nutrition in food security assessments: the links
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Nutrition in food security assessments: The links
5th - 9th December 2011, Rome
Nutrition basics
Malnutrition? An abnormal physiological condition causes by deficiencies,
excesses or imbalanced of nutrients or energy. Measurement of Malnutrition:
Anthropometry and Nutritional Status
Why is anthropometry important?
INDIVIDUALS
Defines who has adequate nutrition and who is malnourished
POPULATIONS
Determines prevalence of malnutrition in surveyed populations
• Anthropometry is the easiest way to measure nutritional status.
Why anthropometry in CFSVAs?
Primary goal: To link food security with nutritional outcomes, controlling for other influences (health/hygiene, caring practices)
Secondary goal: to provide indicative levels of key nutritional outcomes by zone, administrative boundary, or other grouping, as appropriate.
These goals must be clearly stated, agreed upon internally and with partners, and technically supported before the survey takes place!
Why anthropometry in CFSVAs? 2 The distinction between these two purposes has large
implications on CFSVA design. If nutrition is being collected only to relate to food security
(primary goal), then tight sampling restrictions and other considerations are less.
If nutrition is being gathered to also provide prevalences by certain strata, then issues such as tight sampling restrictions will become more important
It is preferable, when possible, to satisfy both goals.
Occurs as a result of recent rapid weight loss
or a failure to gain weightACUTE MALNUTRITION
Occurs as a result of inadequate nutrition over a
long period of timeCHRONIC MALNUTRITION
WASTING(thinness)
STUNTING(shortness)
What are the two types of Growth Failure?
The building blocks of anthropometric indicators
Sex Age
Height (standing
or lying)Weight
1 2
3 4
MUAC
5
Oedema
6
Use of building blocks
Weight
Height/length
To assess if child is “wasted” or thin for his height acute malnutrition
To assess if child suffers from any type of protein-energy malnutrition underweight
Weight
Age
Height/length
Age
To assess if child is “stunted” or short for his age chronic malnutrition
MUACor
Oedemaand
The key indicators of nutritional status 1
Weight-for-Height (WFH) reflects recent weight loss
ACUTE MALNUTRITION WASTING
Height-for-Age (HFA) reflects skeletal growth
CHRONIC MALNUTRITIONSTUNTING
Weight-for-Age (WFA) is a composite indexWASTING
UNDERWEIGHT &/or STUNTING
MUAC:Mid Upper Arm Circumference
WASTING
THE RISK OF MORTALITY FROM ACUTE MALNUTRITION
MUAC measurement Classification
<115 mm Severe acute malnutrition
115 – 124 mm Moderate acute malnutrition
125 - 135 cm At-risk of malnutrition
> 135 mm Normal
Bilateral oedema (SAM)
Marasmus
Kwashiorkor
A B C D
A: HealthyB: StuntedC: WastedD: Stunted & Wasted
Who is stunted and who is wasted?
Source: SMART training modules
Hidden hungerMicronutrient deficiency
Iodine, vitamin A and iron deficiencies are the most important
Their lack represents a major threat to the health and development, particularly children and pregnant women
Vitamin A deficiency (VAD) Leading cause of preventable blindness in
children Increases the risk of disease and death from severe
infections An estimated 250 000 to 500 000 vitamin A-deficient
children become blind every year, half of them dying within 12 months of losing their sight
Example of hidden hunger
Cordillera
(n=884)Chuquisaca
(n=477)Tarija
(n=420)
Acute malnutrition (12) 1,4%(0,8 - 2,4 95% C.I.)
(4) 0,8%(0,3 - 2,3 95% C.I.)
(3) 0,7%(0,2 - 2,3 95% C.I.)
Underweight (52) 5,9%(4,2 - 8,1 95% C.I.)
(12) 2,5%(1,2 – 5,4 95% C.I.)
(15) 3,6%(2,1 - 6,1 95% C.I.)
Chronic malnutrition (197) 22,3%(18,9-26,0 95% C.I.)
(90) 18,8%(15,0-23,4 95% C.I.)
