Population-Based Survey (PBS) Dataset Harmonization and ......REPs should ensure codebooks are...

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Population-Based Survey (PBS) Dataset

Harmonization and Pooling: Potential

Value to USAID and Challenges

A Report from the Food Aid Quality Review

PREPARED BY:

Gabrielle Witham

Audrey Karabayinga

Beatrice Rogers

Patrick Webb

January 2021

PBS DATASET HARMONIZATION AND POOLING JANUARY 2021

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This report was made possible by the

generous support of the American people

through the support of the United States

Agency for International Development’s

Bureau for Humanitarian Assistance

(USAID/BHA) and the legacy Office of Food

for Peace (FFP) under the terms of Contract

No. AID-OAA-C-16-00020, managed by Tufts

University.

The contents are the responsibility of Tufts

University and its partners in the Food Aid

Quality Review (FAQR) and do not

necessarily reflect the views of USAID or the

United States Government.

The authors have no conflict of interest to

declare.

January 2021

Recommended Citation

Witham, Gabrielle; Karabayinga, Audrey;

Rogers, Beatrice; and Webb, Patrick. 2021.

Population-Based Survey Dataset

Harmonization and Pooling: Potential Value to

USAID and Challenges. Report to USAID.

Boston, MA: Tufts University

This document may be reproduced without

written permission by including a full citation

of the source.

For correspondence, contact:

Patrick Webb

Friedman School of Nutrition Science and

Policy

Tufts University

150 Harrison Avenue

Boston, MA 02111

[email protected]

mailto:[email protected]


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ACRONYMS

ACDI/VOCA Agricultural Cooperative Development International/Volunteers in Overseas

Cooperative Assistance

ADIPO Asociación de Desarrollo Integral para el Occidente

ADRA Adventist Development and Relief Agency

AIM Association Interco-operation Madagascar

BDEM Bureau de Développement de l’Ecar Mananjary (Development Office of Ecar

Mananjary)

BHA Bureau for Humanitarian Assistance

CDD Development Council of the Diocese

CNFA Cultivating New Frontiers in Agriculture

CRS Catholic Relief Services

CSB Corn Soy Blend

CSB+ Corn Soy Blend Plus

DEC Development Experience Clearinghouse

DFAP Development Food Assistance Program

DFSA Development Food Security Activity

DMEAL Design, Monitoring and Evaluation, and Applied Learning

EI Emmanuel International

ENSURE Enhancing Nutrition, Stepping Up Resiliency and Enterprise

FAQR Food Aid Quality Review

FFW Food for Work

GHG Growth, Health, and Governance

HAZ Height-for-age Z-score

HKI Helen Keller International

IMC International Medical Corps

IMPEL Implementer-Led Evaluation & Learning Associate Award

IP Implementing Partner

IYCF Infant and Young Children Feeding Practices

LAHIA Livelihoods, Agriculture and Health Interventions in Action

MAD Minimum Acceptable Diet

MCHN Maternal and Child Health and Nutrition

MDD Minimum Dietary Diversity

MMF Minimum Meal Frequency

NASFAM National Smallholder Farmers’ Association of Malawi

NCBA/CLUSA National Cooperative Business Association/Cooperative League of the

United States of America

ORAP Organization for Rural Associations for Progress

PAISANO Programa de Acciones Integradas de Seguridad Alimentaria y Nutricional del

Occidente

PASAM-TAI Programme d’Appui à la Sécurité Alimentaire des Ménages - Tanadin Abincin Iyali

PBS Population-Based Survey

PCI Project Concern International

PLW Pregnant and Lactating Women

REP Research and Evaluation Partner

RWANU Resiliency through Wealth, Agriculture, and Nutrition in Karamoja

SBCC Social and Behavioral Change Communication

SC Save the Children

SEGAMIL Seguridad Alimentaria Enfocada en los Primeros 1,000 Días

SNV Stichting Nederlandse Vrijwilligers ("Foundation of Netherlands Volunteers")

UBALE United in Building and Advancing Life Expectations (UBALE means

“partnership” in Chichewa)


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USAID United States Agency for International Development

WASH Water, Sanitation and Hygiene

WAZ Weight-for-age z-score

WHZ Weight-for-height z-score

WV World Vision


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TABLE OF CONTENTS

ACRONYMS ......................................................................................................................................................... 3

1. Executive Summary ..................................................................................................................................... 7

2. Introduction................................................................................................................................................ 10

3. Methods ...................................................................................................................................................... 12

3.1. Data Source and Activity Selection ............................................................................................... 12

3.2. Activity Designs................................................................................................................................. 13

3.3. Standardized Variable Selection ..................................................................................................... 16

3.4. Creation of Pooled Dataset ............................................................................................................ 16

3.4.1. Pooling Process ........................................................................................................................ 16

3.5. Challenges to Pooling and Data Quality Issues ........................................................................... 19

3.5.1. Geographic Identifiers ............................................................................................................. 19

3.5.2. Z-scores .................................................................................................................................... 19

3.5.3. Missing Unique Identifiers ....................................................................................................... 19

3.5.4. Codebook Values..................................................................................................................... 19

3.5.5. Codebook Variables ................................................................................................................ 20

3.5.6. Missing Variables Needed for Indicator Calculations ........................................................ 20

4. Results ......................................................................................................................................................... 20

4.1. Database Content............................................................................................................................. 20

4.2. Exploratory Demographic and Anthropometric Analyses ........................................................ 21

5. Discussion ................................................................................................................................................... 26

5.1. Potential Use of Pooled Datasets for Programming and Research .......................................... 26

5.2. Limitations of Pooled Datasets ...................................................................................................... 26

5.3. Recommendations for M&E Data Standardization and Reporting ........................................... 27

5.3.1. Data and Metadata ................................................................................................................... 27

5.3.2. Program Design and Reporting ............................................................................................. 28

5.4. Potential Avenues for Expansion of this Work and Further Analysis ..................................... 29

6. References .................................................................................................................................................. 28

Annex 1: Tables .................................................................................................................................................. 30

Annex 2: Codebooks ......................................................................................................................................... 52


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LIST OF TABLES

Table 1. Availability of Activity Documents ................................................................................................... 12

Table 2. Technical Sectors for Development Food Security Activities .................................................... 14

Table 3. Total Mother-Child Unit Rations (PLWs and Children 6-23 Months) ...................................... 15

Table 4. Recoded Rehydration Questions in Guatemala Endline Data..................................................... 18

Table 5. Observations in Pooled Child Dataset by Activity........................................................................ 18

LIST OF ANNEX TABLES

Annex Table 1. Activity Characteristics ......................................................................................................... 30

Annex Table 2. Evaluation and Dataset Details ............................................................................................ 31

Annex Table 3. Strategic Objectives (SO) by Activity ................................................................................. 34

Annex Table 4. Intermediate Objectives (under the main SO outlined in Annex Table 3) by Activity

............................................................................................................................................................................... 35

Annex Table 5. Recoded ICYF Questions ..................................................................................................... 38

Annex Table 6. Data Quality Issues and Solutions ....................................................................................... 42

Annex Table 7. Outputs .................................................................................................................................... 43

Annex Table 8. Baseline Characteristics and Descriptive Statistics .......................................................... 44

Annex Table 9. Endline Characteristics and Descriptive Statistics ............................................................ 45

Annex Table 10. Baseline Nutritional Status Tables .................................................................................... 46

Annex Table 11. Endline Nutritional Status Tables ...................................................................................... 49

LIST OF FIGURES

Figure 1: Age distribution by sex in pooled dataset at baseline and endline............................................ 20

Figure 2: Z-score distribution by age group in pooled dataset at baseline and endline. Dotted line

represents WHO standards. ............................................................................................................................ 21

Figure 3: Z-score distribution by sex in pooled dataset at baseline and endline. Dotted line


Figure 4: Nutrition status by age group and sex in pooled dataset at baseline and endline. Dotted line


https://tuftscloud-my.sharepoint.com/personal/gwitha01_tufts_edu/Documents/FAQR/BHA/FAQR%202020%20Report_BHA_Final%20Draft.docx#_Toc61616738








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1. EXECUTIVE SUMMARY

The Food Aid Quality Review (FAQR) project managed by Tufts University Friedman School of

Nutrition Science and Policy undertook a review of the feasibility of harmonizing and pooling

project-level performance evaluation data collected through population-based surveys (PBS) by

implementing partners of the United States Agency for International Development (USAID). The

Monitoring and Evaluation (M&E) process is designed to ensure USAID’s accountability to its

stakeholders and to promote improvements in development outcomes.1 The goal of this review,

which ran from May 2020 to December 2020, was to examine the possibility of tapping into the

unrealized potential of PBS data collected at project baseline and endline to deepen USAID’s

understanding of program effectiveness and its determinants. The datasets were drawn from the

baseline and endline evaluations of 13 USAID-funded Development Food Security Activities

(DFSAs)2 implemented by international non-governmental organizations (INGOs) and local partners

between 2012 and 2019 in Guatemala, Madagascar, Malawi, Niger, Uganda, and Zimbabwe.

The primary objectives of the project were to:

1. Demonstrate how the standardization of data collection and reporting can facilitate future

efforts by USAID to harmonize and pool datasets.

2. Enhance USAID’s understanding of how pooled PBS data could inform future programming

and policy decisions.

3. Provide a more nuanced understanding of the effectiveness of DFSAs based on its own PBS

data and calibrate the expectations of USAID/Bureau for Humanitarian Assistance (BHA)

regarding DFSA outcomes.

This project included a review of related DFSA documents (proposals, BL/midterm/Endline

evaluations, and annual reports), creation of pooled and harmonized datasets for the child health and

nutrition technical sector, exploratory analyses of the resulting pooled data, development of a set of

recommendations for USAID to facilitate future efforts to pool PBS data, and direction for how the

pooled datasets can be leveraged and expanded upon in the future.

The 13 DFSAs used for this project were selected by FAQR and BHA due to the fidelity of their

evaluations and the data collected to USAID’s M&E standards. Some of the documents and datasets

were publicly available on the Development Experience Clearinghouse (DEC), USAID’s online

repository that houses technical and program documents from USAID-funded activities, or on

implementing partner websites. The rest were provided to the FAQR team by representatives at

BHA. The interventions of selected DFSAs were categorized in the following core technical sectors:

Agriculture and Livelihoods, Risk Management and Disaster Risk Reduction, Maternal and Child

Health and Nutrition (MCHN), Natural Resource Management (NRM), Water, Sanitation and

1 USAID LEARN, “Evaluation Toolkit,” Text, USAID Learning Lab, February 19, 2015,

https://usaidlearninglab.org/evaluation-toolkit. 2 The name of these activities has evolved over time; titles that have been utilized over the years include:

Development Assistance Program (DAP), Multi-Year Assistance Program (MYAP), Development Food Assistance Project (DFAP), Development Food Aid Project (DFAP), Development Food Security Activity (DFSA) and most currently, Resiliency Food Security Activity (RFSA). For simplicity, this report will refer to all

activities as DFSAs.


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Hygiene (WASH), Market Analysis, Food Assistance for Improved Nutritional Outcomes, and Social

and Behavioral Change Communication (SBCC).

Of the variables from the original datasets, 106 variables were selected for the pooled child health

and nutrition datasets, including two additional identifier variables and four recalculated z-score

variables that were added during the harmonization and pooling process. Not all selected variables

were available for every DFSA and missing/absent data was coded as such. The variable names,

variable labels, values, and value labels were harmonized, and data was pooled using R statistical

software. Inclusion criteria were applied to the resulting pooled datasets, removing all child records

with ages less than zero months and greater than 59 months.

The harmonized aggregated child health and nutrition datasets have the following advantages over

single-DFSA datasets:

1. They can facilitate more robust analyses of correlates of undernutrition with greater

statistical power.

2. They allow for analyses to be disaggregated by sociodemographic factors, project

performance on health and nutrition behaviors and indicators, and other factors.

3. They can be used to compare outcomes across a range of geographic contexts.

4. They provide additional data for researchers and policymakers to analyze with country-level

data, climate data, and other external data to explore questions related to food assistance

for nutrition.

The systematic approach outlined in this report and the R syntax files that are included as annexes

allow for the results of this project to be replicated using datasets from other technical sectors that

were not used here (e.g. WASH, Agriculture, and others), as well as new datasets collected by

USAID. Several quality control recommendations are spelled out which would allow USAID to

improve the accessibility and interoperability of DFSA PBS data, and to facilitate future analyses of

program design characteristics in conjunction with these data. These recommendations include:

1. USAID should provide guidance to Research and Evaluations Partners (REPs) to use a

standardized set of variable names, variable labels, values, and value labels to facilitate future

efforts to pool and compare data among activities.

2. REPs should ensure codebooks are comprehensive (include all variable names, variable

labels, values, value labels, etc.) and that all other accompanying documentation needed to

use and understand the datasets, such as readme files, are readily available.

3. REPs should state in their reports and accompanying documentation which exclusion criteria

and/or case flags were applied to the values of each variable for the omission of records

from the calculation of indicators or in analyses.

4. USAID should provide guidance to REPs on how to group datasets by technical sector (e.g.,

WASH, Poverty) or other categorical division (e.g., persons, households) for consistency.

5. To facilitate future cross-DFSA analysis of food assistance interventions, USAID should

standardize how implementing agencies document their food ration distribution modalities

so that these program details can be used in analyses across all activities.


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Standardizing the ways implementing partners document their food assistance program design and

how REPs collect, organize, and store data will enable USAID to facilitate future efforts to

harmonize and pool PBS datasets. These efforts will provide program staff, researchers and

policymakers with quality data to use to support decision-making and bolster other research data

without undertaking further costly data collection endeavors, ultimately benefitting the vulnerable

populations served by USAID/BHA.


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2. INTRODUCTION

The Food Aid Quality Review (FAQR) project at Tufts University, funded by the United States

Agency for International Development’s Bureau for Humanitarian Assistance (USAID/BHA) and the

legacy Office of Food for Peace (FFP), provides actionable recommendations to USAID and its other

partners on ways to improve nutrition among vulnerable populations. As a review under FAQR’s

Option Year 2, FAQR used baseline and endline Population-Based Survey (PBS) data from 13

Development Food Security Activities (DFSAs) to create harmonized and pooled multi-DFSA

datasets. BHA collects high quality quantitative data on outcome indicators to evaluate performance

of DFSAs and to support adaptive management decisions. These datasets have unrealized potential

for use beyond the specific DFSA to inform external programming, policy and research, and this

project sought to leverage the underutilized potential of the datasets by demonstrating a procedure

to make them accessible and usable for both internal audiences and those external to USAID.

The primary objectives of the project were to:

1. Demonstrate how the standardization of data collection and reporting can facilitate future

efforts by USAID to harmonize and pool datasets.

