Life Plans of Young Adults in Rural KwaZulu-Natal: an Intervention Study
Phase: NA
Conditions: HIV/AIDS, Chronic HIV Infection, Health Knowledge, Attitudes, Practice
Sponsor: Boston University

 
 
STUDY PROTOCOL AND PRE-ANALYSIS PLAN 
 
 
Title: Life Plans of Young Adults in Rural South Africa: an Intervention Study 
 
 
September 17, 2018 
 
 
Principal Investigator 
Jacob Bor, ScD, Department of Global Health, Boston University, USA 
 
Co-Investigators 
Till Bärnighausen, MD/PhD, Institute of Global Health, University of Heidelberg, 
Germany and Africa Health Research Institute, South Africa 
Jeremy Barofsky, MA, ScD, Ideas42, USA 
Frank Tanser, PhD, Africa Health Research Institute, South Africa 
 
Study Coordinator 
Dan Flanagan, MPH, Boston University 
 
Partner institution 
Africa Health Research Institute, KwaZulu-Natal, South Africa 
 
 
Description of this document 
This document contains: (A) Study Protocol, as approved by the Boston University 
Medical Campus IRB and University of KwaZulu-Natal Biomedical Research Ethics 
Committee, with additional information on study procedures including: justification 
of the attention placebo control; sampling protocol; and randomization protocol; 
and (B) Pre-Analysis Plan for the primary evaluation of the intervention.  
 
 
 
 
 
1 
 

 
 
A. STUDY PROTOCOL 
 
A1. Summary of project 
 
Can information on population life expectancy gains increase survival expectations 
and human capital investment? In rural South Africa, adult life expectancy increased 
by 18 years from 2003 to 2015, with the scale-up of HIV treatment. We set out to 
conduct a randomized evaluation of a video intervention conveying information on 
these population life expectancy gains and assessed impacts on beliefs and on 
behaviors related to HIV testing, risk compensation, and human capital investment. 
The study is to be implemented in a population-representative sample (target 
n=450) of young adults ages 18-25 years residing in rural KwaZulu-Natal, South 
Africa.  
 
The aims of the study are: 
• To describe beliefs about HIV, ART, and life expectancy in the age of mass HIV 
treatment access 
• To determine the impact of information on survival gains due to ART on beliefs 
about life expectancy 
• To establish whether information on survival gains due to ART affect future-
oriented health and education behaviors 
 
 
A2. Rationale  
 
Economic theory predicts that increases in life expectancy should lead to more 
forward-looking behavior [1]. Individuals who face a higher probability of living to 
future periods are expected to invest more resources in education and job training 
today, in order to enjoy greater total consumption over their lifetime. Similarly, with 
greater longevity, individuals are less likely to take risks – e.g. drunk driving, 
smoking, or sex work – that jeopardize their enjoyment of future utility. In spite of 
its theoretical appeal, the evidence on a causal link between subjective life 
expectancy and economic behavior is limited [2–4], particularly related to young 
adults and HIV. Perhaps the strongest evidence comes from a study in rural Malawi 
which found that scale-up of antiretroviral therapy led to increased survival 
expectations, greater labor supply, and greater human capital investment and 
savings [5,6]. However, without randomization of ART scale-up it is possible that 
other factors could have confounded this relationship. 
 
Antiretroviral treatment (ART) for HIV has dramatically reduced mortality due to 
HIV/AIDS, turning what was once a death sentence into a manageable chronic 
condition. In rural KwaZulu-Natal, roll out of ART in the public sector has led to 
large gains in adult life expectancy, with gains of 18 years since 2003 (update of Bor, 
et al. 2013 [7]). These changes may have widespread impacts on community norms, 
perceptions, and behaviors. Indeed, nearly 40% of community members in the area 
2 
 

 
 
live in a household or compound with someone who has sought care in the 
government ART program [8]. These longevity improvements likely influence how 
young adults think about their future. Little is known, however, about young adults’ 
health perceptions in the age of ART and how perceptions affect health behaviors 
and feedback to the HIV epidemic itself. This increase in life expectancy may raise 
incentives for young adults to engage in preventive health behaviors – e.g., testing 
and linkage to HIV care – and future oriented behaviors like schooling and savings. 
However, limited information about these survival changes and bandwidth 
constraints to acquire this information [9] may minimize the impact these changes 
population changes in survival have on behavior. Young adults that are fatalistic 
about their future or believe they have low chances of survival may not perceive 
benefits to preventive health behaviors.  
 
Formative qualitative research conducted in advance of this intervention study (and 
data from elsewhere [10]) revealed that many young adults have survival 
expectations substantially below the life expectancy implied by prevailing mortality 
rates in the population. Our qualitative data also showed that young adults who 
were pessimistic about their survival chances were often fatalistic about investing in 
their future. These beliefs may be self-fulfilling, such that fatalism about the future 
leads to riskier health behaviors and diminished opportunities later in life, 
generating a behavioral poverty trap [11]. Low survival expectations and associated 
fatalism may also reduce a young adult’s likelihood of HIV testing and early ART 
initiation. This may help explain that, even though treatment is free, HIV mortality 
remains high and many deaths occur without the HIV-infected person seeking care 
[12].  
 
We undertook a randomized evaluation to determine whether a video providing 
information on the gains in population life expectancy due to ART scale-up can shift 
survival expectations, and whether such an intervention can increase future-
oriented behaviors, such as HIV testing, human capital investment, and savings. To 
provide this information, we developed a video intervention in collaboration with a 
local HIV support group and the community engagement unit of the Africa Health 
Research Institute, a research organization which operates a large demographic 
surveillance site in the study area.  
 
Informational interventions have been used to affect perceived returns to education 
in the Dominican Republic [13] and Madagascar [14], and to change mental models 
of future opportunities [15,16] to increase education and savings. In addition, 
greater HIV prevalence and mortality risk reduced schooling in sub-Saharan Africa 
[3], while Malawi’s rapid scale-up of HIV treatment was found to raise labor supply 
among the HIV negative attributable to changes in subjective mortality risk and 
mental health improvements [5] as well as savings and educational expenditure [6]. 
Although existing evidence is consistent with information on rising life expectancies 
affecting future-oriented behavior, to date no experimental study has sought to shift 
survival expectations in the context of rapidly rising life expectancy. If a video 
intervention is effective, then it would be easily scalable to other populations highly 
3 
 

 
 
affected by HIV where treatment is recently available. There is strong rationale to 
assess whether a video intervention can shift perceptions and future-oriented 
behaviors.   
 
The study also sought to describe beliefs about the treatment and prevention 
benefits of ART; to describe subjective life expectancy in the era of mass HIV 
treatment; and to describe the validity of survey instruments to capture subjective 
probabilities in this setting, building on work in other sub-Saharan African settings 
[10].  
 
 
A3. Study context 
 
The study was conducted in rural KwaZulu-Natal, South Africa, in partnership with 
the Africa Health Research Institute (AHRI). AHRI has maintained a health and 
demographic surveillance system since 2000 covering a geographically defined 
population of about 100,000 people residing in a 438 km2 area [17]. The 
surveillance involves annual visits to all households and includes a complete 
population census and additional health, economic, behavioral, and HIV biomarker 
surveys. The database is also linked with the local public sector HIV treatment 
program. The surveillance area includes both rural and peri-urban areas, and is 
located in one of the poorest districts in South Africa.  
 
HIV prevalence in this population is very high, with one third of adults HIV-infected. 
The availability of ART in the public sector, beginning in 2004, led to a major shift in 
the health risk environment. Based on data from AHRI’s complete population 
surveillance of births, deaths, and migrations, we previously computed an increase 
in population adult life expectancy of 11.3 years between 2003 and 2011 [7]; by 
2015, the increase in adult life expectancy had reached 18 years. This study was 
designed in part to evaluate a video-based dissemination strategy to share these 
findings with the population whose data gave rise to our prior research.  
 
In formative qualitative research preceding this randomized trial, we conducted 
interviews and focus groups with young adults ages 16-29 years. Many respondents 
reported low survival chances, e.g. 
 
“I can’t believe that I can reach 40, people are dying, especially the youth…. We 
are dying fast now, we are dying like flies.... We are living in difficult times.”   
 
 
 
 
 
    
 
- Female, 24 years old 
 
“I was answering the question about, do I think I will live up to 60, I thought 60: I 
would be dead by that time”  
- Male, 20 years old  
 
There are many reasons why young adults might not have updated their beliefs 
about future survival in the age of mass HIV treatment. First, they may base their 
4 
 

 
 
beliefs on the experiences of the previous generation – their parents, aunts, uncles, 
many of whose lives were cut short by HIV. Second, deaths among young people 
may be more salient than deaths among older people and have an outsized influence 
on beliefs. Third, people may think about the ages of people who have recently died 
in the community. This heuristic performs very poorly in a population where the age 
distribution is heavily skewed. Due to the much larger number of young people, the 
modal age at death among people who died in 2014 was 33 years, similar to what it 
was in 2003, even as the overall number of deaths has declined dramatically. 
 