(71) 16,9%(12,5 - 22,5 95% C.I.)
ANAEMIA Cordillera Chuquisaca Tarija
6- 59 months 57,0 49,7 48,8
6 -23 months 71,9 56,5 61,1
CALCULATING
www.smartmethodology.org
www.who.int
ENA for SMART
SMART plausibility check
ENA results
At which age do we see most wasting?
Wasting is more common amongst children during the weaning period.
Complementary foods are often inadequate. Complementary foods are often not prepared in hygienic
conditions which can result in diarrhoea. Young children are susceptibles to other diseases.
ENA Report All
n = 877Boys
n = 444Girls
n = 433Prevalence of global malnutrition (<-2 z-score and/or oedema)
(8) 0.9 %(0.5 - 1.8 95% C.I.)
(6) 1.4 %(0.6 - 2.9 95% C.I.)
(2) 0.5 %(0.1 - 1.8 95% C.I.)
Prevalence of moderate malnutrition (<-2 z-score and >=-3 z-score, no oedema)
(8) 0.9 %(0.5 - 1.8 95% C.I.)
(6) 1.4 %(0.6 - 2.9 95% C.I.)
(2) 0.5 %(0.1 - 1.8 95% C.I.)
Prevalence of severe malnutrition (<-3 z-score and/or oedema)
(0) 0.0 %(0.0 - 0.0 95% C.I.)
(0) 0.0 %(0.0 - 0.0 95% C.I.)
(0) 0.0 %(0.0 - 0.0 95% C.I.)
Table 3.2: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sexPrevalence of oedema is 0.0 %
Severe wasting
(<-3 z-score)
Moderate wasting
(>= -3 and <-2 z-score )
Normal(> = -2 z score)
Oedema
Age (mo)
Total no.
No. % No. % No. % No. %
6-17 204 0 0.0 6 2.9 198 97.1 0 0.018-29 188 0 0.0 1 0.5 187 99.5 0 0.030-41 203 0 0.0 1 0.5 202 99.5 0 0.042-53 198 0 0.0 0 0.0 198 100.0 0 0.054-59 84 0 0.0 0 0.0 84 100.0 0 0.0Total 877 0 0.0 8 0.9 869 99.1 0 0.0
Table 3.3: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema
Who is at risk of stunting? Stunting occurs over a long period.
The poor quality of food is one of the factors in stunting.
Action is necessary in the formative years to prevent stunting.
Analysis and reportingNumber of children Wasting Underweight Stunting Age
group (months) CFSVA DHS CFSVA DHS CFSVA DHS CFSVA DHS
< 6 176 529 8.5% 5.9% 3.6% 7.2% 10.8% 17.8% 6-9 135 399 10.4% 11.3% 23.0% 31.5% 30.4% 32.0% 10-11 40 150 12.5% 16.1% 41.5% 50.1% 41.5% 46.9% 12-23 316 1129 11.9% 18.4% 46.9% 50.3% 58.5% 57.3% 24-35 323 946 8.7% 14.8% 47.2% 46.3% 49.2% 49.9%
This example presents an external validation of the CFSVA nutritional findings against DHS findings.
This example is lacking 95% CI, which would allow a better comparison between the survey findings.
Analysis and reporting
Cluster N Whz < -2.00 SD Waz < -2.00 SD Haz < -2.00 SD
% 95% CI % 95% CI % 95% CI
1 164 4.3% (1.1, 7.4) 25.2% (18.4, 31.9) 30.7% (23.5, 37.8%)
2 169 9.9% (5.2, 14.5) 24.1% (17.4, 30.7) 30.9% (23.7, 38.1)
3 194 8.3% (4.4, 12.3) 37.5% (30.6, 44.4) 50.0% (42.9, 57.1)
4 215 6.3% (3.0, 9.7) 43.7% (36.9, 50.5) 64.6% (58.0, 71.1)
5 131 10.6% (5.1, 16.1) 24.4% (16.7, 32.1) 36.6% (28.0, 45.2)
6 173 9.1% (4.7, 13.6) 34.8% (27.4, 42.1) 48.8% (41.0, 56.5)
7 111 7.5% (2.4, 12.7) 45.3% (35.7, 54.9) 47.2% (37.5, 56.8)
8 216 10.8% (6.6, 15.1) 43.4% (36.7, 50.1) 47.2% (40.4, 53.9)
9 174 5.8% (2.3, 9.3) 26.0% (19.4, 32.6) 39.3% (32.0, 46.7)
Nutrition and food security
Assessments within WFP follow the conceptual framework: the nutritional status is an outcome of food security (and/or other factors).
