Nutrition Baseline Surveys Summary

Table of contents
Survey methods
Country overview
Definitions of Key Indicators
Results
• Key Indicator Results
• Availability and access to food
• Water, Sanitation and Health
• Care practices and MAD
• Dietary intake of women and children
• Preliminary Logistic regression results
Conclusions
Recommendations
Bioversity InternationalS. Collins
2

Survey methods
• Sample size needed was calculated at
n=347, sample size of the NBS ranged
from 396-487 with the exception of India
(n=803)
• Data from 4700 women-child pairs
• Selection criteria: Respondents were
women (15-49 yrs) who also had at least
one child (6-23 months)
Bioversity InternationalJ. Lauderdale
3

Country overview: Africa
Mali
• GDP per capita* (World Bank,
2015): 744.3
• Human Development Index
(UNDP, 2014): 0.419
• Prevalence of stunting (UNICEF,
2013): 39%
Burkina Faso
Togo
Benin Malawi
Zambia
Kenya
Ethiopia
2015): 613
(UNDP, 2014): 0.402
2013):33%
• GDP per capita* (World Bank, 2015):
548
• Human Development Index (UNDP,
2014): 0.484
2013): 30% • GDP per capita* (World Bank,
2015): 779.1
(UNDP, 2014): 0.480
2013): 45%
2015): 381.4
(UNDP, 2014): 0.445
2013): 48%
2015): 619.1
(UNDP, 2014): 0.442
2013): 44%
2015): 1376.7
(UNDP, 2014): 0.548
2013): 35%
2015): 1307.8
(UNDP, 2014): 0.586
2013):46%
*current US$
4

Country overview: Asia
India
Cambodia
2015): 1581.6
(UNDP, 2014): 0.609
2013): 48%
2015): 1158.7
(UNDP, 2014): 0.555
2013): 41%
*current US$
5

Definitions of Key Indicators of the Program
Individual Dietary Diversity Score – Women of Reproductive age -
IDDS-W - and Minimum Dietary Diversity – Women of reproductive
age - MDD-W
Minimum Acceptable Diet - MAD (6-23 months of age)
• MDD (Minimum Dietary Diversity)
• MMF (Minimum Meal Frequency)
Food Insecurity Experience Scale-Household - FIES-H
6

Individual Dietary Diversity Score and MDD-W
Photo credit: Klaus Wohlmann
7
IDDS-W: is the sum of ten food groups consumed
over the past 24 hours by women 15-49 years of
age.
MDD-W: is the proportion of women 15-49 years of
age who consumed food items from at least 5 out of
10 defined food groups the previous day or night.
Interpretation: Higher prevalence of MDD-W is a
proxy for better micronutrient adequacy among
women of reproductive age in the population

Food groups for women
• Eggs
• Dark green leafy vegetables
• Other vitamin A-rich fruits &
vegetables
• Other vegetables
• Other fruits
• Grains, white roots/tubers, plantains
• Pulses (beans, peas and lentils)
• Nuts and seeds
• Dairy
• Meat, poultry and fish
8

Minimum Acceptable Diet (MAD)
Minimum Dietary Diversity (MDD)
Minimum Meal Frequency (MMF)
Source, WHO, 2008
Minimum acceptable diet (MAD): Proportion of children 6-23 months of age who receive a minimum
acceptable diet which is a composite indicator of minimum dietary diversity and minimum meal frequency
during the previous day
Minimum dietary diversity (MDD): Proportion of children 6-23 months of age who receive foods from 4
or more food groups out of seven
Interpretation: proxy for adequate micronutrient-density of foods and liquids other than breast milk
Minimum meal frequency (MMF): Proportion of breastfed and non-breastfed children 6-23 months of
age who receive solid, semi-solid, or soft foods (but also including milk feeds for non-breastfed children)
the minimum number of times or more.
Interpretation: proxy for energy intake from foods other than breast milk
9

