Review of Survey Methodology
Bradley A. Woodruff, MD MPH
International Emergency and Refugee Health Branch,
U.S. Centers for Disease Control and Prevention
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Review of Survey Methodology
Review some basic principles in surveys and sampling
Describe some issues which • Often done incorrectly or inefficiently• Remain without strong consensus
Opinions expressed are only my own• Given to provide basis of discussion• Recommendations are strictly personal and not
meant to be adopted by this meeting
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Review of Survey Methodology
1. When to use surveys
2. Sampling methods
3. Final sampling stage (in multistage sampling)
4. Sample size
5. Number of clusters
6. Select 1 or all eligible persons?
7. Judging age eligibility
8. Children <6 months of age
9. Measuring mortality
10. High mortality and low malnutrition
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When to use surveys
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When to use surveys
A cross-sectional survey is a collection of data from a specific
population at a single point in time.
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When to use surveys
Survey Surveillance
Collects data at single point in time
Collects data over time period
Can gather wide variety of data Gathers limited data
Usually collects data on sample of population
Often tries to collect data on every case of illness
Collects data allowing calculation of prevalence or incidence rates
Collects only data for numerator; must get data for denominator from separate source
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When to use surveys
Alternate data collection methods Morbidity and mortality surveillance Birth and death registration Qualitative methods Program data Others
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When to use surveys
What a single survey can tell you The point prevalence of health or nutrition
outcome Retrospective measure of cumulative
incidence, such as mortality rates Can assess outcomes when other data
collection systems absent or impossible
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When to use surveys
What a single survey cannot tell you Cause-effect relationships Why events occur or why things are the way
they are Trends over time
• However, repeated surveys can measure change
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When to use surveys
Purposes of a survey Determine need for new program Design new program Evaluate existing program
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When to use surveys
Strengths of surveys Can give reasonably accurate estimate of prevalence of health
condition in population Can be replicated to evaluate health outcomes Can be done when other data collection systems (e.g. surveillance)
not feasible
Weaknesses of surveys Difficult to assess cause/effect Often confined to short, simple questions Difficult to answer “why” questions Must be repeated to follow trends over time Sometimes difficult to define population of interest Sampling can be complex and is often not done well
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When to use surveys
Surveys cost money, manpower, and time Collect only data useful for program purposes If primary objective is measuring malnutrition
prevalence or mortality rate• Do not add extraneous data collection• However, large part of resources spent in
traveling to selected households
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When to use surveys – and where?
National-level data • Requires only one survey for country• Provides overall picture• Can serve as general assessment of nutritional status
NGOs often work on local level• Local data needed for program decisions• Difficult to formulate overall picture from many local
surveys• Lack of standardization inhibits comparison• Difficult to achieve good coverage
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When to use surveys - recommendations
Before beginning preparations• Carefully consider if survey necessary before
beginning• Identify specific uses of survey results• Design survey to answer specific questions
Rigidly follow correct sampling procedures
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Sampling methods
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Sampling methods
The goal of sampling is to estimate some measure in the larger population.
Probability sampling: A selection of elements in a population, such that every element has a known, non-zero probability of being selected.*
* Last, Dictionary of Epidemiology
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Sampling methods - definitions
Sampling universe Sampling frame Sampling unit Basic sampling unit or elementary unit Sampling fraction Respondent Survey subject Unit of analysis
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Sampling methods – simple and systematic sampling
Simple random sampling• Most basic type of sampling• Statistical theory based on SRS
• Calculate p values and confidence limits• Output from most statistics computer programs assume
SRS• Selection of units is random and independent
