be used by itself to evaluate the effects of nutrition interventions as these are more likely to affect measures of stunting. On the other hand, arm circumference might be an excellent variable for selecting "wasted" individuals in need of prompt food and medical attention, as was done in Biafra and Bangladesh.
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