We therefore set out to provide information on the changes in adult life expectancy 
that have occurred in this population with HIV treatment scale-up. To the extent 
that such information leads individuals to revise upwards their beliefs about 
survival and to more future-oriented investment behaviors, our study will shed light 
on a potentially-important but little-utilized approach in public health information 
campaigns: providing good news on complementary health risks. Whereas most 
public health information campaigns seek to amplify perceived risks as an approach 
to motivate behavior change (e.g. smoking kills), if people over-estimate the risks in 
one domain (e.g. I’ve smoked, so I will die young), this has potential to result in sub-
optimal behavior across other domains (e.g. if I’m going to die young anyway, I may 
as well drink too), leading to clustering of risk behaviors. Health risk-taking in one 
domain reduces returns to healthy behaviors in other domains, leading to a negative 
health behavior trap due to complementarity of risks [18]. 
 
In addition, our video intervention provides information on changes in survival as 
told through the personal experiences of HIV-positive individuals from the 
community who have live successfully for many years on ART. This design is 
intended to both to enhance the information’s credibility and beliefs that the 
information applies to the viewer [19].  
 
The randomized evaluation of the video intervention is nested in the AHRI 
surveillance. The surveillance population provides the sampling frame of eligible 
young adults to participate in the study. This will enable us to ensure balance 
between study arms on some key baseline covariates observed in the surveillance. 
Additionally, the video intervention will be timed so that it occurs 6-8 weeks prior 
to the annual AHRI surveillance visit, which includes an offer of home-based HIV 
counseling and testing, with referral to the local public sector HIV treatment 
program. Coordination with AHRI will enable us to assess uptake of HIV testing via 
home-based HIV testing offered in the surveillance, as well as longer run follow-up 
of education and labor supply outcomes. 
 
 
 
 
5 
 

 
 
A4. Hypotheses 
 
We hypothesize that a video informing young adults about large recent life expectancy 
gains due to HIV treatment scale-up will: 
• Increase human capital investment, by increasing their own survival 
expectations, hope for the future, and lead to increases in positive health and 
investment behaviors  
• Increase HIV testing and care-seeking, by improving attitudes towards and the 
perceived benefits of ART  
 
Subjective life expectancy. We will test the null hypothesis that the video 
intervention described will have no effect on subjective life expectancy, as measured 
through ascertainment of quantitative beliefs about probabilities of living to 
different ages. 
 
Pooled measure of forward-looking behaviors. We will test the null hypothesis that 
the video will have no effect on an index of measured forward-looking investment 
behaviors, including: uptake of HIV testing, participation in a job-search skills 
training workshop, and use of a savings device. For the pooled behavioral outcome, 
these behaviors will be aggregated using a simple count of the following: individual 
tested for HIV using either the voucher or surveillance; individual attended a job 
search skills workshop; individual allocated at least 50 Rand to the labeled savings 
tin at baseline.  
 
Because uptake of HIV testing could increase as a result of either the human capital 
channel or the perceived benefits of ART channel, we will additionally disaggregate 
HIV testing from the other outcomes and test the following null hypotheses separately:  
 
HIV testing. We will test the null hypothesis that the video intervention will have no 
effect on uptake of HIV testing as measured via either use of the HIV testing voucher 
or participation in home-based HIV testing in the AHRI surveillance visit occurring 
about six weeks after baseline. 
 
Job search skills workshop. We will test the null hypothesis that the video 
intervention will have no effect on uptake of a job search skills workshop occurring 
1-3 weeks after the baseline interview that all participants will be invited to. 
 
Savings. We will test the null hypothesis that the video intervention will have no 
effect on the amount (out of 100 Rand) allocated to the savings tin offered at the end 
of the baseline visit. 
 
Additionally, to better understand changes in beliefs and attitudes, we will test the 
null hypothesis that the video intervention will: 
- 
Have no effect on attitudes towards HIV treatment and its efficacy 
- 
Have no effect on perceptions of survival in the community 
6 
 

 
 
- 
Have no effect on measures of future orientation, locus of control, life 
satisfaction, and mental health 
- 
Have no effect on beliefs about future education and employment 
- 
Have no effect on attitudes towards the importance of using condoms 
 
Additionally, to better understand changes in self-reported behaviors, we will test 
the null hypothesis that the video intervention will: 
- 
Have no effect on time use allocated to education or labor supply activities, 
including job search 
- 
Have no effect on self-reported HIV testing  
- 
Have no effect on self-reported smoking, drinking, seatbelt use 
- 
Have no effect on sexual activity and condom use  
 
Additionally, it is possible that providing information on the benefits of HIV 
treatment would reduce demand for HIV prevention, a response known as risk 
compensation. It is also possible that increased optimism about the future would 
lead to greater demand for HIV prevention. Therefore, we will test the null 
hypothesis that the video intervention described will: 
- 
Have no effect on demand for HIV prevention, as measured by number of 
condoms purchased when offered at the end of the baseline survey 
 
Subgroup analyses 
Theory predicts that the impact of the video on subjective life expectancy (and 
human capital investment behaviors) should be greater the lower the person’s 
baseline survival expectations, as the informational intervention is stronger relative 
to the person’s baseline beliefs. Additionally, theory predicts that the impact of the 
video on subjective life expectancy (and human capital investment behaviors as well 
as HIV testing) should be greater the higher a person’s baseline perceived lifetime 
risk of HIV acquisition, as information on the life-prolonging benefits of ART will be 
irrelevant to someone who believes that they are unlikely ever to contract HIV. 
Therefore, we will test the null hypotheses that subjective life expectancy and 
perceived lifetime HIV risk are not linear effect modifiers of the primary outcomes: 
change in subjective life expectancy, HIV testing (with the voucher or home-based 
testing), participation in the training workshop, savings, and the pooled behavioral 
outcome described above.  
 
The benefits of HIV testing vary with prior knowledge of status. Individuals who are 
certain that they are HIV-infected and individuals who are certain that they are HIV-
uninfected have little incentive to test, suggesting an inverse-U relationship. 
Therefore, we will assess for effect modification of the impact of the video on HIV 
testing by current beliefs about HIV infection, comparing effects among respondents 
with greater uncertainty about HIV status vs. those with greater stated certainty.  
 
Finally, we will assess for linear effect modification by age (the information may be 
more impactful on younger participants who have yet to establish behavioral 
patterns) and sex (a common effect modifier in HIV interventions).  
7 
 

 
 
A5. Overview of Study Design 
 
We will conduct a pilot intervention study to learn whether a short video providing 
young adults with information on recent longevity gains affects survival 
expectations, hope for the future, and ultimately health and educational behaviors. 
We intend to recruit approximately 450 young adults ages 18 to 25 for this research 
project. The study involves the following components: 
 
• Conduct a survey to measure survival expectations and other perceptions of HIV 
and ART that affect health decision-making as well as health and education 
(future-oriented) actions,  
• Randomize half of the study participants (50%) to view a 9-minute 
informational video explaining that life expectancy in their area has substantially 
increased since 2003 and that this gain can primarily be attributed to reduced 
HIV-related mortality because of increased access to treatment. The other half of 
study participants will be randomized 1:1 to an attention placebo control video 
(25%) and a pure control arm (25%). 
• Conduct immediate post-video survey and assess uptake of job skills workshop, 
HIV testing voucher, condom offer, and a savings device 
• Assess uptake of HIV testing during the home-based AHRI surveillance visit, 
which occurs about 6 weeks after the baseline survey 
• Conduct follow-up survey at 8 weeks to measure changes produced by the video 
in survival expectations and health behaviors, and  
• Assess longer run effects on linkage to care as well as other schooling and 
employment behaviors observed in the surveillance  
 
The primary endpoint is an index of all human capital investment behaviors 
including use of the HIV testing voucher, uptake of HIV testing in the surveillance 
visit, participation in a job-skills workshop, and use of the savings device. 
 
 
A6. Study Population and Inclusion Criteria 
 
The proposed study will investigate survival expectations and health behaviors 
among young adults residing in the AHRI surveillance who are ages 18 to 25. 
 
Inclusion criteria 
• Member of a household that participates in the AHRI population surveillance (i.e. 
not a visitor to the area) 
• Resided in the AHRI surveillance area in the previous year (and was therefore 
included in the population listing that formed the sampling frame) and was still 
residing in that location when the first contact attempt was made 
• Resides in one of the specific AHRI week-blocks that we sampled from for the 
study (further details below) 
• Ages 18-25 years 
8 
 

 
 
 
Exclusion criteria 
• <18 years at time of baseline interview 
• >25 years at time of baseline interview 
• Not able to provide consent 
• Did not consent to participate 
 
 
A7. Sampling Strategy and Sample Size Determination 
 
Participants were sampled at random from a population listing from AHRI/Africa 
Centre’s surveillance. AHRI’s surveillance is organized by geographically defined 
week blocks. Specific week blocks were chosen for this study based on the timing of 
AHRI’s surveillance. We included respondents residing in week blocks where AHRI 
was scheduled to conduct its annual surveillance and HIV testing offer about 6-8 
weeks after the baseline survey. 
 