2. Enhance USAID’s understanding of how pooled PBS data could inform future programming

and policy decisions.

3. Provide a more nuanced understanding of the effectiveness of DFSAs based on its own PBS

data and calibrate the expectations of USAID/BHA regarding DFSA outcomes.

A similar effort to pool and harmonize PBS data was undertaken by Feed the Future as an activity of

the Gender, Climate Change, and Nutrition Integration Initiative (GCAN) with the goal of enhancing

access to Feed the Future PBS and interoperability of these datasets with other datasets.3 Beyond

this project, other efforts to harmonize and pool PBS data have either not been made or are not

well-documented. However, other studies have underscored the value of pooled data for analysis,

including: increased statistical power in analyses, allowing for the assessment of outcomes across

contexts and for a variety of treatments and subpopulations, and reproducing correlational analyses.4

FAQR’s efforts to harmonize and pool PBS data from DFSAs implemented in several countries

across a wide range of contexts represents a novel approach to expanding the knowledge base of

food assistance for nutrition interventions and their impacts.

An initial interaction between FAQR and USAID/BHA’s Design, Monitoring and Evaluation, and

Applied Learning (DMEAL) Division in May 2020 led FAQR to select the 13 activities whose data and

documents were used for this report. Implemented in different countries and contexts, these DFSAs

were all designed to reduce food insecurity among vulnerable populations and help build resilience in

communities facing chronic poverty and recurrent crises.

3 “Gender, Climate Change, and Nutrition Integration Initiative (GCAN),” Feed the Future, accessed

November 23, 2020, https://gcan.ifpri.info/. 4 J. T. van der Steen et al., “Benefits and Pitfalls of Pooling Datasets from Comparable Observational Studies:

Combining US and Dutch Nursing Home Studies,” Palliative Medicine 22, no. 6 (September 2008): 750–59, https://doi.org/10.1177/0269216308094102.

van der Steen et al.


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Following technical reference guidance for program design from USAID,5&6 all 13 activities employed

variations of interventions in the same core technical sectors: Agriculture and Livelihoods, Risk

Management and Disaster Risk Reduction, Maternal and Child Health and Nutrition (MCHN),

Natural Resource Management (NRM), Water, Sanitation and Hygiene (WASH), Market Analysis,

Food Assistance for Improved Nutritional Outcomes, and Social and Behavioral Change

Communication (SBCC). While the core technical sectors were consistent across DFSAs, there was

considerable heterogeneity in the individual contexts and variations in strategic objectives, design,

and implementation of programs. Despite these programmatic differences, FFP’s Indicators

Handbook7 provides research and evaluation partners (REPs) and program implementers with a

standardized PBS customizable to the local context, as well as guidance on the tabulation of

indicators required for FFP (now BHA) DFSAs. Thus, the PBS datasets included a relatively

homogeneous set of variables, simplifying the process to harmonize and pool data.

USAID contracted with several REPs (ICF International, Tango International, and others listed in

Annex Table 2) to conduct data collection, cleaning, and analysis for baseline and endline surveys

for evaluations. As a supplement to the datasets, FAQR used proposals (with budgets redacted),

evaluations, annual reports, and publicly available guidelines on data collection, program design, and

indicator tabulation to facilitate the data harmonization process and exploratory analyses included in

this report.

Aside from the harmonization and pooling of PBS datasets, several secondary goals were considered

but ultimately not achieved, reasons for which are explained below. These secondary goals included:

1. Using DFSA design to evaluate the context in which these moderate acute malnutrition

(MAM) and severe acute malnutrition (SAM) interventions were implemented, with the hope

of better understanding the contribution of complementary programs like water, sanitation,

and hygiene (WASH) and agricultural interventions to assessed effectiveness;

2. Using program budgets to conduct an analysis of cost-effectiveness; and

3. Expanding the information base on MAM/SAM interventions that do not use specialized

nutritious foods (SNFs), like cash transfer programs.

However, for several reasons, the secondary goals were not possible to achieve. As previously

mentioned, all 13 activities worked in some variation of the same core technical sectors, so a

comparison between DFSAs that did and did not implement WASH, for example, was not possible.

Additionally, in the program proposals provided to FAQR by BHA, budgetary information was

redacted, rendering the cost-effectiveness analysis impossible. Finally, the initial program designs of

all activities included use of specialized nutritious foods, and due to a non-standardized approach in

5 “Technical References for FFP Development Projects” (U.S. Agency for International Development Bureau of Democracy, Conflict, and Humanitarian Assistance Office of Food for Peace (FFP), April 8, 2015), https://www.usaid.gov/sites/default/files/documents/1866/Technical%20References%20for%20FFP%20Developm

ent%20Projects%204-23-15%20%282%29.pdf. 6 “Technical References for Development Food Security Activities” (Office of Food for Peace, Bureau for

Democracy, Conflict and Humanitarian Assistance, USAID, February 2018), https://www.usaid.gov/sites/default/files/documents/1866/FFP_Technical_References_Feb2018.pdf. 7 “Food for Peace Indicators Handbook. Part 1: Indicators for Baseline and Final Evaluation Surveys”

(Washington, DC: Food and Nutrition Technical Assistance III Project (FANTA III), April 2015).


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reporting changes to food assistance program designs, a separate analysis of cash transfer or voucher

programs was not possible.

What resulted from this project is the following:

1. A complete set of harmonized, pooled child health and nutrition datasets and codebooks;

2. The report that follows, which outlines the systematic approach to harmonize and pool BHA

PBS datasets and provides a set of recommendations for BHA to facilitate future efforts to

leverage PBS data; and

3. The associated R syntax that will allow for replication with other technical sectors that were

not pooled during this project (such as WASH, Agriculture, and others).

3. METHODS

3.1. DATA SOURCE AND ACTIVITY SELECTION

This review used evaluation data and information from proposals and data collection rounds from 13

DFSAs (see Annex Table 1) awarded to partner organizations in Guatemala, Niger, Uganda,

Zimbabwe, Madagascar, and Malawi by USAID/BHA and the legacy Office of Food for Peace (FFP)

between 2012 and 2019. The FAQR team at Tufts University, in consultation with BHA’s monitoring

and evaluation (M&E) team, selected 13 closed-out DFSAs8 with consistent and high-quality data and

evaluation reports. BHA’s M&E team provided FAQR with those proposals (with budgetary

information redacted), evaluations, deidentified datasets, and codebooks which were not available

publicly on DEC or from other online sources (see Table 1). Documents marked with ☒ and

highlighted in blue were not available and thus were not provided to FAQR.

Table 1. Availability of Activity Documents

Country Activity Name

Activity Proposal

Evaluations Annual Reports

Datasets, Codebooks, & ReadMe

Files Baseline Midterm Endline

Guatemala SEGAMIL ☑ ☑ ☑ ☑ ☑ ☑

PAISANO ☑ ☑ ☑ ☑ ☑ ☑

Niger

LAHIA ☑ ☑ ☑ ☑ ☑ ☑

Sawki ☑ ☑ ☒ ☑ ☑ ☑

PASAM TAI

☑ ☑ ☑ ☑ ☑ ☑

Uganda RWANU ☑ ☑ ☒1 ☑ ☒ ☑

GHG ☑ ☑ ☒2 ☑ ☒ ☑

Zimbabwe Amalima9 ☒ ☑ ☑ ☑ ☑ ☑

ENSURE ☑ ☑ ☑ ☑ ☑ ☑

8 FAQR omitted one project (SHOUHARDO, Bangladesh) from the original list provided by BHA due to a

corrupted proposal file which prevented a coherent analysis of project objectives against outcomes. 9 Amalima is the Ndebele word for the social contract by which families come together to help each other engage in productive activities such as land cultivation, livestock tending, asset building and their own

development initiatives.


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Madagascar

ASOTRY10

☑ ☑ ☑ ☑ ☑ ☑

Fararano11 ☒ ☑ ☑ ☑ ☑ ☑

Malawi NJIRA12 ☑ ☑ ☑ ☑ ☑ ☑

UBALE ☑ ☑ ☑ ☒ ☑ ☑ 1&2Midterm evaluation was not conducted.

The following publicly available technical guidance documents were also used in this project to

provide context for the datasets, DFSA designs, and indicators, and to assist in their interpretation.

1. Technical References for Food for Peace Development Projects (USAID, 2015)

2. Technical References for Development Food Security Activities (USAID, 2018)

3. Food for Peace Indicators Handbook. Part 1: Indicators for Baseline and Final Evaluation

Surveys (USAID, 2019)

4. Food for Peace Indicators Handbook - Supplement to Part 1: FFP BL/endline Questionnaire

and Indicator Tabulations for Development Food Security Activities (USAID, 2019)

5. Food for Peace Indicators Handbook Part II: Annual Monitoring Indicators (USAID, 2019)

6. Indicators for Assessing Infant and Young Child Feeding Practices, Part 2: Measurement

(WHO, 2010)

7. WHO child growth standards and the identification of severe acute malnutrition in infants

and children (WHO, 2009)

8. Minimum Dietary Diversity for Women – A Guide to Measurement (FAO, 2016)

USAID contracted with various REPs (see Annex Table 2) to conduct the evaluations from which

the data used in this report are derived. There were variations in how comprehensive datasets and

codebooks were, and how data were catalogued and coded across DFSAs, an issue which is

discussed in Section 3.5 in more detail.

3.2. ACTIVITY DESIGNS

The goal of DFSAs is to reduce food insecurity among vulnerable populations and help build

resilience in communities facing chronic food insecurity and recurrent crises though interventions in

various technical sectors. The sectors included in both the 2015 and 2018 Technical References for

DFSAs are i) Agriculture and Livelihoods, ii) Risk Management and Disaster Risk Reduction, iii)

Maternal and Child Health and Nutrition (MCHN), and iv) Water, Sanitation and Hygiene (WASH).13

The sector defined in the 2015 guidelines only is Natural Resource Management (NRM), and the

10 “ASOTRY” means “harvest” in Malagasy. 11 “Fararano” means “harvest season” in Malagasy. 12 “NJIRA” means “footpath or way of achieving something” in Chichewa. 13“Technical References for FFP Development Projects” (U.S. Agency for International Development Bureau of

Democracy, Conflict, and Humanitarian Assistance Office of Food for Peace (FFP), April 8, 2015), https://www.usaid.gov/sites/default/files/documents/1866/Technical%20References%20for%20FFP%20Development%20Projects%204-23-15%20%282%29.pdf.


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sectors defined in the 2018 guidelines only are i) Market Analysis, ii) Food Assistance for Improved

Nutritional Outcomes, and iii) Social and Behavioral Change.14

These technical sectors were further divided into subcategories, which are listed in Table 2. Each

DFSA was required to incorporate in to its program design a Theory of Change, a Log Frame (logical

framework), an Annual Monitoring Plan, an M&E Staffing Plan, an Organogram, and a Capacity

Development Strategy, as well as cross-cutting objectives of Gender, Climate Risk Management, and

Environmental Safeguards and Compliance. A typical life cycle for DFSAs is five years, with baseline

PBS data collected at the start of the DFSA and endline PBS data collected towards the end of the

cycle. Some DFSAs may be extended beyond the initial five-year period.

Table 2. Technical Sectors for Development Food Security Activities

2015 Technical Sectors 2018 Technical Sectors

1. Agriculture and Livelihoods 1. Agriculture and Livelihoods

Profitable, sustainable farm and land management

Household economics (including nutrition

pathways)

Human and institutional capacity building

Profitable, sustainable farm and land management

Household economics (including nutrition

pathways)

Human and institutional capacity building

2. Risk Management and Disaster Risk Reduction 2. Risk Management and Disaster Risk Reduction

3. Maternal and Child Health and Nutrition 3. Maternal and Child Health and Nutrition

Health and nutrition systems strengthening

Social and behavior change communication

Food assistance for improved nutritional

outcomes

Health and nutrition systems strengthening

Essential nutrition actions

Community-based management of acute

malnutrition

Health and nutrition of women of reproductive age

Reproductive health and family planning

Nutritional counseling, assessment and support

4. Water, Sanitation and Hygiene 4. Water, Sanitation and Hygiene

Linking WASH and nutrition

Water supply infrastructure

Sanitation infrastructure

Hygiene promotion

Irrigation

Environmental health

Linking WASH and nutrition

Drinking water access, service delivery, and

governance

Sanitation: Behavior change and facilitating access

Hygiene promotion and behavior change

Water quality - centralized and household water

treatment

5. Natural Resource Management 5. Social and Behavior Change

Soil productivity

Water management

Diversified and productive landscapes

Infant and young child feeding

Early childhood development

6. Food Assistance for Improved Nutritional

Outcomes

Commodity selection and ration design

Locally procured specialty nutrition products

7. Market Analysis

14 “Technical References for Development Food Security Activities” (Office of Food for Peace, Bureau for

Democracy, Conflict and Humanitarian Assistance, USAID, February 2018),

https://www.usaid.gov/sites/default/files/documents/1866/FFP_Technical_References_Feb2018.pdf.


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A complete list of strategic objectives and intermediate results for each DFSA included in this

project is included in Annex Tables 3-4.

Most interventions involving food assistance for nutrition (food rations) targeted the first 1,000 days

of life (PLW and children up to the age of 2) and some also included a household or family ration, a

lean season ration, or a food for work (FFW) ration. Rations of Corn Soy Blend (CSB) or Corn Soy

Blend plus (CSB+) and fortified vegetable oil were distributed to participants; however, there was

heterogeneity among DFSAs in how those ration quantities were reported in initial proposals (see

Table 3) and the quantities, frequency, and distribution was sometimes different in implementation

than the original plan laid out in the proposal. Knowing that the food ration modalities used across

activities varied, having standardized requirements for how modalities are documented (quantities,

frequencies, recipients, changes after midterm evaluation, etc.) would have facilitated the comparison

of food ration program design across activities, a secondary objective that was not ultimately

achieved during the present review.

Table 3. Total Mother-Child Unit Rations (PLWs and Children 6-23 Months)

a PLW ration provided until 6 months after birth (i.e. only during exclusive breastfeeding window). At 6 months after birth,

child ration commences. b A proposal/technical narrative was not provided for this project, which was the source of the ration details provided in this table. c Some reports did not specify whether PLW ration is stopped or reduced at 6 months when child ration commences.

