TRAITEMENT OF NUTRITIONAL AND MORTALITY
SURVEYS
DATA ANALYSIS WITH NUTRISURVEY
ACKNOWLEDGEMENTS
We would like to thank Yvonne Grellety and Michal Golden for revising the present work.
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TABLE OF CONTENTS
A. Description & Preparation………………………………………….4
1. Opening…….……………………………………………………….4
1.1
Nutrisurvey………………………………………………….4
1.2
An existing file……………………………………………..4
2. Useful Icons……………………………………………………..…4
3. Planning………………………………….…………………………5
3.1. Naming the Survey…..……………………………...…..5
3.2. Sampling…………………………………………………….6
3.3. Sample size calculation………………………………….6
3.4. Sample size for Mortality Rate Survey………………7
3.5. Sample size for both anthropometry
and mortality analysis…………………………………..8
3.6. Calculating Clusters……………………………………...8
3.7. Selecting Clusters ...….……………………………….…9
4. Training………………………………………………………….…11
5. Options……………………………………………………………..13
5.1. Data Entry…………………………………………………..13
5.2. Plausibility Check……………………….………….…….14
5.3. Report…………………………………………………..…..14
6. Data Entry Anthropometry……………………………………16
6.1. Variable View………………………………………………16
7. Data Entry Mortality……………………………………………18
B. Introducing Data
1. Anthropometry
1.1. Data Entry ………………….………..…….…………...19
1.2. Pasting data from Excel……………………………….20
2. Mortality…………………..……………………………………...21
C. Data Quality Check………..…………………………………….....23
D. Results & Data Analysis
1. Anthropometry…….....……..………………………………...24
2. Mortality………………………………………………………….24
3. Analysis……………………………………………………………25
3.1. Excel………………………………………………………..25
3.2. EPI Info…………………………………………………….25
E. Annexes…..………………………………………………..…..……..26
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NUTRISURVEY
This program can be downloaded free from www.smartindicators.org or
www.nutrisurvey.de/ena/ena.html . (If it is already installed in a computer, it is important to check
that is the latest update).
A. DESCRIPTION AND PREPARATION
1.
1.1.
OPENING
Nutrisurvey ena
Once installed, the first things that will appear on the screen are the names of the persons who
designed this software, as well as the e-mail and website from where ena has been downloaded.
Press click on <OK>.
1.2.
An existing file
When closing down the software, after saving your file, the way to
open it (or any other existing Nutrisurvey file) is by opening first
Nutrisurvey ena, secondly by clicking on
searching the file.as that you want to open.
and thirdly by
Or using the icon shown in chapter 2 (below), after opening
Nutrisurvey ena.
2.
4
USEFUL ICONS
New
Opens a new file in .as format
Open
Opens an existing .as file
Save
Saves the actual file.as
Save as
Saves file.as with a chosen name
Import
Imports files .rec EPI-Info 5/6 or DBase .dbf
Exit
Exits from actual module and returns to principal menu
The first page to appear will be Data Entry, but you have to go to:
3.
PLANNING
STEPS TO FOLLOW
3.1.
Naming the survey
You need to give a unique name to your survey. However, it is important to be consistent in the
naming of files and directories and to give all files names that can be recognized later by any team
member.
The name of the file should start with a three letter code for the country (e.g., SUD for Sudan, ZAM
for Zambia, ANG for Angola, etc.), then the file-name should have the date of the survey in YYMM
format (year, year, month, month). In certain circumstances the region, type of subject (refugee,
IDP, resident) or the agency involved can be usefully included in the name of all the files. Then there
is a code for the type of file: REP for report, DAT for the data file, etc.
5
Thus, a file named <LIB_0409_rep.doc> would be the report of a survey taken in August 2004 in
Liberia. There may be several simultaneous surveys conducted in Liberia around that time,
<LIB_0409_IDP_Buchanan_AAH_dat.xls> would be the data file for a survey with IDPs in Buchanan,
Liberia in September 2004 conducted by Action Against Hunger.