Details on the Sampling Strategy are contained in Appendix A. 
 
We chose a target sample size of 450 individuals. The final actual sample size will be 
determined based on the number of successful interviews achieved within a pre-
specified period, from a pre-specified roster of eligible participants. The number of 
eligible participants included in the study roster is based our target of 450 baseline 
interviews conducted. Based on prior studies of this age group at AHRI, we 
anticipate that we will be able to find and recruit 50- 60% of the eligible population. 
We anticipate that our study team is capable of conducting 60-75 interviews per 
week. We therefore sampled 120 individuals in each of seven week-blocks for a total 
of 840 individuals eligible for recruitment.  The final sample size was the number of 
respondents successfully interviewed from the pre-specified study rosters. 
 
With a target sample of 450 individuals, this study is powered at 80% to detect a 
0.26 standard deviation difference in outcomes between the treatment and pooled 
control groups.  
 
Example 1) We are powered to detect an increase in subjective (perceived) life 
expectancy from 50 to 54 years, assuming a standard deviation of 15 years. The 
baseline numbers – 50, sd=15 – are based on survey pre-tests conducted as part of a 
prior study: “Life plans of young adults in rural KZN: a qualitative study”. A four-
year increase would be about a quarter of the actual change in life expectancy that 
occurred between 2003 and 2014. 
 
Example 2) We are powered to detect an increase in the proportion using the HIV 
testing voucher (or taking up the job search skills workshop) from 10% to 20%. As 
this is a pilot study, we are only powered to detect intervention effects if they are 
large.  
9 
 

 
 
A8. Interventions and Randomization 
 
A8.1. Study Arms 
Active treatment. We developed a 9-minute video entitled “Iksasa E’lihle” (A 
Beautiful Future) that provides information on the changes in HIV-related mortality 
and adult life expectancy in the Demographic Surveillance Area between 2003 and 
2015 (an increase of 18 years). The video weaves public health information on life 
expectancy gains with narratives from members of a local HIV support group who 
have lived long and fulfilling lives on HIV treatment. Some of these individuals were 
among the first to receive ART in the area and have been on treatment for over a 
decade. We incorporated the stories of individuals on HIV treatment for several 
reasons including: to enhance the credibility of the information on rising life 
expectancy; to increase the personal relevance and salience of the information 
through real world examples; to convey information with no numeracy 
requirement; to facilitate comprehension using emotional signposting based on the 
narratives; to illustrate by example how specific individuals have been motivated by 
their longer life expectancy on ART to invest in themselves, their families, and in a 
small business; and to increase viewer engagement with the video content. The 
video was developed in partnership with AHRI’s Community Engagement Unit and 
was filmed by Jive Media Africa (www.jivemedia.co.za), a South African media 
company with expertise in scientific communication through film. The goals of the 
video are (1) to increase survival expectations, hope, locus of control, and future 
orientation; and (2) to improve perceived benefits of HIV treatment and attitudes 
towards living on HIV treatment.  
 
Attention placebo control. The attention placebo control arm will receive a 9-
minute video clip of a video on another topic. Attention placebo controls should 
mimic the theoretically inactive components of the active treatment under study, 
while having none of the theoretically active components of that intervention 
(Freedland, http://europepmc.org/articles/pmc3091006). The attention placebo 
control video was matched to the active treatment video in length, pace, tone, 
narrative structure, visual imagery (daily life in a local community), and language 
(Zulu with English subtitles), but it was lacking specifically in the active intervention 
content (i.e. information about longevity gains with ART). The video selected was 
about the challenges of raising children in households where the household head 
has a physical disability. It was filmed in a nearby community that was not in the 
surveillance area. The video was found online and we received permission to use 
this video from its creators. The video was edited to be 9 minutes and to contain no 
discussion of HIV treatment and longevity. We note that in clinical behavioral 
intervention studies, attention placebo controls should induce the same expectation 
of therapeutic benefit as the treatment. Our study was not conducted in a clinical 
setting, and neither the active treatment nor the attention placebo control was 
presented as a therapeutic intervention. 
 
Pure control. The pure control arm will view no video and will simply continue 
with the baseline survey. 
10 
 

 
 
A8.2. Randomization procedures 
 
The study is a population-based randomized controlled trial. To reduce potential for 
contamination, treatment assignment will be determined at the level of the 
household, such that if there are multiple eligible participants per household, they 
will receive the same treatment. Households will be randomized to study arm ex 
ante, i.e. before going into the field. Ex ante randomization has two benefits. First, as 
a quality control, ex ante randomization limits the opportunity for violations of 
treatment assignment. Second, because the study is nested in the AHRI population 
surveillance, ex ante randomization enables us to achieve balance across study arms 
with respect to several baseline characteristics of interest among eligible 
participants. We will use a constrained randomization approach to achieve 
approximate balance on: sex, age, whether the person was in school, orphans status, 
HIV positive status, HIV negative status (unknown is the third category), and 
household asset quintile. These factors were selected because of their potential 
influence on survival expectations. (For further details on randomization 
procedures, please see Appendix C.) 
 
We note that sampling and randomization will be conducted using AHRI’s public use 
(de-identified) data. We will provide a list of public use identifiers and treatment 
assignments to AHRI’s data management staff, and they will create an operational 
database to be used during data collection. Information on the characteristics above 
(e.g. HIV status) will never be linked with personally-identifiable information.  
 
Participants will be randomized using a ratio of 50% active treatment, 25% 
attention placebo control, 25% pure control. 
 
 
Active Treatment Video 
Attention Placebo Video  
Arm 1, Active Treatment 
X 
 
Arm 2, Attention Placebo 
Control 
 
X 
Arm 3, Pure Control 
 
 
 
 
A8.3. Blinding 
 
Participants and interviewers 
During recruitment, consent, and the first half of the baseline survey, participants 
and interviewers will be blinded as to the participant’s treatment assignment. 
Randomized treatment assignment will be revealed at the moment when 
participants received the intervention. However, we will not indicate to the 
participants whether the video they see is the treatment or attention placebo video. 
 
Researchers 
11 
 

 
 
The researchers will be blinded to the randomization status of participants 
throughout the data collection, extraction, and cleaning/quality control process, up 
until the date when randomized assignments are unmasked. To maintain blinding of 
treatment assignments vis-à-vis the research team during the study, we will use the 
following procedures: 
- 
Treatment assignment will be stored as a hidden field in the REDCap 
database. I.e. during the baseline survey it will generate a pop-up message 
telling the interviewer to show the participant the active treatment video, 
active control video, or no video, however this field will not be visible to 
anyone on the research team.  
- 
The treatment assignment field will not be exported during routine quality 
control procedures. 
- 
Treatment assignment will be extracted along with study ID into its own 
table and will not be merged with any of the other data until randomization 
is unblinded. 
- 
Randomization will be unblinded only after the final Pre-Analysis Plan is 
posted to www.clinicaltrials.gov. In developing our pre-analysis plan, we 
conducted some descriptive analysis of the blinded baseline pre-intervention 
survey data. Additionally, we monitored overall levels of uptake of the HIV 
testing voucher and job search training workshop in order to determine 
potential statistical power with these outcomes. For guidance on using 
baseline data to inform Pre-Analysis Plans, see Olken (2015). 
- 
We will submit an update to our clinicaltrials.gov study profile after 
treatment assignment is unblinded, noting the date when we have done so. 
 
As this is a short (4-month) pilot study and the intervention is minimal risk, we do 
not have a DSMB and do not have stopping rules for the trial based on preliminary 
analysis of efficacy.  
 
 
A9. Baseline interview and intervention 
 
The baseline survey visit is designed to take about 1.5 hours, and consists of the 
following sub-components: 
 
A9.0. Recruitment and Informed Consent 
 
Potential respondents will be recruited via visits to their homesteads. Permission 
will be obtained from the head of household to enter and to speak to the sampled 
participant. The study will be described and participants will provide written 
informed consent to participate in the study, including the baseline visit, a follow-up 
visit, and permission to link with demographic surveillance data. See Appendix B for 
the approved consent form.  
 
A9.1. Survey module 
 
12 
 

 
 
First, we will conduct a survey questionnaire with young adults ages 18 to 25 to 
learn about their survival expectations, perceptions of HIV risks and treatment, 
future-oriented behaviors such as smoking, alcohol use, HIV risk behaviors, savings, 
locus of control, mental health, time and risk preference, and life satisfaction.  
 
A9.2. Video intervention 
 
All study participants will have been randomly assigned ex ante to one of the three 
study arms: active treatment, attention placebo control, and pure control. Both 
interview participants and interviewers will be blinded to treatment assignment 
during the baseline survey and treatment arm will only be revealed at the time 
when the video will be shown. During the baseline survey, randomization status will 
be revealed, and the participant will be shown the video corresponding to their 
treatment arm, or will not be shown a video if randomized to pure control. 
 
A9.3. Re-survey module 
 
Immediately after the video intervention, all study participants will be asked a brief 
subset of the survey module questions to re-measure survival expectations, locus of 
control, and life satisfaction. This step will aim to assess the immediate impact of the 
video intervention on beliefs.  
 
