Innovative Partnership to Target Antimalarial Subsidies in the Retail Sector
Phase: NA
Conditions: Fever, Malaria
Sponsor: Duke University

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Innovative public-private partnership to target subsidized 
antimalarials in the retail sector 
 
 
 
DMID Protocol Number:  
15-0022 
 
 
Sponsored by: 
 
National Institute of Allergy and Infectious Diseases (NIAID) 
 
DMID Funding Mechanism: 
 
 
Principal Investigator: 
                                            Wendy Prudhomme O’Meara 
 
 
 DMID Program officer: 
 
                                                Malla Rao, DrPH, MEng 
 
 
Draft or Version Number:  
1.6 
 
Day Month Year 
23 June 2016 

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Statement of Compliance 
The study will be carried out in accordance with the principles set forth in The Council for 
International Organizations of Medical Sciences (CIOMS) International Ethical Guidelines for 
Biomedical Research Involving Human Subjects.  
 
 
 
 
 
 

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SIGNATURE PAGE 
The signature below constitutes the approval of this protocol and the attachments, and provides 
the necessary assurances that this trial will be conducted according to all stipulations of the 
protocol, including all statements regarding confidentiality, and according to local legal and 
regulatory requirements and applicable US federal regulations and ICH guidelines. 
 
Site Investigator: 
 
Signed: 
 
Date: 
 
 
Name 
Title 
 
 
 
 
 

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Table of Contents 
 
page 
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iii 
Statement of Compliance ............................................................................................................... i 
Signature Page .............................................................................................................................. ii 
List of Abbreviations ..................................................................................................................... iv 
Protocol Summary ..................................................................................................................... …v 
 
1 
Key Roles ............................................................................................................................. 2 
2 
Background Information and Scientific Rationale ................................................................. 4 
2.1 
Background Information ............................................................................................ 4 
2.2 
Scientific Rationale ................................................................................................... 5 
2.3 
Potential Risks and Benefits ..................................................................................... 6 
2.3.1 Potential Risks .............................................................................................. 6 
2.3.2 Known Potential Benefits .............................................................................. 7 
3 
Objectives ............................................................................................................................. 8 
4 
Study Design ........................................................................................................................ 9 
5 
Study Population ................................................................................................................ 13 
5.1 
Selection of the Study Population ........................................................................... 13 
5.2 
Inclusion/Exclusion Criteria ..................................................................................... 13 
6 
STUDY PROCEDURES/EVALUATIONS ........................................................................... 15 
6.1 
Study Procedures ................................................................................................... 15 
6.2 
Laboratory Evaluations ........................................................................................... 19 
6.2.1 Laboratory Evaluations/Assays ................................................................... 19 
6.2.2 Specimen Collection, Preparation, Handling and Shipping ........................ 19 
7 
Statistical Considerations ................................................................................................... 20 
7.1 
Study Outcome Measures ...................................................................................... 20 
7.2 
Sample Size Considerations ................................................................................... 22 
7.3 
Participant Enrollment and Follow-Up .................................................................... 25 
7.4 
Analysis Plan .......................................................................................................... 25 
8 
Subject Confidentiality ........................................................................................................ 27 
8.1 
Future Use of Stored Specimens ............................................................................ 27 
9 
Informed Consent Process ................................................................................................. 28 
9.1 
Informed Consent/Assent Process (in Case of a Minor or Others Unable to 
Consent for Themselves) ........................................................................................ 29 
10 
Literature References ......................................................................................................... 30 
 
 

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APPENDICES  
Note: Files provided separately 
 
Appendix 1: CHV client encounter documents 
A. Client consent  
B. Client assent (For children 8 years or older and younger than 18 years) 
C. CHV encounter form 
D. Voucher form 
  
Appendix 2: Shop and Facility documents 
A. Shops and facilities verbal consent  
B. Shop and facility survey  
C. Terms of Reference and written consent for shop participation in voucher program 
  
Appendix 3: Household survey documents 
A. Household survey verbal consent 
B. Household survey instrument 
 
Appendix 4: CHV evaluation documents 
A. CHV observation checklist 
B. CHV survey instrument 
C. Verbal consent script for CHVs participating in CHV evaluation activities 
D. Verbal consent script for clients participating in CHV evaluation activities (observation) 
 

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List of Abbreviations 
 
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ACT 
AE 
AMFm 
Artemisinin Combination Therapy 
Adverse Event 
Affordable Medicines Facility - malaria 
CFR 
 
CHV 
Code of Federal Regulations 
 
Community Health Volunteer (also often called Community Health 
Worker) 
CIOMS 
Council for International Organizations of Medical Sciences 
CRF 
Case Report Form 
DMID 
Division of Microbiology and Infectious Diseases, NIAID, NIH, DHHS 
DSMB 
Data and Safety Monitoring Board 
FWA 
Federal-Wide Assurance 
GCP 
Good Clinical Practice 
ICF 
Informed Consent Form 
ICH 
International Conference on Harmonisation 
IEC 
Independent or Institutional Ethics Committee 
IRB 
Institutional Review Board 
ISM 
Independent Safety Monitor 
JAMA 
Journal of the American Medical Association 
MOP 
Manual of Procedures 
N 
Number (typically refers to subjects) 
NEJM 
New England Journal of Medicine 
NIAID 
National Institute of Allergy and Infectious Diseases, NIH, DHHS 
NIH 
National Institutes of Health 
OCRA 
Office of Clinical Research Affairs, DMID, NIAID, NIH, DHHS 
OHRP 
Office for Human Research Protections 
ORA 
Office of Regulatory Affairs, DMID, NIAID, NIH, DHHS 

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PI 
RDT 
Principal Investigator 
Rapid Diagnostic Test for Malaria 
SAE 
Serious Adverse Event 
SMC 
Safety Monitoring Committee 
SOP 
Standard Operating Procedure 
WHO 
World Health Organization 

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Protocol Summary 
 
Title: Innovative public-private partnership to target subsidized antimalarials in the retail sector  
 
Population:               This study will take place in Bungoma East and Kiminini sub-county in 
Kenya. Both are high malaria transmission areas but with different 
patterns of access to health services. The study population will be 20 
community units in Bungoma East sub-county and 12 community units in 
Kiminini. Half of the community units in each study area (10 in Bungoma 
East and 6 in Kiminini) will be randomly selected to be included in the 
intervention arm. The remainder of the community units will be the 
comparison arm.  
 
Number of Sites: 
(2) Bungoma East sub-county in Bungoma County, Kiminini sub-county in 
Trans Nzoia County, Kenya 
 
 Study Duration: 
24 months (intervention period 18 months) 
 
  Subject Duration:     Approximately 1 hour for each type of visit (3 possible visit types:            
 
1) CHV visit; 2) Follow-up supervisor visit; 3) cross-sectional survey) 
 
 
Objectives:  
Our overall objective is to evaluate the public health impact of targeted 
antimalarials subsidies through scale-up by determining the 
community-wide effects of targeting an antimalarial subsidy through a 
partnership between Community Health Volunteers (CHVs) and the 
private retail sector. 
 