3.2.
Sampling
The next step is to choose the type of sample you will use: 1) random survey or 2) cluster survey.
3.3.
Sample size calculation
Calculate the sample size for the anthropometric survey. Introduce the target population size. The
population will be children under 5-years-old. If that number is unknown, an estimate of 20% or less
(in case of having a high mortality rate in the area) of total population will be used as our population
size.
3.3.1.
Estimated prevalence
Enter estimated prevalence of GAM. With a fixed sample size, the higher the malnutrition prevalence,
the lower the precision obtained. When making this assessment always decide upon a plausible range
of values, rather than a single one. In many situations, a reasonable statement would be: “Given the
situation, the malnutrition prevalence is unlikely to be above 20% or below 10%”.
In other words, if there is no certainty of this value, the higher (maximum) prevalence
expected from a range of similar values must be introduced.
Nevertheless, if you are interested in a particular prevalence (e.g. the level that would trigger an
emergency response), and you suspect the actual prevalence is below this threshold, enter the
threshold number.
3.3.2.
Desired precision
The first consideration is the minimum precision needed to meet the objectives of the survey. (For
further explanation –descriptions and tables-, please go to the SMART METHODOLOGY manual.)
The desirable precision and expected malnutrition prevalence rate are interconnected. If there is a
very high prevalence of acute malnutrition (e.g. 40%) the precision does not need to be high to
enable agencies to make appropriate decisions. At a prevalence of over 35% or so, services will be
overwhelmed and urgent and substantial intervention will be needed. A confidence interval of plus or
minus 10% (25-45%) is perfectly acceptable under these circumstances. Normally, it can be set a
5% precision or more for high prevalence, falling to about 2.5% precision for lower prevalence.
In general, the lower the prevalence the greater the precision needed.
Ex. For 5% PREVALENCE, you will need 2.5 to 3% precision
3.3.3.
Design effect
If it’s a random sampling survey, then the design effect will always be 1.
In cluster sampling, design effects can vary from 1 (if the population is homogeneous so that all the
clusters are similar to one another) to 4 or higher where some clusters are not affected and others are
severely affected.
In most nutritional emergencies, the design effect is about 1.5 increasing to 2 or more in more
heterogeneous or large-scale surveys.
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If the design effect is thought to be much greater than 2 then the population is sufficiently
heterogeneous and therefore it is better to conduct two separate surveys, each focused upon more
homogeneous sections of the population: e.g. Two cluster surveys, each with a design effect of 1.5,
can be conducted with the same effort as one survey with a design effect of 3.
The advice to anticipate a range of likely values for the prevalence and for the design effect, within
which you anticipate the results will fall is important. In Nutrisurvey ena you should enter:



The widest confidence interval that is acceptable = the minimum acceptable precision
The highest prevalence that is anticipated
The largest design effect that is likely to be encountered
(For further explanation –descriptions and tables-, please go to the SMART METHODOLOGY manual.)
Upon entering all these values in their respective boxes, the sample size will be automatically
calculated and will appear in the turquoise box.
3.3.4. Increase sample size
By 5% or 10% to allow unforeseen contingencies.
EXAMPLE 1:
Let’s look at the following example:
Our sample size is 754. And if we increase this number 5%, according to the last step, then our
sample size will be 792.
3.3.5. Divide the sample size by the average number of U5 children to have a household
sample size.
e.g. if the average U5 children is 1.5, divide 792 by 1.5 = 528
3.4.
Sample size for mortality rate survey
For the death rate component of the survey (you may need information from governmental and/or
non-governmental organizations (NGOs) working in health):
1. Enter an estimate of the total population that is targeted by the survey.
2. Enter the expected mortality rate (x.xx/10,000 persons/day)
3. Enter the required precision (x.xx/10,000 persons/day). For example, if your expected mortality
rate is 2.0/10,000 persons/per day and you want a confidence interval of 1.4 - 2.6, enter a
required precision of 0.6 (that is 2.0 -/+ 0.6 which gives 1.4 - 2.6). The precision chosen has a
substantial effect upon the sample size needed.
