Anthropometry as an indicator of access to food in populations prone to famine

It is generally believed that anthropometry cannot provide early warning of famine; some would say that even for purposes of relief targeting it has little or no value. This article argues that at the level of the community, anthropometric status can serve as a proxy indicator of access to food, and that its limitations are in fact common to most of the other indicators commonly used in the assessment of food security. For populations in which dietary austerity is an early response to food insecurity, both early warning and targeting can be improved by monitoring an anthropometric index of ‘biomass’.

The author is Lecturer in Public Health at the Joint Centre for Public Health Studies, Department of Epidemiology and Com- munitv Medicine, Universitv of Wales Col- lege df Medicine, Heath Park, Cardiff, CF4 4XN, UK (Tel: 0222 742900; Fax: 0222 742898).

This article is an offshoot of a research project funded by the Health and Popula- tion Division of the UK government’s Over- seas Development Administration, and carried out by me at the Centre for Human Nutrition, Departments of Public Health and Policy and Clinical Sciences, London School of Hygiene and Tropical Medicine. I would like to thank Philip Payne, Jeremy Shoham, Tony Nash, Sarah Atkinson and Julius Holt for helping me to develop the ideas I have tried to set out in this article, and at the same time to exonerate them from blame for any defects in it.

‘John B. Mason, Jean-Pierre Habicht, H. Tabatabai and V. Valverde, Nutritional Surverllance, World Health Organization, Geneva, 1984; Frances D’Souza,

continued on page 444

Anthropometry as an indicator of access to food in populations prone to famine

Marion Kelly

The majority of the international agencies, government ministries, NGOs and assorted experts involved in famine early warning and relief operations regard anthropometry as a ‘late’ indicator of famine, ie one that does not respond until famine is an established fact.’ Most proponents of this view conclude that although anthropometric status cannot function as an early warning indicator it is useful for targeting relief. Others doubt even its validity for targeting, arguing that it fails to discriminate between those most and least at risk. These issues are examined critically in this article.

Definitions

Famine can be understood as the outcome of a process in which an acute reduction in access to food causes a group of people to alter their behaviour in ways that ultimately result in excess mortality among some or all subgroups. Famine is qualitatively different from chronic poverty or chronic undernutrition in that it is characterized by severe economic and social disruption, as well as above-average rates of mortality caused by exposure, malnutrition, disease or any combination thereof.

Food security exists when individuals, households or communities have access to the amount and kinds of food needed for good health and normal activity. In order to initiate and direct operations to prevent or relieve famine, information on food security is essential. Temporal changes in access to food help to predict the approach of famine, while local differences in food security show where the need for assistance is greatest. These two functions are known as early warning and targeting, and a set of arrangements for the collection and utilization of information geared to one or both of these functions may be referred to as a food security information network.

A food security information network obtains information on food supplies and/or access to food and/or health status within a population by monitoring the behaviour of a number of indicators. The remainder of this section sets out what indicators are and how they differ, using the framework provided by Dowler et ul.’

0306-9192/92/060443-l 2 0 1992 Butterworth-Heinemann Ltd 443

Anthropometry as an indicator of access 10 food in populatioru proton’ /o famine

continued from page 443 ‘Famine: social security and an analysis of vulnerability’, in G.A. Harrison, ed, Famine, Oxford University Press, Oxford, 1988; Philip Nieburg, ‘Assessment of the food and nutrition situation among refugees and famine victims’, Working Paper No 3, UN ACC/SCN International Nutrition Planners Forum, Conference on Nutrition in Times of Disaster, Geneva, 27-30 September 1988; Relief and Rehabilitation Commission/Early Warning and Planning Services, People’s Democratic Republic of Ethiopia, Guidelines on Nufritional Status Data and Food Relief, Relief and Rehabi- litation Commission, Addis Ababa, 1990; Jean Dreze and Amartya Sen, Hunger and Public Action, Clarendon Press, Oxford, 1989, p 83. ‘Elizabeth A. Dowler, Philip R. Payne, Young Ok Seo, Anne M. Thomson -and Erica F. Wheeler. ‘Nutritional status indicators: interpretation and policy making role’, Food Policy, Vol7, No 2, 1982, pp 99-112. 3John Borton, ‘UK food aid and the African emergency, 198386’, food Policy, Vol 14, No 3, August 1989, pp 232-240.

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The function of an indicator is to provide information about the state of a system. An indicator is a variable for which critical points have been established; in order to set critical points it is necessary to make subjective judgements about the most desirable state of the system. An indicator therefore differs from a measurement, which merely quanti- fies.