Food Insecurity Experience Scale-H (FIES-H)
Prevalence of Experienced Food Insecurity at moderate or severe levels (FImod+sev)
Prevalence of Experienced Food Insecurity at severe levels (FIsev)
Interpretation: estimates of the proportion of the population facing difficulties in
accessing food, at levels of moderate and severe food insecurity -
During the last 4 weeks, was there a time when:
1. You or others in your household worried you would run out of food because of a lack of money or other resources?
2. You or others in your household were unable to eat healthy and nutritious food because of a lack of money or other resources?
3. You or others in your household ate only a few kinds of foods because of a lack of money or other resources?
4. You or others in your household had to skip a meal because there was not enough money or other resources to get food?
5. You or others in your household ate less than you thought you should because of a lack of money or other resources?
6. Your household ran out of food because of a lack of money or other resources?
7. You or others in your household were hungry but did not eat because there was not enough money or other resources for
food?
8. You or others in your household went without eating for a whole day because of a lack of money or other resources?
Source: FAO, Voices of the Hungry, 2016
10

Results according to the UNICEF Framework
Health Services and WASH
Presence of soap in household,Use of soap for hand-
washing, Access to improved sanitation facility, Access to
improved water
Care Environment
Access to nutrition counselling
Knowledge about prevention of
malnutrition
Availability and access to food
Land access. Crop diversity,
Home gardening, Livestock keeping, Access to fruit trees
Prevalence of moderate and severe food insecurity (FIES-H)
Nutritional Status
(Prevalence of stunting, underweight, overweight
and micronutrient deficiencies)
Food Intake
MAD (MDD and MMF) in children 6-23 mo
MDD-W of women 15-49 years
Health Status
Prevalence of diarrhea
11
Education
Women’s education level

Results: Presentation of the Nutrition Baseline Survey
results
In order to summarize visually
different percentages across
the ten countries, a dashboard
categorization was used.
Red: ‘Very Poor”
Yellow: ‘Poor”
Green: ‘Better’
0-32% Very low/poor
33-66% Low
67-100% Better
12
The inverse percentages used for Food
Insecurity as increasing percentage
reflects a worse situation

Results: Summary of Key Indicators of the Program
Many patterns are consistent
(Kenya, Togo, Zambia, Benin)
Malawi does a bit better with
MDD-W and MAD than could be
expected with such high food
insecurity
Households experience of
moderate and severe food
insecurity is relatively low in
Ethiopia and India, however MAD
and MDD very low.
FIES-H here refers to moderately and severely food insecure
Numbers represent %
Country FIES-H MAD MDD-W
Kenya 87 15 12
Malawi 86 34 43
Mali 55 11 8
Togo 55 33 28
Zambia 41 34 57
Benin 32 26 34
Cambodia 24 41 53
Ethiopia 23 19 7
Burkina Faso 22 38 38
India 18 18 20

Results: Female education
Very large range across countries in female education (20-100%)
According to MDG for sub-Saharan Africa in 2000, 60% of all children were enrolled in
primary school (MDG report of the United Nations, 2015). 5 out of 8 project sites in Africa are
below this benchmark.
Benin
Burkina
Faso
Cambodia Ethiopia India Kenya Malawi Mali Togo Zambia
Some
school
20% 33.6% 90.4% 45% 51% 100% 87% 33.6% 56% 69%
14

Results: Availability and access to food - access to land
Benin
Burkina
Faso
Access to
land for
agriculture
97% 81% 85% 95% 71.5% 21% 90% 78% 92% 99%
Access to land for agriculture is OK for all countries except Kenya.
Kenya reports a low access to land; it should be noted that the survey area in Kenya
represents an area where the population is mainly pastoral and practice a nomadic lifestyle.
15

Results: Availability and access to food - Main crops grown
(% of households growing the crop)
Benin
Burkina
Faso
Maize
94
Maize
97
Rice
95
Maize
70
Wheat
93
Maize
88
Maize
91
Rice
88
Maize
98
Maize
100
Sorghum
75
Millet
78
Cassava
15
Teff
60
Mustard
66
Legumes
74
Groundnuts
45
Maize
19
Manioc
94
Groundnuts
67
Soya
76
Groundnuts
63
Beans
8
Barley
50
Sesame
56
-
Soya
20
Millet
19
Beans
66
Sunflower
57
Yams
77
Red sorghum
61
-
Legumes
43
Bengal
gram
55
-
Rice
20
-
Groundnuts
58
-
16

