Understanding Longer-term Effects of a Father-engaged, Play-based Home Visiting Intervention to Promote Early Childhood Development and Prevent Violence in Rwanda: The Sugira Muryango Longitudinal & Spillover Study
Conditions: Cognitive Change, Growth; Stunting, Nutritional, Language, Depression, Behavior, Violence, Parenting
Sponsor: Boston College

 
 
Understanding longer-term effects of a father-engaged, play-based home visiting intervention to 
promote early childhood development and prevent violence in Rwanda: The Sugira Muryango 
Longitudinal & Spillover Study 
(A follow-up of “Effectiveness Trial of a Family Home Visiting Intervention to Promote Early Child 
Development Among Families Served by the Social Protection System in Rwanda”, NCT02510313) 
Finalized: May 25, 2022 
 
 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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1. Background 
This study is the culmination of several years of research in Rwanda by the investigators. Initial 
research led by Dr. Betancourt and Dr. Sezibera explored the local perceptions of mental health 
problems among HIV/AIDS affected youth and adversely affected households to identify coping 
strategies and sources of support relevant to the Rwanda context and culture. Building on this 
work the team used findings to guide the development and adaption of quantitative measures to 
assess prevalence of mental health problems in children and caregivers. In later years, the team 
developed an adapted Mental Health Intervention for HIV/AIDS-Affected Children, which was 
piloted to test feasibility and acceptability of the intervention. This intervention, referred to as 
Sugira Muryango, was further adapted in recent years to be focused on early childhood 
development in general. The Early Childhood version of Sugira Muryango is a family-based, 
home-visiting intervention targeted at early childhood development and implemented with 
families living in extreme poverty in three districts of Rwanda. This version of Sugira Muryango 
was first tested in two small pilot studies (Betancourt et al. 2018; Barnhart et al. 2020) and a 
large cluster randomized trial (CRT) was implemented between February 2018 and September 
2019 (Betancourt et al. 2020; Jensen et al. 2021). Pre- to post-intervention findings demonstrated 
that Sugira Muryango led to improvements in caregiver behaviors linked to child development 
and health as well as reductions in violence, which were sustained 12 months after the 
intervention, at which time improvements in child development were observed. 
The Research Program on Children and Adversity in the Boston College School of Social 
Work is led by Dr. Theresa S. Betancourt and will, in partnership with the University of Rwanda, 
FXB-Rwanda and Laterite, conduct a longitudinal follow-up study to investigate the longer-term 
outcomes of the Sugira Muryango intervention in families who participated in the CRT. The 
four-year follow-up will examine the long-term and sustained outcomes of the intervention. In 
particular, we will look at key indicators of long-term positive outcomes for children such as 
school readiness and transition to formal schooling. Given the lack of longitudinal research on 
intervention programs supporting ECD in sub-Saharan Africa, this study will contribute greatly 
to the body of knowledge on the costs and benefits of investments in ECD and guide policy 
makers and government leaders on making impactful investments in children, leading to long-
term benefits for the population at large.  
The follow–up study involves two activities: 
Activity A: Pilot to assess measures performance of newly added measures and field test 
study protocols.  
Activity B: Four-year follow-up of families who participated in the CRT of the Sugira 
Muryango intervention. 
 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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2. Study Aims and Objectives 
The aim of the study is to assess the impacts of Sugira Muryango on treated families compared 
to families receiving only usual care (controls) four years after the intervention. Moreover, at 
this visit, we will also for the first time examine potential spillover effects of Sugira Muryango 
onto siblings of children enrolled in the original study. The longitudinal follow-up study will 
assess whether and to what extent Sugira Muryango has an impact on caregivers’ awareness and 
utilization of available services; support for children’s education and playful learning; home 
hygiene; parenting practices, including child feeding/nutrition, stimulation and sensitive care; 
experiences of intimate partner violence; use of harsh discipline; mental health, including 
depression and alcohol use; gender attitudes; and overall wellbeing. It will also assess whether 
and to what extent Sugira Muryango has an impact on children’s physical development; 
cognitive and linguistic development; temperament; enrollment in early or formal education; 
school readiness, including self-regulation and early literacy and numeracy; mental health, 
including internalizing and externalizing and depression; behavior, including conduct problems; 
and gender attitudes. Finally, the study will assess potential covariates such as the impact of the 
COVID-19 pandemic and associated lockdowns, child disability, and household size and 
composition. 
The specific objectives are: 
1. To assess the long-term (4 years post-intervention) impact of Sugira Muryango on 
caregiver behaviors, attitudes, mental health and wellbeing, especially as related to 
parenting and intimate partner violence. 
2. To assess the long-term (4 years post-intervention) impact of Sugira Muryango on 
previously enrolled children’s physical and cognitive development and health. 
3. To assess the long-term (4 years post-intervention) impact of Sugira Muryango on 
previously enrolled children’s behavior, mental health, school readiness and gender 
attitudes. 
4. To assess the long-term (4 years post-intervention) impact of Sugira Muryango on 
younger and older siblings of children enrolled in the intervention, including on their 
physical and cognitive development and health, and in the case of older siblings their 
school readiness, behavior and gender attitudes. 
The following activities will be conducted during the proposed 4-year follow up study: 
● Activity A: Pilot to assess measures performance and field test study protocols.  
○ Phase A1: Translation and adaptation of newly selected measures. 
○ Phase A2: A Pilot study of selected new child measures with 150 children and their 
primary caregivers in a sector of the Sugira Muryango PLAY Collaborative 
Expansion Study to test the feasibility and validity of new tools. 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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● Activity B: Four-year follow-up of families who participated in the Cluster Randomized 
Trial (CRT) of the Sugira Muryango intervention. 
○ Phase B1: Household tracking and re-enrollment of 1,049 households that 
participated in the prior CRT (April-May 2022). 
○ Phase B2: Quantitative (full sample) and qualitative (subsample) data collection 
with the 1,049 households from the prior CRT, including children and caregivers 
who were in the original CRT, and siblings in a subset of households to test for 
spillover effects (June [Quantitative] & December [Qualitative] 2022). 
Our specific hypotheses are as follows: 
Longitudinal Hypotheses 
1) Sugira Muryango will have effects (superior when compared to usual care households) 
on a range of outcome domains, both new domains and those assessed in the prior CRT. 
a) Sugira Muryango will continue to have effects among eligible caregivers and 
children compared to controls on a range of outcomes assessed at previous waves 
of data collection, and 
b) Sugira Muryango will have effects on new outcomes that have become relevant as 
the children have aged, including aspects of school readiness. 
 