For some kinds of system it is possible to achieve a clear understand- ing of the exact processes by which the value of a given variable is related to those of other variables, and to express these relationships quantitatively. Whenever the values of several variables are related by precise mathematical formulae, any one of them can serve as a shorthand indicator of the state of the whole system. In such cases the setting of critical points is straightforward: once the boundaries between desirable and undesirable states of the system have been agreed, the critical points can simply be ‘read off’.

For complex biological and social systems, however, the relationship between the value of a particular variable and the state of the system as a whole is usually known only through statistical associations, and the underlying processes are not well understood. In these circumstances a variable may be considered a proxy indicator of the other variables with which it is associated. If the probabilities of desirable and undesirable outcomes associated with different values of the variable are not known, critical points may be based on normative scales.

Early warning and targeting indicators

Famine involves biological and social systems of immense complexity, and is one of the least desirable configurations of any such system. At the same time, relief operations involving the mobilization of food aid are costly and slow-moving, often requiring a lead time of six m0nths.s Hence there is a need for indicators that can accurately detect the approach of famine months before it becomes a reality. Once a relief response has been elicited, indicators that can discriminate between communities most and least at risk are required.

However, the variety and complexity of the interactions that lead to famine militate against the development of simple shorthand indicators. Because the processes that contribute to the development of famine can vary from place to place and from time to time, the variables that can be monitored for early warning and targeting purposes are at best proxy indicators, for which country-specific normative critical points could be devised. However, for the countries that most often require outside assistance to prevent or relieve famine, there is still no agreement among donors, governments and relief agencies as to which indicators to monitor, let alone any consensus on critical points.

In theory, objective assessment of the validity of an indicator or the usefulness of a cut-off point is straightforward. The ability of a variable to provide early warning of famine can be evaluated by examining its behaviour over a series of famine and non-famine years. If the behaviour of the variable during pre-famine periods is significantly different from that associated with periods of food security, the variable has potential as an early warning indicator. In order to assess the value of a potential targeting criterion, it is necessary first to specify what outcome or condition the targeted intervention is intended to prevent or remedy (eg excess mortality, destitution, malnutrition). The variable in question is then measured in subjects (people, households or communities) that

FOOD POLICY December 1992


are alike in not having been exposed to the intervention, but different in terms of whether or to what extent they exhibit the condition in

question. The ability of a dichotomous variable to predict either famine or an

undesirable outcome such as mortality can be quantified by calculating its sensitivity and specificity; in the case of continuous variables,

sensitivity and specificity can be calculated and compared for different cut-off points. The most useful indicators are those that give an optimum balance of sensitivity, specificity and cost of measurement. The results of such an evaluation are applicable only to the population from which the data were collected.

A few such systematic evaluations of the performance of food security indicators have been attempted.” However, data of the required quality and quantity are difficult to obtain for most populations prone to famine, which tend to inhabit remote areas of countries where systems of data collection are rudimentary or non-existent. In practice, therefore, many variables are used for early warning and targeting even though all that is really known about them is their likely direction of change as food insecurity intensifies.

Having no established critical points, such variables do not qualify as indicators according to the criteria stipulated by Dowler et al. They can nonetheless be of use to decision makers as long as their behaviour accurately indicates whether famine is becoming more or less likely. However, in the absence of consensus on either critical points or their validity as indicators, the behaviour of these variables requires careful interpretation based on knowledge of the area and the population concerned. This is the state of the art (or craft, as Holt prefers to call it),’ the context within which the actual and potential role of anthropometric status will now be considered.

The case for anthropometry

The central thesis of this article is that for many famine-prone populations change in anthropometric status can serve as a proxy indicator of access to food, which reflects the likelihood of future famine as well as current levels of stress. In other words, anthropometry can be used to improve both early warning and relief targeting in certain circumstances.

Timeliness of chunge in anthropometric status

The conventional view of famine is that households whose access to food is threatened give priority to maintaining food intakes, and will go into debt or liquidate assets in order to obtain the same quantities and kinds of food consumed habitually; in other words, they will reduce consumption only when forced to do so by destitution. It follows that

4Alex de Waal, ‘Famine early warning sys- once a household’s income-earning capacity and fungible resources are

terns and the use of socio-economic data’, exhausted its members rapidly begin to starve and die, so that by the Disasters, Vol 12, No 1, 1988, pp 81-91; time a change in anthropometric status is apparent immediate interven- Philippe Autier, ‘Nutrition assessment through the use of a nutritional scoring system’, Disasters, Vol 12, No 1, 1988, DD

tion is needed if lives are to be saved. At this point, according to the conventional wisdom, differences in anthropometric status can be used

76-80. 5Julius Holt, ‘Ethiopia: review of the SCF

to target food aid to those at greatest risk of dying.

