Malaria Diagnostic Testing and Conditional Subsidies to Target ACTs in the Retail Sector: the TESTsmART Trial Aim 2
Phase: NA
Conditions: Malaria, Febrile Illness
Sponsor: Duke University

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A randomized experiment of malaria diagnostic testing and 
conditional subsidies to target ACTs in the retail sector: the 
TESTsmART trial AIM 2 
Sub-Title: Outlet-based trial of RDTs and conditional ACT 
subsidies among patients with fever 
 
NCT04428307 
 
Sponsored by: 
National Institute of Allergy and Infectious Diseases (NIAID) 
 
 
 
Principal Investigators: 
Wendy Prudhomme O’Meara 
Theodoor Visser 
Jeremiah Laktabai 
 
 
 
Draft or Version Number: v1.1 
 
Day Month Year 
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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 and 45 CFR Part 46. 
All key personnel (all individuals responsible for the design and conduct of this study) have 
completed Human Subjects Protection Training. 

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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: 
 
 
Wendy Prudhomme-O’Meara, PhD 
Principal Investigator 
 
 
 
* The protocol should be signed by the local investigator who is responsible for the study 
implementation at his/her specific site; ie, if Investigational New Drug study, the individual who 
signs the Form FDA 1572. 
 

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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 
Background Information and Scientific Rationale ................................................................ 5 
1.1 
Background Information ........................................................................................... 5 
1.2 
Scientific Rationale ................................................................................................... 7 
1.3 
Potential Risks and Benefits ..................................................................................... 8 
1.3.1 
Potential Risks .............................................................................................. 8 
1.3.2 
Known Potential Benefits .............................................................................. 8 
2 
Objectives ............................................................................................................................ 9 
3 
Study Design ...................................................................................................................... 10 
4 
Study Population ................................................................................................................ 12 
4.1 
Selection of the Study Population .......................................................................... 12 
4.2 
Inclusion/Exclusion Criteria .................................................................................... 12 
5 
STUDY PROCEDURES/EVALUATIONS .......................................................................... 13 
5.1 
Study Procedures ................................................................................................... 13 
5.2 
Laboratory Evaluations ........................................................................................... 13 
6 
Statistical Considerations ................................................................................................... 14 
6.1 
Study Outcome Measures ...................................................................................... 14 
6.2 
Sample Size Considerations .................................................................................. 15 
6.3 
Participant Enrollment and Follow-Up .................................................................... 16 
6.4 
Analysis Plan .......................................................................................................... 17 
7 
SUBJECT CONFIDENTIALITY .......................................................................................... 19 
7.1 Future Use of Stored Specimens ................................................................................. 19 
8 
Informed Consent Process ................................................................................................. 20 
8.1 
Informed Consent/Assent Process (in Case of a Minor or Others Unable to 
Consent for Themselves) ....................................................................................... 20 
9 
RESULT DISSEMINATION PLAN ..................................................................................... 21 
10 
Literature References ......................................................................................................... 22 
 
 
SUPPLEMENTS/APPENDICES 
1. Exit Interview Questionnaire 
2. Adult Verbal Consent Form (attached separately) 
3. Outlet Flyer (attached separately) 
4. Outlet enrollment questionnaire 
5. Outlet consent form  
 

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ACT 
AE 
AM 
Artemisinin Combination Therapy 
Adverse Event 
Antimalarial 
CFR 
Code of Federal Regulations 
CIOMS 
Council for International Organizations of Medical Sciences 
CRF 
CHW 
DFID 
Case Report Form 
Community Health Worker 
Department for International Development, UK 
DMID 
Division of Microbiology and Infectious Diseases, NIAID, NIH, 
DHHS 
DSMB 
Data and Safety Monitoring Board 
FWA 
Federal-Wide Assurance 
GCP 
GEE 
Good Clinical Practice 
Generalized Estimating Equation 
ICF 
Informed Consent Form 
ICH 
ID 
International Conference on Harmonisation 
Identification 
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 
PI 
Principal Investigator 
PPMV 
Patent and Proprietary Medicine Vendors, Nigeria 
RA 
Research Assistant 
RD 
Risk Difference 
RDT 
Rapid Diagnostic Test 
RR 
Risk Ratio 
SAE 
Serious Adverse Event 
SMC 
Safety Monitoring Committee 
SOP 
Standard Operating Procedure 
USAID 
United States Agency for International Development 
WHO 
World Health Organization 
 

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Title: 
A randomized experiment of malaria diagnostic testing and conditional 
subsidies to target ACTs in the retail sector: the TESTsmART trial AIM 2 
 
Population: 
40 registered pharmacies in western Kenya, 6800 participants seeking 
care from these pharmacies 
 
Number of Sites: 
1 
 
Study Duration: 
24 months 
 
Subject Duration: 
30 minutes 
 
 
Objectives: 
The ultimate goal of a conditional subsidy scheme is to limit inappropriate use of ACTs and 
reduce the programmatic costs of ACT subsidies by offering them conditional upon a positive 
test at the point of sale.  
 
The objective of this study is to test the effect of provider-directed and patient-directed 
incentives on improving the management of suspected malaria fevers that receive care in the 
retail sector. Provider-directed incentives include small payments for taking the time to conduct 
malaria-RDT testing for participants with malaria-like illness. Patient-directed incentives are 
inexpensive RDT testing coupled with a conditional ACT discount. The ACT discount is only 
applied if the RDT is positive for malaria. Outcomes will be measured by exit interviews on 
random days each month at each participating outlet.  
 
Primary: 
• 
The primary outcome will be the proportion of all ACTs that are sold to individuals with a 
positive malaria diagnostic test. For this outcome, a positive test is anyone who has a 
malaria-RDT performed at the outlet and receives a positive result or is referred from a 
health facility with a documented positive test result. 
 
Secondary: 
• 
The major secondary outcome is the proportion of suspected malaria cases that are tested. 
This outcome will allow us to determine whether the conditional subsidy can drive demand 
for testing.  
• 
Other secondary outcomes will measure 1) adherence to the RDT result among all those 
tested at the outlet, 2) proportion of all suspected malaria cases that are managed 
appropriately (tested for malaria, and use ACT following a positive test or do not purchase 
an ACT after a negative test), and 3) ACT use by untested clients.  
 

