Optimizing Viral Load Suppression in Kenyan Children on Antiretroviral Therapy
Phase: NA
Conditions: Chronic HIV Infection
Sponsor: University of Alabama at Birmingham

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Project Title: Optimizing viral load suppression in Kenyan children on antiretroviral therapy (Opt4Kids) 
 
 
 
 
Project Team: 
 
Principal Investigator:  
Rena Patel, MD, MPH 
Assistant Professor 
Division of Allergy and Infectious Diseases 
Department of Medicine 
University of Washington 
Phone: 206-520-3800 
Email: rcpatel@uw.edu  
 
Co-investigators/collaborators: 
 
 
 
Lisa Abuogi, MD, MSc 
Associate Professor 
Department of Pediatrics 
University of Colorado, Denver 
Phone: 303-358-5061 
Email: lisa.abuogi@ucdenver.edu 
 
Katherine Thomas 
Research Scientist, Biostatistician 
International Clinical Research Center  
Department of Global Health  
University of Washington 
 
Dr. Patrick Oyaro 
MBChB, DLSHTM, MPH-Epidemiology 
COP: LVCT Health - USAID Stawisha Pwani 
 
 
Anjuli Wagner, MPH, PhD 
Acting Assistant Professor 
Department of Global Health 
University of Washington 
 
Irene Mukui, MBChB 
Ministry of Health  
Kenya 
 
Evelyn Brown, clinical officer 
Study coordinator/co-investigator 
UW-Kenya 
Kenya 
 
 
Version Date: 2nd March 2022 
 
Funding: National Institutes of Mental Health 1R34MH115769 & 1 R21 MH122361, Thrasher Foundation grant 

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ABSTRACT 
Among nearly 1 million HIV-infected children receiving antiretroviral treatment (ART), as many as 40% of those 
living in resource limited settings have not achieved virologic suppression. Kenya, a UNAIDS fast-track and PEPFAR priority 
country, has an estimated 98,000 children aged 0-14 years living with HIV. Virologic suppression is achieved by only 65% 
of Kenyan children on ART translating to only 38% of the final UNAIDS 90-90-90 goal for population-level viral suppression. 
Feasible, scalable and cost-effective approaches to maximizing durability of ART regimens and ensuring viral load (VL) 
suppression in HIV-infected children are urgently needed. The goal of this pilot study is to determine the feasibility and 
impact of point-of-care (POC) VL drug resistance mutation (DRM) testing to improve VL suppression in children on ART 
within a PEPFAR-funded HIV care and treatment program in Kenya. 
The proposed research is a randomized, controlled study to pilot the use of POC VL and DRM testing in children 
aged 1-14 years on ART at sites with on-site access to GeneXpert® technology. Children enrolling at each site will be 
randomized 1:1 to intervention (POC VL and targeted DRM testing) vs. control (standard-of-care) arms and followed for up 
to 5 years. The proposed study will be conducted in PEPFAR-supported HIV treatment facilities in western Kenya, a 
PEPFAR priority country. This pilot study will evaluate two critical components related to viral suppression in children via: 
1) POC VL testing (Aim 1a) and 2) targeted DRM testing (Aim 2a) among children on ART. Our primary outcome is the 
proportion of children achieving VL suppression 12 months after enrollment for each child, with two secondary outcomes: 
1) VL suppression and DRM patterns up to 5 years and, 2) time to viral suppression in those children not suppressed or 
initiating ART. We will also validate HIV-1 POC VL testing against current in-country gold standard VL testing (Aim1b) in 
order to eventually scale up POC technologies for VL testing at national levels. We will also validate HIV-1 POC DRM 
testing, also named OLA Simple, against current in-country gold standard, consensus sequencing, DRM testing (Aim 2b). 
In order to maximize the impact of POC technologies to improve health outcomes for children with HIV, it is critical 
to explore health care provider, caregiver, and adolescent perspectives on the use of POC VL testing. We will also query 
caregivers and adolescents to understand how the novel coronavirus disease 2019 (COVID-19) has impacted intervention 
delivery and participants’ overall well-being. Additionally, the costs and cost-effectiveness of implementing POC VL and 
targeted DRM testing for children on ART will critically inform key clinical and policy stakeholders for future implementation, 
whether in Kenya or other resource-limited settings. Therefore, we also propose to 1) use qualitative research methods to 
better understand how our POC VL intervention functions and how policymakers might use tools to scale up POC VL (Aim 
3a) and 2) determine the cost-effectiveness of POC VL combined with targeted DRM testing in this group, as well as model 
the potential POC machine placement within a geographic area (Aim 3b).   
This pilot study helps build the foundation for a future adaptive trial which packages optimized POC VL and DRM 
monitoring algorithms with socio-behavioral interventions to maximize VL suppression rates in children on ART and facilitate 
timely switch of ART in resource-limited settings. This pilot study will provide critical information on the impact of POC VL 
testing on viral suppression among children on ART in a resource-limited setting. It will additionally show current patterns 
and impact of DRM testing among children undergoing routine VL monitoring. Findings from this pilot will inform the 
development of an adaptive clinical trial which evaluates the impact of combination interventions, including POC VL and 
DRM testing at programmatic scale, facility- and community-based care packages, and the cost-effectiveness of 
implementing each strategy in achieving viral suppression. This proposal directly addresses the urgent need to find 
interventions to maximize viral suppression among children on ART and achieve the UNAIDS 90-90-90 goals. 
 

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LIST OF ABBREVIATIONS 
 
ART- antiretroviral treatment 
CDC- Centers for Disease Control and Prevention 
DBS- Dried Blood Spot 
DRM- Drug resistance Mutations 
EDTA- Ethylenediaminetetraacetic acid 
FACES- Family AIDS Care and Education Services 
FWA- Federal Wide Assurance 
HIV- Human Immunodeficiency Virus 
KEMRI- Kenya Medical Research Institute 
mL- milliliter 
MOH – Ministry of Health 
NASCOP-National AIDS and STI Control Programme 
NIAID- National Institute of Allergy and Infectious Disease 
NICHD- National Institute of Child Health and Development 
NIH- National Institutes of Health 
NGS- Next-generation sequencing 
NNRTI- Non-nucleoside/nucleotide reverse transcriptase inhibitor 
OLA- Oligonucleotide Ligation Assays 
PEPFAR – President’s Emergency Plan For AIDS Relief 
PI- Protease Inhibitor 
PMTCT -   Prevention of mother to child transmission 
POC- Point of Care 
RCTP-FACES- Research Care and Training Program- Family AIDS Care and Education Services 
SOC- Standard of Care 
UCD – University of Colorado at Denver 
UCSF- University of California San Francisco 
UW- University of Washington 
UNAIDS- Joint United Nations Programme on HIV/AIDS 
VL- Viral Load 
 

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TABLE OF CONTENTS 
Table of Contents 
List of Abbreviations 
3 
INTRODUCTION 
5 
A. Specific Aims 
5 
B. Hypotheses 
5 
C. Background and Significance 
5 
D. Preliminary Studies/Progress Report 
6 
RESEARCH METHODS 
8 
A.  Outcome Measure(s) 
8 
B. Description of Population to be Enrolled 
8 
C. Study Design and Research Methods 
8 
Overall Study Design 
8 
Study Setting 
9 
Study Collaborators 
9 
Study methods 
9 
Start-up activities, including training 
11 
Recruitment and randomization 
11 
Study Timeline 
12 
Procedures 
12 
Data Collection 
13 
E.   Potential Scientific Problems 
13 
F.   Data Analysis Plan 
14 
G.  Summarize Knowledge to be Gained 
15 
HUMAN SUBJECTS PROTECTIONS 
15 
Risks to the Subjects 
16 
Sources of materials 
16 
Potential Risks 
17 
Adequacy of protection against risks 
17 
Protection against risk 
18 
Potential benefits of the proposed research to the subjects and others 
18 
Adverse event reporting 
19 
Data safety monitoring plan 
19 
REFERENCES: 
19 
 
 
 

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INTRODUCTION 
 
 
 
 
 
  
 
A. Specific Aims   
Among nearly 1 million HIV-infected children receiving antiretroviral treatment (ART), as many as 40% of those living in 
resource limited settings have not achieved virologic suppression. The proposed research is a randomized, controlled study 
to pilot the use of point of care (POC) viral load (VL), drug resistance mutation (DRM) testing, and clinical decision support 
in children aged 1-14 years on antiretroviral treatment (ART) at sites with on-site access to GeneXpert® technology. Children 
enrolling at each site will be randomized 1:1 to intervention (POC VL and targeted DRM testing) vs. control (standard-of-
care) arms and followed for up to 5 years. The proposed study will be conducted in PEPFAR-supported HIV treatment 
facilities in western Kenya, a PEPFAR priority country.  We will evaluate two critical components related to viral suppression 
in children in this pilot study: 1) POC VL testing and 2) targeted DRM testing among children on ART.  This pilot study helps 
build the foundation for a future adaptive trial which packages optimized POC VL and DRM monitoring algorithms with 
socio-behavioral interventions to maximize VL suppression rates in children on ART and facilitate timely switch of 
ART through the following specific aims:  
 
Aim 1: To determine the impact of POC VL on the proportion of children on ART achieving viral suppression and time to 
viral suppression among those not suppressed or initiating ART (Aim 1a) and to validate HIV-1 POC VL testing against 
current in-country gold standard VL testing (Aim 1b).   
 
Aim 2: To determine the impact of targeted DRM testing and patterns of DRMs among children on ART without viral 
suppression, to detect emergency DRM to dolutegravir via next-generation sequencing for genotypic resistance and 
phenotypic resistance (Aim 2a),  and to validate HIV-1 POC DRM testing, also named OLA Simple, against current in-
country gold standard, consensus sequencing, DRM testing (Aim 2b). 
 
Aim 3: To understand how our intervention functions and how policymakers might use tools to scale up POC VL and DRM 
testing, by qualitatively exploring the barriers to achieving viral suppression and evaluating the feasibility of incorporating 
POC VL and DRM testing at programmatic scale using different sharing strategies to overcome of these barriers (Aim 3a) 
and to estimate the costs of implementing pediatric POC VL testing and DRM monitoring and the incremental cost-
effectiveness per disability adjusted life year (DALY) averted, compared to standard clinical care, and determine the optimal 
placement of limited POC VL machines and DRM testing (Aim 3b).  
B. Hypotheses 
 
We hypothesize that 1) viral suppression rates will be higher among children with access to POC VL testing; 2) time to 
suppression will be shorter compared to children with standard VL testing; 3) we will detect >90% concordance for 
categorization of plasma VL as <1000 or =>1000 copies/mL for POC (via GeneXpert) vs. gold standard (via Abbott) VL 
testing; 4) hypothesize that targeted DRM testing will shorten time to viral suppression; 5) pediatric POC VL testing and 
DRM can be implemented in HIV care clinics in Kenya at reasonable costs and is cost-effective compared to standard-of-
care adherence monitoring; and 6) modeling tools to inform placement of POC VL machines in a geographic area will be 
usable to policymakers. 
C. Background and Significance 
C.1 Background 
Viral suppression in children lags UNAIDS goals and the optimal approach for achieving VL suppression and 
maximizing the duration of ART regimens for children in resource-limited settings is unknown. A recent meta-
analysis of 72 studies including over 50,000 children from resource-limited settings showed pediatric viral suppression rates 
of 60-75%.1 These rates are much lower than the >90% suppression rates in high-income countries, well below the target 
goal of 90% established by the UNAIDS, and significantly lower than rates among adults in resource-limited settings.2-4  
Kenya, a UNAIDS fast-track and PEPFAR priority country, has an estimated 98,000 children and young adolescents aged 
0-14 years living with HIV and 6,600 new pediatric infections annually.5 Similar to many other PEPFAR countries, Kenya 
has continued to increase access to pediatric ART, yet viral suppression rates among children remain poor. Based on 
national survey data, only 65% of children and adolescents on ART achieve VL suppression in Kenya.6 Kenya’s internal 
gap analysis shows that this translates to achieving only 38% viral suppression of the final 90-90-90 goal (Figure 1). Such 

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dismal viral suppression rates not only contribute to pediatric HIV morbidity and mortality but will undoubtedly slow down 
global efforts to control the HIV epidemic. 
 