A9.4. Rapid assessment behaviors 
 
Following the baseline survey participants will be offered several behavioral 
prompts: 
  
HIV Testing Voucher. Immediately after the re-survey module, we will offer 
participants a voucher for free HIV testing at a local pharmacy. We will provide 
respondents with information about the benefits of testing, the benefits of HIV 
treatment, and the availability of free testing and treatment in public sector 
facilities. We will then offer a voucher for free HIV testing in local private 
pharmacies. Participants will be provided R10 with the voucher, to subsidize 
transport. The cost of testing in these facilities is usually R100, but will be available 
to people for free during a time-limited period if they bring the voucher. At the end 
of the study, we will visit the pharmacies to collect the vouchers that have been 
redeemed and we will pay the pharmacies for the cost of the testing provided. We 
will record the voucher numbers redeemed, as an indicator of participation in HIV 
testing, but will not record any information about participants’ test results. This 
information will be stated clearly on the voucher. 
 
Condom offer. Immediately after the giving participants the HIV testing voucher, all 
participants will be offered the opportunity to purchase condoms. Each participant 
will be provided with R20 in the form of ten (10) R2 coins. They will be offered the 
chance to use that money to buy condoms of a locally popular brand for a 
13 
 

 
 
discounted price (up to 10 condoms at R2 each) and to keep any remaining money. 
We will record how many condoms the respondent purchases. 
 
Invitation to Job Search Skills Workshop. The Job Search Skills Workshop will be 
delivered by a local consultant or organization with experience in human resources 
and career counseling, as well as the local employment context. The workshop will 
last 3 hours. The curriculum, developed by the researchers in consultation with a 
local HR expert, will cover the following: (30 min) introduction and review of 
process of job search, including identifying opportunities, building up relevant skills 
and qualifications, and submitting applications; (60 min) hands on tutorial on how 
to write an application cover letter; (15 min) break; (60 min) interview skills and 
practice; (15 min) presentation on local resources for career counseling and job 
fairs, with hand outs from these existing organizations. Other topics related to job 
search skills may be covered as well. The workshop will be offered either at AHRI or 
at a local community hall. The workshop will be held 1-3 weeks after the baseline 
interview and attendance of study participants will be recorded. The goal is to 
measure differences in uptake across treated and control arms, which will provide 
short-run evidence of the impact of the video on human capital investment 
behaviors. In order to plan for the number of attendees, we will require participants 
to reserve a space in the workshop. We will give them a cash payment and give them 
the option to keep the cash or use it to reserve a space. Participants will be 
randomized to receive R30 (and pay R20 to reserve) vs. R15 (and pay R5 to 
reserve); in both cases they will have R10 for transport. We are randomizing the 
incentive size in order to understand whether differences in price affect demand for 
the workshop, which will provide a point of comparison for the effect of the 
intervention video. We will be collecting data on attendance of the Job Search Skills 
Workshop at the individual level. Participants will reserve a spot in the workshop 
during the baseline survey. A workshop roster of registered attendees will be 
generated that contains: study ID, name, sex, date of birth, phone number, and 
household head name. Phone numbers will be used to remind all registered 
attendees 1-2 days before the workshop event. When participants arrive at the 
workshop, they will be asked to confirm their identity by correctly identifying one of 
the following: date of birth, phone number, or name household head. Attendance 
information will be entered into the study database and linked based on study ID. 
The physical attendance roster will be scanned and destroyed after data are 
entered. The scanned copy will be retained in a password-protected folder on the 
AHRI server, along with other identified operational data.  
 
Savings choice. Lastly, all participants will be provided with a labeled savings tin. 
During the disbursement of participant compensation (R100), the participants will 
be given the opportunity to save a portion of the money by placing it in the sealed 
savings tin. We will record the amount of money the participant chooses to save and 
hold a brief discussion with the participant about saving for the financial aspects of 
their future goals. We provide this option for study participants so that we can 
immediately measure intentions for future-oriented behavior and to estimate the 
14 
 

 
 
video’s short-term effect based on differences in the amount saved between the 
treatment and control groups. 
 
 
A10. Follow-up interview at 2 months  
 
Study participants will be contacted 2 months after their initial interview and 
provided with a follow-up survey that measures survival expectations, locus of 
control, life satisfaction, mental health, savings, health behaviors, time use, and HIV-
related attitudes and beliefs. Participants will receive R50 in compensation for 
participation in the follow-up survey.  
 
 
A11. Follow-up in the AHRI Surveillance 
 
Home-based HIV testing. Data from this study will be linked at the individual level 
to the AHRI/Africa Centre population surveillance. The baseline survey is timed so 
that, about 6-8 weeks afterwards, the same household will be visited by the AHRI 
surveillance team for their routine annual surveillance visit. During this visit, all 
people ages 15 and older are offered home-based HIV testing and counseling, with 
referral to the public sector HIV program. Uptake of HIV testing in the AHRI 
surveillance visit at 6-8 weeks will be combined with use of the HIV testing voucher 
offered at baseline, to assess impacts on HIV testing uptake. Linkage with the 
surveillance will be conducted by AHRI data management staff. 
 
Other outcomes. Participants will be followed up in future rounds of AHRI’s 
routine population surveillance. In addition to HIV testing, the AHRI surveillance 
collects information on schooling, employment, and migration, among other 
outcomes, and is also linked to the public sector clinical record system. These 
linkages will be conducted by AHRI data management after data are de-identified. 
 
 
A12. Project Timeline 
 
April 2016 – December 2016 formative qualitative research 
December 2016 – April 2017 creation of video intervention 
May – July 2017 REDCap database development, pre-testing 
June 2017 Registered on www.clinicaltrials.gov 
August 2017 Recruitment and training of field interviewers 
September – October 2017 Baseline survey and intervention 
September – November 2017 Job search workshops, HIV testing vouchers 
November – December 2017 Follow-up survey, home-based HIV testing 
15 
 

 
 
December 2017 Updated on www.clinicaltrials.gov 
January – August 2018 Data extraction, quality checks, passive follow-
up of HIV testing in the AHRI surveillance 
September 17, 2018 Final Protocol and Pre-Analysis Plan posted to 
www.clinicaltrials.gov 
September 18, 2018 Randomization status unblinded to researchers 
and posted to www.clinicaltrials.gov 
March 2018 – January 2019 Analysis and preparation of manuscripts  
 
 
A13. Funding and Registration 
 
This randomized trial was jointly funded by two grants from the National Institutes 
of Health: 1K01MH105320 (Bor) “Economic, health, and behavioral dimensions of 
HIV treatment scale-up” and 1R01HD084233 (Tanser/Barnighausen) “Causal 
pathways to population health impact of HIV antiretroviral treatment”.  
 
The trial is registered on www.clinicaltrials.gov as NCT03215901. 
 
 
 
 
* 
* 
* 
 
 
 
16 
 

 
 
B. PRE-ANALYSIS PLAN 
 
B1. Outcomes Variables and Definitions 
 
B1.1 Primary Outcome Measures 
 
Behaviors 
 
1. Participation in HIV testing 
Definition: Use of HIV testing voucher to be provided OR participation in HIV testing 
during surveillance household survey visit conducted at 1 to 2 months follow-up. 
[Time Frame: 2 months] 
 
2. Composite measure of future-oriented behaviors 
Definition: Count variable (range 0:3) equal to the sum of the following indicators: 
- 
Participation in HIV testing (either via the voucher or home-based testing) 
- 
Attendance at a job search skills workshop: 1=attended 
- 
Allocation of funds to savings tin during baseline interview: 1=participant 
allocated at least 50 Rand (out of 100 Rand) to the savings tin 
 
Beliefs and Attitudes 
 
3. Change in subjective life expectancy (at 2 months) 
Definition: Subjective life expectancy is defined as the area under the subjective 
survival curve. Respondents will be asked about the chances of surviving to age 30, 
40, ..., 80 years. We will linearly interpolate the responses beginning at their current 
age and ending at 80 years. We will then calculate the area under this curve, which 
we define as subjective life expectancy (number of years expected to live between 
current age and age 80). Our primary outcome for beliefs will be the change in 
subjective life expectancy between baseline (pre-intervention) and follow-up (at 2 
months). We will also assess change in subjective life expectancy between baseline 
and immediate post-intervention assessment as a secondary outcome. 
[Time Frame: 2 months] 
 
4. Positive attitudes towards HIV and HIV treatment (at 2 months) 
Definition: Immediately after the intervention and at 2 months, we ask participants 
whether they strongly disagree, disagree, agree, or strongly agree with a series of 
statements regarding attitudes towards HIV and HIV treatment. As a primary 
outcome, we will assess differences in a composite index of 12 of these measures, 
comparing attitudes between treatment arms at 2 months. We will assign points 
(1=strongly disagree, 2=disagree, 3=agree, 4=strongly agree) based on responses, 
we will sum over the following items, and we will construct a z-score based on the 
mean and standard deviation in the baseline data. “Reverse code” denotes that the 
variable will be scored (4=strongly disagree, 3=disagree, 2=agree, 1=strongly 
agree). We will also assess differences immediately after the intervention as a 
17 
 