Systematic random sampling• Similar to simple random sampling• First sampling unit chosen randomly• Systematic selection of subsequent units• Statistics same as simple random sampling
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Sampling methods – simple and systematic sampling
Both simple and systematic random sampling require a complete list of all sampling units in sampling universe OR that sampling units be organized to allow systematic selection
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Sampling methods – cluster sampling
Cluster sampling is probability sampling in which sampling units at some point in the selection process are collections, or clusters, of population elements*
* Kalsbeek, Introduction to survey sampling
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Sampling methods – cluster sampling
Is almost always multistage Randomly choose geographic areas as primary
sampling units Selection is probability proportional to size Final stage of sampling – choose basic
sampling unite (For example, households or children)
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Sampling methods – cluster sampling
Advantages
Cheaper - basic sampling units closer together Does not need complete list of basic sampling units
Disadvantages
Decreased precision of estimate Calculation of p values and confidence limits more
complicated
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Sampling methods – cluster sampling
D is tr ic t A D is tr ic t B
S u b d is tric t 1 S u b d is tric t 2 S u b d is tric t 3
H H 1 H H 2 H H 3 H H 4 H H 5
S u b d is tric t 4 S u b d is tric t 5
D is tr ic t C D is tr ic t D D is tr ic t E D is tr ic t F
Country
Stage 1
Stage 2
Stage 3
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Sampling methods – cluster sampling
Sampling probability proportional to size
A B C DDistrict: E F
NotPPS
231 912 3,099 376 484 763
231 912 3,099 376 484 763PPS
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Sampling methods – cluster sampling
Selecting sample of districts not affected by severe weather
Total Total children
No Province District Population <5 years of age
1 Bayan-Ulgii Tsagaannuur 1,853 2312 Bayan-Ulgii Nogoonnuur 7,094 9123 Bayan-Ulgii Ulgii 25,763 3,0994 Bayan-Ulgii Altantsogts 3,168 3765 Bayan-Ulgii Bugat 3,541 4846 Bayan-Ulgii Bayannuur 5,140 7637 Bayan-Ulgii Tolbo 4,773 6728 Bayan-Ulgii Deluun 8,347 1,2519 Bayan-Ulgii Bulgan 5,916 816
10 Uvs Bukhmoron 2,435 32611 Uvs Davst 1,918 24812 Uvs Ulaangom 24,888 2,45513 Uvs Khovd 2,804 38914 Uvs Umnogobi 4,723 51215 Uvs Ulgii 2,861 43816 Khovd Erdeneburen 3,414 45717 Khovd Khovd 4,834 46418 Khovd Myangad 4,015 45519 Khovd Buyant 3,362 42520 Khovd Jargalant 26,418 2,83721 Khovd Dorgon 3,077 41222 Khovd Chandmana 3,417 54523 Khovd Darvi 3,061 356
etc.
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Sampling methods – cluster samplingSelecting sample of districts not affected by severe weather
Total Total children Culmulative number
No Province District Population <5 years of age children < 5 years
1 Bayan-Ulgii Tsagaannuur 1,853 231 2312 Bayan-Ulgii Nogoonnuur 7,094 912 1,1433 Bayan-Ulgii Ulgii 25,763 3,099 4,2424 Bayan-Ulgii Altantsogts 3,168 376 4,6185 Bayan-Ulgii Bugat 3,541 484 5,1026 Bayan-Ulgii Bayannuur 5,140 763 5,8657 Bayan-Ulgii Tolbo 4,773 672 6,5378 Bayan-Ulgii Deluun 8,347 1,251 7,7889 Bayan-Ulgii Bulgan 5,916 816 8,604
10 Uvs Bukhmoron 2,435 326 8,93011 Uvs Davst 1,918 248 9,17812 Uvs Ulaangom 24,888 2,455 11,63313 Uvs Khovd 2,804 389 12,02214 Uvs Umnogobi 4,723 512 12,53415 Uvs Ulgii 2,861 438 12,97216 Khovd Erdeneburen 3,414 457 13,42917 Khovd Khovd 4,834 464 13,89318 Khovd Myangad 4,015 455 14,34819 Khovd Buyant 3,362 425 14,77320 Khovd Jargalant 26,418 2,837 17,61021 Khovd Dorgon 3,077 412 18,02222 Khovd Chandmana 3,417 545 18,56723 Khovd Darvi 3,061 356 18,923
etc. etc.184 Dornod Matad 2335 267 129,177
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Cluster sampling – probability of selection
Number of HHs in district
———————
Number of HHsin country
Number of HHs in subdistrict
———————
Number of HHs in district
15——————
Number of HHs in subdistrict
30 x 15——————
Number of HHs in country
x x =
30 x
30 clusters of 15 households each
Probability of
selecting district
Probability of
selecting subdistrict
within selected district
Probability of
selecting HH within
selected subdistrict
Total probability
of selecting any
HH in country
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Sampling methods – recommendations
Choose sampling method appropriate to availability and organization of data on population
Use simple or systematic random sampling when possible if logistic savings from cluster sampling not important• Most refugee camps are small enough to so that cluster
sampling offers little logistic advantage For cluster sampling, always sample probability
proportional to size in all but the last sampling stage
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Final sampling stage
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Final sampling stage – EPI method
EPI (bottle spinning) method of selecting households
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Final sampling stage – EPI method