To optimize management of children on ART and provide early intervention for those failing to achieve virologic 
suppression, access to timely laboratory monitoring is required. POC VL testing can drastically improve time-
sensitive clinical decision-making, and potentially increase viral suppression. Laboratory technologies have become 
a critical component of clinical decision-making, including in resource-limited settings. Starting in 2013, the WHO has 
recommended monitoring both adults and children on ART with routine VL testing.7 Kenya, has adopted these 
recommendations.8 Kenya recommends obtaining viral loads in children six months after ART initiation and annually 
thereafter for those with VL <1000 copies/mL or repeated three months after adherence intervention for those who remain 
viremic.8 While these recommendations are being 
scaled-up on the ground, several limitations exist to 
laboratory-based VL testing.  A CDC report shows 
between  2014-2015, only 38% of individuals on ART in 
Kenya had evidence of routine VL monitoring defined as 
more than one VL documented since initiation of ART.9 
Currently in Kenya, laboratory-based VL testing is 
conducted in centralized facilities that require highly 
trained staff and specialized, expensive equipment.  
Given the need to transfer the samples to these facilities, the turn-around time to results reaching health care providers and 
patients is prolonged (often greater than 4-6 weeks), significantly delaying the time to clinical decision-making.9-11 Our pilot 
work shows that a third of children in Kenya do not have follow up viral loads and 77% with virologic failure are not 
suppressed up to one year after initial VL.12 Failure to follow National Guidelines on timing of follow up VL and clinicians 
reluctance to order resource-intensive VL tests may contribute to this poor uptake. Delaying interventions to address 
treatment failure, such as improving adherence or switching treatment, increases the risk of clinical deterioration and multi-
drug resistance. POC testing, done at or near the site of care, with the results available to the ordering provider and patient 
within hours or days to expedite clinical decision-making, has the potential to dramatically overcome the limitations of 
centralized laboratory-based testing.13 In the field of HIV, rapid HIV antibody and POC CD4 count tests are reliable and 
have shown to improve clinical decision-making.14-22 With VL assessments becoming integral to routine HIV care, POC VL 
testing has the potential to facilitate drastic improvement in viral suppression rates and longevity of ART regimens, especially 
among children. Furthermore, POC VL-based care models are predicted to be even more cost-effective than laboratory-
based VL testing.23  
 
The WHO and others have expressed concerns that HIV DRM will undermine the attainment of the global targets 
for HIV.24,25 Understanding current patterns of DRM among children failing  ART is critical to determining the 
optimal management of these children. Among children not achieving viral suppression, small cohort reports in East 
Africa indicate between 68-77% may have DRMs while on NNRTI-based regimens, while much fewer (<10%) have drug 
resistance on PI-based regimens.26-28 This problem is likely to be worsening, with more ART exposure through earlier 
initiation and combination ART now used for prevention of mother to child transmission (PMTCT).29 Among newly diagnosed 
children <2 years of age in South Africa, 60% of those with PMTCT exposure and 24% with no reported PMTCT exposure 
demonstrated major non-nucleos(t)ide-reverse transcriptase inhibitor (NNRTI) resistance.30 The first-ever Kenya national 
pediatric HIV DRM surveillance study conducted in 2013, showed detectable viremia in more than 30% of children on ART 
for 12-36 months. More than 90% of these children had at least one DRM detected; 77% had both nucleos(t)ide-reverse 
transcriptase inhibitor (NRTI) and NNRTI DRMs, while none had major resistance to protease inhibitors (PIs).31 Notably, 
this study was conducted prior to Kenya’s roll-out of lifelong ART for all pregnant women and before guidelines changed to 
treat children <3 years of age with PI-based regimens as 1st line ART. The current levels and pattern of DRMs among 
children failing 1st line ART, now including PIs, and 2nd// 3rd line ART in Kenya are not known. If current DRM remains at 
90% or is even higher due to more pre-treatment antiretroviral exposure, it is highly likely that the current management 
strategies of children with elevated VL will be unsuccessful, since no amount of adherence intensification will overcome 
these multiple DRMs. Understanding DRM patterns in children on ART in Kenya may provide evidence for revision of 
algorithms used to manage children with virologic failure that can be rigorously tested in the future. 
 
Current challenges in DRM monitoring in Kenya include pre-consultation with centralized committees, extremely delayed 
turn-around times, and testing only for those failing 2nd or 3rd line regimens. A POC DRM assay could significantly 
overcome these barriers and extend longevity of 1st and 2nd line regimens.  While the causes of treatment failure in 
individuals on ART are multifactorial, from health systems32-34 to sociobehavioral factors,7,14,35 this proposal begins to 
address the biomedical issue of DRMs.36 We believe that rapid return of results via POC DRM testing will facilitate earlier 
and more appropriate clinical decision-making, acting as “cues to action” as postulated in the Health Belief Model.37  
Collaborators on this proposal, Drs. Frenkel and Lutz, have developed exactly that—a POC DRM assay named OLA Simple. 

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Oligonucleotide Ligation Assays (OLA) Simple is an extremely promising POC technology.  OLA Simple is the POC 
version of the “parent” lab-based OLA technology.  These OLA tests overcome a major limitation of other past, failed single 
point mutation technologies which did not tolerate nearby polymorphisms.38 They hybridize well below melt temperatures, 
which allows tolerance to unknown variable polymorphisms.  The parent, lab-based OLA has been tested in studies of 
pretreatment DRM in Kenya using DNA from peripheral blood mononuclear cells (PBMC).  DRMs that arise during treatment 
failure often occur at very low levels in PBMC DNA, but at higher levels in plasma RNA; thus, the Lutz lab has needed to 
adapt OLA Simple to test DRMs in plasma RNA. Therefore, this will be the first study to evaluate the performance of a POC 
DRM test using plasma RNA in treatment failure in children.  Lastly, the OLA Simple has only been previously tested for 
codons for nucleotide reverse transcriptase inhibitors (NRTIs) and NNRTIs; the OLA Simple kit we plan to use now includes 
codons for protease inhibitor (PI) mutations, which has yet to be validated.  The lab has developed probes for integrase 
strand transfer inhibitor (INSTI) DRMs (manuscript under review), so while we anticipate very few children will be on INSTI-
containing ART in our study as noted above, if we find that a significant portion of the children in our study are on INSTI-
containing ART, we can easily adapt the OLA Simple kits to include the relevant INSTI probes.  No study to-date examines 
the use of a POC DRM technology to determine the minimum number of mutant codons needed to detect clinically 
meaningful DRMs for pretreatment or acquired DRMs in a RLS. 
 
OLA Simple requires validation prior to clinical use.  While the parent, lab-based OLA, the predecessor to OLA Simple, 
has undergone validation and is Clinical Laboratory Improvement Amendments (CLIA)-certified,38,39 validation of OLA 
Simple must occur prior to regulatory approval for its use clinically.  OLA Simple has significant technology differences from 
lab-based OLA, including reformulation of reagents and probes, different enzymes concentrations, dry reagents, and assay 
timing.  These changes are substantial enough that they require rigorous validation prior to clinical use.  Initial testing of the 
OLA Simple was conducted in Seattle with a small set of sixty dried blood spot samples collected from HIV-infected infants 
in South Africa and against previously-sequenced samples of HIV, with 95% sensitivity, and 100% specificity.56,57  In 
addition, a digital lab assistant “app” now exists for OLA Simple that provides step by step tutorial for running tests; 40 
untrained users in Seattle piloted OLA Simple using cloned plasmids to test the automated instructions and collection of 
results on a tablet with 97% accuracy with no prior instructions about the test (manuscript in preparation).  However, OLA 
Simple has not been evaluated for technician performance using clinical specimens in a RLS where its use is intended.  
While the above incremental advances help in optimizing OLA Simple kits, they are not sufficient to be used for making 
treatment decisions in patients.  The next step in validation for clinical use is field-testing OLA Simple and validating its 
performance—a WHO pre-qualification requirement for approval of diagnostic technologies.40 Without the type of validation 
of OLA Simple we propose here, it would be unethical to begin using OLA Simple for clinical decision-making. 
 
Monitoring dolutegravir (DTG) resistance. A rapid transition to more effective ART regimens, specifically DTG, holds 
promise for improving VS in CLHIV, but on its own, cannot address the multifactorial challenges that reduce ART adherence 
in CLHIV and result in VF.23 Despite its higher barrier to resistance, DTG resistance can develop and will undermine the 
durability of first- and second-line regimens in LMIC.24,25 While ART-naïve individuals treated with DTG, including tenofovir 
+ lamivudine + DTG (or TLD), rarely select DRMs, ART-experienced individuals with some pre-existing resistance prior to 
switch to DTG may be at higher risk for developing additional DRMs, including to the tenofovir or lamivudine components 
of TLD. Further, it is not clear if minority variant populations, those HIV-1 subpopulations that are not detected by current 
consensus sequencing techniques due to being <10% of the population of viruses circulating, play a role in fostering DRM.  
Losing our ability to use DTG more widely, including in children and for potent viral suppression among pregnant and 
postpartum women, will be a tremendous loss for the global HIV community. Therefore, proactive DRM monitoring of 
individuals on DTG are needed urgently. 
 
C.2 Innovation 
Our proposal contains two key innovations: (1) we will utilize novel, emerging technologies, namely POC VL testing and 
POC DRM assay (OLA Simple), for ART monitoring among children, and (2) we are proposing a more aggressive VL 
monitoring algorithm than current standard of care (SOC).  First, POC diagnostic and laboratory monitoring technologies 
are improving delivery of healthcare in resource-limited settings.40 For example, POC testing for early infant diagnosis of 
HIV is increasingly being scaled up, yet POC VL testing for HIV treatment monitoring lags behind.41-44  POC VL testing has 
the potential to dramatically improve HIV care in small, rural or high-volume urban facilities.  Several studies have now 
demonstrated high analytical performance of POC VL testing.45-48 However, a more comprehensive evaluation of its clinical 
end-point, viral suppression, is critically needed to guide its programmatic adoption.49 Our study proposes to accomplish 
precisely this by scaling up POC VL testing at the facility level.  Second, our proposed POC VL monitoring plan is more 
aggressive than current practice, and if successful in promoting better viral suppression, may lend support to implementing 
similar VL monitoring algorithms among children. Coupling POC DRM monitoring with a POC VL testing strategy brings 
together two powerful innovations that could revolutionize HIV treatment monitoring. 

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D. Preliminary Studies/Progress Report   
Pediatric viral suppression at FACES: Drs. Abuogi, multiple-PI on this proposal, Oyaro, co-investigator, and colleagues 
have assessed prevalence of treatment failure amongst children undergoing routine VL testing at FACES sites between 
June 2014–May 2015.12,50-52 Among 1190 children and young adolescents 0-14 years of age undergoing routine VL testing, 
748 (63%) were virologically suppressed. In multivariable analysis, unsuppressed children were more likely to be male 
(adjusted odds ratio (aOR) 2.1, 95% Confidence Interval (CI) 2.1-3.6) and have had an ART regimen change (aOR 2.0, CI 
1.0-3.7) than suppressed children. A third of children with virologic failure never had a follow-up viral load. Among those 
that did, VL suppression was only achieved by 23% versus 93% among those suppressed at baseline (p<0.0001). Mean 
time for a follow-up VL in children with virologic failure was 11.3 months (95% CI 9.5-13.2).  
Table 2. Patterns of HIV DRMs 
among children and adolescents 
in Kenya, 201331 
Specific mutations 
with VL > 1000 
copies/mL 
N (%) 
(n=136) 
NRTI, any 
108 
(79%) 
M184V 
103 
(75%) 
K65R 
6 (4%) 
1 TAM 
13 (10%) 
2 TAMs 
7 (5%) 
3 TAMs 
15 (11%) 
L74V 
36 (27%) 
NNRTI, any 
117 
(86%) 
K103N 
46 (34%) 
Y181C 
43 (32%) 
G190A/C 
39 (29%) 
PIs, any 
0 
Any mutations 
121 
(89%) 
 
National drug resistance surveillance for children in Kenya: In 2013, Dr. Irene Mukui, site-PI on this proposal, led the 
National AIDS and STI Control Programme’s (NASCOP) cross-sectional, nationally-representative survey of acquired HIV 
DRMs among adults and children on ART for at least 12 months in Kenya.31 Virologic failure was defined as a VL !"###$
%&'()*+,-$').$/01(&/02$34(5)2(/)*$0/5$6#7$&8$19)$%9(25.)/$:).)$&/$0$;;<=>?%&/10(/(/3$.)3(,)/@$$AB).022C$D"7$&8$19)$EF"$
%9(25.)/$905$B(.&2&3(%$80(24.)C$0*$%&,'0.)5$1&$&/2G$""7$&8$19)$05421*@$$A8$19)$%9(25.)/$:(19$B(.&2&3(%$80(24.)C$H67$905$01$2)0*1$
&/)$I<J$5)1)%1)5@$K)B)/1G?/(/)$').%)/1$905$I<J*$1&$;<=>*C$HF7$1&$;;<=>*C$LL7$1&$M&19$;<=>*$0/5$;;<=>*C$0/5$#7$
1&$N>*$O19&439$N>$4*)$:0*$(/8.)P4)/1$(/$19(*$%&9&.1Q@$$=9)$,&*1$%&,,&/$I<J*$:).)$J"HER$0/5$-LER$8&.$;<=>*$0/5$S"#D;C$
T"H"UC$0/5$V"6#W$8&.$;;<=>*$OTable 2).  In addition to conducting this survey, Dr. Mukui was involved in training and 
capacity building of providers in handling DRM testing results and results dissemination. 
 
E. Conceptual Framework  
Based on socioecological frameworks that encompass factors from individual to societal levels, we conceptualize the causes 
of treatment failure in children on ART under three broad, overlapping, and multifactorial themes of: 1) biomedical; 2) health 
system; and 3) socio-behavioral factors (Figure 2).53 Biomedical causes of treatment failure may include transmitted or 
acquired resistance, complex drug regimens that are difficult for caregivers to dispense, or failure to provide the appropriate 
weight-based dosing for younger children.54,55 Health system barriers include unresponsive centralized laboratory systems, 
undertrained health care workers, or drug stock outs.1,4,54 Socio-behavioral factors, such as adherence, disclosure, and 
stigma are critical to treatment success but complex for children who are dependent on caregivers. It is clear that a 
multifactorial approach is ultimately necessary to achieve optimal viral suppression rates in this population.  Nonetheless, 
our current pilot proposal begins to address the biomedical and health systems components of treatment failure in children 
and provides the foundation for our larger vision of addressing the barriers to optimal viral suppression in this population. 

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As conceptualized in the Health Belief Model, we believe that timely feedback on viral suppression and DRM to both patients 
and providers, will act as “cues to action” driving their perceived severity and threat of their HIV disease progressing and 
their likelihood of taking positive actions, such as improving adherence or switching to more optimal ART regimens, to 
improve HIV disease status.37 Thus, our fundamental hypothesis is that earlier and more frequent VL monitoring will facilitate 
earlier and more appropriate clinical decision-making.  
 
 
 
 
RESEARCH METHODS 
 
A.  Outcome Measure(s)   
Study Aim 1: Our primary outcome for Aim 1a is rates of viral suppression 
(defined as VL <1000 copies/mL) at 12 months after enrollment for each 
child at the study facilities.  Our secondary outcomes for Aim 1 are VL 
suppression and DRM patterns up to 5 years and time to viral suppression 
among those children without viral suppression at their 1st POC VL testing 
or newly initiating ART after POC VL testing implementation. We will also 
examine a set of process outcomes in this study, such as the feasibility of 
POC VL testing by describing the uptake, which we define as the proportion 
of children undergoing VL testing within each group at the scheduled intervals. We will also examine the turn-around time 
for the VL testing results, retention-in-care, proportion of children switching ART, time to switch to 2nd or 3rd line ART, etc. 
for each arm.  We hypothesize viral suppression rates will be higher among children with access to POC VL testing and 
time to viral suppression shorter compared to children with standard VL testing in the control arm one year after POC VL 
testing implementation. In Aim 1b, we intend to validate HIV-1 POC VL testing against current in-country gold standard VL 
testing by generating concordance/discordance rates and Cohen’s kappa to determine assay agreement. We will also 
conduct sensitivity analyses to the concordance rates at lower thresholds of viral load cut offs for viral suppression (down 
to <40 copies/mL, the lower limit of quantification for the POC VL testing). 
 