WFP Conceptual Framework
Underlying causes of malnutrition
Malnutrition
Inadequate Food Intake Infection
Household Food Security
Social and Care
Environment
Access to Health Care &
Healthy Environment
Immediate Causes
Underlying Causes
Formal & Informal InfrastructurePolitical Ideology
Resources
BasicCauses
Nutrition in food security assessments
Improved food security and nutrition analysis should lead to more appropriate and targeted responses.
In assessments include core information on the factors influencing both food and nutrition security.
Non-food causes of nutrition insecurity are incorporated in assessments (poor health environment, inadequate care practices), but are less developed than food-related causes.
IMPORTANT !!!
Good quality data allows us to have good analysis. We cannot have good analyses from poor quality data.
Key crosstabulation - Descriptive
Nutrition Strata
Sex of the child Child age groups Sex of the household head Region/province Livelihood groups Ethnic group Urban / rural Total
Wasting (WHZ and or MUAC)
Stunting (HAZ) Underweight (WAZ)
Under 5 nutrition by ethnic group- Laos
0
10
20
30
40
50
60
70
80
Lao Thai Austro-Asiatic
Sino-Tibetan
Hmong-luMien
Main Ethnic Group
Prev
alen
ce <
-2 z
-sco
res
95%
CI
StuntingUnderweightWasting
Key crosstabulation – analysis
Nutrition Food security / access
Wasting (WHZ and or MUAC)
Stunting (HAZ) Underweight (WAZ)
Economic vulnerability Expenditure / income Share food expenditure CSI
Diet quality FCS FC groups Diet diversity Food energy from staples
Diet quantity Kcal
Key crosstabulation – analysis
Nutrition Other household critical indicators
Wasting (WHZ and or MUAC)
Stunting (HAZ) Underweight (WAZ)
Improved source of water Improved sanitation Crowding index Household size Percentage of dependents Asset /wealth index Household has chronically
sick member Etc…
Key crosstabulation – analysis
Nutrition Mothers
Health status Antenatal cares Age of the mother? Education Number of kids
Wasting (WHZ and or MUAC)
Stunting (HAZ) Underweight (WAZ)
Key crosstabulation – analysis
Nutrition Health – child care
Health care access Hand washing Breastfeeding practices Complementary feeding
practices Child diet diversity score (IDDS) Child food consumption score? Immunizations Prevention (mosquito net)
Wasting (WHZ and or MUAC) Stunting (HAZ) Underweight (WAZ)
Reporting For anthropometry
Prevalence % Number of measured children CI
For FS analysis Correlations levels and/or if the relation is significant Level of Sig. (<0.05 or 0.01)
Incorporating nutrition into food security analysis Relating food security to nutritional outcomes.
Nutrition can be analyzed as an outcome of food consumption/security (nutritional status as the dependant variable) CAUSAL (two-way or multi-way) ANALYSIS
Nutrition can be a descriptive variable of food consumption/security DESCRIPTIVE (one-way) ANALYSIS
Nutrition can be viewed as a component of food security (utilization) NUTRITIONAL STATUS + X = FOOD SECURITY
Incorporating nutrition into food security analysis (or.. food security into nutrition analysis) There are several methodologies for making the link
between food security and nutritional outcomes. This depends on the approach to food security taken (remember the analytical frameworks). Descriptive analysis (one way analysis)
Food security = nutrition security (depending on interpretation)
Causal/relational analysis (descriptive analysis, plus two-way or multivariate analysis, examples following)
Is food consumption/household food security an underlying cause of nutritional status, or are other factors at play?