Spillover Hypothesis 
 
2) Sugira Muryango will have positive effects on younger and older siblings of children 
who were eligible for and participated in the intervention compared to siblings in usual 
care households. 
 
Secondary exploratory analyses 
Analyses evaluating potential child and caregiver sex differences will be performed across all 
outcomes to examine differences in parenting behaviors and child outcomes as children get older 
and approach school age. 
Study benefits and justification 
Potential societal benefits of the study include increased knowledge about evaluating ECD and 
parenting in Rwanda, increased knowledge of ECD in Rwanda, and increased knowledge 
regarding family-based ECD interventions to improve child development outcomes in low-
resource settings. Intervening in early childhood has been demonstrated to be highly cost-effective 
for improving child development and life outcomes, yet interventions in low-resource settings—
particularly in sub-Saharan Africa—are limited and not always well-evaluated or systematically 
implemented (Britto et al. 2017). At the individual level, families who participate in the study will 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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receive free disability and behavioral screening for eligible children and will be connected with 
support services should a disability or other risk of harm be identified. 
The key research question is whether Sugira Muryango provides lasting benefits to children in 
households that received the intervention. Little longitudinal research into home-visiting ECD 
interventions in this region exists; findings from the proposed research will add critical evidence 
to inform Rwanda’s expansion of ECD support to families as well as learnings for other 
countries. 
The study has been reviewed and approved by the Rwanda National Ethics Committee, the 
National Institute of Statistics of Rwanda, and the National Council for Science and Technology 
in Rwanda. 
3. Methodology 
Sampling 
Target population 
The target study population (referred to as “beneficiaries”) are families who participated in the 
Sugira Muryango CRT in 2018-19 (Trial Registration Number NCT02510313). Family inclusion 
criteria for the CRT were (a) living in the Rubavu, Ngoma or Nyanza District of Rwanda, (b) 
being VUP-eligible (according to the Rwandan government), (c) having at least one child aged 
6–36 months living in the home, and (d) having at least one caregiver who was willing to discuss 
and enhance their caregiving practices by interacting with a home-visiting coach (a community-
based volunteer or CBV). Further caregiver inclusion criteria were: (a) was aged 18 or older and 
cared for child(ren) and (b) lived in the same household as the child(ren). Single and dual 
caregiver families were enrolled to reflect population dynamics and other legal guardians, 
including parents, aunts, uncles, grandparents, and/or foster parents, were enrolled if no 
biological parent lived in the household. To ensure that families enrolled in the CRT were socio-
economically vulnerable and VUP-eligible, they were randomly selected from an administrative 
records list, part of the government social protection information infrastructure.  
Other than not meeting inclusion criteria, specific family exclusion criteria for the CRT were a) 
caregiver(s) having severe cognitive impairments which precluded their ability to speak to the 
research questions under study, b) families or caregivers being in the midst of crisis (e.g., a 
caregiver(s) with active suicidal attempts or psychosis). Families with ongoing crises or 
disabilities were excluded from the study and were referred to appropriate services.  
For the current Longitudinal Study, inclusion criteria for families are (a) having participated 
in the prior CRT, (b) living in Ngoma, Nyanza, or Rubavu districts, (c) having at least one child 
who participated in the CRT and who is currently living in the household. Exclusion criteria 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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are: (a) caregiver having severe cognitive impairments which precluded their ability to speak, to 
understand the research questions, or to get involved in program activities, or (b) families or 
caregivers who are in the midst of crisis (e.g., a caregiver with active suicidal attempts or 
psychosis). 
Due to the passage of time between waves of data collection, we expect some households to have 
changed in composition between the one-year post-intervention wave and the current 
(longitudinal, four-years post-intervention wave). In such cases the following replacement 
criteria will be observed: 
● The current primary caregiver (the person who spends the most time with the child) 
responds to the caregiver report on self, caregiver report on household, and caregiver 
report on child. If the current primary caregiver did not participate in the CRT, this will 
be flagged in the dataset. Field preparation for the study found 72 households where this 
is the case. 
● The secondary caregiver who participated in the CRT responds to the caregiver report on 
self and caregiver report on household if they are available and eligible (still living in the 
household), even if they are no longer acting as the secondary caregiver. If the secondary 
caregiver who participated in the CRT is not available, the current secondary caregiver (if 
one exists) responds to the caregiver report on self and caregiver report on household. 
In order to test the Spillover hypothesis, the current study will also enroll siblings of CRT-
eligible children in a subsample of eligible households. The sampling methodology is described 
below. Inclusion criteria for younger siblings are: (a) aged 3 months or older; (b) was not eligible 
to participate in the original CRT due to age (below 6 months or not yet born at the time of 
CRT). Inclusion criteria for older siblings are: (a) aged 12 years or younger; (b) lived in the 
household but was not eligible to participate in the original CRT due to age (above 36 months at 
the time of CRT); (c) currently lives in the household that participated in the CRT. Although 
caregivers and children who participated in the CRT will be eligible for the follow-up study 
regardless of caregiver’s presence in the home, siblings will be excluded from sampling for the 
spillover study if the primary caregiver has been away from the household for six months or 
more. 
The reference period 
The CRT began in February 2018 with enrollment, baseline data collection, and subsequent 
implementation of the Sugira Muryango intervention with treatment families. Immediate post-
intervention data were collected in August-September 2018. Families received a three-month 
post-intervention booster session in November-December 2018 and a six-month post-
intervention booster session in March 2019. In addition, a follow-up assessment was completed 1 
year post-intervention in August-September 2019. 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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The current longitudinal study will conduct a new follow-up assessment approximately 4 years 
after the intervention ended. The pilot tool-testing activity enrolled families in the usual care 
group (UC) of the Sugira Muryango PLAY Collaborative Study (Trial Registration Number 
NCT04257383) (these families did not participate in the original CRT). Piloting took place in 
March 2022. Full quantitative data collection will take place with the longitudinal sample in June 
2022.  
 
