WASH.
2
3
ACKNOWLEDGEMENTS
This National Guidelines for Nutrition Surveys in Bangladesh are
designed to provide
clear instructions and guidance on area based nutrition surveys. I
wish to convey my
gratitude to the Director DGHS and the director IPHN/NNS for the
strong leadership in
preparing this guidelines. I do also acknowledge the contributions
of the Program
Manager and deputy program manager - Nutrition In Emergencies –
under IPHN/NNS
for their valuable administrative and technical support in the
preparation of the
guidelines.
I gratefully acknowledge the contribution of the members of the
technical committee
formed under IPHN to review the draft guidelines. Contributions by
staff from the
following organisations is who were members of the technical
committee is highly
appreciated.
- MOHFW
- NNS/IPHN
- UNICEF
- ICDDR,B
- Institute of Nutrition and Food Science (INFS) of Dhaka
University.
I would also like to appreciate the time, review and useful
recommendations provided by
the curriculum committee towards finalisation of the guidelines.
Thanks too to all the
participants from government and development partners who attended
and contributed at
consultative workshops at national level.
Regards.
Name
4
ACRONYMS
FCS Food Consumption Score
FGD Focus group discussion
GAM Global acute malnutrition
MAM Moderate acute malnutrition
NCHS National centre for health statistics
PPS Probability proportional to population size
RNA Rapid nutrition assessment
SAM Severe acute malnutrition
WASH Water Sanitation and Hygiene
WAZ Weight-for-age z-score
5
3.1 Decide whether to conduct a survey
................................................................................................
12
3.2 Define survey objectives
.................................................................................................................
12
3.3 Define geographic area and population groups
...............................................................................
13
3.4 Meet local leadership and authorities
..............................................................................................
13
3.5 Determine the timing of the survey
.................................................................................................
14
3.6 Gather population data and other data
.............................................................................................
14
3.7 Select the sampling method, determine sample size
.......................................................................
14
3.8 Decide which data to collect
............................................................................................................
14
3.9 Prepare supplies and equipment
......................................................................................................
15
3.10 Select and train survey teams
.......................................................................................................
15
3.11 Collect data and manage survey teams
..........................................................................................
16
3.12 Enter and clean
data.......................................................................................................................
16
4. SAMPLING
.......................................................................................................................................
18
4.3 Setting parameters for sample size
calculation................................................................................
20
4.5 Cluster sampling
..............................................................................................................................
26
5. SURVEY FIELD PROCEDURES
.............................................................................................................
30
5.1 Bias
..................................................................................................................................................
30
6
6.1 Selection of survey teams
................................................................................................................
33
6.2 Training of survey teams
.................................................................................................................
33
6.3 Roles and responsibilities of team members
..................................................................................
34
6.4 Standardisation test
..........................................................................................................................
36
7. DATA COLLECTION
....................................................................................................................
39
8.1 Data entry
........................................................................................................................................
54
8.2 Plausibility check
.............................................................................................................................
54
9.1 Data analysis
....................................................................................................................................
57
10. REPORT WRITING AND DISSEMINATION
..............................................................................
64
10.1 Preliminary Report
........................................................................................................................
64
10.2 Final report
....................................................................................................................................
65
11.1 Decide whether to conduct a RNA
................................................................................................
67
11.2 Define RNA
objectives..................................................................................................................
68
11.4 Meet local leadership and authorities
............................................................................................
68
11.5 Determine the timing of the RNA
.................................................................................................
68
11.6 Gather population data and other data
...........................................................................................
69
11.7 Select sampling method, sample size
............................................................................................
69
11.8 Decide which data to collect
..........................................................................................................
71
11.9 Prepare supplies and equipment
....................................................................................................
71
11.10 Select and train assessment teams
...............................................................................................
71
7
11.12 Enter and clean
data.....................................................................................................................
73
11.14 Write and disseminate the RNA
report........................................................................................
75
Annex 2 Local calendar of events
.........................................................................................................
77
Annex 3 Referral
form...........................................................................................................................
78
Annex 5 Mortality module
....................................................................................................................
81
Annex 6 IYCF module
..........................................................................................................................
82
Annex 7 Food security
module..............................................................................................................
84
Annex 8 Food security
module..............................................................................................................
86
Annex 10 Cluster control form
..............................................................................................................
93
Annex 11 MUAC screening form
.........................................................................................................
94
Annex 12 Key informant interview guidance sheet
..............................................................................
95
Annex 13 Focus group discussion guidance sheet
................................................................................
96
Annex 14 Glossary of terms
..................................................................................................................
97
References
.............................................................................................................................................
99
Figure 2. Cluster sampling
.....................................................................................................................
19
Figure 3. Effect of changing estimated prevalence on sample size
...................................................... 20
Figure 4. Sample size calculation
(SRS)..................................................................................................
24
Figure 6. Sample size calculation (cluster sampling)
.............................................................................
27
Figure 7. Modified EPI method
.............................................................................................................
28
Figure 8. Adjustment for small sample size
..........................................................................................
29
Figure 9. Standardization test
................................................................................................................
37
Figure 10. Indices of nutritional status
..................................................................................................
39
8
Figure 12. Salter hanging scale
.............................................................................................................
42
Figure 13. Mother-to-child scale
..........................................................................................................
42
Figure 14. Height measurement
............................................................................................................
43
Figure 15. MUAC measurement
...........................................................................................................
44
Figure 16a. Identification of nutritional oedema
..................................................................................
44
Figure 16b. Nutritional oedema grade 2
...............................................................................................
44
Figure 16c. Nutritional oedema grade
3................................................................................................
44
Figure 19. Conceptual framework of
malnutrition................................................................................
48
Figure 22. Flagged values on data entry screen
....................................................................................
52
Figure 23. SMART and WHO flags
.....................................................................................................
53
Figure 24. Check for double entry
........................................................................................................
54
Figure 25. Plausibility report
.................................................................................................................
54
Figure 26. Results anthropometry
.........................................................................................................
57
Figure 27. Results mortality
..................................................................................................................
58
Figure 28. Food Consumption Score weights
.......................................................................................
61
Figure 29. Food Thresholds for FCS
.....................................................................................................
62
Figure 30. Timing of rapid assessments
................................................................................................
69
Figure 31. MUAC data analysis
............................................................................................................
74
LIST OF TABLES Table 1. Precision: anthropometry
........................................................................................................
21
Table 2. Precision: mortality
..................................................................................................................
21
Table 3. Cluster sampling example
......................................................................................................
26
Table 4. Standardization test
.................................................................................................................
36
9
Table 7. Mortality rate calculation
........................................................................................................
47
Table 8. Plausibility report criteria
........................................................................................................
55
Table 9. Vitamin A supplementation and measles
vaccination.............................................................
59
Table 10. Morbidity results
...................................................................................................................
59
Table 11. Antenatal care and iron folate
...............................................................................................
59
Table 12. IYCF analysis
........................................................................................................................
59
Table 13. Household analysis
................................................................................................................
60
Table 14. Classification of severity of malnutrition
..............................................................................
62
10
1. INTRODUCTION.
According to the World Risk Report 2012, of 173 countries,
Bangladesh is the 5th most disaster
prone country in the world, particularly susceptible to devastating
tropical cyclones, storm surges
and floods. There are 27 million people across the 12 districts
vulnerable to tropical cyclones and
storm surges. Forty percent of these 12 districts, covering around
10 million people, are considered
high risk areas and with an average poverty rate of 40%, people are
particularly vulnerable to
natural hazards. Malnutrition has remained high in Bangladesh,
although a decline has been noted
in stunting and underweight between 2004 and 2011 (Bangladesh 2011
DHS). Negative
consequences of a cyclone on other sectors may aggravate the
already poor nutritional status of the
population. This in the background that under nutrition prevalence
is chronically high in the
coastal areas as is in the entire country. The nutritional status
of especially under five children as
well as pregnant and lactating women can deteriorate in quickly in
the event of a disaster. It is
therefore important to regularly assess the nutritional status in
Bangladesh, particularly in disaster-
prone areas. Currently, the Food Security Nutrition Surveillance
Project (FSNSP), implemented by
BRAC University in collaboration with Hellen Keller International,
is the only source of seasonal,
nationally representative estimates of malnutrition in Bangladesh.