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4. Enter the design effect. The default design effect for sample size calculations for mortality is 2.0.
If violence-related-mortality is limited, a design effect of 1.5 for crude death rate may also be
sufficient. You can also use the last survey raw data and check the design effect in ena: it
calculates the design effect.
5. Enter the chosen recall period in days. In most situations, 90 days (or from 30 to 120 days) will
be used. However, the decision should be made individually for each emergency context
according to the date of the last event that occurs in this area.
EXAMPLE 2
3.4.1.
Divide the sample size by the average number of persons per household to have
a household sample size.
e.g. 1646 divided by the average persons per HH e.g. 4 = 416
3.5.
Sample size for both anthropometry and mortality analysis
3.5.1.
For anthropometry analysis, e.g. 528
3.5.2.
For mortality analysis, e.g. 416
The final sample size per household that will be retained will be 528
3.6.
3.6.1.
Calculating Clusters
For Anthropometric Survey
In cluster surveys, sample size should be divided by the number of households that can
be visited each day. This will provide the number of clusters.
To continue with EXAMPLE 1, let’s say that each of our teams can visit 14 households (HH) per day.
(For further details on how to calculate this number, please go to SMART Methodology Manual).
Dividing 528/14 = 37.7 Whenever the result has a decimal, it is advised to round up to the next
whole number. Then we will have 38 clusters for our survey.
In pastoral/nomadic zones we can have difficulties finding children. If this is the case,
it is advised to decrease the number of children/cluster and increase the number of
clusters.
8
Statistically, the minimum amount of clusters that each of the surveys (anthropometric
& mortality) can have for them to be valid is 26. However, in ACF we STRONGLY
RECOMMEND having 30 clusters, minimum.
3.7.
Selecting clusters
When designing a combined survey (with both components: nutrition & mortality), the sample size to
estimate malnutrition prevalence, as well as the number of households needed to estimate mortality
rate are calculated, and then:
The greater of these numbers is chosen for the survey.
If you are attempting to undertake a survey in an area of nomads where the population
frequently moves large distances, it is likely that you may travel to an area to find that
there is no-one there and no-one nearby.
If you suspect that this might happen, you should select some extra clusters
before you start the survey. This way, if one cluster is deserted you can replace it with
another one.
3.7.1.
Table for Cluster Sampling
1. Enter number of clusters
To randomize the clusters obtain the best available census data for each village, district,
or section on the map using the smallest existing unit:
It is important to define your geographical area very realistically in the planning
stage, taking into account: travel, security, and any other factor that could influence your
ability to get to the cluster site before listing the sites in the planning table.
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2. Define what constituents a “village”: it should be the smallest unit with population figure. Then,
under Geographical unit column enter the names of all the towns, cities, districts or other
areas that will potentially be chosen to include in a cluster. All potential areas have to be
entered. It does not matter what order the areas are entered. But if any village is omitted at
this step, it then will not be part of the surveyed population.
The smallest available geographical unit is always chosen, as long as population data are
available and the geographical unit has a name. If the number of children is less than 30,
add another area with that one. Each area should have a local name, so that the
inhabitants are familiar with the boundaries of the area when the local name for the area
is used.
3. Under Population size column enter the estimated population size for each “village”.
4. With the Number of Clusters and their names entered, click on <Assign Cluster>
The computer will then select the areas where there will be clusters. This should be done
only ONCE. It will potentially introduce a bias if they are reselected.
3.7.2. Random Number Table
If you want to do a random survey, then you need to generate a random table based in the box
<Random Number Table>. For that you need the following data: for Range from and to the total
range of children of the total population, i.e. 1-1,350 and enter for Numbers the sample size to use.