The primary outcome of this study is to compare the percent of fevers that receive a malaria test 
from any source between the intervention and control arms. 
The secondary outcomes of this study will also be measured and compared between intervention 
and control arms. The main secondary outcome is the percent of all ACTs used that were taken by 
people with a malaria positive test. Additional secondary outcomes are: the percent of all ACTs 
used that were taken by people without a test, the percent of those with a positive test who got an 
ACT, and the percent of those with a negative test who got an ACT.  
 

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1 
KEY ROLES 
Individuals: DMID Representative: 
 Malla Rao, DrPH, M.Eng  
DMID, NIAID 
610 Rockledge Dr, MSC 6604, Room 5095 
Bethesda MD 20892 
301-451-3749 
301-402-0659 
MRao@niaid.nih.gov  
        Principal Investigator: 
Wendy Prudhomme O'Meara, PhD 
Assistant Professor of Medicine and Global Health 
Duke University 
Box 90519  
Trent Hall, Durham, NC 27708 
+254 728 306 521  
wpo@duke.edu 
Institutions: 1. Duke University 
 
Box 90519 Durham, NC 27710 
 
Darlene McCain 
 
PHONE: 919 681 7717 
 
FAX: 919 681 7677 
 
2. Moi University School of Medicine 
 
MTRH OPD Complex 
Nandi Road 
 
PO Box 4046 
 
Eldoret 30100 KENYA 
 
Christine Tonui, Research and Sponsored Projects Office 

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FAX: +254 532061195 
 
PHONE: +254 532033471 ex. 3528 
 
Cchuani@iukenya.org 
 
 
 
 

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2 
BACKGROUND INFORMATION AND SCIENTIFIC 
RATIONALE  
2.1 
Background Information  
In most malaria-endemic countries, a large fraction of fevers are treated in the informal health 
sector where diagnostic testing is uncommon and effective drugs are expensive. For many families, 
particularly in rural areas, the first source of treatment for fevers are retail medicine outlets such as 
chemists, pharmacists and small, unregulated medicine shops (Abuya, et al., 2007; Amin et al., 
2003; Chuma et al., 2009; Malik et al., 2006; Littrell et al., 2011). These retail outlets, also referred 
to as the ‘informal health sector’, are more accessible than formal health services, but effective 
drugs are expensive and most clients purchase cheaper, ineffective therapies to which high levels 
of resistance exist. For patients with malaria, treatment failure is common and contributes to 
increased morbidity and mortality from this treatable disease. Fewer than 15% of fevers treated for 
malaria receive appropriate, effective therapy (World Health Organization, 2012). 
Subsidized antimalarials in the retail sector improve access to effective drugs. The Global Fund 
piloted a drug subsidy called the Affordable Medicines Facility – malaria (AMFm) to reduce the 
prices of effective, high quality ACTs in the private sector. AMFm was launched in 2010 and 
provided quality-assured ACTs to wholesale markets at substantially reduced prices in seven pilot 
countries, including Kenya. $339 million dollars were earmarked for subsidies and 155.8 million 
doses were delivered in the first 18 months of the program (ICF International, 2012).  Prices of 
subsidized ACTs in most pilot countries dropped below that of cheaper, ineffective drugs and 
substantial cost savings were seen by the end consumer. In Kenya, the retail market share of ACTs 
increased from 12% to 61% in the first 18 months of the program (Tougher et al., 2012). However, 
there is concern that dramatically lowering the price of ACTs opened the door to over-treatment and 
overuse of ACTs.  
As the price of drug declines, the demand for it increases amongst both appropriate and 
inappropriate users. Between 36-77% of fevers treated in the retail sector do not have malaria 
(Mangham, et al., 2012; Cohen et al., 2012). A study in Tanzania showed that 80% of ACTs are 
sold to patients without parasitemia and only 69% of parasitemic patients purchased an ACT 
(Briggs, et al., 2012). This mismatch between who needs an ACT and who purchases one 
highlights the importance of improving targeting of antimalarials purchased in the retail sector.  
Very few of the millions of cases treated in the retail sector have a diagnostic test before treatment, 
including clients who purchased one of the 14.4 million courses of AMFm ACTs that were delivered 
to Kenya in 2011. It has been argued that RDTs could effectively be deployed in the retail sector. 
Experimental evidence from a study in western Kenya shows high uptake of free or heavily 
subsidized RDTs, exceptional adherence to the results of a positive test (>98% purchasing a 

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subsidized ACT), but low adherence to the results of a negative test. Sixty-three percent of clients 
with a negative RDT result purchased an ACT (Cohen, et al., 2012).  
In 2012, the Global Fund revised the AMFm strategy and ended the stand-alone subsidy. Countries 
receiving Global Fund support for malaria control can incorporate wholesale drug subsidies into 
their malaria control portfolios, but within their existing budgets. In addition, they can now use 
Global Fund money to provide subsidized malaria rapid diagnostic tests (RDTs) to the private 
sector. Ideally, incorporating diagnosis into a subsidy program would allow subsidized ACTs to be 
targeted to those with confirmed malaria infection, thus reducing unnecessary overuse of ACTs and 
reducing the cost of the subsidy program per patient treated. However, there is little evidence to 
guide policy makers in the implementation of diagnostic testing within a subsidy framework in the 
retail sector. 
In 2014, this team implemented a pilot study to test the use of a voucher to improve uptake of 
testing. We also included testing for free compared to charging a small amount of money for the 
test. Although results of that pilot are still being analyzed, we have shown that 1) uptake of testing is 
improved when offered through CHVs, 2) far fewer clients chose to be tested when asked to pay for 
testing and 3) clients with vouchers were charged a higher base price for drugs. This information 
has informed our study design. Specifically, we will not charge for RDTs and we will offer a voucher 
for a fixed reduced price rather than a discount.   
This study will evaluate the public health impact of targeted antimalarials subsidies through 
scale-up by determining the community-wide effects of targeting an antimalarial subsidy 
through a partnership between CHVs in the public sector and the private retail sector. In the 
intervention arm, CHVs will offer household members free RDTs and a voucher allowing the 
purchase of a qualified ACT at a reduced fixed price in the retail sector conditional on a positive test 
while individuals in the comparison arm will only receive standard community health volunteer 
(CHV) visits. Cross-sectional household surveying at pre-intervention, and 6 months, 12 months, 
and 18 months post-baseline will allow us to determine any change in the percent of fevers that are 
tested for malaria and the effect of testing on subsequent drug purchasing decisions.  
The primary hypothesis to be tested is that offering a fixed-price voucher that reduces the cost 
for ACT purchase in the retail sector conditional on a positive malaria test (targeted subsidy) 
can improve uptake of testing for malaria and will increase the proportion of fevers tested for 
malaria before treatment. 
2.2 
Scientific Rationale  
There are compelling medical and public health reasons to reduce unnecessary consumption of 
antimalarials and strong evidence to support the use of RDTs in malaria case management. 
However, the large numbers of fevers that are treated in the informal health sector go undiagnosed. 
Consumption of first-line antimalarials has increased due to availability of subsidized ACTs in the 
retail sector. Inappropriate consumption is a drain on public funds and jeopardizes the useful 
therapeutic life of ACTs.    