There is, however, scope to
increase the coverage of nutrition surveys, and the development of
these guidelines is one
important step towards achieving the same.
These guidelines are designed to provide clear instructions and
guidance to survey managers on
nutrition surveys and rapid assessments by outlining steps and
procedures to be followed in
planning, implementation and evaluation of nutrition surveys and
rapid assessments. The
guidelines are divided into: a. Comprehensive nutrition survey
guidelines, and b. Rapid nutrition
assessment guidelines. The development of these guidelines has been
triggered by the need for
standardizing the approach and methodology used to conduct
nutrition surveys and rapid
assessments for the purposes of comparability of results and
compliance to international
standards. The methods and instructions are based on the SMART
methodology
(www.smartmethodology.org), which is based on the assessment of
malnutrition and mortality
to establish the magnitude of a crisis. The SMART Methodology draws
from core elements of
several existing methods and current best practices.
Recommendations are based on varying
degrees of evidence including methods for which there is clear
scientific evidence to support its
recommendation. A practical consideration that initiated the
development of the SMART
method, and guided the decision process in its development, is that
partners should be able to
collect data in nutrition surveys with a minimum of added burden to
their programs. In addition,
the method’s level of difficulty is a conscientious balance between
technical soundness and
simplicity for rapid assessment of acute emergencies to obtain
early, accurate, quantitative
profiles of a population’s nutritional status and mortality rate.
For these reasons, the SMART
method is iterative, with continuous upgrading and building on this
basic version, informed by
research, experience, and current best practices.
The current guidelines are to be used to undertake surveys in both
emergency and none
emergency contexts. The timing of nutrition surveys during an
emergency should be as per the
Joint Needs Assessment (JNA) Planning that propose detailed
sectorial surveys at the 6-8 week
after a disaster. However, rapid nutrition assessments could be
done immediately - 1st week -
after a disaster either as a part of the JNA or as stand alone.
(Please see page 69 for the
diagram). In none emergency contexts, the guidelines should be used
to undertake nutrition
surveys to provide nutrition status information of the population
for specific programmatic
reasons.
2. BACKGROUND
In order to gain an understanding of the extent to which an
emergency is impacting nutrition it is
important to analyse data on the affected population and area. Data
relating to nutrition can be
collected, and existing evidence should be reviewed. Nutrition
assessments are essential to guide
response during an emergency. There are three main methods used to
assess the nutrition of
populations: rapid nutrition assessments, nutrition surveys and
nutrition surveillance. In a chronic
or complex emergency the situation is ongoing and nutrition
surveillance is carried out.
Anthropometric surveys are included as part of this; their purpose
being to collect, analyse,
interpret and report on information about the nutritional status of
populations over time and to
inform appropriate response strategies. However, in a rapid-onset
emergency the priority is to
obtain a snapshot of the nutrition situation as quickly as possible
and therefore rapid nutrition
assessments are carried out. The information may not always be
representative and thus not
statistically valid, but the results from a rapid assessment can
verify the existence or threat of a
nutrition emergency, provide an estimate of the numbers affected
and establish immediate needs.
Rapid assessments are also done where there is poor security and
very limited access. Data in
rapid assessments is collected directly from the field and is
usually qualitative.
Acute malnutrition in children 6-59 months is closely linked with
risk of death and is used to
draw conclusions about the situation of the health status of the
whole population, not just young
children. Children aged 6-59 months are more vulnerable than other
age groups to external
factors (such as food shortage or illness) and their nutrition
status is more sensitive to change than
that of adults in many (although not all) populations. Mortality is
the most critical indicator of a
population’s improving or deteriorating health status and is the
type of information to which
donors and relief agencies most readily respond.
Nutrition surveys using a statistically representative sample of
children remain the best method to
determine the magnitude of malnutrition in a population. However,
there are certain limitations to
the use and interpretation of nutrition survey findings. Accurate
population data is needed to list
the population in villages or population units. This may not be
available in an emergency.
Additionally, the data cannot be disaggregated to produce
statistically reliable results for
geographical sub-samples when cluster sampling is used. Surveys are
also time and resource
consuming, but are often necessary to assess the anthropometric
situation with accuracy.
Interpreting results of anthropometric nutrition surveys in
relation to contextual factors and
interventions is also not straightforward and requires a wealth of
information including food
security and public health.
3. STEPS IN CONDUCTING NUTRITION SURVEYS.
This section describes the steps to be followed when conducting a
nutrition and/or mortality
survey.
12
2. Define survey objectives
4. Meet local leadership and authorities
5. Determine the timing of the survey
6. Gather population data and other data
7. Select sampling method, determine sample size
8. Decide which data to collect
9. Prepare supplies and equipment
10. Select and train survey teams
11. Collect data and manage the survey team
12. Enter and clean data
13. Analyse data and interpret results
14. Write and disseminate the survey report
3.1 Decide whether to conduct a survey It is always important to
make sure that the decision on whether or not to conducts a
nutrition
survey is made with consideration to the following points:
Avoiding overlap: the decision to undertake an assessment is
usually made in
conjunction with the government, the nutrition cluster and other
agencies so as to
prevent overlap by different agencies.
Are the results crucial for decision making? If a population’s
needs are obvious,
immediate program implementation takes priority over doing a
survey, and the survey
should be deferred. For example, after a natural disaster, such as
a flood, where it is clear
that the population’s food stocks have been destroyed, the current
nutritional status may
reflect the pre-disaster state.
It should be anticipated that the results will lead to action:
there is little point of doing a
survey if you know a response will not be possible. If the agency
cannot itself implement
a program where needed, the results must be useful in advocating
for a response.
Is the affected population accessible? Insecurity or geographical
constraints may result in
limited access to the population of interest. If this is extreme, a
survey cannot be
conducted. 3.2 Define survey objectives It is important to be clear
about what the survey seeks to achieve. In most cases, nutrition
surveys
seek to quantify the level of malnutrition (and/or mortality) in a
given population at a defined
point in time. Nutrition surveys also provide a baseline from which
future trends can be
monitored. Nutrition and mortality surveys also provide opportunity
for collecting additional data
on relevant interventions and nutrition-related variables such as
food security. These include
immunization and nutrition program coverage, vitamin A, anaemia, or
other micronutrient
deficiency and morbidity. However, caution must be exercised given
that a survey presents a
greater likelihood of inaccuracy as more data is included.
The broad objective (aim) of a nutrition survey is to assess the
current nutrition and health
status of a specific population. (The population may be the
district, village, camp or urban
settlement, or even the region or country).
13
The specific objectives may vary depending on the interventions,
situations
or circumstances in place or intended, and may include the
following:
To determine the prevalence of malnutrition (wasting, stunting
and
Under weight) among children aged 659 months.
To determine the nutritional status of a specific subgroup (e.g.
women of
reproductive age, adolescents or the elderly).
To determine the coverage of health interventions (e.g. measles
vaccinations,
Vitamin A supplementation and oral polio vaccine) among children
aged 659
months.
To determine the levels of retrospective crude mortality rates and
age-
specific mortality rates for under5s in a specific time
period.
To determine the incidence of common diseases (diarrhoea, measles
and ARI)
among the target population, two weeks prior to the
assessment.
To identify possible interventions that addresses the causal
factors of
malnutrition.
Note that, in defining specific objectives, the target group must
be specified where applicable.
