|Nutrition Guidelines (MSF, 1995, 191 p.)|
|Part II: Rapid Nutrition Surveys|
The analysis is composed of two parts:
· A descriptive analysis, which consists of building distributions according to the variables.
· An interpretive analysis where cross tabulations are used to make comparisons between groups.
The analysis uses the weight/height index values. There are two approaches when analysing and presenting results:
- the first approach estimates the proportion of children with W/H index falling below a cut off value.
- the second approach describes the whole distribution of children according to index values (= standard prevalence).
These two approaches are complementary. If the survey's objectives are to quantify the number of children who may benefit from an intensive feeding programme or from supplementary rations based on a cut-off value of the index, the first approach is the most appropriate.
However, if the objective is to assess the overall impact of a programme on the whole population of children, the second approach is preferred.
In this Part, we will only develop the first approach.
Description of the sample
The first step in the analysis is to describe the sample, by describing the distribution of characteristic variables. This will indicate if the sample is made up from eligible children. For example, a distribution according to age will give the proportion of children less than 6 months or more than 59 months, which should not have been included in the sample. An age pyramid can demonstrate an under-representation of an age group in the sample (see Annex 9).
An under-representation of an age group, such as the 6-17 months group, in the sample may reflect a higher mortality in this subgroup, but may also reflect the fact that these children were not present on the day of the survey.
In the same way, a distribution according to sex allows us to verify that both sexes are equally represented, and hence, that no selection bias has occurred.
A cluster sample survey has been carried out. The expected prevalence was 12%, the desired precision was 4%. The required sample size was 254 x 2 = 508 children, implying 30 clusters of 17 children (510 children). The following distribution according to age and sex was observed.
Expression of results with their confidence intervals
The age classes proposed here are centred around the months representing fullyears: 12 months (6 - 17 months),24 (18 - 29), 36 (30 - 41) and (48 (42 - 32) months. Many ages are mis - reported and age biasing is toward the full years (i.e. the child is 1 year if it is really 10 months). Making age classes around the full year months is meant to evenly distribute peaks of distribution usually seen around these values
In our example the distribution by sex is not statisticaly different from an even distribution.
In our example, W/H and oedema were distributed as follows:
Definition of nutrition indicators
The two main signs of acute malnutrition described in the chapter concerning the antropometric measures are: a decrease in the value of the W/H index and the presence of oedema. The combination of these two signs and a cut off value for the index are used to define 2 classes of malnutrition:
Global acute malnutrition: proportion of children with a weight / height index <- 2 Z-Scores or oedema
Severe acute malnutrition: proportion of children with a weight / height index <- 3 Z-Scores or oedema
Calculation of malnutrition indicators
To start with, the number of children presenting with oedema is calculed (2 in our example). Then the number of children presenting with a weight / height index?<- 3 Z-Scores and not presenting oedema is calculated. These two numbers are added in order to determine the total number of children with severe acute malnutrition (5 in our example).This number is then expressed as a proportion of the total number of children (5/510 = 10%)
Then these steps are repeted using - 2 Z-Scores as the cut off, in order to determine the number of the children who are defined as globally acutely malnourished (39 in our example). Again this result is expressed as a proportion of the whole sample (39 /510 + 7.6%).
In our example,the following figures are obtained:
Index <- 3 Z-Scores
Index <- 2 Z-Scores
Severe acute malnutrition:
Severe acute malnutrition 7.6%
Oedema + index <-3 Z-Scores
Oedema + index <-2 Z-Scores
When calculating the sample size, the notion of precision was introduced. This is the reason why the proportion of children presenting with malnutrition should be expressed with a corresponding precision which determines the 95% confidence interval. The confidence interval is the prevalence found plus or minus the precision achieved. Calculation of the precision uses the formula already used for determining the sample size but in another way. As a matter of fact, when calculating the sample size (n), an expected prevalence (p) was estimated and a desired precision (d) was used. Now that the survey has been carried out, the approach is reversed: the sample size is known, and the prevalence has been measured, what is going to vary is the precision achieved. If the observed prevalence is closer to 50% than the predicted one, the precision will be worse than expected. If the observed prevalence is less than expected, the precision will be better than expected. This highlights the importance of overestimating the expected prevalence when calculating the sample size, in order to be on the safe side when the survey is completed. Formula for calculation of random/ systematic survey precision:
d = t * sqrt(p * q/n)
The above formula concerns random sample or systematic sample surveys. In a cluster sample survey, the exact formula for the precision uses the prevalence observed in each of the clusters in order to take into account the design effect.