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Community diagnosis of malaria using RDTs by CHVs has been done in several settings in Africa. 
Evidence shows that CHVs can safely and correctly use RDTs with appropriate training and 
supervision (Briggs et al., 2012; Chanda et al. 2011; Cohen et al., 2012; Hamer et al., 2012.  This 
approach has now been adopted by Kenya’s Malaria Control Unit as part of their National Malaria 
Control Plan.  
Using a cluster randomized trial, we will describe the effect of a conditional ACT subsidy for only 
clients with a positive malaria test on the proportion of fevers tested for malaria and consequent 
drug purchasing decisions after testing.  
 
 
2.3 
Potential Risks and Benefits 
2.3.1 Potential Risks 
It should be noted that the RDTs used in this study are the same brand and test 
as those used by the Government of Kenya in public health facilities and in their 
community-based case management for malaria. The Malaria Control Unit has 
embraced the strategy of community-based diagnosis for malaria using RDTs 
and has begun to train CHVs in some areas, although the program remains small 
due to funding constraints. Neither the RDT itself, nor the use of the RDT by 
CHVs are experimental in this study. The RDT itself is not the point at which the 
subject is participating in research.   
The intervention proposed is the use of a conditional voucher for a positive test. 
This voucher is offered to patients with a positive test, but the client is free to 
choose whether or not to use it. Participating in this study involves agreeing to 
receive or not a voucher depending on the test results and allowing us to record 
information about treatment seeking behavior following a test. There is a small 
risk of breach of confidentiality of these information.  

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While unlikely, there is a small potential for excess bleeding or infection 
associated with finger pricks conducted in the course of administering an RDT. 
Proper training of the CHVs according to strict protocols will further minimize 
these risks. Study participants will be advised to contact the CHV in the case of 
any adverse events which may occur after the visit; CHVs will be trained in the 
recognition of and response to the unlikely occasion of any adverse events. 
These risks are equivalent to the risk of seeking the same test from a facility or 
CHV outside of the study.  
2.3.2 Known Potential Benefits 
There is significant benefit to the client to know their malaria infection status prior 
to purchasing a drug. There is also a benefit to the client to be able to purchase 
an effective drug at a reduced, fixed price when they have a confirmed malaria 
infection, which may also reduce the likelihood that they would purchase an 
inappropriate or outdated therapy.  
More broadly, there are important future benefits to rigorous testing of subsidy 
schemes that promote testing before treatment. This work will contribute to 
evidence-based policy making, improved access to malaria diagnosis and 
ultimately reduced potential for spread of antimalarial resistance.  
 
  
 

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3 
OBJECTIVES 
The overall objective of this study is to evaluate the public health impact of targeted antimalarial 
subsidies through scale-up by determining the community-wide effects of targeting an 
antimalarial subsidy through a partnership between CHVs and the private retail sector. We 
will use a cluster-randomized design using established community health volunteers (CHVs) in both 
areas. The conditional subsidy will be offered in the form of a voucher providing for the purchase of 
a WHO-qualified ACT at a reduced, fixed price to those with a positive malaria test that can be 
redeemed at a local drug retailer. 
 
The primary outcome of this study is to compare the percent of fevers that receive a malaria test 
from any source between the intervention and control arms. 
The secondary outcomes of this study will also be measured and compared between intervention 
and control arms. The main secondary outcome is the percent of all ACTs used that were taken by 
people with a malaria positive test. Additional secondary outcomes are: the percent of all ACTs 
used that were taken by people without a test, the percent of those with a positive test who got an 
ACT, and the percent of those with a negative test who got an ACT.  
 
 

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4 
STUDY DESIGN 
We will use a cluster-randomized design to assign community units to either an intervention or 
control arm. The study will be carried out in two sub-counties in Kenya with similar malaria burden 
but different access to health services. Community Units (CUs) in each sub-county are the clusters 
to be randomized. A community unit consists of 1000 households (approximately 5000 people) 10 
CHVs and One Community Health Extension Worker (CHEW). Each CHV is responsible for 100 
households while the CHEW supervises the 10 CHVs. There are 32 CUs in total across both sub-
counties, 20 in Bungoma East  (10 in Ndivisi and 8 in Bokoli) and 14 in Kiminini. Four of the CUs in 
Bungoma East and 4 of the CUs in Kiminini have health facilities. To ensure that the randomization 
is balanced with regard to the presence of health facilities, we use this as a strata in our 
randomization. Half of the community units in each study area within each strata (9 in Bungoma 
East sub-county and 7 in Kiminini) will be randomly allocated to the intervention and the remainder 
of the community units to the comparison arm (refer to Table 1). We will screen  2884 subjects for 
eligibility in each arm at each of 4 outcome assessment surveys (refer to Section 7.2 for calculation 
of sample size, refer to Section 6.1 for study procedures, including data collection) and we expect to 
enroll 640 assuming a fever prevalence of 22.21% (see Section 7.2 for calculation of expected 
prevalence) of households have had an individual with fever in the last one month. We expect 
minimal contamination between the intervention and the control arm since CHVs are organized by 
geographic location and are responsible for specific households. 
Table 1 
  
Health 
Facility? 
Number of Community Units (CUs) 
Number of 
subjects with 
fever in each 
of the 4 
cross-
sectional 
surveys1 
 
Number of 
subjects 
screened in 
each of the 4 
cross-
sectional 
surveys2 
 
Group 
  
Bungoma East 
Kiminini 
Row Total 
  
  
  
Ndivisi 
Bokoli 
  
  
Intervention 
arm 
YES 
2 
0 
2 
4 
160 
721 
NO 
3 
4 
5 
12 
480 
2162 
Comparison 
arm 
YES 
2 
0 
2 
4 
160 
721 
NO 
3 
4 
5 
12 
480 
2162 
Total number   
10 
8 
14 
32 
1280 
5766 
1Number of CUs x 40 individuals per CU 
2Number of subjects with fever / 22.21% prevalence 

