Columbia University Analysis for the Scaling Up Nutrition (SUN) Secretariat

Columbia University Analysis for the Scaling Up Nutrition (SUN) Secretariat

Simulating Potential of Nutrition Sensitive Investments

Slide Deck to Accompany the Technical Report

January 2014

The Lives Saved Tool (LiST) Visualizer Five outcome areas, intermediary to stunting, served as key starting point for

our study

Source: http://list.cherg.org/

FAMILY PLANNING

MATERNAL NUTRITION

Lancet, 2013

http://list.cherg.org/

Health

Environment and Water

Social Protection

Agriculture

Maternal Nutrition

[low birth weight and dietary patterns]

Family Planning

[Contraceptive use]

Exclusive Breastfeeding

[EBF for 0-6 months}

Complementary Feeding

[minimum acceptable diet]

1.Selected outcomes delivered through nutrition sensitive channels for literature search.

2.Used data available at the national level to model associations of outcomes with contextual factors.

3.Used data available at the national level and in the literature review to model associations of interventions with outcomes.

Strong or Weak

Associations for decision

making

Nutrition Sensitive Sector

Literature Review And

Modeling

Outcome Areas Predictors

Diarrhea incidence[diarrhea rate]

Education

Theoretical Framework

Public Health Model: Theoretical Framework

Public Health

Maternal Nutrition

[Low-birth weight]

Strong or Weak

Associations for

decision making

Nutrition Sensitive

Sector

Interventions/Delivery Channels

Outcome Areas

Predictors

Peer counseling Commercial packets Facility based education

Maternal education Health professional training Supplement provision

Community Health WorkersSchool promotion Media campaign

Iron/ Folic Acid Supplementation Multiple Micronutrient Supp Calcium Supplementation Balanced Energy Protein Supp

ContextualFactors

Done by LIST:

GNI per capita

Adult literacy rate

Adolescent birth rate

Female labor participation

Sec. School Enrollment

Maternity leave

Family Planning

[Contraceptive use]




[minimum acceptable diet]

Environment Model: Theoretical Framework

Environment and Water

Strong or Weak

Associations for

decision making

Nutrition Sensitive

Sector

Interventions/Delivery Channels

OutcomeAreas

Predictors

Reduced diarrhea

[diarrhea rate]

Promotion of access to water and sanitation

Safety of complementary foods

ContextualFactors

% Rural Population

Adult Literacy Rates

Vaccination Rates

Agriculture Model: Theoretical framework

Agriculture

Complementary feeding (minimum

acceptable diet)

Maternal nutrition

[low birth weight; dietary patterns:

% energy from non staples, calories

per capita, micronutrient availability]

Strong or Weak

Associations for

decision making

Nutrition Sensitive

Sector

Agricultural InvestmentsOutcome areas

Predictors

Inputs• Ag biodiversity• Fertilizer • Land available for

agriculture• Water available for

agriculture• MechanizationRural Infrastructure• Irrigation• Crop storage facilities• Road infrastructure• Port infrastructure• Mobile networkInstitutions and governance• Access to finance • Policy and legal

framework• Accountability and

transparancy• Allocation of pub

resourcesMarkets• Ag imports and exports• Ag import tarifsResearch• Ag R&D

Contextual factors

Economic setting and agricultural role in society• GNI per capita• GINI index• % rural

population• % Ag value

added• % Ag

employmentHealth setting• Life expectancy• # physicians

per 1000Gender• Girls/boys ratio

in secondary school

Education• Literacy

We investigate the estimated effect of interventions in these two sectors on the following key outcomes: exclusive breastfeeding, complementary feeding, maternal diet and family planning (public health sector), and promotion of access to improved water and sanitation (environment and water sector).

Contextual models- we used country level data from The State of World Children (SOWC) 2013 report, and most recent

data available from International Labor Organization, World Bank or other sources . The available data ranges from period 2006-2010 for adolescent birth rate to year 2011 for vaccination data.

- we ran cross country multiple regression analysis with 1000 simulations to investigate the predictors of the outcome measures. We estimated the 95% confidence interval

Intervention models: Interventions in these two sectors were assessed via meta-analysis, as following: - we conducted an extensive literature review on intervention impacts on the following outcome

measures: exclusive breastfeeding, minimum acceptable diet, contraceptive use, percent of low births and diarrhea rate.