Study Aim 2: In our Aim 2a, we intend to evaluate the impact of targeted HIV DRM testing on viral suppression in the 
intervention arm and for all children newly initiating ART in both arms. We will describe the proportion of children tested for 
DRMs with significant mutations within each class of HIV drugs, e.g. NRTIs, NNRTIs, and PIs. We will also explore how 
sociodemographic, behavioral, clinical, and facility factors may be contributing to the DRM patterns we observe. We 
hypothesize that targeted DRM testing will shorten time to viral suppression among children on ART without viral 
suppression, high levels of antiretroviral DRMs in this population, and both individual factors, such as prior exposure to 
antiretrovirals, and facility-level factors, such as facility type, will predict the presence of DRMs. The same descriptive and 
analytic approaches will be used for data generated from the next-generation sequencing genotypic resistance and 
phenotypic resistance, especially to monitor emerging DRMs to DTG. In Aim 2b, we intend to determine and validate the 
test performance of a novel POC DRM assay, OLA Simple, against current in-country gold standard VL testing by generating 
concordance/discordance rates and Cohen’s kappa to determine assay agreement.   
 
Study Aim 3: In our Aim 3a, we intend to understand how our intervention functions and how policymakers might use tools 
to scale up POC VL and DRM testing by conducting focus group discussions (FGDs) and in-depth interviews with key 
informants. In these FGDs, we will assess policymakers’ views on using systems engineering to inform decision-making. 
We will seek diverse opinions about the feasibility, acceptability, and scalability of systems engineering tool utilization, to 
understand how such tools would modify, support, enhance, or interfere with decision-making. In the KIIs, we will interrogate 
factors which acts as both facilitators and barriers to children achieving viral suppression and focus specifically on how POC 
VL and DRM testing may improve viral suppression. Furthermore, due to the on-going coronavirus disease 2019 (COVID-
19) pandemic, we will query participants through study visit questionnaires and in-depth interviews to understand how the 
novel coronavirus disease 2019 (COVID-19) has impacted intervention delivery and participants’ overall well-being.  In 
addition, we will query particular logistical aspects of optimally operationalizing POC VL testing, e.g. how caregivers prefer 
to learn of the results or where facility staff see the most need for POC VL testing.  These logistical aspects, such as the 
preferred approach to delivering results, result counseling content and methods, and provider reaction to results and 
additional capacity building needs for the providers and health facilities, need to be explored further in order to optimize 
implementation and scale up of POC testing. To understand how policymakers might use tools to scale up POC VL and 
DRM testing, we will use KIIs to understand how usable the model built in Aim 3b; we will cover domains of learnability, 
efficiency, memorability, error recovery, and satisfaction. In addition to qualitatively assessing these domains, we will 

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additionally collect a quantitative survey from the same participants to assess learnability, efficiency, memorability, error 
recovery, and satisfaction. For Aim 3b, we will estimate three main outcomes 1) the incremental cost-effectiveness of POC 
VL testing alone compared to standard laboratory testing, and 2) the effectiveness threshold needed to reach in order for a 
combination intervention scenario, which goes beyond POC VL testing alone (e.g. combines financial incentives for 
remaining virally suppressed), to be considered more cost-effective than POC VL testing alone, and 3) the optimal 
placement of POC machines in Kenya to minimize turnaround time. For the first two outcomes, we will calculate ICER as 
the ratio of the difference in costs divided by the difference in effects across simulations for the intervention compared to 
standard-of-care over a 10 year horizon. Consistent with guidelines, we will discount costs and health benefits at 3% 
annually, and consider ICERs below Kenya’s per capita GDP to be cost-effective.56 We will perform extensive sensitivity 
analyses to identify influential assumptions. For the third outcome, we will create a queuing model – used in industrial 
engineering to model waiting times – to identify optimal placement of POC machines in Kenya. We will model the reduction 
in turnaround time and waiting time associated with placement of POC machines in a hub-and-spoke model, where select 
“hub” facilities (sites with a POC machine) and “spoke” (sites that send samples to a hub) facilities are chosen. We will also 
model a platform sharing model, in which POC machines rotate between different facilities within a sharing network. At 
different budget levels, we will identify the optimal number, placement, and network of POC machines.  
 
 
 
B. Description of Population to be Enrolled   
The study population will be recruited from HIV-infected children and young adolescents age 1 to 14 years newly initiating 
or already receiving ART at the study facilities (n= approx. 700 children in total). Infants <1 year of age will not be included 
in the study as they frequently require more than 6 months of ART to suppress their initially high VL, and thus require 
specialized interpretation and management of VL results.57-59   
 
C. Study Design and Research Methods   
Overall Study Design  
We propose an open-label randomized, controlled study to pilot the use of POC VL testing and targeted DRM testing among 
HIV-positive children on ART age 1-14 years over a 12-month period in high-volume HIV treatment facilities in Kisumu, 
Kenya.  We have chosen the study facilities to leverage existing POC technologies, as they are already equipped with 
GeneXpert® IV systems. Currently, these GeneXpert® systems are primarily being used for tuberculosis management. 
While validated for HIV VL monitoring, the technology remains to be tested for optimal integration into routine HIV clinical 
care.13,60 At each facility, eligible children will be randomized 1:1 to either receive the intervention testing, consisting of 
quarterly POC VL testing and 
targeted DRM testing, or standard-
of-care (SOC) testing based on the 
existing Kenyan national guidelines. 
We will follow the viral outcomes up 
to 24 months after enrollment for 
each 
child 
and 
compare 
VL 
suppression rates, defined as VL 
<1000 copies/mL by the Kenyan 
national 
guidelines, 
among 
intervention 
vs. 
control 
arms, 
accounting for pre-intervention VL 
suppression rates (Figure 4). Of 
note, after the 12-month study visit, 
the 
study 
will 
enter 
an 
observational-only 
phase 
to 
continue to monitor for viremia and DRMs, especially among children on DTG-containing ART. During this observational-
only phase of the project, participants will continue to receive testing, treatment, and other procedures as part of routine 
care, according to the standard-of-care then; study staff will not be providing any interventions, but only collect blood 
samples and administer questionnaires every 6 months up to 5 years of follow-up.  

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Study Setting  
The proposed study will occur in western Kenya in Kisumu County. Adult HIV prevalence in Kisumu is 19.9%, 3.4 times 
higher than the national prevalence.61 Kisumu County accounts for the second highest incidence of pediatric HIV infections, 
with 8,600 children estimated to be living with HIV and over 500 annual HIV-related deaths in children.61 The study will be 
implemented at low-resource, high-volume, high-HIV burden primary health care facilities and compare viral suppression 
rates in children randomized to intervention vs. control arms. These facilities are among the more than 70 health facilities 
in Kisumu County supported by the Family AIDS Care and Education Services (FACES), a PEPFAR/CDC-funded HIV 
prevention, care and treatment program run through a collaboration between the University of California, San Francisco 
(UCSF), the Kenya Medical Research Institute (KEMRI), and the Kenya Ministry of Health (CDC NU2GGH001947). 
FACES’s unique model of technical support in direct collaboration with the local Ministry of Health (MOH) and CDC Kenya 
for provision of HIV care will allow implementation of the study interventions within government health facilities with strong 
technical support from FACES and study staff, thus promoting local ownership.62   
Table 1. Preferred 1st and 2nd line antiretroviral therapy regimens 
for children and adolescents in Kenya8 
Age 
Preferred 1st Line 
Preferred 2nd Line 
< 3 years 
ABC+3TC+LPV/r 
National referral for DRM 
testing/consultation 
3-15 years 
<35 kilograms 
> 35 kilograms 
 
ABC+3TC+EFV 
TDF+3TC+EFV 
 
AZT-3TC-LPV/r 
AZT+3TC+LPV/r or ATZ/r 
 Using a family model of care, FACES implements comprehensive pediatric HIV care and treatment services in 
accordance with the Kenyan MOH ART Guidelines. The latest guidelines, released in 2016, support initiation of ART for all 
individuals living with HIV, regardless of CD4 cell count or disease severity.8 Recommended 1st and 2nd line ART regimens 
are outlined in Table 1. Routine VL monitoring is offered through a laboratory network connected to a national, centralized 
laboratory and is recommended for children after six months of continuous ART and annually thereafter for those with VL 
<1000 copies/mL.  Management of children with VL > 1000 copies/mL includes adherence intensification and repeat of VL 
testing in three months followed by possible switch of ART for those who are still not virologically suppressed. Only those 
children on 2nd or 3rd line ART who continue to have viremia after adherence optimization, undergo DRM testing at a national 
reference laboratory.  This testing process requires initial consultation with the Kenyan MOH HIV ART treatment committee, 
who then approves centralized DRM testing, receives the results, and eventually guides the local provider on management 
of the child failing their 2nd or 3rd line ART.  
 
Study Collaborators 
The proposed intervention builds upon long-standing and strong collaborations among the co-investigators and 
collaborating institutions. FACES serves as a platform for multiple clinical and implementation science research studies. 
Drs. Abuogi, Patel, and Oyaro have jointly led a series of implementation science projects and pediatric HIV studies with 
FACES in Kenya.  Dr. Patel is an infectious diseases/HIV physician who conducts implementation science research in 
optimizing concomitant ART and hormonal contraceptive use for HIV-positive a and adolescents (NIH/NIAID K23AI20855, 
PI Dr. Patel) and was recently awarded a CFAR pilot grant to study POC VL testing for pregnant women in Kenya for which 
Drs. Abuogi, Oyaro, and Thomas are collaborators. She has worked in Kenya since 2013, collaborating with FACES on 
crucial operations research, which has impacted the care of adolescents and women. Dr. Abuogi is a pediatrician, who 
previously served as FACES Deputy Director Clinical, and alongside Dr. Oyaro, has spearheaded FACES efforts to improve 
pediatric HIV prevention and treatment services through programmatic interventions and key operational research since 
2008. Key studies by Dr. Abuogi include the ongoing MOTIVATE Study (Mother and Infant Visit Adherence and Treatment 
Engagement Study) (NICHD R01HD080477-01, co-PI Dr. Abuogi), a cluster community randomized trial in Kenya to 
evaluate the impact of community mentor mothers versus text messaging to optimize adherence and retention for women 
and infants on lifelong ART. Dr. Abuogi, in collaboration with FACES, has also studied the uptake of routine viral load testing 
among children and evaluated of risk factors for treatment failure in children on ART. Dr. Oyaro, a medical doctor and 
Country Director of FACES and current Chief Executive Officer of RCTP-FACES NGO, has led FACES to expand HIV care 
and treatment for children and served as co-investigator and/or site PI on a series of implementation science studies within 
FACES. He is currently Co-PI for the LIVING study, which is studying the feasibility of Lopinavir/Ritonavir pellets amongst 
HIV-infected children (NCT02346487).   Dr. Irene Mukui was the NASCOP HIV Care and Treatment Manager for five years 
and has been intensely involved in implementation science research in Kenya including treatment outcomes for children, 
sustainability of the national HIV response, and HIV drug resistance. She has led and participated in development of key 
national and global HIV strategies and policy. Drs. Frenkel and Lutz, developed the POC DRM assay, OLA Simple.  Dr. 
Frenkel’s lab will lead the next-generation sequencing for genotypic resistance and Dr. Gotlieb’s lab will lead phenotypic 
resistance testing. Dr. Anjuli Wagner has led several investigations using systems sciences to improve the HIV care cascade 
in Kenya and other settings. 