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For questions regarding this protocol, contact: 
Individuals: DMID Representative: 
 
Walt Jones, RN, MPH 
 
Clinical Project Manager NIH/NIAID/DMID/PIPB 
 
5601 Fisher’s Lane, Room 8A37, Rockville, MD 20892 
 
PHONE: 240-627-3283 
 
joneswalter@niaid.nih.gov 
Principal Investigator:  Site investigator responsible for conducting the study: 
Wendy Prudhomme O’Meara, PhD 
Associate Professor of Medicine and Global Health, Duke University 
Box 90519, Trent Hall, Durham, NC  27708 
PHONE: +254 728 306 521  
wpo@duke.edu 
 
Subcontract Principal Investigators: 
Theodoor Visser 
Senior Manager, Malaria Commodity Access, Clinton Health Access Initiative 
383 Dorchester Ave., Ste 400, Boston, MA 02127 
FAX: 617 774 0220 
PHONE: 706 296 4141 
tvisser@clintonhealthaccess.org 
 
Jeremiah Laktabai, PhD 
Lecturer, Department of Medicine, Moi University School of Medicine 
P.O. Box 4605-30100 MTRH Complex, Nandi Road, Eldoret, Kenya 
FAX: +254 532 061 992 
PHONE: +254 722 685 542 
jlaktabai@gmail.com 
Institutions:  
 
Duke University  
 
Box 90519, Room 219, Trent Hall Durham, NC  27708 
 
Jamie Mills, Lead Grants and Contracts Administrator, DGHI 
 
FAX: 1 919 681 7748 
 
PHONE: 1 919 684 1755 
   
jamie.mills@duke.edu 
 
 
Clinton Health Access Initiative (CHAI) 
 
383 Dorchester Ave., Ste 400, Boston, MA 02127 
 
Molly Baker, Finance and Operations Manager, Access Programs, CHAI 
FAX: 617 774 0220 

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PHONE: 617 774 0110 
mbaker@clintonHealthAccess.org 
 
 
Moi University 
 
PO Box 4606-30100 MEC Building, Nandi Road, Eldoret, Kenya 
 
Robert Rono, Head of Research and Sponsored Projects Office 
 
FAX: +254 532 061 992 
 
robertrono@iukenya.org 

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1 
BACKGROUND INFORMATION AND SCIENTIFIC 
RATIONALE  
1.1 
Background Information  
Artemisinin combination therapies (ACTs) – the WHO-recommended first line therapy for 
uncomplicated malaria – have played a significant role in reducing global malaria mortality [1], 
but their overuse is rampant. In 2016 an estimated 216 million cases of malaria occurred 
worldwide, yet more than 400 million treatment courses of ACT were consumed1 [2]. 
Approximately 75% of global ACT demand is subsidized with international public funds from 
sources such as The Global Fund, DFID, and USAID2 [3] . Overconsumption of ACTs is an 
unnecessary drain on scarce public health resources and threatens the future sustainability of 
publicly-funded subsidies. In addition, it puts both present and future patients at risk; 
inappropriate treatment of a non-malaria illness with an antimalarial increases case fatality rates 
[4, 5] and contributes to population-wide drug pressure that accelerates the spread of drug 
resistance [6-8].  
 
Global over-consumption of ACTs is largely driven by its increased over-the-counter 
distribution in private retail outlets as a result of publicly-funded subsidies directed to the private 
sector [9]. In 2015, 44% of all donor-funded ACTs consumed world-wide were distributed 
through the private retail sector [9] where studies have shown that between 65-91% of ACTs 
dispensed for malaria are actually purchased by people without malaria [10-13]. Targeting 
ACTs to only those who receive a confirmatory diagnosis could dramatically reduce 
inappropriate ACT consumption, in alignment with WHO policy that stipulates that all febrile 
patients be tested before administering antimalarials.  
 
Although more than half of families in sub-Saharan Africa seek care for febrile illness 
through the retail sector [14, 15], it has remained largely outside of efforts to improve rational, or 
diagnosis-directed, use of antimalarials. Private medicine retail outlets are made up of for-profit 
outlets that specialize in medicines, such as pharmacies and drug stores. These outlets are 
generally poorly regulated, often operate outside of formal channels, and have weak, 
sometimes antagonistic relationships with the formal health care sector. The lack of 
accountability and incentive structure undermines adherence to national case management 
guidelines and contributes to poor ACT stewardship in the retail sector.  
 
Point-of-care malaria rapid diagnostic tests (RDTs), which have excellent sensitivity and 
specificity and are simple enough to be used by trained laypersons with limited formal training 
[16], could expand the reach of diagnostics into the retail sector and help improve the rational 
                                                
1 This includes both 311 million courses of subsidized, WHO-prequalified ACTs as well as an estimated figure of 
more than 100 million courses of other brands not subsidized or tracked by the WHO. Quality-assured or pre-qualified 
ACTs are pre-approved ACT brands that meet WHO quality standards. Only quality-assured ACTs are subsidized. 
2 USAID – United States Agency for International Development; DFID – Department for International Development, 
UK 

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use of antimalarials. However, in the context of subsidized ACTs and a for-profit business 
model, it may not be in the economic interest of clients to test when the treatment is less 
expensive than the RDT. Clients may prefer to forgo the RDT in favor of the treatment if they 
strongly suspect they have malaria, particularly if testing may increase their out-of-pocket costs. 
Similarly, use of a test may also mean loss of a sale for the retailer if the test is negative, 
particularly if there is an expectation that the provider will refer all clients who test negative for 
further evaluation at a formal health facility, as has been suggested by policy-makers. 
Therefore, in the presence of highly subsidized ACTs, incentives for testing are not aligned with 
provider or client interests in the private retail sector. As a result, the private retail sector 
remains the largest contributor to inappropriate use of ACTs. 
Currently, recipients of Global Fund grants may use funds to subsidize RDTs in the private 
sector, including in private retail outlets. However, outside of a few countries (i.e., Cambodia, 
Ghana, Myanmar), the use of RDTs in the retail sector is uncommon as is evidenced by the 
very low testing rates (<10%) in the private sector in sub-Saharan Africa [2, 17]. Several studies 
have explored the potential role of RDTs in improving case management in the retail sector with 
mixed and often poor results. In most of these studies retail providers received case 
management training, followed by supportive supervision visits by researchers. In a few studies, 
the wholesale RDT price was partially or fully subsidized but retail providers were permitted to 
set their own price to the consumer and offer testing at their discretion [18, 19]. More often, 
outlets were required to provide testing free of charge or at a low fixed price. Instructions to the 
outlets regarding when an RDT should be performed and an ACT should be dispensed were 
quite rigid [20-23]. Providers were not explicitly incentivized to conduct RDTs. All of these 
studies shared two features – 1) ACTs were heavily subsidized for all customers and 2) there 
was no relationship between the RDT result and the ACT subsidy. This range of implementation 
strategies resulted in a wide range of testing uptake; between 7 - 100% of suspected malaria 
cases were tested [24]. Adherence to a negative malaria test was inconsistent (between 1-40% 
of those with a negative test purchased an ACT) and often a significant portion of those testing 
positive, up to 70%, did not take an ACT [24]. 
 