Two-way analysisStarting to make statements of
association
Multi-way analysis… and linking food security and nutrition
Case study - Liberia Areas found to have high food insecurity (by several
methodologies) do not have the highest prevalences of stunting, wasting, underweight.
How is this reconciled?
Liberia (nutritional outcomes)
Liberia (food security)
From the regression analysis The analysis revealed:
Food consumption is not associated with wasting. Food access is not associated with wasting. Wasting is primarily associated with perceived birth size, caring practices
(age of introduction of solid foods), and episodes of diarrhea Female children and older children are significantly less wasted than male
children and younger children MCH food assistance played a role in improving nutritional status of children Continued breastfeeding in addition to intake of cereals and liquids as
revealed by 24-hour recall had better impact on the nutritional status of young children
Children who were introduced to solid foods especially before the age of four months were more wasted than other age groups
Example question:
In Swaziland, is there a difference in nutritional status depending on socio economic status, estimated by assets (asset poor/not asset poor), independent of education attainment?
Example from Swaziland
Report
weight-for-age z-score
-.8407 54 1.00960-.9150 12 1.06679-.8542 66 1.01220-.6726 183 .94419
-1.4316 19 .84888-.7440 202 .95973-.6034 116 .81132-.6723 13 1.17787-.6103 129 .84964-.4576 125 .92338-.8250 10 .72365-.4848 135 .91296-.6186 478 .92049
-1.0217 54 .99198-.6595 532 .93499
asset poornot poorpoorTotalnot poorpoorTotalnot poorpoorTotalnot poorpoorTotalnot poorpoorTotal
highest level educationnone
primary, incomplete
primary, complete
secondary or higher
Total
Mean N Std. Deviation
From Swaziland data.
Education of mother, asset poor/not asset poor
Regression Analysis
Tests of Between-Subjects Effects
Dependent Variable: weight-for-age z-score
15.131a 4 3.783 4.439 .002125.730 1 125.730 147.548 .000
6.753 1 6.753 7.925 .0057.247 3 2.416 2.835 .038
449.070 527 .852695.584 532464.201 531
SourceCorrected ModelInterceptpoormeducErrorTotalCorrected Total
Type III Sumof Squares df Mean Square F Sig.
R Squared = .033 (Adjusted R Squared = .025)a.
We could also do a regression analysis to explore the statistical significance of this same question.
Regression Analysis
Parameter Estimates
Dependent Variable: weight-for-age z-score
-.832 .147 -5.670 .000 -1.120 -.544 .058.375 .133 2.815 .005 .113 .637 .015
0a . . . . . .-.329 .139 -2.360 .019 -.603 -.055 .010-.252 .103 -2.452 .015 -.453 -.050 .011-.115 .114 -1.016 .310 -.339 .108 .002
0a . . . . . .
ParameterIntercept[poor=0][poor=1][meduc=1][meduc=2][meduc=3][meduc=4]
B Std. Error t Sig. Lower Bound Upper Bound95% Confidence Interval Partial Eta
Squared
This parameter is set to zero because it is redundant.a.
Example question two:
In Swaziland, is stunting status related to the age of the child, independent of sex of child?
Is stunting status related to the sex of child, independent of age?
Stunting by age group and sex: Swaziland
0%
10%
20%
30%
40%
50%
60%
70%
6 to 11 12 to 17 18 to 23 24 to 35 36 to 47 48 to 59
BoysGirls
Swaziland
Are the trends presented in the previous graph statistically significant? Controlling for age group, are boys more stunted than girls? Does age group have a significant relationship with stunting?