 
 
secondary outcome. Note: the attitudes questions are asked after the intervention. 
There is no baseline. 
[Time Frame: 2 months] 
 
ITEM 
REVERSE CODE? 
If I were HIV infected, I could have children and a family 
 
If I were HIV infected, it would be difficult to find a 
romantic partner 
X 
If I were HIV infected, I could still do all the activities I enjoy  
If I were HIV infected, it would make it harder to get and 
keep a job 
X 
If I were HIV infected, it would change my career plans  
X 
If I were HIV infected, I could expect to live to pension age 
(age 60) 
 
If I were HIV infected, I could expect to live long enough to 
see my children grow up and to meet my grandchildren 
 
I am scared to find out my HIV status 
X 
If I were HIV infected, it would be hard to start ARVs (HIV 
treatment) 
X 
If I were HIV infected, I would want to start ARVs (HIV 
treatment) as soon as possible, even if I didn’t feel sick 
 
If I were HIV infected, I would prefer to wait as long as 
possible to start ARVs 
X 
If I were HIV infected, I would choose not to start ARVs. 
X 
 
 
 
B1.2 Secondary Outcome Measures 
 
Behaviors 
 
Attendance at job search skills workshop 
Definition: Participants will be invited to a job search skills workshop; workshops 
were held in nearby locations 1-3 weeks after each survey week-block; attendance 
will be measured as a binary indicator 
[Time Frame: 2 weeks] 
 
Condoms purchased 
18 
 

 
 
Definition: Participants will be offered the opportunity to purchase discount 
condoms; the number purchased will be assessed; range 0 to 10 condoms 
[Time Frame: Baseline survey] 
 
Savings allocated 
Definition: Participants will be offered the opportunity to allocate a portion of their 
participation incentive to a savings device (lock box); amount allocated will be 
assessed; range 0 to 100 Rand 
[Time Frame: Baseline survey] 
 
Savings at 2-month follow-up 
Definition: At follow-up, participants are asked if they still have the savings tin and if 
they have added money to the tin since baseline. They are then asked to estimate 
the total savings inside today or on the date when they opened it. If they still have 
the tin on hand, we then open it, count their savings, and provide them with a new 
tin to continue saving. We will define 2-month savings in two ways: first, as the 
amount the participants self-report they have in the tin on the follow-up interview 
date or, if they no longer have the tin, on the last day they had it; and second, as the 
amount we validate by opening the tin, with zeroes imputed if the tin is not available 
or money used.  
[Time Frame: 2-months] 
 
Use of HIV testing voucher 
Definition: Participants will be provided with a voucher for free HIV testing at a 
local service provider (we partnered with two local pharmacies that provided 
government certified HIV testing services); use of voucher will be assessed by 
obtaining voucher numbers from the service provider 
[Time Frame: 1 month] 
 
Uptake of HIV testing in the AHRI surveillance 
Definition: Participants will be offered the opportunity to test for HIV via a 
household visit buy the AHRI surveillance team 4-6 weeks after the baseline survey. 
By linking with the AHRI surveillance, we will assess whether individuals tested in 
the AHRI home visit. All those who tested will be coded as 1; all others, including 
those who were not at home will be coded as 0. 
[Time Frame: 1 month] 
 
Use of clinical HIV services 
Definition: For individuals who are HIV-infected, the intervention could increase 
linkage to care and retention on HIV treatment. We will assess use of clinical HIV 
services as a binary indicator for whether the individual sought care in the public 
sector HIV care and treatment program in the three months after baseline. Clinical 
data from the public sector HIV treatment program are routinely linked into the 
population surveillance platform by AHRI data management.  
[Time Frame: 3 months] 
 
19 
 

 
 
 
Self-reported behaviors 
 
Change in time allocated to education or labor supply  
Definition: At both baseline (pre-intervention) and 2-months follow-up, we ask 
respondents to report hours spent on different activities in the last week. These 
questions were adapted from the Malawi Living Standards Measurement Survey, 
Module E (p17). We will assess change in the total hours spent on the following 
activities (aggregating time spent across these activities): 
- 
a household business 
- 
formal work for pay 
- 
informal work for pay 
- 
apprenticeship/internship   
- 
looking for work 
- 
schooling 
- 
job training 
(The activities that we measured but excluded from this list are: housework, 
caregiving, socializing with friends, doing nothing, and sleep) 
[Time Frame: 2 months] 
 
Change in self-reported savings  
Definition: Difference between baseline and 2-month follow up in response to the 
following question: how much have you saved in bank accounts, at home, or 
elsewhere? 
[Time Frame: 2 months] 
 
Change in self-reported non-HIV health risk behaviors 
Definition: We will assess changes between baseline and 2-month follow-up in self-
reported health risk behaviors. We will conduct statistical testing on an additive 
index of the following: any smoking in the last week; any drinking in the last week; 
any heavy drinking (3+ drinks) in the last week; does not always use a seatbelt. 
Range of this index will be 0 to 4. 
[Time Frame: 2 months] 
 
Change in self-reported unprotected sexual activity 
Definition: We will assess changes between baseline and 2-month follow-up in self-
reported unprotected sex. Specifically, we will assess number of times a person 
reported having condomless sex in the last 30 days. 
[Time Frame: 2 months] 
 
Change in self-reported current HIV care-seeking  
Definition: We will assess change between baseline and 2-month follow-up in a 
combined self-report measure of whether the respondent either tested for HIV 
within the last 3 months and whether the respondent is currently taking ARVs.  
[Time Frame: 2 months] 
 
20 
 

 
 
Employment, education, and care-seeking outcomes in the AHRI surveillance  
Definition: We will assess longer run education, employment, migration, and care-
seeking outcomes observed through household proxy report and clinic linkage in 
the AHRI population-based surveillance platform. Specifically, we will assess 
whether the respondent is reported to be in school, working, looking for work, or 
none of the above; whether the respondent is still residing in the surveillance area; 
and whether the respondent has linked to clinical HIV services. 
[Time Frame: 1 year, 2 years] 
 
 
Attitudes and beliefs 
 
Change in beliefs about life expectancy in the community 
Definition: at baseline and at 2-month follow-up, we ask people to consider 20 
people their age and gender in the community and to estimate how many they think 
will be alive at age 30, 40, … 80. We will construct a measure of perceived 
community life expectancy by linearly interpolating the responses to these 
questions (starting at the respondent’s age) and computing the area under the 
curve. This value is the average number of years they expect a demographically 
similar person in the community to live. 
[Time Frame: 2 months] 
 
Change in beliefs about lifetime risk of HIV infection  
Definition: change between baseline and 2-month follow-up in the subjective 
probability (out of 20) of becoming HIV-infected at some point in their life. Although 
we do not expect that the video will change underlying beliefs about HIV risk; it 
could change people’s willingness to consider these risks, which would be important 
to know in interpreting our results. 
[Time Frame: 2 months] 
 
Change in beliefs about work and schooling  
Definition: At baseline and 2-month follow-up we asked respondents about the 
chances (out of 20) that they would complete different levels of schooling and about 
the chances they would be employed in the future. We will assess changes in these 
beliefs. 
[Time Frame: 2-months] 
 
Change in future orientation 
Definition: change between baseline and 2-month follow-up in an index of 
statements (strongly disagree, disagree, agree, strongly agree) measuring future 
orientation. 
 
ITEM 
REVERSE CODE? 
Sometimes I act spontaneously instead of thinking 
too much about the consequences of my actions. 
X 
21 
 

 
 
If I get money, I tend to spend it too quickly. 
X 
I am a patient person 
 
I tend to avoid thinking about the future 
X 
I enjoy thinking about the future 
 
I am excited for what the future will bring 
 
I worry about what the future will bring 
X 
Things in life never seem to get better  
X 
My future is bright 
 
I am usually able to anticipate what the future will 
bring 
 
I tend to be good at predicting what will happen in 
the future. 
 
I find it difficult to anticipate what the future will 
bring 
X 
It is bad luck to try to predict what the future will 
bring 
X 
Thinking about what the future will bring is 
important to help me plan for and achieve my goals  
 
 
The above questions include different facets of future orientation including 
patience, optimism, ability to form accurate future expectations, attitudes towards 
forming future expectations. To reduce the likelihood of false positives, we create an 
index of all questions related to future orientation by aggregating scores (reverse 
coding such that higher score for all questions reflect greater degree of future-
orientation). If we observe statistical significance at the index level, we then will 
investigate treatment effects by individual components. 
 