The objectives must be measurable, and should be feasible within
the context of a nutrition
survey, bearing mind the limitations of nutrition surveys due to
their cross-sectional nature,
which does not allow for determining the cause-and-effect.
3.3 Define geographic area and population groups
In planning a nutrition survey, a decision must be made as to the
area and population groups that
will be covered, with clear justification. It is useful to have a
map of the selected area for
reference, and for inclusion in the final survey report. A survey
should be conducted in an area where the population is expected to
have a similar nutritional and mortality situation. If an area is
assessed that has two or more very different agro- ecological
zones, the results will be an average of the two zones and not give
an appropriate perspective of either zone. Such heterogeneity can
be resolved by doing separate assessments, although this usually
increases the cost. In general, urban and rural areas, refugee/IDP,
and resident populations should be assessed separately. If there
are areas which are unreachable due to insecurity, these must be
defined before the survey and must be reported as having been
excluded from the survey. Anthropometric measurements and oedema
assessments for children ages 6 to 59 months, and crude death rate
(CDR) for the entire population (all deaths within a defined period
of time) are the priority for nutrition and mortality surveys. The
6 to 59 month-old children are considered the most sensitive to
acute nutritional stress and thus a proxy of the severity in the
whole population. Globally, there is also more experience in
collecting data from this age group.
3.4 Meet local leadership and authorities Meeting local leadership
and authorities before a survey is important for the
following
reasons:
• To obtain letters of permission from the local authorities
addressed to the district or
village leaders, stating that you will be conducting the survey,
stating the reasons for
the survey.
• It is necessary to agree with the community about the objectives
of the survey. If the
population does not understand why you are doing an assessment they
may not
14
cooperate during the survey.
• To obtain a map of the area to plan the survey.
• To obtain detailed information on population figures.
• To obtain information on security and access to the prospective
survey area.
• To agree on the dates of the survey with the community and local
authorities.
• To agree on how the results will be disseminated and used and, in
particular, to discuss the
likely intervention if the situation is found to be as poor as
expected.
3.5 Determine the timing of the survey The exact dates of the
assessment should be chosen with the help of community leaders
and
local authorities to avoid market days, local celebrations, food
distribution days, vaccination
campaigns, or other times when people are likely to be away from
home. Roads may be
impassable during the rainy season. In agricultural areas, women
may be in the fields for most of
the day during ground preparation, planting, or harvesting.
Wherever possible, community
leaders should inform the villages chosen to be surveyed in
advance. In determining the timing
of a survey, it may be decided that a survey be conducted at the
start of an intervention, and then
at the end, so as to determine the impact of an intervention. This
may be challenging, as there
may be several factors contributing to the impact of an
intervention and it may be difficult to
establish attribution. Nutrition surveys may also monitor trends of
malnutrition and identify
possibly impact of a crisis, which is generally a more relevant
rationale.
3.6 Gather population data and other data Before starting the
survey, it is important to learn as much about the population as
possible from
existing sources, including population characteristics and figures.
Population figures are key for
sample size calculation. Data on previous surveys and assessments,
health statistics, food
security information, situation reports (security and political
situation), maps, and
anthropological, ethnic, and linguistic information is also
important.
3.7 Select the sampling method, determining sample size
A decision must be made as to which sampling method to apply, based
on knowledge about the
size of the population, the layout of households, and the presence
of household lists. Simple
random sampling, systematic random sampling and cluster sampling
are the three methods
recommended by SMART. With a small sample size, exhaustive sampling
may be used. The
population data is then used to calculate the required sample size
for the survey, which assists in
anticipating the expected duration and required personnel and
equipment.
3.8 Decide which data to collect
The objectives of the survey will guide the decision on what data
will be collected, and hence
what materials and equipment could be required.
Nutrition
For determination of the magnitude and related factors influencing
malnutrition, data normally
collected includes:
15
1. age, in months (from a known date of birth or based on an
estimate derived from a
calendar of local events)
6. mid-upper arm circumference (MUAC)
7. measles immunization
10. morbidity information
Mortality To estimate the mortality rate (and causes of death), the
following information needs to be
collected:
1. total number (of all ages) currently in the household
2. number who were in the household at the start of the recall
period
3. number of deaths
4. number of births
5. number who left the household during the recall period
6. number who joined the household during the recall period
7. age and sex of each household member
8. number of deaths of children below age 5
9. information about cause of death
Additional data to collect may include:
-Food security
3.9 Prepare supplies and equipment
It is essential to take measures to ensure that measuring material,
including scales and height
boards, are procured in good time and are in good condition. During
the survey, scales should
always be calibrated each day against a known weight. Faulty
equipment should never be used
and there should always be spare equipment. Additional equipment
and supplies include:
vehicles, fuel, paper and pens, questionnaires, and referral
forms.
3.10 Select and training survey teams Team members do not have to
be health professionals. In fact, anyone from the community
can
be selected and trained. They need to be fit, as there is usually a
lot of walking. They should
have a relatively high level of education, as they will need to
read and write fluently, and count
accurately. Ideally, they will speak the local language. If not
possible, there should be
interpreters as part of the survey teams. Women generally have much
more experience dealing
with young children and should usually lead the interviewing of
mothers/caretakers of children.
This is also important as some cultures do not allow women to be
interviewed by men. The
gender composition of the team should conform to the local
context.
The composition of survey teams depends on the data to be
collected. Two people are required
16
for measurement of children (measurer and recorder) in addition to
an interviewer. A team
leader is also required for quality control and leadership of the
survey team. If there are
additional modules such as food security and water and sanitation,
which are household
modules, an additional member may be required. Generally, four to
six teams survey teams may be needed depending upon the number
of
households to be visited, the time allocated to complete the
survey, and the size and the
accessibility of the area covered. The number of teams should never
be too many despite the fact
that the more the teams, the faster the data collection. The
quality of the data deteriorates with too
many teams as it is much more difficult to train, supervise,
provide transport and equipment, and
organize a large number of teams. Supervisors should be assigned to
each team. If the teams are
to collect data in nearby areas, there may be a supervisor for two
teams, but if they are far apart, a
supervisor may be required for each team. The supervisor must be
experienced in undertaking
nutrition and mortality surveys, training team members, organizing
logistics, and managing
people. Adequate training of the survey team members before the
survey is crucial. All scheduled training
must be completed prior to data collection, and every team member
should undergo exactly the
same training, whatever their former experience, to ensure
standardization of methods. During the
survey the supervisor must continually reinforce good practice,
identify and correct errors, and
prevent declining measurement standards.
3.11 Collect data and manage the survey team After having trained
survey teams and assigning members to respective teams, the
data
collection is ready to begin. Supervisors have the overall
responsibility of management of survey
teams in the field. The supervisor must ensure that households are
selected properly, ensuring
the equipment is checked and calibrated each morning during the
survey and that measurements
are taken and recorded accurately. Unexpected problems nearly
always arise during a survey,
and the supervisor is responsible for deciding how to overcome
them. Each problem encountered
and decision made must be promptly recorded and included in the
final report. The survey
supervisor is also responsible for overseeing data entry and for
the analysis and report writing. The survey manager should organise
a review session at the end of each day for a discussion on
the day’s progress and any possible challenges. Before leaving the
field, each team leader should
review and sign all forms to ensure that no pieces of data have
been left out. If there were people
absent from the house during the day, the team should return to the
household at least once before
leaving the area.
It is also the duty of the supervisor to regularly supervise teams
in the field. It is particularly
important to check cases of oedema, as there are often no cases
seen during the training and some
team members may therefore be prone to mistaking a fat child for
one with oedema (particularly
with younger children). The supervisor should note teams that
report a lot of oedema, and visit
some of these children to verify their status.