Then, click on <Generate Table>.
The children to be interviewed will be shown in a Word sheet.
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4. TRAINING
This page is used to standardize our teams in measuring weight and height (or length). The aim of
the standardization test consists on improving the quality of the measurements. All the members of
the teams should measure twice 10 or more children with a time interval between individual
measures. (Check SMART METHODOLOGY to observe the appropriate procedure for standardization.)
The outcome of this exercise will be analyzed by Nutrisurvey Ena: you only need to enter the
measures and then click on <Report>. Precision and accuracy are assessed by 1) calculating the
variation between their repeated measurements (repeatability of measurements); 2) calculating the
variation between each team member’s measurements and the ones of the supervisor. Each team
member is then given a score of competence in performing measures (OK or POOR). If the results
are OK it means the enumerator is standardized, if the results are POOR the enumerator should
repeat the exercise completely, perhaps with different people paired in teams.
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Report for Evaluation of Enumerators
Weight:
Precision:
Sum of Square
[W2-W1]
Supervisor
Enumerator 1
7,29
6,76 OK
Accuracy:
Sum of Square
[Superv.(W1+W2)Enum.(W1+W2]
1,69 OK
Height:
Precision:
Sum of Square
[W2-W1]
Supervisor
Enumerator 1
4,84
10,89 POOR
Accuracy:
Sum of Square
[Superv.(W1+W2)Enum.(W1+W2]
0,25 OK
For evaluating the enumerators the precision and the accuracy of their measurements is calculated.
For precision, the sum of the square of the differences for the double measurements is calculated. This value
should be less than two times the value of the supervisor.
For the accuracy, the sum of the square of the differences between the enumerator values (weight1+weight2) and
the supervisor values (weight1+weight2) is calculated. This value should be less than three times the accuracy
value of the supervisor.
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5.
OPTIONS
This is the last of the screens of Nutrisurvey, but is useful to fill out before entering the data. The
software has an automatic way for entering data in Data Entry. We could agree and save these
options or simply modify them based on our needs. If we save them, we should always remember
that we are establishing these options for future surveys. However, they can be modified any time a
new survey is planned.
5.1.
Data Entry:
5.1.1. Automatic filling out:
The program selects all these options; it is advised, nevertheless, that the number
of household be introduced manually, because the number of children in one
household could be greater than one.
We recommend not filling out Household No. automatically.
5.1.2. Entering of age mainly:
Either method can be selected depending on the document you find at household
level: e.g. in some countries like Tanzania, every child has a birthday date…in
13
other countries, which is mostly the case, you have no birthdate and you
have then to estimate the age in months; in this case, you should use
“with months”.
5.1.3. Entering of data:
You can select introducing data directly or “with Pull Down Editors” for
some of the variables (sex, birthdate, edema). See the example below.
We recommend using ● directly as 1.1.99, 10199 or 010199 option.
5.1.4.
In this page we can also select if we want the program to
calculate anthropometric indices after pasting data from another
file.
5.1.5. Weight of clothes
Here we can introduce average weight in grams of
clothing of survey subjects to be automatically subtracted
from the weight introduced in Data Entry page.
5.1.6. Correction for Edema:
We can select automatic correction of weight for edema found:
“n/y” = average weight of body weight edema
“0,1,2,3 = mild, moderate or severe edema
We don’t recommend using it, unless strictly necessary.
5.2. Plausibility Check:
Enter the z-scores and the age range (in months) for the plausibility
report in the box.
The plausibility check report, after entering all your data, will give
you the questionable ID no.(s) out of range that you have selected,
e.g. >3Z-score and <-3Z-score from the mean of the WH of the
sample size.
In the Data Entry sheet, the software will highlight gross errors similar to flag in Epi Info in pink.
but they are not the same as the ones in the plausibility check in this version.
5.3. Report:
If we want our report to be grouped by ages (in months).