In order to align both the provider and customer incentives towards testing and 
targeting, the provider must be willing to perform the test and sell the appropriate 
medicine and, at the same time, the customer must be motivated to purchase the test 
and adhere to the results. We hypothesize that offering ACT subsidies for the client 
(conditional on a positive test), and incentives to the provider to offer malaria testing, will 
each, on their own, have a very modest impact on uptake of testing and targeting of 
ACTs in the retail sector. We further hypothesize that when combined, they will have a 
synergistic effect on RDT testing and ACT targeting. However, it is important to estimate the 
effect of each intervention separately and in combination in order to provide cogent evidence to 
support joint implementation. We will test the combination of subsidies for ACTs and RDTs 
(selected from Aim 1) paired with a provider-directed testing incentive in a four-arm cluster-

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randomized controlled trial to evaluate their impact on the proportion of ACTs sold to individuals 
with parasitologically-confirmed malaria among those seeking care in the retail sector.  
1.2 
Scientific Rationale  
 
The study will be conducted in western Kenya, where the retail sector is an important 
source of malaria treatment. 100% of the population is considered at risk for malaria, and the 
private sector is an immensely important avenue for fever management. Nationwide surveys 
show that 70.6% of ACTs are distributed through the private sector [25]. Given the large 
proportion of individuals obtaining anti-malarial drugs outside the formal sector, efforts to 
improve targeting of ACTs must include the retail sector.  
 
Kenya has the programmatic goal of universal access to prompt parasitological diagnosis 
before treatment and has endorsed the use of malaria RDTs in the public health sector. 
However, very high rates of self-treatment in the retail sector undermine the explicit policy that 
all malaria cases should be confirmed by parasitological diagnosis. Private medicine retailers in 
Kenya, consisting of private pharmacies and chemists, are not routinely permitted to conduct 
RDTs. Instead, emphasis has been placed on shifting care to the formal health sector or 
deploying RDTs in the community through lay health workers. Persistently low testing rates 
have led Kenya to recently reconsider its position on RDT use in the retail sector, but the 
country has delayed any changes due to lack of rigorous data to guide policy.  
 
While there exist simulations of clients’ 
decisions around testing and ACT 
purchasing based on prices of these 
commodities [25], they do not consider 
the motivations of providers in the retail 
sector to offer malaria diagnostic testing. 
The proposed project is a real-world 
implementation study that will provide 
insights into whether an innovative and 
scalable approach can help overcome 
the practical challenges of working 
through the private retail sector while 
improving the targeting of ACTs to 
individuals with confirmed malaria 
infection.  
 
Table 1: Study country characteristics 
 
Kenya 
First line drug 
AL1 
Year adopted universal testing policy 
2010 
Estimated malaria cases (2016) [26] 
3.5 million 
Total population 
50M 
Percent of cases confirmed [26] 
33% 
Percent of fevers that seek care in 
retail sector 
60% 
Current price of ACT in retail sector 
(adult dose) 
$1.20 
Expected price in 2018 
$1.20 
Use of RDTs in retail sector?  
No 
Current retail cost of RDT  
$1.00 [27] 
Study area 
Rural 
Prevalence of malaria in study area 
25% 
1

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1.3 
Potential Risks and Benefits 
1.3.1 Potential Risks 
The intervention proposed is an incentive to providers to perform malaria diagnostic 
testing and the offer of a conditionally subsidized ACT for the client in case of a positive test. 
The provider incentive is relatively small, so it is not likely to distort the providers’ behavior to the 
extent where they would perform unnecessary malaria tests in order to obtain the incentive. The 
subsidized ACT is offered only to patients with a positive test (and only in some arms) but, the 
client is free to choose whether or not to use the subsidy. Participating in this study involves 
allowing us to record information about clients’ malaria testing and treatment decisions. This 
includes the clients’ decisions about whether to get tested for malaria using the RDT, and 
whether to buy an ACT. There is a small risk of breach of confidentiality of this information.  
RDTs will be provided to all participating pharmacies for wholesale price. The RDTs to 
be 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 study 
will ensure availability of proper sharps disposal equipment and services to participating outlets. 
All clients who choose to be tested will be advised on what to do if they experience any adverse 
events as a result of the finger prick.  
1.3.2 Known Potential Benefits 
There is significant health benefit to the client in knowing 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.  
In some arms, the providers get a direct monetary incentive for performing malaria 
diagnostic tests.  
More broadly, there are important future benefits to rigorous testing of subsidy schemes 
that promote appropriate testing before treatment. This work will contribute to evidence-based 
policy making, improved access to malaria diagnosis and ultimately reduced potential for the 
spread of antimalarial resistance. 

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2 
OBJECTIVES 
The ultimate goal of a conditional subsidy scheme is to limit both inappropriate use of ACTs by 
those without a test or with a negative test, as well as to reduce the programmatic cost of ACT 
subsidies by offering them conditionally on a positive test at the point of sale. The objective of 
this study is to test the effect of provider-directed and patient-directed incentives on improving 
the management of suspected malaria fevers that seek care in the retail sector. 

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3 
STUDY DESIGN 
This will be a four-arm cluster-randomized trial based on an underlying 2x2 factorial design. A 
random sample of 40 pharmacies (clusters) will be selected from a complete sampling frame of 
all eligible outlets and subsequently randomly assigned in a 1:1:1:1 ratio to each of the 4 arms 
(see Table 2).  
 
All clusters (Arm 1-4) will have access to RDTs at the wholesale price that enables the outlet to 
charge the desired retail price. The study will pre-specify the retail price so that it is consistent 
across all the arms and pharmacies and will work with in-country wholesalers to provide RDTs 
at the appropriate price to all participating outlets. In addition, all outlets will be trained on a 
mobile reporting app and asked to use it to record RDT and ACT sales and to facilitate payment 
of financial incentives. The four treatment arms are as follows:  
 
(1) Control (Arm 1): No price subsidy or incentive. RDTs are made available at 
wholesale price to the retail outlet.  
 
(2) Provider-directed intervention (Arm 2): Retail outlet in this arm receives a small 
incentive to perform an RDT for suspected malaria cases (approximately USD $0.10 for 
each RDT test they report)  
 
(3) Client-directed conditional ACT subsidy (Arm 3): Clients visiting outlets in this arm 
will receive a free ACT if they purchase a malaria test and have a positive test result.  
 
(4) Combined interventions (Arm 4): Retail outlets in this arm receive an incentive to test 
for malaria and clients visiting these outlets receive a free ACT conditional on a malaria 
positive test (i.e. this arm is a combination of the provider-directed and client-directed 
interventions that are offered in Arm 2 and Arm 3).  
 
Our four-arm study design will allow us to measure the effect of joint incentives to the 
provider and consumer, relative to no incentives and relative to either incentive alone.  
 