- we selected relevant studies and formatted the results as needed for meta regressions- we estimated the pooled relative risks (RR) and their respective confidence intervals (CI) using a

restricted maximum likelihood estimator (REML) on a random effect model. The pooled estimates were calculated using the natural logarithms of the RRs and their standard errors from the individual studies.

- we explored sources of heterogeneity using sub-group analysis. The sub-groups (moderators) were identified based on participant or study characteristics. For moderators that were not systematically available at the study level, we used country level data that was matched to the country of the study. Significant moderator were identified using contextual models – described below.

Public Health and Environment: Methodology

Agriculture model: Methodology

We focused on two intermediary outcome areas for the agriculture model including: maternal nutrition and complementary feeding. For both outcome areas, we developed a national-level contextual model that allows us to identify associations rather than causal relationships between agricultural and nutrition variables.

Quantitative model: - National level data from seven publicly available databases were collated and

organized to populate the model integrating agriculture (FAO, IFAD, EIU), socio-economic (World Bank), human nutrition and health (WHO NILS, WHO IYCF) data using the country as the unit

- we first identified model indicators for maternal nutrition and complementary feeding that are significantly related to stunting, while controlling for income level

- Starting from these results we fit the agricultural factors into multivariate regressions against each of these indicators while taking into account a set of contextual factors

Additional literature review:- Starting from the results of the quantitative model, we revisited the literature to

identify specific programs/ interventions and delivery channels that contribute to the implementation of the agricultural investments that were identified as significantly related to nutrition specific indicators.




[Minimum acceptable diet]

Family Planning

[Contraceptive use]

Maternal Nutrition

[Low-birth weight]

GNI per

capita

0.01***[0.0001, 1.84]

8.60***[0.022,3295]


-0.10 [-0.21,0.03]

0.28***[0.07,0.71]

Female to male labor

participation *

maternity leave

-6.13** [-10.79,-1.60] Female

to male labor

participation

-64.31***[-95.5,-35.0]

Adult literacy: females as a % of males

0.24*[0.004, 0.48]

Ethnicity

African -17.90*[-32.16, -3.39]Asian -14.41* [-28.89, 1.09]

Asian 19.10*, [0.64, 37.66]Mixed24.59*[4.98, 44.50]

% urban

-0.56**[-0.90,-0.19]

-0.18*[0.02,0.33]

Secondary school

enrollment

female/male ratio

0.47*** [0.26,0.69]

Latino/Hispanic 12.46* ,95% CI [1.48, 23.75]

Access to

improved rural sanitati

on

0.21**[0.09,0.34]

Public Health Model: ResultsContextual model



Peer counseling

Contextual factors influencing the intervention effect

RR 2.46***

Public Health Model: ResultsIntervention model EBF

Non provision of commercial packets

RR 1.55***

Facility based education

RR 1.33***

Duration of BF

Adult literacy rate

% rural population

Female

labor participation

Maternity

leave


For interpretation: Relative Risk (RR) = 1 indicates that the outcome in intervention and control groups are equally likely to occur; RR<1 outcome in intervention is less likely to occur compared with control; RR>1 outcome in intervention is more likely to occur compared with RR>control. E.g. RR 0.6 is usually interpreted in the following way (exp for RR=0.6). (1-0.6)*100=40%, the outcome in intervention is 40% less likely to occur. If RR is 1.5, the outcome is 1.5 times more likely to occur in the intervention compared with control (or 50% increased risk.

Results of meta-regressions for the effect of peer counseling on exclusive breastfeeding in randomized controlled trials and quasi-

experimental studies Covariate

No. of observations* RR (95% CI) p-value

Duration of breastfeeding (study)3 months or less 36 1.84 (1.44 to 2.35) <0.00014 to 6 months 26 3.82 (2.80 to 5.22) <0.0001Adult literacy rate, female as % of male (country)<80 13 3.09 (1.94 to 4.92) <0.0001>=80 36 2.61 (1.96 to 3.48) <0.0001Rural population, % (country)<=30 41 2.24 (1.72 to 2.92) <0.0001>30 21 2.89 (2.04 to 4.10) <0.0001Maternity leave, # of weeks (country)<=12 19 2.79 (1.93 to 4.02) <0.000113 to 20 34 2.63 (2.00 to 3.47) <0.0001>20 9 1.38 (0.80 to 2.38) 0.2462Female labor participation rate (%) (country)<40 16 3.99 (2.63 to 6.03) <0.0001>=40 46 2.11 (1.69 to 2.64) <0.0001Adolescent birth rate (%)<=60 53 2.61 (2.07 to 3.29) <0.0001>60 9 1.81 (1.06 to 3.07) 0.0286