We can change the age groups by only changing the first number in each
range, ena is automatically changing the other one from the range above.
14
Once finished introducing desired parameters, click on
right hand side of the page.
which is located at the bottom
15
6. DATA ENTRY ANTHROPOMETRY
Fill out this page with the data gathered in the field questionnaire.
Take in account that the field questionnaire follows the program’s
same order to facilitate data entry.
Nutrisurvey ena has a form that we can use and can be obtained clicking on:
Form for anthropometric survey
Other uses of this module are:
Form for mortality
Paste Data
Copy Data
Plausibility Check
Check of double entry
Report in Word
6.1.
Form to gather mortality information
Pastes data from clipboard
Copies data from current survey to Excel format
Verifies presence of errors
Corroborates presence of errors when two persons are
entering the same data
Reproduces report in Word format
Variable View:
This is one of the two sections of page Data Entry. In Variable View we should save the file using the
same instructions we used in Planning. (We can find these in the 1st. step.) In <Variable View>
section and before introducing data we should establish ranks to be used in the survey.
16
Some variables in ena are automatically ranked; however, you can change some of them, like for
example height ranks if the targeted population has growth retardation. These changes should be
included in the report.
Ena will then check all those data which are out of range and highlight them in pink in <Data View>
section.
In this latter section, data cleaning also can be made by using “Plausibility check” clicking on Check
Table every time you enter some data; this facilitates to underline errors.
The entered data have to be checked using the original written data collection sheets. Any error in
data entry should be corrected immediately.
If a child is excluded from the survey, his information will disappear from
the page and we should click again on Check Table.
Column/Variable:




Add: place mouse in <Data View> section and over line No. 1 of the column
where we want a variable to appear and click on Add. A little box will appear,
where the name of the extra column should be entered (ex. Measles), then
click on Enter or OK.
Delete: place mouse in <Data View> section on any spot below the column
title we want to eliminate and click on Delete; a box will appear asking if we
want to erase this column. Click on Yes or Enter.
Sort: place mouse in <Data View> section on any spot below the column
title that we want to use to classify our database. Click on it. A box will
appear telling that our database will be classified by: column name. Click on
Yes or Enter.
Filter: click on Filter. A box will appear asking us to define which variable
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we need for selecting cases from database. Select the variable, introduce the
desired ranges, and then click OK.
Row/Data:
For adding a child to the survey we click on New. A new line will appear at
the end of the listing.
For adding data and to insert a blank line in a specific place of your list, place
the mouse and click over the line where you want the new one to appear and
then click on Insert.
To erase data of a child, place mouse on the line you want to eliminate and
click on Delete.
We can choose the references for evaluating our data, NCHS or WHO. Results will
automatically be shown in <Data View> and in Results Anthropometry page.
At clicking on Disclaimer you’ll see the following:
It will disappear with a second click.
Go To:
If you click on this icon, you automatically go to subject ID 1.
If you click on this icon, you automatically go to the last subject.
If you click on this icon, a box will ask you the ID number you are looking for; enter the
number and click on OK, then the software will automatically go to this ID number.
7. Data Entry Mortality
To enter the data in this sheet, you can use the Word sheet of the software as a questionnaire. This
form is located under the command Extras at the left top of the screen; click on Form for mortality
rate survey. There is one called the SMART Standard form and the other one called Simple form (one
sheet/cluster).
Don’t use the simple form at the moment but the standard form as a questionnaire.
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Icons on this page:
Save Survey
Add line
Erase line
Paste data from clipboard
Transfer data to Excel format
B. INTRODUCING DATA
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1.
Anthropometry
1.1. Data Entry in Data View Section
On the first line introduce survey date, cluster number (if used), and team number.
The population that has been chosen for a cluster has already been
introduced with a number in our database during Planning stage, so we
should use that same number of cluster in this panel.
The identification number of each child WILL BE ENTERED automatically one by one for each
new introduced case.
