Strengthening analysis of the nutrition situation through linking food security and nutrition information: Pitfalls and potentials
By Claire Chastre and Sonya le Jeune
Claire Chastre is the SC(UK) Regional Food Security Adviser based in Nairobi.
Sonya le Jeune is the SC(UK) Food Security Programme Manager in Liberia.
It has become the practice in several countries1 to routinely combine nutrition surveys with a food security component in order to contextualise anthropometric results, leading to more informed and appropriate intervention design.
This type of approach can sharpen our understanding of the causes of malnutrition. However, care must be taken to guard against the following assumptions - "there is food insecurity therefore there is child malnutrition" and "there is child malnutrition therefore there is food insecurity."2
This paper considers the main features of nutrition surveys and food security assessments, in particular household economy assessments, and looks at what conclusions can realistically be drawn from a joint analysis when conducted within a conceptual framework such as the causal model presented below (adapted from UNICEF).
Rapid nutrition surveys are routinely conducted in emergencies. These surveys are usually based on anthropometric measurement of a representative sample of children aged 6 - 59 months.
Nutrition surveys usually assess prevalence of acute malnutrition (measured with the weight/height index) amongst children 6-59 months at the time of the survey. Surveys also collect information on health status, for example morbidity, mortality and vaccination rates as well as general contextual information on food consumption.
Food security assessments
There are many different approaches to food security assessment3. Here we consider the household economy analysis (HEA) which provides a comprehensive framework for food and livelihood security analysis. The methodology is based on understanding the various options/strategies that people employ to secure access to food and income. It explores how typical households from different wealth groups in a given area live in a normal year. It also looks at how households cope when events/'shocks' occur and how sources of food and income change.
The three case studies below demonstrate the advantages of combining nutrition surveys and food security assessments.
Case study 1: Liberia (Vahun County, 1998), Combined Information Influencing Targeting of resources.
Both nutritional survey and food security assessment were conducted simultaneously.
The nutrition survey found that one specific age group amongst the 6 - 59 months group presented a higher malnutrition prevalence than the others. Meanwhile, the food security assessment indicated that newly-arrived refugees were more food insecure than the residents and the old caseload refugees. Based on a combined analysis of the food security, nutrition, water and health data, the main factors associated with malnutrition were identified as poor access to food amongst the new arrivals and insufficient access to safe drinking water resulting in a high incidence of diarrhoea amongst children. This analysis led to programmes such as food aid distributions, improvement in the water supply and an increase in capacity to treat malnutrition. It also led to increased targeting of the home-visiting programme. Due to limited resources it was not possible to visit all the households with children in the age range most vulnerable to malnutrition, i.e. under fives. Instead, home visits were targeted at recently arrived refugee households only.
Case study 2: Northern Sudan (Northern Darfur State, 2000) Combined Information leads to more appropriate interventions
A nutritional survey was conducted at the same time as a household economy assessment. The HEA predicted that there would be a food deficit at some point in the future, based on poor cereal production, high grain prices and low groundnut prices. The anthropometric survey showed a current high rate of global malnutrition as well as signs of Vitamin A deficiency. The nutrition survey also indicated that there had recently been a measles epidemic. If the malnutrition rates had been interpreted in the absence of the HEA data, the high rate of wasting may have been attributed mainly to food insecurity as there had been a harvest shortfall and the role of the measles epidemic as a major contributing factor may have been overlooked.
Case study 3: Bugesera (Kirundo province, Burundi) Using combined data sources to predict oncoming crisis can ensure preventative programmes are in place earlier.
The province of Kirundo4 in Burundi experienced three consecutive years of inadequate rainfall and reduced crop production. Three nutritional surveys and two Household Food Economy (HFE) assessments were conducted between January 1999 and January 2000. The HFE assessments covered the area most affected by the drought within Kirundo province: the Bugesera agro-ecological zone.
The first survey (Jan. 1999) showed higher overall levels of malnutrition mainly reflecting a high prevalence of oedema while subsequent surveys indicated a lower but stable prevalence of malnutrition with low levels of oedema.