(UK) nutritional surveillance programme’, A different view is taken by Shoham, who argues that anthropometric

mimeo. Save the Children Fund. London. status may not in fact be one of the last indicators to change as famine 1990, p 11. develops. He points out that since many communities regularly deal


Anthropometry US an indicator of access to Jood in populatiom prone to famine

‘Jeremy Shoham, ‘Does nutritional surveillance have a role to play in early warning of food crisis and in the management of relief operations?‘, Disasfers, Vol 11, No 4, 1987, pp 282-285. ‘N.S. Jodha, ‘Famine and famine policies: some empirical evidence’, Economic and Polifical Week/v. Vol 10. 11 October 1975. pp 1609-1625;’ David ‘J. Campbell and David D. Trechter, ‘Strategies for coping with food consumption shortage in the Mandara Mountains region of North Came- roon’, Social Science and Medicine, Vol 16, 1982, pp 2117-2127; John C. Cald- well, P.H. Reddy and Pat Caldwell, ‘Periodic high risk as a cause of fertility decline in a changing rural environment: survival strategies in the 198&83 South Indian drought’, Economic Development and Cultural Change, Vol 34, 1986, pp 677-701; Dessalegn Rahmato, Famine Survival Sfrategies: A Case Study from Northeast Ethiopia, Food and Famine Monograph Series No 1, Addis Ababa Uni- versity, Addis Ababa, 1987; Alexander de Waal, Famine Thaf Kills: Darfur, Sudan, 7984-1985, Clarendon Press, Oxford, 1989. *De Waal, op tit, Ref 7, p 225. ‘David L. Pelletier, Relationships Between Child Anthropometry and Mortality in De- veloping Countries: implications for Policy, Programs and Future Research, Mono- graph 12, Cornell Food and Nutrition Poli- cy Program, Ithaca, NY, 1991. ‘O/bid, p 19.

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with an annual hungry season by reducing the size and frequency of their meals, it would hardly be surprising if they tightened their belts whenever they experienced or anticipated extreme or unseasonal food insecurity.’ Groups in which dietary austerity is a deliberate and early response to food insecurity are described by Jodha, Campbell and Trechter, Caldwell et al, Rahmato and de Waal.’

In his book on the 1984-85 famine in Darfur, Sudan, de Waal argues against the use of anthropometric surveillance for early warning, calling it ‘conceptually misleading’x because people in Darfur give priority to preserving their way of life rather than avoiding hunger. However, it seems highly likely that people who cope with the threat of destitution by reducing their food intakes will fail to maintain body weight and/or normal rates of growth, resulting in adverse effects on anthropometric status (which would be amenable to detection by anthropometric surveillance) at a fairly early stage.

However, even if this argument allays concerns about the timeliness of change in anthropometric status, objections to the use of anthropometric surveillance for early warning and/or targeting might still be raised on any or all of the following grounds: research evidence shows inconsistencies in the relationship between anthropometry and mortality; famine mortality is not necessarily caused by undernutrition; anthropometric status is influenced by factors other than food intake; the samples used for anthropometric surveys are prone to selection bias in times of food insecurity; and anthropometric surveillance is expen- sive. The remainder of this section deals with each of these issues in turn.

Nutritional status and mortality

As regards the relationship between nutritional status and mortality, two questions arise. First, is anthropometry useful for predicting mortality in general? Second, to what extent is famine mortality attributable to nutritional deficits?

In fact, neither of these questions has any direct bearing on the potential of anthropometric status as an indicator of access to food, which is a matter of whether responses to food insecurity lead to deterioration in anthropometric status, and of how early this happens in the sequence of events leading to famine. However, they do have important implications for the selection and targeting of interventions.