Simple Widely known Easy to train Results in biased sample
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Final sampling stage – selecting households
1
2
34
5
678
109
EPI (bottle spinning) method of selecting households
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Final sampling stage – selecting households
EPI (bottle spinning) method of selecting households
12
3
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Final sampling stage – selecting households
1
2
34
5
678
109
EPI (bottle spinning) method of selecting households
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Final sampling stage – alternate methods
Less simple Not as widely known Requires somewhat more training Results in less-biased sample
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Final sampling stage – selecting households
Map and dart throw method of selecting households
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Final sampling stage – selecting households
1
6
5
4
32
Segmentation method of selecting households
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Final sampling stage – selecting households
1
2
3
26
14
13
12
10
9
8
7
5
4
29
21
28
27
25
24
22
2016
15
18
19
11
17
30
6
23
Simple or systematic random selection of households
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Final sampling stage - recommendations
Do not use EPI method Continue sampling stages until reach small
enough area to do alternate method Map and segment or list, then do simple or
systematic random sampling
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Sample size
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Sample size
To estimate sample size for single survey using simple or systematic random sampling, need to know:
1. Estimate of the prevalence of the outcome
2. Precision desired
3. Design effect
4. Size of total population
5. Level of confidence (always use 95%)
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Sample size
Effect of Changing the Estimated Prevalence(assume 95% CI, +/- .05, large population)
0
100
200
300
400
500
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Estimated prevalence
Req
uire
d sa
mpl
e si
ze
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Sample sizeEffect of Changing the Desired Precision
(assume 95% CI, +/- .05, large population)
0
500
1000
1500
2000
2500
3000
0.00 0.05 0.10 0.15 0.20
Width of Confidence Interval
Req
uire
d sa
mpl
e si
ze
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Sample sizeEffect of Changing the Population Size
(assume 95% CI, prevalence=.50, +/- .05)
0
100
200
300
400
500
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000
Population size
Req
uire
d sa
mpl
e si
ze
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Sample size
As long as the target population is more than a few thousand people, you do not need to consider it in the sample size.
You do NOT generally need a larger sample size if the population is bigger.
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Sample size
Where do get information to make assumption about prevalence?
Prior surveys Qualitative estimates Wild guesses Err toward a prevalence of 50%
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Sample size
Effect of Changing the Estimated Prevalence(assume 95% CI, +/- .05, large population)
0
100
200
300
400
500
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Estimated prevalence
Req
uire
d sa
mpl
e si
ze
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Sampling – Sample size
To estimate prevalence with 95% confidence limit and simple or systematic random sampling:
N = 1.962 x (P)(1-P)
d2
1.96 = Z value for p = 0.05 or 95% confidence limitsP = Estimated prevalenced = Desired precision (for example, 0.05 for ± 5%)
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Cluster sampling
To estimate prevalence with 95% confidence limit and cluster sampling:
N = DEFF x 1.962 x (P)(1-P) d2
DEFF = Design effect1.96 = Z value for p = 0.05 or 95% confidence limitsP = Estimated prevalenced = Desired precision (for example, 0.05 for ± 5%)
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Sample size
Examples of design effect for wasting: Mongolia 1.3 Badghis Province, Afghanistan 1.6 Sar-i-Pul Camp, Afghanistan 1.4 Mazar-i-Sharif, Afghanistan 2.0
Source: Mongolia MOH/CDC; UNICEF/CDC; MSF-B; ACF
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Sample size - 30 x 30 cluster design
Assumptions:• Prevalence = 50%
• Precision = +/- 5 percentage points
• Design effect = 2
• Non-response = 15%
Why assumptions not valid:• Prevalence rarely so high
• Often do not need such precision
• Design effect often higher or lower than 2
• Non-response rarely so high
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Sample size
Larger sample size increases precision (by decreasing sampling error)• It does NOT guarantee absence of bias• Bias may result in very incorrect estimate• If little sampling error, may have confidence in this
wrong estimate• Often difficult to detect bias; cannot quantify bias
Quality control is more difficult the larger the sample size
Therefore, you may be better off with smaller sample size, less precision, but much less bias.