Formula for calculation of cluster survey precision:
d = 1.96 * sqrt(sum(pi - p) ^ 2/((k * (k - 1)))
pi: proportion observed in the cluster i
p: proportion observed in the whole sample
k: number of clusters
In fact, unless a computer is available, a more simplified formula is used in estimating precision for cluster surveys, assuming a design effect of 2. Simplified formula for calculation of cluster survey precision:
d = t * sqrt(2 * p * q/n)
The confidence interval for the estimation of malnutrition is the observed prevalence plus or minus the precision as calculated above. The confidence interval has a 95% of chance of including the actual proportion of malnutrition in the whole population.
In our example, calculation of the precision gives the following:
d = 1.96 * sqrt(2 * 0.076 * 0.924/510) = 0.033
Therefore, a 95% confidence interval is:
C.I. = p + d
Results would be presented as follows:
510 CHILDREN AGED 6 - 59 MONTHS
Proportion of children with oedema = 0.4%
Global acute malnutrition defined by an index < -2 Z-Scores or presence of oedema: 7.6%
95% confidence interval of this estimation: 4.8% to 10.9% (7.6 + 3.3%).
Severe acute malnutrition defined by an index < -3 Z-Scores or presence of oedema: 1.0%
95% confidence interval of this estimation (corrected): 0.2% to 3.4%.
From these two proportions, we can estimate the actual number of malnourished children in the population. The proportions can be applied to the number of children in the population. In our example we estimate the total number of globally acutely malnourished children in the population to be between 430 (4.3% of 10,000) and 1090 (10.9% of 10,000); and between 20 (0.2% of 10,000) and 340 (3.4% of 10,000) children are severely acutely malnourished.
These results can also be expressed for a sub-group of the children, based on age, in order to avoid masking malnutrition in this group by including them in a broader group.
252 CHILDREN AGED 6 - 29 MONTHS
Global acute malnutrition defined by an index < -2 Z-Scores or presence of oedema:10.7%
95% confidence interval of this estimation: 5.3% to 16.1%
Severe acute malnutrition defined by an index < -3 Z-Scores or presence of oedema: 0.8%
95% confidence interval of this estimation (corrected): 0.0% to 5.2%.
Expression of results with their confidence intervals
If a correction was used for calculating the sample size because the sample was more than 10% of the size of the total population, the value of n appearing in the above formulae should be the one before the correction in order to calculate the precision.
A corrected formula exists (quadratic method) which should be used for proportions close to 0% or 100%:
Lower limit = ((2 np + t2 - 1(t2 - (2 + 1/n) + 4p(np + 1))1/2)/2(n + t2)
Upper limit = ((2 np + t2 + 1) + t(t2 + (2 -1/n) + 4 p (np -1))1/2)/2(n + t2)
t: error risk = 1.96
n: sample size
p: proportion of children with malnutrition
q = 1 - p: proportion of children without malnutrition
Some variables can be cross-tabulated. For example, nutritional status (defined according to a cut off value of the weight/height index) and the date of arrival in the camp.
Nutritional status according to the date of arrival
Children 6 - 59 months, Region X, Period Y
Such a table only gives an indication of the trend. The sample size will often not allow a stratified analysis.
Interpretation of the results in context
NOTION OF A <<SNAPSHOT>>
Figures obtained through a single cross-sectional nutrition survey only reflect the nutritional status of the population at the moment of the survey, in a certain region. Taken alone, these figures do not give any indication of the trend, whether the nutritional status is improving or deteriorating. Additional information, collected at the preparatory phase, will allow the interpretation of the results in context.
