Guidelines for Nutrition Surveys - Bangladesh

99663_7bd_nut_survey_guidelines_ver211015.pdf 1

Guidelines

for Nutrition Surveys - Bangladesh

June 2015

Anthropometry, Mortality, IYCF, Food Security and

WASH.

2 Funded By: ECHO

Prepared by:

IPHN, INFS, and UNICEF

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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 - Action Contre La Faim (ACF) - 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

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ACRONYMS

CI Confidence Interval

CDR Crude death rate

DEFF Design effect

DR Death Rate

ENA Emergency nutrition assessment

EPI Expanded programme on immunisation

FCS Food Consumption Score

FGD Focus group discussion

GAM Global acute malnutrition

HAZ Height-for-age z-score

HFA Height-for-age

IYCF Infant and young child feeding

MAM Moderate acute malnutrition

MUAC Mid upper arm circumference

NCHS National centre for health statistics

PPS Probability proportional to population size

RNA Rapid nutrition assessment

SAM Severe acute malnutrition

SMART Standardized Monitoring and Assessment for Relief and Transitions

WASH Water Sanitation and Hygiene

WAZ Weight-for-age z-score

WFA Weight-for-age

WFH Weight-for-height

WHM Weight-for-height median

WHO World Health Organisation

WHZ Weight for Height Z score

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS ........................................................................................................................... 2

ACRONYMS .............................................................................................................................................. 3

TABLE OF CONTENTS ............................................................................................................................... 4

1. INTRODUCTION ................................................................................................................................... 9

2. BACKGROUND ................................................................................................................................... 10

3. STEPS IN CONDUCTING NUTRITION SURVEYS ................................................................................... 11

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

3.13 Analyze data and interpret results ................................................................................................ 17

3.14 Write and disseminate the report ................................................................................................... 17

4. SAMPLING ....................................................................................................................................... 18

4.1 Defining sampling ............................................................................................................................ 18

4.2 Selecting the sampling method ....................................................................................................... 18

4.3 Setting parameters for sample size calculation................................................................................ 20

4.4 Simple/systematic random sampling .............................................................................................. 24

4.5 Cluster sampling .............................................................................................................................. 26

4.6 Adjustment for small sample size .................................................................................................... 29

5. SURVEY FIELD PROCEDURES ............................................................................................................. 30

5.1 Bias .................................................................................................................................................. 30

5.2 Supervising data collection .............................................................................................................. 31

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5.3 Special circumstances ...................................................................................................................... 31

6. SELECTION AND TRAINING OF TEAMS .............................................................................................. 33

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

7.1 Anthropometry ................................................................................................................................ 39

7.2 Mortality .......................................................................................................................................... 45

7.3 Additional indicators ....................................................................................................................... 47

8. DATA ENTRY AND CLEANING ................................................................................................... 51

8.1 Data entry ........................................................................................................................................ 54

8.2 Plausibility check ............................................................................................................................. 54

9. DATA ANALYSIS AND INTERPRETATION .............................................................................. 57

9.1 Data analysis .................................................................................................................................... 57

9.2 Interpretation of results .................................................................................................................. 58

10. REPORT WRITING AND DISSEMINATION .............................................................................. 64

10.1 Preliminary Report ........................................................................................................................ 64

10.2 Final report .................................................................................................................................... 65

11. RAPID NUTRITION ASSESSMENTS .......................................................................................... 67

11.1 Decide whether to conduct a RNA ................................................................................................ 67

11.2 Define RNA objectives.................................................................................................................. 68

11.3 Define geographic area and population groups ............................................................................. 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

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11.11 Collect data and manage assessment teams ................................................................................. 71

11.12 Enter and clean data..................................................................................................................... 73

11.13 Analyze data and interpret findings ............................................................................................. 73

11.14 Write and disseminate the RNA report........................................................................................ 75

Annex 1 WHO reference table .............................................................................................................. 76

Annex 2 Local calendar of events ......................................................................................................... 77

Annex 3 Referral form........................................................................................................................... 78

Annex 4 Child health and nutrition module .......................................................................................... 79

Annex 5 Mortality module .................................................................................................................... 81

Annex 6 IYCF module .......................................................................................................................... 82

Annex 7 Food security module.............................................................................................................. 84

Annex 8 Food security module.............................................................................................................. 86

Annex 9 Survey questionnaires in Bengali ........................................................................................... 88

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

LIST OF FIGURES

Figure 1. Systematic random sampling ................................................................................................. 19

Figure 2. Cluster sampling ..................................................................................................................... 19

Figure 3. Effect of changing estimated prevalence on sample size ...................................................... 20

Figure 4. Sample size calculation (SRS).................................................................................................. 24

Figure 5. Random number generator .................................................................................................... 25

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

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Figure 11. Anthropometric equipment ................................................................................................. 41

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 17. Death rate calculation ........................................................................................................... 46

Figure 18. Death rate formula ............................................................................................................... 46

Figure 19. Conceptual framework of malnutrition................................................................................ 48

Figure 20. Variable view ....................................................................................................................... 51

Figure 21. Data view ............................................................................................................................. 52

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

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Table 5. Classification of acute malnutrition ......................................................................................... 40

Table 6. Classification of nutritional oedema ........................................................................................ 40

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

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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, e between technical soundness and

simplicity for rapid assessment of acute emergencies to obtain early, accurate, quantitative

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 - 11 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

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. The steps for conducting nutrition surveys are as follows: 12

1. Decide whether to conduct a survey

2. Define survey objectives

3. Define geographic area and population groups

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 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 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 6Ǧ59 months. To determine the nutritional status of a specific subǦgroup (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 6Ǧ59 months. To determine the levels of retrospective crude mortality rates and age- specific mortality rates for underǦ5s 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)

2. sex

3. weight in kilograms

4. height, in centimetres

5. presence or absence of oedema

6. mid-upper arm circumference (MUAC)

7. measles immunization

8. micronutrient supplementation status, particularly vitamin A

9. infant and young child feeding practices

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 -Water, sanitation and hygiene

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

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 lar 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, 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.

Box 1: Sampling method decision-making

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

Large population (eg.

District, region)

Systematic random

sampling

Simple random

sampling

Relatively small population

(eg. Camp, small urban settlement)

Updated list of

households not available

Households arranged in

clear pattern

Updated list of

households available

Figure 1 Systematic random sampling

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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 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

20

Source: SMART Methodology (2012)

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. Table 1 Precision: anthropometry Malnutrition Confidence Desired prevalence Interval precision % Range ± % 5 3 7 2.0 7.5 5 10 2.5 10 7 13 3.0 13 10 16 3.0 15 11 19 3.0 20 15 25 5.0 30
22.5 37.5 7.5
21

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

CDR

Confidence Desired

Interval precision

Range

0.5 0.2 0.8 0.30

1.0 0.6 1.4 0.40

1.5 1.0 2.0 0.50

2.0 1.25 2.75 0.75

3.0 2.0 4.0 1.00

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

Simple/systematic sampling

Sample size (n) = z² x p x q

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 d=precision (as a decimal)

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 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 - Number of children to be included = 669 children. - Number of households to be included = 702 households.

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:

n = z² x (CDR) 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
n = DEFF x t² x (CDR) 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. 4.4 Simple/systematic random sampling

Example: Location A with a population of

50,000 people and 10,000 households:

Estimated prevalence of GAM: 20%

Estimated CDR: 2/10,000/day

Desired precision- 5%

anthropometry

Desired precision 0.75

Recall period: 90 days % children under 5: 20%

Average household size: 5

% of non-response households: 10% Recall period: 90 days

The calculated sample size is 304 households and

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- (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