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Study methods 
Aims 1 & 2 
POC VL testing: In this study, we propose the use of POC VL testing to optimize viral suppression rates in children and 
young adolescents.  Several POC VL tests have undergone analytical performance evaluations and are becoming 
increasingly available in resource-limited settings.40,41,46-48,63 Of such POC VL tests, the GeneXpert® HIV-1 VL Assay 
developed by Cepheid is a leading test that has been validated and found feasible and reliable in rural African communities.63 
It is an in-vitro test designed for rapid quantification of HIV-1 in human plasma from infected individuals over a range of 40 
to 10 million copies/mL.64  The lower limit of quantification for the GeneXpert® VL assay is 40 copies/mL, with the lower 
limit of detection as low as 15 copies/mL. This assay uses reverse transcriptase polymerase chain reaction (PCR) 
technology to detect HIV in plasma, achieving a high sensitivity and specificity for quantification of HIV. The GeneXpert® 
HIV VL assay utilizes the existing GeneXpert® Instrument Systems. These systems consist of the instrument, personal 
computer, and preloaded software, and are already in place at the intervention facilities for detection of Mycobacterium 
tuberculosis. CDC Kenya has supported the purchase and maintenance of these GeneXpert® IV systems at some of these 
facilities and continue to provide ongoing training to MOH laboratory staff on its use. For facilities that do not have 
GeneXpert®, a hub and spoke lab networking model will be used to scale up POC VL. The sites with GeneXpert® 
technology will serve as the hub and near-by sites will transport their samples to the hub with quick turnaround times. 
Operationalizing such a model will foreshadow real-life implementation of such systems, where a hub and spoke model 
could be more feasible and sustainable. As such, the proposed study leverages the existing GeneXpert® technology already 
being supported by the Kenya MOH and CDC Kenya. In addition, the GeneXpert® VL assay kit is self-contained, including 
the necessary PCR reagents and supplies. The only additional tool required is a centrifuge to separate the plasma from 
whole blood which is available at all the intervention facilities in addition to trained laboratory staff capable of performing 
such procedures. This assay requires <1 minute of hands-on time with an average run time of 90 minutes reported from 
cartridge insertion to VL report generation. The GeneXpert® platform does not require batching tests and since the VL 
assay can be run with other tests simultaneously on the same machine, study use of the machine should not interfere with 
routine clinical use of the GeneXpert® machines. Ultimately, while the analytical performance of GeneXpert® HIV VL assay 
has been established, its clinical effect and optimal use remain to be determined.32-34 As POC VL testing becomes 
increasingly popular, rigorous evaluations, which include clinical outcomes, of such testing is necessary.  
For those assigned to the intervention arm, our proposed POC VL testing will be done earlier than SOC, for those 
children newly initiating ART, and more frequently than SOC for children already on ART. For children newly initiating ART, 
we will conduct a POC VL test and DRM test pre-ART initiation, then POC VL only 3 months post-ART initiation, and every 
3 months thereafter for up to 24 months. For children already on ART, we will conduct a POC VL test 3 months after their 
last VL test (or at the time of their next visit, if no VL testing has been done before), and every 3 months thereafter for up to 
24 months. A blood sample will be collected by a study nurse during study visits every 3 months, which will be coordinated 
with their routine clinical visits; children on ART return to the clinic monthly for routine visits. Approximately 4mL (<1 
tablespoon) of whole blood will be collected in EDTA plasma tubes from participating children for analysis of POC VL, 
potential DRM testing, and storage for any future studies. If phlebotomy of 4mL is difficult to achieve, we will aim to collect 
a minimum of 2mL of blood, using most of the sample for POC VL testing, and then prepare dried blood spots (DBS) for 
potential DRM testing if needed. All samples can be stored at room temperatures up to 25° Celsius, and in a cooler box if 
study facility temperatures rise above 25° Celsius, for up to 6 hours after sample collection. The samples will be processed 
within 6 hours of collection for plasma POC VL testing by existing lab staff already trained on GeneXpert® at intervention 
sites only after obtaining approval from the study nurses. Remaining plasma will be placed in aliquots and transported to 
the KEMRI-CDC laboratory in coolers for DRM testing or stored for any future testing. The study staff will return POC VL 
results to the ordering providers and caregivers within 24 hours of sample collection via text messages or phone (and paper 
results for providers). A generic message that lab results are available and a phone number to call free of charge for results 
will be sent to caregivers. Results will be given after verifying the caregiver and child’s identity followed by appropriate 
counseling on viral load results. Name of the participant will not be included in the text messaging.   
For those assigned to the control arm, VL testing will be conducted per national guidelines (as outlined above) 
through laboratory networking and transport to a centralized laboratory as the SOC. Children in the SOC arm who are newly 
initiating ART will have blood collected prior to ART initiation for future VL and DRM testing to be conducted at completion 
of study follow up. These results will be returned to clinical providers and patients/caregivers after the study. Additionally, in 
order to have comparable outcomes, POC VL will be conducted on ALL SOC participants at 12 and 24 months of follow up. 
Study nurses will prompt providers to order follow-up VL tests for those who qualify to ensure uptake of VL testing in both 
study arms. 
 
We will batch test all stored samples for gold standard VL testing towards the end of the study period for both the 
arms (n= up to 700 samples), and generate concordance/discordance rates and Cohen’s kappa to determine assay 
agreement. We will also conduct sensitivity analyses to the concordance rates at lower thresholds of viral load cut offs for 
viral suppression (down to <40 copies/mL, the lower limit of quantification for the POC VL testing). To conduct the in-country 

Opt4Kids Protocol V5.1 2 March 2021 
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gold standard HIV-1 VL testing (or quantification), we will build on our existing partnership with the KEMRI-CDC HIV 
Research Laboratory, led by Dr. Maxwell Majiwa, a collaborator on this proposal.  The KEMRI-CDC facilities are located 
immediately outside of Kisumu. The KEMRI-CDC HIV Research laboratory utilizes state of the art HIV-1 quantification 
technologies by using Abbot m2000sp/RT, Roche CAP/CTM, and Roche C8800 systems (cite).  Overall, the two main assay 
platforms, the Abbot vs. the Roche platforms, both perform extremely well, with subtle difference in accuracy and precision 
of each platform (cite).  As we get closer to the time of running our samples for this validation, we will determine which 
specific platform to use depending on logistical aspects, such as current usage of the platforms.  These platforms can run 
the assays with 0.5-1mL of plasma, which will already be stored in cryovials of this volume at the same facility.  For each 
sample analyzed, we will receive printed reports noting exact quantification or below or above the limits of quantification.   
 
HIV DRM testing: We will conduct targeted HIV DRM testing at the Kisumu-based KEMRI-CDC HIV Research laboratory 
using consensus sequencing on all plasma samples which lack viral suppression, defined as VL > 1000 copies/mL per 
national guidelines. “Targeted” DRM testing will include DRM testing for each child on the first detection of such a VL and 
in children newly initiating ART. Subsequent samples from the same child on the same ART regimen will not be routinely 
retested, since costs of repeat DRM testing outweigh detection of new DRMs. Since approximately 65% of children achieve 
viral suppression currently in the FACES-supported facilities, including the study facilities, i.e. 35% have viremia VL > 1000 
copies/mL, we anticipate conducting DRM testing in at least 100 samples in participants in the intervention arm over the 
course of this pilot study. Such plasma samples, once processed from whole blood within 6 hours of collection, will be 
transported from the local health facility to the KEMRI-CDC laboratory the same day.  
To conduct the HIV DRM testing, we are partnering with KEMRI-CDC, which has extensive facilities, including the 
HIV Research Laboratory led by Dr. Maxwell Majiwa, a collaborator on this proposal, located immediately outside of Kisumu. 
The KEMRI-CDC HIV Research laboratory utilizes Applied Biosystems (ABI) 3130xl Genetic Analyzers to conduct its HIV 
drug resistance testing on 0.5-1mL of plasma. These analyzers have all steps automated, from polymer loading to sample 
injection to data analysis. This laboratory utilizes an optimized in-house assay to detect reverse transcriptase- and PI-based 
mutations. This validated, assay is broadly sensitive in genotyping HIV-1 subgroups, detecting all mutations classified in 
the IAS-USA mutations list, and more sensitive than commercially available assays for mixed viral populations.65 The 
KEMRI-CDC in-house assay can run samples from plasma or DBS; the ability to also use DBS is a key asset for our study, 
in case adequate volume of plasma cannot be obtained from our younger study population. DBS samples will be prepared 
by spotting 100 μL of whole blood onto each of the preprinted circles on the DBS paper, then dried overnight at the local 
health facility at ambient temperature, and then transport wrapped DBS cards the next day.   
At the KEMRI-CDC laboratory, the samples will be batched together daily, or at most weekly as this laboratory 
experiences a high volume of testing for other clinical and research projects. Results will be returned to the study staff within 
24 hours of assay results, noting that the assay can take up to 24 hours to run. The DRM results report contains a list of the 
major and minor DRM genotypes as well as phenotypic interpretations, based on the scoring systems generated by the 
Stanford Genotypic Resistance Interpretation Algorithm. Study staff will then forward the DRM results to the ordering 
providers within 24 hours of their receipt and enter them into the patient file. Providers will use the results to guide patient 
management. Providers at the study facilities will undergo training on interpretation of the DRM testing results and study 
staff will work closely with facility leadership to assist providers in managing children with DRM including making any 
potential switches in accordance with national guidelines. As noted above, Dr. Mukui has extensive background from the 
2013 Kenya DRM survey in training and helping to build capacity for local HIV providers in understanding and interpreting 
DRM testing results. She will lead the standard operating procedures related to DRM testing results sharing with the facility 
providers and actively be involved in clinical decision-making as needed. We anticipate the turn-around time for the DRM 
testing to be 1-2 weeks at most; remarkably faster than the months-long process currently in place as SOC in Kenya.  We 
anticipated that very few children in the control arm will undergo DRM testing as DRM testing is not currently recommended 
for children on 1st or 2nd line ART per national guidelines. 
To further analyze emerging DRM for DTG, we will follow participants longitudinally after they reach the study 
primary endpoint (of 12 months after enrollment) and follow their routine care VL patterns while on DTG via an observational 
extension of this project for up to 5 years.  If VL>X##$%&'()*+,-$(*$5)1)%1)5$MG$.)B():(/3$.&41(/)$%0.)$.)%&.5*C$*145G$*1088$
:(22$%&/10%1$19)$'0.1(%('0/1$8&.$)(19).$055(1(&/02$M2&&5$%&22)%1(&/$&.$').,(**(&/$1&$4*)$0/G$*1&.)5$*0,'2)$8&.$/)Y1?3)/).01(&/$
*)P4)/%(/3$O;VKQ$0/5$'9)/&1G'(/3@$$W22$*0,'2)*$:(19$R-$!X##$%&'()*+,2$:(22$M)$1)*1)5$MG$;VK$4*(/3$19)$>224,(/0$J(K)P$
'2018&.,$4*(/3$N.(,).$>I$(/$I.@$Z.)/[)2\*$20M$01$K)0112)$U9(25.)/\*$<)*)0.%9$>/*1(141)$(/$K)0112)C$]W$O19)$%9(25.)/\*$9&*'(102$
088(2(01)5$:(19$19)$^/(B).*(1G$&8$]0*9(/31&/Q@$=9(*$;VK$,)19&5$:(22$022&:$8&.$5)1)%1(&/$&8$2&:?8.)P4)/%G$5.43$.)*(*10/%)$
,4101(&/*$O,(/&.(1G$B0.(0/1*QC$:9(%9$,0G$M)$'.)*)/1$01$M0*)2(/)$0/5+&.$01$1(,)$&8$B(.),(0@$K)P4)/%)*$:(22$M)$0/02G_)5$8&.$
`>R$5.43$.)*(*10/%)$,4101(&/*$4*(/3$19)$K10/8&.5$`>R$5.43$.)*(*10/%)$5010M0*)$O`>R5MQ$3)/&1G'(%$.)*(*10/%)$(/1).'.)101(&/$
*G*1),C$0/5$5.43$.)*(*10/%)$,4101(&/*$:(22$M)$.)'&.1)5$01$5)1)%1(&/$19.)*9&25*$&8$"7C$X7C$a7C$"#7C$0/5$X#7@$=9)$.)*421*$

Opt4Kids Protocol V5.1 2 March 2021 
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CF-146, Effective 7/10/11 
 
:(22$ M)$ .)14./)5$ 1&$ 19)$ 80%(2(1G$ *1088@$ Z&.$ '9)/&1G'(/3$ .)*(*10/%)$ '011)./*C$ I.@$ V)&88$ V&112()M\*$ 20M$ 01$ 19)$ ^/(B).*(1G$ &8$
]0*9(/31&/$:(22$').8&.,$'9)/&1G'(%$.)*(*10/%)$1)*1(/3$8&.$19&*)$*0,'2)*$:(19$.)2)B0/1$3)/&1G'(%$,4101(&/*@$`(*$20M$.)P4(.)*$
1:&$X?,-$%.G&B(02*$&8$'20*,0C$:9(%9$:(22$M)$%&22)%1)5$01$19)$1(,)$&8$M2&&5$%&22)%1(&/$8&.$;VK$*0,'2)$%&22)%1(&/@$$;)(19).$
B02(501)5$;VK$/&.$'9)/&1G'(%$.)*(*10/%)$1)*1(/3$(*$%4..)/12G$0B0(20M2)$(/$S)/G0C$194*C$.)P4(.(/3$*9(',)/1$&8$*0,'2)*$1&$19)$
^@K@ During this observational-only phase of the project, participants will continue to receive testing, treatment, and other 
procedures as part of routine care, according to the standard-of-care then; study staff will not be providing any interventions, 
but only collect blood samples and administer questionnaires every 6 months up to 5 years of follow-up. However, if any 
irregularities in routine care are noted in regards to treatment or monitoring by study staff during this observational-only 
phase, study staff will alert the health facilities for possible routine care interventions. 
 
OLA Simple-based DRM testing: We will evaluate the ability of OLA Simple to identify DRMs by comparing its results against 
DRM testing results from CS from study samples, as well as from the de-identified stored samples at the Kenya National 
HIV Reference Lab (NHRL), a key partner and stakeholder in future scale up of POC HIV DRM testing in Kenya.  We will 
also evaluate the usability of OLA Simple when performed in the field in a clinical setting, bringing OLA Simple one step 
closer to being implemented as a POC DRM test.  To increase efficiency, the OLA Simple testing will be conducted 
retrospectively, once the majority of samples to be tested have been collected, with direct observation from the Lutz team.  
Nonetheless, to gain better insight into technician performance without direct supervision from the developers, we will also 
conduct a limited, prospective one-month long OLA testing towards the end of the participant follow-up.  We will also partner 
with NHRL to cross-validate samples that have already undergone CS for clinical purposes and where leftover specimens 
are now stored and available for analysis at NHRL.  These specimens are collected as part of routine clinical care from 
individuals experiencing viral failure, and are used for routine surveillance and evaluation of new test kits within the Kenya 
Ministry of Health.  For NHRL, the use of their de-identified, stored specimens falls under the existing NASCOP-MOH routine 
lab validation procedures.  Of note, results of the OLA Simple testing will not be returned to patients or providers, as this 
technology has not yet been approved for clinical use.  The following procedures will be followed:  
a. Approximately 500uL of plasma from the samples genotyped by CS will be tested by staff at each facility trained in the 
use of OLA Simple, with oversight and assistance by the Lutz team over a 3-week period. The facility staff performing 
OLA Simple and the Lutz team will be “blinded” to CS results.  Results from OLA Simple will be interpreted as presence 
or absence of resistance using test strip lines for each codon tested, based on a visual guide for test interpretation and 
automated analysis from captured test images. For OLA Simple tests that show INDETERMINATE results for one or 
more codons, samples will be re-tested for that codon in two separate kits; if the two new results concur, that result will 
be used, but if the two new sets do not concur the result will be reported as a test failure for that codon. (Specimens 
with indeterminate or discordant results will be further analyzed as noted in “d” below.) 
b. The OLA Simple results (MUTANT, WILD-TYPE, or INDETERMINATE at each codon) will be compared to sequencing 
results to calculate test sensitivity, specificity, indeterminate rate, and other parameters.  
c. OLA Simple will target specific NRTI, NNRTI, and PI DRMs, including: (1) NRTI M184V/I, K65R; (2) NNRTI K103N, 
Y181C, G190A, V106M; and (3) PI V82A, I50V/C, I54V/M/L, I84V/A mutations (based on published analysis of DRMs 
among Kenyan adults in the Frenkel laboratory using the Illumina platform38).  
d. Prior studies have shown that OLA Simple may be more sensitive than CS; specifically, OLA Simple can detect DRMs 
at ~10% of a patient’s viral population while CS detects DRMs at about ~20%.  To address this sensitivity gap, any 
sample negative for DRMs by CS but positive by OLA Simple will be tested by the more sensitive Illumina sequencing 
(detects DRM down to 1% of viral population, requiring as few as 2000 copies of HIV RNA).  Illumina sequencing will 
be performed in Dr. Frenkel’s laboratory in Seattle. To avoid shipping biohazardous material, we will ship non-infectious 
leftover RNA from OLA Simple testing in Kisumu. 
 