The household number should NOT BE ENTERED automatically, because you can have more
than one child in the same household. The ID Household number should be the same as the one in
mortality entry data.
Enter age in months in corresponding square.
Anthropometrical variables are calculated automatically. Any major error or “flag” will appear in pink
but they are not the same as the ones selected in the plausibility check.
1.2. Pasting data from Excel to Nutrisurvey ena
If you have survey data in excel format, Nutrisurvey can analyze it by pasting it in Data
Entry sheet. To do so, follow the steps:
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a.
On your excel sheet select only those data of the variables that you included in your <Data
View> section, then click on Copy icon.
b. Go to Nutrisurvey ena <Data View> section and place your mouse and click over line 1,
under SURVDATE column, and then click on Paste icon.
2.
Mortality
21
This screen has the instructions written down at the top of it. It is important to take in account the
following:
 In box of “HH members” “total” enter the total number of members in the HH and “<5” the
total number of children <5 years of age in this HH.
 In join HH exclude births during Recall Period.
 In leave HH exclude deaths during this same period.
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C.
DATA QUALITY CHECK
This step is useful for finding and interpreting errors.
In Data Entry page we find the box:
In Plausibility Print Report, ena lists values out of range from the ones that you put in Options (e.g.
WH<-3 & WH>3Z-score). It points errors in your data, first for your total sample and then by teams.
(See Annex 1 with an example of the way Nutrisurvey reports errors found with their
interpretation).
Remember that in order to find errors as soon as possible, we mentioned before to enter the ranges
of the variables needed in your survey in both <Variable View> of Data Entry and in Options pages.
We can also check mistakes by making two persons entering the same data. Using the Extras icon,
and if you click on:
Check of double entry, the software will
check data quality corroborating data introduced by
two persons.
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D.
1.
RESULTS & DATA ANALYSIS
Anthropometry
When all the data are entered, we can move on to Results Anthropometry sheet. Here you will
find your results based on the references you chose earlier in Data Entry Anthropometry sheet:
(NCHS or WHO) and also you are able to obtain the graphs according to the chosen indicators, and
then by sex, age or cluster; you can also obtain a partial report in Word and Excel formats.
2.
Mortality
Once introduced all mortality data in this screen, the software will automatically calculate and show
results in Results Mortality sheet. Here, it is the Crude Death Rate which is calculated as well as its
components [a], [b], [c], [d], [e], and [f].
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3.
Analysis
Nutrisurvey does not analyze MUAC or any additional variables, so we can choose between
Excel or EPI Info Analysis.
3.1.
Excel Analysis
You can enter your data directly on excel or transfer your data from Ena to Excel format by clicking on
Data Entry
sheets for both Anthropometry and Mortality.
Or using the box displayed by clicking on Extras, and then clicking again on Copy Data to Excel.
Excel will show in the last columns all the data in percentage of the medians.
At the same time that you enter the data, you should transfer them to
Excel to save your data.
To sort your data by variables in Excel, place the mouse over line 1 (the one with the variables
names) and select the whole line with a left click. Then click on Datos, place Mouse over Filtros, click
and then re-click on Autofiltro.
At this moment we
can use filters, also, to
select for e.g. MUAC
or which children are
eligible
to
get
admitted to a center.
3.2.
EPI Info Analysis
Unlike Excel, this type of analysis can calculate confidence interval easily. You can find it
under File icon.
