Nutrition in food security assessments: The links 5 th - 9 th December 2011, Rome.

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: Healthy

B: Stunted

C: Wasted

D: Stunted & Wasted

Who is stunted and who is wasted?

Source: SMART training modules

Hidden hunger

Micronutrient 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 = 433

Prevalence 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 reporting

Number 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 Infrastructure

Political 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

Pre

va

len

ce

< -

2 z

-sc

ore

s 9

5%

CI

Stunting

Underweight

Wasting

Key crosstabulation – analysis

Nutrition Food security / access



Economic vulnerability Expenditure / income Share food expenditure CSI

Diet quality FCS FC groups Diet diversity Food energy from staples

Diet quantity Kcal


Nutrition Other household critical indicators



Improved source of water Improved sanitation Crowding index Household size Percentage of dependents Asset /wealth index Household has chronically

sick member Etc…


Nutrition Mothers

Health status Antenatal cares Age of the mother? Education Number of kids


Stunting (HAZ)

Underweight (WAZ)


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)


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 poor

poor

Total

not poor

poor

Total

not poor

poor

Total

not poor

poor

Total

not poor

poor

Total

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 .002

125.730 1 125.730 147.548 .000

6.753 1 6.753 7.925 .005

7.247 3 2.416 2.835 .038

449.070 527 .852

695.584 532

464.201 531

SourceCorrected Model

Intercept

poor

meduc

Error

Total

Corrected 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 Bound

95% Confidence Interval Partial EtaSquared

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

Boys

Girls

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

% o

f chi

ldre

n un

derw

eigh

t

Malawi

Zambia

Swaziland

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

Stu

ntin

g pr

eval

ence

Malawi

Zambia

Swaziland

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 AM

N Mean SD N Mean SD N Mean SD

FCS dry season 450 52.3 16.2 247 53.7 15.7 85 50.2 15.1

p<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 PoorFSC Poor

43.3%

3.6%8.6%

9.3%

3.6%

28.5%

3.1%

FSC Border lineFSC Border line

25.8%

8.9%

7.7%

22.1%

7.5%0.7%

21.4%

5.9%

FSC AdequateFSC 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

Vitam

in A

Veget

ables

Fruits

Mea

t Egg

Fish

Pulses

Milk

Oil

Sugar

Misc

ellan

eous

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

Vitam

in A

Veget

ables

Fruits

Mea

t Egg

Fish

Pulses

Milk

Oil

Sugar

Misc

ellan

eous

< 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/wasting

Chronic malnutrition/stunting

Underweight

MUAC

Body Mass Index (BMI)

Anthropometric thresholds

Global, moderate, severe

FrenchMalnutrition Aiguë/Emaciation

Malnutrition chronique/ Retard de croissance

Insuffissance pondérale

Périmètre brachial (PB)

Indice de masse corporel (IMC)

Seuils anthropométriques

Globale, moderée, sévère

Nutrition in food security assessments: The links 5 th - 9 th December 2011, Rome.

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Transcript of Nutrition in food security assessments: The links 5 th - 9 th December 2011, Rome.