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CHVs will be trained to use RDTs to diagnose malaria in household members with documented or 
reported fever. Households in intervention CUs will be informed of the intervention and encouraged 
to contact the CHV for any febrile illness in the home. Household members with a negative RDT will 
be given a referral note documenting the results of the RDT and the date it was performed in the 
event they choose to go to a health facility to seek diagnosis and treatment for a different cause.  
Intervention: Households with a positive RDT will be given a serialized voucher. The voucher will 
entitle the holder to purchase a quality assured ACT in the retail sector at a reduced, fixed price. 
The holder will redeem the voucher at any participating retailer by presenting both the voucher and 
the positive RDT provided by the CHV in a sealed plastic pouch.  
All retail shops that serve customers in the intervention clusters will be identified through a 
comprehensive census. Shop owners and shop attendants will be invited to participate in the 
intervention. When a community member presents a voucher for redemption, participating outlets 
will collect both the voucher as well as the used positive RDT. Both will be given to the study team 
in return for payment on the value of the voucher. 
The CHV will attempt to follow up on all clients receiving an RDT 4 days after testing to record what 
action was taken and what treatment, if any, was obtained. The CHV will provide further advice or 
referral depending on persisting symptoms. 
Comparison Arm: Community health volunteers will conduct standard health education and health 
promotion activities. ACTs are available in the retail sector at unsubsidized and government 
subsidized prices. 
Study outcomes and additional data will be collected via population-based cross-sectional 
household surveys in all of the 32 CUs at four regularly spaced time points: baseline, 6 months, 
12 months, and 18 months post-baseline. We will use independent survey teams blinded to the 
assignment of arms to collect household data in a random sample of homes. The households 
will be selected randomly using a systematic sampling approach and selection will be 
independent of whether they received service from a CHV. Only one fever case from each 
household will be included. Data obtained from these population-based surveys will be used to 
evaluate the impact of the community intervention. 
The overall intervention strategy is summarized in Figure 1 below.  
The primary outcome of this study is to compare the percent of fevers that receive a malaria test 
from any source between the intervention and control arms. Testing is the gateway to targeting and 
if we cannot improve the proportion tested, then the voucher subsidy program will not have any 
effect. The main secondary outcome is the percent of all ACTs used that were taken by people with 
a malaria positive test. Additional secondary outcomes are: the percent of all ACTs used that were 
taken by people without a test, the percent of those with a positive test who got an ACT, and the 
percent of those with a negative test who got an ACT. Completion of the treatment course of ACT 
may be influenced by the cost of the drug; therefore we will also compare drug adherence amongst 

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those who redeemed a voucher for their ACT and those who paid the retail price. See Table 2 for 
detailed definitions of study outcomes. 
 
Table 2. Study Outcomes Derived from Survey Questions 
Outcomes 
Survey questions used 
Primary Outcome Measures: 
  
1. Comparison of percent of fevers 
that receive a malaria test from any 
source between arms. 
45. Did you have a malaria test? (1="Yes") 
Secondary Outcome Measures: 
  
1. Percent of all ACTs used that 
were taken by people with a malaria 
positive test. 
44. Did you have a malaria test? (1="Yes") 
50. What were the results of the malaria test? (1="Positive") 
60. Which medicines did you take?  (1=”Coartem/Artefan/other AL“) 
65. Did you start taking the Antimalarial before or after the malaria 
test? (2="After") 
2. Percent of all ACTs used that 
were taken by people without a test 
45. Did you have a malaria test? (2="No") 
60. Which medicines did you take?  (1=”Coartem/Artefan/other AL“ ) 
3. Percent that take AL if positive 
44. Did you have a malaria test? (1="Yes") 
50. What were the results of the malaria test? (1="Positive") 
60. Which medicines did you take?  (1=”Coartem/Artefan/other AL“ ) 
65. Did you start taking the Antimalarial before or after the malaria 
test? (2="After") 
4. Percent that take AL if negative 
44. Did you have a malaria test? (1="Yes") 
50. What were the results of the malaria test? (2="Negative") 
60. Which medicines did you take?  (1=”Coartem/Artefan/other AL“ ) 
65. Did you start taking the Antimalarial before or after the malaria 
test? (2="After") 
Other Outcome Measures: 
1. Percent of people that took ACTs 
and received a correct dose* 
64. If AL, how many pills were given? If not AL, skip to 64 
1=6 
2=12 
3=18 
4=24 
5=Don’t know/remember 
6=OTHER: _____________ 
66. If AL, did you / patient complete the full course? 
1=Yes 
2=No 
3=Not sure 
4=Don’t remember 
68. If AL, how many days to complete the course? 
 
Figure 1: Diagram of intervention strategy 

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5 
Study Population 
This study will take place in Bungoma East and Kiminini sub-County. Both are high malaria 
transmission areas. The study population will be all individuals resident in the 20 community 
units in Bungoma East sub-county and 12 community units in Kiminini.  
5.1 
Selection of the Study Population 
Half of the community units in each study area (10 in Bungoma East and 6 in Kiminini) will be 
randomly selected to be included in the intervention arm. The remainder of the community units 
will be the comparison arm. CHVs are responsible for specific households and they know the 
household members in their allotted households.  
5.2 
Inclusion/Exclusion Criteria 
INCLUSION CRITERIA 
Subjects who meet all of the following inclusion criteria will be eligible to participate in the study 
Intervention participation criteria: 
• 
Client is older than 1 year  
• 
Client has fever or history of fever or feeling unwell with a malaria-like illness within 
the last 2 days  
• 
Client or their parent/legal guardian (if under 18) consents to participate  
Cross sectional survey participation criteria: 
• 
Household representative in the intervention or control arm 
• 
At least one member in the respondent’s household with a history of fever or feeling unwell 
with a malaria like illness within the last four weeks 
• 
Respondent is older than 18 years 
EXCLUSION CRITERIA 
Intervention exclusion criteria: 

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• 
Client has signs of severe disease or other problem requiring immediate referral to a 
health facility 
• 
Client has already visited a health facility, taken or purchased antimalarials for the 
current illness. 
Cross sectional survey exclusion criteria: 
• 
Households not in the intervention or control arms 
 
 