Results: Availability and access to food - households with
home gardens, access to fruit and livestock
 India, Kenya and Togo
twenty percent or fewer
hh had home gardens.
 1/3 of hh or fewer with
access to fruit in
Ethiopia, India, Kenya
and Mali
 Fifty percent or more of
all hh keep livestock,
except Malawi
Photo: Kuldeep Singh Jadon
17

Results: Availability and access to food - food insecurity
experience scale (FIES-Household)
Prevalence rates are
representative of project area
(not national).
Within the surveyed areas
food insecurity is greatest in
Kenya, followed by Malawi,
Mali and Togo.
Results from Ethiopia are
better than expected given
one of the lowest MDD-W and
MAD.
Country FImod+sev (%) FIsev (%)
Kenya 86.6 46.5
Malawi 86.1 35.8
Mali 54.8 29.7
Togo 54.6 2.5
Zambia 41.4 10
Benin 31.7 12.8
Cambodia 23.8 0.09
Ethiopia 22.7 0.8
Burkina Faso 21.9 4.7
India 17.7 8.4
18

Results: Health - drinking water and sanitation
2015 MDG targets
for sub-Saharan
Africa 74% access
to safe water and
62% access to safe
sanitation.
Water goal met in at
least one season for
six project areas in
Africa.
Sanitation in nearly
every project area is
very far away from
goal.
0
10
20
30
40
50
60
70
80
90
100
Ethiopia Kenya
East Africa
Access to improved sanitation
Access to improved water – Wet
season
Access to improved water – Dry
season
0
10
20
30
40
50
60
70
80
90
100
Benin Burkina
Faso
Mali Togo
West Africa
0
10
20
30
40
50
60
70
80
90
100
Malawi Zambia
Southern Africa
0
10
20
30
40
50
60
70
80
90
100
Cambodia India
Asia

Results: Care - Maternal knowledge compared to prevalence of
MAD
20
Gap between knowledge
and practice is high in Kenya
and Ethiopia. WHY?
Knowledge and practice are
similar for other countries, so
behavior change
communication a plausible
strategy.
What should we do to prevent malnutrition in
children?

Results: Dietary Intake Women (15-49 years)
The IDDS-W ranged from 3.1 (0.9)
(Ethiopia) to 4.7 (1.3) (Zambia) with
percent of women achieving MDD
ranging from 6.8 % in Ethiopia to 57 %
in Zambia.
IDDS-W and MDD-W pretty consistent
except in Cambodia.
In Cambodia, high percentage of
women consume the same 4 food
groups.
Country IDDS-W MDD-W (%)*
Ethiopia 3.1 7
Kenya 3.2 12
Mali 3.2 8
Cambodia 3.3 53
India 3.6 20
Malawi 3.9 43
Togo 3.9 28
Benin 4.1 34
Burkina
Faso
4.2 38
Zambia 4.7 57
21

Results: Dietary intake children 6-23 months
MMF higher than MDD for all countries, focus needed on diversification.
Important to also disaggregate results by breastfeeding and age
Results are presented for full sample not by breastfed/non-breastfed as >75% of children breastfed yesterday
Mali Kenya Ethiopia India Benin Togo Malawi Zambia
Burkina
Faso
Cambodia
MAD 11 15 17 18 26 33 34 34 38 41
MDD 27 22 19 23 33 43 43 55 50 47
MMF 57 71 66 58 67 71 70 77 63 93

Hypothesis 1: Women’s dietary diversity, measured by the Individual Dietary
Diversity Score Women (IDDS-W) or Minimum Dietary Diversity Women (MDD-W),
is higher for households with a more diverse agricultural production pattern and a
better knowledge of adequate nutrition.
Hypothesis 2: Children aged 6-23 months are more likely to receive a minimum
acceptable diet (MAD) the more diverse the household’s agricultural production and
the higher the household’s level of nutrition knowledge.
Results: Preliminary testing of hypotheses
23