Anthropometric status as u predictor of mortality. Different anthropometric indices vary in their ability to predict individual mortality. Pelletier reviewed six studies that attempted or permitted comparison of predictive power, and found that in young children, arm circumference (AC) and weight for age (WFA) are clearly better predictors of mortality than weight for height (WFH).’ Even for a single anthropometric index, the mortality risk associated with a given deficit varies somewhat from one study to another, but Pelletier concludes that ‘the same fundamental relationship between anthropometry and mortality is seen in all populations studied’.“’

Where targeting to individuals is possible, present knowledge of the relationship between anthropometry and mortality justifies the allocation of life-saving interventions to those with the lowest anthropometric status, as judged by the index with the greatest predictive power. Obviously, it is not feasible to use WFA among people whose birth


Anthropometry as an indicator of LICC~SS to food in populutions prone to famine

“Bahru Zewde, ‘A historical outline of famine in Ethiooia’. in A.M. Hussein, ed, Rehab: Droughi and Famine in Ethiopia, African Environment Special Report No 2, International African Institute, London, 1976, p 57; D&e and Sen, op tit, Ref 1, p 44. “De Waal, op tit, Fief 7, p 189. 13/bid, pp 189-l 90. ‘?bid, p 190. “/bid, p 193. “Andrew Tomkins and Fiona Watson, Mu/nutrition and Infecfion: A Review, Un- ited Nations Administrative Committee on Coordination/Sub-Committee on Nutrition, Geneva, 1989, p 42. 17Pelletier, op cif, Ref 9, p 42

dates cannot be ascertained accurately, but using AC rather than WFH would make for more efficient targeting to individuals. However, since individual targeting and even household-level targeting are so often impracticable under field conditions, there is a need for criteria by which communities likely to experience high mortality can be identified.

For any population in which the relative risk of mortality for a particular attribute (eg anthropometric status below a certain cut-off point) can be quantified, it is a matter of simple arithmetic to predict how many will die when the attribute occurs in any given proportion of the population. Obviously, such quantitative predictions would not be valid for another population in which the attribute carried a different relative risk, but as long as low anthropometric status carries some extra risk of death in all populations, the mortality rate must rise as the proportion with low anthropometric status increases.

Consequently, although differences in anthropometric status might constitute a rather arbitrary criterion for allocation of life-saving interventions to communities living in vastly different environments, it would not be unreasonable to use anthropometric status as a criterion (perhaps one of several) for targeting such interventions among communities sharing a similar environment.

Undernutrition as a cause offamine mortality. The use of anthropometry to identify communities at risk of excess mortality makes little sense if, owing to a change in circumstances, the predictive power of anthropometry has been superseded by that of some other factor. In this case the targeting criterion - and possibly even the nature of the intervention - would need to be re-examined.

The contribution of disease epidemics to famine mortality has often been noted;” some would go so far as to say that famine mortality can be explained almost entirely in terms of disease. De Waal observed that death rates varied with location during the Darfur famine of 198445, and inferred that differences in exposure to infection were the main determinants of famine mortality. He also found associations between survival and access to milk, but concluded that ‘if undernutrition affected mortality, the effect was slight relative to other factors’.12

Mild and moderate malnutrition were widespread in Darfur during 198445, but de Waal argues that only severe malnutrition carries a significant risk of death.” He states that the rarer ‘cases [of severe malnutrition] were almost always a consequence of disease rather than the result of lack of food consumption per se’,14 and concludes that disease was also the main cause of a sixfold increase in mortality among young children. I5

In fact, even under relatively well-controlled conditions the effects of disease are notoriously difficult to disentangle from those of diet.” However, results of four studies in which attempts were made to control for the effects of morbidity suggest that the observed association between severe WFA deficits and mortality cannot be attributed simply to weight loss caused by illness.”

De Waal’s assessment of the relative importance of nutrition and infection, which leads him to conclude that famine relief should emphasize public health interventions rather than grain distribution, is based mainly on a lack of association between mortality and measures of poverty (which he treats as proxy indicators of staple food consumption), and on local health workers’ impressions that diarrhoea, malaria



“De Waal, op tit, Ref 7, pp 182-187. 19Helen Young and Susanne Jaspars, Nut- ritional Surveillance: Help or Hindrance in Times of Famine?, Discussion Paper, In- stitute for Development Studies, University of Sussex, Brighton, UK, 1992, pp 155- 158. “De Waal, op cit. Ref 7, pp 132-135. 2’Pelletier, ob tit, Ref 9, b 22. “De Waal, op tit, Ref 7, pp 184-185. 23Philip Nieburg, Angela Berry, Richard Stekeiee, Nancy Binkin, Timothy Dondero and Nabil Aziz, ‘Limitations of anthropometry during acute food shortages: high mortality can mask refugees’ deteriorating nutritional status’, Disasters, Vol 12, No 3, 1988, pp 253-257; Philippe Autier, Jean- Pierre d’Altilia, Bart Callewaett, Baalti Tamboura, Jean-Pierre Delamalle and Vincent Vercruysse, ‘Migrations and nutritional status in the Sahel’, Disasters, Vol 13, No 3, 1989, pp 247-254.