Data will be collected by two independent mechanisms – provider reporting and exit 
interviews. First, we will examine routine reporting data submitted via mobile phones using the 
mobile app. All shopkeepers within each enrolled outlet will be trained to use the mobile app 
which reports on volume of clients, number of ACTs or other antimalarials sold, number of RDT 
sold. The mobile reporting app will also require the user to take a picture of every RDT test. 
These photos will be uploaded to the server in real-time and will be reviewed continuously by 
registered lab techs. This will allow the lab tech to be responsible for the diagnostic decision. 
Data reported through the app will primarily be used to track RDT and ACT sales in real-time 
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visualization of a random sample of uploaded RDT photos. This routine monitoring will detect 
potential problems (i.e. providers who have unusually high or low test positivity rate, problems 
with RDT interpretation).  Problems detected will trigger support supervision and/or additional 
on-the-job training to ensure compliance and quality of diagnosis.  
Data for our main study outcomes will be collected by exit interviews with customers in order 
to avoid bias that may arise by relying on provider-reported data. In previous retail-sector 
studies, results from exit interviews and provider reports differed significantly, with exit interview 
results considered more reliable [22, 26]. Trained data collectors will approach customers who 
have transacted at the outlet and ask them if they are willing to participate in an exit interview. In 
our experience, refusal rates are less than 2% for these types of surveys so we expect our 
sample to accurately represent all transactions at the outlet. Customers will be eligible if they 
sought treatment for a febrile illness or malaria-like symptoms for themselves or their child, 
provided the child is present. Exit interviews will be conducted on randomly selected days each 
month and data collectors will be randomly assigned to outlets in order to minimize behavior 
change prompted by the presence of the interviewer. The number of days of data collection at 
each outlet will depend on the sales volume with a target of 10 participants per outlet per month. 
Customers will be asked to report whether they had a test, the results of the test, and what 
medicine they purchased. Basic information about household assets will also be collected in 
order to stratify participants on socioeconomic status.  
Exit interview and provider reporting data will be compared to assess agreement between 
the sources. Specifically, we will compare the following indicators aggregated by outlet: the 
proportion tested, the proportion of tests positive and, the proportion of individuals using ACTs 
by test status (untested, positive or negative).  
The primary outcome will be the proportion of all ACTs that are sold to individuals with a 
positive malaria diagnostic test. The ultimate goal of a conditional subsidy scheme is to limit 
both inappropriate use of ACTs by those without a test or with a negative test as well as to 
reduce the programmatic cost of ACT subsidies by offering them conditionally on a positive test 
at the point of sale. For this outcome, a positive test is anyone who is tested at the outlet and 
receives a positive result or is referred from a health facility with a documented positive test 
result. Although all ACTs purchased after a test, whether tested at the outlet or health facility, 
are included in the outcome measure, only ACTs purchased following a test at the outlet are 
eligible for the conditional subsidy. Our major secondary outcome is proportion of suspected 
malaria cases that are tested. This outcome will allow us to determine whether the conditional 
subsidy can drive demand for testing. Other secondary outcomes will measure 1) adherence to 
the RDT result among all those tested, 2) proportion of all suspected malaria cases that are 
managed appropriately (tested for malaria, and use ACT following a positive test and do not 
purchase an ACT after a negative test), and 3) ACT use by test status (untested, negative, 
positive). 
 

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4 
Study Population 
4.1 
Selection of the Study Population 
All clients attending a participating outlet on the day selected for exit interviews will be eligible to 
be screened for inclusion into the interview sample.  Only those who meet all the criteria will be 
counted towards the sample size.  
4.2 
Inclusion/Exclusion Criteria 
OUTLETS: Only outlets who are registered with the Kenya Pharmacy and Poisons Board and 
who stock ACTs will be asked to participate 
PARTICIPANTS- 
INCLUSION CRITERIA: 
- 
Participants with fever, or history of fever in the last 48 hours, or suspects they may have 
malaria 
- 
Individual with malaria-like illness must be present at recruitment 
- 
Older than one year of age 
EXCLUSION CRITERIA:  
- 
Any individual with signs of severe illness requiring immediate referral 
- 
Individuals who have taken an antimalarial in the last seven days, including for the 
current illness 
- 
Patients <18 years without a parent or legal guardian present 
- 
Adults who are unable to consent for themselves 
 

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5 
STUDY PROCEDURES/EVALUATIONS 
5.1 
Study Procedures 
On random days of the month, clients leaving the pharmacy will be asked to participate in a brief 
survey. Those who meet the inclusion/exclusion criteria will provide verbal consent before 
responding to questions about their current illness and their decisions regarding testing and 
medicines purchased. These clients will have been offered an RDT at the pharmacy if they have 
malaria-like symptoms. RDTs will be available in all participating pharmacies at the same price. 
Those who do not wish to purchase an RDT are free to conduct their transaction as planned.  
The exit survey will be conducted in one session and last approximately 15-20 minutes.  
5.2 
Laboratory Evaluations 
None 

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6 
STATISTICAL CONSIDERATIONS 
6.1 
Study Outcome Measures 
The primary outcome measure is the proportion of all ACTs that are sold to individuals with a 
positive malaria diagnostic test defined as anyone who is tested at the outlet and receives a 
positive test result or is referred from a health facility with a documented positive test result. 
Among secondary outcomes, behavior will be measured for individuals who were tested at the 
outlet. Untested clients are those who chose not to be tested and did not come with a test result 
from another facility. The major secondary outcome is the proportion of suspected malaria 
cases that are tested. This outcome will allow us to determine whether the conditional subsidy 
can drive demand for testing. Note that “untested people” here refers specifically to individuals 
that present at the shop without a referral but decline to be tested 
 
Other secondary outcomes will measure 1) adherence to the RDT result among all those tested 
at the outlet, 2) proportion of all suspected malaria cases that are managed appropriately 
(tested for malaria, and use ACT following a positive test or do not purchase an ACT after a 
negative test), and 3) ACT use by untested clients.  
- Secondary outcome 2: Adherence to the RDT result among all those tested in the shop 
 
 For those who are negative, if they buy any antimalarial (AM), including monotherapies and 
older therapies, they are not adhering to test result. 
- Secondary outcome 3: Proportion of all suspected malaria cases that are managed 
appropriately in the shop 
 
“Untested people” here refers specifically to individuals that present at the shop without a 
referral but decline to be tested 
- Secondary outcome 4: ACT use among untested clients 
# 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑤𝑤ℎ𝑜𝑜 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑛𝑛𝑛𝑛𝑛𝑛 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖 𝑡𝑡ℎ𝑒𝑒 𝑠𝑠ℎ𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎𝑎𝑎 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎 𝐴𝐴𝐴𝐴𝐴𝐴
# 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑤𝑤ℎ𝑜𝑜 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑛𝑛𝑛𝑛𝑛𝑛 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑖𝑖𝑖𝑖 𝑡𝑡ℎ𝑒𝑒 𝑠𝑠ℎ𝑜𝑜𝑜𝑜, 𝑑𝑑𝑑𝑑𝑑𝑑 𝑛𝑛𝑛𝑛𝑛𝑛 ℎ𝑎𝑎𝑎𝑎𝑎𝑎 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎 𝐴𝐴𝐴𝐴 
 
For the primary outcome, we include in both numerator and denominator those individuals who 
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primary outcome we are interested in evaluating the degree to which the interventions impacted 
the purchasing behavior of all suspected malaria cases seeking treatment. Our secondary 
outcomes do not include these individuals (i.e. the outcomes are calculated on the subset of 
individuals that present at the shop for testing without a referral). That is, for the secondary 
outcomes we are specifically interested in evaluating the degree to which the interventions 
impacted purchasing behavior among individuals who present at the shop for testing. 
 