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6 
STUDY PROCEDURES/EVALUATIONS 
6.1 
Study Procedures 
CHVs in the intervention arm will be trained to use RDTs to diagnose malaria in 
household members with documented or reported fever. The RDT that the study will use 
is the CareStart™ Malaria HRP-2 Pf Test. HRP2 malaria rapid diagnostic tests have been 
shown to have an average sensitivity and specificity of 94.8% and 95.2%, respectively, 
for P. falciparum infection when compared to light microscopy (Abba et al., 2011). This 
particular brand of test was reported to have sensitivity >90% even for low density 
infections and a false positive rate of <1% (World Health Organization, 2014).  Several 
studies have demonstrated that CHVs can safely and correctly use rapid diagnostic tests 
(Briggs et al., 2012; Chanda et al. 2011; Cohen et al., 2012; Hamer et al., 2012). 
Although community-based diagnosis has been formally adopted by the Kenya Malaria 
Control Unit, roll-out has been limited due to funding constraints. Therefore we will have 
an opportunity to help implement this policy in the intervention areas and will train the 
CHVs in comparison areas at the conclusion of the study.  
CHVs will be trained using a validated 3-day curriculum (based on the Kenya Ministry of 
Health curriculum) in conjunction with practical, skills-oriented sessions. Training session 
facilitators will include members of the study team, CHEWs, members of the sub-county 
health management team (SC-HMT), and peer mentors with extensive experience 
administering RDTs during previous studies. During the intervention, CHVs will be 
continuously monitored by both the study team and the CHEWs to ensure proper use and 
interpretation of RDT results. 
Households in the 16 intervention CUs will be informed of the intervention and 
encouraged that any household member who experiences a febrile illness should visit the 
CHV in his/her village. The specific place where the CHV can be found may already be 
common knowledge within the community, but will also be made clear during community 
sensitization efforts. A mobile number at which the CHV can be reached will also be 
provided to the community; the CHV will attempt to make arrangements to visit sick 
clients who contact the CHV but who face difficulties in reaching the CHV’s established 
location. For participants presenting for evaluation, the project CHV will assess and 
record on a standardized encounter form basic medical and medications history. Data 
about febrile household members and their RDT results will be collected by CHVs using 
customized carbonless-copy client registers designed to be read and digitized by 
Captricity (Captricity, Inc.) for automated data entry. Data will be routinely scanned and 
digitized by field supervisors. The CHV will take the participant’s temperature and 
evaluate participant-reported fever history in the past two days. The CHV will record 

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participant-reported information on any medications taken for the treatment of malaria in 
the past two weeks. The CHV will assess whether women of child-bearing age presenting 
for evaluation may be pregnant based on self-reported date of last menstrual period. A 
participant having a measured temperature above 37.5 C or self-reporting a fever in the 
past two days will be administered an RDT (at no cost); the results will be recorded and 
provided to the participant.  
A participant with a negative RDT will be issued a referral note documenting the results of 
the RDT and the date it was performed in the event they should choose to go to the 
health facility. This would eliminate the need to be re-tested at the facility. Children under 
5 with signs of pneumonia (fast breathing, cough) or any individual with danger signs will 
be referred to a facility regardless of the results of the malaria test.  
A participant having a positive RDT will be given a serialized voucher for a quality-
assured ACT purchased at a participating drug shop. The voucher will entitle the holder 
to purchase a quality-assured ACT at a reduced, fixed price in the retail sector (Table 3). 
The fixed price will vary by age group according to the dosage required for the patient; 
the fixed price for a full course of treatment will be 10 KES for a child under three years of 
age (6 tablets), 15 KES for a child aged 3-8 years old (12 tablets), 20 KES for an 
adolescent aged 9-15 years old (18 tablets), and 40 KES for an adult (aged 16 years or 
older, 24 tablets). The voucher will be valid for 3 days from the date of issue. The holder 
will redeem the voucher at any participating retailer by presenting both the voucher and 
the positive RDT (provided by the CHV in a sealed plastic pouch). The retailer will be 
reimbursed the difference between the normal retail price and the study price plus 5 
shillings. Women with a positive RDT who the CHV determines may be pregnant will not 
be offered a voucher, but will instead receive a referral.  
 
Table 3: Pricing scheme for voucher holders compared to standard retail prices.  
 
Age group 
Average 
unsubsidized 
price 
Study-subsidized 
price for voucher 
holders 
Adult dose 
(>16 years) 
100-120 KES 
40 KES 
9-15 years 
80 KES 
20 KES 
3-8 years 
50 KES 
15 KES 

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<3 years 
40 KES 
10 KES 
 
All retail medicine outlets that serve customers in the intervention clusters will be 
identified through a comprehensive census. We estimate a total of 40-50 shops will be 
enrolled in total across both sites. Shop owners and shop attendants will be invited to 
participate in the intervention and will attend a one-day training on current Government of 
Kenya malaria treatment guidelines, the role of RDTs in case management, and the study 
procedures. The training of shop keepers/owners will also include identification of danger 
signs and procedures for referral when necessary. They will be encouraged to purchase 
ACTs at the normal government-subsidized price and will be provided with a list of 
wholesalers in order to participate in the voucher scheme. When a community member 
presents a voucher for redemption, participating outlets will collect both the voucher as 
well as the used positive RDT (stored in a sealed plastic pouch). Both will be given to the 
study team in return for payment on the value of the voucher. Saving and re-reading the 
used RDT will ensure that exactly one voucher is redeemed for each positive RDT. 
Patients who redeem vouchers will be logged in a register and a unique study number will 
be written on the RDT.  
 
The CHV will attempt to follow up on all clients receiving an RDT 4 days after testing to 
record what action was taken and what treatment, if any, was obtained. The CHV will 
provide further advice or referral depending on persisting symptoms or danger signs. 
They may provide education on adherence to full course of medication.  
In the comparison areas, CHVs will provide standard care including home-based health 
education and referral services. ACTs will be available in the retail sector at unsubsidized 
and government-subsidized prices. No free RDTs or ACT vouchers will be offered by the 
CHVs in the comparison area clusters during the intervention period.  
Survey teams who are blinded to the assignment of arms and independent of the 
intervention will collect household data in four regularly spaced cross-sectional surveys 
(baseline, 6 months, 12 months, and 18 months post-baseline). Household survey data 
will be collected in a random sample of households through face-to-face interviews. The 
households will be selected randomly according to village rosters and selection will be 
independent of whether members of that household received service from a CHV. The 
sample of homes will be unique in each survey round. The surveys will ask household 
representatives whether members experienced any fevers in the preceding four weeks. 
For one reported fever per household, the survey team will record whether any drug was 
taken, which drug or drugs were taken, and where they were obtained. They will also ask 
the individual or caregiver whether any diagnostic test for malaria (RDT or microscopy 