Results of meta-regressions for the effect of facility based education on exclusive breastfeeding in randomized controlled trials and quasi-

experimental studies Covariate No. of

observations*RR (95% CI) p-value

Duration of breastfeeding (study)3 months or less 10 1.60 (1.28 to 2.01) <0.0001

4 to 6 months 9 1.56 (1.20 to 2.04) 0.001Adult literacy rate, female as % ofmale (country)<80 4 1.98 (1.33 to 2.95) 0.0008

>=80 7 2.09 (1.58 to 2.77) <0.0001Rural population, % (country)<=30 13 1.33 (1.11 to 1.59) 0.0016

>30 7 2.05 (1.60 to 2.63) <0.0001Maternity leave, # of weeks (country)<=12 10 1.27 (1.05 to 1.54) 0.013613 to 20 9 2.07 (1.66 to 2.58) <0.0001

>20 1 1.12 (0.63 to 2.00) 0.6974Female labor participation rate (%)(country)<40 5 2.24 (1.63 to 3.08) <0.0001

>=40 15 1.39 (1.16 to 1.65) 0.0002Adolescent birth rate (%)<=60 14 1.43 (1.19 to 1.71) <0.0001

>60 6 1.89 (1.42 to 2.51) <0.0001

Public Health Model: ResultsIntervention model Family planning

Family Planning

[Contraceptive use]

School promotion, media campaign, community-based education

RR 1.15*

Environment model: ResultsContextual model

Diarrhea treatment

[% treatment with ORS]

Adult literacy

rate: females as % of males

Vaccination rate

0.31* [0.07, 0.56]

0.34* [0.03, 0.68]

Environment Model: ResultsIntervention model

Diarrhea incidence

[Diarrhea rate]

Hand washing

RR 0.76***

Water treatment

Contextual factors controlled for

Adult literac

y

% rural population

Vaccination rate

GNI per

capita

RR 0.71**

Agriculture ModelIdentification of model indicators for nutrition-related

intermediary outcomes

Stunting

Dietary patterns (proxy for maternal nutrition)

% Energy from non staples in

supply(-4.75***)

Calories per capita(-6.86***)

Fe availabilit

y from animal-products(-4.15*)

Low-birth weight(proxy for maternal nutrition)

% Low-birth

weight(2.82***)

Complementary feeding

% Minimum acceptabl

e diet(-6.51***)

CONTEXT- SPECIFICITYLog GNI per capita

Adj R2 0.84 Adj R2 0.73 Adj R2 0.32

Adj R2 0.43% energy from non staples in national food supply significantly related to % low birth weight (-1.91**)

Adj R2 0.63Fe availability from animal based products significantly related to % minimum acceptable diet (9.93*)

Agriculture ModelAgricultural investments related to dietary patterns

Dietary patterns (proxy for maternal nutrition)

% Energy from non staples in supply

Calories per capita

Fe availabilit

y from animal-products

Contextual factors influencing outcomes

6.88*** to -1.81 dependent on income level

% Energy from non staples in production

-0.39 ***

Access to finance for farmers

0.14**

# tractors per land unit

-0.34 **

Road infrastructure

Exports as % of GDP

Log GNI per capita

Fertilizer use per land unit

0.48**

% land for agriculture

0.15**

Ag R&D as % of GDP

Ag import tariffs

0.47** -0.48 **

Agriculture ModelSupply diversity as a function of production diversity

The relationship between food supply diversity and food production diversity depends on the income level of a country. For low-income countries the diversity of agricultural goods produced by a country is a strong predictor for food supply diversity; for middle- and high-income countries national income and trade are better predictors.

Agriculture ModelAgricultural investments related to complementary feeding

Complementary feeding

% Minimum acceptabl

e diet Fe availabilit

y from animal-products

Contextual factors influencing outcomes

9.02*

% Energy from non staples in production

-4.85*

Exports as % of GDP

Log GNI per capita

% land for agriculture

9.93*

Ag R&D as % of GDP

Ag import tariffs

0.47** -0.48 **

Summary Table of Model Results

Intervention Outcome Impact

Peer counselling EBF The likelihood of EBF is 2.46 higher for mothers who received peer counseling than for mothers who weren’t counseled (95% CI: 1.99 to 3.04, p<0.001).