Clinical decision support for early management of children on ART: Overall, providers will be instructed to follow current 
Kenya National Guidelines for the management of any child with VL > 1000 copies/mL which includes assessment of 
barriers to adherence and other potential factors related to virologic failure. For the intervention arm, standardized protocols 
and clinical decision support flow sheets will be developed to facilitate clinical management and closely record decision-
making steps. Both caregivers and providers will receive POC VL results within 24 hours of the study team receiving the 
results. Providers will perform routine counseling and adherence intensification, then reassess VL results three months from 
initial VL in the intervention arm only. If DRM testing is performed, study staff will assist providers in interpreting the DRM 
results and assessing the need to switch to 2nd or 3rd line ART. If a provider determines a child should be switched to 2nd or 
3rd line ART based on DRM testing or POC VL results, the child and caregiver will be called back within one week of VL/DRM 
results for counseling, adherence support, and regimen change. To facilitate caregiver response, we will sensitize 
patients/families on implications of VL results and provide standardized results counseling. Special consideration will be 
given to children newly initiating ART as they may not achieve virologic suppression after only 3 months of ART. In such 
cases, providers will be trained to monitor trends in VL over the first six months on ART and to consider intervention (e.g. 

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switch to 2nd or 3rd line) only after child has completed a minimum of 6 months on ART, unless significant primary transmitted 
mutations are found.  
 
Children in the control arm will receive the SOC, which includes clinical management consistent with national 
guidelines including assessment of adherence and multi-disciplinary team review. Providers will follow their standard 
protocol for notification and follow up of children with high viral loads. 
 
Aim 3a: Qualitative interviews 
Qualitative evaluation: We will conduct the key informant interviews (KIIs) with five subgroups of key informants (Table 2), 
with 10-20 KIIs conducted within each group with a total of 50-100 persons interviewed, some of whom will be interviewed 
serially up to four times during the study period, and up to 20 who have participated in Aim 3 will also fill a brief quantitative 
survey. We will additionally conduct up to 4 focus group discussions (FGDs) (with 6-10 participants each) with policymakers 
at the county or national level. 
 
 
Table 2: Groups and timings of key informant interviews  
Groups 
Enrollment or 3 
month visit 
12 month visit  24 month visit  
One Time (towards the 
end of study period) 
Group 1: Caregivers 
X 
X 
 
 
Group 2: Adolescents 
X 
X 
 
 
Group 3: Providers, lab staff and 
other facility staff discussing POC 
VL and DRM testing questions 
 
 
 
X 
Group 4: Policy makers and others 
discussing POC VL and DRM 
testing questions 
 
 
 
X 
Group 5: Providers and policy 
makers discussing modeling of POC 
VL scale up  
 
 
 
X 
Focus group discussion guide: Our FGD guide will be developed from an implementation science framework focusing on 
feasibility, acceptability, and scalability, seeking to understand how utilizing systems engineering tools would modify, 
support, enhance, or interfere with decision-making.  
  
 
Interview guide- Our first KII guide will be developed from a socioecological model of pediatric viral suppression, 
which include individual, interpersonal, organizational, and structural/policy factors that influence pediatric viral suppression 
(Figure 5).  In these KIIs, we will 
interrogate factors which acts as both 
facilitators 
and 
barriers 
to 
children 
achieving viral suppression and focus 
specifically on how POC VL testing and 
OLA 
Simple 
may 
improve 
viral 
suppression. 
We 
will 
also 
query 
participants 
through 
study 
visit 
questionnaires and in-depth interviews to 
understand how the novel coronavirus 
disease 2019 (COVID-19) has impacted 
intervention delivery and participants’ 
overall well-being. In addition, we will 
query particular logistical aspects of 
optimally operationalizing POC VL testing, 
e.g. how caregivers prefer to learn of the 
results or where facility staff see the most 
need for POC VL testing.  These logistical 
aspects, such as the preferred approach to 
delivering 
results, 
result 
counseling 
content 
and 
methods, 
and 
provider 
reaction to results and additional capacity 
building needs for the providers and health facilities, need to be explored further in order to optimize implementation and 
scale up of POC testing. Our second KII guide will have three phases: in phase I, each participant will be asked first to 
describe how they might make a decision about where to place POC VL testing machines in their geographic area. Prompts 
will include factors like cost, equity, coverage, competing disease priorities, and inclusion of other decision-makers. In phase 

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II, each participant will be asked to look at a computer screen with the Excel-based tool from Aim 3b, be read a short set of 
standardized instructions, and asked to complete a series of tasks within the tool from Aim 3b. Within the tool, they will be 
asked to: change 1) the overall budget available to purchase POC VL machines, and 2) the maximum waiting time allowable 
for each sample. During this phase, policymakers will be asked to narrate their thoughts aloud, borrowing methodological 
approaches from cognitive interviewing, a technique used in psychology to assess comprehension. They will be asked to 
reflect on their experience of interacting with the tool and the interpretability of the model results. During phase III, the tool 
will be removed and they will be asked to describe how satisfied they felt while using the tool and how they could envision 
this tool being used to make decisions related to placement of POC VL machines. Of note, each interview guide for the 
appropriate group will be adapted for that group, e.g. the language and content of the interview guides for the adolescents 
may vary markedly from that we use to interview a policy maker. Following the qualitative interview, participants will complete 
a brief quantitative survey assessing learnability, efficiency, memorability, error recovery, and satisfaction. 
 
 
 
Aim 3b: Modeling 
Costing and cost-effectiveness: Costing- We will use activity-based micro-costing, staff interviews, and time and motion 
studies, to estimate the annual cost of HIV monitoring per child living with HIV in the control (SOC) and intervention (POC 
VL + DRM testing and OLA Simple) arms. An experienced research assistant will conduct time and motion studies to 
estimate the nurse and clinician times necessary to complete the clinic visits for both arms, and to estimate the average 
number of patients seen in the clinic each day. We will assess the time required to complete each step of the HIV care visit 
(VL testing and DRM, adherence counseling). Observing multiple visits by various staff members will allow estimation of 
the average time taken for each step; time needed for research activities (e.g. administering informed consent) will be 
removed from intervention time to provide an estimate of the intervention, if implemented as a government program.  For 
the OLA Simple testing, we will only conduct the micro-costing activities during the prospective portion of its use, as this 
time frame best mimics real-life use of the test and optimizes efficiency once the facility staff have gained experience 
conducting the tests. We will use standardized cost menus to collect site costs, including start-up costs, clinic space, human 
resources, supplies, and VL test costs. When data are not available from our cohort, we will utilize data from population-
based studies from Kenya and sub-Saharan Africa. Additional cost data may be obtained from health facilities, published 
government information on labor costs, and health economics literature. Analyses will follow the guidelines for costing HIV 
interventions66,67, and will reflect the provider perspective. We will also collect data on patient out-of-pocket costs to assess 
if POC VL testing saves participants time and expense, reflecting the societal perspective. 
 
Mathematical model- We will parameterize a model with cost data from our microcosting activities and viral 
suppression data from Opt4Kids and project HIV infections, HIV-related deaths, and DALYs associated with various POC 
VL testing interventions.  We will use EMOD-HIV, an open-source agent-based transmission model to project the cost and 
clinical impact of implementing POC VL testing alone and as part of a CIS in Western Kenya.68,69 EMOD includes geographic 
patterns of (1) age-specific demographics 
(fertility, mortality, migration); (2) vertical 
transmission, 
(3) 
a 
detailed, 
user-
configurable care continuum for HIV 
treatment and prevention interventions that 
can be configured to reflect differences in 
treatment access and health seeking 
behavior among children and adolescents, 
including heterogeneities in access and 
retention in care (e.g., age and sex 
differences in diagnosis and linkage), and 
(4) a detailed within-host model of HIV 
progression and treatment to simulate the 
health and transmission effects of pediatric ART (Figure 6).68,70-72 
 
Queuing model design: Operational research models are meant to simulate the behavior of a system and use data to inform 
decision-making. We will use queuing models, a specific kind of model commonly used in industrial engineering to model 
waiting lines or “queues”. Queuing models represent queuing systems (i.e., waiting lines) using mathematical formulas to 
describe the system performance, such as average waiting time and expected queue length in the system. A queuing model 
has the following components; an input source generating the customer arrival process, the queue capacity, the queue 
discipline (e.g., first-come-first-served) and the service mechanism (e.g., number of servers, service time distribution).  The 
queuing model example input and output parameters are delineated in Table 4, which will be collected from a representative 
sample of 12 clinics; we will include 5 health facilities where Opt4Kids is taking place and at 7 additional facilities selected 
to reflect a range of pediatric patient volumes, facility sizes, and distances to the central laboratory.  To further model the 
parameters for a wider range of facilities in Kenya, In conjunction with our colleagues at the Kenyan Ministry of Health, we 

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will identify 100 facilities in Kisumu (out of ~260) that are representative of the distribution of facility sizes, locations, patient 
volumes, staffing and level of care, using the existing Kenyan Master Health Facility List. Lastly, we will create the Excel-
based queuing model. Our objective is to identify conditions (number of POC machines in the region and the structure of 
hub-and-spoke or platform sharing networks) that minimize the total expected waiting time and service costs. The model 
will simulate various hub-and-spoke and platform sharing network configurations under a user’s specification (e.g. options 
on entering maximum travel distance or maximum number of spoke per hub or facilities in a sharing network). For each type 
of facility, defined by a unique set of input parameters on sample arrival distribution and service mechanism (i.e., the type 
of queuing model), the model will estimate average turnaround time (including travel time) and the expected number of 
samples processed at each facility under a particular network structure. Using these estimates, the model will select the 
optimal network structure with the minimum expected cost.  
 
Start-up activities, including training  
Start-up activities will include laboratory strengthening and training in conjunction with the MOH to ensure high quality and 
accurate VL testing using the existing GeneXpert® equipment at the study facilities. Health care providers and laboratory 
staff at study facilities will undergo training on POC VL testing (including sample collection, lab procedures, and 
documentation), all relevant study logistics, review of National ART Guidelines and interpretation of DRM testing results. 
Study staff, including study coordinator, study nurses, and data clerks/manager will be trained on recruitment, consenting, 
and all study protocols. Study staff, led by Dr. Mukui, will work with the intervention facilities to develop standard procedures 
for clinical management of children with viremia following national guidelines. 
 
Recruitment and randomization  
Caregivers of HIV-infected children already on ART or newly initiating ART at one of the study facilities will be referred by 
clinic staff during a routine clinic visit to participate in the study. Since the majority of children are seen on a monthly basis 
(maximum of three months for older children), we anticipate recruiting the majority of eligible participants in 3-4 months; 
historically, in FACES-supported research studies we have been able to recruit over 80-90% of the eligible participants. As 
of December 2016, FACES had enrolled 3,727 HIV positive children on ART at all its health facilities. At the proposed study 
facilities, 683 children were actively enrolled in care on ART as of December 2016, with approximately two children newly 
initiating ART per month at each site. We therefore anticipate that by time of study recruitment targeted for April 2018, we 
should have more than 700 eligible children to recruit at the intervention facilities even with some attrition, which tends to 
be approximately 10% in one year due to loss to follow-up or death, prior to study start. We will encourage referral of eligible 
participants by providing site staff a small incentive of around $1USD for referral of children who are enrolled. The FACES 
retention model combines multiple interventions to maintain retention in care including text message reminders, phone calls, 
home visits, and defaulter tracing which study nurses will supplement with additional phone tracing as needed. We anticipate 
these efforts will minimize losses to follow up and allow us to maintain sufficient participants to estimate our study outcomes 
well and minimize any bias due to missing outcomes.  
The University of Washington’s (UW) International Clinical Research Center staff has extensive experience running 
high-profile randomized clinical trials, including in the design and implementation of randomization. In order to assure 
balance by arm within site and age group (1-9 or 10-14 years of age), children will be randomized 1:1 to the intervention vs. 
control arms in varying sized blocks stratified by facility and age. This means that a randomized allocation list will be made 
for each age group within each facility. Each list will be composed of blocks (e.g. 8, 10, or 12) with equal numbers of 
participants assigned to intervention and control arms with random order of assignment within each block. The resulting 
allocation lists will bear no indication of where each block starts or stops, preventing guessing the next allocation even in 
an open-label study. Study nurses will ensure fidelity to the arm allocations by flagging participant charts with the study arm 
and limiting access of POC VL testing to the intervention arm.  
 
Inclusion criteria: 
● 
Children age 1-14 years living with HIV (documented HIV positive or HIV viral load) 
● 
On antiretroviral medication (ART) per Kenya National Guidelines OR 
● 
Newly initiating first line ART 
 
Table 3: Study timeline by study activities 
Timeline (quarters) 
Pre- 
awar
d 
Q
1 
Q
2 
Q
3 
Q
4 
Q
5 
Q
6 
Q
7 
Q
8 
Study preparations, IRB 
 
 
 
 
 
 
 
 
 
Recruitment and enrollment 
 
 
 
 
 
 
 
 
 
Participant follow up 
 
 
 
 
 
 
 
 
 

Opt4Kids Protocol V5.1 2 March 2021 
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Data analyses 
 
 
 
 
 
 
 
 
 
SMART protocol and  
procedures development 
 
 
 
 
 
 
 
 
 
Manuscripts, R01 
application 
 
 
 
 
 
 
 
 
 
 
Study Timeline   
This study will be conducted over 2 years. Considerable budgetary resources are required to complete POC VL and DRM 
testing in quarters 2-6, necessitating a two-year project period to meet budget limits. While this is an ambitious timeline, 
based on our prior experience conducting research in this setting and the need to urgently address suboptimal viral 
suppression in children on ART, we believe it is achievable. The pre-award and initial two quarters of the study period will 
be used for protocol development, obtaining ethical approval from relevant institutional review boards, start-up activities, 
including hiring study staff, trainings, laboratory infrastructure preparation, data collection tool development, and study 
activation visits. Recruitment will occur rapidly over 3-4 months with continuous follow-up thereafter for a minimum of 12 
months and up to 24 months after enrollment for each child at the facility. The final two quarters of the study period will be 
used for data cleaning, analyses, manuscript(s) writing, and results dissemination.  
 