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and measles were the main killers. ” But because it is not supported by either anthropometric or morbidity data, this assessment remains open to argument.

An alternative interpretation, albeit one based on equally circumstan- tial evidence, is that much of the child malnutrition and mortality that occurred in Darfur during 198445 was actually attributable to drastic reductions in nutrient intakes. First, socioeconomic status is not a good proxy for anthropometric status in Darfur,‘” so the fact that deaths occurred in both rich and poor households has no bearing on the relationship between nutritional status and mortality. Second, during 1984435 drought-affected households in Darfur ate mukheit instead of grain, and reduced meal frequency to once a day or less.*” Mukheit is a wild berry that is poisonous when raw; when cooked it is unappetizing and not very energy-dense. It is practically inevitable that these dietary changes would have adversely affected the nutritional and anthropometric status of young children, who need frequent energy-dense meals for normal growth. Third, there is evidence that for a given anthropometric deficit the relative magnitude of the increase in mortality is greatest where mortality from all causes is highest,*’ which implies that where exposure to disease is intensified by famine conditions, the mortality implications of anthropometric status become more, not less, important. Finally, the fact that access to milk or donated sup- plementary foods was associated with lower mortality** also suggests that nutrition was a factor in determining who lived and who died, even if starvation per se was rare.

Although its impact is subject to variation and difficult to quantify, undernutrition probably makes a substantial contribution to mortality in many famines. It is nonetheless true that it is not the only cause of death during famine, and food distribution is not the only way to save lives. The case for diversifying relief strategies, to include economic support measures and public health interventions as well as food distribution, is therefore a good one.

However, if excess mortality from all causes is greatest among those who are most food-insecure, and if changes in access to food are reflected in the trend in a community’s anthropometric status, then knowledge of the anthropometric status of communities can be used, in conjunction with other indicators, to improve targeting of all kinds of relief interventions at community level. Contrary to the conventional wisdom, targeting based on anthropometric status and other indicators of access to food would, in many populations, be more appropriate in the early stages of a crisis, before the threat of epidemic disease is heightened by the activation of more desperate coping mechanisms like migration.

Selection bias in sample surveys

Given that rates of out-migration and death tend to rise in a famine zone, the use of anthropometry for either early warning or targeting is complicated by the possibility of selection bias. As Nieburg et al and Autier et al have shown, the poorest and thinnest members of a community are often the first to migrate and/or the first to die.*” Because they take no account of those who have left or died, anthropometric surveys can give a falsely reassuring picture of conditions in such circumstances.

This is a serious pitfall not only for anthropometric surveillance but


Anthropometry as an indicator of acce.~ to Jood in populations prone to famine

also for other kinds of indicator, as Shoham and Clay point out.24 Market data, for example, could be misleading if changes in demand due to migration and mortality were confused with changes caused by variations in food availability. Although such biases cannot be elimin- ated, their extent can be estimated by collecting information on population movements and deaths, which can then be taken into account in interpreting the behaviour of anthropometric and other indicators.

Confounding influences on anthropometry

Anthropometric status cannot be considered a shorthand indicator of access to food because it is influenced by energy expenditure and disease processes as well as by food intakes, and the effects of these influences are extremely difficult to separate in free-living humans. Thus, if food insecurity is associated with weather conditions or activity patterns that reduce either energy expenditure or exposure to infection, anthropometric data might suggest a stable situation despite a drop in food intakes. Conversely, an epidemic of infectious disease could cause a decline in anthropometric status which would mimic the effects of a food shortage even though access to food remained normal.

However, the potentially confounding effects of disease and energy expenditure on anthropometric status should not rule out its use as a proxy indicator of access to food, since the behaviour of socioeconomic indicators is also affected by factors other than access to food. Changes in grain price can be caused by the activities of parastatals, by transport constraints or by payment of taxes at certain times of year; livestock prices can be influenced by the celebration of religious feasts, by livestock disease outbreaks and by seasonal changes in requirements for transport or draught power; migration patterns can be distorted by war and by changes in the availability of employment.

Any assumption that the influence of such factors can readily be separated from the effects of changes in access to food implies the availability of fairly detailed and up-to-date information on local conditions; if a system for collecting such information is in operation it will often be possible to extend it to include human disease and activity patterns, which could then be taken into account in interpreting the anthropometric data. This kind of approach enabled Young and Jaspars to conclude that changes in anthropometric status in Darfur during 1988-91 were caused by changes in food security rather than altered disease patterns.2”

Costs of anthropometric surveillance

Anthropometric surveillance is a labour-intensive activity requiring specially trained personnel, who must be paid not just for the time it takes them to make measurements but also for the time they spend travelling to survey sites, explaining their work to community leaders and locating sample subjects. Hence it costs much more to carry out anthropometric surveillance than it does to monitor certain socioeconomic indicators such as market trends.