 
6.2 
Sample Size Considerations 
 
Our primary comparison of interest is the effect on targeting of ACTs of offering a 
combination of outlet incentives for testing (provider-directed intervention), and ACT subsidies 
for malaria test-positive clients (client-directed intervention) relative to a control arm where 
outlets are able to offer malaria testing but neither the provider nor the client receives any 
extrinsic incentive to test (Arm 4 versus Arm 1). In order to evaluate whether the client-directed 
intervention and the provider-directed intervention do, in fact, have a synergistic effect on the 
outcome, we also have two secondary comparisons of interest: (1) the combined provider- and 
client-directed interventions relative to the provider-directed intervention alone (Arm 4 versus 
Arm 2) and (2) the combined provider- and client- directed interventions relative to the client-
directed intervention alone (Arm 4 versus Arm 3).  
  
We calculated power based on a cluster randomized two-sample two-tailed t-test for the 
comparison of two proportions using standard formulae [27]. We assumed 40 clusters (outlets) 
would be available to be enrolled based on information about the number of registered 
pharmacies in the study area. We calculated power for differences in our primary outcome for 
each of the three comparisons of interest noted above. To ensure that our overall two-tailed 
Type I error (alpha) is 5%, we fixed the alpha level at 1.667% (i.e. 5%/3) for each of the 3 
comparisons of interest, using the conservative Bonferroni correction [28]. We estimated the 
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Our primary 
outcome—the 
proportion of 
ACTs that are 
taken by malaria 
test-positive 
clients— is a 
combination of the 
proportion of 
individuals who 
get tested for 
malaria and the 
proportion of 
tested individuals 
who treat 
according to the test result. We anticipate that 12% of ACTs will be purchased by clients with a 
positive test in the control arm (with 88% of ACTs purchased by those without a test or with a 
negative test). Moreover, we hypothesize that both the provider-directed intervention, and the 
client directed intervention on their own will only increase testing marginally (on the order of ~5 
percentage points), but that among those who do test, the client-directed intervention alone will 
substantially increase the proportion of malaria-positive individuals who take an ACT [29]. As a 
result, we expect that the provider-directed intervention (Arm 2) will have only a small effect on 
ACT targeting, that the client-directed intervention (Arm 3) will have a somewhat larger effect, 
but that the largest effect will come from combining the two interventions (Arm 4) (i.e., we 
assume a statistical interaction). With a sample of 170 exit interviews per outlet (40 outlets in 
Kenya), we will have >90% power to detect a minimum difference between Arms 1 and 4 in the 
primary outcome of 23 percentage points. We will also have >89% power to detect a minimum 
difference of 16 percentage points for the main secondary comparison of interest (testing 
uptake). 
Due to the nature of the interventions, it is not possible to blind participants and the 
implementation team to the allocation received. Data collectors will be blinded throughout 
collection and study statisticians will be blinded during the analysis phase.  
 
6.3 
Participant Enrollment and Follow-Up 
Medicine retail outlets will be eligible if they regularly stock and sell regulatory approved 
artemether-lumefantrine (AL) and are licensed medicine outlets. From this roster of all eligible 
outlets, 48 will be randomly selected (8 additional above the required sample size). Of those 
retail outlets, 40 outlets will be randomly selected for training and enrollment in the study and 
will be randomized to one of the 4 study arms in a 1:1:1:1 ratio. The remaining 8 outlets will be 
alternates in the event that any of the initial outlets drop out of the intervention. The alternate 
Table 2: Assumptions in Kenya and Nigeria for sample size calculation for Aim 2  
  
Expected Percentage 
of ACTs taken by 
Clients Testing 
Positive for Malaria  
Power 
 (# of exit 
interviews  
per arm) 
  
Kenya 
Nigeria 
Kenya 
(1700) 
Nigeria 
 (2040) 
Arm 1: Control Arm  
(RDT only, no incentives) 
12% 
15% 
>99% 
>99% 
Arm 2: Provider-Directed 
Intervention (Shopkeeper 
incentives for malaria testing) 
14% 
17% 
>99% 
>99% 
Arm 3: Client-Directed 
Intervention (ACT subsidy to client 
conditional on positive test) 
19% 
26% 
91% 
89% 
Arm 4: Combined Provider- and 
Client-Directed Interventions  
35% 
40% 
Ref. 
Group 
Ref. 
Group 

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outlets will also be randomized equally across the 4 study arms (2 alternates per arm per site), 
but they will not undergo training unless they are needed for replacement. 
 
We will retain these enrolled outlets through effective supportive supervision and 
communication between providers at participating outlets and study staff. All outlets will receive 
thorough, in-person training on using the mobile reporting tool, conducting RDTs and overall 
study procedures. They will also receive extra support during the start of the intervention and 
burn in period (3 months), including an initial stock of RDTs provided by the study at no cost. At 
least once per month, the PI, Project Manager, and/or Field Coordinator will visit each outlet in 
person to answer questions, check RDT stock and testing performance, and identify and 
address any problems. Providers will also receive phone calls from the Field Coordinator and 
Project Manager between in-person visits and will themselves be provided with phone numbers 
and encouraged to call the study staff with any questions or concerns. Additionally, outlets will 
be reimbursed promptly to ensure participation in the study presents no financial strain.  
 
In our previous work, we have had excellent retention of outlets in research activities and 
therefore have no reason to expect attrition of clusters. However, should one of the outlets 
selected choose to leave the study within the first 6 months of the intervention, the outlet will be 
replaced by one of the alternate outlets assigned to the same arm. If any outlet chooses to 
leave the study after the first 6 months of the intervention, they will not be replaced because, 
after joining the intervention at such a late stage, a newly added outlet is not likely to be 
comparable to that of all other participating outlets. Changes in the clusters (dropped and/or 
replaced) will be accounted for in the analysis.  
 
Our sample size estimates correspond to a total of 6800 exit interviews with clients. 
Since not everyone interviewed will have purchased an ACT, our estimates account for the fact 
that only a subset will enter into our analysis for the primary outcome.  
6.4 
Analysis Plan 
We will analyze client-level outcomes by fitting a modified Poisson regression model [30, 
31] with log link to estimate risk ratios (RRs) and identity link to estimate risk differences. Such 
an approach assumes a Poisson distribution for the binary outcome and then ‘fixes’ the 
estimated standard errors to correct for model misspecification.  
 
To account for clustering by outlet we will use a generalized estimating equations (GEE) 
[32, 33] approach with exchangeable working covariance matrix and robust standard errors (to 
correct for model misspecification due to specifying a Poisson distribution). The outcome will be 
regressed on three binary indicators for each of the treatment arms 1-3, with treatment arm 4 
(the combined interventions) serving as the reference group. The model will also include a 
vector of potential confounder variables (e.g., age, gender, education, household distance to 
closest health facility, study quarter) to account for possible imbalances between study arms. All 
analyses will be based on the intention-to-treat principle whereby all clients will be included in 
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they sought care (e.g. even if they did not use the ACT subsidy if they tested positive in an 
outlet in Arms 3 and 4 that received the client-directed intervention).  
 
