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Factors associated with undernutrition & household food security (Findings from the PoSHAN Community Studies)Nutrition Innovation Lab‐ Asia (Nepal)

SWETHA  MANOHARJOHNS  HOPKINS  BLOOMBERG  SCHOOL  OF  PUBLIC  HEALTH

Nepal

© Google Images & CBS Nepal, 2012

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PoSHAN Community Studies

Agriculture to Nutrition Pathways

From Agriculture to Market/Home

……to Household Food Availability, Access

Dietary Intake & Nutrition & Health Outcomes of Women & Young Children

POLICY

ENVIRONMENT

PROGRAM

EXPOSURE

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Design & MethodsDesign

Longitudinal, observational study

Representative, annual  panel surveys (21 sites) balanced across mountains, hills, flatlands)

Conduct seasonal data collection in a nested sample (3 sites)

Duration: 3 years (2013‐2015)

Eligible households (N=~5000): < 5 children, newly married women

Major outcomes of interest: nutrition status, HH food security, dietary patterns 

Measurements

Community: food prices, infrastructure

Household: food security, income, expenditure, ag production & practices,  program participation 

Individual: Dietary patterns, nutritional status, anemia status, access to health & nutrition services, morbidity, IYCF, family planning, knowledge of key health and nutrition messages

Map of PoSHAN Study Sites

Sentinel sites (n= 3 VDCs)

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Undernutrition in Under‐ Five Children

2013%

2014%

Mountains(N=932)

Hills(N=1264)

Terai(N=3111)

Mountains(N=826)

Hills(N=1307)

Terai(N=3276)

Stunting* 37.1 36.2 34.8 39.1 36.9 37.2

Wasting* 8.3 10.8 23.3 7.28 8.79 21.56

Underweight* 26 29.1 39.1 25.7 27.1 39.8* < ‐2 SD

Under nutrition in under‐five children varies by age

Klemm et al, unpublished

Wasting peaks to 30% @ 8-18 mo

Stunting plateaus @>40% by end of 2nd

year

SAM peaks to 4% @ 6-15 mo

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Under nutrition in under‐five children varies by age AND by agro ecology. Wasting 2x higher in the plains 

‐5

0

5

10

15

20

25

30

35

40

45

50

Prevalence

Age, months

Mountains Hills Terai

Is Household Food Insecurity (HFI) associated with child undernutrition in 6‐ 59.9 month olds in Nepal?

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Sample characteristics: individual

Child Characteristics (6‐59.9 months) N= 4943

Age in months, mean (SD) 33.0 (15.6)

Sex, %

Male 52.6

Female 47.4

Child Dietary Diversity Score, mean (SD) 5.3 (1.7)

Consumed > 4 food groups, % 71.8

Hemoglobin, g/dL,  mean (SD) 10.5 (1.3)

Acute Respiratory Illness ≤ 30 days, % 17.1

Diarrhea ≤past 30 days, % 29.9

Mothers’ characteristics N= 4929

Age in years, mean (SD) 27.3 (6.7)

Education, %

No schooling (0 years) 58.4

Primary schooling (1‐5 years) 12

High School/SLC (6‐10 years) 21

College or higher (>10 years) 8.6

Short stature: <145 cm, % 11.9

Sample characteristics: householdHousehold characteristics N=3665Head of Household, %

Male 72.4Female 27.6

Household size, mean (SD) 5.8 (2.6)Wealth index, %

Lowest quintile 21.1Second quintile 20.5Middle quintile 20.1Fourth quintile 19.1Highest quintile 19.1

Caste, %Brahmin or Chettri 24.7Other terai 30

Caste, contd. [%]Dalit 17.4Newar 2.2Janjati 20.3Others  5.4

Household Food Insecurity, %None 59.1Mild 18.4Moderate  16.2Severe 6.3

Agro ecological Zone, %Mountains 18.4Hills  26.1Terai 55.5

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Model 1 = UnadjustedModel 2 = Adjusted household size & region, maternal age & height, child age, sex, ARI, diarrhea

Odds of stunting and wasting increases (but not consistently) as HFI increases

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Model 1 Model 2 Model 1 Model 2

Odds Ratio (Stunting) Odds Ratio (Wasting)

Odds Ratio

Food Secure Mildly Food Insecure Moderately & severely food insecure

**

** *

* P < 0.05 

Manohar et al. unpublished

N= 4493

0

10

20

30

40

50

60

70

80

90

N= 2789 N= 901 N= 828 N= 333

None Mild Moderate Severe

HFIAS

%

Stuntingǂ, % Wasting, % Consuming ≥ 4 food groupsǂ,% Diarrheaǂ,% (past 7 days) ARIǂ,% (past 7 days)

Prevalence of child stunting and recent history of diarrhea & ARI increase with severity of HFI  

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Coping strategies & social networks during times of food insecurity

0

10

20

30

40

50

60

Take cashloan

Take in‐kind loan

Colect wildfood

Consumeseed

Sell assets Selllivestock

No families 1 family 2‐4 families >5 families

%

Coping Strategies                                           Social support available  N= 4286

0 20 40 60 80

None

Mild

Moderate

Severe

Household Food In

security

% HH taking cash loans

0

5

10

15

20

25

30

35

40

Low Second Middle Fourth Highest

Wealth Index

% HHs taking loans 

Higher proportions of food and income stressed households take cash loans

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Reports of seasonal food insecurity by agro ecological zone

PLANTING SEASON

Jan Feb March April May June July Aug Sept Oct Nov Dec

Paddy Hills X X X X

Terai X X X X

Maize Mtns X X

Hills X X X X

Terai X X X X

Wheat Mtns X X

Hills X X X

Terai X X

Adapted from © FAO 2007

If you don’t have food available at home, you need to buy it….what does that cost? And how does that differ based on where one lives?

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How affordable the most nutritious diets that people are actually consuming vs. what they could consume?

Cost of Diet Tool, SCBiehl, et. al, unpublished

USD

Key MessagesTARGETTING MATTERS

The burden of under nutrition, specifically wasting and underweight in under‐five is children & HFI is significantly different across agro ecology. 

The most food and income‐stressed household are more likely to resort cash & in‐kind loans 

As HFI worsens, the odds of stunting and wasting increase

As HFI worsens, child dietary diversity decreases ‐ creating access and promoting  cheap, available and diverse diets during these time is important

Even during “leaner” seasons, affordable nutritious diets are available for young children 

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This type of ongoing agriculture – nutrition research system and infrastructure can provide insights on national trends in nutritional status, diet and food security and its relationship with nutrition‐specific and nutrition sensitive factors which can inform program and policies to retarget resources and bridge policy and programmatic gaps

Key Messages contd.

AcknowledgementsFunding:  through Feed the Future Innovation Lab for Collaborative Research on Nutrition

Management Entity: Tufts University, Friedman School of Food Policy & Nutrition

Co‐Investigators:  Dr. Keith West, Dr. Rolf Klemm, Dr. Devendra Gauchan, Dr. Ramesh Adhikhari, Dr. Shibani Ghosh, Dr. Patrick Webb 

Data Collection & Human Resource Management: New Era Pvt. Ltd; NTAG; NNIPS

PoSHAN‐JHU Technical Team: Ruchita Rajbhandary, Abhigyna Bhattarai, Dr. Raman Shrestha, Hari Krishna Shah, Dev Raj Gautam, Dev Mandal, Dr. SudeepShrestha

PoSHAN‐Tufts Technical Team: Diplav Sapkota