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Calculate sample size for every survey• Tells you what precision you may have when finished• Assists in planning logistics• Use this sample size to maximize efficient use of
resources Estimate prevalence somewhat closer to 50% than you
expect Overestimate design effect if using cluster sampling Include population size if more than a few thousand and
requested by software• Can result in smaller sample size• If not sure, enter large number (e.g.100,000)• If calculating by hand, can safely ignore population size
Sample size - recommendations
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Number of clusters
Number of clusters
Trade-off between statistical precision and logistic requirements
Usually use 30• Design effect increases rapidly with < 30• Design effect not decrease rapidly with > 30
30 clusters not permit subgroup analysis
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Number of clusters
From Binkin N. Rapid nutrition surveys: how many clusters are enough? Disasters 16(2): 97-103.
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Number of clusters
From Binkin N. Rapid nutrition surveys: how many clusters are enough? Disasters 16(2): 97-103.
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Number of clusters - recommendations
Use at least 30 clusters Sample size calculations for prevalence will
allow only calculation of estimates for entire sample
Add clusters if subgroup analysis is important Add clusters in some cases if wish to
substantially increase precision
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Select 1 or all eligible persons?
Select 1 or all eligible persons? – selecting 1
Advantages May decrease design effect because selected children
come from more households
Disadvantages Must visit more households to recruit subjects Requires additional sampling step Produces biased sample Requires weighted analysis
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Select 1 or all eligible persons? – selecting 1
D is tr ic t A D is tr ic t B
S u b d is tric t 1 S u b d is tric t 2 S u b d is tric t 3
C h ild 1 C h ild 2 C h ild 3
H H 1 H H 2
C h ild
H H 3 H H 4
C h ild 1 C h ild 2
H H 5
S u b d is tric t 4 S u b d is tric t 5
D is tr ic t C D is tr ic t D D is tr ic t E D is tr ic t F
Country
Stage 1
Stage 2
Stage 3
Stage 4
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Select 1 or all eligible persons? – selecting 1
Child123456789
10
Child11121314151617181820
Select 8 households in population where 50% of children alone in household, 50% of children have 1 sibling
HouseholdABCDEFGHIJ
Household
K----L
----M----N
----O
10 children alone(50% of all children)
10 children with siblings(50% of all children)
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Select 1 or all eligible persons? – selecting 1
Child123456789
10
Child11121314151617181820
Select 8 households in population where 50% of children alone in household, 50% of children have 1 sibling
HouseholdABCDEFGHIJ
Household
K----L
----M----N
----O
5 children alone(63% of sampled children)
3 children with siblings(37% of sampled children)
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Select 1 or all eligible persons? – selecting 1
Child123456789
10
Child11121314151617181820
Select 8 households in population where 50% of children alone in household, 50% of children have 1 sibling
HouseholdABCDEFGHIJ
Household
K----L
----M----N
----O
5 children alone(45% of sampled children)
6 children with siblings(55% of sampled children)
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Select 1 or all eligible persons? – selecting 1
x x =
Number of HHs in district———————Number of HHs
in country
30 x Number of HHsin subdistrict
———————Number of HHs
in district
15——————
Number of HHs in subdistrict
30 clusters of 15 households each
Probability of
selecting district
Probability of
selecting subdistrict
within selected district
Probability of
selecting HH within
selected subdistrict
1——————
Number of children in HH
x
Probability of
selecting child within
selected HH
30 x 15———————————————
Number of HHs X Number in
in country children in HH
Overall probability of
selecting any child in
sampling universe
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Select 1 or all eligible persons? – selecting 1
Number of eligible children in selected households differs for different children
Therefore, overall probability of selection differs for different children• Must do weighted analysis
• Requires weighting for all prevalence estimates• Each child in multi-child household represents all the
children in that household
30 x 15———————————————
Number of HHs X Number in
in country children in HH
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Select 1 or all eligible persons?- recommendations
In most cases, include all eligible persons in selected households
If clustering expected to be very high:• Choose 1 eligible person• Record number of eligible persons in every
selected household• Do weighted analysis
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Judging age eligibility
Judging age eligibility
Must select specific age group for survey• Usually 6 - 59 months of age for nutrition
assessment of young children Age often not well known Family records may be lost How to judge age of children in selected
household?• Some use height/length• Exact age not necessary for measuring
wasting
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Judging age eligibility
If height cut-off is same as population’s height at 60 months of age (e.g. 100 cm in stunted population):
Includes older, stunted children Excludes younger, taller children Results in same bias toward older, stunted
children
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Do not use height/length criteria to judge eligibility unless absolutely necessary
Use local calendar to estimate age• Use one-time important events to determine year
of birth• Use seasonal events to determine month of birth
If must use height/length criteria, how to minimize bias?