Figure 1: Sugira Muryango study timeline 
 
Geographical coverage 
The original CRT took place in Ngoma, Nyanza, and Rubavu Districts of Rwanda. The same 
families will be followed up in the proposed longitudinal and spillover study, in the same 
geographies. Families in which the entire family has moved out of the district they lived in 
during the CRT will be excluded. 
Figure 2: District map 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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Sample size determination and sampling 
Longitudinal Sample (follow-up with CRT sample) 
Given that this is a follow-up study, all 1,028 families who participated in the one year post-
intervention wave of  the CRT,  who meet current inclusion criteria, will be considered the 
sample of interest. The following values were used to calculate statistical power when sampling 
for the CRT. We assumed a total of 200 clusters with 5 families per cluster. We assume a linear 
relationship over time. The significance level is set at 0.05. The statistical test was the likelihood 
ratio test for the inclusion of the time by treatment group interaction. The ICC was set at 0.10. A 
variable for public works type which was categorical with 3 levels was included in the model. 
Within public works type a ratio 1:1:2 for expanded:both:classic was assumed. Variability was 
set to one so that effect sizes may interpreted as “Cohen-Like” effect sizes. We found that we 
have at least 90% power to detect an effect size of 0.25 and at least 80% power to detect an 
effect size of 0.22. Details on the original sampling strategy for the CRT can be found in 
Betancourt et. al. 2020, and Jensen et. al 2021, and are also summarized below.  
Families for the CRT were randomly selected from government-provided lists of families who 
were eligible for the Vision Umurenge Program (VUP). Families’ participation in the VUP and 
selection into one of two versions of the VUP program, namely classic public works (cPW) or 
expanded public works (ePW), was determined by governmental policies and was not under the 
control of the research team. Non-overlapping, geographically defined clusters were created 
from one or more contiguous villages so one community-based volunteer (CBV) could provide 
services to all participating families in the cluster. Each cluster comprised at least 30 families 
participating in the classic Public Works (cPW) program or ten families participating in the 
expanded Public Works (ePW) program, with some clusters containing both ≥30 cPW and ≥10 
ePW households. Randomization to either treatment or control conditions occurred at the cluster 
level within strata defined by public works type (ePW only, combined ePW/cPW, and cPW only) 

Sugira Muryango Longitudinal & Spillover Study Protocol 
9 
 
and geographic sector. After cluster randomization, households were contacted by the data 
collection contractor and invited to participate in the study. Clusters were retained if at least five 
families in the cPW strata or at least one family in the ePW strata enrolled and had at least one 
child aged 6–36 months. We retained 48 ePW-only clusters, 38 ePW/cPW clusters, and 112 
cPW-only clusters (see Cluster Sampling Strategy, Figure 3). The final CRT sample comprised 
1,049 households, with 48.41% (n=508) in the usual care (UC) and 51.57% (n=541) in the 
treatment (Sugira Muryango, SM) group. Within households, we sampled n=1084 children ages 
6-36 months and n=1498 primary and secondary caregivers. The longitudinal study will sample 
these same families from the CRT.  
Figure 3: Cluster Sampling Strategy and Flowchart of Participants in the Sugira 
Muryango Cluster Randomized Trial 
 
Spillover study - sibling sample 
As discussed above, this study also incorporates a Spillover study examining the effects of the 
intervention on younger and older siblings of currently eligible children who were not eligible to 
be enrolled when the original intervention was implemented. Since Sugira Muryango is meant to 
work with the whole family, and teach beneficiaries transferable skills, we expect siblings of 
previously enrolled children to also benefit from the intervention. The sibling spillover study will 
allow us to test hypothesis 2: “Sugira Muryango will have positive effects on younger and older 
siblings of children who were eligible for and participated in the intervention compared to 
siblings in usual care households”.  

Sugira Muryango Longitudinal & Spillover Study Protocol 
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To select children for the spillover study we will sample younger (age 3 months and older) or 
older (up to age 12) siblings of the children that were eligible for the original CRT (for 
qualitative interviews, siblings up to 15 years old may be sampled). For statistical power 
efficiency, and to prevent a second level of clustering at the household level, younger and older 
siblings will be randomly selected from different households (only one sibling per household). 
We will seek to enroll 50% male and 50% female siblings across age categories outlined below. 
We will seek to balance gender within each village and cluster. 
Sampling plan for siblings:  
 
 
3 months -2 
years 
2-4 years 
8-10 years 
10-12 years 
Intervention 
Female 
31 
31 
31 
31 
Male 
31 
31 
31 
31 
Control 
Female 
31 
31 
31 
31 
Male 
31 
31 
31 
31 
 
The main outcome of interest for siblings is the child development score, a continuous outcome. 
We are interested in testing whether the means in the original control and treatment groups are 
different. The appropriate statistical test for this outcome is a two-sample equality test. The null 
and alternative hypotheses to be tested are as follows, with 𝜇𝑐,𝑡 the mean in the control and 
treatment groups, respectively: 
𝐻0: 
The difference in means of the two groups is equal to 0, 𝜇𝑡−𝜇𝑐= 0 
𝐻1:  
The difference in means of the two groups is different from 0, 𝜇𝑡−𝜇𝑐≠0. 
Table 1: Parameter for sample size calculation 
Parameter 
Value 
ICC (rho)  
𝜌= 0.01 
Significance level 
 𝛼 =  0.05  