Change in locus of control 
Definition: change from baseline to 2-month follow-up in an index of questions on 
locus of control. Research in economics that investigates the effect of perceptions on 
human capital formation (Bernard et al. 2014, Heckman and Kautz 2012, Heckman 
et al. 2006) commonly uses the Internality, Powerful Others, and Chance (IPC) scale 
to explore three separate components of locus of control (Levenson 1981, [20]). We 
use a subset of Levenson’s scale, two questions per component, for each component 
of the IPC scale. We score respondents with a 1 if the strongly disagree and a 4 if the 
strongly agree. To create a composite score for each component of the scale, we then 
combine the total per question. A score of 8 on the internality scale implies that an 
individual feels the highest level of control over their lives. Based on our formative 
qualitative research, we also added a question on the role of external forces, e.g. 
God, witchcraft, etc. To reduce the likelihood of false positives, we create an index of 
all questions related to locus of control by aggregating scores (reverse coding such 
that higher score for all questions reflect greater perceptions of control). We will 
construct a z-score by subtracting the baseline mean and dividing by the baseline 
standard deviation. If we observe statistical significance at the index level, we then 
will investigate treatment effects by sub-scale and individual components.  
22 
 

 
 
ITEM 
SUB-SCALE 
REVERSE 
I can mostly determine what will happen in 
my life 
Internality 
 
My life is determined by my own actions 
Internality 
 
I feel like what happens in my life is mostly 
determined by powerful people, including 
parents, friends, and politicians.  
Powerful 
others 
X 
People like myself have very little chance of 
protecting our personal interests when they 
conflict with those of more powerful people 
Powerful 
others 
X 
I feel like what happens in my life is mostly 
determined by external forces, such as god, 
the ancestors, or witchcraft.  
External 
forces 
X 
To a great extent my life is controlled by 
accidental/chance happenings, luck, or 
fate   
Chance 
X 
It is not always wise for me to plan too far 
ahead because many things turn out to be a 
matter of good or bad fortune 
Chance 
X 
 
Change in health-related locus of control 
Definition: We adapted the locus of control scale to focus on the domain of health. 
We will assess change from baseline to 2-month follow-up in a 4-item index of 
questions on health-related locus of control. We score respondents with a 1 if the 
strongly disagree and a 4 if the strongly agree. We will construct a z-score by 
subtracting the mean and dividing by standard deviation in the baseline survey. If 
we observe statistical significance at the index level, we then will investigate 
treatment effects by individual components. 
 
ITEM 
REVERSE 
My health is mostly the result of decisions I make 
 
My health is mostly the result of decisions other 
people make.  
X 
My health is mostly the result of chance happenings 
and luck.  
X 
My health is mostly the result of forces beyond my 
control.  
X 
 
 
Change in life satisfaction 
Definition: change from baseline to 2-month follow-up in reported life satisfaction 
today (0-10 scale) and in five years (0-10 scale). Subjective wellbeing (SWB) can be 
separated into three distinct concepts: evaluative, hedonic, and eudaimonic 
(Graham and Nikolov 2014; Stone and Mackie 2014). Evaluative SWB or life 
satisfaction is reflective of an individual’s perceptions of their life as a whole, 
23 
 

 
 
instead of reflecting current emotional states. Hedonic SWB reflects an individual’s 
affective state and current emotional experiences in daily life.  Eudaimonic refers to 
wellbeing related to an evaluation of overall life purpose. Because various authors 
find that objective measures of wellbeing such as income and education are 
correlated most with evaluative well-being (Kahneman and Deaton 2010), we use 
this definition of SWB. To measure evaluative SWB, we use the best possible life 
(BPL) Cantril’s Ladder question, which asks respondents to compare their life to the 
best possible life they can imagine on a scale from 0 to 10 (Cantril 1965) with 10 
corresponding to the best possible life and 0 corresponding to the worst possible 
life. Because Cantril’s Ladder asks respondents to compare themselves to a notional 
best life, answers also correlate to open-ended happiness questions and other life 
satisfaction questions (Graham et al. 2010). We also ask respondents to answer 
Cantril’s ladder in 5 years, which can be interpreted as a measure of future 
optimism. 
[Time frame: 2 months] 
 
Change in mental health and well-being 
Definition: mental health will be assessed using 7 mental health questions from the 
SF-12 Mental Component Survey with the greatest discrimination with respect to 
mental health. We will aggregate these questions using published weights [21,22], 
also shown below: 
https://www.researchgate.net/publication/291994160_How_to_score_SF-12_items 
 
(GH1) In general would you say your health is? 
 
Poor = -1.71175 
Fair = -0.16891 
Good = 0.03482 
Very Good = -0.06064 
Excellent = 0 
During the past 4 weeks, have you had any of the following problems with your 
work or other regular daily activities as a result of any emotional problems (such as 
feeling anxious or depressed)?  
(RE2) Accomplished less than you would like  
Yes = -6.82672  
No = 0 
(RE3) Didn’t do work or other activities as carefully as usual  
Yes = -5.69921 
No = 0 
How often in the past 4 weeks:  
(MH3) have you felt calm and peaceful?  
None of the time = -10.19085 
Some of the time = -6.31121 
A good bit of the time = -4.09842 
Most of the time = -1.94949 
All of the time = 0   
(VT2) Did you have a lot of energy?  
24 
 

 
 
None of the time = -6.02409 
Some of the time = -3.29805 
A good bit of the time = -1.65178 
Most of the time = -0.92057 
All of the time = 0   
(MH4) Have you felt downhearted and blue (reverse code)?  
None of the time = 0   
Some of the time = -4.59055 
A good bit of the time = -8.09914 
Most of the time = -10.77911 
All of the time = -16.15395 
 
(SF2) During the past 4 weeks, how much of the time has your physical health or 
emotional problems interfered with your social activities (like visiting friends, 
relatives, etc.)?  
None of the time = 0 
Some of the time = -3.13896 
A good bit of the time = -5.63286 
Most of the time = -8.26066 
All of the time = -6.29724 
 
After aggregating with these weights, we will construct a z-score based on the mean 
and standard deviation in the baseline data. 
 
[Time frame: 2 months]  
 
 
 
B2. Comparisons to be made across treatment arms 
 
We have three treatment arms: 
1. Intervention video (50%) 
2. Attention placebo control (active control) (25%) 
3. No video (pure control) (25%) 
 
Our primary analyses will assess: intervention video (arm 1, 50%) versus no 
intervention video (arms 2 and 3, 50%). This is the highest powered test we have. It 
is justified under the a priori assumption that the attention placebo control video 
has no effect on outcomes. Indeed, this is the same assumption that is made 
(implicitly) any time a placebo control is used, with the assumption justified by the 
design of the placebo as not having any theoretically active components. We note 
that this primary analysis rests on an a priori assumption. We are not powered to 
test the null hypothesis that Arms 2 and 3 are the same. And because of limited 
power, failure to reject the null hypothesis that arms 2 and 3 are the same would not 
be strong evidence that the null hypothesis is true. Hence, approach 1 is identified 
under an a priori assumption of no difference between arms 2 and 3. 
25 
 

 
 
 
In secondary analyses, we will loosen the a priori assumption and now compare: 
A. intervention video (arm 1, 50%) versus attention placebo control (arm 2, 25%) 
This comparison assesses the impact of information on survival gains due to ART on 
outcomes, holding constant the nature of the video intervention (attention control). 
Therefore, it comes closest to capturing the theoretical construct of interest. 
 
B. intervention video (arm 1, 50%) versus pure control no video (arm 3, 25%) 
This comparison assesses the impact of the informational video vis-à-vis standard of 
care, which is no video. This comes closest to capturing the effect of policy interest. 
Additionally, this comparison insures us against the possibility that our a priori 
assumption is incorrect (i.e the placebo video does affect outcomes), giving us an 
estimate of effect that does not include the effect of the placebo. 
 
 
B3. Assessment of Balance 
 
We conducted constrained randomization to ensure balance among individuals who 
were sampled and eligible to participate in the study. However, balance on these 
factors – as well as other factors not considered in the randomization – is unknown 
among the participants who are actually recruited into the study. 
  
We will assess balance on the following characteristics that were included in the 
randomization:  sex, age, whether the person was in school, orphan status, HIV 
positive status, HIV negative status (unknown is the third category), and household 
asset quintile.  
 
We will additionally assess balance on all primary and secondary outcomes 
described above that are measured at baseline.  
 
Through linkage to the AHRI surveillance, we will additionally assess balance on: 
whether the person has a child; household size; distance to the nearest clinic, 
whether anyone in the household died from HIV in the prior 10 years; whether 
anyone in the household is receiving public sector ART at time of baseline. 
 
For each baseline covariate, we will report a t-test for the null hypothesis of equal 
means in Arm 1 vs. Arms 2+3 (pooled).  
 