Judging age eligibility - recommendations
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Children < 6 months of age
Children <6 months of age
Reasons cited for exclusion*: Thought to be protected by breastfeeding Anthropometric measurements substantially influenced by
birthweight and intrauterine factors Small sample size in surveys of children < 5 years of age Reference population less applicable because reference
infants virtually all bottle-fed Survey team members hesitant to manipulate young infants Difficult to weigh and measure
• Imprecision in measurements larger as proportion of measurement
• Most scales accurate to +/- 100 grams
* Golden and Prudhon, Field Exchange
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Children <6 months of age
Reasons cited for exclusion: Thought to be protected by
breastfeeding
Anthropometric measurements substantially influenced by birthweight and intrauterine factors
Small sample size
Reference population less applicable because reference infants virtually all bottle-fed17
However: Non-exclusive breastfeeding leads
to exposure to enteric pathogens producing wasting
Breastfeeding and complementary feeding practices still important
Can include and analyze data as collected or oversample infants
Mortality very high for infants defined as wasted using current reference. Therefore, regardless of applicability, current reference useful.
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Children <6 months of age
Age of child
(months)
Median weight for boys in reference
population (kg)
50 gm as % of
weight
Median height/length for boys
in reference population (cm)
0.5 cm as % of height/
length
1 4.3 1.2% 54.6 1%
6 7.8 0.6% 67.8 0.7%
12 10.2 0.5% 76.1 0.7%
24 12.3 0.4% 85.6 0.6%
36 14.6 0.3% 94.9 0.5%
48 16.7 0.3% 102.9 0.5%
60 18.7 0.3% 109.9 0.5%
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Children <6 months of age
0%
5%
10%
15%
20%
25%
30%
35%
40%
< 12 12-23 24-35 36-47 48-59
Age group (months)
Pre
va
len
ce
& c
on
fid
en
ce
in
terv
al
Age-specific wasting prevalence and 95% confidence intervals,
by 1 year age groups, Badghis Province, 2002
N = 87 125 149 101 77
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Children <6 months of age
May need to be included because Case fatality ratio of wasting much higher in infants
• More important to detect and treat wasting May be at higher risk of wasting than commonly
thought Many nutritional interventions very different in infants
• Normal feeding programs not appropriate
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Children <6 months of age - recommendations
Include infants if any suspicion of wasting in this age group
Take length and weight measurements carefully• Measure length to nearest 0.1 cm• Measure weight to nearest 100 grams• Use usual rules for rounding
• Do not routinely round up or down• If possible, use uniscales
• But how do uniscales round? If possible, collect data on breastfeeding in this age
group
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Mengukur Mortalitas
Mengukur Mortalitas
Cross-sectional survey gathers data at single point in time
Mortality measurement is rate• Requires counting deaths over period of time
Therefore, must gather data from retrospectively• Ask about deaths during specific period in
the past (recall period)
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Recall period• Beginning of period must be well-known date
• Major holiday or festival• Occurrence everyone remembers
• End of period is usually day of survey data collection• Recall period should be short enough to allow
accurate recall & for results to be meaningful• Not usually interested in mortality rate from distant past
• Recall period should be long enough to detect enough deaths for statistical precision
• Often choose about 1 year
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Mengukur Mortalitas – Prinsip Umum
Kelahiran & Kematian• Birth and death information reported by living
household member• Method should account for births during
recall period• Detection of deaths must be nearly complete
Denominators of mortality rates are survey sample itself• Does not depend on estimates of population
size
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Mengukur Mortalitas – Prinsip Umum
Mengukur Mortalitas – Prinsip Umum
Beginning of recall period
End of recall period(usually when survey data collected)
HH member
Moved into HH during recall period
Birth during recall period
Death during recall period
Birth and death during recall period
Moved out of HHduring recall period
Time
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Beginning of recall period
End of recall period(usually when survey data collected)
Time
Deaths
Births
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Mengukur Mortalitas – Prinsip Umum
At least 3 methods currently used:• Current household census• Previous birth history• Past household census
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Mengukur Mortalitas
Measuring mortality – current HH census
Determines number of household members alive on day of survey
Asks number of deaths and births within household during recall period
Ask separately about number of household members and deaths for children < 5 to derive mortality rate for this age group
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality – current HH census
Beginning of recall period
End of recall period(usually when survey data collected)
HH member
Moved into HH during recall period
Birth during recall period
HH census
Moved out of HHduring recall period
Death during recall period
Birth and death during recall period
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality – current HH census