Sugira Muryango Longitudinal & Spillover Study Protocol 
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Statistical Power 
1 −𝛽 =  0.8  
Standardized Minimum 
Detectable Effect 
𝜇𝑡−𝜇𝑐
𝜎
= 0.25, where  𝜎 is the standard deviation of the 
outcome, 𝜇𝑐 is the mean in the control group and 𝜇𝑡 is the 
mean in the treatment group 
The relevant parameters needed to calculate the appropriate sample size to conduct this test are 
outlined in Table 1. For the level of significance and statistical power, the social sciences 
standard values of α=0.05 and power=80% are used. The standardized minimum detectable 
effect (MDE) is estimated based on a target of 0.3 standard deviations maximum or smaller, as 
interventions of the type of Sugira Muryango have achieved effects of this magnitude in the past 
with similar outcomes. To ensure adequate power, the standardized MDE for this study is set 
slightly lower at 0.25 standard deviations.  
The following formula for a two-sample two-sided hypothesis test is used to compute sample 
size in each group (control and treatment) based on above parameters (Chow et al. 2008): 
𝑛𝑐,𝑡= (𝜎𝑧1−𝛼/2 + 𝑧1−𝛽
𝜇𝑡−𝜇𝑐
)
2
× (1 + 1
𝜅 ) 
Where, 
● n is sample size 
● 𝜅=
𝑛𝑐
𝑛𝑡 is the matching ratio and is equal to 1 
● 
𝜇𝑡−𝜇𝑐
𝜎
 is the standardized MDE 
● α is Type I error  
● β is Type II error, meaning 1−β is power 
● 𝑧1−𝑎
2 is the critical z value for 1 −
𝛼
2  
● 𝑧1−𝛽 is the critical z value for 1 −𝛽 
𝑛𝑐,𝑡= ( 1
0.25(1.96 + 0.84))
2
× 2 = 250 
With those parameters, we find a total sample size of 500 siblings, with 250 siblings in control 
clusters and 250 siblings in treatment clusters. 
As the sampling strategy includes clustering, the sample size calculation also takes into account 
the effects of the within and between cluster variation in the calculation of the required effect 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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size. The formula below is used for the calculation of the design effect where 𝑚 is the size of 
each cluster and 𝜌 is the Intra-Cluster Correlation (ICC) (Collett 2014): 
𝐷𝐸𝐹𝐹= (1 + (𝑚−1)𝜌) 
The average number of households per cluster is 5. The sibling sample will be drawn from all 
available clusters in the study to maximize power. As this study sample includes 197 clusters, we 
can calculate: 𝑚= 
500
197 = 2.54 participants per cluster. In practice this means that some clusters 
will include 3 siblings while others will include 2 siblings.  
The ICC estimate is taken from the initial CRT and is estimated to be 0.01 at the cell level. Such 
a small ICC implies that the clustered design does not result in significant data heterogeneity 
between clusters. Note that actual clusters for this study are between village and cell size, and do 
not directly align with Rwanda’s administrative units. However, for the purpose of power 
calculations, we assume the ICC for the cell is comparable to the ICC for the actual cluster size.  
Using this, we calculate a design effect as follows: 
𝐷𝐸𝐹𝐹= (1 + (2.54 −1)0.01) 
𝐷𝐸𝐹𝐹= 1.0154 
 
Due to the very small ICC, the effect of the design on the sample size is very limited. Given that 
we use for this calculation a MDE of 0.25, versus the target of 0.3, the calculation will provide a 
conservative (slight overestimate) of the sample needed, and so we can assume the calculated 
sample size accounts for this very limited design effect.     
Therefore, with 197 clusters (k), we will sample 2 to 3 siblings (m) per cluster for a total sample 
size of 500 siblings (k*m=n), with 250 siblings in control clusters and 250 siblings in treatment 
clusters.  
Qualitative sampling 
From the quantitative sample, 90 caregivers (30 from single-caregiver homes [mostly female; if 
we find single-male-headed homes in our sample, some of these will be purposively included in 
the sample], 30 male caregivers from dual-caregiver homes, and 30 female caregivers from dual-
caregiver homes) will be selected to additionally participate in qualitative interviews. Families 
will be selected as high- and low-responsive to the intervention based on quantitative data. We 
will also sample 60 CRT-eligible children and/or older siblings from the same households. 
Siblings will be selected to participate in qualitative interviews (60 children in total). Selection of 
children will be stratified by gender (50% male and 50% female).  

Sugira Muryango Longitudinal & Spillover Study Protocol 
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Analysis Plan 
Outcomes: CRT-eligible children (longitudinal sample) 
Continuous Outcomes 
The goal is to determine the efficacy of SM four years from study initiation. Measurements of 
most outcome variables (see section 6) were taken at baseline, immediately post-intervention, 
and 12 months post-intervention. In this study, several of these measures will be collected again 
four years post-intervention. Subjects in the CRT were cluster randomized (Section 4, “The 
sample size and its calculations and the sampling methods used”) into a treatment group (SM) 
and usual care (UC). Continuous outcomes collected during the original CRT will be analyzed 
using linear mixed-effect models and intervention effects will be evaluated based on the 
differences in slopes between SM and UC. The linear mixed-effect model, with a continuous 
outcome variable (e.g., KABC-2 total score) as the response variable, will include the treatment 
status (SM vs UC), time, and their two-way interaction as fixed effects. Mixed effect models, 
also known as hierarchical linear models (HLM) or multilevel models are a flexible tool for 
analyzing associations and changes over time in longitudinal studies when there are clusters of 
correlated data in the outcome variable. This design has three levels of nesting: families are 
measured within measurement waves, and measurement waves are nested within randomization 
clusters (i.e., geographical regions). Since we expect region- and time-level effects, subject-
specific slopes and intercepts will be modeled as random effects nested within randomization 
cluster, also modeled as a random effect. Additionally, although these changes were neither 
expected nor hypothesized, a quadratic term for time will be added as a fixed effect in order to 
explore possible changes (acceleration) in the rate of growth.  Lastly, even though in prior 
analysis no differences between VUP programs were found,the type of VUP program will be 
modeled as a fixed effect to evaluate potential long-term impacts of the VUP program. A 
mathematical representation of this model is:  
Yijk = B0 + b0i + B1*TreatmentGroup + B2*Time + b1i*Time + 
B3*Time*Treatment group + b2k + b3k*time + B4m*PubicWorksGroup + e 
Where, Yijkm represents the value of the ith subject (i = 1 . . . I), at the jth time point (j=1, . . . 4), in 
the kth cluster (k = 1, . . 200) and m is the VUP indicator with levels expanded public works, 
classic public works and mixed. The set of b0i variables represent subject-specific intercepts and 
are assumed b0i  ~ N(0, σI). The b1i  are subject-specific slopes and assumed b1i  ~ N(0, σslopes), 
the b2k are randomization cluster intercepts and assumed b2k  ~ N(0, σregion) and the b3k are 
region-specific slopes and assumed b3k  ~ N(0, σsregion). The value e represents residual variation 
and is assumed e ~ N(0,σ). Model efficacy will be evaluated using the likelihood ratio test for 
inclusion of the Time*Treatment interaction (null hypothesis B3 = 0). Random effects will be 
tested during the model building but we will seek to simplify models where appropriate. For 
example, we will test if we can assume a common cluster slope versus a unique slope for each 