The survey teams must be managed in such a way that they are not
overworked, as this may
introduce bias due to short cuts and errors. This is achieved by
the survey manager making a
realistic determination of the number of households which a team
can realistically complete in
a day without fatigue. 3.12 Enter and clean data It is important to
note that data cleaning in nutrition surveys begins from the moment
data
17
collection begins, rather than at the end. By conducting data
cleaning as data collection proceeds,
errors can be swiftly rectified to enhance the accuracy of data
collected.
The process begins with the team leaders, who must check the
questionnaires during the day for
errors, which may include omissions. Each evening, or during the
next day while the teams are in
the field, the supervisor should arrange for data to be entered
into the computer. Recording
errors, unlikely results, and other problems with the data may
become clear at this stage. The
ENA for SMART software will automatically flag abnormal values as
data are entered. Each
morning, before the teams set out for the day, there should be a
short feedback session. If any
team is getting a large number of “flagged” results, the supervisor
should accompany that team
the next day. If the results are very different from those obtained
by the other teams, it may be
necessary to repeat the cluster from the day before.
3.13 Analyze data and interpret results ENA for SMART is
recommended for analysis of anthropometry data, which may either
be
entered directly, or copied from other software such as Microsoft
Excel. Individual-level data on
additional indicators may be analyzed with other software such as
EPI-INFO given the
limitations for ENA for SMART. Similarly, household-level data may
neither be entered in nor
analyzed in ENA for SMART.
3.14 Write and disseminate the report The final part of a survey is
report writing and dissemination. The results of the survey
should
be presented in a standard format so that different surveys can be
compared, and no important
information should be left out. After being introduced to the
standard format, it is also becomes
much easier for readers to find particular pieces of information in
the report. ENA for SMART
automatically generated a standard report format with standard
headlines, which the survey
manager can build upon. This is very convenient for the survey
manager.
Preliminary results from a nutrition survey should be available
within approximately a week,
with the full report being available within a month, assuming that
there are no unforeseen
problems. The survey report must clearly articulate the objectives,
implementation steps and
findings of the survey in clear language. An important aspect of
the report is recommendations
for possible intervention.
4. SAMPLING
This section will define and explain the different procedures and
methods for sampling in
nutrition surveys, followed by a step-by-step description of
procedures for sample size
calculation.
4.1 Defining sampling
If all the children aged 6-59 months from a given population were
measured, we would get a
precise picture of the nutrition status of the population. This is
called a census, or exhaustive
survey, and it is possible in a small population. However, an
exhaustive survey is normally long,
costly and difficult to carry out in a large population. Instead of
surveying all the children, we
normally survey only a sub-group of the population, called a
sample, which “represents” the
whole population. Instead of interviewing all the households and
measuring all the children, a
sample may be taken to represent the whole population. It is
important that the sample be
chosen that indeed is representative of the whole population. This
is done by choosing
18
households at random, whereby the selection of one household is
independent of the selection of
another, so as to give each household and child in the population
an exactly equal chance of
being selected into the sample.
4.2 Selecting the sampling method
Box 1 summarizes the decision-making process for selecting the
suitable sampling method for a
nutrition survey.
No
Simple random sampling
When a complete and updated list of households is available and the
population is relatively
small, it is recommended to use simple random sampling, whereby
each household is randomly
selected using a random number generator.
Systematic random sampling
Where the population is relatively small and the numbers of
households are known, households
may be arranged in a clear pattern as shown in Figure 1, with
survey teams able to move
systematically from one household to another. In this case,
systematic random sampling is used. This method is a
variant of simple random sampling. In this method, a
sampling interval is determined by dividing the total
number of households by the required number of
households. A random number is then selected between 1
and the sampling interval to determine the starting point.
Cluster sampling
Systematic random
19
From the starting point, the sampling interval is applied
continuously to select subsequent
households until the sample has been achieved. Simple or systematic
random sampling is
normally useful in contexts such as small refugee camps and urban
settlements.
Cluster sampling
In a relatively large population such as a district, cluster
sampling is more preferable as it may
not be feasible for teams to travel long distances if households
are to be randomly selected and
may be far apart. Another reason is that the likelihood of
having an updated list of all households in a large area is
very low. This method is implemented in two stages.
There are two stages, which are: 1. selection of clusters
and, 2. selection of households (Figure 2) Stage 1:The
whole population is divided, on paper, into smaller
discrete geographical areas, such as villages as in the
case of a district-level survey.
The population of each smaller area must be known or
page42 be estimated with reasonable accuracy. Clusters
are then randomly selected from these villages with the
chance of any village being selected being proportional to
the size of its population. This is called sampling with
“probability proportional to population size” (PPS).
Stage 2: Households are chosen at random from within
each cluster area or village.
4.3 Setting parameters for sample size calculation
In order to calculate the required sample size for a
nutrition survey, the following parameters are considered:
Estimated prevalence/death rate
Anthropometry
The estimated prevalence of acute malnutrition is estimated, and
can be estimated from previous
survey data, or surveillance data. Emergency thresholds may also be
used when previous data is
unavailable. It must be noted that, to determine the sample size it
is recommended that the most
conservative value be selected, which is the prevalence as close to
50% as possible, given that the
required sample size increases as estimated prevalence increases up
to 50% (Figure 3).
Figure 3 Effect of changing estimated prevalence on sample
size
Figure 2 Cluster sampling
Mortality
The expected Crude Death Rate (CDR) in a mortality survey can also
be estimated from previous
surveys or from discussion with key informants. It may also, in the
absence of these sources, be
set as CDR of 2 deaths per 10,000 per day, which is the level that
is often used to declare an
emergency (WHO, 1995).
Standard error, probability and sampling interval
Data gathered from a sample population only provides an estimate of
the true population value.
Thus the true population value can only be calculated through
exhaustive sampling (by measuring
every child in the population). Hence, whenever a sample is drawn,
there is a risk that it will not
be truly representative and, therefore, that the results do not
reflect the true situation. Inevitably,
if a second sample is drawn from the same population, slightly
different results are likely to be
obtained. This risk is known as the standard error. In
anthropometric surveys, the generally
accepted standard error is five per cent. That is to say that if a
hundred sample surveys were
carried out on the same population, five would give results that
were not representative of the
total population. When we undertake a survey, therefore, we
calculate not only an estimate of the
rate of malnutrition but also the range of values within which the
real rate of malnutrition in the
entire population almost certainly lies. This range is usually
called the confidence interval (C.I).
In nutrition surveys we generally accept that a 95 per cent
confidence interval is appropriate (5
per cent standard error). This means that we are 95 per cent
certain that the true prevalence of
malnutrition lies in the range given. If a survey found the
prevalence of global acute malnutrition
(GAM) to be 29.7% (23.8-36.4 95% CI), this would mean that we are
95% confident that the true
GAM prevalence lies between 23.8% and 36.4%.
The 95% is automatically calculated when using ENA for SMART
software.
Precision
Precision measures the consistency of the results and is related to
sampling error. The larger the
proportion of the target population that is measured, the lower
this uncertainty becomes.
Therefore, the higher the sample size, the higher the precision. A
larger sample size increases the
precision of the results.
prevalence Interval precision
40 30 – 50 10.0
There are recommended ranges for precision which are recommended by
the SMART
methodology for different levels of prevalence of malnutrition and
death rate (Table 1 and 2):
Table 2 Precision: mortality
Design effect
The design effect (DEFF) is a correction factor to account for the
heterogeneity between clusters
with regard to the measured indicator. Therefore, it is only used
to determine sample size in
cluster sampling. Generally, if there is no previous information
about design effect, 1.5 can be
used as a default for GAM. DEFF depends on the prevalence and the
size of the clusters. The
higher the expected prevalence, the higher would be DEFF. For
example, if your expected
prevalence is around 10%, expected DEFF may be 1.5, whereas if
expected prevalence is around
25-30% you would increase your expected DEFF to 1.7-1.8. The
smaller the number of children
per cluster, the smaller the DEFF will be. For example, if you are
measuring 15 children per
cluster, your DEFF may be 1.5, whereas if you plan to measure 25-30
children per cluster, you
would increase your expected DEFF to 1.7-1.8. If heterogeneity is
expected to be high, the
maximum value used is 2. DEFF multiplies the sample size, meaning
that a DEFF of 2 doubles
the required sample size. A higher DEFF than 2 would mean that more
than one survey must be
conducted due to very high heterogeneity. In this case, stratified
sampling may be considered.