In a cluster sample survey, figures should not be analyzed for each cluster. Malnutrition observed in one cluster is never representative of the section in which the cluster was drawn, but it is representative of many similar clusters that could be drawn in different sections. As a matter of fact, it is the whole sample which is representative of the population. Practically, in order to avoid misinterpretation by people not acquainted with the cluster sampling method, results should not be presented by cluster.
INTERPRETATION IN CONTEXT
The proportion of malnutrition observed in the sample can be compared to malnutrition rates observed in a previous survey. If there is information from different surveys, some idea of the trend in nutritional status can be inferred. One can only conclude that there was a statistically significant difference between two surveys if confidence intervals do not overlap. Hence, in our example a previous survey had shown a rate of malnutrition of 15.4% + 4.4% (11.0% to 19.8%). The current situation is significantly better, since the lower limit of the first survey (11.0%) is greater than the upper limit of the current one (10.9%).
Information about infanto-juvenile mortality may suggest that a number of malnourished children have died in the past few months. The significance of the survey figures will depend on context factors such as the level of mortality and the timing of the harvest. Complementary information will help in interpreting the results. A stable nutritional situation with moderate levels of malnutrition may deteriorate rapidly if there is an outbreak of measles or a high seasonal transmission of malaria.
A <<Standard recipe>> does not exist for interpreting nutrition survey results. Interpretations and recommendations cannot be based on observed figures only.
However, when the level of malnutrition reaches certain values, emergency actions have to be taken. These actions focus on the children who are classed as malnourished by anthropometric indicators as these indicators are associated to high risk of mortality. In a population where more than 2% of children present oedema, action directed towards these children should be taken without delay. In the same way, a lower limit of the confidence interval of the proportion of children presenting an index value < - 2 Z-Scores of more than 10% depicts an alarming situation.
Survey results may be used to assess the current nutritional situation, to see if nutrition programme objectives have been reached and rates of malnutrition have been reduced. However, this measure of the impact of a programme does not allow us to assess the reasons for success or failure in reaching the objective. The anthropometric survey is just a tool which is part of a more global approach.
Writing of the report / recommendations
A report on the survey should be written as soon as preliminary results are available. Such a report should indicate the procedure used in defining the survey.
The first page should include a short summary (1 page), covering the objectives of the survey, the methodology used, the main results and recommendations. It is meant for the use of people in charge of the population in order to make appropriate decisions guided by the findings of the survey.
In the introduction, the context in which the survey was carried out should be described. What population was surveyed, at which period, in which geographic area.
Furthermore, any relevant information concerning the status of the population should also be reviewed. Nutrition programmes, surveillance data, morbidity and mortality information are included.
OBJECTIVES OF THE SURVEY
The objectives of the survey should be clearly stated. What was measured, in which population and why?
· Survey methodology and sampling frame
The choice of a cross-sectional survey is justified by the necessity for getting information in a short period of time. The sampling frame must be indicated as well as all parameters used, such as error risk, expected prevalence and expected precision.
· Variables measured and recording information
The type of measuring instruments used should be noted. For example, the weight was assessed using a Salter spring scale, to the nearest 100 grams.
· Training of the data collectors
The schedule of the training and its duration should be mentioned, including the realization of a standardization test, if it was possible.
Distribution of the sample, according to age and sex is the first stage of the analysis. The mode of expression of indices should be recorded as well as the definition of grades of malnutrition used. Distribution according to the indices is presented.
INTERPRETATION OF THE RESULTS, DISCUSSION
The discussion puts the results back into the context. Comparison can be made with previous surveys, or surveys from a similar situation. Tentative explanations may be suggested at this stage.
A report should always include recommendations. A nutrition survey is meant to promote rational decision making. As an example, an intensive feeding centre might be recommended in the area if the proportion of children with severe acute malnutrition is high. On the other hand, an active screening programme through home visits can be proposed if the number of malnourished children found during the survey is much higher than the number of children currently seen in an ongoing feeding programme. A global approach might be recommended if the whole population is suffering from malnutrition. However, if the situation is demonstrated to have improved, one can recommend the interruption of a "vertical supplementary programme" and the integration of the nutrition programme in the daily MCH activities of the health system.