Sugira Muryango Longitudinal & Spillover Study Protocol 
14 
 
cluster. Cluster and subject-specific effects will be dropped from the model based on a 
Likelihood Ratio Test (LRT) for their inclusion. The estimation procedure while model building 
random effects will be Restricted Maximum Likelihood (REML). The primary hypothesis test 
which is based on the inclusion of a fixed effect will require models estimated by full maximum 
likelihood. Because of randomization, we have controlled in theory for imbalances in observed 
and unobserved variables (confounders). Moreover, previous analyses from the CRT sample 
have shown that group differences at baseline tend to be minimal. However, maturation and 
developmental processes might play a role so models with some key demographic variables will 
be evaluated and models will be compared with the main results.  
Model assumptions and alternative tactics: Model assumptions will be tested. In particular, we 
will examine the distributions of the residuals to verify that normality assumptions hold. 
Residual plots will reveal violations of the normality assumptions. Panel plots will be examined 
to look at functional shape assumptions within subjects. In the case of non-normality of 
outcomes, we will consider response transformations or the use of models that do not make 
distributional assumptions (e.g., generalized linear mixed effect models). If the linearity 
assumption does not hold across time (baseline, immediately post-intervention, one year post-
intervention, and four years post-intervention) marginal effects of the treatment at each timepoint 
will be estimated, focusing on the 4-year group differences adjusted for baseline. Lastly, if the 
examination of panel plots reveals a large amount of heterogeneity among trajectories of the 
outcomes by subjects, Growth Mixture Models will be used in order to first identify clusters of 
similar trajectories from which we can test for treatment group differences in terms of 
membership in the clusters of similar trajectories. Model outliers will be flagged through visual 
inspection and standardized residual values greater than |3|. A sensitivity analysis will be done to 
determine if outlier removal changes the coefficient values. If it is found that outliers are having 
an oversized effect on the estimated coefficient values,  preference will be given to the models 
where the outliers are removed. However, both sets of results will be presented. 
Binary Outcomes  
The analysis for these outcomes will be similar to the analyses above with accommodations 
being made for the binomial distribution of the outcome. In particular, mixed-effect logistic 
regression models for binomial distributions and a logistic link function will be used, a specific 
form of generalized linear mixed models (GLMM). Outside of accounting for the correct 
distribution of the response variable, the model nesting structure and modeling strategy (i.e., 
covariates, fixed, and random effects) will be the same as the one outlined in the previous section 
for the continuous outcomes. 
Model Assumptions and Alternative Tactics: We will examine Pearson residuals to assess the 
model goodness of fit. Alternative methods include the use of quasi-binomial mixed-effects 
regression (in the case of over-dispersion). However, such models do not have a full likelihood 
and instead of LRT test, a Wald-type type test for regression coefficients will be considered. In 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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fitting logistic mixed-effect regression models, it is possible that we may run into numerical 
difficulties in computing a likelihood ratio test, so score-type tests may be needed. This is more 
likely to occur in the case of testing for variance components. 
Count Outcomes 
Prior CRT data examination and analysis of outcomes like harsh disciplinary actions or physical 
and sexual abuse as suffered or inflicted showed that these variables were not normally 
distributed but resembled a Poisson distribution, with a great concentration of zero values. In 
fact, previous experience with these scales is that there are more values of zero than what would 
be predicted by a standard Poisson distribution. Our analysis will make use of Zero Inflated 
Poisson (ZIP) models (Everitt, & Hothorn 2006; Long 1997). These models belong to the more 
general class of mixture models. In particular, they model the binomial probability of a count of 
zero versus a count greater than zero and the Poisson probability for counts greater than zero. In 
particular, we will fit mixed effect models as described in the continuous variable sections but 
under the ZIP distribution assumption. 
A key difference between a mixture model and more standard probability distributions is that the 
fixed-effects structure and tests for significance can vary between the two parts (binomial and 
Poisson). Likelihood ratio testing for the Time*Treatment group interaction will be performed 
for both components of the mixture model. A significant result of the LRT test for the binomial 
portion would indicate that the probability of no violence (inflicted or suffered) differed between 
the treatment groups. A significant result of the LRT on the Poisson portion of the ZIP model 
would indicate that among those who inflicted or suffered harsh discipline or violence the mean 
amount of violence differed between the treatment groups. The two portions are contained in a 
single likelihood function and parameter estimation is done simultaneously. 
Model Assumptions and Alternative Tactics: The key assumption in these models is the ZIP 
distribution of the response variables. We will visually inspect the distribution of the outcome 
variables to verify that we are fitting the correct model. Alternative distributions could be 
Poisson, Quasi-Poisson, Binomial (data dichotomized due to lack of variability), or Negative 
Binomial. These are non-mixture models and modeling will follow the process outlined in the 
continuous outcomes section. 
Linking procedures to address CRT sample children’s maturation 
The current study design addresses developmental changes and maturation effects by using age-
appropriate instruments to measure the constructs of interest. This posits a measurement 
challenge given some of the children that during the CRT were assessed using a certain 
instrument (e.g., ASQ-3) will now need to be assessed using a different age-appropriate tool 
(e.g., KABC-II) considering they are currently out of the original instrument recommended age 
span. To address this, statistical methods for test linking will be used. Linking is an umbrella 
term covering different processes and techniques that involve transformations between the scores 