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using blood from a finger-prick) was done prior to treatment and the results of the test 
(self-reported). Whenever possible, health records from a facility or CHV will be reviewed 
for test results and the drug packaging will be observed to reduce recall bias. Survey 
teams will also have examples of antimalarial drug packaging to help with identification of 
type and brand used. The household survey data will be collected on android netbooks 
running the OpenDataKit platform. Internal consistency checks and data quality checks 
will be programmed into the forms. Data will be uploaded onto a secure laptop after each 
day of data collection and reviewed by the data managers.  
Monitoring and supervision activities will be ongoing throughout the intervention to ensure 
compliance and data quality. The study team will randomly visit participating retail outlets 
several times each month. The availability of ACTs and prices of antimalarials will be 
monitored during supervisory visits. The team will check the list of patients who 
redeemed vouchers and cross-check with the used positive RDTs. Monitoring of the 
implementation of the intervention will occur through routine program records kept by 
both CHVs and retail medicine outlets in the intervention arm. Supervisors will also 
conduct random visits to observe a test being done to ensure adherence to proper 
procedures and study protocol. In addition, monitoring for adherence to the intervention 
will occur through collection and re-reading of used RDTs, and unannounced supervisory 
visits to retail outlets. At the conclusion of the study, all shop owners and shop attendants 
from participating outlets will be invited to participate in a focus group discussion to give 
their views about participating in the intervention (Focus group discussion guide can be 
found in Appendix_29_March_1). This will help us understand the perceived value, 
advantages, and disadvantages of the voucher program from the perspective of the 
shops. We anticipate conducting 3 focus groups with 10-12 participants per group.  
In addition, the performance and satisfaction of CHVs participating in implementation of 
the study will be evaluated through standardized observation and questionnaire. A 
randomly-selected sample of 90 CHVs are expected to complete both evaluation 
activities. First, CHVs will be surveyed via a structured questionnaire regarding their 
understanding of, and satisfaction with, their role in the study as well as information 
regarding their demographics and experience as a CHV. Then, in order to monitor the 
quality of the rapid diagnostic test procedure performed by CHVs, trained observers will 
use a standardized checklist and note which, if any, steps are misconducted or omitted 
as they observe the CHVs perform RDTs. The CHVs will also be asked to interpret about 
10 used RDT cassettes. For this specific component, clients receiving RDTs for CHV 
evaluation will be recruited from amongst patients presenting to local health facilities for a 
malaria test (i.e., that would be receiving a malaria test anyway). Clients will be invited to 
receive a test from a CHV participating in this study, but then sent back to the referring 
clinician for treatment after the test has been performed. Note that if the RDT is positive, 
the client will not receive a voucher for an ACT (as is the case for standard clients 
receiving a CHV-administered RDT outside the context of this observation activity). 
Rather, clients receiving an RDT in the context of this observation activity would receive 

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the treatment determined by a health facility clinician, who would be informed of the 
results of the RDT. ACTs are free at the health facility. Encounter forms will not be 
completed for these clients. Satisfaction and motivation of CHVs in both comparison and 
intervention areas will also be measured at the conclusion of the study. 130 CHVs in 
each area (total of 260) will be interviewed to understand what aspects of their 
community work contribute to their motivation and retention and whether that differs 
between intervention and comparison areas (Appendix_29March_2)  
 
6.2 
Laboratory Evaluations 
6.2.1 Laboratory Evaluations/Assays 
Evaluation of samples is limited to RDTs performed and evaluated in the field. No 
evaluations will take place in the laboratory.  
6.2.2 Specimen Collection, Preparation, Handling and Shipping 
Positive RDTs will be provided to the client in a sealed plastic bag, to be presented to 
the drug shop when redeeming a voucher. Supervisors will retrieve these tests from the 
drug shop regularly for diagnosis confirmation.  
 
6.2.2.1 
Instructions for Specimen Preparation, Handling, and Storage 
Collection of samples will be limited to the extent described above. All RDTs (after 
supervisor confirmation) and associated waste will be disposed of properly according 
to strict protocol. 
6.2.2.2 
Specimen Shipment 
NA 

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7 
STATISTICAL CONSIDERATIONS 
7.1 
Study Outcome Measures 
All study outcomes will be those measured in the four regularly spaced population-based 
cross-sectional surveys (baseline, 6 months, 12 months, and 18 months post-baseline).  
The primary outcome will be the percent of fevers that are tested for malaria either in the 
community or in the facility. Testing is the gateway to targeting and if we cannot improve 
the proportion tested, then the voucher subsidy program will not have any effect. The 
secondary outcomes of this study are to measure and compare between intervention 
and control arms. The main secondary outcome is the percent of all ACTs used that 
were taken by people with a malaria positive test. Additional secondary outcomes are: 
the percent of all ACTs used that were taken by people without a test, the percent of 
those with a positive test who got an ACT, and the percent of those with a negative test 
who got an ACT. Completion of the treatment course of ACT may be influenced by the 
cost of the drug; therefore we will also compare drug adherence amongst those who 
redeemed a voucher for their ACT and those who paid the retail price. All endpoints will 
depend on self-report supplemented by any medical records that describe diagnosis and 
treatment of the eligible fever episode and inspection of medicine packaging of drugs 
taken for the illness, if available. We will evaluate our study endpoints across all 
individuals >1 year of age. Although studies often focus on the treatment of fevers in 
children, the majority of ACT overuse occurs in adults. Adults are less likely to have a 
febrile illness caused by malaria, less likely to have a diagnostic test before treatment, 
and more likely to receive treatment in the informal retail sector. Therefore, increasing 
testing and reducing unnecessary use amongst adults has the largest potential for 
improving targeting. 
 
To help clarify the population to which our outcomes apply, we show in Figure 2 the 
expected participant flow in the intervention and comparison arms.  
 
 
 
 
 
 
 
 
 
 
 
Figure 2: Participant Flow and assumed probabilities.  

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Note that percentages on the branches of the tree are probabilities conditional on the previous step. Percentages 
at the end of the branch represent overall percentages out of 100% for each arm. 
 
 

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7.2 
Sample Size Considerations 
 
The study will survey 40 people with fever in the previous 4 weeks in each CU at each of 
the 4 measurement time points (baseline, 6, 12 and 18 months post-baseline). With 16 
CU per arm, this will total 640 fevers per arm at each time point for a total of 1280 across 
both arms. We anticipate that approximately 22F% of households will have a member 
with a self-reported fever within the previous 4 weeks, so that the study team outcome 
assessors will need to visit 2884 households per arm at each measurement time point. All 
endpoints will be evaluated at 6 months post-baseline to determine whether high 
coverage of testing can be scaled-up quickly. Evaluation of the endpoint at 12 and 18 
months will measure the saturation level of the intervention. 
 
Based on pilot data from Bungoma, we anticipate that the proportion of fevers in the past 
4 weeks who underwent testing for malaria (i.e. our primary outcome) will be 70% vs. 
31% in intervention vs. control arms. These assumptions together with other assumptions 
are shown in Figure 2. Specifically, pilot data suggests that 43% of tested fevers will be 
positive (with no difference between arms). Of those with a positive test, 90% vs. 70% will 
purchase ACT in the intervention arm vs. control arm. Only 10% of those with a negative 
test are assumed to purchase ACTs (at the retail price) in both arms. Of those without a 
test, pilot data indicates that 21% purchase an ACT, and we expect this to be the same in 
both arms. Based on these assumptions, Table 4 shows the expected proportions for 
each of our primary and secondary outcomes and the number of fevers per CU which will 
be included in the denominator of each outcome (since all our secondary outcomes are 
conditional on testing or on ACT use so that the denominators are smaller than our 
assumed 40 fevers per CU). The table also reports the calculated interclass correlation 
coefficient and anticipated power of the comparisons. We base the main calculation (titled 
“Original” in Table 4) on the results of our preliminary pilot study, then expand into various 
scenarios that may occur during our proposed full-scale cRCT to demonstrate how power 
may change under various conditions. Details of the rationale and other assumptions of 
power calculations are listed below.     
 