Results: Preliminary testing of hypotheses IDDS-W and MDD-W
In 9 out of 10 country models at least one and max. three agricultural variables have
a causal positive effect on women's dietary diversity:
(crop diversity, home garden, access to fruit/fruit production in homestead, year-
round vegetable production, vegetable diversity, fruit diversity)
Nutrition counselling was significant in some but not all countries
Other significant predictors are very country specific and do not occur consistently
amongst countries
(secondary education, geographical location, ethnicity, income score)

Results: Preliminary testing of hypotheses: Minimum acceptable
diet
 Results seem to confirm the program’s hypotheses of relationships between
agriculture production and particularly women’s dietary diversity.
 For Minimum acceptable diet, interventions need to incorporate a focus on youngest
children and breastfeeding
In 5 out of 10 country models at least one and max. two agricultural variables
have causal positive effect on infants’ MAD (vegetable diversity, crop diversity,
home garden)
In 9 out of 10 country models infant’s age, breastfeeding status, and/or under 5
child clinic visits positively and significantly influence MAD
Within countries district was also often a significant predictor

Conclusions
1. The project sites represent vulnerable areas and are appropriate sites for the proposed
interventions.
2. The chosen key indicators of IDDS-W and MAD match well with the program intervention
packages in most countries.
3. There are plausible impact pathways to achieve the objectives of the program given the
combination of interventions in most countries.
26

Recommendations for the program
General
 Try to assess level and intensity of participation in interventions
Availability and access to food
 Own production was a big focus of the NBS but market and market access was not, more
exploration of market access and food availability in markets could be undertaken at mid-term
 Seasonal fluctuations in food availability should be further explored and addressed when
designing interventions
 Divergence between access to fruit and vegetables and consumption should be explored
using qualitative methods
27

Recommendations for the program
Care
 Country specific qualitative data collection is needed to understand divergence in knowledge
and practice of dietary diversity for children in Ethiopia and Kenya
 Most knowledge questions were based on maternal recall of general topics, the uptake of
program specific messaging should be tested
 Assess different channels of communication for uptake of messages
Health/WASH
 Advocate especially access to sanitation
28

Thank you
www.bioversityinternational.org/subscribe
@BioversityInt
Gina Kennedy
g.kennedy@cgiar.org

Results: Dietary intake - percent food group consumption
across countries using dashboard classification
75 % or more of all
women in every country
consume staple foods.
Other vegetables are the
next food group most
frequently consumed.
Food groups rarely
consumed include nuts
and seeds, dairy, eggs,
vitamin A rich fruits/veg
and other fruit.
30

Country Agriculture
/production
(9/10)
Direct nutrition
education
(6/10)
Support to extension
staff to provide
education (9/10)
Multi-sectoral
coordination (8/10)
Other (media
campaigns, WASH,
Social transfers)
Benin X X X X x
Burkina Faso X X X X x
Cambodia X X X X X
Ethiopia X X X X
India X Improve efficiency of TPDS
Kenya X X X X
Malawi x x x X Support to education and health
facilities
Mobile advisory services via
telephone for pregnancy and post-
pregnancy
Mali X x
Togo X X X X
Zambia X X X X x
Project intervention types by country
38
31

• Based on the project interventions in support of agricultural production, training
and support to extension staff, direct nutrition education (including cooking
demonstrations and other methods) and support to multi-stakeholder groups for
improving nutrition several of the NBS indicators could be expected to change,
including;
– Agriculture indicators of home gardening, access to fruit and potentially number of
crops grown
– Increased Knowledge, attitudes and practices
– Improved MDD-W and MAD
Interventions (II)
39
32

• Basic services including primary education and access to safe
sanitation need urgent attention in some of the project areas
• The NBS also showed uneven coverage of access to safe water in
wet/dry season, this aspect also needs to be addressed with national
stakeholders
• The project interventions working with multi-sectoral bodies need to
make sure to highlight the lack of basic services in some of the
project areas
Recommendations for the project area: Basics for development
30
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Nutrition Baseline Surveys Summary

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