Sugira Muryango Longitudinal & Spillover Study Protocol 
16 
 
from one test and those of another in order to achieve comparability across different instruments. 
In general terms, these transformations can then be divided into three big categories: Predicting, 
Scale Aligning, and Equating (Holland 2007). In particular, considering 1) instruments are 
measuring the same construct 2) scores come from a common population (single-group design), 
and 3) reliability and difficulties may vary between test forms, equipercentile equating 
techniques will be used. In short, as described by Gonzalez and Wiberg (2017), with 
equipercentile equating, test form X is equated to test form Y by identifying X scores on test 
form X that have the same percentile ranks as Y scores on test form Y. This will allow for the 
transformation of scores from two different tests onto a common scale. These scores will be later 
analyzed using the three-level linear mixed model for continuous outcomes as specified before.  
Analysis of new outcomes (CRT and Siblings samples) 
Outcomes that were not originally collected from eligible children during the original CRT (e.g., 
school readiness) and outcomes for younger and older siblings who were not assessed during the 
original CRT will be analyzed using either linear mixed models (continuous outcomes), mixed-
effect logistic regression models (binary outcomes), or mixed-effect Zero Inflated Poisson (ZIP) 
models (count outcomes). These models will have two levels of nesting: individuals nested 
within randomization clusters. Considering these subsamples will have data from only one 
measurement wave, the longitudinal nature of previous models (i.e., individuals nested within 
measurement waves) is not present. In this particular case, intervention effects will be evaluated 
using the coefficient for the treatment group variable (SM vs UC). As before, since we expect 
region-level effects, subject-specific slopes and intercepts will be modeled as random effects 
nested within randomization cluster, also modeled as a random effect. And the type of VUP 
program will be modeled as a fixed effect to evaluate potential long-term impacts of the VUP 
program. Although the use of one timepoint is not ideal to evaluate intervention effectiveness, 
this study will take advantage of the original randomized design (see Section 4, Subsection 
“Sample Selection”), which generated comparable intervention groups that are supposed to be 
similar in all relevant observed and unobserved characteristics except for the treatment 
allocation. In short, randomization ensures no a-priori knowledge of treatment allocation, and 
also that groups do not differ in any systematic way in terms of observed or unobserved 
characteristics (Altman and Bland, 1999; Suresh 2011). Lastly, in order to account for potential 
imbalances between the siblings’ treatment and control subsamples or imbalances due to changes 
in household structure and other potential observed confounders, group equivalence analysis will 
be performed. When statistically significant imbalances are found those variables will be 
included as statistical controls in the respective mixed model fitted.  
Missing Data 
Data will be analyzed under the Intention-to-Treat approach, a data analysis strategy according to 
which all participants are included in the analytic sample and considered part of the group to 
which they were originally randomized, regardless of intervention completion. This will help in 

Sugira Muryango Longitudinal & Spillover Study Protocol 
17 
 
preventing bias due to sample loss. Missing data, either from attrition or from survey non-
response, will be addressed using HOTDECK multiple imputation techniques. Lost cases will be 
replaced by randomly selecting cases matched on key demographic, and socioeconomic variables 
such as age, gender, educational attainment, household structure, and also the treatment group.  
 
 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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4. Measures 
Table 2: Caregiver measures 
Caregivers 
Data collection 
type 
Outcome 
Measure 
New 
measure 
Outcome 
type 
Caregiver report 
on household 
(taken at home) 
Household 
composition 
“Household composition” survey  
No 
Covariate 
Caregiver report 
on self (taken at 
home) 
Service 
utilization 
“Service utilization” survey 
Yes 
Secondary 
Caregiver report 
on household  
Support for 
child’s 
education 
Epstein survey 
No 
Secondary 
Caregiver report 
on household 
(taken at home) 
Financial 
decision 
making; father 
engagement 
DHS/Promundo “Father Engagement 
& Financial Decision-Making” 
survey 
No, but 
some 
changes 
Secondary  
Caregiver report 
on household 
(taken at home) 
Stimulating 
care/play 
“The Role of Play in Children’s 
Learning” survey  
 
Yes 
Primary 
“Parental Acceptance-Rejection 
Questionnaire”  
No 
Secondary 
Caregiver report 
on household 
(taken at home) 
Home 
sanitation & 
hygiene 
“Multiple Indicator Cluster Survey 
(5)” WASH module 
No 
Secondary  
Caregiver report 
on household 
(taken at home) 
COVID-19 
pandemic  
impact  
“COVID Questionnaire”  
Yes 
Covariate 
Caregiver report 
on self (taken at 
home) 
Caregiver 
conflict; 
victimization & 
DHS “Intimate Partner Violence” 
survey 
No 
Primary 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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perpetration of 
violence 
Caregiver report 
on self (taken at 
home) 
Depression/ 
anxiety 
“Hopkins Symptom Checklist (25)”  
No 
Secondary 
Caregiver report 
on self (taken at 
home) 
Caregiver 
alcohol 
problems 
World Health Organization “Alcohol 
Use Disorders Identification Test 
(AUDIT)” 
No 
Secondary  
Caregiver report 
on self (taken at 
home) 
Quality of life 
“Flourishing Scale” 
Yes 
Secondary  
Caregiver report 
on self (taken at 
home) 
Views of 
gender/ 
masculinity 
“Gender Equitable Men” scale 
“Gender Equitable Men” norms scale  
Yes 
Secondary 
Caregiver report 
on household 
(taken at home) 
Attitudes 
towards 
children’s 
education 
“International Development & Early 
Learning Assessment” parent 
attitudes module  
Yes 
Secondary 
Caregiver report 
on household 
(taken at home) 
Expectations 
towards 
children’s 
education 
“International Development & Early 
Learning Assessment” parent 
expectations module 
Yes 
Secondary 
Caregiver report 
on self (taken at 
home or central 
location) 
Various 
Qualitative interviews  
Yes 
NA 
(qualitative) 
 
Table 3: Child measures 
Children 
 
 
Outcome 
 
Measure 
 
Younger 
siblings 
 
CRT- 
eligible 
children 
 
Older 
siblings 
 
New 
measure 
 
Outcome 
type 

Sugira Muryango Longitudinal & Spillover Study Protocol 
20 
 
Data 
collection 
type 
 
 
 
Caregiver 
report on 
child 
(central 
location) 
Physical 
development 
(stunting, 
wasting, 
underweight) 
Anthropometric measures  
Younger 
CRT- 
eligible 
 
No 
Primary 
Caregiver 
report on 
child 
(central 
location) 
Child 
development 
“Ages and Stages 
Questionnaire-3”  
Younger 
 