We calculated power based on a cluster randomized two-sample two-tailed t-test for the 
comparison of two proportions using standard formulae, which use the coefficient of 
variation as the measure of between-cluster variability (Hayes & Moulton, 2009). 
Specifically, we calculated power for the two-tailed comparison of the intervention group 
vs. the control group for the primary outcome of the proportion of clients with fever in the 
previous four weeks who reported being tested for malaria prior to any treatment. To 
ensure that our overall two-tailed Type I error (alpha) was 5%, we fixed the alpha level at 
1.667% (i.e. 5%/3) for each of the 3 follow-up time points (6, 12 and 18 months post-
baseline), using the conservative Bonferroni correction (Aickin & Gensler, 1996). In 
practice, we will use the less conservative Benjamini Hochberg procedure to adjust the 
actual alpha level for simultaneously testing the primary outcome at those 3 time-points 
(Benjamini & Yekutieli, 2001). To further protect against possible losses in power due to 
the stratified study design, we conservatively based the power on a matched-cluster 
design, as the stratified design is expected to have more power than a matched design. 
 
 

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Table 4: Summary of study outcomes under varying scenarios, assumed effects, ICC, n per 
cluster, and power at each of 3 follow-up time points for a cluster-randomized trial of 16 
control CUs vs 16 intervention CUs at overall 5% type-1 error rate for each outcome 
(Bonferroni correction for 3 time points) 
 
Scenario (with differences from scenario 1 
highlighted) 
 
Original 
1. Calculated 
from pilot 
data 
2. Like 
Scenario 1, 
except 
Positive (%) 
= 43% for 
intervention 
 
3. “Best 
case”:  
Like Scenario 
2, except in 
intervention 
Positive (%) 
with ACT = 
No test (%) 
with ACT = 
50% 
4. “Worst case”:  
Like Scenario 2, 
except in 
intervention 
positive (%) and 
take ACT 70% 
Assumptions 
Intervention vs. Control 
Percent of 
fevers with test 
70% vs. 
31% 
70% vs. 31% 
70% vs. 31% 
70% vs. 31% 
70% vs. 31% 
Percent of 
tested fevers 
that are 
positive 
43% vs. 
43% 
69% vs. 69% 
43% vs. 69% 
43% vs. 69% 
43% vs. 69% 
Percent that take 
ACT if positive 
90% vs. 
70% 
90% vs. 90% 
90% vs. 90% 
90% vs. 90% 
70% vs. 90% 
Percent that take 
ACT if negative 
10% vs. 
10% 
70% vs. 70% 
70% vs. 70% 
50% vs. 70% 
70% vs. 70% 
Percent that take 
AL with no test 
21% vs. 
21% 
80% vs. 80% 
80% vs. 80% 
50% vs. 80% 
80% vs. 80% 
Outcome 
Intervention vs. Control  
ICC; Assumed n per cluster (set at min of intervention and control); Power 
Percent of fevers 
with test 
70% vs. 31% 
 0.073; 40; 98% 
Percent of ACT 
taken by those 
who test positive 
72% vs. 
36% 
0.027; 
10; 90% 
53% vs. 24%;  
0.027; 32; 98% 
34% vs. 24% 
0.027; 32; 
36%  
44% vs. 24% 
0.027; 25; 
81% 
29% vs. 24% 
0.027; 29; 9% 
Percent of ACT 
taken by those 
with no test 
17% vs. 
57%  
0.064; 
10; 100% 
29% vs. 68%;  
0.064; 32; 
>99% 
30% vs. 68% 
0.064; 32; 
>99% 
24% vs. 68% 
0.064; 25; 
>99%  
33% vs. 68% 
0.064; 29; >99% 

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Percent that take 
ACT after 
positive test 
90% vs. 
70%  
0.074; 5; 
50% 
90% vs. 90%;  
0.074; 9; - 
90% vs. 90% 
0.074; 9; - 
90% vs. 90% 
0.074; 9; -   
70% vs. 90% 
0.074; 9; 62% 
Percent that take 
ACT after 
negative test 
10% vs. 
10%  
0.007; 7; 
 - 
70% vs. 70%;  
0.007; 4, - 
70% vs. 70% 
0.007; 4, - 
50% vs. 70% 
0.007; 4; 40%  
70% vs. 70% 
0.007; 4; -   
* See Figure 2 
 
 
The coefficient of variation (CV) for each outcome was estimated based on preliminary 
(unpublished) pilot data and substantive knowledge of the research setting. In the 
absence of measurements on any of the proposed outcomes, we used the following 
procedure as recommended in the literature (Hayes & Moulton, 2009). First, we took the 
assumed value of the outcome in the control group (see Table 4), assumed a normal 
distribution on the CU-specific proportions with the outcome centered around the control 
arm proportion. The SD of that distribution (i.e. the between-CU SD) was then calculated 
based on a plausible range for 95% of possible CU-specific proportions agreed by the 
study team. We used a conservative width of 0.5 for the percent of fevers that take a test, 
percent of ACT taken by those with no test, and percent that take ACT after a negative 
test; a width of 0.25 for percent of ACTs taken by those with a positive test, and a width of 
0.10 for percent that take ACT after a positive test. Corresponding intra-cluster correlation 
coefficients (ICCs) were all estimated according to the formula ICC = CV2*π/(1-π), where 
π is the expected proportion for the outcome in the control group (Hayes & Moulton, 
2009).    
 
As noted above, with 40 fevers per cluster (1280 in total across all 32 CUs), we are very 
well powered for our primary outcome (under most scenarios), and for our secondary 
outcomes except for the two secondary outcomes related to the proportion buying ACT 
following a test result (either positive or negative). As we do not expect the intervention to 
influence the proportion who buy ACT following a negative result, we are not concerned 
that we are not powered for this outcome. Even though we do not expect this to be 
different between the arms, this is an important metric of appropriate malaria treatment 
and still important to evaluate in our study area.  
 