Procedures 
Blood sample collection: For children in the intervention arm: blood samples will be collected every 3 months for POC VL 
testing and DRM testing per protocol for the first 12 months. For children in the control arm: blood samples will be collected 
per standard of care by non-study clinic staff per routine clinic schedule after study enrollment. Newly initiating children will 
undergo baseline VL and DRM testing as outlined above. After either intervention or control arm participants reach the 
primary endpoint of 12 months after enrollment, for the observational-only phase of this project, they may be followed every 
6 months up to 5 years by routine clinic staff for VL monitoring; if VL>200, then study staff will request routine staff to share 
any leftover or stored plasma sample to obtain NGS genotyping and phenotyping. If no stored samples exist (for example, 
at the VL testing facility) or not in sufficient quantities, then the participant will be asked to return to the clinic for additional 
sample collection for NGS genotyping and phenotyping. Phlebotomy required for POC VL testing and DRM testing is also 
required for routine lab and viral load monitoring already a part of HIV care in Kenya. However, this will be more frequent 
than routine lab monitoring. Participants may experience some additional pain and anxiety due to this. This risk will be 
minimized by utilizing trained study nurses with experience collecting blood in children. Additionally, if the required 4ml of 
blood is not obtained, we will utilize dried blood spots which require less blood to reduce the need to perform additional 
phlebotomy.  Once the testing for the study is completed, remaining samples will be stored in Kenya and later batch 
transported to the UW ICRC Laboratory Repository for storage for future studies.  
 
Blood sample storage and transfer: Existing standards and best practices will be followed for blood sample storage and 
transfer.  Greater details are found in the laboratory standard operating procedure. 
Data Collection  
Our overall data collection strategy leverages the existing electronic medical record systems already running at the study 
facilities, and supplements data collection by study staff for important gaps in the existing records (Table 4). FACES uses 
standard MOH encounter forms and registers to capture HIV care and treatment data, which are then entered into electronic 
medical record systems by data clerks daily after clinic visits. These encounter forms support program activities and facilitate 
thorough history taking, guide medical care, and allow for rigorous research. Data entry into the electronic medical records 
is performed by FACES-supported and trained MOH data clerks and includes pre-programmed data quality checks. In 
addition, FACES conducts robust data quality checks daily and audits weekly for each data clerk and provider. We will, 
therefore, leverage this existing resource for some data collection at the study facilities. Over the last decade, FACES has 
been able to implement a robust electronic medical record system; several research studies have taken advantage of the 
robust data and published pioneering and impactful findings.73,74 Because recording of VL results from centralized 
laboratories back into the routine HIV encounter forms is sometimes incomplete, we will supplement VL outcome information 
from the NASCOP online VL database and facility laboratory VL log books. We will also supplement the electronic medical 
records data with additional data that our study team will collect, targeting important data gaps in the electronic records 
particularly pertinent to sociobehavioral aspects of our study (e.g. adherence). Study data will be abstracted by trained 
research data clerks using direct, electronic data entry via tablets into standardized data collections forms developed in 
REDCap, a data collection and management system hosted by the UW Institute of Translational Health Sciences. REDCap 
meets all required standards for protection of personal health information.  
Table 4: Example characteristics to be captured by existing electronic medical records1 or planned study data tools2 

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Sociodemographics 
Behavioral 
Clinical 
Laboratory 
Process 
▪ Child’s age at diagnosis1 
▪ Number of siblings and 
HIV status of each2 
▪ Caregiver HIV status1,2 
▪ Socioeconomic status of 
family2 
▪ Food security2 
▪ Ethnic, religious 
background2 
▪ Disclosure of 
HIV status1,2 
▪ Adherence (e.g. 
pill count,1 refill 
pick-up from 
pharmacy logs,2 
validated 
adherence 
scale2) 
▪ 
Date of HIV diagnosis1 
▪ 
Health status (e.g. WHO clinical stage, 
weight-for-age, etc.)1 
▪ 
Start of ART, regimens used, dosing, 
side effects1 
▪ 
Exposure to any antiretrovirals prior to 
HIV diagnosis1,2 
▪ 
Development and treatment of any 
opportunistic infections1 
▪ Basic labs1 
(e.g. 
hematology
, chemistry) 
▪ HIV labs1 
(e.g. CD4/8 
cell counts, 
VL testing) 
▪ Turn-around 
time for VL 
and DRM 
testing results2 
▪ Clinical 
decision-
making during 
study2 
We will program in-built data verification and quality tools within our REDCap database. In addition, the data team will 
conduct daily quality checks for accuracy and completeness of data. The data team will also generate random and routine 
quality checks for appropriateness and quality of data. Errors detected will be verified against the source documents and 
an audit trail of corrections will be maintained. The data will be de-identified with a unique, database-specific identifier for 
confidentiality of personal health information. Data entered into mobile devices, such as tablets, will be synchronized with 
the REDCap database when internet connection is next available. The REDCap database will undergo daily backups at the 
UW and the data manager will download the data files on a regular basis and store on HIPAA-compliant and password-
protected locations. Proper documentation and storage of the metadata and any files relevant to data management will be 
handled with utmost care. Only certain study staff will have access to the data files containing personal health information, 
and only de-identified data will be shared amongst the researchers. 
All participants in the KIIs and FGDs will undergo informed consent with additional assent obtained from the 
participating adolescents (ages 13-14 whose caregivers will provide the consent).  The interviewers will audio record the 
KIIs and FGDs with a digital recorder and take field notes.  The interviews or FGDs will last 30-60 or 60-120 minutes, 
respectively, and occur in the preferred language of the participants, and participants will be provided approximately 6-10 
USD as reimbursements for their time. The location of the interviews will vary depending on the groups being interviewed.  
For example, the interviews for caregivers and adolescents will occur in or near the health facilities on days that they are 
attending the facility for their routine clinical care visit.  The interviews for the facility staff will likely take place in or near the 
health facilities too and scheduled at times most convenient for the staff.  The interviews with policy makers and other 
stakeholders will take place at locations and times most convenient for them. The FGDs will likely be conducted virtually, if 
the COVID-19 pandemic precautions persist during study implementation of this activity, either on phone or on a virtual 
platform like Zoom.  
For the costing and modeling studies, in addition to the staff interviews, we will use standardized cost and time 
menus to collect site costs and times, including start-up costs, clinic space, human resources, supplies, VL test costs, and 
timing of client arrival at a facility and VL sample collection, processing, and return. When data are not available from our 
cohort, we will utilize data from population-based studies from Kenya and sub-Saharan Africa. 
CS, NGS, phenotyping, and OLA simple-based DRM testing: Scanned copies of the DRM testing results from CS, 
NGS, and phenotyping will be directly uploaded into a data form in REDCap by the study staff after receipt of each result.  
Similarly, the DRM testing results from OLA Simple will be entered into a data form and a picture of the resulted assay 
uploaded into REDCap.  The data forms will be linked to the same participant by a unique study ID.  Study investigators, 
namely Drs. Patel and Lutz, will conduct the interviews and surveys in-person or by phone/Skype, if needed.  The interviews 
will be digitally audio-recorded when feasible.  Study investigators will conduct the costing studies, including time/motion 
studies and interviews with clinical staff. 
    
E.   Potential Scientific Problems   
First, though we will strive to conduct the POC VL testing while the patients are still at the facility, so that the patients and 
providers have immediate feedback, because we are leveraging existing MOH resources, i.e. the GeneXpert® platforms, 
to conduct this pilot we cannot guarantee this. Thus, the ultimate utility of a POC test is diminished. However, our proposed 
work will undoubtedly reduce the current turn-around times of 4-6 weeks for centralized laboratory VL testing. Further, return 
of results to caregivers and providers via text messaging or phone will facilitate more rapid interventions for those with 
viremia than current practice. Likewise, as already noted, our current DRM testing process uses a central laboratory. 
Currently, point of care DRM testing technology is under development but is not available for use in this study.  Second, 
GeneXpert® is arguably one of the more resource-intensive existing POC technologies. Other existing or in-development 
platforms, such as the AlereQ®, can run on battery power, are more compact, and hence more user friendly for more rural 
facilities. Nonetheless, the generalizability of our findings will not be limited to the GeneXpert® platform, as we are not 
validating this specific platform but rather testing a new approach to HIV management for children on ART which may 
include any POC VL testing platform. A key aspect of our proposal is leveraging existing POC technologies at these facilities, 
and, again, careful considerations will be made in selecting the most appropriate POC technologies for future studies. Third, 
crossovers or contamination may occur in our study if providers may want to utilize POC VL testing for children in the control 

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arm, since the POC VL testing is being implemented at the facility but only being offered to those children randomized to 
the intervention arm. Such crossovers will be minimized by limiting access to POC VL test ordering to study nurses to ensure 
only children randomized to the intervention arm undergo POC VL testing. Also, because all providers will receive additional 
training on management of children with virologic failure, it is possible that viral suppression will increase for both groups. 
Even if this occurs, we likely still have sufficient power to detect a significant difference between study arms. Fourth, prior 
research has shown that assessment of adherence—an integral element for achieving viral suppression—in this population 
is extremely difficult. We intend to use multiple methods to assess adherence including pill counts, pharmacy logs, and a 
standardized and validated pediatric assessment tool for children on ART in resource-limited settings.75 However, none of 
these measures are 100% accurate. While measurement of drug concentrations, for example, adds accuracy to adherence 
assessments, the complexity of measuring timed blood collections, performing testing out of country, and inability to return 
results real-time to inform clinical management, makes its use less feasible and meaningful. Electronic pill monitoring 
devices may not reflect actual adherence or may be an intervention in of itself in promoting adherence. Ultimately, VL 
testing, in addition to targeted DRM testing, is arguably the most sensitive measure of adherence. Fifth, we have found 
some caregivers and children find blood draws difficult and may be, therefore, less willing to participate in this study. We 
plan to support demand creation for VL testing through health talks provided by research staff in the waiting areas to provide 
education on the importance of VL testing and address concerns for blood draws. Finally, there are few children newly 
initiating ART at study facilities, thus we will not have a sufficient sample to describe primarily transmitted DRMs nor will it 
be meaningful to block randomize the expected 24 ART-naïve children. However, our data on DRM in this group will certainly 
add to current knowledge and we will address any major imbalance in distribution of ART-naïve children between arms as 
described in our analysis.  
F.   Data Analysis Plan   
Table 5: Effect sizes and power calculations 
Viral suppression rates, 
control (SOC) vs. 
intervention (POC) arms 
Difference 
(effect size) 
Power 
65% vs. 70%  
5.0% 
0.22 
65% vs. 72.5%  
7.5% 
0.45 
65% vs. 75%  
10% 
0.71 
65% vs. 76% 
11% 
0.80 
65% vs. 77.5%  
12.5% 
0.89 
65% vs. 80%  
15% 
0.98 
70% vs. 80% 
10% 
0.76 
70% vs. 82.5%  
12.5% 
0.93 
70% vs. 85%  
15% 
0.99 
Sample size and power: Power for the study is based on the primary outcome of viral suppression rates in children in the 
intervention vs. control arms at 12 months after enrollment for each child (Aim 1). With approximately 700 eligible 
participants at the study sites, 90% enrolling in our study, and 10% loss to follow-up over 12 months, we anticipate having 
a total of 567 children (or 284 per arm) with outcomes for analysis. With this number, we estimate power of !H#7$1&$5)1)%1$
0$5(88).)/%)$&8$""7$&.$9(39).$M)1:))/$19)$(/1).B)/1(&/$0/5$%&/1.&2$0.,*$OTable 5). These calculations are based on Fisher’s 
exact test, two-sided α=0.05, and initial viral suppression rates of approximately 65% (estimated from historical FACES 
facility data, stubbornly stable since 2014).  
Data analysis and interpretation: 
Study Aim 1: For Aim 1a, we will collect individual-level data on viral suppression up to 24 months, time to viral suppression, 
and process outcomes, such as uptake of VL testing. We note that viral suppression is assessed on different schedules for 
the intervention arm (quarterly) versus control arm (6-monthly unless unsuppressed and then quarterly until suppression). 
Although testing frequency differs by arm, note that both arms include viral suppression testing at 12 months, which is our 
primary outcome. In addition, both arms test quarterly in those not currently suppressed, i.e., the group for analysis of our 
secondary outcome (VL suppression at 24 months and time to viral suppression). For both outcomes, the assessment plan 
is similar in spite of the overall difference in testing frequency by arm. We will provide descriptive statistics by randomization 
arm for study population baseline demographics and facility characteristics, viral suppression rates at baseline and quarterly 
afterwards, and process outcomes of interest, such as uptake of VL testing, turn-around time for the VL testing results, 
retention-in-care, and proportion of children switching ART. Analyses of the intervention will be intent-to-treat (ITT), meaning 
that all participants will be analyzed as randomly assigned, regardless of any issues in participant receipt of the intervention, 
crossovers, or any other post-randomization information. 
Our primary analysis in this randomized study will compare the proportion of children with viral suppression at 12 
months after enrollment for each child (primary outcome) in the intervention vs. control arms (primary predictor) using a 
logistic regression, adjusting for facility and age group strata (1-9 or 10-14 years). Because VL suppression in young 