 
No 
Primary 
Caregiver 
report on 
child 
(central 
location) 
Playful 
parenting; 
childcare 
arrangement 
“International 
Development & Early 
Learning Assessment” 
stimulating care and play 
module  
Younger 
CRT- 
eligible 
Older 
No 
Primary 
Caregiver 
report on 
child 
(central 
location) 
Child 
discipline 
“Multiple Indicator 
Cluster Survey” child 
discipline module  
Younger 
CRT- 
eligible 
Older 
No 
Primary 
Caregiver 
report on 
child 
(central 
location) 
Feeding 
practices 
World Health 
Organization “Infant and 
Young Child Feeding 
Practices” survey  
Younger 
 
 
No 
Secondary 
Caregiver 
report on 
child 
(central 
location) 
Enrollment in 
early education 
“Enrollment in early 
education” survey 
Younger 
CRT- 
eligible 
Older 
Yes 
Secondary 
Caregiver 
report on 
child 
(central 
location) 
Temperament 
“Infant Behavior 
Questionnaire” ; 
“Early Childhood 
Behavior Questionnaire”  
Younger 
 
 
Yes 
Secondary 

Sugira Muryango Longitudinal & Spillover Study Protocol 
21 
 
Caregiver 
report on 
child 
Disability 
“Multiple Indicator 
Cluster Survey” child 
functioning module  
Younger 
 
Older 
Yes 
Covariate 
Observation 
(at home) 
Stimulation 
“Home Observation 
Measurement of the 
Environment” 
infants/toddlers survey  
Younger 
 
 
No 
Primary 
“Home Observation 
Measurement of the 
Environment” early 
childhood survey  
 
 
CRT- 
eligible 
 
Yes 
Primary 
Observation 
(at home or 
central 
location) 
Sensitive care 
“Observation of Mother-
Child Interactions”  
Younger 
 
 
No 
Primary 
Observation 
(central 
location, in 
the presence 
of caregiver) 
Developmental 
milestones & 
language 
“Malawi Developmental 
Assessment Tool” (under 
consideration)  
Younger 
 
 
No 
Primary 
Observation 
(central 
location, in 
the presence 
of caregiver) 
Cognition; 
language 
“Mullen Scale of Early 
Learning”   
Younger 
 
 
Yes 
Primary 
“Kaufman Assessment 
Battery for Children-2”  
 
CRT- 
eligible 
Older 
Yes 
Primary 
“Weschler Preschool & 
Primary Scale of 
Intelligence”  
 
CRT- 
eligible 
 
Yes 
Primary 
Caregiver 
report on 
child 
(central 
location) 
Enrollment in 
formal 
education 
“Child Enrollment in 
Formal Education” survey  
 
CRT- 
eligible 
Older 
Yes 
Secondary 

Sugira Muryango Longitudinal & Spillover Study Protocol 
22 
 
Caregiver 
report on 
child 
(central 
location) 
Internalizing/ 
externalizing 
“Child Behavior 
Checklist”  
 
CRT- 
eligible 
Older 
Yes 
Primary 
Child report 
on 
self 
(central 
location, in 
the presence 
of caregiver) 
Youth Self Report  
 
 
 
Older 
Yes 
Primary 
Observation 
(central 
location, in 
the presence 
of caregiver) 
Self-regulation 
“Preschool Self-
Regulation Assessment”  
 
CRT- 
eligible 
 
Yes 
Secondary 
Child report 
on self 
Child 
discipline 
Adapted “Multiple 
Indicator Cluster Survey” 
and “ISPCAN Child 
Abuse Screening Tool”  
 
 
Older 
Yes 
Primary 
Caregiver 
report on 
child 
(central 
location) 
Child disability 
NCDA “Disability 
Screener”  
 
CRT- 
eligible 
 
Yes 
(replaced 
previous 
disability 
screener) 
Covariate 
Observation 
(central 
location, in 
the presence 
of caregiver) 
Early reading 
“Early Grade Reading 
Assessment”  
 
 
Older 
Yes 
Secondary 
Observation 
(central 
location, in 
the presence 
of caregiver) 
Early math 
“Early Grade 
Mathematics Assessment”  
 
 
Older 
Yes 
Secondary 
Child report 
on self 
(central 
location, in 
the presence 
of caregiver) 
Depression 
“Center for 
Epidemiological Studies 
Depression Scale”  
 
 
Older 
Yes 
Primary 

Sugira Muryango Longitudinal & Spillover Study Protocol 
23 
 
Child report 
on self 
(central 
location, in 
the presence 
of caregiver) 
Conduct 
problems 
“Youth Conduct Problems 
Scale - Rwanda”  
 
 
Older 
Yes 
Primary 
 
Child report 
on self 
(central 
location, in 
the presence 
of a 
caregiver) 
 
Views of 
gender/ 
masculinity 
“Gender Equitable 
Men” scale  
 
CRT- 
eligible 
 
Yes 
Secondary 
“Gender Equitable 
Men” scale  
 
 
Older 
Yes 
Secondary 
Child report 
on self 
(central 
location, in 
the presence 
of a 
caregiver) 
Various 
Qualitative interviews 
 
CRT- 
eligible 
Older 
Yes 
NA 
(qualitative) 
 
5. Safety & Adverse Events 
Risks to Subjects 
This study does not involve a new intervention. Potential risks are therefore limited to risks 
associated with research participation. Potential risks include: 
● In some cases, fatigue from the psychosocial assessment administered may occur. 
● There is a small risk of loss of confidentiality. 
We consider most risks associated with participation in the study to be unlikely. Two previous 
pilots of SM have been carried out with vulnerable families in Rwanda. Previous study iterations 
have demonstrated that processes related to confidentiality are upheld throughout the entire 
study. Further, no participants have indicated distress or fatigue with the assessments. Weekly 
field reports from the Laterite enumerators have not indicated any issue with the assessments, 
length of time to administer, issues with confidentiality, or distress caused. 