24J. Shoham and E. Clay, ‘The role of In order to decide whether anthropometric surveillance is worth the socio-economic data in food needs assessment and monitoring’, Disasters,

extra time and money, it is necessary to consider what benefit is

Vol 13, No 1, 1989, pp 44-60. obtained from it. In Wollo region, Ethiopia, the extra cost of anthro-

“Young and Jaspars, op cif, Ref 19, pometric surveillance during 1987-88 was large in relation to NGO and pp 144-146. government budgets for early warning activities, but modest in relation

FOOD POLICY December 1992 449


to expenditure on relief operations.2h In these circumstances anthropometric surveillance is worth paying for, provided the information it generates can improve the cost-effectiveness of relief operations by more than a few per cent.

Using anthropometry in practice

The preceding section argued that anthropometry can be used as a proxy indicator of access to food. Its targeting potential is independent of its ability to predict deaths directly, as is its early warning potential, which depends on the timing of dietary austerity and the timeliness of change in weight or growth. This is not to imply that anthropometric status is superior to other proxies for access to food, nor to suggest that it should replace other indicators for assessing food security. Rather, anthropometric surveillance components could be added to existing food security information networks to improve the accuracy and precision of early warning and targeting. This section considers how best to put the theoretical potential of anthropometric surveillance into practice.

Collection, analysis, reporting arzd interpretation of survey data

Traditionally, anthropometric survey data have been analysed and written up solely in terms of malnutrition rates, ie the percentage falling below a specific cut-off point. The tendency to focus exclusively on the proportion classed as malnourished stems from the practice of using anthropometry to identify individuals at risk (eg in growth monitoring, or as a screening tool to determine eligibility for enrolment in selective feeding programmes).

An alternative way to describe the anthropometric status of a group is to calculate the mean and standard deviation of the relevant anthropometric index. In fact the mean (together with its standard deviation) is, in conceptual terms, a more appropriate proxy indicator of a community’s access to food because it summarizes the status of the entire group. In addition, the use of mean and standard deviation rather than proportion malnourished (which can in any case be estimated from the mean and standard deviation) increases the precision with which change can be detected, thus reducing somewhat the sample size required.27

Since most famine-prone populations are, even in times of relative plenty, smaller and thinner than Western populations, sample means should be compared with baseline values for the at-risk population rather than with international norms. To establish an adequate baseline a representative sample of several hundred subjects would need to be assessed at monthly or two-monthly intervals over a period of at least one non-crisis year, so as to give an indication of the magnitude and

26Marion Kelly, ‘Operational value of nutri- timing of normal seasonal variations. Ideally, data on infectious disease

tional surveillance in famine early warning prevalence, market prices, population movements and mortality would and relief targeting’, Disasfers, Vol 17, No also be collected as part of the baseline survey. 1, 1993 (in press). “A. Briend, K.Z. Hasan, K.M.A. Aziz, B.A.

Once a baseline has been established, changes in anthropometric

Hoque and F.J. Henry, ‘Measuring change status can be detected through regular follow-up of the original sample in nutritional status: a comparison of diffe- (or a representative subsample). Longitudinal surveillance is ideal for rent anthropometric indices and the sample sizes required’, European Journal of

detecting deviations from normal patterns because it increases the

Clinical Nutrition, Vol 43, 1989, pp 769- precision with which trends can be measured. 778. This approach was used in a study of Wollo region, Ethiopia, covering

450 FOOD POLICY December 1992

Anthropometry as an indicator of access to food in populations prone IO famine

Mean WFL

95 -

Figure 1. Mean WFL (% NCHS me- dian) of children 70-l 10 cm in length (average for 85 samples of 50 children assessed monthly), Wollo region, Ethiopia.

*‘Marion Kelly, ‘Entitlements, coping mechanisms and indicators of access to food: Wollo Region, Ethiopia, 1987-88’, Disasters, Vol 16, No 4, 1992, pp 332- 338. *‘Young and Jaspars, op tit, Ref 19, p 147. 3oFrances D’Souza and Jeremy Shoham, ‘Evaluation of CRS emergency programmes: Lesotho’, mimeo, Relief and De- velopment Institute, London, 1986; John Borton and Jeremy Shoham, ‘A review of the 1983/84 CRS emergency programme in Ghana’, mimeo, Relief and Develop- ment Institute, London, 1986.