Country Activity Name

CSB/CSB+ (g/day)

Vegetable Oil (g/day)

Total Calories (kcal/d)

Protein (g/d)

Other Rations

Guatemala

SEGAMIL 150 15 932 35 36.3 g/d rice

30.3 g/d pinto

PAISANO 167 60 - - 167 g/d rice

167 g/d beans

Niger

LAHIA 167 17 774 28.7 -

SAWKI 166 - - - -

PASAM TAI 167 25 847 29 -

Uganda

RWANU PLW: 133

6-23 mos: 75 15 - -

PLW: 50 g/d split green

peas

GHG

PLW: 165

Malnourished CU2: 100

PLW: 30


PLW: 1107


PLW: 44


PLW: 65 g/d peas

Zimbabwe AMALIMAb - - - - -

ENSURE 100 30 641 15 -

Madagascar ASOTRYa

PLW: 400

6-24 mos: 100

PLW: 23.125

6-24 mos: 30

PLW: 1708

6-24 mos: 641

PLW: 61.2

6-24 mos: 15.3 -

FARARANOb - - - - -

Malawi NJIRAc

PLW: 133

6-23 mos: 50

PLW: 30

6-23 mos: 15 - -

PLW: 50 g/d pinto beans

UBALE 100 30 641 15.3 -


16

3.3. STANDARDIZED VARIABLE SELECTION

The FAQR team conducted a rigorous cross-program comparison of original codebooks and

datasets to select 106 variables for inclusion in the final child health and nutrition pooled dataset. A

complete list of standardized variables, codes, and labels is included in the Child Health and

Nutrition Codebook in Annex 2 - Codebooks. The variables are divided into the following

categories:

1. Record identifiers (e.g., id, country, DFSA name, implementing partner, interview date,

household number, child line number)

2. Geographic identifiers (e.g., district, region, ward, commune, enumeration area, village

number)

3. Demographic (e.g., gender, age in days and months)

4. Child anthropometric (e.g., weight, height, if height/length was measured laying down or

standing up, edema, weight/height-for-age and weight-for-height z-scores, underweight,

stunted, wasted)

5. Statistical (e.g., sampling weight, stratum, cluster)

6. Child health and nutrition (e.g., breastfeeding status, diarrhea, dietary questions for

Minimum Dietary Diversity (MDD), Minimum Acceptable Diet (MAD), Minimum Meal

Frequency (MMF))

7. Dietary indicator tabulations (e.g., MDD, MAD, MMF)

3.4. CREATION OF POOLED DATASET

3.4.1. POOLING PROCESS

Baseline and endline data were recoded, cleaned and merged using R Version 1.3.959 and two R

packages,15&16 resulting in CSV files of the data and R syntax files for the R code used. A codebook

was created to accompany each of the pooled baseline and endline datasets with variables, codes,

labels, and values included in the datasets. These items, as referred to throughout this report, are

defined as follows:

• Variable name – the numeric, alphanumeric, or character string used to represent the

variable in the dataset (e.g., “D16”).

• Variable label – the full description of the measure (e.g., “Has the child ever been

breastfed?”).

• Value – a possible observation/response for a given variable recorded in the dataset (e.g.

0/1, yes/no, a number [age in days/months], or name [name of district], etc.).

• Value Label – the corresponding description of the value in the codebook if the dataset

value is numeric (e.g., “no” if the value was “0”).

15 Hadley Wickham et al., Dplyr: A Grammar of Data Manipulation, 2020, https://CRAN.R-project.org/package=dplyr. 16 Nicholas Tierney et al., Naniar: Data Structures, Summaries, and Visualisations for Missing Data, 2020,

https://CRAN.R-project.org/package=naniar.


17

Prior to pooling, variable names were standardized to match either to the most commonly used

variable name among all activities or to a name that clearly and succinctly described the variable.

Columns were added to pooled datasets when the variables selected for the pooled datasets were

missing from the original datasets, with values appearing as “NA” if there was no data available for

that variable. The following variables were added to all records if not already present: country

(“country”), implementing partner (“partner”), DFSA name (“activity”), whether the data came from

a baseline or endline evaluation (“bl_el_indicator”), and what year the data was collected

(“bl_el_year).

Nominal data in the original datasets had variations in coding (i.e. “yes” and “no” were sometimes

coded “yes” = 1, “no” = 2 and other times as “no” = 0, “yes” = 1); and some datasets retained the

character strings associated with the nominal response (e.g. “yes” or “no”), while others associated

the nominal response with a numeric code (“yes” = “1”, “no” = “2”). All character strings, with the

exception of geographic identifiers (see Section 3.5.1.), were recoded to numeric values, and all

numeric value labels for a given value were kept consistent throughout the pooled datasets (e.g. a

“yes” response is always “1” and a “no” response is always “2”). In addition, where “NA” was coded

as numeric (e.g. “99”, “999”, “998”, or a non-integer variation), it was replaced with “NA” in the

pooled dataset.

A unique child identifier (“id”) was created for all activities by combining the variables country

(“country”), DFSA (“activity”), and a serial identification number generated in R (“casenum”). The

“casenum” variable was an automatically generated unique number 1–n for n observations in a

dataset (e.g., row/observation 1 was assigned the number 1, row/observation 2 was assigned the

number 2, etc.). A unique variable (“unique_ID”) was also added, as requested by BHA, to permit

unique record identification when baseline and endline datasets are merged. It is a combination of

the unique child identifier (“id”) and the variable that indicates whether a record is from the baseline

or endline evaluation (“bl_el_indicator”).

Infant and young child feeding (IYCF) practices indicators from Module D of the surveys17 were

retained in the pooled child health and nutrition dataset. There were variations in food categories

between countries, so the FAQR team adhered to the core food groups included in the sample

questionnaire of the IYCF Module of WHO’s indicators for assessing IYCF practices18 and merged

related categories from datasets that had more expansive options so that if the respondent

answered “yes” to at least one of the related questions, then the response to the merged category

would be coded as “yes” (see Annex Table 5). Regional variations in category examples were

retained in the final codebook. The same was done in the Guatemala endline dataset for questions

related to the treatment of diarrhea that varied slightly from the possible responses that were

standard across all other datasets (see Table 4).

17 “Food for Peace Indicators Handbook - Supplement to Part 1: FFP Baseline/Endline Questionnaire and Indicator Tabulations for Development Food Security Activities,” May 21, 2020, https://www.usaid.gov/food-

assistance/documents/ffp-indicators-handbook-supplement-part-1. 18 “Indicators for Assessing Infant and Young Child Feeding Practices, Part 2: Measurement” (Geneva,

Switzerland: World Health Organization, 2010),

https://apps.who.int/iris/bitstream/handle/10665/44306/9789241599290_eng.pdf?ua=1.


18

Table 4. Recoded Rehydration Questions in Guatemala Endline Data

Once datasets were pooled, records with a child age of less than zero months or greater than 59

months were removed and implausible data values (i.e. age as a negative number) were discarded.

Observations were also omitted where they were missing values required for calculation of

anthropometric values (i.e. gender, age, height, and weight). Z-scores beyond the WHO cut-offs19

were retained in the pooled datasets but were not included in analyses. The final pooled baseline

dataset includes 31,184 records, and the final pooled endline dataset includes 16,635 records (see

Table 5).

Table 5. Observations in Pooled Child Dataset by Activity

Country and Activity # of Observations:

Child Baseline Data # of Observations: Child Endline Data

Guatemala

SEGAMIL

PAISANO

5,621

3,015

2,606

2,604

1,339

1,265

Niger PASAM-TAI

SAWKI

LAHIA

9,329 2,864

2,674

3,791

8,020 2,677

2,399

2,944

Uganda GHG

RWANU

5,551 2,855

2,686

2,249 1,064

1,185

Zimbabwe

AMALIMA ENSURE

3,232

1,647 1,585

1,062

288 774

Madagascar

ASOTRY FARARANO

3,710

1,901 1,809

1,536

749 787

Malawi

NJIRA

UBALE

3,741

2,120

1,621

1,164

431

733

Total, combined 31,184 16,635

19 World Health Organization, WHO Child Growth Standards: Length/Height-for-Age, Weight-for-Age, Weight-for-

Length, Weight-for-Height and Body Mass Index-for-Age: Methods and Development (World Health Organization,

2006).

FAQR Dataset

Variable Name

FAQR Dataset

Variable Label

Endline Dataset

Variable Name

Endline Dataset

Variable Label

D62A

Was the child given any

fluid made from a special

packet? (Sachet SRO)

D62c Hydration Saline Solution

D62B

Was the child given any

govt. recommended

homemade fluids (e.g. ESS/SSS: sugar-salt water

solution)?

D62d Homemade Remedies

D62b Was Child Given A Home-

Made liquid


19

3.5. CHALLENGES TO POOLING AND DATA QUALITY ISSUES

3.5.1. GEOGRAPHIC IDENTIFIERS

Due to fact that geographic identifiers (including “strata”) were dependent upon the administrative

divisions used by the census in each country, there were many possible geographic identifier variable

names included in the final codebook. Additionally, since these data values varied in their

classification among DFSAs (character or numeric) and didn’t always have corresponding codebook

entries, the original child health and nutrition dataset values were retained even if the values were

character strings or were not unique in the pooled dataset (e.g. if “strata” = 602 was used in both

Zimbabwe and Uganda to represent what are obviously different strata). The implications of this

decision for data analysis are included in Section 4.1. However, the nominal geographic identifiers

that were either included in the dataset as character strings or coded as numeric with their

corresponding nominal values listed in the codebook (“district,” “department,” “region,”

“commune”) were recoded with unique numeric values and listed in the pooled datasets codebooks.

3.5.2. Z-SCORES

Due to errors in the z-scores in some of the original datasets, child anthropometric z-scores for all

activities at both baseline and endline were re-calculated using R package ‘Anthro’.20 The following

datasets were found to have either errors or missing z-scores:

• Guatemala Persons – endline (e.g., “whz” = -145)

• Uganda Persons – GHG – endline (e.g., “waz” = -176)

• Uganda Persons – RWANU – endline (e.g., “haz” = -204)

• Niger Persons – endline (e.g., “waz” = -370)

• Niger Child – baseline (missing z-score calculations for 3342 records, despite some having

valid age in days, gender, weight, and height/length variables)

The z-scores from the original datasets (where applicable) were retained in the pooled datasets and

the recalculated z-scores were included in adjacent columns. For Niger, the 3342 records missing z-

scores had been omitted in the baseline evaluations by the original REPs as they could not be linked

to other technical sectors. Those that did not have any missing age, height, or weight were retained

in the pooled baseline dataset.

3.5.3. MISSING UNIQUE IDENTIFIERS

Due to missing identifiers in the original Malawi’s UBALE and Njira datasets, these DFSAs were

initially excluded from the pooled child dataset. The child anthropometric data and IYCF data were

stored in separate datasets (child and persons, respectively), and the child line number that was used

to link records between the two datasets was missing from the original child dataset. Therefore,

child anthropometry data could not be linked to IYCF data. In addition, the “unique_id” and

“unique_mem_id” variables were not unique, so could also not be used to link the same record in

20 Dirk Shumacher, Anthro: Computation of the WHO Child Growth Standards, version R package version 0.9.3, 2020, https://CRAN.R-project.org/package=anthro.


20

the child and persons datasets. The datasets were sent back to the REPs for correction, and the

revised (corrected) datasets were included in the pooled child dataset.

3.5.4. CODEBOOK VALUES

Of the datasets used in this report, four (Uganda baseline, Zimbabwe endline, Madagascar baseline

and Malawi endline) either had values that were not defined in the codebook or the datasets had

different values than the ones that were defined in the codebook. Where value labels could be

inferred based on other labels in the codebook, they were recoded using the standard label for that

value. Where labels were missing entirely from the corresponding codebook and could not be

inferred, all values of that dataset were omitted from the pooled dataset. A list of variables that

were recoded or omitted due to missing codebook values is included in Annex Table 6.

3.5.5. CODEBOOK VARIABLES

The endline evaluations for Guatemala and Niger erroneously used identical codebook sections,

despite differences in variables and labels for each country. These codebooks used the same regional

examples for IYCF questions despite being relevant only to Niger (see variables highlighted in yellow

in Annex Table 5). For example, for variable D31, which corresponded to the IYCF Module

question “Any other liquids such as [list other water-based liquids available in local setting]?”, listed

“Thea, decoction, sugared water roubout” as examples for Guatemala endline (the same regional

examples listed for Niger endline) instead of the contextually appropriate examples of “Corn, rice

water, barley water, pelo de maiz, chamomile?” listed for Guatemala BL. These variables were

verified with the questionnaires to ensure a match with the corresponding IYCF question prior to

recoding.

3.5.6. MISSING VARIABLES NEEDED FOR INDICATOR CALCULATIONS

The activities from Zimbabwe (ENSURE and Amalima) were initially omitted from the exploratory

analyses in Section 4 because the original endline datasets provided did not include the “agedays”

(age in days) variable needed for calculation of anthropometric z-scores and prevalence. Upon

consultation with the REP responsible for this evaluation, new Zimbabwe endline datasets that had

this variable were provided to FAQR. However, these new datasets included only anthropometric

data (not IYCF data) and did not have a unique ID that would allow the new anthropometric dataset

to be merged with the IYCF data from the old dataset. Further consultation resulted in the

resolution of these issues and allowed incorporation of these DFSAs.

4. RESULTS

4.1. DATABASE CONTENT

This review yielded the pooled datasets listed in Annex Table 7 and the following codebooks, also

included in the annexes: Child Health and Nutrition Baseline Codebook and Child Health and

Nutrition endline Codebook (Annex 2: Codebooks).

Because numeric and character values from the original dataset were retained in the pooled dataset

for “ward,” “VN” (village number), “strata,” and “cluster” without being recoded into unique values,


21

analyses using these variables must also include the country and DFSA variables. In addition, DFSA

sample sizes included in the pooled datasets may differ from sample sizes used in evaluations because

only the exclusion criteria outlined in Section 3.4.1. were applied to datasets, and in some cases

(e.g. Niger baseline) this differed from flags used by REPs.

4.2. EXPLORATORY DEMOGRAPHIC AND ANTHROPOMETRIC ANALYSES

Exploratory analyses were conducted to summarize and visualize the main characteristics of the

pooled datasets. Some characteristics and corresponding descriptive statistics were first generated

for all activities at baseline and endline (see Annex Tables 8 and 9). Further exploratory analysis

was conducted using the WHO Anthro Survey Analyser,21 an online tool based on R Shiny Package22

built to analyze child anthropometric survey data and provide a set of outputs including z-scores,

prevalence estimates by stratification variables and a summary report, as well as graphics and tables.

Unweighted exploratory analyses were conducted on the pooled data in January 2021. Sampling

weights from the original datasets were retained in the pooled datasets to allow for future re-

calculation but were not used in the analyses, as they are not appropriate to use when pooling data

for meta-analysis. Stratification was done at the level of standard age groups, sex, and DFSA. Output

plots were generated to compare age distribution by sex (Figure 1), z-score distribution by age

group (Figure 2), z-score distribution by sex (Figure 3), and nutrition status by age group and sex

(Figure 4). Nutrition status tables (height-for-age, weight-for-age, and weight-for-height z-scores)

for both baseline and endline grouped by age group, sex and DFSA are also available in Annex

Tables 10 and 11.