Food consumption and underweight
0.0%5.0%
10.0%
15.0%20.0%25.0%30.0%
35.0%40.0%
poor borderline adequate
Food consumption group
% of
chi
ldre
n un
derw
eigh
t
MalawiZambiaSwaziland
Food consumption and stunting
0.0%5.0%
10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%
poor borderline adequate
Food Consumption Group
Stun
ting
prev
alen
ce
MalawiZambiaSwaziland
Rates of GAM by FS Group
Global wasting by food security groups
0
2
4
6
8
10
12
14
16
Food insecure Highly vulnerable Moderatly vulnerable Food secure
Rates of WHZ<-2
Rural Liberia Sudan 2006
Rates of GCM by FS group Global stunting by food security groups
20
25
30
35
40
45
Rates of HAZ<-2
Rural Liberia Sudan 2006
Food insecure Highly vulnerable Moderatly vulnerable Food secure
Total NAM AMN Mean SD N Mean SD N Mean SD
FCS dry season 450 52.3 16.2 247 53.7 15.7 85 50.2 15.1p<0.069
FCS rainy season 450 50.5 17.0 260 52.0 15.7 90 47.4 17.0
p<0.028
Means comparison. FCS and malnutrition (Sierra Leone)
Seasonality
Case study – Burundi – unexpected results 1?
FCS Wasting WHZ
Poor 7.9%
Borderline 10.3%
Acceptable 7.9%
72.7%
1.0%1.0%0.9%
4.9%
1.4%18.1%
FSC Poor
43.3%
3.6%8.6%
9.3%
3.6%
28.5%
3.1%
FSC Border line
25.8%
8.9%
7.7%22.1%
7.5%0.7%
21.4%
5.9%
FSC Adequate
72.3%
12.7%
43.4%
31.8%
9.8%
56.6%
83.2%
57.2%
45.1%
85.5%
27.2%
57.2%
87.3%
39.6%
13.5%
27.0%
75.7%
32.4%
0%
10%20%
30%40%
50%
60%70%
80%90%
100%
Cereals
Tubers
Vitamin
A
Vegeta
bles
Fruits
Meat
Egg
Fish
Pulses
Milk
Oil
Sugar
Miscell
aneo
us
NAM AM
Malnutrition and food groups consumption
NAM = -1 SD < WHZ < 1SD AM = WHZ < -2SD
42-59 months
15.4%
96.2%
7.7%
34.6%
57.7%
84.6%
61.5%
50.0%
30.8%
73.1%
96.2%88.5%
75.0%
87.5%100.0%
25.0%25.0%
100.0%
25.0%
0%
20%
40%
60%
80%
100%
120%
NAM AM
Malnutrition and food groups consumption by age
6-17 months
7.0% 8.1%
77.9%
66.3%
80.2%
43.0%
20.9%
29.1%
24.4%
77.9%
59.3%
38.4%
56.4%
30.8%
10.3%
28.2%25.6%
2.6%
15.4%
53.8%
25.6%
53.8%59.0%
17.9%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Cereals
Tubers
Vitamin
A
Vegeta
bles
Fruits
Meat
Egg
Fish
Pulses
Milk
Oil
Sugar
Miscell
aneo
us
< 2 symptoms ≥ 2 symptoms
Food consumption and sickness
1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.510.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
f(x) = − 6.65887314943182 x + 45.8073243092111R² = 0.52219435851757
Vegetables
% U
nder
wei
ght
0.5 1.0 1.5 2.0 2.55.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
f(x) = − 11.5697854015114 x + 35.6020747631473R² = 0.663382483622398
Fruits
% W
astin
g
Comparison among geographic units
Comparison among geographic units 2
10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.010.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
f(x) = 0.387767663308546 x + 8.33020393288584R² = 0.448669338994744
Poverty incidence
% U
nder
wei
ght
10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.00.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
f(x) = − 0.0374865208075246 x + 4.57068796555953R² = 0.356043277113651
Poverty incidence
Vege
tabl
es co
nsum
ption
Key nutrition terms in FrenchEnglishAcute Malnutrition/wastingChronic malnutrition/stunting
UnderweightMUAC
Body Mass Index (BMI)Anthropometric thresholdsGlobal, moderate, severe
FrenchMalnutrition Aiguë/EmaciationMalnutrition chronique/ Retard de croissanceInsuffissance pondérale Périmètre brachial (PB)
Indice de masse corporel (IMC)Seuils anthropométriques Globale, moderée, sévère
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