Given that the literature indicates that when there are fewer than 40 clusters in a cRCT, 
small sample correction methods should be used to ensure that standard error estimates are 
correctly estimated when using GEE to analyze binary outcomes, and given that the size of the 
cRCTs in each country are close to this cut-off, we plan to adopt the use of the Kauerman-
Carroll correction to avoid any possible problems [34, 35]. We will compare secondary 
outcomes using the same modeling approach. 
 
 
 

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7 
SUBJECT CONFIDENTIALITY 
Participant confidentiality will be maintained during and after the interview in several ways: 
- 
The interview will be conducted in a private place where the interview cannot be 
overheard. 
- 
Interviewers will be trained in research integrity and ethics, including protecting 
participant information. 
- 
Informed consent will be conducted verbally, thereby reducing the risk of a breach of 
client confidentiality by eliminating any paper record of clients’ participation in the study 
with signature. 
- 
Data will be collected on tablets which will be encrypted and password protected. 
- 
Individuals will be assigned a unique study ID. No information that could be used to 
identify the participant will be recorded such as names, identification numbers, dates of 
birth or address.  
- 
Anyone older than 80 years of age will be recorded as ‘80.’ 
- 
Data will be transferred from the tablet to a secure, password protected computer once 
per week.  
- 
Data will be stored on an encrypted, password protected computer and backed up on 
Duke Box. The data will only be accessible to the Data Manager and the PI. It will be 
reviewed regularly to ensure quality and completeness. 
- 
Only fully de-identified data will be provided to other study personnel or statisticians. 
Only fully de-identified data will be shared.  
 
 
7.1 Future Use of Stored Specimens 
Not applicable. 
 

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8 
INFORMED CONSENT PROCESS 
Pharmacies will be approached and the study described. If they agree to participate, they will be 
asked to consent to a baseline survey that collects details about the outlet (Appendix 4,5). This 
will be conducted in a private area and at a time that is convenient for their business.  
Informed consent will be obtained prior to collecting any participant information for research 
purposes. Clients will be approached as they leave the pharmacy and asked if they would be 
willing to participate in a brief interview. If so, the interviewer will conduct screening and consent 
in a private area away from other clients. The client will be given time to ask questions and will 
be asked to give verbal consent for the interview, which will be documented by the interviewer. 
The interviewer will explain the purpose of the study, the risks, benefits, and safeguards in place 
to protect the participant’s information. A printed copy of the consent script will be available for 
them to read and to keep. Contact information for the Study Coordinator or PI will be provided 
so the participant can ask questions after the interview.  
No individual identifying information will be collected during the interview, therefore withdrawing 
consent after the interview is closed will not be possible. To mitigate this, the interviewer will 
confirm consent by asking at the end of the survey if the participant is comfortable with all the 
answers and agrees for their information to be retained.  
8.1 
Informed Consent/Assent Process (in Case of a Minor or 
Others Unable to Consent for Themselves) 
 
The study will only enroll minor participants present with a parent or guardian and all questions 
will be directed to the parent/guardian. Therefore, we do not anticipate interacting with minors or 
needing assent from the minor.  
 
We will not enroll individuals who are unable to consent for themselves such as those who are 
mentally impaired or are experiencing impaired consciousness.  

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9 
RESULT DISSEMINATION PLAN 
The results from this trial will be disseminated to the county and sub-county health teams, 
National Malaria Control Program, and the Director of Medical Services through meetings and a 
policy brief.  
 
We shall also support the sub-county health teams to disseminate the results at the community 
level through barazas or CHWs depending on plans for scale-up coming from national partners.  

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10 
LITERATURE REFERENCES 
1. 
Gething, P.W., et al., Mapping Plasmodium falciparum Mortality in Africa between 1990 
and 2015. N Engl J Med, 2016. 375(25): p. 2435-2445. 
2. 
World Health Organization., World Malaria Report 2016. 2016, World Health 
Organization: Geneva. 
3. 
Unitaid., Global Malaria Diagnostic and Artemesinin Treatment Commodities in Demand 
Forecast 2017-2020. 2018, World Health Organization: Vernier, Switzerland. 
4. 
Lindblade, K.A., et al., Mortality of sick children after outpatient treatment at first-level 
health facilities in rural western Kenya. Trop Med Int Health, 2007. 12(10): p. 1258-68. 
5. 
Opoka, R.O., et al., Inpatient mortality in children with clinically diagnosed malaria as 
compared with microscopically confirmed malaria. Pediatr Infect Dis J, 2008. 27(4): p. 
319-24. 
6. 
Nsanzabana, C., et al., Quantifying the evolution and impact of antimalarial drug 
resistance: drug use, spread of resistance, and drug failure over a 12-year period in 
Papua New Guinea. J Infect Dis, 2010. 201(3): p. 435-43. 
7. 
Hastings, I.M., Modelling parasite drug resistance: lessons for management and control 
strategies. Trop Med Int Health, 2001. 6(11): p. 883-90. 
8. 
O'Meara, W.P., D.L. Smith, and F.E. McKenzie, Potential impact of intermittent 
preventive treatment (IPT) on spread of drug-resistant malaria. PLoS Med, 2006. 3(5): p. 
e141. 
9. 
Cohen, J.M., et al., Public health. Optimizing investments in malaria treatment and 
diagnosis. Science, 2012. 338(6107): p. 612-4. 
10. 
World Health Organization., World Malaria Report 2015. 2015, World Health 
Organization: Geneva. 
11. 
Mbonye, A.K., et al., Treatment of fevers prior to introducing rapid diagnostic tests for 
malaria in registered drug shops in Uganda. Malar J, 2013. 12: p. 131. 
12. 
Briggs, M.A., et al., Prevalence of malaria parasitemia and purchase of artemisinin-
based combination therapies (ACTs) among drug shop clients in two regions in 
Tanzania with ACT subsidies. PLoS One, 2014. 9(4): p. e94074. 
13. 
Nwokolo, E., et al., Misuse of Artemisinin Combination Therapies by Clients of Medicine 
Retailers Suspected to Have Malaria Without Prior Parasitological Confirmation in 
Nigeria. Int J Health Policy Manag, 2018. 7(6): p. 542-548. 
14. 
Montagu, D., et al., Recent trends in working with the private sector to improve basic 
healthcare: a review of evidence and interventions. Health Policy Plan, 2016. 31(8): p. 
1117-32. 
15. 
Sudhinaraset, M., et al., What is the role of informal healthcare providers in developing 
countries? A systematic review. PLoS One, 2013. 8(2): p. e54978. 
16. 
Boyce, M.R. and W.P. O'Meara, Use of malaria RDTs in various health contexts across 
sub-Saharan Africa: a systematic review. BMC Public Health, 2017. 17(1): p. 470. 
17. 
Hanson, K. and C. Goodman, Testing times: trends in availability, price, and market 
share of malaria diagnostics in the public and private healthcare sector across eight sub-
Saharan African countries from 2009 to 2015. Malar J, 2017. 16(1): p. 205. 
18. 
Cohen, J., et al., Introducing rapid diagnostic tests for malaria to drug shops in Uganda: 
a cluster-randomized controlled trial. Bulletin of the World Health Organization, 2015. 
93(3): p. 142-151. 