21 World Health Organization, “The WHO Anthro Survey Analyser,” World Health Organization [Internet].

Available. Available: Https://Whonutrition.Shinyapps.Io/Anthro/, n.d. 22 shiny: Web Application Framework for R. https://cran.r-project.org/web/packages/shiny.

https://cran.r-project.org/web/packages/shiny


22

Figure 1: Age distribution by sex in pooled dataset at baseline and endline.

Baseline (n = 31,184)

00-05 mo 06-11 mo 12-23 mo 24-35 mo 36-47 mo 48-59 mo

Standard Age Group

6000

4000

2000

0

Count

Endline (n = 16,635)

00-05 mo 06-11 mo 12-23 mo 24-35 mo 36-47 mo 48-59 mo

Standard Age Group

Count

3000

2000

1000

0


23

Figure 2: Z-score distribution by age group in pooled dataset at baseline and endline. Dotted line

represents WHO standards.

Baseline

Endline


24

Figure 3: Z-score distribution by sex in pooled dataset at baseline and endline. Dotted line

represents WHO standards.

Baseline

Endline


25

Figure 4: Nutrition status by age group and sex in pooled dataset at baseline and endline. Dotted

line represents WHO standards.

Endline

Baseline


26

5. DISCUSSION OF FINDINGS

5.1. POTENTIAL USE OF POOLED DATASETS FOR PROGRAMMING AND

RESEARCH

USAID’s implementing partners and REPs collect quality data on widely accepted indicators for child

malnutrition that are used exclusively for DFSA evaluation and adaptive management purposes.

Pooling DFSA-level PBS data creates larger datasets from existing data that include a range of

demographic and anthropometric variables and indicators related to dietary habits, health behaviors,

and other modifiable risk factors for malnutrition.

The finalized pooled child health and nutrition datasets can be used to explore associations between

suboptimal IYCF practices and malnutrition indicators like wasting, stunting, underweight and

overweight as well as concurrent wasting and stunting. The larger sample size in the pooled data

provides increased statistical power, especially among small but important subgroups (e.g. children

who have concurrent stunting and wasting or those who are overweight), thus making more robust

analyses possible for these subgroups. Deeper analysis of correlates to undernutrition enables

USAID, researchers, and policymakers to explore associations between undernutrition and program

indicators, identify knowledge gaps in M&E frameworks, and explore novel questions without

undertaking additional data collection efforts.

Pooling data also facilitates analysis across or between geographic locations. Geographic identifiers

included in the datasets allow for stratification by country, DFSA, and narrower designations such as

region, district, or commune, and raise the possibility of linking these data with climate and other

geographic information from external sources. This facilitates evaluations of the impact of program

design, implementation, and/or environment on outcomes and an understanding of which

conclusions are idiosyncratic to a certain setting and which are universal. The pooled datasets allow

for disaggregation by sociodemographic factors, performance on health and nutrition

behaviors/indicators, and other factors that may add nuance to these analyses.

5.2. LIMITATIONS OF POOLED DATASETS

There are several limitations to the use of this pooled dataset in future analysis.

1. Pooled DFSA/RFSA PBS datasets are not nationally representative and should not be used to

draw conclusions at the national level nor in country-to-country comparisons.

2. The DFSAs used targeting criteria to select participant households. Surveyed households did not

universally participate in all technical sectors (e.g. WASH, SBCC, livelihoods, agriculture) or all

interventions in each sector, and some may not have participated in any intervention. Data

collection targeted geographic areas in which the activities were implemented but not

exclusively individuals or households who participated in specific interventions. The goal of the

evaluations was to assess overall impact at the community level, not on individual program

participants.

3. The evaluation used non-experimental designs: baseline and endline, but without a randomly

assigned comparison or control group. Thus baseline-endline comparisons demonstrate only


27

whether changes have occurred and not causal linkages between participation in an intervention

and an outcome.

4. Interventions were not implemented in controlled environments. Confounding factors such as

natural events, interventions run by other donors, policy changes, and other external factors

could influence outcomes in numerous but unquantifiable ways.

5. Sampling weights included in the pooled dataset were drawn from the original datasets and

should be recalculated for pooled analyses.

5.3. RECOMMENDATIONS FOR PBS DATA STANDARDIZATION AND

REPORTING

5.3.1. DATA AND METADATA

This process led to several recommendations for USAID to consider as means to improve the

management of data for performance evaluation so that data can be used beyond the context of

specific evaluations. Suggestions emerging from this process include:

1. Providing guidance on the use of a standardized set of variable names, variable labels, values, and

value labels to facilitate future efforts to pool and compare data across programs.

i. Instructing REPs to use the same names for the same variables across countries to avoid the

need for recoding to create a pooled dataset.

ii. Providing REPs with consistent capitalization guidelines for codes (i.e., either always

capitalized or always lowercase), so programming languages, such as R and Stata, recognize

that they are the same variable (e.g. D29 vs. d29 are not read by R or Stata as the same

variable).

iii. Recommending that REPs use variable labels to be specific and descriptive (i.e., “child weight

in kilograms” vs. “child weight”).

iv. Setting the standard for REPs that a single variable label should have a single variable name

(e.g., variable names used for the variable label ‘child sampling weight’ included sw, CHWT,

d_wgt and weighting, requiring recoding during the pooling process).

v. Consistently using the same values and value labels across countries and datasets to facilitate

data pooling (e.g., “1” = no, “2” = yes, “8” = don’t know, “9” = refused to answer).

vi. Recommending that REPs use numeric values in datasets instead of corresponding character

values (e.g., “1” and “2” instead of “no” and “yes”).

2. Encouraging REPs to ensure codebooks are comprehensive and include all metadata needed for

external interpretation of the datasets, including:

i. All variables included in datasets;

ii. Corresponding variable names for all variables;

iii. Corresponding value labels if dataset values are numeric; and

iv. Flag for case inclusion/omission (for both calculation of indicators and omission from

dataset) (e.g., missing height for height-for-age z-scores or implausible height).

3. Employing quality control checks on data and metadata, including:

i. Verifying that data in datasets are consistent with their codebook entry (i.e., if a variable is

coded that “1” = no, “2” = yes in the codebook, the values in the dataset are “1” and “2”

and not “yes” and “no” and do not contain other numbers that are not assigned a meaning).


28

ii. Verifying that codebooks (including all variable names, variable labels, values, and value

labels) are representative of the datasets for the correct country and/or DFSA.

iii. Reviewing CSV datasets exported from final analysis datasets to ensure they are usable, with

no export errors or data inconsistencies.

iv. Checking handling of missing values in export process. Cross-reference with codebooks for

accuracy.

4. Providing guidance to REPs on dividing and organizing datasets, including:

i. Standardizing how variables are organized into split datasets (i.e., by technical sector or as

“persons” and “household” datasets) and require these split datasets to have a minimum

defining set of variables to permit them to be linked.

ii. Ensuring data stored across datasets for the same observation/household/child can be linked

using a unique identifier. Verify that this identifier is included in each technical sector dataset

and is indeed unique.

iii. Including in metadata the algorithm by which child records were linked to their caregiver in

the Maternal Health and Nutrition datasets.

iv. Ensuring anthropometry data is merged with corresponding child/women's data to avoid

problems with matching. Anthropometry datasets should not be standalone datasets.

v. Defining variables needed to merge individual data records with data in other technical

sectors for that individual.

5.3.2. PROGRAM DESIGN AND REPORTING

1. USAID may want to standardize the way in which implementing agencies document their food

ration and cash/voucher distribution schema so that program design details are documented

comparably across all activities. It is recommended that expectations for how and when to report

the following are established for these categories:

i. Quantities and units for each type of food (i.e., grams vs. ounces; kilocalories).

ii. Voucher amount and currency unit.

iii. Specific requirement for food or voucher recipients and frequency (i.e. work, participation in

SBCC).

iv. Units for frequency of distribution (i.e., per day, per week, biweekly, etc.).

v. Intended target (i.e., PLW, child, household).

vi. Changes to quantities/frequency/target of food ration distribution based on milestones (e.g.,

changes in distribution to PLW and to child when child reaches 6 months of age).

vii. Programmatic updates/changes to the intended schema at midterm, endline or at any time

during the program should be clearly recorded and reported in a standard format.

Standardizing how implementing partners document their food assistance program design and how

REPs collect, organize, and store data will enable USAID to facilitate future efforts to harmonize and

pool PBS datasets. These efforts will provide program staff, researchers, and policymakers with

quality data to use to support decision-making and bolster other research without the need to

undertake new data collection endeavors.


29

5.4. POTENTIAL AVENUES FOR EXPANDING THIS WORK AND FURTHER

ANALYSIS

The systematic approach outlined in this report as well as the R syntax files that are included in the

annexes will facilitate future efforts to pool data from other technical sectors that were not included

in the deliverables of this review (such as WASH or agriculture). With the pooling of these

additional technical sectors, further analysis could join program design details (e.g., ration amount or

duration) with pooled data to draw associations between individual program components and their

impact at the child or household level.

This review provides researchers and policymakers with additional data to analyze in conjunction

with country-level data, climate data, or other external data, adding complexity to analyses,

providing a better understanding of the interventions’ impacts and the context in which they were

implemented, and raising opportunities to answer novel questions related to food assistance for

nutrition. This adds context to extant program evaluation efforts, which can be leveraged to

improve program design, implementation, and, consequently, program effectiveness.


30

ANNEX 1: TABLES

Annex Table 1. Activity Characteristics

Activity Name Country

Primary

Implementing

Partner

Additional Implementing

Partners Activity Dates

SEGAMIL Guatemala CRS Caritas (San Marcos), ADIPO

(Totonicapan)

August 1, 2012-July

31, 2018

PAISANO Guatemala SC PCI 2013- 2018

LAHIA Niger SC WV August 1, 2012-August

1, 2018

Sawki Niger Mercy Corps HKI, Africare 2012-2018

PASAM TAI Niger CRS

International Crop Research Institute for the Semi-Arid

Tropics, Misola Foundation 2012-2018

RWANU Uganda ACDI/VOCA Concern Worldwide,

Welthungerhilfe July 2012-July 2017

GHG Uganda Mercy Corps

Peace for Development Agency,

Tufts University’s Feinstein

International Center

July 2012-July 2018

Amalima Zimbabwe CNFA ORAP, IMC, The Manoff Group,

Africare, Dabane Trust 2014-2019

ENSURE Zimbabwe WV

CARE, SNV USA, Southern

Alliance for Indigenous

Resources and International Crops Institute for the Semi-

Arid Tropics

June 1, 2013-June 1,

2018

ASOTRY Madagascar ADRA Land O’Lakes, Association Inter-

cooperation Madagascar (AIM

December 1, 2014-

September 1, 2019

Fararano Madagascar CRS NCBA/CLUSA, ODDIT, BDEM,

Caritas Morombe, CDD 2014-2019

NJIRA Malawi PCI EI 2014-July 2019

UBALE Malawi CRS CARE, Chikwawa Diocese,

NCBA/CLUSA, NASFAM, SC 2015-2019


31

Annex Table 2. Evaluation and Dataset Details

Evaluation Datasets REP Subcontractor(s) Evaluation

dates

Baseline Evaluation

Baseline Study for the

Title II Development

Food Assistance

Programs in

Guatemala

Guatemala_Agric_Practices_Data

Guatemala_ChildHealth_Data

Guatemala_Food Consumption_Data

Guatemala_HH Description_Data

Guatemala_Maternal Health and HH

Sanitation_Data

GUATEMALA_weights_annotated

ICF International Aragon y Asociados January-June

2013



Food Assistance

Programs in Niger

Niger_Access Health Services_Data

Niger_Agric Practices_Data

Niger_Child Health_Data

Niger_Food Consumption_Data

Niger_HH Description_Data

Niger_Mothers Pregnancy_Data

Niger_Sanitation and Maternal

Health_Data

ICF International A.C. Nielson January-June

2013



Food Assistance

Programs in Uganda

Uganda_Agric Practices_Data

Uganda_Child Health_Data

Uganda_Food Consumption_Data

Uganda_HH Description_Data

Uganda_Maternal Health and HH

Sanitation_Data

ICF International A.C. Nielson January-June

2013

Baseline Study of the


Food Assistance

Programs in

Zimbabwe

ZM_MB_FD_AMALIMA

ZM_MD_FD_AMALIMA

ZM_MG_FD_AMALIMA

ZM_MH_FD_AMALIMA

ZM_MJ_FD_AMALIMA

ZM_PR_FD_AMALIMA

ICF International

PROBE Market

Research

M-Consulting Group

January-

August 2014

Baseline Study of Food

for Peace

Development Food

Assistance Projects in

Madagascar

ffp_mad_poverty_asotry_data_CSV

MAD_Children_Anthro_Asotry_CSV

MAD_H_Mod_Asotry_CSV

MAD_Household_Asotry_CSV

MAD_Persons_Asotry_CSV

MAD_Women_Anthro_Asotry_CSV

ffp_mad_poverty_fararano_data_CSV

MAD_Children_Anthro_Fararano_CS

V

MAD_H_Mod_Fararano_CSV

MAD_Household_Fararano_CSV

MAD_Persons_Fararano_CSV

MAD_Women_Anthro_Fararano_CS

V

ICF International Agence CAPSULE

January-

September

2015

Baseline Study of Food

for Peace

Development Food

Assistance Projects in

Malawi

ffp_mal_children_anthropometry_mas

ter file_Combined


ter file_Njira


ter file_UBALE

ffp_mal_women_s_anthro_Combined

ffp_mal_women_s_anthro_Njira

ffp_mal_women_s_anthro_UBALE

ICF International

Center for Agricultural

Research and

Development

Centre for Social

Research at the

University of Malawi

January-

December

2015

Midterm Evaluation

SEGAMIL Midterm

Evaluation Report

2015

FFP, SC and CRS

May-

November

2015

Livelihoods,

Agriculture and Health

Interventions in

Action (LAHIA)

Project Mid-Term

Evaluation Report

SC Federation and

True Panacea, LLC,

SC International

and Souley

Aboubacar, and SC

International and

Chaibou Dadi.