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adolescents may differ from younger children due to unique adherence challenges, they may have a quite different response 
to our intervention. Therefore, a key secondary analysis will stratify by age group in the sense that we will estimate the effect 
of the intervention separately by age group, using an interaction term between age group and intervention group in the 
model. While the study is not powered for the intervention effect within age subgroups, particularly in the subgroup of 10-
14 year old, this analysis will provide data for the intervention effect within each, as well as for the difference in effect 
between the younger and older children. Our randomized study design should protect us from bias between intervention 
and control groups, but should we find that strong predictors of viral suppression vary between these groups at baseline, 
we will also perform the analysis adjusting for any such differing characteristics. Additional analyses including multivariate 
logistic regression models will estimate associations between viral suppression and all potentially related individual-level 
factors, such as age, sex, duration on ART, prior viral suppression patterns, and family demographics, as well as facility, in 
order to explore predictors of viral suppression in this context. We will also do a secondary analysis separating the outlined 
outcomes for children on 1st vs. 2nd line therapy.  
For our secondary analysis, we will compare the proportion with viral suppression at 24 months after enrollment 
and follow the analytical plan detailed above for the primary outcome. We will also compare time to viral suppression using 
a Cox regression model limited to individuals who were not already suppressed at 1st POC VL testing or those initiating 
ART. The primary predictor will be intervention vs. control arm, with stratified baseline hazards by site, adjusting for other 
predictors that could confound this relationship, such as the ones listed above. As noted above, although the VL testing 
schedule is generally different in intervention vs. control arms, the frequency of VL assessment is not different among those 
on ART but not suppressed; in both intervention and control arms, children without viral suppression at their first VL test 
while in the study are subsequently tested every 3 months until achieving suppression. The exception is for children newly 
initiating ART at study enrollment, the first VL test is at 6 months in the control arm and 3 months in the intervention arm; to 
align the outcome assessment in those newly initiating ART, we will exclude from the analysis the initial 3-month viral load 
in the intervention arm for those participants. In a sensitivity analysis, we will exclude those newly initiating ART altogether 
(estimated to be 24 individuals). 
For Aim 1b, the quantified values will be transformed on a log scale. Then we will correlate log copies when 
detected, <40 copies/mL but detected, or not detected.  We will use Cohen’s kappa statistic, generating 95% confidence 
intervals, to determine the agreement in concordance and discordance of the POC VL testing against the gold-standard 
testing.  We expect that as the quantity of viral copies/mL detected increases, so will the concordance rates between the 
two platforms.  We will also assess the agreement between the two platforms using a Bland-Altman plot.  For the discordant 
results, we will compare descriptively the results found in each platform.    
 
Study Aim 2: To estimate (Aim 2a) the effect of providing timely DRM results on viral suppression for intervention 
participants undergoing DRM, separately from the effect of POC alone, a secondary analysis will be performed with outcome 
of time to viral suppression. As in Aim 1, we will use a Cox model with primary predictor of intervention vs. control arm. To 
distinguish the effect of DRM results from that of POC VL testing, we will add a time-varying covariate which indicates, for 
each visit with a VL test, whether the clinician was notified of a positive DRM result since the child’s prior VL test. If 
implemented as expected, all children without viral suppression will be provided DRM testing and providers notified of the 
results. This model will allow us to divide the estimated effect of the intervention between the effect of POC VL testing alone 
(on those who do not have DRM), and the effect of POC VL testing plus DRM testing for those who do undergo DRM testing. 
We will perform these Cox models both unadjusted and adjusted, accounting for other factors likely to affect viral 
suppression, particularly those that may vary at baseline in the intervention vs. control arms. 
We will also describe the proportion of samples within the intervention arm that have any targeted DRMs by HIV 
drug classes, e.g. NRTIs, NNRTIs, and PIs. We will report the proportion of samples with each type of mutation detected 
by drug class, and further group these mutations into major and minor ones. For example, we will examine major mutations 
in M184V/I and K65R for NRTIs, K103N, Y181C, G190A, and V106M for NNRTIs, and V82A, I76V, 184V, L47A, L90M, 
M46I, and D30N for PIs; one or more of the NRTI and NNRTIs mutations alone accounted for 99% of the mutations detected 
among adults on 1st line ART from resource-limited settings with similar findings in small studies conducted with children, 
though the rates of PI-based mutations are increasing with more children on PI-based regimens.37 With 284 participants 
randomized to the intervention arm, we expect approx. 35% (100) to not achieve viral suppression at their 1st POC VL test 
and undergo DRM testing. With n=100, we will be able to estimate prevalence of DRMs to within +/- 5% to 10% of the 95% 
CIs. For example, for a class of DRMs with prevalence of 80%, we anticipate generating exact 95% CIs of 71.3%-87.0%; a 
less common DRM at 10% will generate 95% CIs of 5.1%-17.1%.   
 
We will use logistic regression models to identify risk factors associated with major and any DRMs. Potential risk 
factors of interest include individual-level (age, sex, duration on ART, prior antiretroviral exposure, prior viral suppression 
patterns, WHO clinical stage), clinical parameters (such as weight- or height-for-age), immunological status, family-level, 
HIV status of caregivers, socioeconomic status, and facility-level factors, such as volume of patients and urban/rural 
location. Covariates with p<0.20 in univariate analyses will be included in the multivariate models. We will also consider 
including interaction terms for any suspected effect modifiers such as age group of the child. The above analytic approaches 
will be taken for analyzing data from the NGS genotyping and phenotyping; in addition to descriptively generating the 

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patterns and distribution of NGS-detected DRM, including for minority variants and for mutations relevant to DTG, we will 
also compare them to the results obtained via CS when concurrent results are available. 
To evaluate Aim 2b, CS and OLA simple-based DRM testing (Aim 4): To evaluate the test performance of OLA 
Simple (Aim 4a), we will compare test results for each codon from the OLA Simple test against a composite “gold standard” 
constructed from CS and Illumina sequencing (the latter performed on samples that are negative for DRMs by CS), stratified 
by retro- or prospective testing.  We will calculate sensitivity, specificity, and positive and negative predictive values for the 
detection of DRMs per codon, organized by drug classes, for the OLA Simple.  The above are the fundamental metrics the 
WHO, for example, considers in its prequalification application for in-vitro diagnostic and laboratory technologies 
approvals;40 achieving sensitivity and specificity >90% would demonstrate high test performance. 
 
Study Aim 3: For Aim 3a, the interviewers themselves will transcribe the first few audio files from the interviews in the 
language that the interview occurred in. And if it is different from English, then translate them to English.  A second study 
staff will verify the English translations against both the non-English transcripts and the audio files.  Once we are confident 
of the accuracy of our transcription and translation, the interviewers will directly transcribe the non-English interviews into 
English.  The English transcripts will be imported into Nvivo 10.1 for coding.  Serial interviews for the same person will be 
coded together to maintain context for that person.  We will develop a codebook documenting codes, definitions, guidelines 
on their use, and example quotes. 2-3 study staff will independently code the transcripts, including initial double coding.  
The initial, primary codes will be developed from the interview guides and expanded into more detailed, secondary codes 
during the coding process.  After the initial round of coding, the researchers will meet to discuss their coding process, assess 
intercoder agreement, and resolve discrepancies through consensus.  Once the coding is complete, for the first question 
guide, we will use framework analysis to organize the data for further analysis, prioritizing longitudinal over cross-sectional 
analysis.76,77  Finally, analytic memos will be written to lift the primary and secondary codes into thematic analyses that 
represent a full range of perspectives, both convergent and divergent.  Several measures will be taken to ensure high quality 
data and rigorous analysis, such as principles of reflexivity78 and rigor.79-81 For the second question guide, we will use 
thematic analysis using a deductive approach based on Nielsen’s framework (Table 6). Special attention will be paid to 
identify domains of learnability, efficiency, memorability, error recovery, and satisfaction that could be improved in future 
iterations of the Excel-based model. For the FGD guide, thematic analysis using a deductive approach based on the 
concepts of acceptability, feasibility, and scalability of tools, as well as modifications, support, enhancement, or interference 
with process.  
 
 
For Aim 3b, the micro-costing data, time and motion studies, and clinical outcomes will be used to estimate the 
average cost per HIV-positive child achieving VL suppression and retained in care in the intervention compared to the 
control arm. The mathematical modeling will project clinical outcomes and estimate the cost-effectiveness of the 
intervention. These data are key to informing decision makers considering implementation of HIV adherence interventions 
for children.  We expect to demonstrate that the intervention is cost-effective for improving VL suppression and retention in 
care. Queuing model parameterization and analysis: Finally, we will use our model to conduct analysis for a range of 
scenarios. The base case scenario of the model will be built to reflect 100 facilities in Kisumu, Kenya, using a fully SOC VL 
processing system (Figure 7). Several additional model scenarios will be run to represent: 1) a hub-and-spoke model, in 
which samples are sent from neighboring facilities (spokes) to a facility equipped with a stationary POC VL machine (hubs); 
and 2) a platform sharing model, in which a single POC machine is transported from one facility to the next on a regular 
basis, rather than transporting the samples in the hub-and-spoke model. Within the hub-and-spoke scenarios, we will model 
a range of combinations of SOC and already GeneXpert® POC-equipped sites (hubs), ranging from 2-30% of facilities 
having a dedicated POC machine. We will further modify the number of spoke sites. Within the platform sharing scenarios, 
we will model a range of combinations of the number of sites within a sharing network, duration of time that a machine stays 
stationary, and geographic distribution of networks. We will also include a combination model of both hub-and-spoke and 
platform sharing, pending our initial results. We will run the full queuing models with a range of constraints that reflect the 
public health priorities of Ministries of Health in resource-limited settings, including differing budgets available to purchase 
different POC machines and different turnaround times acceptable for VL sample return.  Use of data: This model will yield 
direct, practical suggestions for POC machine placement at different budgetary levels in Kisumu, Kenya and may be easily 
adapted for other regions within Kenya. The model will also be modifiable for future use in other African regions and settings. 
The goal of this Excel-based model is to provide a user-friendly tool to Ministry of Health policymakers at the county or 
regional level to assist in resource allocation to optimize chronic care monitoring tests for populations.  
G.  Summarize Knowledge to be Gained   
The findings from this pilot study will inform programmatic scale-up of POC VL and DRM testing in Kenya and elsewhere. 
Additionally, our findings will provide crucial HIV drug resistance information for children on ART, which is currently lacking 
and very likely to undermine current management approaches for children with virologic failure. Specifically, data collected 
from this study will help optimize future POC VL and DRM testing strategies. This work will directly inform national policy 
approaches to ART regimen choices for children in Kenya. Children living with HIV have continually lagged adults regarding 

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HIV treatment access and health outcomes including viral suppression; our study strives to contribute to the evidence-base 
that will close this gap.  
 
HUMAN SUBJECTS PROTECTIONS 
 
Ethical approval for this study will be obtained from all relevant institutional review boards before initiating any of the study 
data collection activities.  In the US, UW and UCD’s Institutional Review Boards will review this protocol. UCSF already 
has ethical approval from both UCSF and CDC for routine data collection and evaluation at FACES sites in Kenya that will 
be facilitated through UCSF.  In Kenya, the protocols will be reviewed by AMREF IRB.  We will ensure that all procedures 
conform to US, Kenyan, and international ethical standards regarding research involving human subjects.  Future studies 
or analyses conducted with the data collected in this study will undergo separate ethical reviews. 
 
The proposed research will be conducted in collaboration with Family AIDS Care and Education Services (FACES), a 
CDC/PEPFAR-funded HIV prevention, care, and treatment program operating in western Kenya since 2005, which 
already has long-standing positive relationships with the local Ministry of Health (MOH) and the study communities. Our 
study design takes advantage of the extensive existing infrastructure of FACES and current laboratory infrastructure. 
Additionally, the feasibility, acceptability and potential for scale up of the proposed interventions were carefully considered 
with MOH and FACES investigators. The proposed research builds on our findings and our experience protecting human 
subjects in the proposed study regions gained during the implementation and operational research by FACES such as the 
Rapid Results Initiative for PMTCT as well as the MOTIVATE Study (Mother and Infant Visit Adherence and Treatment 
Engagement Study) for which Dr. Abuogi is a multiple-PI (NICHD 1R01HD080477-01). 
 
Risks to the Subjects 
Human subjects involvement, characteristics, and design 
Our proposed study design is an open-label randomized, controlled study piloting the use of POC VL testing and targeted 
DRM testing among HIV-positive children on ART age 1-14 years over a 12-month period in high-volume HIV treatment 
facilities in Kisumu, Kenya. At each facility, eligible children will be randomized 1:1 to either receive the intervention 
testing, consisting of quarterly POC VL testing and targeted DRM testing, or standard-of-care (SOC) testing based on the 
existing Kenyan national guidelines. We will follow the viral outcomes up to 24 months after enrollment for each child and 
compare VL suppression rates, defined as VL <1000 copies/mL by the Kenyan national guidelines, among intervention 
vs. control arms. 
 
Aim 1 of this study aims to determine the impact of point of care (POC) VL on the proportion of children achieving viral 
suppression and time to viral suppression for those who are either initiating ART or not suppressed. 
 
The target population for aim 1 of the study includes approximately 700 HIV-infected children and young adolescents 
aged 1-14 years on ART at one of the intervention sites supported by FACES. Children who are outside of this age range 
will be excluded. These children will be randomized 1:1 to intervention vs. control arms.  Therefore, approximately 350 
children will receive the intervention (POC VL and DRM testing) and another 350 will receive the current standard of care 
as recommended by the Kenyan Ministry of Health, including routine viral load monitoring through centralized laboratory 
testing and DRM testing only for those failing 2nd line ART.  
 
We conservatively estimate that around 90% of HIV infected children and adolescents and their families will agree to 
participate in the intervention portion of the study. Our sample size takes into account the potential for as much as 10% 
attrition due to loss to follow up, transfers out, or death based on current retention rates within the FACES program for 
children. Therefore, even if we experience as much as 10% attrition, we will still have a sufficient number of participants to 
evaluate the impact of the interventions. 
 
Aim 2 will explore the impact of targeted HIV drug resistance mutation (DRM) testing and patterns of DRM among HIV-
infected children on ART without viral suppression. 
 