I Jan Mar May Jul Sep NOY Jan Mar May Jul Sep NO" Jan 1987 1988 1989

the period 1987-88. Following a reasonably good harvest at the end of 1986, young children’s mean weight for length (WFL) began 19X7 at nearly 94% (see Figure 1). After April it began to decline, but from June to November it remained more or less stable at about 93.4%. As the main harvest at the end of 1987 was drastically reduced by drought, mean WFL recovered only briefly after November. Relative to 1987, the 1988 ‘hungry season’ decline in anthropometric status began earlier and went deeper: after January mean WFL fell initially to around 93%, reaching 92% by July and going even lower a few months later. This deterioration in anthropometric status was apparent before there was any detectable change in livestock market indicators, migration or mortality.*s Similarly, Young and Jaspars’s study of Darfur shows that over the period 1988-91 change in mean WFH coincided with change in goat/millet terms of trade, making it a very timely indicator of food security trends.*”

Choice of index group

Why ussess children? Other things being equal, the mean anthropometric status of any subgroup can serve as an indicator of a community’s access to food, as long as that subgroup’s food intakes and anthropometric status are responsive to changes in food security. However, in populations prone to famine, children under five are virtually the only age group whose anthropometric status is ever documented.

Most primary health care programmes involve routine growth monitoring, but restrict it to children under five because the risk of growth faltering and mortality are greatest in this age group. In areas where such data are already being routinely collected there is an argument for utilizing them for early warning and targeting rather than setting up a new surveillance system from scratch. Of course, self-selected clinic attenders do not necessarily constitute a representative sample, but evidence from Lesotho and Ghana suggests that trends in growth monitoring data from clinics gave early indications of change in communities’ access to food.“’


Under famine conditions young children are considered a vulnerable group, more prone to excess mortality than older children and adults are. Hence the under-fives’ anthropometric status is often assessed during emergencies in order to gauge the need for targeted sup- plementary feeding programmes, or to screen individual children for entry into such programmes.

Young children may or may not experience a disproportionate amount of excess mortality when access to food is impaired,“’ but in any case it is rarely practicable or even desirable to target them as individuals when other members of the household, on whom the children ultimately depend for their survival, are also suffering; hence the priority accorded the distribution of family rations in the planning of relief operations.32 However, even when the importance of the nutritional well-being of the entire household or community is recognized, it is usually assumed that it is unnecessary to assess other age groups because their anthropometric status is reflected in that of the under- fives. According to the Ethiopian Relief and Rehabilitation Commis- sion, ‘small children . , . are the most sensitive to changes in food intake . . . [they] serve as an indicator group for the whole population’; an additional reason cited for focusing on under-fives is that ‘appropriate international standards exist for this group’.“”

Way not assess other age groups? In fact, the avaiIability of international standards is only an issue if the aim of assessment is to make international comparisons. It is irrelevant if the aim is to detect changes or differences in access to food at the level of the community; after all, there are no international standards for market prices, migration rates or any of the other widely used socioeconomic indicators of access to food.

Children’s anthropometric status might respond more readily than adults’ to reductions in food intake, inasmuch as their higher energy requirement per unit body weight obliges them to catabolize a greater percentage of their own body tissue for a given percentage reduction in energy intake, assuming energy expenditure remains unchanged. However, one of the first responses to a reduction in energy intake may well be a compensatory reduction in energy expenditure through a reduction in activity, and young children, who are not obliged to work, have more scope for compensating in this way than adults do. In practice, therefore, the supposedly greater sensitivity of children is debatable. (It is probably true to say that donors and the general public in developed countries are more sensitive to images of emaciated babies than to those of thin adults, but this is a different issue altogether.)

For any population in which children’s anthropometric status is ‘protected’, the anthropometric status of adults ought to give an earlier iIldication of change in the comnlunity’s access to food. This would be the case if, as was noted by Autier in some parts of Chad, children were ensured preferential access to food within the household when supplies

31Dr&e and Sen, up cif, Ref 1, p 80. were inadequate to meet the needs of all members.34

32Judith Appleton and the SCF Ethiopia Another factor to consider in choosing an index group is the

Team, Droughf Relief in Ethiopia: A Prac- possibility of bias due to migration and mortality. For example, in a

tical Guide to the Planning and Manage- community in which adult men were the first to migrate and young ment of Feeding Programmes, Save the Children Fund (UK), London, 1987, p 36.

children the first to die, the group least likely to be affected by selection

33RRCIEWPS, op tit, Ref 1, p 10. bias would be the adult women. For populations in which the group

34Autier, op tit, Ref 4, p 71. least likely to migrate or die is not the first to experience change in



anthropometric status, more than one index group could be used. Since most of the expense, time and effort that go into sample surveys in vulnerable populations are taken up not in making measurements but in travelling to survey sites, liaising with local leaders and locating sample subjects,‘5 only a moderate amount of extra effort is needed to cover two age groups.