In order to determine how many people will need to be screened in order to obtain the 
required sample size of 1280 fevers across all 32 study CUs, we use data collected in 
previous studies in the region. From our preliminary results, we know that the period 
prevalence of fevers in children under 5 in the last one month is 33% in the Bungoma 
East area and 38% in Kiminini. We expect the incidence of fever in older children and 
adults to be roughly half of that in children. Based on a population mix of 1:3 for under 5s 
vs. older children and adults we assume that the prevalence of fever in the previous four 
weeks is 22.21%. Therefore, overall we assume that 22.21% of the population in both 
arms will experience a fever during the previous four weeks. Consequently we will need 
to sample just over four-times as many clients, which corresponds to a total of 5766 

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individuals (2884 per arm) to be sampled at each of 3 follow-up surveys (6, 12 and 18 
month post-baseline). Assuming comparable intervention effects at the final time point, 
we will use the same sample size at all 4 data collection time points. With a team of ten, 
surveys should take approximately 2 months to complete at each time point. 
 
7.3 
Participant Enrollment and Follow-Up 
 
Eligibility for enrollment will be determined according to the specific inclusion and 
exclusion criteria outlined in Section 5.2 of this protocol. The CHV in the intervention arm 
will attempt to follow up on all clients receiving an RDT 4 days after testing to record 
what action was taken and what treatment, if any, was obtained.  
 
 
7.4 
Analysis Plan 
 
All study outcome measures are individual-level binary outcome measures. Results will 
be presented as effect sizes with 95% confidence intervals. We will compare each of the 
study outcomes at each time point between study groups. We will account for the 
stratified design and clustering by CU for all analyses. All analyses will be based on the 
intention-to-treat principle.  
 
Models with adjusted and unadjusted estimates will be presented for all analyses. 
Adjustment covariates will be pre-specified based on prior knowledge of relationships 
with the outcome variables. Adjustment covariates will include: age, sex, household size, 
and household socioeconomic status. Analyses may be adjusted for the baseline level of 
the outcome variable in order to improve the precision of the measurement of the 
treatment effect. 
 
Baseline levels of demographic variables as well as outcome variables will be compared 
to assess balance of covariates between treatment arms. No formal hypothesis testing 
will be performed in these comparisons. 
 
Autonomy of testing and treatment decisions depend on the age of the febrile individual. 
It is possible that the effects of the intervention may differ based on age (i.e. children vs. 
adults). We will examine this possibility by testing the presence of an interaction effect 
between age and treatment group. Differences between sub-groups will be identified by 
the significance of the interaction effect. If outcomes do indeed differ by subgroup, we 
will report results separately by subgroup. 
 
Sub-analysis will also be performed to examine the sensitivity of results to the precise 
definition of the outcome. The first set of sub-analyses will look at the set of main 
outcomes using only those malaria tests for which documentation was provided to the 
data collectors (i.e. duplicate form for those who tested at the CHV center, health booklet 
for those who tested at facilities). 

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Our primary aim is to determine whether there is significant difference between the 2 
study arms in the proportion of clients with fever who are tested prior to any treatment 
after adjusting for relevant covariates at each of the follow-up periods. As noted above in 
Section 7.2 (Sample Size Considerations), we will use the Benjamini Hochberg 
procedure for determining significance of the 3 tests of the difference between groups at 
each follow-up time point. We will also compare secondary measures using the same 
modeling and adjustment approach. 
 
 
 
 
 

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8 
SUBJECT CONFIDENTIALITY 
Data collected by CHVs about client visits will be entered into electronic databases using web-
based technology and will not include names and birthdates of individuals. Client information 
from cross-sectional household surveys will be collected on encrypted, password-protected 
tablets. Data will be removed from the tablets weekly and stored in a secure study server. 
Participants will be assigned a study number which will link them to their study data. For all 
databases, all personal identifying information will be stored separately from the study data and 
will be linked only by the unique study number. Field supervisors may review data including 
identifying information to ensure compliance with study procedures. All data released to co-
investigators or statisticians for analysis will be anonymized. Research assistants will be trained 
on proper data collection techniques and protection of client data.  
All data released to co-investigators or statisticians for analysis will be anonymized. GPS 
coordinates of retail outlets and households is considered to be personal identifying information. 
Therefore, the data manager will use the coordinates to calculate distances between the 
household and the health facilities and only distance information will be released for analysis. If 
coordinates are required to make maps of the study area, a random error of 50-100 meters will 
be added to each coordinate to protect individual household identities before the data is 
released to the analyst. Recipients of study data will be asked to sign a data sharing agreement 
that specifies what the data may be used for (specific analyses), criteria for acknowledging the 
source of the data, and the conditions for publication. It will also stipulate that the recipient may 
not share the data with other investigators. Requests for data use must be made directly to the 
PI and not through third parties. 
 
 
8.1 
Future Use of Stored Specimens 
NA (No samples will be stored). 

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9 
INFORMED CONSENT PROCESS 
Potential subjects presenting to a CHV for testing will be screened for eligibility in privacy. 
Those who meet the inclusion criteria will be asked to provide written consent before testing. A 
consent document will be read and explained to the participant. A copy will be provided if 
requested. The subject will sign the encounter form to indicate consent (Appendix 1). A copy of 
the encounter form will be released to the patient and a copy will be retained by the study.  
Clients selected to receive an RDT from amongst health facility attendees under the CHV 
observation (evaluation) activity will provide verbal consent, as their participation will meet the 
following criteria for a waiver - this study presents no more than minimal risk to the participant, 
and does not include any procedure for which consent would normally be required outside of the 
research setting; and the only record linking the subject and the research would be the consent 
document.  
Potential respondents to the baseline, interim, and final household questionnaires will be asked 
to provide verbal consent for participation. We are requesting a waiver of documentation of 
informed consent for the household surveys only. Clients will complete a questionnaire which 
meets the following criteria for a waiver - this study presents no more than minimal risk to the 
client and does not include any procedure for which consent would normally be required outside 
of the research setting. The consent document would be the only information linking the 
respondent to his or her study id.  
Shop owners of participating retail medicine outlets in the study areas will be asked to provide 
written informed consent for participating in the intervention, including honoring client vouchers 
and collecting used RDTs. For shops that choose not to participate in the voucher scheme, we 
will seek verbal consent for the study to collect study-related data only such as stocking and 
sales of antimalarials. We are requesting a waiver of documentation of informed consent for the 
survey as this study presents no more than minimal risk and no personal identifying information 
will be collected about the shop attendant/owner.  
 
CHVs will provide verbal consent for CHV evaluation activities (surveying and observation), 
which meet the following criteria for a waiver - this study presents no more than minimal risk to 
the participant, and does not include any procedure for which consent would normally be 
required outside of the research setting.  
 
 

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9.1 
Informed Consent/Assent Process (in Case of a Minor or 
Others Unable to Consent for Themselves) 
The assent process for minors (defined as those under 18 years of age) will be to obtain 
consent from the minor’s parent or legal guardian for the participation of the minor and 
verbal assent from minors over the age of 8 years. Eight years is considered the age 
above which assent is required in Kenya. This applies only to subjects presenting to the 
CHV for testing. Minors will not be eligible to participate in the household survey. 
 

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10 
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