September-

November

2015


32


dates

CRS Niger

PASAM-TAI Mid-Term

Evaluation

Tango International

August-

September

2015

Mid-Term Evaluation

Report for the

Zimbabwe

Development Food

Assistance Programs:

ENSURE and Amalima

The Mitchell

Group, Inc JIMAT Consult Pvt Ltd

March-

August 2016

ADRA ASOTRY Joint

Midterm Review

FFP, the USAID

Mission in

Madagascar,

Catholic Relief

Services (CRS) and

ADRA

January-May

2017

Joint Mid-Term

Review of the UBALE

and Njira

Projects

FFP, CARE, CRS

and PCI

January-May

2017

Endline Evaluation

Final Performance

Evaluation of the Food

for Peace PAISANO

Development Food

Assistance Project in

Guatemala

FFP_GUA_2018_EL_HOUSEHOLD_F

INAL

FFP_GUA_2018_EL_PERSONS_FINA

L

ICF Macro, Inc. January 17,

2019

Final Performance

Evaluation of the Food

Security Program

Focused on the First

1,000 Days (SEGAMIL)

ICF Macro, Inc. January 22,

2019

Summative

Performance

Evaluation of Food for

Peace Title II Projects

LAHIA, PASAM-TAI

and Sawki in Niger

eve_nig_el_anc_USAID

EVE_NIG_EL_HOUSEHOLD_USAID

EVE_NIG_EL_PERSONS_USAID

ME&A

NORC at the

University of Chicago

ICF International

BAGNA, Inc.

January 24,

2018

Evaluation of the

Northern Karamoja

Growth, Health and

Governance Project in

Karamoja Region,

Uganda

FFP_UG_EL_HOUSEHOLD_FINAL_

GHG

FFP_UG_EL_PERSONS_FINAL_GHG

Advanced

Marketing Systems

January 9,

2017

Final Performance

Evaluation of the

ENSURE Development

Food Assistance

Project in Zimbabwe

ZIM_HH_Anthro_Endline_Women

Zim_HH_Endline_child

Zim_HH_Endline_expenditures

Zim_HH_Endline_farmer

Zim_HH_Endline_FSWASH

Zim_HH_Endline_gender

Zim_HH_Endline_hhinfo

Zim_HH_Endline_hhroster

Zim_HH_Endline_weights_v3

SC (IMPEL)

Tango International

Tulane University March 2020

Final Performance

Evaluation of the

Amalima Development

Food Assistance

Project in Zimbabwe

Save the Children

(IMPEL)

Tango International

Tulane University March 2020

Final Performance

Evaluation of the

ASOTRY

Development Food

Security Activity in

Madagascar

MDG_HH_Endline_agriculture_indicat

ors

MDG_HH_Endline_agriculture_results

MDG_HH_Endline_children_anthro_i

ndicators

MDG_HH_Endline_children_anthro_r

esults

MDG_HH_Endline_children_indicator

s

MDG_HH_Endline_children_results

MDG_HH_Endline_food_security_wa

sh_indicators

SC (IMPEL)

Tango International Tulane University March 2020

Final Performance

Evaluation of the

Fararano

Development Food

Security Activity in

Madagascar

SC (IMPEL)

Tango International Tulane University March 2020


33


dates

MDG_HH_Endline_food_security_wa

sh_results

MDG_HH_Endline_gender_indicators

MDG_HH_Endline_gender_MCHN_in

dicators

MDG_HH_Endline_gender_MCHN_r

esults

MDG_HH_Endline_gender_results

MDG_HH_Endline_hh_roster_info

MDG_HH_Endline_hhinfo

MDG_HH_Endline_poverty_indicator

s

MDG_HH_Endline_poverty_results

MDG_HH_Endline_women_anthro_in

dicators

MDG_HH_Endline_women_anthro_r

esults

MDG_HH_Endline_women_indicators

MDG_HH_Endline_women_results

Final Performance

Evaluation of Njira

Development Food

Assistance Project in

Malawi

Malawi_EL_Anthro

Malawi_EL_Household

Malawi_HH_Endline_agriculture_resul

ts

Malawi_HH_Endline_children_anthro_

results

Malawi_HH_Endline_children_results

Malawi_HH_Endline_food_security_w

ash_results

Malawi_HH_Endline_gender_MCHN_

results

Malawi_HH_Endline_gender_results

Malawi_HH_Endline_hh_roster_info

Malawi_HH_Endline_hhinfo

Malawi_HH_endline_pov_indicators

Malawi_HH_Endline_women_anthro_

results

Malawi_HH_Endline_women_results

Malawi_weights

SC (IMPEL)

Tango International Tulane University July 2020

Final Performance

Evaluation of

Resiliency through

Wealth, Agriculture,

and Nutrition in

Karamoja (RWANU)

FFP_UG_EL_HOUSEHOLD_FINAL_R

WANU

FFP_UG_EL_PERSONS_FINAL_RWA

NU

ICF Macro, Inc. February 18,

2019


34

Annex Table 3. Strategic Objectives (SO) by Activity

Country DFSA

Name Strategic Objectives

Guatemala SEGAMIL

SO1: Food access of farmer households improved SO2: Chronic malnutrition among vulnerable populations in targeted micro-watersheds

reduced

SO3: Local and municipal resilience systems in food security improved

Cross-cutting: Female empowerment to make decisions for the food security of their families improved

Guatemala PAISANO

SO1: Household access and availability to food increased

SO2: Malnutrition among girls and boys under five years reduced

SO3: Community resilience improved Cross-cutting: Status of women within their target households and communities improved

Niger Sawki

SO1: Reduce chronic malnutrition among pregnant and lactating women and children

under five with an emphasis on children under two

SO2: Increase the local availability of and households’ access to nutritious food by diversifying agricultural productivity, rural households’ income and increasing resilience to

shocks

Niger PASAM-

TAI

SO1: Households with PLW and children under five have reduced chronic malnutrition

SO2: Vulnerable households have increased the production and consumption of food for nutrition and income

SO3: Targeted communities have enhanced and protected food security

Cross-cutting: Gender roles expanded to enhance sustainable results

Niger LAHIA

SO1: Nutritional status of children under five years of age and pregnant and lactating

women (PLW) improved SO2: Access to food by vulnerable households increased

SO3: Vulnerability to food security shocks reduced

Cross-cutting: Status of women within target households and communities improved

Uganda GHG SO1: Livelihoods strengthened SO2: Nutrition among children under two improved

SO3: Governance and local capacity for conflict mitigation improved

Uganda RWANU

SO1: Improved access to food for men and women

SO2: Reduced malnutrition in pregnant and lactating mothers and children under age two Cross-cutting: Gender, conflict mitigation, natural resource management, and disaster

risk-reduction

Zimbabwe Amalima

SO1: Household access to and availability of food improved

SO2: Community resilience to shocks improved SO3: Nutrition and health among PLW and boys and girls under two improved

Zimbabwe ENSURE

SO1: Nutrition among women of reproductive age and children under five years improved

SO2: Household income increased

SO3: Resilience to food insecurity of communities improved

Madagascar ASOTRY

SO1: Improved health and nutrition status of women of reproductive age and children

under five

SO2: Increased sustainable access to food for vulnerable households

SO3: Improved disaster preparedness and response and natural resource management in vulnerable communities

Madagascar Fararano

SO1: Undernutrition is prevented among children under two

SO2: Increased household incomes (monetary and non-monetary)

SO3: Community capacity to manage shocks is improved

Malawi UBALE

SO1: Smallholder farming households sustainably increase productivity of nutritious and

profitable farm products

SO2: Vulnerable rural households successfully engage with markets

SO3: Stunting among children under five is reduced SO4: Households and communities are more resilient to shocks

Cross-cutting: Underlying systems and structures sustainably contribute to reducing

chronic malnutrition and food insecurity while building resilience

Malawi Njira

SO1: Increased income from agricultural and non-agricultural activities SO2: Improved health and nutrition of pregnant and lactating women and children under

five

SO3: Improved capacity to prepare for, manage, and respond to shocks


35

Annex Table 4. Intermediate Objectives (under the main SO outlined in Annex Table

3) by Activity

Country DFSA Name

Intermediate Results from Proposal

Guatemala SEGAMIL

1.1: Farmer households adopt sustainable production practices.

1.2: Communities adopt sustainable watershed resource practices.

1.3: Farmer households sustainably finance productive activities. 1.4: Farmer households enter competitive markets and participate in value chains.

2.1: Households adopt practices to improve health and nutrition of pregnant women and

children under two (based on AIEPI/AINM-C).

2.2: Households access improved health services for pregnant women and children under two.

2.3: Pregnant/lactating mothers and children under two have increased intake of diverse,

nutritious food.

3.1: Community organizations have improved organization capacity. 3.2: Communities and municipalities improve their capacity to respond to increase in food

insecurity and disasters.

3.3: Municipalities support community efforts to improve food security

Guatemala PAISANO

1.1: Use of improved agricultural services and inputs increased 1.2: Use of improved production and post-production practices increased

1.3: Use of local and regional market opportunities improved

2.1: Use of quality MCHN preventive services increased

2.2: Use of improved MCHN practices at HH level increased 3.1: Community capacities to participate in FtF opportunities increased

3.2: Community disaster risk management capacity improved

Niger Sawki

1.1: Pregnant women, mothers and caretakers adopt appropriate nutrition practices during

their children’s first 1,000 days 1.2: Health centers and other community staff promote and respond efficiently and

appropriately to community demand for counseling and care

1.3: Adolescents adopt appropriate nutrition practices and healthy timing of first pregnancy

2.1: Women in target areas more efficiently manage their resources for nutrition and energy-saving purposes

2.2: Vulnerable households in target areas consolidate and diversify their revenue sources

2.3: Improved governance structures efficiently assist communities to become more

resilient to shocks

Niger PASAM-

TAI

1.1: HH (especially pregnant and lactating women and children U5) have adopted

appropriate health, hygiene and nutrition behaviors

1.2: MCU have accessed quality community and facility-based health, WASH and nutrition

services 2.1: HH have increased and diversified the production of more nutritious foods for

consumption and income

2.2: HH have adopted improved varieties of staple crops for consumption and income

2.3: HH have managed environmentally responsible integrated crop production systems 2.4: HH have increased sources of revenue

3.1: Community-based early warning systems are integrated into the national EWS

3.2: Targeted communities have managed disaster responses

Cross-cutting 1.1: Target communities have improved gender equity Cross-cutting 2.1: Women and men have increased basic literacy and numeracy skills

Cross-cutting 3.1: Governance of targeted communities and national structures

strengthened

Niger LAHIA

1.1: Adoption of key Maternal Child Health and Nutrition (MCHN) practices increased 1.2: Utilization of key MCHN services at community and health facility levels increased

1.3: Access to potable water and sanitation facilities increased

2.1: Agricultural production increased

2.2: Agricultural marketing improved 3.1: Capacity of communities to respond to and mitigate shocks improved

3.2: Capacity of communes to monitor and respond to shocks improved

4.1: Staff and community capacity to address gender equity improved

4.2: Women’s participation in agricultural and non-agricultural markets increased

Uganda GHG

1.1: Improved productivity among male and female agriculturalists, agro-pastoralists and

pastoralists

1.2: Market access and marketing behaviors improved


36

Country DFSA Name


1.3: Business environment improved

2.1: Access to quality maternal and child health and nutrition services improved

2.2: Household maternal and child health and nutrition practices improved 2.3: Sustainable access and appropriate use of safe water and sanitation facilities improved

3.1: Local conflict management capacity strengthened

3.2: Cooperation between formal and informal governance structures increased

3.3: Constructive male and female youth engagement in peace and development initiatives enhanced

Uganda RWANU

1.1: Improved smallholder farm management practices adopted

1.2 – Improved smallholder livestock management practices adopted

1.3 – Increased linkages to markets 2.1 – Improved health and nutrition practices at the household level

2.2 – Improved prevention and treatment of maternal and child illness

Zimbabwe Amalima

1.1: Agricultural productivity increased

1.2: Agricultural marketing improved 1.3 Post-harvest losses reduced

2.1: Basic agricultural infrastructure and other production assets developed/rehabilitated

2.2: Community-managed disaster risk reduction systems strengthened

2.3: Community social capital leveraged 3.1 Consumption of diverse and sufficient foods for pregnant and lactating women and

boys and girls under 2 improved

3.2 Health and hygiene and caring practices of pregnant and lactating women, caregivers,

boys and girls under 2 improved 3.3 Accessibility to and effectiveness of community health and hygiene services improved

Zimbabwe ENSURE

1.1: Nutritional practices improved

1.2: Water safety and sanitation improved

2.1: Food production and storage improved 2.2: Profitability of vulnerable HHs increased

2.3: Agricultural marketing improved

3.1: Community risk management strengthened

3.2: Assets impacting livelihoods sustainably managed Cross-cutting 1: Targeted support to mothers increased

Cross-cutting 2: Time sharing strategies improved

Madagascar ASOTRY

1.1: Improved health and nutrition behaviors of caregivers and children under five

1.2: Increased utilization of health and nutrition services for women of reproductive age and children 0 to 59 months

1.3: Reduced incidence of water- and hygiene-related illnesses for children under five

2.1: Increased agriculture production

2.2: Increased agricultural sales 2.3: Increased engagement of women and men in micro-enterprises

3.1: Community disaster mitigation assets improved

3.2: Community response capacities improved

Madagascar Fararano

1.1 Women and children have improved consumption of diverse and nutritious foods 1.2 Women and children (especially during the 1,000 days) utilize preventive and curative

maternal and child health and nutrition services

1.3 Households practice optimal water management, hygiene, and sanitation behaviors

2.1 Increased diversified agriculture production 2.2 Increased on- and off-farm sales by households and producer organizations

3.1 Community-based disaster mitigation systems meet national standards

3.2 Community-based disaster preparedness systems meet national standards

3.3 Community-based disaster response systems meet national standards 3.4 Community-based social safety net mechanisms strengthened

Malawi UBALE

1.1 Smallholder farming households improve their farm-management skills

1.2 Smallholder & vulnerable farming households sustainably increase productivity

1.3 Public and private extension and agricultural advisory services are strengthened 1.4 Women have increased influence over household decisions

2.1 Market linkages for (segmented) vulnerable smallholder farmers' marketing groups

strengthened

2.2 Access to sustainable financial services improved 2.3 Men, women and youth diversify their income options

2.4 Women have increased access to and control over income


37

Country DFSA Name


3.1 Health systems and capacities are strengthened to support GoM’s multi-sectoral

response to prevent stunting

3.2 Communities are mobilized to take ownership of sustainable approaches that prevent stunting

3.3 Targeted households adopt evidence-based behaviors that prevent malnutrition

3.4 All WRA have improved agency and relationships to effectively address the causes of

stunting within their households 4.1 Communities implement gender responsive disaster preparedness, mitigation, and

management systems

4.2 Communities adopt equitable livelihood-centered NRM strategies

4.3 Women increasingly participate in decision-making structures

Malawi Njira

1.1: Increased sustainable nutrition-friendly and market-oriented agriculture production

1.2: Increased sustainable HH income

2.1: Improved health and nutrition practices

2.2: Improved RMNCH prevention and treatment services 2.3: Improved hygiene, sanitation & water facilities

3.1: Improved disaster preparation, prevention, response and recovery

3.2: Improved community risk reduction


38

Annex Table 5. Recoded ICYF Questions

FAQR

Dataset

Variable

Name

FAQR Dataset

Question

Language

WHO ICYF

Module

Question

Language

Regional Variation from Items in

WHO ICYF Module

Country and

Evaluation

How many times yesterday during the day or night did (Child name) consume any (Item from list)?