Recall period
In mortality surveys, a recall period, in days, is applied, and is
defined as the interval over which
deaths are counted. It is determined by looking at the period most
relevant to the purposes of the
survey, the risk of mortality being measured, and the context of
the study. To improve the
accuracy of mortality estimates in cross-sectional surveys, the
beginning of the recall period
should be a memorable date known to everyone in the population. For
example, the start of the
recall period may be a major holiday or festival (Christmas,
beginning of Ramadan, etc.), an
election, an episode of catastrophic weather or other remarkable
event. The end of the recall
period should be the interview date. The recall period is commonly
set at around 90 days.
Average household size and percentage of children under 5
years
In nutrition surveys, although children are the primary target, it
is households which are selected,
hence the necessity for calculating the number of households and
estimating the number of
children and vice versa. This calculation requires knowledge of the
average household size and
the proportion of children below 5 years.
22
Non-response rate Non-Response Rate (NRR) accounts for households
that could be either absent, not accessible,
refuse to be surveyed, or any other reason that prevent survey
teams from surveying a selected
household. The sample size is accordingly inflated using this
NRR.
Procedures for sample size calculation
In calculating the sample size for anthropometry, the following
formula is applied by ENA for
SMART in automatic calculation to determine the number of children
required:
Anthropometry
d²
where:
z =critical value for the normal distribution at 95% Confidence
Interval (C.I) = 1.96
p:=estimated prevalence (as a decimal)
q=1-p
Cluster sampling
Sample size (n) = t² x p x q X DEFF
d²
t=2.045 (Note that the t-distribution is used for cluster
sampling).
The number of children is then converted to the number of
households using the following
formula, which assumes that children 6-59 months constitute 90% of
all children below 5 years:
Sample size households (N) = Sample size children (n)
(Average household size x % children under 5 x 0.9)
Example of determining the number of clusters.
The sample size for anthropometry was done on ENA for SMART
software planning
screen and was based on the following assumptions; .
i) Average Household size 6.1 (SHHS 2006)
ii) Under five children 16.7%. = 1.02 under fives in each
household
iii) Children 6-59 months 17.9% = 1.09 under fives in each
household.
iv) Estimated prevalence – 20% Realistic estimate based on Rapid
assessments
throughout 2006 to 2011. Couldn’t use SHHS 2006 since the time is
too long before.
v) Desired precision = 4; Reason: There is a plan to repeat the
survey during the post
harvest of 2012 for comparison purposes.
23
vi) Design effect = 1.5 (DEFF for malnutrition usually falls
between 1.4 and 1.8)
vii) Percentage of none response households = 3 based on reports of
rapid assessments
that have shown zero refusals.
viii) Therefore anthropometric sample
Calculation of cluster size.
i) Working day from 7 am to 7 pm = 12 hours
ii) Time taken to and from the field on average = 1.5 hours
(90minutes).
iii) Time to sample and identify 1st household = 30minutes
iv) Time for lunch and breaks = 1hour (60 minutes)
v) Time to interview once household = 25 minutes.
vi) Time to walk from one household to another = 5 minutes.
vii) Calculation.
- Total working hours = 12 x 60 = 660 minutes.
- Total time spend in the field undertaking various survey tasks =
90min +
30min = 120minutes.
- Time available for the teams to undertake the survey = 660 – 120
=
540minutes
- Therefore, number of households to be visited / day = Time
available for the
team divided by time spend interviewing and moving from one
household to
another= 540/30 = 18 households.
viii) Calculation of number of clusters per survey = Sample size1
(Household) ÷ size of
cluster = 713/18 = 40 clusters.
Note: This is an hypothetical example and parameters may differ
case by case depending on the
situation.
Mortality
Systematic random sampling
The sample size (households) is calculated as follows, whereby CDR
is the estimated Crude
Death Rate:
d²
Cluster sampling
11 The higher of the sample among Anthropometry and Mortality is
used for the calculation of number of clusters per
survey. In this case 713 households for the mortality.
24
d²
To calculate the sample size, the parameters explained in Section
4.4 are entered into the planning
screen of ENA for SMART. For ease of understanding, we will use a
single hypothetical
population and apply the three sampling methods separately to show
how the sample size will be
calculated.
50,000 people and 10,000 households:
Estimated prevalence of GAM: 20%
Estimated CDR: 2/10,000/day
Desired precision- 5%
246 children for anthropometry and 337
households (corresponding to 1,518 population)
for mortality (Figure 4). Given that, in reality, the
anthropometry and mortality data is collected
from the same households in a single survey, the
higher number of households is taken as the
sample size (337 in this case) to ensure that the
sample is sufficient for both. It is important to
note that survey teams should collect data from
all 337 households even if the 246 children are measured before all
the households are completed.
This ensures that survey teams do not select households only with
children, which can introduce
bias. This method, known as the fixed household method, is also
useful as nutrition surveys
frequently include the collection of household-level data such as
food security, which requires all
households to be included for a more representative picture.
Simple random sampling
In terms of implementation, with simple random sampling, the next
stage is to randomly select
the 337 households from the 10,000 households, using the random
number generator, also found
on the ENA for SMART planning screen, by entering the range of
required households (1-
10,000) and the number required (337) and clicking “Generate Table”
(Figure 5) to produce a list
of randomly selected households. These numbers should then be
sorted in ascending order. From
the list of households, the households are then selected. If, for
example, the first number on the
list is 964, meaning that, from the list of households, the 964th
household will be the first to be
selected.
25
Systematic random sampling
The total number of households (10,000) is divided by the sample
(337) to determine the
sampling interval (30 rounded to the nearest whole number in this
case). A random number is
then selected between 1 and 30 to determine a starting point (using
the procedure described
above). Assuming the random starting point is 5, the survey team
will go to the 5th household as
per the layout. This is the first household that will be
interviewed. The next household will be
determined by adding the sampling interval, which will be the
(5+30)th household, which will be
the 35th household. The next household will be the (35+30)th, which
is the 65th household. This
process is repeated until the 337 households are achieved.
Figure 5 Random number generator
Random Number table
Range: 1 to 10000, Number: 337
964 5461 1320 4544 8169 5635 1270 1456 7280 7644 2105 2098
5471 6759 4630 298 8404 1340 8435 412 7369 2932 4219
8311 1124 9839 3543 9534 3528 9653 3363 1319 556 8712
5178 7722 2795 8945 1144 9559 2006 5082 1993 3377 2864
7696 4101 6176 3444 35 5556 5104 571 9130 3555 3844
8612 4137 7989 9897 2810 7386 8555 9512 755 6097 5227
9443 8079 5245 4455 7279 6978 4535 5906 4558 8645 4958
7115 9381 3221 2891 5874 7747 3338 2220 2718 5324 9870
7449 8330 3885 968 3256 3230 9118 7985 8489 9581 9028
4373 5036 7462 4659 6157 4569 435 7344 5078 8615 7780
9216 7986 9921 3207 6175 8597 5597 2422 5059 188 4981
4359 344 7726 3451 9202 1327 44 2761 7828 2182 9603
329 1272 1165 2036 3577 470 9584 4521 9113 9511 650
4421 7778 256 9433 3432 7283 4890 2516 226 7277 640
8437 7039 4189 3361 2517 8195 5693 9226 2169 1009 7128
6225 1277 2064 4735 9700 4740 4310 3037 8629 6777 2965
6216 1887 9088 682 5826 6187 7458 4953 9689 9938 7901
9319 9725 5949 3659 5709 8636 5153 2099 5835 8154 3425
8066 4718 7831 8278 5398 381 9466 4589 5412 1558 4999
959 3082 481 9852 3273 5609 5922 6549 4078 1623 953
8672 3502 2111 6500 8335 5226 2199 1735 5741 4691 3089
983 880 9445 4963 4899 3245 5551 8599 7149 7585 6318
5879 1348 454 4436 7347 3198 2790 4236 393 5801 8358
26
3301 4173 8621 417 5140 9843 3046 1427 7140 7218 42
3175 525 1434 7846 7126 1536 5717 183 7381 8848 7964
1051 8354 8419 9949 3413 1296 3473 318 7688 9541 2627
3650 1777 5128 6258 254 6804 9067 3866 7335 4813 9193
191 3955 5456 2978 6180 262 4019 7045 3411 3961 5629
7583 9482 2993 8389 7827 7496 5761 8917 7558 6017 1949
9001 9896 8306 1394 1393 3403 2250 6468 81 6207 6896
1930 4011 6249 5552 9829 8451
4.5 Cluster sampling
ENA calculation
With the same example, to determine the sample size for cluster
sampling, a DEFF must now be
entered in the planning screen of ENA. The population sizes of the
smaller units must also be
entered to generate clusters. Let us use a DEFF of 1.5 and the
sub-populations in Table 3.