 
B4. Statistical analysis of treatment effects 
 
All statistical analyses will involve comparisons across the treatment arms 
described above. For each outcome, we will generate the following data: 
 
 
 
26 
 

 
 
Example Results Table (fake data) 
 
  
Sample Means 
Treatment Effect Estimate (95% CI) 
 
Arm 1, 
n=XXX 
Arms 2+3, 
n=XXX 
Arm 2, 
n=xxx 
Arm 3, 
n=xxx 
Crude 
difference 
Adjusted for 
design variablesa 
Adjusted for design, 
strong predictors, and 
baseline imbalanceb 
Outcome 1 
1.2 
1.5 
 
 
-0.3 (-0.5 to 0.2) 
-0.3 (-0.5 to 0.2) 
-0.3 (-0.5 to 0.2) 
 
1.2 
 
1.7 
 
-0.5 (-0.8 to 
0.0)* 
-0.5 (-0.7 to 0.0)* 
-0.5 (-0.7 to 0.0)* 
 
1.2 
 
 
1.4 
-0.3 (-0.5 to 0.3) 
-0.3 (-0.5 to 0.2) 
-0.3 (-0.5 to 0.2) 
 
 
 
 
 
 
 
 
Outcome 2  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
… 
 
 
 
 
 
 
 
Outcome k  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
aDesign variables are those variables on which balance was explicitly assessed during randomization. bStrong 
predictors of the outcome are those factors in the baseline balance table that are djusted differences include 
regression adjustment for stratifying variables, variables used in the constrained randomization, and variables that 
were not balanced at baseline despite randomization (p-value <0.1 in F-test). 
 
Sample means will be reported for Arm 1, the combined control (Arms 2+3), and for 
Arm 2 and 3 separately. When assessing changes from baseline to follow-up, we will 
simply define the outcomes as within-subject “differences” to facilitate comparisons 
with other outcomes observed only after the intervention. 
 
Crude comparisons of outcomes between treated and comparison arms will be 
assessed in linear regression models, regressing the outcome on an indicator for 
treatment arm and indicators for week-block, a stratification variable. We will 
adjust our standard errors for clustering at the household level, given that error 
terms without households may be correlated. 
 
Adjusted effect estimates. We will estimate adjusted effect estimates in multivariate 
linear regression models. Adjustment for covariates in an RCT has three aims[23]:  
a) (design variables) to obtain appropriate standard errors when conducting 
stratified or constrained randomization;  
b) (strong predictors of the outcome) to reduce residual variance in the outcome, 
leading to smaller standard errors and narrower confidence intervals; and  
c) (factors with strong baseline imbalance) to eliminate the impact of any 
imbalances on observed baseline factors, despite randomization  
 
(a) In our adjusted analyses, we will include the following design variables:  
- 
Stratification variables: week block 
- 
Constrained randomization variables: sex, age, whether the person was in 
school, orphan status, HIV positive status, HIV negative status (unknown is 
the third category), and household asset quintile  
 
(b) We will include the following variables believed a priori to be strong predictors 
of HIV testing and human capital investment: 
27 
 

 
 
- 
Distance to nearest clinic 
- 
Baseline subjective life expectancy 
- 
Baseline beliefs about lifetime HIV infection risk 
 
(c) Finally, to eliminate imbalances on observables, we will adjust for variables with 
p<0.1 for the t-test of differences between Arm 1 and Arm 2+3. 
 
 
B5. Subgroup analyses 
 
The informational intervention is expected to have the greatest impact for 
individuals with low baseline survival beliefs and for individuals with high baseline 
beliefs about lifetime chances of contracting HIV. 
 
Perceived likelihood of lifetime HIV infection. The impact of the 
informational video is likely to vary by baseline perceived risk of lifetime HIV 
infection. We will assess for linear effect modification of our primary outcomes 
by perceived lifetime infection risk. 
 
Baseline survival expectations. The impact of the informational video is likely 
to vary by baseline survival expectations, with the greatest potential shift in 
beliefs for individuals with previously low survival expectations. We will assess 
for linear effect modification of our primary outcomes by baseline subjective life 
expectancy.  
 
Finally, we will assess effect modification by age (continuous, 18-25) and sex of the 
respondent (M/F). 
 
 
B6. Robustness checks 
 
We will subject our primary results (Arm 1 vs. Arms 2+3 for primary outcomes) to 
robustness checks.  
 
Logistic regression model. We will also estimate our primary models using 
multivariate logistic regression to assess sensitivity to specification. In lieu of odds 
ratios, we will report marginal effects associated with the treatment arm, to enable 
comparison with the estimate from the OLS models. Again, standard errors will be 
clustered at the household level. 
 
Restrict to HIV-uninfected. HIV testing uptake – one of our primary outcomes – is 
less relevant for individuals who already know their current HIV status. In a 
robustness check, we will assess HIV uptake after excluding: individuals who 
reported that they had ever tested for HIV and were certain that they were HIV 
positive at baseline (20 out of 20 beans); and individuals who reported testing for 
28 
 

 
 
HIV in the prior 3 months and reported that they were sure that they were HIV 
negative (0 out of 20 beans). 
 
Remove participants with inconsistent answers and extreme outliers. A small 
number of participants reported inconsistent answers to the survival expectations 
questions at baseline. We will assess robustness of results after removing: 
participants who reported non-zero chances of survival at ages greater than the 
“oldest possible age” they could imagine living to; participants who reported that it 
was unlikely they would live to 60 years but allocated >10 beans; participants who 
reported that it was likely they would live to 60 years but allocated <10 beans. 
Demographic differences between persons with consistent and inconsistent 
answers will be reported. 
 
 
 
B7. Adjusting Inference for Multiple Hypothesis Testing 
 
No adjustments will be made for our primary outcomes. 
 
For secondary outcomes, the primary manner in which we adjust for multiple 
hypotheses being tested is by grouping outcomes into indices such that their joint 
significance will be tested. If we do not find that there is overall significance for 
aggregated measures of behaviors such as local of control, we will not move test 
statistical significance of individual index components. In addition, for families of 
outcomes where we are interested in testing the treatment effect both for an index 
overall as well as individual components, when the number of tests is large (greater 
than 10), we adjust our standard errors using the Benjamini-Hochberg 
procedure[24] for controlling the false discovery rate to less than 0.05.  
 
 
 
B7. Handling attrition 
 
Attrition will be quantified across treatment arms. We will adjust for attrition using 
inverse probability weighting if we lose more than 20% of the sample between 
baseline and follow-up.  
 
 
 
 
 
29 
 

 
 
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31 
 

 
 
LIST OF APPENDICES 
 
Appendix A. Sampling Strategy 
 
Appendix B. Consent Form 
 
Appendix C. Randomization Procedures 
 
 
32 
 

 
 
Appendix A. Sampling Strategy 
 
Sampling Protocol 
 
The sampling frame was all young adults who resided in bounded structures located 
in PIPSA week blocks 33-41 and were part of the AHRI PIPSA eligibility list.  Young 
adults were defined as individuals who were 18 years or older on August 15, 2017 
and were 25 years or younger on October 1, 2017. Week blocks 34-41 were selected 
because the AHRI surveillance was scheduled to visit these weekblocks for the 
annual surveillance visit and HIV testing 4 weeks after we would be there. With the 
AHRI surveillance 2 weeks behind schedule, we are expecting our respondents to be 
visited by the surveillance 4-6 weeks after the baseline survey visit. The AHRI 
eligibility list includes those known to reside in the surveillance and includes all 
those who resided in the surveillance at last visit in 2016 as well as some known to 
have moved in. 
 
We randomly sampled Bounded Structures within each week block and included all 
individuals in each Bounded Structure. We proceeded until we had 120 individuals 
per Week Block, or 20 per field worker per week. We anticipate that each field 
worker will be able to complete 10-15 interviews per week.  
 
The target sample size for interviews completed was 450. We assumed a 62.5% 
response rate, which is similar to prior studies of young adults at AHRI. Based on 
this response rate, we estimated that we needed to sample 720 individuals to 
complete 450 interviews. With a 6-field worker team and 2.5 interviews conducted 
per field worker per day, we project that the baseline survey will be completed in six 
weeks.  
 
We sampled 8 weeks of data to ensure that if unforeseen circumstances (e.g., 
difficulties locating respondents, fieldworker absences, etc.) dictate lower 
productivity, then we will have respondents already sampled to finish fieldwork. 
 
Our “stopping rule” for study enrollment is defined as: “Data collection will proceed 
for six weeks. If at the end of six weeks, we have not yet recruited 450 participants, 
then data collection will continue for a seventh week. If at the end of seven weeks, 
we have not yet recruited 450 participants, then data collection will continue for an 
eighth week. If after eight weeks, we still have not attained 450 participants, we will 
cease enrollment.” 
 
We have sampled 20 potential respondents per interviewer. Each interviewer is 
responsible for making three contacts with these respondents during the week. The 
total number (currently 20) of respondents allocated to each interviewer may 
change during the pilot stage, depending on what are acceptable workloads. 
 
The following table describes the sampling strategy: 
33 
 

 
 
 
Week 
Block 
Sample 
Size 
Expected 
Interviews 
completed 
Under the lower 
productivity 
scenario 
34 
120 
75 
58 
35 
120 
75 
58 
36 
120 
75 
58 
37 
120 
75 
58 
38 
120 
75 
58 
39 
120 
75 
58 
40 
120 
0 
58 
41 
120 
0 
50 
TOTAL 
960 
450 
450 
 
 
 
 
34 
 

 
 
Appendix B. Consent Form 
 
 
Information Sheet and Consent to Participate in Research 
 
Re: Life expectations of young adults in rural KwaZulu-Natal: An Intervention 
Study 
 
Dear Sir/Madam: 
 
My name is ________, from the African Health Research Institute (AHRI), also 
known as the Africa Centre for Population Health, in Somkhele. 
 