Number of current residents+ ½ number of deaths during recall period– ½ number of births during recall period
Number of deaths during recall periodMortality
rate=
x constant / time period
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality – previous birth history
Asks about births to women in household in prior 5 years• Does not take household census
Determines current status for each child born in prior 5 years
Collects data only one children born in prior 5 years, therefore, on children < 5 years of age
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality – previous birth history
Beginning of recall period
End of recall period(usually when survey
data collected)
Older than 5
Under 5, bornbefore recall period
Under 5, bornduring recall period
Under 5, deathduring recall period
Under 5, born anddied during recall period
Older than 5, diedduring recall period
5 years priorto survey
Under 5, left HHduring recall period
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality – previous birth history
Number of births in prior 5 years– ½ number of deaths during recall period+ ½ number of births during recall period
Number of deaths during recall periodamong children born in prior 5 years Mortality
rate(for children
<5 years of age)
=
x constant / time period
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality – past HH census
1. Asks about number of household members at beginning of recall period
2. Determines number of births during recall period
3. Asks about status of these individuals
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality – past HH census
Beginning of recall period
End of recall period(usually when survey data collected)
HH member
Moved into HH during recall period
Time
Birth during recall period
Death during recall period
Birth and death during recall period
Moved out of HHduring recall period
HH census
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality – past HH census
Number of residents at beginning of recall– ½ number of deaths during recall period+ ½ number of births during recall period
Number of deaths during recall periodMortality
rate=
x constant / time period
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality - difficulties
1. Manipulation by respondents
2. Taboo regarding reporting or counting deaths
3. Poor recall of deaths, especially in young children
4. Poor recall or definition of live births, especially those > 2 years in past
5. Poor recall of date of deaths or births
6. Determining age
7. Time required at each household
8. Deaths of women or entire household
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Measuring mortality - difficulties
Present in method
DifficultyCurrent
HH censusPrior birth
historyPast HH census
Manipulation
Taboo
Recall of deaths
Recall of births
Recall of date of death or birth
Determining age
Time required
Deaths of women or household
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Methods need to be studied• Compare to reliable measure of mortality rate,
for example, death registration• Compare to each other
When results reported, technique must be described in detail
Keep in mind potential difficulties and biases
Measuring mortality - recommendations
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
High mortality and low malnutrition
High mortality and low malnutrition
Some believe that high mortality can mask high malnutrition prevalence
The argument:• Malnourished children at greater risk of death• High mortality preferentially kills
malnourished children• Therefore high mortality lowers apparent
malnutrition prevalence
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
High mortality and low malnutrition
The history• Paper in 1992 suggested that continued high mortality
may cause plateau in malnutrition prevalence• Some suggest that high mortality can produce normal
or near-normal malnutrition prevalence The reality
• Mortality rate correlated with malnutrition prevalence• When one rises, so does the other
• Very high mortality rate and malnutrition prevalence have coexisted in many emergencies
• Somalia• South Sudan
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
High mortality and low malnutrition
High mortality cannot explain normal or low malnutrition prevalence
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Review of Survey Methodology – Potential recommendations from SMART
When to use surveys Sampling methods Final sampling stage (in multistage sampling) Sample size Number of clusters Select 1 or all eligible persons? Judging age eligibility Children <6 months of age Measuring mortality High mortality and low malnutrition
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Additional data to collect
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Additional data to collect
Nutritional status of other age groups• Adult women• Older persons
Infant feeding practices Household food security? Micronutrient status Vaccination status Water/sanitation dll.
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Additional data to collect - caveats
Inclusion of only households with children eligible for anthropometric measurement results in biased household sample• Cannot make valid conclusions about all
households in sampling universe Must keep in mind differences between unit
of sampling and unit of analysis Must write valid questions and organize data
collection form correctly
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...
Additional data to collect
Trade-off between useful data and logistic and time requirements
Collect only data essential to make program decisions
SUMBER: www.smartindicators.org/.../B%20Woodruff%207-23%20Review%20of...