Sugira Muryango Longitudinal & Spillover Study Protocol 
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Monitoring 
A risk of harm protocol, including flagged items from the assessment battery, provides a 
structured protocol for monitoring and responding to adverse effects on participants. This 
protocol has been reviewed and approved by the Rwanda National Ethics Committee and is used 
in enumerator training and fieldwork. 
6. Study Limitations 
A primary study limitation is the potential introduction of confounding effects in the three years 
since the last data collection (and four years since intervention). In particular, the shock of the 
COVID-19 pandemic is not yet well-understood. In addition, the targeted families are among the 
most vulnerable in Rwanda and therefore they may have been enrolled in other interventions 
targeting similar outcomes in the intervening years. Time effects will be modeled as random 
effects in the multilevel model, and a quadratic term has been added to the model as a fixed 
effect to explore possible changes in the rate of growth. Further, questionnaires on COVID-19-
related experiences and participation in other programs have been added to the battery to 
improve understanding of possible covariates related to the pandemic and to other interventions. 
A second study limitation regards the reliance on Western-created measures to assess primary 
and secondary study outcomes. Extensive work was done to refine and adapt measures to fit the 
Rwandan context with assessment questions forward and back translated into Kinyarwanda 
following best practices (Van Ommeren et al., 1999) through two pilot studies (Betancourt et al. 
2018; Barnhart et al. 2020). New measures added for this wave of data collection were piloted 
with demographically similar households in Rwanda, and this pilot indicated strong reliability 
and validity of the new measures. 
7. Compensation, Consent & Assent 
Households will receive 5000 Rwandan Francs for quantitative data collection . Households 
selected to participate in qualitative interviews will receive an additional 3000 Rwandan Francs. 
All caregivers gave written informed consent for themselves and their eligible children ages 6–
36 months to participate in the CRT and will provide consent again for the follow-up study. 
Children ages four and older will also provide assent for themselves. 
8. Provisions for vulnerable subjects 
All households in the study had an Ubudehe 1 poverty categorization at the time of intervention. 
As such, all study procedures are created and implemented with provisions for vulnerable 
participants. As mentioned above, risk of harm/adverse event procedures are in place to identify 
any risk of harm situations related or not related to participation in the study. Further, funds are 

Sugira Muryango Longitudinal & Spillover Study Protocol 
25 
 
available if any participant requires a referral or transfer to a higher level of care, such as 
transport to and treatment at a district hospital, for example.  
9. Data archiving and dissemination 
Results from the trial will be published in peer-reviewed journal articles and presented at high 
level conferences. A formal dissemination event involving study funders and stakeholders will 
be held in Kigali, Rwanda. 
10. References 
Altman, D. G., & Bland, J. M. (1999). Statistics notes: Treatment allocation in controlled trials:  
why randomise? BMJ, 318(7192), 1209–1209. 
https://doi.org/10.1136/bmj.318.7192.1209 
 
Barnhart, D. A., Farrar, J., Murray, S. M., Brennan, R. T., Antonaccio, C., Sezibera, V., Ingabire,  
C., Godfroid, K., Bazubagira, S., Uwimana, O., Kamurase, A., Yousafzai, A., Betancourt, 
T. (2020). Lay-worker delivered home promotes early childhood development and 
reduces violence in Rwanda; a randomized pilot. Journal of Child and Family Studies; 
29, 1804-1817. https://doi.org/10.1007/s10826-020-01709-1 
 
Betancourt, T. S., Franchett, E., Kirk, C. M., Brennan, R. T., Rawlings, L., Wilson, B., ... &  
Ukundineza, C. (2018). Integrating social protection and early childhood development: 
open trial of a family home-visiting intervention, Sugira Muryango. Early Child 
Development and Care; 190(2), 219-235. 
 
Betancourt, T.S., Jensen, S.K.G., Barnhart, D.A., Murray, S.M., Yousafzai, A.K., Farrar, J,  
Godfroid, K., Bazubagire, S.M., Rawlings, L.B., Wilson, B., Sezibera, V., & Kamurase, 
A. (2020). Promoting parent-child relationships and preventing violence via home-
visiting: a pre-post cluster randomised trial among Rwandan families linked to social 
protection programmes. BMC Public Health; 621(20), 2-11. 
 
Britto, P.R., Lye, S.J., Proulx, K., Yousafzai, A.K., Matthews, S.G., Vaivada, T., Perez-
Escamilla,  
R., Rao, N., Ip, P., Fernald, L.C.H., MacMillan, H., Hanson, M., Wachs, T.D., Yao, H., 
Yoshikawa, H., Cerezo, A., Leckman, J.F., Bhutta, Z.A. (2017). Nurturing care: 
promoting early childhood development. The Lancet; 389(10064), 91-102. 
 
Chow, S. C., Shao, J., Wang, H., & Lokhnygina, Y. (2017). Sample size calculations in clinical  
research. chapman and hall/CRC. 

Sugira Muryango Longitudinal & Spillover Study Protocol 
26 
 
 
Collett, D. (2014). Modelling Binary Data (2nd ed.). Chapman and Hall/CRC.  
https://doi.org/10.1201/b16654 
Everitt, B. & Hothorn, T. (2006). A handbook of statistical analyses using R. Chapman &  
Hall/CRC. 
González, V. & Wiberg, M. (2017). Applying test equating methods: using R. Springer. 
Holland, P. W. (2007). A framework and history for score linking. In N. J. Dorans, M.  
Pommerich, & P. W. Holland (Eds.), Linking and aligning scores and scales (pp. 5–30). 
Springer Science + Business Media. https://doi.org/10.1007/978-0-387-49771-6_2 
   
Jensen, S. K., Placencio-Castro, M., Murray, S. M., Brennan, R. T., Goshev, S., Farrar, J., ... &  
Betancourt, T. S. (2021). Effect of a home-visiting parenting program to promote early 
childhood development and prevent violence: a cluster-randomized trial in Rwanda. BMJ 
Global Health, 6(1), e003508. 
  
Long. S. (1997). Regression models for categorical and limited dependent variables. Sage  
Publications. 
 
Suresh, K. (2011). An overview of randomization techniques: An unbiased assessment of  
outcome in clinical research. Journal of Human Reproductive Sciences, 4(1), 8. 
https://doi.org/10.4103/0974-1208.82352 
 
Van Ommeren, M., Sharma, B., Thapa, S., Makaju, R., Prasain, D., Bhattarai, R. & de Jong, J.  
(1999). Preparing instruments for transcultural research: Use of the translation 
monitoring form with Nepali-speaking Bhutanese refugees. Transcultural Psychiatry, 36, 
285-301. doi.org/10.1177/136346159903600304