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19. 
Maloney, K., et al., Expanding access to parasite-based malaria diagnosis through retail 
drug shops in Tanzania: evidence from a randomized trial and implications for treatment. 
Malar J, 2017. 16(1): p. 6. 
20. 
Onwujekwe, O., et al., Effectiveness of Provider and Community Interventions to 
Improve Treatment of Uncomplicated Malaria in Nigeria: A Cluster Randomized 
Controlled Trial. PLoS One, 2015. 10(8): p. e0133832. 
21. 
Awor, P., et al., Increased access to care and appropriateness of treatment at private 
sector drug shops with integrated management of malaria, pneumonia and diarrhoea: a 
quasi-experimental study in Uganda. PLoS One, 2014. 9(12): p. e115440. 
22. 
Mbonye, A.K., et al., A Cluster Randomised Trial Introducing Rapid Diagnostic Tests into 
Registered Drug Shops in Uganda: Impact on Appropriate Treatment of Malaria. PLoS 
One, 2015. 10(7): p. e0129545. 
23. 
Ansah, E.K., et al., The impact of providing rapid diagnostic malaria tests on fever 
management in the private retail sector in Ghana: a cluster randomized trial. Bmj, 2015. 
350: p. h1019. 
24. 
Visser, T., et al., Introducing malaria rapid diagnostic tests in private medicine retail 
outlets: A systematic literature review. PLoS One, 2017. 12(3): p. e0173093. 
25. 
Hansen, K.S., T.H. Lesner, and L.P. Osterdal, Optimal price subsidies for appropriate 
malaria testing and treatment behaviour. Malar J, 2016. 15(1): p. 534. 
26. 
Hutchinson, E., et al., Introducing rapid tests for malaria into the retail sector: what are 
the unintended consequences? BMJ Glob Health, 2017. 2(1): p. e000067. 
27. 
Hayes, R.J. and L.H. Moulton, Cluster Randomized Trials, ed. C.H.C.P.I. Statistics. 
2009. 
28. 
Aickin, M. and H. Gensler, Adjusting for multiple testing when reporting research results: 
the Bonferroni vs Holm methods. Am J Public Health, 1996. 86(5): p. 726-8. 
29. 
Prudhomme O'Meara, W., et al., Assessing the independent and combined effects of 
subsidies for antimalarials and rapid diagnostic testing on fever management decisions 
in the retail sector: results from a factorial randomised trial in western Kenya. BMJ Glob 
Health, 2016. 1(2): p. e000101. 
30. 
Zou, G., A modified poisson regression approach to prospective studies with binary data. 
Am J Epidemiol, 2004. 159(7): p. 702-6. 
31. 
Zou, G.Y. and A. Donner, Extension of the modified Poisson regression model to 
prospective studies with correlated binary data. Stat Methods Med Res, 2013. 22(6): p. 
661-70. 
32. 
Zeger, S.L., K.Y. Liang, and P.S. Albert, Models for longitudinal data: a generalized 
estimating equation approach. Biometrics, 1988. 44(4): p. 1049-60. 
33. 
Preisser, J.S., et al., An integrated population-averaged approach to the design, analysis 
and sample size determination of cluster-unit trials. Stat Med, 2003. 22(8): p. 1235-54. 
34. 
Adeyi, O. and R. Atun, Innovating for impact: the Affordable Medicines Facility-malaria 
(AMFm). Nat Med, 2009. 15(9): p. 991. 
35. 
Amfm Independent Evaluation Team, et al., Independent Evaluation of Phase 1 of the 
Affordable Medicines Facility - malaria (AMFm), Multi-Country Independent Evaluation 
Final Report. 2012, ICF International and London School of Hygiene and Tropical 
Medicine: Calverton, Maryland and London. 

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SUPPLEMENTS/APPENDICES 
 
 

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Appendix A.1 
 
Aim 2: TESTsmART Participant exit interview 
 
SCREENING QUESTIONS 
Number QUESTION 
RESPONSE 
SKIP 
0.01 
Did the person meet all of the 
inclusion criteria? 
1. Yes   
2. No 
If yes, skip to 
0.03 
0.02 
If no, why were they excluded 
from participating in the study 
today? (select all that apply) 
1. Person did not have a fever or malaria-
like illness 
2. Patient with malaria-like symptoms not 
present 
3. Patient younger than 1 year of age 
4. Had symptoms of severe malaria and 
referred for care 
5. Took antimalarial in the last seven days 
for current illness 
6. No parent or guardian present 
STOP 
0.03 
Did the person consent to 
participate in the study? 
1. Yes  
2. No 
If No, STOP 
 
 
GENERAL STUDY INFORMATION 
Number QUESTION 
RESPONSE 
SKIP 
0.1 
Date 
MM/YYYY 
 
0.2 
Participant ID 
 
 
0.3 
Outlet ID 
 
 
0.4 
Interviewer ID 
 
 
0.5 
Language of interview 
 1. English 
 2. Swahili 
 3. Bukusu 
 4. Other 
 
 

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SECTION 1: RESPONDENT INFORMATION 
Number QUESTION 
RESPONSE 
SKIP 
1.1 
Who is the respondent? 
Adult with fever……………………….1 
Guardian of the child………………….2 
Q1.5 
 
1.2 
What is your relationship with the 
child?  
 
Parent…………………………………..1 
Grandmother/grandfather…………….2 
Brother/Sister………………………….3 
Uncle/Aunt…………………………..…4 
Other……………………………………5 
        Specify:  
 
1.3 
Gender of the child 
Female…………………………..1 
Male……………………………..0 
 
1.4 
 
 
How old is the child? 
 
      __  ___  .  __ years  
 
 
1.5 
Gender of the respondent 
Female…………………………..1 
Male……………………………..0 
 
 
1.6 
How old are you? 
[In the case the child is ill, please 
collect this information for the 
parent/guardian of the child] 
 
 
  
     _____  _____  years 
 
 
 
SECTION 2:  CURRENT ILLNESS 
 
Number QUESTION 
RESPONSE 
 
SKIP 
2.1 
Which symptoms do you/your child 
have or had in the last 24 hours? 
[Mark all that apply] 
 
1. Fever  
2. Nausea  
3. Headache  
4. Body aches  
5. Vomiting  
6. Shivering  
7. Stomach ache  
8. Other______________________ 
99. Don’t know  
    

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2.2 
How serious is this illness in your 
opinion? 
 
[Guardian of the child can guess how the 
child feels]  
1. Not very serious/minor 
2. Moderate 
3. Very serious 
 
 
2.3 
 How many days ago did the symptoms 
start?  
_______days 
99=Don't 
know 
2.4 
How likely is that the illness that 
you/your child have today is malaria? 
 