HFE data showed that food and cash income from production is traditionally earned during the first seven to eight months of the year. This was the case in 2000 although the two main harvests were reduced compared to normal. The poorest households coped through a set of strategies; reduced food consumption (while protecting their children's food intake), eating food they would not normally eat and increased migration in search of labour. These strategies allowed households to cover their minimum energy requirements over the first eight months of the year.
The July 2000 HEA assessment anticipated for the remaining months of the year an increased reliance on the labour market to access food and income (in an almost saturated labour market) and an increase in basic commodities prices. Findings also indicated that the poorest households would be confronted with a food deficit over the last four months of the year in the absence of an intervention.
During the last part of the year, when food security was expected to be at its worst, only half of the recommended food aid was distributed due to shortage of food stocks in country. In addition, the area was hit by epidemics in November.
Coping strategies protected the children's food intake. Therefore the nutrition status of children had not yet been affected by September 2000. It is however possible that nutritional status of the poorest households had been adversely affected but that this was masked by the fact that the nutritional survey findings were aggregated for the whole population.
The predictive value and seasonal dimensions of Food Economy Analysis (FEA) should be taken into account when planning a nutritional survey. It was justifiable to request a nutritional survey in September 2000 as at that stage it was not clear how much the households' reduction in food intake had impacted on children's nutritional state. However, the interpretation of the results needed to take into account the fact that the survey was conducted just after the most food secure part of the year and just before food insecurity was expected to worsen (in the absence of intervention).
In the case, of the January 1999 survey the harvest that month had probably had little time to impact significantly on the nutritional status of children. The high rates of oedema may have been a function of changes in diet and/or the end of the food deficit period. Following the September 2000 nutrition survey, it is possible to predict that nutritional status would have worsened again (as happened in neighbouring provinces) given the food distribution problems and the epidemics that occurred. This example shows how nutritional surveys in the absence of food security analysis have a limited value in terms of prediction and planning interventions.
Linking Household Economy Analysis and nutrition information
Malnutrition is the result of a variety of possible factors, only some of which are food related. By analysing the food related causes in isolation of the other possible factors we risk misinterpreting the results. Analysis is therefore strengthened by combining food security and nutritional data. However, caution is necessary when considering the results from nutritional surveys and household economy assessments together because they have different features/characteristics (summarised in the table below).
|Unit of analysis
||Individual (child 6-59 months)
||Breakdown by socioeconomic category
||Prevalence relates to the entire 6-59 months or breakdown by age groups (i.e. no breakdown by wealth group)
|Time period covered by the results
||Tells of the situation over previous months Can make projections over coming months Describes seasonal variation
||Reflects the situation at one point in time (snapshot)
- Anthropometric surveys describe an outcome (nutrition status), HEA describes processes.
- HEA assessments take the household as the unit of analysis whereas nutritional surveys use the individual (child 6 - 59 months). A cause-effect relationship cannot be assumed without considering the intermediary level: the intra-household food distribution and the child's food intake/utilisation. Without any intra-household information, for instance, one cannot rule out the possibility that food insecure households may preferentially feed the younger children during difficult periods.
- HEA data is analysed by wealth group, whereas nutrition surveys relate to ALL children within the population. Therefore the results are not directly comparable.
- It is equally crucial to take into account the timeframe and the dates at which the assessments were conducted. For example, if a household food deficit translates into inadequate food for the child, the impact of this on the nutritional status might not be seen straight away.
Food security and nutrition information can complement each other, as illustrated by the case studies attached. However, when combining nutrition and food security assessments, in order to understand better the causes of malnutrition, the field worker needs to assess the reliability and the coverage of the data in order to be confident about interpretation. Otherwise it is easy to misinterpret results. With this in mind, a well conducted joint analysis of the food security situation in conjunction with a nutrition survey can lead to better programming and more appropriate interventions.
1Sudan and Burundi as examples
2Experience shows that this is not always the case. For example in Rwanda, discussion with parents in food insecure households revealed that they tended to reduce their food intake to ensure sufficient for their children. In North Sudan, a high level of malnutrition was in part explained by an earlier measles outbreak.
3Food Security Assessment in Emergencies: Report of an Inter-Agency Workshop. Amsterdam, 2-3 December 1997. MSF-Holland
4The majority of the people in the province are engaged in agriculture and pastoralism.
Taken from Field Exchange Issue 13, August 2001