Table 3 Cluster sampling example Geographical unit Estimated total
population
Location 1 5,000
Location 2 6,000
Location 3 6,500
Location 4 6,200
Location 5 7,000
Location 6 5,900
Location 7 7,100
Location 8 6,300
Total 50,000
A decision must then be made as to the number of clusters to be
used for the survey. The higher
the number of clusters, the higher the probability will be that the
sample will be truly
representative of the population from which it is selected. This is
because, the more clusters there
are, the smaller the confidence interval will be around the
estimate of the prevalence of
malnutrition and the more accurate our estimate of malnutrition
will be. According to SMART
guidance, the number of clusters are recommended to be 30 or more,
and cannot go below 25
under any circumstanced. In this example, let us assume that we
will use 30 clusters. The output
from ENA for SMART, using the same assumptions above, gives 551
households and 401
children. In this example, we will use 30 clusters. After entering
the variables in ENA for
SMART, and clicking “Assign Cluster’, the clusters are selected
from the population sub-units.
By clicking the icon , the table with the selected clusters is
copied to Microsoft Excel (Figure
6).As shown, the 30 clusters have been selected, meaning that
location one will contain clusters 1
to 3 and so on. Note that the software also generates replacement
clusters (RC) which are
substitute clusters to be used in the event that 10% or more of the
clusters are unreachable, either
due to insecurity of other reasons. In such circumstances, all the
RCs will be used.
27
Output:
Geographical
unit
Population
Location 1 5000 1,2,RC,3
Location 2 6000 4,RC,5,6
Location 3 6500 7,8,9,10
Location 4 6200 11,12,13,14
Location 5 7000 15,16,17,18,19
Location 6 5900 20,21,22,23
Location 7 7100 RC,24,25,26,27
Location 8 6300 28,29,30,RC
Having selected the clusters, the next stage is to select
households. In this example, the sample
required is 551 households, meaning that there will be 551/30=18.4
children per cluster. This will
be rounded up to 19 (it is advisable to round up rather than down).
The 19 households within each
cluster must be randomly selected. The recommended approach is to
use simple random sampling
of households from a list, or systematic random sampling if a list
is unavailable and households
are arranged in a clear pattern. It may be that villages are not
very large but village leaders are
still unable to list all households. In such situations, survey
team members can walk around the
village and identify all households, by writing a number (starting
at 1 to the total number of
households in the village) with a chalk on their door, for example.
If clusters have been selected
from villages, the list will be obtained from the village leader.
Only in the event that the list of
households is unavailable, the modified EPI method is used.
Step 1
Step 2
Figure 7 Modified EPI method
When the team arrives at the village that will contain the cluster,
the following procedure should
be followed after discussions with the village leaders (Figure
7).
Go to somewhere near the centre of the selected cluster area.
Randomly choose a direction by spinning a bottle, pencil, or pen on
the ground and
noting the direction it points when it stops.
Walk in the direction indicated, to the edge of the village. At the
edge of the village
spin the bottle again until it points into the body of the village.
Walk along this
second line counting each house on the way.
Using a random number list select the first house to be visited by
drawing a random
number between 1 and the number of households counted when walking.
For
example, if the number of households counted was 27, then select a
random number
between one and 27.
The team will not have a computer in the field, so each day before
setting out, the
supervisor should print out the list of random numbers. If the
number 5 was chosen,
go back to the fifth household counted along the walking line. This
is the first house
you should visit.
Go to the first household and interview all children aged 6–59
months in the household
for the nutritional survey and complete the mortality
questionnaire.
The subsequent households are chosen by proximity. In a village
where the houses are
closely packed together, choose the next house on the right.
29
Continue in this direction until the required numbers of households
are interviewed.
Continue the process until the required number of children has been
measured.
The modified EPI method understandably gives less
representativeness as children will be
selected with close proximity to each other, which has potential
for bias. Additionally, the
selection of a household is actually determined by the selection of
another, which contradicts the
principles of probability sampling.
Segmentation
In some cases, villages selected randomly to contain a cluster
might be very large or households
very dispersed and sample selection can then become very tedious;
teams will have long distances
to walk and not enough time to complete one cluster per day. In
those scenarios (approximately
more than 100 households in the village), segmentation can be used
in order to reduce the area
that will be covered by the survey teams. The objective of this
procedure is to divide the village
into smaller segments and choose one segment randomly to include
the cluster. This division can
be done based on existing administrative units, such as natural
landmarks (river, road, mountains,
etc.) or public places (market, schools, churches, mosques,
temples, etc.) The segments should be
preferably be of approximately equal size, whereby the team will
randomly select one segment to
be the cluster. This should be accompanied by a sketch map.
4.6 Adjustment for small sample size
If the target population (number of children 6-59 months) is below
approximately 10 000, the
“Correction for small population size” box is clicked in ENA for
SMART (Figure 7 and 8). This
is because, If the target population is small, a smaller sample
size would be needed to achieve the
required precision. ENA for SMART calculates the target population
from the total number
entered in the cluster selection table and % of Under-5 children
entered into the calculator. For
example, if the total population size in the cluster selection
window is 40,000, and % of under-5
is 15%, ENA for SMART would assume that there are
40,000x0.15x0.9=5,400 children aged 6-
59 months in this sampling universe, and use this number for
adjustment for small population size
Figure 8 shows the effect of adjusting for small population size.
With the same assumptions, the
required sample size has reduced from 496 to 485 households for
anthropometry.
5. SURVEY FIELD
This section will highlight ways of
ensuring that field teams are well managed, and will discuss
measures for reducing bias during
data collection. Special field circumstances in sampling and
selection will also be explained.
Figure 8 Adjustment for small population size
30
5.1 Bias
Bias is anything other than sampling error that causes the results
of the survey to be different
from the actual population prevalence. Bias cannot be calculated
nor its effect upon the result
assessed, but is the main reason for inaccurate survey results. It
is important for survey teams to
understand potential sources of bias and to minimise them.
Common sources of bias in nutrition surveys include:
Systematic errors due to faulty weighing equipment or measuring
techniques.
Non-calibration of weighing equipment.
Recall error: Respondents often fail to recall all deaths during a
given recall period.
Infant deaths, in particular those within a short time after birth,
are particularly under-
reported. Respondents may also misreport ages, dates, and salient
events.
"Calendar" error: Respondents may report events as happening within
the recall period
when they did not (or vice versa) due to lack of clarity about
dates.