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ANNEX 1
Plausibility check:
Anthropometric Indices out of usual range (mean -3,0, mean +3,0):
Line 104:
Line 193:
Line 282:
Line 363:
Line 497:
Line 647:
Line 703:
Line 754:
WHZ (4,187), probably height is incorrect
WHZ (3,562), probably weight is incorrect
HAZ (3,413), probably age is incorrect
WHZ (-3,318), probably weight is incorrect
HAZ (3,674), WHZ (-3,272), probably height is incorrect
HAZ (-5,486), probably age is incorrect
HAZ (4,397), WHZ (-4,491), probably height is incorrect
WHZ (-3,267), probably height is incorrect
Age distribution:
Month 6 : ######
Month 7 : ##############
Month 8 : ##########
Month 9 : #############
Month 10 : #####################
Month 11 : #################
Month 12 : ################
Month 13 : ##########
Month 14 : #############
Month 15 : ######################
Month 16 : ###############
Month 17 : #################
Month 18 : #####################
Month 19 : ################
Month 20 : ###############
Month 21 : ##################
Month 22 : #######################
Month 23 : #####################
Month 24 : ################
Month 25 : ######################
Month 26 : ##################
Month 27 : ######
Month 28 : #########
Month 29 : ############
Month 30 : ###########
Month 31 : ########
Month 32 : ########
Month 33 : ########
Month 34 : ###########
Month 35 : ############
Month 36 : #########
Month 37 : ###########
Month 38 : ################
Month 39 : ###############
Month 40 : #########
Month 41 : #############
Month 42 : ################
Month 43 : ##############
26
Month 44 : ################
Month 45 : ######
Month 46 : ############
Here, the software indicates
Month 47 : #######
that a lot of cases were
Month 48 : #############
59months old, each case
Month 49 : ##############
being noted by a “#”
Month 50 : #########
according to its age in month.
Month 51 : #######
In this example, age 59
Month 52 : ##############
months has the majority of
Month 53 : ###########
cases. It would be appropriate
Month 54 : #########
Month 55 : ###########
to verify if that is the case.
Month 56 : ############
Month 57 : #########
Month 58 : ######
Month 59 : #############################################################
Digit preference Weight:
Digit .0
Digit .1
Digit .2
Digit .3
Digit .4
Digit .5
Digit .6
Digit .7
Digit .8
Digit .9
Here, Ena software tells you if the teams measure
repeatedly the weight and rounded or not to .0 or .5 .
In this example, it is obvious that the teams rounded
often to .0 and .5. There is a digit preference, so you
will have to go back and review where the mistake
occurs.
: #############################
: #####
: ##
: ########
:#
: ##########################################################
: ######
: ####
:############
: ########
Digit preference Height:
Digit .0
Digit .1
Digit .2
Digit .3
Digit .4
Digit .5
Digit .6
Digit .7
Digit .8
Digit .9
: ##############################################################
: ##########
: #############
Here, the Ena software tells you if the
: #####
teams rounded or not to a certain digit
: #########
preference decimal when they took the
: ####################################
height/length. It is obvious that they
: #############
: #####
rounded to .0 and 0.5, so you will have
: #######
to go back and review where the mistake
:#
occurs.
27
Standard deviation of WHZ:
Standard Deviation SD: 0,929 (The SD should be between 0.8 and 1.2)
0,929 is the SD of our survey, in this case is between 0,8 and 1,2. It is ok
Prevalence (< -2) counted: 2,5%
2,5 % is the % of children wasted in the survey
Prevalence (< -2) calculated with current SD: 2,9%
2,9% is the prevalence of wasting with SD of 0,929
Prevalence (< -2) calculated with a SD of 1: 3,9%
3,9% is the prevalence of wasting with a SD of 1
Standard deviation of HAZ:
Standard Deviation SD: 1,206 (The SD should be between 1.10 and 1.30)
Prevalence (< -2) counted: 64,6%
Prevalence (< -2) calculated with current SD: 61,0%
Prevalence (< -2) calculated with a SD of 1: 63,1%
Skewness and Kurtosis of WHZ:
Skewness of WHZ: 0.078 => probably not skewed (value < 2*(6/n)½)
(Skewness characterizes the degree of asymmetry around the mean, positive skewness indicates a long right tail,
negative skewness a long left tail)
Kurtosis of WHZ: -1.478 => probably kurtosis problem (value > 2*(24/n)½)
(Kurtosis characterizes the relative peakedness or flatness compared with the normal distribution, positive
kurtosis indicates a relatively peaked distribution, negative kurtosis indicates a relatively flat distribution)
See the graphic below which illustrates when we have a not skewed (green graphic), but kurtosis (red
graphic) of WHZ.
