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1
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
3
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)
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
represents WHO standards. ............................................................................................................................ 22
Figure 4: Nutrition status by age group and sex in pooled dataset at baseline and endline. Dotted line
represents WHO standards. ............................................................................................................................ 23
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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).
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
Malnourished CU2: 10
PLW: 1107
Malnourished CU2: 464
PLW: 44
Malnourished CU2: 17
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 -
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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.
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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,
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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Figure 2: Z-score distribution by age group in pooled dataset at baseline and endline. Dotted line
represents WHO standards.
Baseline
Endline
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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Figure 3: Z-score distribution by sex in pooled dataset at baseline and endline. Dotted line
represents WHO standards.
Baseline
Endline
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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Figure 4: Nutrition status by age group and sex in pooled dataset at baseline and endline. Dotted
line represents WHO standards.
Endline
Baseline
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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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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).
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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.
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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.
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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
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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
Baseline Study for the
Title II Development
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
Baseline Study for the
Title II Development
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
Title II Development
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
ffp_mal_children_anthropometry_mas
ter file_Njira
ffp_mal_children_anthropometry_mas
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
32
Evaluation Datasets REP Subcontractor(s) Evaluation
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
33
Evaluation Datasets REP Subcontractor(s) Evaluation
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
36
Country DFSA Name
Intermediate Results from Proposal
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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Country DFSA Name
Intermediate Results from Proposal
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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?
D22 Did the child have
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?
D27 Did the child have
any clear broth? Clear broth?
D28 Did the child have
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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FAQR
Dataset
Variable
Name
FAQR Dataset
Question
Language
WHO ICYF
Module
Question
Language
Regional Variation from Items in
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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FAQR
Dataset
Variable
Name
FAQR Dataset
Question
Language
WHO ICYF
Module
Question
Language
Regional Variation from Items in
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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FAQR
Dataset
Variable
Name
FAQR Dataset
Question
Language
WHO ICYF
Module
Question
Language
Regional Variation from Items in
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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FAQR
Dataset
Variable
Name
FAQR Dataset
Question
Language
WHO ICYF
Module
Question
Language
Regional Variation from Items in
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
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Annex Table 8. Baseline Characteristics and Descriptive Statistics
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Annex Table 9. Endline Characteristics and Descriptive Statistics
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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: 36-47 mo.Female 3118 26.4 (24.8; 27.9) 51.2 (49.4; 52.9) 1.56 Age + sex: 48-59 mo.Female 2560 21.6 (20.0; 23.2) 49.3 (47.4; 51.2) 1.38
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
Age group: 36-47 mo 6311 10.6 (9.9; 11.4) 30.0 (28.9; 31.1) 1.19 62 Age group: 48-59 mo 4998 7.3 (6.6; 8.1) 25.9 (24.7; 27.1) 1.08 44
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: 36-47 mo.Female 3176 10.7 (9.6; 11.8) 30.3 (28.7; 31.9) 1.20 33 Age + sex: 48-59 mo.Female 2587 7.3 (6.3; 8.3) 25.2 (23.6; 27.0) 1.05 29
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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: 06-11 mo.Female 888 10.4 (8.5; 12.5) 32.3 (29.3; 35.5) 1.52 Age + sex: 12-23 mo.Female 1639 21.4 (19.5; 23.5) 52.9 (50.5; 55.3) 1.47
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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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: 24-35 mo 3532 11.8 (10.8; 12.9) 33.7 (32.2; 35.3) 1.20 17 Age group: 36-47 mo 3438 8.6 (7.7; 9.6) 27.9 (26.4; 29.4) 1.14 11
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: 24-35 mo.Female 1741 10.9 (9.5; 12.4) 32.5 (30.3; 34.7) 1.23 7 Age + sex: 36-47 mo.Female 1726 8.3 (7.1; 9.7) 27.9 (25.8; 30.0) 1.15 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
PBS DATASET HARMONIZATION AND POOLING JANUARY 2021
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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
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ANNEX 2: CODEBOOKS