In this aim we will determine the impact of DRM testing of children with viral load > 1000 copies/ml among the 350 
children 1-14 years on ART randomized to the intervention arm only. Based on current viral suppression rates in children 
at FACES sites, we anticipate at least 35% of children will have one or more POC VL results of > 1000 copies/ml during 
the first 12 months of study follow up. This translates to around 100 DRM tests which allows us to generate point 
estimates with reasonable confidence intervals to assess our outcomes for this pilot study. 

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Aim 3 of this study aims to better understand how our POC VL intervention functions using qualitative research methods 
and estimate the costs and time and feasibility of implementing pediatric POC VL testing and DRM monitoring at scale. 
 
 
Sources of materials  
Data for Aims 1&2 will be collected in the following manner: 
As part of standard care at participating health facilities, a standardized Ministry of Health routine HIV encounter form is 
filled for each child at every clinic visit. This form will provide baseline demographic and clinical data on participants at 
intervention and non-intervention sites. Additionally, clinic registers which document laboratory testing and results, patient 
visits, and pharmacy refills will also be utilized. A standardized, electronic data collection tool will be used to capture 
supplemental study information for both intervention and control participants. Data will be abstracted from by trained 
research data clerks using direct data entry via tablets into REDCap. REDCap meets all required standards for protection 
of personal health information. Data collected from study activities, namely process and outcome data on POC VL and DRM 
testing, will also be captured into the REDCap-supported data collection forms. 
Finally, because currently VL results from routine VL testing are erratically recorded on the routine HIV encounter form, and 
therefore not always available in patient charts or electronic medical records, we will supplement VL outcome information 
from the National AIDS and STI Control Programme (NASCOP) online VL database and facility laboratory VL log books.  
Data for Aim 3: For Aim 3a, the interviewers will audio record the KIIs or FGDs with a digital recorder and take field notes. 
Later the audio files will be transcribed from the interviews in the language that the interviewer occurred in, and if different 
from English, then translate them to English. Quantitative surveys assessing learnability, efficiency, memorability, error 
recovery, and satisfaction will be captured either on paper or electronically and stored on REDCap. For Aim 3b, we will use 
standardized cost and time menus to collect site costs and times, including start-up costs, clinic space, human resources, 
supplies, VL test costs, and timing of client arrival at a facility and VL sample collection, processing, and return. When data 
are not available from our cohort, we will utilize data from population-based studies from Kenya and sub-Saharan Africa. 
Additional cost data may be obtained from health facilities, published government information on labor costs, and health 
economics literature. 
 
 
Of note, prior to shipment of any samples outside of Kenya, permission from appropriate authorities and agencies will be 
pursued as is standard in such cases. 
Potential Risks 
Potential risks to participants in this study include social risks involved if information about participants or their family 
members, specifically HIV status, or other personal details were to be disclosed outside of the research setting. In particular, 
children and caregivers might face serious social risks (disruption of family, discrimination, and/or physical harm) if their HIV 
status were to be disclosed without their consent. These risks could be posed if a) clinic staff do not maintain confidentiality, 
or b) laboratory specimens are not handled in a manner that maintains confidentiality. The proposed study approach 
considers these potential risks. Clinic staff including service providers, laboratory technicians and phlebotomists, are already 
trained on maintaining privacy and confidentiality for all patients and this will be reinforced during clinic-wide trainings pre-
study initiation.  
 
Neither POC viral load testing nor DRM testing are anticipated to pose significant additional risks to study participants 
outside of the risks they are exposed to regularly as part of routine HIV care. Phlebotomy required for POC VL testing and 
DRM testing is also required for routine lab and viral load monitoring already a part of HIV care in Kenya. However, this will 
be more frequent than routine lab monitoring. Participants may experience some additional pain and anxiety due to this. 
This risk will be minimized by utilizing trained study nurses with experience collecting blood in children. Children undergoing 
to POC VL and DRM testing may switch to 2nd or 3rd line more frequently than those at non-intervention sites. This may 
present a risk if they are not adherent to 2nd or 3rd line treatment as 3rd line and salvage regimen options are limited in Kenya. 
However, more frequent VL testing may help to identify these children sooner and intervene such that they are able to re-
suppress on their 2nd line regimen. DRM will also inform if 3rd line treatment options are needed which will facilitate more 
rapid transition to 3rd line when needed.  
 

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The use of ART in children within this study will follow Kenya National ART guidelines. ART risks include potential medication 
side effects, induced viral resistance from poor adherence or long-term exposure to ART limiting future drug options, and 
the burden of initiating lifelong treatment at an early stage of disease. However, Kenyan ART guidelines have clear 
recommendations on side effect monitoring and management and our study is intended to reduce the potential for drug 
resistance. It is important to note that this study is not intended to evaluate the safety and efficacy of ART for children in 
Kenya but rather to explore interventions that will maximize virologic suppression and therefore health outcomes for children 
with HIV.   
 
Adequacy of protection against risks 
Recruitment and informed consent  
Aims 1&2:  Participants will be recruited at the study facilities during routine clinic visits. Caregivers accompanying HIV 
infected children 1-14 years of age receiving or initiating ART will be approached and invited to participate. Recruitment 
may occur as part of pre-clinic information sessions provided by trained study staff (e.g. study nurses), by clinic staff during 
care, or by study staff before or after routine visits. We will offer study site providers a small incentive for referring patients 
who are eligible and enroll in the study. There is currently precedence among other local studies to offer a small incentive 
for successful study referrals. Site providers have been educated on enrollment eligibility and will be directed to not coerce 
or otherwise influence study enrollment.  If the caregiver expresses interest in participating in the study, they will be invited 
into a private room to conduct the informed consent process where study staff will confirm if the caregiver is a parent or 
legal guardian. Informed consent will be obtained from at least one parent, or from the primary caregiver if both parents are 
deceased. Informed consent will be obtained by a study staff, namely study nurses, in the local language preferred by the 
caregiver. Caregivers and children over 13 years of age will be invited to participate and will be given clear explanations 
that this is something separate from their child’s regular care and that they have the option of refusing to participate in any 
part of the research without any effect on their or their child’s routine care received at the facility.     
 
As part of this informed consent process, caregivers will be informed of the purpose and methods of the study, interventions, 
procedures to protect the confidentiality of the information, their rights to withdraw from the study at any time, randomization 
procedures, the fact that their participation or non-participation will not affect the medical care/performance evaluation that 
they or their child receives at the study health facilities, and persons to contact if they have any questions about the study.  
They will be asked to sign an informed consent form, or if illiterate, to provide a thumbprint in the presence of a non-study 
staff witness (the standard practice used in our other Kenyan studies, as approved by the Kenyan IRB). Caregivers will 
receive a small transport and time reimbursement at enrollment of ~$5 USD. This is similar to enrollment incentives being 
offered locally by other research studies. We do not feel this enrollment reimbursement will be coercive or unduly influence 
caregivers to enroll as it is in line with other research being conducted in the same setting.  Transport reimbursement of ~$5 
USD will also be offered at the 12-month, and possibly 24-month, visits to incentivize return for collection of endpoint VL 
samples.  The same transport reimbursement will also be offered during the observational phase of the project after a 12-
month study visit for every 6-month visit up to 5 years, since blood collection may be required out of routine care visit or 
phlebotomy for our study monitoring of DRM. 
 
Children age 13 and older will be given the opportunity to provide assent for participation in the study. They will be provided 
information at an age appropriate level and given the opportunity to assent or decline separate from their caregiver.  
 
For those children who are not aware of their HIV positive status, which will be ascertained by asking their caregivers first, 
we will provide a modified assent form which removes any mention of HIV or HIV-related terms.  All study participants will 
be informed that they will be followed up for a minimum of 12 months and up to 24 months.  
 
Families who do not wish to participate, will continue to receive standard HIV services within the facility.  
 
Aims 3: We will use purposeful sampling to identify key informants for each subgroup of key informants (Table 3).  We will 
purposefully sample Groups 1 and 2 from both the control and intervention arms, ensuring we are capturing a diversity of 
key informant characteristics such as caretakers of younger vs. older children, children with and without viral suppression, 
male vs. female informants, etc.  Study staff will recruit these participants from our enrolled study sample.  For Group 3, we 
will again use purposeful sampling to interview providers and other facility staff, such as pharmacists, medical supervisors, 
nurses in charge, etc., with the goal of ensuring we capture a breath of professionals within the health facilities.  Study staff, 
including our study coordinator, will recruit these participants from the facilities where we are conducting our study.  For 
Group 4, we will use a combination of snowball and purposeful sampling, relying on our community partners and those 
attending our community stakeholder meetings to help us generate our initial pool of key informants.  Once a person agrees 
to interview, we will also ask that person to help us identify another 2-3 persons to consider for interviews.  For the focus 

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groups, we will use purposeful sampling to identify participants from national and county levels. We will ensure we capture 
a diversity in perspectives including local county and national policy makers, NGO staff, and academics working in this field 
and country.  Study staff, including the lead investigators, will recruit these participants from known community partners and 
stakeholders. 
   
 
Protection against risk 
In order to minimize social risks to participants in this study, all study and participating facility staff will be thoroughly trained 
on procedures for maintaining confidentiality and will be asked to sign a pledge of confidentiality. All potential study 
participants will benefit from the facility- based training for staff on pediatric treatment failure identification and management. 
 
All consent and will be kept in locked cabinets, initially at the health facility, and later be transferred for secure storage at 
the central study office in Kisumu. All personal identifiers from study data will be in electronic format only in password 
protected devices and files. When these procedures are followed, it is highly unlikely that any participant information will be 
disclosed to anyone outside the research team.   
 
Given that some caregivers/young adolescents may not be literate, all explanations will be made orally in a language well 
understood by the participant.  The caregivers/adolescents will also be given a copy of the study informed consent/assent 
form written in the local language to keep if he/she wishes.  This page will include names and phone numbers of persons 
to contact with any questions regarding the study. The informed consent forms will be available in Dholuo, Swahili, and 
English.  
 
Training on VL interpretation and treatment failure recognition and management in children will be conducted at all study 
facilities prior to the launch of the intervention. This training will also include additional training on interpretation of DRM 
results and when to switch to second line. Expert pediatric HIV consultation will be available for complex cases provided by 
the multiple-PIs and Scientific Advisory Committee members, including Dr. Betsy McFarland, a pediatric HIV expert at the 
University of Colorado, in collaboration with local experts. Ongoing support for health care providers will be available through 
the study staff, FACES technical support teams and the Ministry of Health. 
 
Importantly, this research will be conducted in collaboration with health facilities where the FACES program already provides 
comprehensive HIV education, counseling, and services.  Thus, it will be possible for the study staff to provide immediate 
referrals to FACES staff for research participants who are in need of help with medical or social issues regarding HIV, 
including issues related to disclosure of HIV status and non-HIV related health problems that may be identified during the 
course of the study.  
 
Children will be receiving the standard of care at all sites based on current Kenyan national ART guidelines. All study sites 
are already providing ART to children living with HIV. All sites have the necessary clinical, laboratory, and psychosocial 
support to provide ART to children. FACES technical support is available to all sites and includes consultation on HIV care 
and treatment, treatment failure, laboratory networking, psychosocial support, and routine support supervision visits.  
 
COVID-19 precautionary measures 
We are adapting study procedures in compliance with COVID-19 precautionary measures recommended by the Country 
and County governments. We have developed standard operating procedures in-line with current guidance to minimize risk 
of COVID-19 infection and transmission among participants and research staff. In order to minimize the risk of COVID-19, 
we will administer most of the questionnaires and conduct interviews through virtual means (by phone, WhatsApp etc.). In 
case the participants access the health facility for routine follow up or specific study procedures, facility screening 
procedures for COVID-19 will be repeated by phone by research staff to identify participants with symptoms or recent 
exposure to COVID-19. If no symptoms or recent COVID-19 exposure, then we shall ensure that both the interviewer and 
participant have their masks on, observed hand hygiene, have their temperature assessed and the requirement 1 meter – 
physical distance to carry out study activities. Of note, collection of POC VL samples does not allow for physical distancing 

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but close contact will be minimized, and above precautions taken. Study participants may benefit from POC VL testing at 
all study time points to achieve viral suppression.  
Potential benefits of the proposed research to the subjects and others 
Through their participation in the interventions to be tested (Aims 1&2), intervention participants will potentially benefit from 
intervention activities designed to achieve viral load suppression. If the interventions are found to be effective, there is the 
potential to scale up this intervention to include all FACES-supported health facilities in Kisumu, Kenya and for further scale 
up through our collaboration with CDC and the Kenyan Ministry of Health. Participants in the control are likely to benefit 
from the increased training and additional awareness of facility providers on VL testing and management of treatment failure 
in children. In addition, the research findings have the potential to benefit other Kenyan and sub-Saharan African HIV 
infected children in the future.  
  
Adverse event reporting   
All serious adverse events associated with the procedures of this study will be reported in the specified time-frame to the 
appropriate Institutional Review Boards. The primary risk to participants in this study is social harm associated with HIV 
status disclosure. Field staff will be trained to complete descriptions of adverse events that will then be communicated to 
the onsite Study Coordinator immediately, and then sent electronically to both the U.S. co-Principal Investigators and the 
Kenyan co- Investigators within 24 hours.   
 
Data safety monitoring plan 
In addition to the above plans which outline thorough assessment and mitigation of participant risk, the multiple-PIs, Drs. 
Patel and Abuogi, will ensure ongoing routine data safety monitoring to ensure participant safety. We will work with our 
local collaborators, study staff, facility staff, and other key stakeholders to ensure feedback to the PIs regarding potential 
increased risks or adverse events. If any increased risks are identified, the PIs will review and determine the adequacy of 
human subject protections, making recommendations for enhancing these protections if deemed necessary. The study 
team will create detailed procedures to be followed in cases of adverse events involving study participants. The PIs will 
immediately review each adverse or serious adverse event report and will establish criteria for stopping the study if any 
arm of the study is associated with increased severe adverse events. As this is a very low risk study, attempting to 
optimize clinical care delivery through enhancing the laboratory monitoring components of HIV care, a formal Data Safety 
Monitoring Board is not required. We believe the above protections and procedures will minimize any potential risk to 
participants. 
 
 
 
 

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