Facility based promotion EBF The likelihood of EBF is 1.55 times higher for mothers receiving the facility based intervention than for mothers who didn’t (95% CI: 1.31 to 1.84, p<0.001).

Commercial packets of infant formula

EBF The likelihood of EBF for mothers who were not given commercial packets is 1.34 higher than for mothers who received the packets (95% CI: 1.12 to 1.59, p=0.0011).

Combined health interventions (minus mass media campaigns which was assessed qualitatively)

EBF The results show that the likelihood of exclusive breastfeeding for mothers that received the public health interventions is 2.02 higher than mothers who did not (95% CI: 1.74 to 2.34, p<0.001)


Intervention Outcome Impact

Family Planning Promotion

Contraceptive Use Contraceptive use for participants who were exposed to school promotions, media campaigns and community based education is 1.16 higher than for the control groups (95% CI: 1.01 to 1.35, p<0.0425).

Hand washing Diarrhea rates The likelihood of diarrhea for those who were exposed to hand washing interventions is 24% less likely than for those in the control group (RR= 0.76% CI: 0.62 to 0.93, p=0.0074)

Water treatment Diarrhea rates The likelihood of diarrhea those who were exposed to water treatment intervention is 29% less likely than for those in control group (RR= 0.7073% CI: 0.56 to 0.90, p=0.0043).


Investment

Examples specific interventions

Outcome Association

Agricultural diversification

Promotion of animal-based products, homegardens, irrigation, legume intercropping, agro-forestry

Dietary patternsComplementary feeding

Increased food supply diversity in low-income countriesPotential trade-off with calories available per capitaIncreased % of children meeting minimum acceptable diet

Agricultural intensification

Increased fertilizer use per land unit (e.g. subsidy program)Increased number of tractors per land unit

Dietary patterns Increased amount of calories available per capitaPotential trade-off with food supply diversity

Agricultural extensification

Increased % land for agriculture


Increased amount of calories available per capitaPotential trade-off with % children meeting minimum acceptable diet

Rural development

Increased access to finance for farmers (e.g. microcredits)Road infrastructure

Dietary patterns Increased food supply diversity

Trade policies/ strategies

Ag import tariffsExport crops

Dietary patterns Potential trade-off with food supply diversity and micronutrient availability

Ag R&D e.g. biofortification, livestock health programs


Increased micronutrient availability

Implications

• With limited evidence, the evidence at hand suggests that public health, environment and agriculture investments could support nutrition specific interventions that address undernutrition.

• A country’s contextual factors (relating to income, social, education and governance) are important to consider in their impact on nutrition outcomes with nutrition sensitive approaches.

• Examining agriculture through large scale investments, rather than nutrition interventions, can provide insight for MoA on impact for nutrition, indirectly.

• Alternative delivery channels for public health and environment, through marketing, commercialization, food safety, and social protection, are less clear in their evidence of impact.

• Evidence published in the literature remains scant and varied for nutrition sensitive interventions, and more implementation science should be published.

• Using a quantitative statistical simulation model can only go so far as with the current literature and data. This has resulted in some interesting insights but unfortunately it is not a tool that is user friendly for countries looking to scale up nutrition.

• Costing tools and perhaps game tools could provide an entry point for decision making in which this quantitative modeling could be used as a first step resource.

Implications

Maternal Nutrition

Complementary

feeding

Diarrhea incidence

Family planning

Exclusive breastfeedi

ngPUBLIC HEALTH

Peer counselingFacility-based educationNon provision of commercial packagesMaternal counselingHealth worker trainingSchool promotionMedia campaignsCommunity-based education

AGRICULTURE

Agriculture diversification - Animal products- Homegardens- Legumes- Agroforestry- Small scale

irrigationAccess to FinanceFertilizer useAgriculture research and development- BiofortificationRural infrastructureWomen empowerment

ENVIRONMENT AND WATER

Water treatment, HandwashingFood safety measures

SOCIAL PROTECTION

Conditional cash transfers

CONTEXT- SPECIFICITYIncome, Education, Urbanization, Geographic Location, Employment Policy,

Schematic Summary of Findings

(LiST results)

Documents

Columbia University Analysis for the Scaling Up Nutrition (SUN) Secretariat