Age heaping/digit preference: Respondents may round ages to the
nearest year i.e. 12,
24, 36 and 48 months.
Sensitivity/taboos about death: In general, the death of a
household member is not a
subject discussed readily with strangers.
Deliberate misleading: In some populations, with experience of
relief operations, some
respondents may deliberately give incorrect answers in the
expectation of continuing or
increased aid.
Interviewer error: Enumerators may ask questions or write down
answers incorrectly,
skip questions, assume answers, or rush respondents in an effort to
complete the
interview quickly.
The best way to minimise bias is to thoroughly train and supervise
teams, ensuring that all
procedures outlined in section 6 are strictly followed.
Additionally, the following minimise bias
and enhance accuracy of data:
Ensure errors in the field are minimised by using good quality
equipment that is regularly
calibrated.
Check the questionnaires and control forms for blank entries at the
end of each day to
make sure no data is left out. The team leader should review all
questionnaires before
leaving an area in order to make sure no pieces of data have been
left out. If there are any
problems the team can return to the household and correct any
identified error.
Check for data collected. Each evening, or during the next day
while the teams are in the
field, the supervisor should arrange for data to be entered into
the computer. Recording
errors, unlikely results, and other problems with the data may
become clear at this stage.
ENA software will automatically flag abnormal values as data are
entered.
Each morning, before the teams set out for the day, there should be
a short feedback
session. If any team is getting a large number of flagged results,
the supervisor should
accompany that team the next day. If the results are very different
from those obtained by
the other teams, it may be necessary to repeat the cluster from the
day before.
5.2 Supervising data collection team
Field supervision is important in ensuring valid data collection
and minimising bias. The
supervisors should:
Make frequent unannounced spot checks on the teams in the
field.
Ensure that the methodology is closely followed and document any
deviations.
31
Check all questionnaires and control forms to ensure that all
sections are accurately
completed.
Ensure that all instruments to be used the survey teams are
calibrated every day.
It is particularly important to check cases of oedema, as there are
often no cases seen
during the training and some team members may therefore be prone to
mistaking a fat
child for one with oedema.
5.3 Special circumstances
There are certain circumstances which field team may encounter,
which must be anticipated.
Some households or children may be absent or refuse to participate,
and in some cases there may
be need for teams to return to the households later in the day. All
these must be documented, and
it is strongly recommended to use a cluster control form for this
purpose (Annex 10).
Impossible to visit a selected household
In the event that a household cannot be interviewed, either due to
refusal or lack of access, the
team should continue to the next household according to the
sampling procedure. The households
that are impossible to visit have already been accounted for in the
planning stage by inflating the
sample size with the non-response rate.
No children in the household
Not all households will have children. All applicable
modules/questionnaires should be
completed if there are no children. Excluding households without
children from selection will
introduce serious selection bias in measuring household-level and
other non-child variables (e.g.,
mortality, WASH, food security).
Absent household
The survey team may find all household members absent. After
confirming with neighbours, this
should be recorded on the cluster control form. The team should
return to absent households
before leaving the village, to see if residents are back. If not,
this should be reported on the
questionnaire and control form. As explained above, absent
households are not replaced.
Absent household
This is a household which has had no one living there for a long
time. Such households should
not be included in the list of households used for household
selection.
Absent household
If a child lives in the household but is not present at the time of
the survey, this should be
recorded on the household questionnaire and control form, and the
household should be revisited
before the end of the day. The rest of the information (age, sex,
feeding practices, immunizations,
etc.) can still be filled completed.
Child with disability
Some disabilities might not allow you to take all anthropometric
measurements needed or might
lead to a biased measure. For example, the weight of a child
missing a limb will not be very
meaningful when comparing it with the standard population. All
other data that is not influenced
by the disability should be collected such as sex, age, oedema (if
the child has both feet), etc
should be collected.
Such children should be recorded as absent.
6. SELECTION AND TRAINING OF SURVEY TEAMS
This section will outline guidance for selecting survey teams, and
the aspects to be included in
training of enumerators.
6.1 Selection of survey teams
Survey team members do not necessarily have to be health
professionals. However, They need
to be fit, as there is usually a lot of walking. They should have a
relatively high level of
education, as they will need to read and write fluently, and count
accurately. Ideally, they will
speak the local language. If not possible, there should be
interpreters as part of the survey teams.
Women generally have much more experience dealing with young
children and should usually
lead the interviewing of mothers/caretakers of children. This is
also important as some cultures
do not allow women to be interviewed by men. The gender composition
of the team should
conform to the local context.
The composition of survey teams depends on the data to be
collected. Two people are required
for measurement of children (measurer and recorder) in addition to
an interviewer. A team
leader is also required for quality control and leadership of the
survey team. If there are
additional modules such as food security and water and sanitation,
which are household
modules, an additional member may be required.
Generally, four to six teams survey teams may be needed depending
upon the number of
households to be visited, the time allocated to complete the
survey, and the size and the
accessibility of the area covered. The number of teams should never
be too many despite the fact
that the more the teams, the faster the data collection. The
quality of the data deteriorates with too
many teams as it is much more difficult to train, supervise,
provide transport and equipment, and
organize a large number of teams. Supervisors should be assigned to
each team. If the teams are
to collect data in nearby areas, there may be a supervisor for two
teams, but if they are far apart, a
supervisor may be required for each team. The supervisor must be
experienced in undertaking
nutrition and mortality surveys, training team members, organizing
logistics, and managing
people.
6.2 Training of survey teams
Survey team members must receive adequate training prior to
conducting a survey, even if they
have prior survey experience. Each survey has its unique
challenges, and survey work requires
constant re-training so as to standardize methods and techniques as
well as to update knowledge.
The survey manager must come up with a survey training schedule and
organise a suitable
training venue, where there is sufficient space. The equipment and
materials for the training need
to be procured and organised in advance of the training.
The main topics to cover in training of data collectors
include:
Introduction to nutrition surveys: To introduce team members to the
rationale behind
surveys and the objectives.
Sampling procedure: Defining sampling, explaining why sampling is
used and how it is
applied. Describe the rationale and importance of
representativeness and outline the
sampling method to be used for the survey.
Interviewing techniques and questionnaires: Explain the best
practice in terms of
interviewing so as to prevent bias. Go through each survey question
to give guidance on
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suggestive questioning but probe where necessary.
Measurement techniques: Introduce the teams to the different
measurements to be used in
the survey and the equipment to be used, and explain procedures for
each. This is
followed by a standardisation test exercise at the end of the
survey for anthropometry.
Composition of survey teams, roles and responsibility of team
members: Discuss and
agree on the composition of the different teams and assign team
leaders and supervisors,
clearly explaining the responsibility of each.
Survey field procedures: Go through the procedures to be followed
by teams before going
to the field, whilst in the field, and after leaving the field on a
daily basis.
Survey logistics: Outline the plans for the survey regarding:
materials and equipment to
be used by each team, communication, travel, food and accommodation
in the field as
well as allowances (if applicable).
6.3 Roles and responsibilities of team members
The roles of the different members of the survey team are generally
as follows:
Survey manager (1 per survey)
1. Gathering available information on the context and survey
planning.
2. Selecting team members.
3. Training team members.
4. Supervision of the survey: Taking necessary actions to enhance
the accuracy of data
collected.
5. Visiting teams in the field and making sure that supervisors are
following up team leaders.
6. Ensuring that households are selected properly and, that the
equipment is checked and
calibrated each morning during the survey, and that measurements
are taken and recorded
accurately.
7. Deciding on how to overcome the problems encountered during the
survey. Each problem
encountered and decision made must be promptly recorded and
included in the final report, if
this has caused a change in the planned methodology.
8. Organizing data entry into ENA for SMART and checking any
suspect data every evening,
by using the appropriate sections of the SMART plausibility
report.
9. Organizing an evening or morning “wrap-up” session with all the
teams together to discuss
any problems that have arisen during the day.