D21 Did the child have

plain water? Plain water?


infant formula?

Infant formula

such as [insert

local example]

Similac, Enfamil, NAN Guatemala baseline

Nani, SMA, Nestle Uganda endline

D24

Did the child consume any milk

such as tinned,

powdered or

animal milk?

Milk such as tinned,

powdered, or

fresh animal

milk?

Fresh cow or goat milk Guatemala baseline

D26

Did the child have

any juice or juice

drinks (including

soda)?

Juice or juice

drinks?


any clear broth? Clear broth?


any yogurt? Yogurt?

D30

Did the child

consume any thin

porridge or atole?

Thin porridge?

Atole Guatemala baseline

Gruel Malawi endline

D31

Did the child have any other liquids

(such as coffee,

tea, water,

corn/rice/barley water, pelo de

maiz, chamomile)?

Any other liquids such as

[list other

water-based

liquids available in local setting]?

Corn, rice water, barley water, pelo de maiz, chamomile?

Guatemala baseline

Thea, decoction, sugared water roubout Guatemala endline

Thea, decoction, sugared water roubout Niger endline

Sodas, Frooti, coke Uganda endline

Any other

liquids?

Please describe everything that (Child name) ate yesterday during the day or night, whether at home or outside the home. What ingredients were in the (Mixed dish)? Yesterday during the day or night, did (Child name)

drink/eat any (food group items)?

D33

Any foods made from grains

(breads, biscuits,

pastries,

doughnuts, pasta, noodles, tortillas,

tamales, cereals,

rice, chapati,

posho, sorg, etc.)?

Porridge, bread,

rice, noodles, or

other foods made from

grains

Tortillas, tamales, bread, rice, pasta,

cereals Guatemala baseline

Doughnut, pasta Guatemala endline

Doughnut, pasta Niger endline

Biscuits, pastries, doughnuts, pasta Madagascar baseline

Biscuits (savory), crackers Madagascar endline

Pastries, doughnut, pasta Malawi baseline

Biscuits (savory), crackers Malawi endline –

D33, D33a

Doughnut, chapati, posho, sorg Uganda endline

D34

Any foods that are

yellow or orange

inside (pumpkin, carrots, squash,

sweet potatoes,

marrow, monkey

bread, gonda, etc)?

Pumpkin,

carrots, squash, or sweet

potatoes that

are yellow or

orange inside

Zucchini, carrots, yellow sweet potatoes? Guatemala baseline

Marrow, yams, monkey bread, gonda Guatemala endline

Carotte, courge orange ou jaune, patate

douce de chair orange, ou tout alim Madagascar endline

Orange-fleshed sweet potatoes or foods

made from orange-fleshed sweet

potatoes

Malawi baseline –

d34a

Other dark yellow or orange fleshed roots, tubers, or vegetables

Malawi baseline – d34b


39

FAQR

Dataset

Variable

Name

FAQR Dataset

Question

Language

WHO ICYF

Module

Question

Language


WHO ICYF Module

Country and

Evaluation

Orange-fleshed sweet potatoes or foods

made from orange-fleshed sweet

potatoes

Malawi endline -

D34a, D34aa

Other dark yellow or orange fleshed

roots, tubers, or vegetables

Malawi endline -

D34b, D34bb

Marrow, yams, monkey bread, gonda Niger endline

D35

Potatoes or any

other foods made

from roots (yams,

cassava/yucca, plantains, etc.)?

White potatoes,

white yams,

manioc, cassava,

or any other foods made

from roots

Potatoes, yucca, white sweet potatoes, other roots

Guatemala baseline

Yams, tarot, sweet potato Guatemala endline

Plantains Madagascar baseline

Malawi endline – D35, D35a

Yams, tarot, sweet potato Niger endline

Matooke Uganda endline

D36

Any dark green leafy vegetables

such as spinach,

etc.?

Any dark green

leafy vegetables

Spinach, lettuce, swiss chard, turnip leaves, amaranth, zucchini leaves,

chickpea leaves, watercress,

hierbamora/macuy

Guatemala baseline

Spinach, lettuce, sorrel, molohiya, baobab leaves (Kouka), yodo, okra leaves, Mo

Guatemala endline

Spinach, pumpkin leaves, kale, okra Madagascar baseline

Feuille de manioc, feuille de harico Madagascar endline

Spinach, pumpkin leaves, kale, okra Malawi baseline

Spinach, lettuce, sorrel, molohiya, baobab

leaves (Kouka), yodo, okra leaves, Mo Niger endline

Spinach, lettuce, chard, dodo (amaranth) Uganda endline

D37

Any ripe mangos,

papayas, melon,

passionfruit, apricots or other

fruits that are

yellow; fruits such

as bananas, apples, avocado, etc?

Ripe mangoes,

ripe papayas, or (insert other

local vitamin A-

rich fruits)

Cantaloupe Guatemala baseline

Melons Guatemala endline

Apricots, cantaloupe melon Madagascar baseline

Apricots, cantaloupe melon Malawi baseline

Apricots, cantaloupe, melons Malawi endline – D37a, D37aa

Melon, passionfruit Niger baseline

Melon, passionfruit Uganda

Melon, passionfruit Zimbabwe

Melons Niger endline

Apricots, cantaloupe melon Uganda endline

D38 Any other fruit or vegetables?

Any other fruits or vegetables

Cabbage, broccoli, tomatoes, onions,

apples, bananas Guatemala baseline

Cabbage, cauliflower, watermelon, squash Guatemala endline

Vegetables like green beans Madagascar baseline

– d36b*

Fruits such as bananas, apples, avocado

Madagascar baseline – d37b*

Madagascar endline

– D37B*

Fresh green beans, tomato Madagascar endline – D36B*

Kaki, mangues et papayes mures, abricots,

melons oranges ou tout fruit

Madagascar endline

– D37A*

Green beans Malawi baseline – d36b

Bananas, apples, avocado Malawi baseline –

d37b

Fresh green beans, tomato Malawi endline – D36b


40

FAQR

Dataset

Variable

Name

FAQR Dataset

Question

Language

WHO ICYF

Module

Question

Language


WHO ICYF Module

Country and

Evaluation

Green beans, tomatoes, mushrooms, cab Malawi endline –

D36bb

Bananas, apples, avocado Malawi endline – D37b, D37bb

Cabbage, cauliflower, watermelon, squash Niger endline

Indigenous vegetables such as eboo,

alilote, ekamalakwang. ekoreete seeds and…

Uganda endline –

d36b*

Any other vegetables, like cucumbers,

tomatoes, cauliflower, cabbage, broccoli

Uganda endline –

d36c*

Any indigenous fruits like ekoreete, ngadekela (white watermelon), ngimongo,

nga

Uganda endline – d37b*

Any other fruits like watermelon,

tamarind, or jackfruit

Uganda endline –

d37c*

D39

Any liver, kidney,

heart, or other organ meats, any

flesh or organs

from wild animals,

blood?

Liver, kidney,

heart, or other

organ meats

Stomach Guatemala baseline

Blood Niger baseline

Blood Uganda baseline

Blood Zimbabwe baseline

Any organs from wild animals Madagascar baseline

– d39a*

Organ meats from domesticated animals Madagascar endline

– D38A*

Organs from wild animals, such as

herissons, chats sauvages, chave-so

Madagascar endline

– D39A*

Organs from wild animals Malawi baseline –

d39a

Other organ meats from domesticated

animals

Malawi endline –

D38a, D38aa

Any organs from wild animals Malawi endline –

D39a, D39aa

Any organs from wild animals Uganda endline –

d39a*

D40

Any meat such as

beef, pork, lamb,

goat, chicken,

rabbit or duck?

Any meat, such

as beef, pork,

lamb, goat,

chicken, or duck

Rabbit Guatemala

Flesh from wild animals Madagascar baseline – d39b*

Flesh from wild animals, such as herissons,

chats sauvages, chauve-sou

Madagascar endline

– D39B*

Rabbit Madagascar endline

Flesh from wild animals Malawi baseline –

d39b

Malawi endline – D38b, D38bb

Any flesh from wild animals Malawi endline –

D39b, D39bb

Any flesh from wild animals Uganda endline – d39b*

D41 Any eggs? Eggs

Eggs? (chicken, turkey, fowl, duck) Madagascar endline

Eggs? (chicken, turkey, fowl, duck) Malawi endline –

D40, D40a

D42

Any fresh or dried

fish, shellfish or

seafood?

Fresh or dried

fish, shellfish, or

seafood

Crabs Madagascar endline

Crabs Malawi endline -

D41, D41a

Crabs Uganda endline

Crabs Zimbabwe endline

D43 Broad beans, peas Guatemala baseline


41

FAQR

Dataset

Variable

Name

FAQR Dataset

Question

Language

WHO ICYF

Module

Question

Language


WHO ICYF Module

Country and

Evaluation

Any foods made

from beans, broad

beans, peas, lentils,

other legumes, nuts or seeds?

Any foods made

from beans,

peas, lentils, nuts, or seeds

Cowpea vouandzou, dan-w Guatemala endline

Beans, peas, lentils, or other legumes Madagascar baseline

– d42*

Nuts and seeds Madagascar baseline

– d43*

Groundnuts Madagascar endline

– D42*

Nuts and seeds such as mahabibo, (sakoa

dans le Sud)

Madagascar endline

– D43*

Groundnut or groundnut products Malawi baseline –

d42a

Soy or soy products Malawi baseline –

d42b

NUA beans such as processed snacks... Malawi baseline –

d42c

Beans, peas, lentils, or other legumes Malawi baseline –

d42d

Sesame or sesame flour Malawi baseline –

d43a

Other nuts and seeds Malawi baseline –

d43b

Groundnut or groundnut products Malawi endline –

D42a, D42aa

Soy or soy products such as soya bean

flour, soy milk, so

Malawi endline –

D42b, D42bb

NUA beans such as processed snacks,

cakes, fritters, dough

Malawi endline –

D42c, D42cc

Beans, peas, lentils, or other legumes Malawi endline –

D42d, D42dd

Sesame or sesame flour Malawi endline –

D43a, D43aa

Other nuts and seeds Malawi endline –

D43b, D43bb

Cowpea vouandzou, dan-w Niger endline

Nuts and seeds Uganda endline – d43

D44

Any milk (liquid or

powder, from

cows or goats), cheese, cream,

yogurt or other

milk products?

Cheese, yogurt, or other milk

products

Cream, liquid or powder milk, cow milk,

goat milk Guatemala baseline

Lait caillé Madagascar endline

Milk, soured milk Malawi endline –

D44, D44a

D45

Any oils, fats, butter, margarine,

lard, peanut

butter, or foods

made from these?

Any oil, fats, or

butter, or foods

made with any

of these

Margarine, lard Guatemala BL

Grease Guatemala endline

Malawi endline –

D45, D45a

Peanut butter Zimbabwe endline

D46

Any sugary foods

such as chocolate,

sweets, candies, pastries, biscuits,

cakes?

Any sugary

foods such as

chocolates,

sweets, candies, pastries, cakes,

or biscuits

Malawi endline -

D46, D46a

D47

Any condiments

such as chilies, spices, herbs, fish

powder or other?

Condiments for flavor, such as

chilies, spices,

Pepper Guatemala endline

Persil, Oregon, laurier Madagascar endline

Curry Malawi endline –

D47, D47a


42

FAQR

Dataset

Variable

Name

FAQR Dataset

Question

Language

WHO ICYF

Module

Question

Language


WHO ICYF Module

Country and

Evaluation

herbs, or fish

powder Pepper Niger endline

D48

Any grubs, snails,

edible insects,

mopane worms?

Grubs, snails, or

insects

Mopane worms Madagascar baseline

Vers, escargots, insectes (locusts, chenille) Madagascar endline

Grasshoppers or flying ants Malawi endline –

D48, D48a

Larvae Niger endline

Mopane worms Uganda endline

D49

Any foods made

with red palm oil,

red palm nut or red palm nut pulp

sauce?

Foods made

with red palm

oil, red palm nut, or red palm nut

pulp sauce

Malawi endline –

D49, D49a

*Variable name from original dataset included if variable was merged with primary category and recoded

Annex Table 6. Data Quality Issues and Solutions

Dataset Variable Data Quality Issue Solution

Uganda Child BL ORT “997” value from dataset not in

codebook Recoded value to “NA”

Zimbabwe Child

endline DD1-DD7 Unlabeled values in codebook

Assumed that 0 = no, 1 = yes based

on other values in codebook

Zimbabwe Child

endline (original

datasets)

agedays Variable not included in dataset Requested updated datasets

including ‘agedays’ variable

Madagascar Child BL

Multiple Values labeled as numeric in codebook, contained in dataset as

character

Recoded values to numeric

Malawi Child BL activity

Values labeled as numeric in

codebook, contained in data as character

Recoded values to numeric

Malawi Child BL district “a03b” in codebook as “district” but

not in dataset -

Malawi Child endline (new

datasets)

unique_id & unique_mem_id

Not unique – cannot be used to link anthro data and ICYF data stored in

different datasets

Requested updated datasets allowing linking of anthro and ICYF

datasets


43

Annex Table 7. Outputs

Sector Datasets Included Number of

Variables

Number of

Observations

Child baseline Guatemala Child Health Niger Child Health

Uganda Child Health

Zimbabwe Child Health – ENSURE

Zimbabwe Child Health – Amalima Madagascar Children – ASOTRY

Madagascar Persons – ASOTRY

Madagascar Children – Fararano

Madagascar Persons – Fararano Malawi Children

106

31,184

Child endline Guatemala Persons

Niger Persons – endline

Uganda Persons – GHG Uganda Persons – RWANU

Zimbabwe Child Health

Madagascar Child Indicators

Madagascar Child Anthro Malawi Child Anthro

Malawi Child Results

106 16,635


44

Annex Table 8. Baseline Characteristics and Descriptive Statistics


45

Annex Table 9. Endline Characteristics and Descriptive Statistics


46

Annex Table 10. Baseline Nutritional Status Tables Note: The following tables show unweighted calculations and thus will not correspond to the

weighted results found in the DFSA evaluation reports.