Choice of anthropometric index

Having established that for some populations the mean anthropometric status of one or more subgroups can serve as a proxy indicator of access to food, the only task remaining is to consider which of the anthropometric indices is most appropriate for this purpose.

Other things being equal, dietary austerity causes weight loss in adults; in young children it leads first to slowing of weight gain, which may be followed by loss of body weight. Thus changes in access to food could be detected simply by regularly monitoring the weights of a representative sample chosen from the index group. In other words, changes in the ‘biomass’ of the index group would signal changes in the community’s access to food, and comparisons with baseline values would show whether or not these changes were normal seasonal events. Bias due to ageing of samples over time could be avoided by monitoring ‘open-ended’ cohorts; for example, for a cohort of children under five, newborns would be continuously recruited while older children would be discharged upon reaching an upper age (or height) limit.

However, this approach would pose problems if young children were chosen as the index group: rates of weight gain are normally extremely age-dependent, and the age structure of this subgroup, while remaining fairly stable over the medium to long term, could fluctuate considerably from year to year (and even month to month), especially within a small sample. Use of an index that adjusts for age (eg WFA) would solve this problem, but is out of the question if children’s exact ages are not known. An alternative would be to use WFH or AC as a proxy for biomass, AC being preferable if the aims of surveillance include predicting individual child deaths as well as monitoring the community’s access to food.

On the other hand, if the index group consisted of adults rather than children, monitoring biomass would be a simple matter of weighing the sample subjects periodically and comparing the rate and direction of weight change with baseline values. For one-off comparisons of two or more communities not covered by regular surveillance, eg for ‘fine- tuning’ of targeting, it would be necessary to adjust for differences in the average heights of the groups, which could be done by using Body Mass Index (weight/height2).

Conclusion

The conventional wisdom restricts the use of anthropometric assessment to confirming the existence of famine and targeting relief assistance to individuals who are likely to die. However, in a number of populations

%. de Ville de Goyet, E. Jeannee, M.F. prone to famine, reduction of food intake, far from being a last resort, is Lechat and A. Bouckaert, ‘Anthropometric actually one of the first coping mechanisms activated in response to food measurements in a relief programme in Niger: a tool for decision making at the

insecurity. This means that change in anthropometric status can occur

community level’, Disasters, Vol 1, No 3, quite early on in the sequence of events culminating in famine. In such 1977, pp 223-229. populations changes in anthropometric status could signify changes in


access to food, which would facilitate the prediction of famine as well as the targeting of relief to communities.

The timeliness of the warning provided by change in anthropometric status depends on how soon dietary change occurs in the sequence of responses to acute food insecurity, and how quickly it is translated into change in either body weights or growth rates. Unanswered questions concerning the relationships between anthropometric status and mortality, and between undernutrition and famine mortality, do not preclude the use of anthropometry for targeting as long as the premise that assistance should be targeted to the most food-insecure c~~lnmunities is accepted. Contrary to the conventional wisdom, there are no other theoretical obstacles to the use of anthropometric status as a proxy indicator of access to food at community level, given that the problems of confounding, selection bias, lack of established critical points and need for baseline data also bedevil the use of socioeconomic measures.

For conceptual reasons, the most appropriate anthropometric proxies for a community’s access to food are those that reflect biomass, and the anthropometric status of a community is best described in terms of its mean and standard deviation rather than the percentage below a cut-off point. Change in anthropometric status and the food security implications thereof are most readily detected by regularly assessing representative samples and comparing current trends with an a~~pr(~priate baseline. Children are not necessarily the best index group for anthropometric surveillance; timeliness can be enhanced and bias minimized by choosing an index group who are among the first to be affected by dietary austerity measures and among the last to migrate or die.

Finally, there is a need for objective, population-specific evaluations of the performance of anthropometric and other indicators of food security. Preliminary efforts in this direction suggest that some food security information networks can be improved by the addition of an anthropometric surveillance component.


Anthropometry as an indicator of access to food in populations prone to famine

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