You are being invited to consider participating in a research study. Research is a 
systematic investigation designed to develop generalizable knowledge. The 
purpose of this study is to understand how young adults in this community think 
about and plan for the future, and how they perceive different health risks in 
the community. The study is expected to enroll 450 participants, ages 18 – 25 
years, from households chosen at random in this area. 
 
Participation in this study involves two sessions: 
1) Interview 1 will last about two hours and will be conducted at your home. We 
will ask you questions about your expectations for the future and perceptions 
of different risks. After completing this questionnaire, you will view a 10-
minute video and afterward we will ask you to answer some additional 
questions. You will be compensated R100 in acknowledgement of your time 
answering our questions and in appreciation of your participation. At the end 
of today’s session, will provide you with some information about an 
opportunity to participate in a job search skills workshop, a voucher for free 
HIV testing, an opportunity to purchase subsidized condoms, and a savings 
opportunity. You do not have to participate in any of these activities to be 
part of the study. 
2) Interview 2 will take place 2-3 months from now, will last about 30 minutes, 
and will be conducted by phone or at your home. We will schedule the 
interview using the contact number you provide us today. You will be 
compensated R50 for this second session.  
 
In order to better understand life plans in this area, your survey responses will 
be linked and analyzed alongside survey data collected by AHRI during other 
visits to your household. All data will be de-identified (made anonymous) prior 
to being analyzed by the researchers. 
 
Participation in this study carries a small risk of psychological or emotional 
discomfort. Some of the questions in the interviews may be of a personal nature. 
For example, we will ask you about what you expect the future to bring. We will 
also ask you to consider the likelihood of different life events, such as getting a 
35 
 

 
 
job, marriage, contracting HIV, or surviving to a certain age. If you find the 
material that we discuss uncomfortable, you are free to skip any questions that 
you do not wish to answer or to stop the interview at any time. The interview 
will take place in a setting that ensures your privacy. This research poses no risk 
of physical injury. 
 
The study provides no direct benefits to participants, however some people find 
it enjoyable to answer questions about their future plans. The scientific benefits 
of this study will be a deeper understanding of how young adults in rural 
KwaZulu-Natal think about the future. There are no costs to you for participating 
in this research study. 
 
In the event of any problems or concerns/questions you may contact Jacob Bor, 
the researcher, or Ncengani Mthethwa, AHRI/Africa Centre’s Community 
Engagement Coordinator, at (0)35 5507500. You may also contact the UKZN 
Biomedical Research Ethics Committee, at (0)31 2604769 (full contact details 
below).  
 
Participation in this research is voluntary. Your alternative is not to participate. 
If you decide to participate, you may later withdraw from the study at any point, 
without any penalty. You will still receive payment for the session that you 
attend.  
 
Who will see the information that is collected? All the information collected is 
kept private and confidential. Your responses to this study will be assigned a 
secret number. All the data will be kept on a secure computer using only this 
number and not your name. Your identifiable information (such as your name, 
the place where you live, or your mobile phone number) will be kept in a 
separate secure location. In this way the data are locked so that scientists cannot 
link the information they are analyzing to named individuals. Scientists at AHRI 
and other institutions can be given permission to analyze the findings from this 
study and may also write about the findings in scientific journals to share the 
information that we learn with scientists, doctors and others in South Africa and 
the world. Scientific writing is never about named individuals. We take all 
possible steps to reduce the risk of people being identified. 
 
Do you have any questions for me about this study? 
 
 
 
36 
 

 
 
CONSENT 
I _____________ have been informed about the study entitled Life plans of young 
adults in rural KwaZulu-Natal: an intervention study. 
 
I understand the purpose and procedures of the study. 
 
I have been given an opportunity to ask questions about the study and have had 
answers to my satisfaction. 
 
I understand that my participation in this study is entirely voluntary and that I 
may withdraw at any time. 
 
I understand that by consenting to participate in this study I do not waive any of 
my legal rights. 
 
If I have any further questions/concerns or queries related to the study I 
understand that I may contact the researcher, Jacob Bor, at AHRI/Africa Centre 
(0)35 5507500, or at Boston University School of Public Health, USA, +1 617 429 
6910. I may also contact the Community Engagement Coordinator, Ncengani 
Mthethwa, at the Africa Centre, (0)35 5507500. 
 
African Health Research Institute (also known as Africa Centre for Population 
Health) 
R618 en route to Hlabisa, Somkhele, KwaZulu-Natal 
P.O. Box 198, Mtubatuba, 3935, KwaZulu-Natal 
(0)35 5507500 
 
If I have any questions or concerns about my rights as a study participant, or if I 
am concerned about an aspect of the study or the researchers then I may 
contact: 
  
BIOMEDICAL RESEARCH ETHICS ADMINISTRATION 
Research Office, Westville Campus  
Govan Mbeki Building  
University of KwaZulu-Natal 
Private Bag X 54001, Durban, 4000  
KwaZulu-Natal, SOUTH AFRICA  
Tel: 27 31 2602486 - Fax: 27 31 2604609  
Email: BREC@ukzn.ac.za    
 
I have been given a copy of this form to keep. 
 
_____________________________  
____________________ 
Signature of Participant                  
 
 
Date 
 
_____________________________  
____________________ 
Signature of Witness (if necessary) 
 
 
Date 
 
37 
 

 
 
Appendix C. Randomization Procedures 
 
Variables Randomized. Study participants are randomized with respect to: 
 
1) Intervention video vs. no intervention video 
2) Pure control vs. attention placebo control (among those randomized to no 
intervention video) 
3) 20 Rand vs. 5 Rand cost to reserve spot at job search skills workshop 
4) Male-vs.-female-oriented HIV transmission questions 
 
1. Intervention video vs. no intervention video 
 
Level of randomization. Individuals are randomized to video intervention vs. 
control. Randomization is at the bounded structure level, so that all respondents 
within a bounded structure get the same treatment. 
 
Stratification. Randomization is stratified by weekblock to ensure approximately 
equal numbers of treated and controls within weekblocks.  
 
Constrained (restricted) randomization. The AHRI data contain robust information 
on baseline covariates. However, because randomization is at the level of the 
bounded structure, it was not possible to stratify by individual characteristics. 
Instead, we use a constrained (aka restricted[25]) randomization approach. For the 
primary analysis – comparison of intervention video vs. no intervention video, we 
constrained randomization to force balance on the following characteristics: sex, 
age, whether the person was in school, orphan status, HIV positive status, HIV 
negative status (unknown is the third category), and household asset quintile. We 
randomized the intervention treatment assignment (intervention video vs. no 
intervention video) 3078 times using different randomization seeds. Each time, we 
assessed balance on key characteristics through a t-test (i.e regression of treatment 
assignment on the covariate). All randomization seeds that lead to a maximum t-test 
value greater that 1.68 (p<0.1) were excluded. This excluded 62% and retained 38% 
of the seeds (1060 seeds). We then randomly selected two seeds from these 1060 
acceptable seeds. One was used to randomize the video, the other was used to 
randomized the incentive for the job search skills workshop. We assessed 
correlation of these two interventions and re-randomized if the Chi2 p-value for the 
cross-tab was less than 0.5 (see tables below). 
 
2. Pure control vs. attention placebo control (among those randomized to no 
intervention video) 
 
Within the control group – i.e. individuals assigned to “no intervention video” – we 
used simple randomization to assign respondents to “pure control” (no video) and 
to “attention placebo control” (video on another topic).  
 
38 
 

 
 
3. 20 Rand vs. 5 Rand to reserve spot at job search skills workshop 
 
Participants were given 30 or 15 Rand and were then asked to pay 20 or 5 Rand 
respectively to reserve a spot in the job search skills workshop. Randomization was 
conducted as described above in concert with the intervention video comparison. 
 
4. Male-vs.-female-oriented HIV transmission questions. 
 
In the survey, respondents were asked to report on the chances that an HIV-
negative woman (man) would become HIV-infected after having sex with an HIV-
positive man (woman). We used simple randomization to assign respondents to the 
male-focused or female-focused HIV transmission questions. 
 
 
Data management procedures 
 
These random treatment assignments – video assignment, job search workshop 
incentive assignment , together with their AHRI internal ID numbers were 
submitted to the AHRI data management core, which linked them with the 
operational data.  
 
Blinding  
 
The investigators are blinded to randomization assignment throughout data 
collection, cleaning, and development of the analysis plan. Randomization status 
with respect to the intervention and placebo videos will not be revealed until after 
all data on the primary outcome (including uptake of testing in the 2017 AHRI 
surveillance) have been collected, the data have been cleaned, and final pre-analysis 
plan is posted.[26]  
 
 
Tables. Intervention video stratified by weekblock and approximately balanced vs. 
Job Skills Workshop incentive 
 
       
 
 
39