Height-for-age Group Unweighted N -3SD (95% CI) -2SD (95% CI) z-score SD

All 30558 24.2 (23.7; 24.7) 50.4 (49.9; 51.0) 1.61 Age group: 00-05 mo 3121 10.1 (9.1; 11.2) 27.9 (26.4; 29.5) 1.52 Age group: 06-11 mo 3292 13.9 (12.8; 15.1) 37.8 (36.1; 39.4) 1.57

Age group: 12-23 mo 6390 27.5 (26.4; 28.6) 55.7 (54.5; 56.9) 1.70 Age group: 24-35 mo 6613 32.3 (31.2; 33.4) 59.9 (58.7; 61.1) 1.67

Age group: 36-47 mo 6205 26.6 (25.5; 27.7) 53.3 (52.1; 54.6) 1.54 Age group: 48-59 mo 4937 21.8 (20.7; 23.0) 49.8 (48.4; 51.2) 1.39 Sex: Female 15330 22.5 (21.8; 23.2) 48.0 (47.2; 48.7) 1.60

Sex: Male 15228 25.9 (25.2; 26.6) 52.9 (52.1; 53.7) 1.62 Age + sex: 00-05 mo.Female 1550 8.6 (7.3; 10.1) 25.2 (23.1; 27.4) 1.46

Age + sex: 06-11 mo.Female 1612 10.9 (9.5; 12.5) 33.2 (30.9; 35.5) 1.51 Age + sex: 12-23 mo.Female 3187 24.4 (22.9; 25.9) 52.2 (50.5; 54.0) 1.68

Age + sex: 24-35 mo.Female 3303 29.9 (28.4; 31.5) 57.6 (56.0; 59.3) 1.67


Age + sex: 00-05 mo.Male 1571 11.6 (10.2; 13.3) 30.6 (28.4; 32.9) 1.58 Age + sex: 06-11 mo.Male 1680 16.8 (15.1; 18.6) 42.1 (39.8; 44.5) 1.61 Age + sex: 12-23 mo.Male 3203 30.6 (29.0; 32.2) 59.2 (57.5; 60.9) 1.71

Age + sex: 24-35 mo.Male 3310 34.6 (33.0; 36.2) 62.2 (60.5; 63.8) 1.66

Age + sex: 36-47 mo.Male 3087 26.9 (25.4; 28.5) 55.5 (53.8; 57.3) 1.52 Age + sex: 48-59 mo.Male 2377 22.1 (20.5; 23.8) 50.4 (48.3; 52.4) 1.40

Guatemala: SEGAMIL 3000 42.4 (40.6; 44.2) 77.5 (76.0; 79.0) 1.11 Guatemala: PAISANO 2598 35.2 (33.4; 37.0) 74.3 (72.6; 76.0) 1.08

Niger: LAHIA 3630 31.9 (30.4; 33.5) 58.3 (56.7; 59.9) 1.67 Niger: PASAM TAI 2752 34.4 (32.7; 36.2) 58.5 (56.6; 60.3) 1.75 Niger: SAWKI 2560 30.0 (28.3; 31.8) 52.9 (51.0; 54.9) 1.86

Uganda: RWANU 2631 18.9 (17.5; 20.5) 38.4 (36.5; 40.2) 1.85

Uganda: GHG 2811 16.4 (15.1; 17.8) 34.9 (33.1; 36.6) 1.92

Zimbabwe: ENSURE 1547 8.3 (7.1; 9.8) 28.4 (26.2; 30.7) 1.36 Zimbabwe: AMALIMA 1624 8.2 (7.0; 9.6) 31.7 (29.5; 34.0) 1.23 Madagascar: ASOTRY 1885 23.3 (21.5; 25.3) 53.6 (51.3; 55.8) 1.32

Madagascar: FARARANO 1800 13.6 (12.1; 15.3) 39.7 (37.4; 41.9) 1.40 Malawi: NJIRA 2105 12.8 (11.4; 14.3) 38.1 (36.0; 40.1) 1.37 Malawi: UBALE 1615 9.8 (8.5; 11.4) 37.3 (34.9; 39.7) 1.20


47

Weight-for-age Group Unweighted N -3SD (95% CI) -2SD (95% CI) z-score SD Edema cases

All 31007 10.7 (10.4; 11.1) 29.9 (29.4; 30.4) 1.30 330

Age group: 00-05 mo 3174 6.1 (5.3; 7.0) 17.3 (16.0; 18.6) 1.38 31 Age group: 06-11 mo 3350 10.6 (9.6; 11.7) 28.7 (27.2; 30.2) 1.41 46

Age group: 12-23 mo 6480 13.6 (12.8; 14.4) 35.2 (34.1; 36.4) 1.37 84

Age group: 24-35 mo 6694 12.8 (12.1; 13.7) 34.5 (33.3; 35.6) 1.29 63


Sex: Female 15544 10.0 (9.5; 10.4) 28.5 (27.8; 29.2) 1.28 182

Sex: Male 15463 11.5 (11.0; 12.0) 31.4 (30.6; 32.1) 1.31 148

Age + sex: 00-05 mo.Female 1574 5.1 (4.2; 6.4) 15.4 (13.7; 17.3) 1.31 21 Age + sex: 06-11 mo.Female 1649 8.4 (7.1; 9.8) 25.2 (23.1; 27.3) 1.39 18

Age + sex: 12-23 mo.Female 3224 11.8 (10.7; 12.9) 32.3 (30.7; 33.9) 1.35 44

Age + sex: 24-35 mo.Female 3334 12.7 (11.6; 13.8) 33.5 (32.0; 35.2) 1.30 37


Age + sex: 00-05 mo.Male 1600 7.1 (5.9; 8.4) 19.1 (17.2; 21.1) 1.44 10

Age + sex: 06-11 mo.Male 1701 12.7 (11.2; 14.4) 32.0 (29.9; 34.3) 1.42 28

Age + sex: 12-23 mo.Male 3256 15.4 (14.2; 16.6) 38.1 (36.5; 39.8) 1.39 40 Age + sex: 24-35 mo.Male 3360 13.0 (11.9; 14.2) 35.4 (33.8; 37.0) 1.28 26

Age + sex: 36-47 mo.Male 3135 10.6 (9.6; 11.7) 29.7 (28.1; 31.3) 1.18 29

Age + sex: 48-59 mo.Male 2411 7.4 (6.4; 8.5) 26.5 (24.8; 28.3) 1.11 15

Guatemala: SEGAMIL 3014 8.0 (7.1; 9.0) 32.9 (31.2; 34.6) 1.03 18 Guatemala: PAISANO 2606 5.7 (4.9; 6.6) 26.7 (25.1; 28.5) 0.99 15

Niger: LAHIA 3746 20.6 (19.3; 21.9) 46.9 (45.3; 48.5) 1.40 71

Niger: PASAM TAI 2833 22.6 (21.1; 24.1) 48.5 (46.7; 50.4) 1.43 34

Niger: SAWKI 2633 20.3 (18.8; 21.9) 44.9 (43.0; 46.8) 1.44 46 Uganda: RWANU 2685 7.5 (6.6; 8.6) 21.8 (20.3; 23.4) 1.35 26

Uganda: GHG 2841 9.6 (8.6; 10.8) 26.9 (25.3; 28.6) 1.31 31

Zimbabwe: ENSURE 1563 3.6 (2.8; 4.6) 10.5 (9.1; 12.1) 1.12 32

Zimbabwe: AMALIMA 1641 4.0 (3.1; 5.0) 15.5 (13.8; 17.3) 1.12 15 Madagascar: ASOTRY 1899 9.0 (7.7; 10.3) 31.5 (29.4; 33.6) 1.11 6

Madagascar: FARARANO 1809 6.9 (5.8; 8.1) 25.4 (23.4; 27.4) 1.10 11

Malawi: NJIRA 2116 2.8 (2.2; 3.6) 12.0 (10.6; 13.4) 1.08 13

Malawi: UBALE 1621 2.5 (1.8; 3.3) 12.6 (11.1; 14.4) 1.03 12


48

Weight-for-height

Group Unweighted

N

-3SD (95%

CI) -2SD (95% CI) +2SD (95% CI)

+3SD (95%

CI)

z-score

SD

Edema

cases

All 30781 3.7 (3.5; 3.9) 9.9 (9.6; 10.2) 3.5 (3.3; 3.7) 1.0 (0.9; 1.1) 1.29 330

Age group: 00-05 mo 3151 3.8 (3.2; 4.5) 8.9 (7.9; 9.9) 11.5 (10.4; 12.6) 3.5 (2.9; 4.2) 1.56 31 Age group: 06-11 mo 3326 5.7 (4.9; 6.5) 15.2 (14.1; 16.5) 3.1 (2.6; 3.7) 0.8 (0.6; 1.2) 1.39 46

Age group: 12-23 mo 6451 4.8 (4.3; 5.4) 14.2 (13.4; 15.1) 2.6 (2.2; 3.0) 1.0 (0.7; 1.2) 1.30 84

Age group: 24-35 mo 6631 3.8 (3.3; 4.2) 9.5 (8.8; 10.2) 2.4 (2.1; 2.8) 0.5 (0.4; 0.8) 1.25 63

Age group: 36-47 mo 6259 2.7 (2.4; 3.2) 7.1 (6.5; 7.8) 2.3 (2.0; 2.7) 0.5 (0.4; 0.8) 1.16 62

Age group: 48-59 mo 4963 1.9 (1.5; 2.3) 5.4 (4.8; 6.1) 2.7 (2.3; 3.2) 0.8 (0.6; 1.1) 1.11 44 Sex: Female 15450 3.3 (3.0; 3.6) 9.0 (8.5; 9.4) 3.3 (3.1; 3.6) 1.1 (0.9; 1.2) 1.25 182

Sex: Male 15331 4.0 (3.7; 4.4) 10.8 (10.4; 11.3) 3.6 (3.3; 3.9) 1.0 (0.8; 1.1) 1.33 148

Age + sex: 00-05 mo.Female 1562 3.7 (2.9; 4.8) 8.7 (7.4; 10.2) 10.1 (8.7; 11.7) 3.5 (2.7; 4.6) 1.52 21

Age + sex: 06-11 mo.Female 1642 4.6 (3.7; 5.8) 13.3 (11.8; 15.1) 2.9 (2.2; 3.8) 0.9 (0.5; 1.4) 1.33 18

Age + sex: 12-23 mo.Female 3217 4.2 (3.6; 4.9) 12.5 (11.4; 13.7) 3.1 (2.6; 3.8) 1.2 (0.9; 1.7) 1.29 44 Age + sex: 24-35 mo.Female 3308 3.5 (2.9; 4.2) 8.7 (7.8; 9.7) 2.4 (2.0; 3.0) 0.5 (0.3; 0.8) 1.20 37

Age + sex: 36-47 mo.Female 3149 2.3 (1.8; 2.9) 6.5 (5.7; 7.4) 1.9 (1.5; 2.4) 0.5 (0.3; 0.8) 1.11 33

Age + sex: 48-59 mo.Female 2572 2.1 (1.6; 2.7) 5.5 (4.7; 6.4) 2.8 (2.2; 3.5) 1.0 (0.7; 1.4) 1.11 29

Age + sex: 00-05 mo.Male 1589 3.8 (3.0; 4.9) 9.0 (7.7; 10.5) 12.8 (11.2; 14.5) 3.5 (2.7; 4.6) 1.60 10 Age + sex: 06-11 mo.Male 1684 6.7 (5.6; 7.9) 17.1 (15.4; 19.0) 3.3 (2.6; 4.3) 0.8 (0.5; 1.4) 1.44 28

Age + sex: 12-23 mo.Male 3234 5.4 (4.7; 6.2) 16.0 (14.8; 17.3) 2.0 (1.6; 2.6) 0.7 (0.4; 1.0) 1.30 40

Age + sex: 24-35 mo.Male 3323 4.0 (3.4; 4.8) 10.3 (9.3; 11.4) 2.3 (1.9; 2.9) 0.6 (0.4; 1.0) 1.29 26

Age + sex: 36-47 mo.Male 3110 3.2 (2.6; 3.8) 7.8 (6.9; 8.8) 2.8 (2.3; 3.4) 0.6 (0.4; 1.0) 1.21 29

Age + sex: 48-59 mo.Male 2391 1.7 (1.2; 2.3) 5.3 (4.5; 6.3) 2.6 (2.0; 3.3) 0.6 (0.3; 1.0) 1.11 15 Guatemala: SEGAMIL 3006 1.0 (0.7; 1.5) 2.5 (2.0; 3.2) 3.4 (2.8; 4.1) 0.5 (0.3; 0.8) 1.02 18

Guatemala: PAISANO 2598 0.7 (0.5; 1.1) 1.8 (1.3; 2.4) 3.7 (3.0; 4.5) 0.8 (0.6; 1.3) 0.97 15

Niger: LAHIA 3700 5.9 (5.2; 6.7) 16.3 (15.1; 17.5) 1.2 (0.9; 1.6) 0.3 (0.1; 0.5) 1.21 71

Niger: PASAM TAI 2799 6.1 (5.2; 7.0) 18.6 (17.2; 20.1) 2.1 (1.6; 2.7) 0.9 (0.6; 1.4) 1.32 34

Niger: SAWKI 2600 7.1 (6.2; 8.1) 19.3 (17.9; 20.9) 2.2 (1.7; 2.8) 0.7 (0.4; 1.1) 1.35 46 Uganda: RWANU 2642 4.4 (3.6; 5.2) 12.1 (10.9; 13.4) 10.9 (9.8; 12.2) 4.3 (3.6; 5.2) 1.64 26

Uganda: GHG 2819 7.7 (6.8; 8.8) 18.3 (16.9; 19.8) 4.7 (4.0; 5.6) 1.7 (1.3; 2.2) 1.53 31

Zimbabwe: ENSURE 1558 2.4 (1.7; 3.3) 3.2 (2.4; 4.2) 4.3 (3.4; 5.4) 1.0 (0.6; 1.7) 1.05 32

Zimbabwe: AMALIMA 1638 1.5 (1.0; 2.2) 4.7 (3.8; 5.8) 2.9 (2.2; 3.8) 0.7 (0.4; 1.3) 1.10 15

Madagascar: ASOTRY 1898 1.6 (1.1; 2.3) 5.9 (4.9; 7.1) 1.5 (1.1; 2.2) 0.4 (0.2; 0.8) 1.04 6 Madagascar: FARARANO 1806 2.0 (1.5; 2.8) 7.5 (6.3; 8.8) 1.1 (0.7; 1.6) 0.3 (0.1; 0.7) 1.02 11

Malawi: NJIRA 2104 1.2 (0.8; 1.8) 2.2 (1.7; 3.0) 3.3 (2.6; 4.2) 0.5 (0.3; 0.9) 1.02 13

Malawi: UBALE 1613 1.2 (0.8; 1.9) 2.7 (2.0; 3.6) 3.7 (2.9; 4.8) 0.4 (0.2; 0.9) 1.03 12


49

Annex Table 11. Endline Nutritional Status Tables Note: The following tables show unweighted calculations and thus will not correspond to the

weighted results found in the DFSA evaluation reports.