10. Ensuring that the teams have enough time to take appropriate
rest periods and has
refreshments with them. It is very important not to overwork survey
teams since there is a lot
of walking involved in carrying out a survey, and when people are
tired, they may make
mistakes or fail to include more distant houses selected for the
survey.
11. Analyse and write the report.
Survey supervisor (1or 2 per team depending on survey)
1. Visiting teams in the field and making sure that before leaving
the field, each team leader
reviews and signs all forms to ensure that no pieces of data have
been left out; making
sure that the team returns to visit the absent people in the
household at least twice before
leaving the area.
2. Checking cases of oedema, as teams are prone to mistaking a fat
child for one with
oedema (particularly with younger children).
3. Ensuring all food/refreshments are ready at the start of the
day.
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4. Participating in deciding on how to overcome the problems
encountered during the
survey. Each problem encountered and decision made must be promptly
recorded and
included in the final report, if this has caused a change in the
planned methodology.
5. Assisting with organizing data entry into ENA for SMART and
checking any suspect
data every evening, by using the appropriate sections of the SMART
plausibility report
and other checks.
Team leader (1 per team)
1. Ensuring that all questionnaires, forms, materials and equipment
are ready at the start of
the day.
3. Organising briefing meeting with team before departure in
morning.
4. Speaking with representatives to explain the survey and its
objectives.
5. Using a local events calendar to estimate the age.
6. Checking if the child is malnourished and making a referral if
necessary.
7. Supervising the anthropometric measurements.
8. Ensuring that households with missing data are revisited before
leaving the field the same
day.
9. Ensuring that all equipment is maintained in a good state.
10. Managing time allocated to measurements, breaks and
lunch.
11. Ensuring the security of team members.
12. Noting and reporting problems encountered to the
supervisor.
Measurer (anthropometry) -1 per team
1. Measuring weight, height, weight and MUAC.
2. Assessing the presence of oedema.
Assistant (anthropometry) -1 per team
1. Using local events calendar to estimate age.
2. Recording age.
3. Ensuring each child is in the correct position for
measurement.
4. Recording the measurements on the questionnaire.
Interviewer -1 per team
6.4 Standardization test
The standardization test consists of all the members of the teams
measuring 10 (or more)
different children twice, with a time interval between individual
measures. The size of the
variation between these repeated measures is calculated to assess
how precisely each person
measures the children (repeatability of measurements).
The standardization exercise is performed with a group of children
whose ages fall within the
range for the study (6–59 months). Before carrying out the
exercise, the supervisor carefully
measures each child without allowing the trainees to see the
values. The supervisor is
automatically given the ID number 0, and should start by filling in
the form. It is important that
the supervisor undertakes the exercise as well as the team members.
The supervisor’s data may
35
be assumed to be of higher quality than the trainees; however the
actual values should relate
closely to the mean value for all the teams.
Each team member is also given a unique ID. Each child that will be
measured is also be
given a child-ID, starting from 1. For the exercise, each child,
with his/her mother, remains at
a fixed location with the ID number clearly marked. The distance
between each child should
be far enough to prevent the trainee from seeing or hearing each
other’s results.
At the beginning of the exercise, each pair of trainees starts with
a different child. The supervisor
instructs the measurers to begin the measurements. The trainees
should carefully conduct the
measurements and clearly record the results on the second, third
and fourth columns of the
standardization form next to the child’s identification number
(Table 4a and 4b).
Table 4 Standardisation test
Enumerator name....................ID......1st measure Enumerator
name....................ID......2nd measure
37
Each pair of measurers should have their own form to complete, and
each should take turns
taking measurements. When each member of the pair has done the
measurement, they should
move on to the next child. At the end of the process, the sheets
are handed in and a second sheet
is taken. The teams then take a break (lunch). The whole process is
then repeated after the break.
Thus, without seeing the measurements they previously made, each
enumerator measures each
child twice.
The equipment used in the exercise should be the same equipment
used to measure children in
the survey itself. The team members will rotate but the equipment
should not, so that each child
is always measured with the same equipment (the team is being
tested not the equipment). Only
one pair of measurers should be with a child at any one time.
Talking between pairs of trainee
measurers during this exercise should not be allowed.
Upon completion of the exercise, the data is entered into the
“Training” screen of ENA for
SMART, as shown in Figure 9. On clicking , a report is
generated in Microsoft Word, giving the accuracy of each measurer
either as “good” or “poor”.
Each team member’s measurements are compared to the mean of the
whole group to assess how
accurately the measurements are made. Each team member is then
given a score of competence
in performing measures. Any misunderstandings or errors in
technique are corrected during the
training. Any team member unable to make the measurements
sufficiently well should be
replaced or given a different job in the survey that does not
require taking the primary
measurements.
39
6. DATA COLLECTION .
This section will explain the procedures for data collection for
anthropometry, mortality as well as
additional indicators, which include morbidity, infant and young
child feeding.
7.1 Anthropometry
Anthropometric measurements (measurements of body proportions, such
as weight and height) are
used to give an approximation of the nutrition status of a
population, or to monitor the growth and
development of an individual. At the individual level,
anthropometric data is used to determine
whether or not an individual is malnourished. In turn, this
information may be used to decide whether
or not the individual should be included in a supplementary feeding
programme, or treated for severe
malnutrition. The information is also used to decide when to
discharge the individual from a feeding
programme. At the population level, anthropometric data is used
either in a one-off survey to assess
what proportion of a population is malnourished, or as a
surveillance tool to follow the nutritional
situation of a population. Collectively, the anthropometric
measurements of children aged 6-59
months may be used to compare different populations, or to make
comparisons of the same population
over time. Anthropometry data is mandatory for all nutrition
surveys as it forms the basis for
determining the magnitude of malnutrition.
Nutrition indices and indicators When body measurements are
compared to a reference value they are called nutrition indices.
Three
commonly used nutrition indices are weight-for-height (WFH),
weight-for-age (WFA) and height-for-
age (HFA). The indices are shown in Figure 10. As an alternative to
weight-for-height (WFH),
wasting can also be measured by Mid Upper Arm Circumference (MUAC),
which is relatively easy to
measure and a good predictor of immediate risk of death for
children 6-59 months. It is used for rapid
screening of acute malnutrition. MUAC can be used for screening in
emergency situations but is not
typically used for evaluation purposes
Figure 10 Indices of nutritional status. Source: ENCU/PPDA
(2002).Guideline on Emergency Nutrition
Assessment
Nutrition indicators are an interpretation of nutrition indices
based on cut-off points. Whereas indices
are simply a figure, indicators represent an interpretation of the
indices. For example, WFH is an
index of nutritional status, whereas low WFH is an indicator.
Anthropometric indices or indicators are
most commonly expressed as z-scores. A z-score is a measure of how
far a child’s measurement is
from the median value of the reference distribution.
For example, weight-for-height z-score (WHZ) is based on:
a) The child’s weight
b) The median weight for children of the same height and sex in the
reference population
c) The standard deviation for the distribution of weights in the
reference population for
40
children of the same height (because the standard deviation of a
distribution increases as
children get older, you need to use the standard deviation for the
reference distribution of
children of the same height).
WHZ = actual weight – median weight
standard deviation for reference population
The median values and standard deviation are contained in the
reference population. The
recommended reference to use is the In WHO 2006 reference (Annex
1), although when data is
analyzed by ENA for SMART, results are also presented using the
NCHS 1977 child growth
standards for purposes of comparison only in the annex.
Classification of nutritional status
Chronic malnutrition, underweight and wasting
A z-score below -2 for any indicator defines moderate malnutrition,
whilst a z-score below -3 defines
severe malnutrition. For example, a WHZ below -2 is classified as
moderate wasting, whilst a WHZ
below -3 is classified as severe wasting.
Acute malnutrition
Acute malnutrition is