28
Poisson distribution of clusters for WHZ:
number of clusters
1 children/cluster with WHZ < -2:
2 children/cluster with WHZ < -2:
3 children/cluster with WHZ < -2:
4 children/cluster with WHZ < -2:
5 children/cluster with WHZ < -2:
6 children/cluster with WHZ < -2:
7 children/cluster with WHZ < -2:
8 children/cluster with WHZ < -2:
9 children/cluster with WHZ < -2:
10 children/cluster with WHZ < -2:
11 children/cluster with WHZ < -2:
12 children/cluster with WHZ < -2:
13 children/cluster with WHZ < -2:
14 children/cluster with WHZ < -2:
15 children/cluster with WHZ < -2:
16 children/cluster with WHZ < -2:
17 children/cluster with WHZ < -2:
18 children/cluster with WHZ < -2:
19 children/cluster with WHZ < -2:
20 children/cluster with WHZ < -2:
21 children/cluster with WHZ < -2:
22 children/cluster with WHZ < -2:
23 children/cluster with WHZ < -2:
24 children/cluster with WHZ < -2:
Detailed Team Evaluation
###
##
#
The software indicates how many
wasted children you have in your
clusters using the #.
In this example, it tells you that:
3 clusters each have 10 wasted
children.
2 clusters have each 12 wasted
children,
1 cluster has 13 wasted children, etc.,
etc.
###
####
##
#
#
Team
1
2
3
4
Digit preference Weight (%):
.0 :
6
4
10
9
.1 :
8
14
11
8
.2 :
9
10
9
17
.3 :
9
8
15
8
.4 :
13
10
11
13
.5 :
13
11
6
10
.6 :
8
14
7
9
.7 :
7
8
8
7
.8 :
14
8
9
8
.9 :
13
14
12
11
Digit preference Height (%):
.0 :
10
5
25
13
.1 :
10
10
12
15
.2 :
14
11
14
13
.3 :
13
6
11
17
.4 :
8
11
8
9
.5 :
11
12
3
11
.6 :
9
12
8
8
.7 :
8
11
5
3
.8 :
9
6
5
7
.9 :
7
15
8
4
Global malnutrition (WHZ < -2):
SD
0,81
0,79
0,82
0,87
Prevalence (< -2) counted:
%
7,1
9,1
7,7
11,6
Prevalence (< -2) calculated with current SD:
%
8,3
8,4
11,1
13,6
Prevalence (< -2) calculated with a SD of 1:
%
13,2
13,7
15,7
17,1
The software also gives detailed
information per team.
Here, it tells you digit preference
per team for weight.
…it also does the same with height.
SD found by each team for Global Malnutrition
% of children with Global Malnutrition *
Prevalence of wasting with current SD
Prevalence of wasting with SD of 1
29
Stunting (HAZ < -2):
SD
1,60
1,24
1,30
1,55
Prevalence (< -2) counted:
%
18,1
15,6
9,8
16,7
Prevalence (< -2) calculated with current SD:
%
17,9
15,5
14,6
16,4
Prevalence (< -2) calculated with a SD of 1:
%
7,0
10,4
8,4
6,4
SD found by each team for Stunting
% of children with Stunting *
Prevalence of stunting with current SD
Prevalence of stunting with SD of 1
* This is the data we should consider as valid.
Poisson distribution of clusters for WHZ:
1
2
3
4
number of clusters
children/cluster with WHZ < -2: ###############
children/cluster with WHZ < -2: #############
children/cluster with WHZ < -2: ###
children/cluster with WHZ < -2: #
30
(Same as above Poisson
distribution of clusters WHZ)