Height-for-age Group Unweighted N -3SD (95% CI) -2SD (95% CI) z-score SD

All 16456 22.0 (21.4; 22.7) 48.3 (47.6; 49.1) 1.53 Age group: 00-05 mo 1725 7.4 (6.2; 8.7) 24.5 (22.5; 26.5) 1.48

Age group: 06-11 mo 1773 13.0 (11.5; 14.7) 36.0 (33.8; 38.2) 1.55 Age group: 12-23 mo 3350 25.6 (24.2; 27.1) 56.2 (54.6; 57.9) 1.51 Age group: 24-35 mo 3516 30.4 (28.9; 31.9) 60.2 (58.5; 61.8) 1.49

Age group: 36-47 mo 3418 23.9 (22.5; 25.4) 49.7 (48.0; 51.4) 1.51 Age group: 48-59 mo 2674 19.6 (18.1; 21.1) 44.7 (42.8; 46.5) 1.39

Sex: Female 8243 19.9 (19.1; 20.8) 46.0 (45.0; 47.1) 1.53 Sex: Male 8213 24.1 (23.2; 25.1) 50.6 (49.5; 51.7) 1.52 Age + sex: 00-05 mo.Female 885 5.5 (4.2; 7.3) 21.1 (18.6; 23.9) 1.46


Age + sex: 24-35 mo.Female 1737 27.6 (25.5; 29.7) 57.5 (55.1; 59.8) 1.50 Age + sex: 36-47 mo.Female 1715 23.4 (21.5; 25.5) 50.1 (47.7; 52.5) 1.52 Age + sex: 48-59 mo.Female 1379 19.5 (17.5; 21.7) 43.2 (40.6; 45.9) 1.41

Age + sex: 00-05 mo.Male 840 9.3 (7.5; 11.4) 28.0 (25.0; 31.1) 1.48 Age + sex: 06-11 mo.Male 885 15.7 (13.5; 18.3) 39.7 (36.5; 42.9) 1.56 Age + sex: 12-23 mo.Male 1711 29.6 (27.5; 31.8) 59.4 (57.1; 61.7) 1.53

Age + sex: 24-35 mo.Male 1779 33.1 (31.0; 35.3) 62.8 (60.5; 65.0) 1.48 Age + sex: 36-47 mo.Male 1703 24.4 (22.4; 26.5) 49.3 (46.9; 51.6) 1.50

Age + sex: 48-59 mo.Male 1295 19.7 (17.6; 21.9) 46.2 (43.5; 48.9) 1.36 Guatemala: SEGAMIL 1333 36.0 (33.5; 38.6) 71.5 (69.0; 73.9) 1.06 Guatemala: PAISANO 1262 31.7 (29.2; 34.3) 68.8 (66.2; 71.3) 1.09

Niger: LAHIA 2900 22.3 (20.8; 23.9) 49.6 (47.7; 51.4) 1.46 Niger: PASAM TAI 2618 27.4 (25.7; 29.1) 55.6 (53.7; 57.5) 1.61

Niger: SAWKI 2368 26.2 (24.5; 28.0) 51.1 (49.1; 53.1) 1.57 Uganda: RWANU 1177 15.2 (13.3; 17.4) 35.5 (32.8; 38.3) 1.67

Uganda: GHG 1061 17.3 (15.2; 19.7) 38.1 (35.2; 41.0) 1.66

Zimbabwe: ENSURE 768 4.6 (3.3; 6.3) 22.8 (20.0; 25.9) 1.33 Zimbabwe: AMALIMA 288 9.7 (6.8; 13.7) 29.9 (24.9; 35.4) 1.33 Madagascar: ASOTRY 744 18.7 (16.0; 21.6) 44.5 (41.0; 48.1) 1.47

Madagascar: FARARANO 780 12.8 (10.7; 15.4) 36.7 (33.4; 40.1) 1.38 Malawi: NJIRA 430 8.4 (6.1; 11.4) 27.0 (23.0; 31.4) 1.63

Malawi: UBALE 727 8.3 (6.5; 10.5) 29.2 (26.0; 32.6) 1.34


50

Weight-for-age

Group Unweighted N -3SD (95% CI) -2SD (95% CI) z-score

SD

Edema

cases

All 16568 10.1 (9.6; 10.6) 29.8 (29.1; 30.5) 1.24 60 Age group: 00-05 mo 1744 5.5 (4.5; 6.7) 16.3 (14.7; 18.2) 1.35 8

Age group: 06-11 mo 1790 13.5 (12.0; 15.2) 32.7 (30.6; 34.9) 1.41 7

Age group: 12-23 mo 3370 13.1 (12.0; 14.3) 36.3 (34.7; 38.0) 1.27 8


Age group: 48-59 mo 2694 6.8 (5.9; 7.8) 25.8 (24.2; 27.5) 1.06 9

Sex: Female 8294 9.0 (8.4; 9.6) 28.2 (27.3; 29.2) 1.24 30

Sex: Male 8274 11.2 (10.6; 11.9) 31.4 (30.4; 32.4) 1.24 30 Age + sex: 00-05 mo.Female 897 3.9 (2.8; 5.4) 14.7 (12.5; 17.2) 1.33 6

Age + sex: 06-11 mo.Female 894 11.4 (9.5; 13.7) 29.6 (26.7; 32.7) 1.36 4

Age + sex: 12-23 mo.Female 1650 11.2 (9.7; 12.8) 33.3 (31.0; 35.6) 1.27 5


Age + sex: 48-59 mo.Female 1386 6.5 (5.3; 7.9) 25.3 (23.1; 27.7) 1.05 3

Age + sex: 00-05 mo.Male 847 7.2 (5.6; 9.1) 18.1 (15.6; 20.8) 1.36 2

Age + sex: 06-11 mo.Male 896 15.6 (13.4; 18.2) 35.8 (32.8; 39.0) 1.45 3 Age + sex: 12-23 mo.Male 1720 15.0 (13.4; 16.8) 39.2 (37.0; 41.6) 1.27 3

Age + sex: 24-35 mo.Male 1791 12.7 (11.2; 14.3) 35.0 (32.8; 37.2) 1.17 10

Age + sex: 36-47 mo.Male 1712 8.9 (7.6; 10.3) 27.9 (25.8; 30.0) 1.13 6

Age + sex: 48-59 mo.Male 1308 7.0 (5.8; 8.6) 26.3 (24.0; 28.8) 1.07 6 Guatemala: SEGAMIL 1337 5.0 (4.0; 6.3) 26.6 (24.3; 29.1) 0.98 0

Guatemala: PAISANO 1264 5.1 (4.0; 6.4) 25.4 (23.1; 27.9) 0.98 2

Niger: LAHIA 2925 13.0 (11.9; 14.3) 37.4 (35.7; 39.2) 1.17 10

Niger: PASAM TAI 2653 18.5 (17.1; 20.1) 44.4 (42.5; 46.3) 1.33 5 Niger: SAWKI 2395 14.0 (12.7; 15.4) 37.6 (35.7; 39.6) 1.23 9

Uganda: RWANU 1182 10.9 (9.3; 12.8) 28.3 (25.8; 30.9) 1.34 22

Uganda: GHG 1063 10.4 (8.7; 12.4) 27.9 (25.3; 30.7) 1.28 10

Zimbabwe: ENSURE 774 1.3 (0.7; 2.4) 6.5 (4.9; 8.4) 1.12 0 Zimbabwe: AMALIMA 288 1.0 (0.3; 3.2) 8.3 (5.6; 12.1) 1.07 0

Madagascar: ASOTRY 745 4.4 (3.2; 6.2) 19.3 (16.6; 22.3) 1.04 0

Madagascar: FARARANO 782 3.7 (2.6; 5.3) 18.0 (15.5; 20.9) 1.08 0

Malawi: NJIRA 431 0.9 (0.3; 2.4) 7.4 (5.3; 10.3) 1.11 0 Malawi: UBALE 729 2.1 (1.2; 3.4) 9.3 (7.4; 11.7) 1.07 2


51

Weight-for-height

Group Unweighted

N

-3SD (95%

CI) -2SD (95% CI)

+2SD (95%

CI)

+3SD (95%

CI)

z-

score

SD

Edema

cases

All 16546 2.3 (2.1; 2.6) 8.3 (7.9; 8.8) 1.8 (1.6; 2.0) 0.5 (0.4; 0.6) 1.16 60

Age group: 00-05 mo 1777 2.1 (1.5; 2.9) 6.4 (5.3; 7.6) 7.4 (6.3; 8.7) 1.5 (1.0; 2.2) 1.37 8

Age group: 06-11 mo 1780 5.2 (4.3; 6.4) 15.2 (13.6; 17.0) 1.9 (1.3; 2.6) 0.7 (0.4; 1.2) 1.35 7

Age group: 12-23 mo 3351 3.6 (3.0; 4.3) 12.9 (11.8; 14.1) 0.9 (0.6; 1.2) 0.2 (0.1; 0.5) 1.16 8

Age group: 24-35 mo 3520 1.9 (1.5; 2.4) 6.9 (6.1; 7.8) 1.1 (0.8; 1.5) 0.3 (0.2; 0.6) 1.06 17

Age group: 36-47 mo 3431 1.1 (0.8; 1.6) 5.1 (4.4; 5.9) 1.1 (0.8; 1.6) 0.3 (0.2; 0.5) 1.02 11

Age group: 48-59 mo 2687 1.0 (0.7; 1.5) 5.4 (4.6; 6.3) 0.8 (0.5; 1.2) 0.3 (0.1; 0.5) 1.01 9

Sex: Female 8278 1.9 (1.6; 2.2) 7.4 (6.8; 8.0) 1.5 (1.3; 1.8) 0.3 (0.2; 0.4) 1.11 30

Sex: Male 8268 2.8 (2.5; 3.2) 9.3 (8.7; 9.9) 2.0 (1.8; 2.4) 0.6 (0.5; 0.8) 1.20 30

Age + sex: 00-05 mo.Female 905 2.3 (1.5; 3.5) 6.4 (5.0; 8.2) 6.0 (4.6; 7.7) 0.6 (0.2; 1.3) 1.30 6

Age + sex: 06-11 mo.Female 890 4.4 (3.2; 5.9) 14.5 (12.3; 17.0) 1.8 (1.1; 2.9) 0.7 (0.3; 1.5) 1.33 4

Age + sex: 12-23 mo.Female 1641 2.5 (1.8; 3.4) 10.9 (9.5; 12.5) 0.9 (0.5; 1.4) 0.3 (0.1; 0.7) 1.11 5

Age + sex: 24-35 mo.Female 1735 1.5 (1.0; 2.2) 6.0 (5.0; 7.2) 1.0 (0.7; 1.6) 0.2 (0.1; 0.5) 1.02 7

Age + sex: 36-47 mo.Female 1725 0.8 (0.5; 1.4) 4.2 (3.4; 5.3) 1.0 (0.6; 1.6) 0.2 (0.1; 0.6) 0.99 5

Age + sex: 48-59 mo.Female 1382 0.9 (0.5; 1.6) 4.8 (3.8; 6.1) 0.5 (0.2; 1.1) 0.1 (0.0; 0.6) 0.97 3

Age + sex: 00-05 mo.Male 872 1.8 (1.1; 3.0) 6.3 (4.9; 8.1) 8.9 (7.2; 11.0) 2.5 (1.7; 3.8) 1.44 2

Age + sex: 06-11 mo.Male 890 6.1 (4.7; 7.8) 16.0 (13.7; 18.5) 1.9 (1.2; 3.1) 0.7 (0.3; 1.5) 1.36 3

Age + sex: 12-23 mo.Male 1710 4.6 (3.7; 5.7) 14.8 (13.2; 16.6) 0.9 (0.5; 1.4) 0.2 (0.1; 0.5) 1.20 3

Age + sex: 24-35 mo.Male 1785 2.4 (1.7; 3.2) 7.8 (6.6; 9.1) 1.2 (0.8; 1.9) 0.4 (0.2; 0.9) 1.09 10

Age + sex: 36-47 mo.Male 1706 1.5 (1.0; 2.2) 6.0 (5.0; 7.3) 1.3 (0.9; 2.0) 0.4 (0.2; 0.8) 1.05 6

Age + sex: 48-59 mo.Male 1305 1.1 (0.7; 1.9) 5.9 (4.7; 7.3) 1.1 (0.7; 1.9) 0.4 (0.2; 0.9) 1.06 6

Guatemala: SEGAMIL 1336 0.3 (0.1; 0.8) 1.1 (0.7; 1.9) 3.1 (2.3; 4.2) 0.4 (0.2; 0.9) 0.95 0

Guatemala: PAISANO 1263 0.5 (0.2; 1.1) 1.4 (0.9; 2.3) 2.8 (2.0; 3.8) 0.4 (0.2; 0.9) 0.96 2

Niger: LAHIA 2924 2.9 (2.4; 3.6) 12.0 (10.9; 13.2) 0.8 (0.6; 1.2) 0.3 (0.2; 0.6) 1.11 10

Niger: PASAM TAI 2649 4.9 (4.1; 5.8) 14.8 (13.5; 16.2) 0.9 (0.6; 1.3) 0.3 (0.2; 0.6) 1.17 5

Niger: SAWKI 2386 3.0 (2.4; 3.8) 10.9 (9.7; 12.2) 1.5 (1.1; 2.1) 0.6 (0.4; 1.0) 1.16 9

Uganda: RWANU 1178 3.7 (2.8; 5.0) 12.2 (10.5; 14.2) 1.3 (0.8; 2.1) 0.2 (0.0; 0.7) 1.12 22

Uganda: GHG 1057 2.6 (1.8; 3.8) 9.5 (7.8; 11.4) 1.0 (0.6; 1.9) 0.5 (0.2; 1.1) 1.10 10

Zimbabwe: ENSURE 766 0.1 (0.0; 0.9) 1.6 (0.9; 2.7) 3.3 (2.2; 4.8) 0.8 (0.4; 1.7) 0.97 0

Zimbabwe: AMALIMA 288 0.3 (0.0; 2.4) 1.7 (0.7; 4.1) 4.2 (2.4; 7.2) 0.3 (0.0; 2.4) 1.07 0

Madagascar: ASOTRY 749 0.5 (0.2; 1.4) 3.2 (2.2; 4.7) 2.7 (1.7; 4.1) 1.3 (0.7; 2.5) 1.02 0

Madagascar: FARARANO 787 0.5 (0.2; 1.3) 3.6 (2.5; 5.1) 1.4 (0.8; 2.5) 0.1 (0.0; 0.9) 0.97 0

Malawi: NJIRA 431 0.5 (0.1; 1.8) 2.8 (1.6; 4.8) 2.8 (1.6; 4.8) 0.2 (0.0; 1.6) 1.04 0

Malawi: UBALE 732 0.4 (0.1; 1.3) 2.7 (1.8; 4.2) 4.0 (2.8; 5.6) 0.8 (0.4; 1.8) 1.06 2


52

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