1. Not possible  
2. Unlikely but not impossible 
3. 50/50 
4. Likely 
5. Absolutely sure 
99.  Don’t know  
 
2.5  
Have you sought treatment or care 
elsewhere for this illness?  
1. Yes   
2. No 
      99. Don’t know 
If no or 
don’t know, 
go to 3.1 
2.6 
What did you do? Check all that apply 
1. Visit hospital  
2. Visit government health 
center/dispensary 
3. Visit private clinic  
4. Visit private laboratory  
5. Visit pharmacy/chemist  
6. Buy medicine at general shop  
7. Gave medicine available at home   
8. Visit traditional healers  
9. Visit religious/cultural healers  
10. Visit CHW    
11. Other_____________________ 
99. Don’t know (i.e., another caregiver 
was involved) 
 
2.7 
Why did you come to this facility? 
1. Treatment was not available in 
other facilities 
2. RDT tests were not available in 
other facilities 
3. It’s the closest chemist shop to me  
4. This chemist shop has better prices 
5. I was referred here for treatment 
6. OTHER 
99. Don’t know (i.e., another caregiver 
was involved) 
 

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2.8 
Did you have a malaria test from 
another provider/facility/lab before 
coming here today? 
1. Microscopy 
2. RDT 
3. Don’t know 
4. No test 
If 4, go to 
3.1 
2.9 
Malaria test results from elsewhere 
1. Negative – report observed 
2. Positive – report observed 
3. Reported negative by respondent 
4. Report positive by respondent 
5. Doesn’t recall result 
 
2.10 
Do you have a copy of the test result? 
1. No, nothing written 
2. Yes  
 
2.11 
How likely is it that the result of that 
test was correct? 
Only if tested elsewhere before coming 
to the shop today 
 
 
1. Not possible  
2. Unlikely but not impossible 
3. 50/50 
4. Likely 
5. Absolutely sure 
 Don’t know 
 
2.12 
Have you taken/given to the child any 
medication for this illness since it 
started?  
1. Yes   
2. No 
99. Don’t know  
If 1, go to 
2.12 
If 2 or 99, 
go to 
Section 3 
2.13 
If so, which medicines (select all that 
apply) 
1. AL (Lonart/CoArtem/Artefan) 
2. Other ACT (DHAP, DP, 
Duocotexin, P-alaxin) 
3. Monotherapy (Artesunate, Quinine, 
Chloroquine, SP/Fansidar) 
4. Antibiotic (Amoxyl/Ceptrin, 
Metronidazole/Flagyl/Ampicillin) 
5. Painkiller/fever medecine 
(Panadol/Brufen/Hedex/ 
Action/Maramoja) 
6. Cough medicine or decongestant 
7. Other________________ 
99. Don’t know 
If 1, 2 or 3 
STOP 
2.14 
Did you come to the pharmacy today 
with a prescription from another 
provider/facility/lab?  
1. Yes   
2. No 
 
If 2 skip to 
Section 3. 
2.15 
If yes, what was the prescription for? 
1. AL (Lonart/CoArtem/Artefan) 
2. Other ACT (DHAP, DP, 
Duocotexin, P-alaxin) 
 

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3. Monotherapy (Artesunate, Quinine, 
Chloroquine, SP/Fansidar) 
4. Antibiotic (Amoxyl/Ceptrin, 
Metronidazole/Flagyl/Ampicillin) 
5. Painkiller/fever medecine 
(Panadol/Brufen/Hedex/ 
Action/Maramoja) 
6. Cough medicine or decongestant 
7. Other________________ 
99. Don’t know 
2.16 
Did the client show documentation of 
the prescription? 
1. No 
2. Yes, a written prescription from the 
provider/facility/lab 
3. Yes, other (Specify:___________) 
 
 
Section 3: Test at Shop 
 
Number 
QUESTION 
RESPONSE 
SKIP 
3.1 
Did you (or your child) have your blood 
tested for malaria today at the shop? 
1. Yes 
2. No 
If yes skip 
to 3.3 
3.2 
Why not? (Mark all that apply) 
1. Too expensive 
2. RDT was not offered 
3. RDT not in stock 
4. No time 
5. Already sure illness is malaria 
6. Already sure illness is not malaria 
7. Don’t want to get finger pricked 
8. Was tested elsewhere before 
coming 
9. Had a prescription for drug I 
needed before coming  
99. Other 
                Specify: ________________ 
Next skip to 
section 4 
3.3 
RDT results from shop (self-report) 
1. Negative 
2. Positive 
3. Invalid 
 
3.4 
How likely is it that the result of the test 
was correct? 
Only if tested  
1. Not possible  
2. Unlikely but not impossible 
3. 50/50 
4. Likely 
 

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5. Absolutely sure 
 Don’t know 
 
Section 4: Purchase Questions 
 
Number QUESTION 
RESPONSE 
SKIP 
4.1 
 
Which medicine(s) did you obtain from 
the shop today to treat your/your child’s 
illness 
Choose all that apply,  
1. AL (Lonart/CoArtem/Artefan) 
2. Other ACT (DHAP, DP, 
Duocotexin, P-alaxin) 
3. Other antimalarial (Artesunate 
injection, Quinine, Chloroquine, 
SP/Fansidar) 
4. Antibiotic (Amoxyl/Ceptrin, 
Metronidazole/Flagyl/Ampicillin) 
5. Painkiller/fever medicine 
(Panadol/Brufen/Hedex/ 
Action/Maramoja) 
6. Cough medicine or decongestant 
7. Other________________ 
8. None 
99. Don’t know 
If 2, 3, 4, 5, 
6, 7 or 99 
Skip to 4.4 
 
If 8, skip to 
5.1 
4.2 
How much did you pay today for the 
ACT? 
   
(The amount refers to how much money 
respondent paid for the ACT. Don’t add 
the amount of the voucher if any was 
used-the amount should be between 0-
5000, or 9999 for don’t know) 
 
 
    
      ACT= ________KES 
 
OR 
 
      Don’t know/remember…9999 
 
4.3 
How much did you spend at the 
pharmacy today? (Total cost to client 
including RDT, ACT and any other 
drugs)  
 
      _________ KES 
 
4.4 
Was there any discount for your ACT at 
the shop today? 
1. Yes  
2. No 
3. Don’t know 
 
 

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4.5 
I would like to record information about 
each drug you purchased today. May I 
see the packaging?  
1. Type _______________ 
2. Brand Name _______________ 
3. Price _______________ KES 
4. Did participant show you the drug? 
1. Yes 
2. No 
Repeat for 
every drug 
purchased 
for illness 
 
Section 5: Household Characteristics 
 
Number 
QUESTION 
RESPONSE 
SKIP 
5.1 
What is the main source of drinking 
water for your household?   
1. Piped water/Public Tap/borehole  
2. Unprotected well 
3. Protected well 
4. Protected Spring 
5. Unprotected Spring 
6. Surface water (river, dam, lake, 
pond, stream, canal, irrigation 
channel) 
7. Rain water 
8. Bottled water 
9. Other ......................................... 
 
 
 
 
 
 
5.2 
Does your household have the 
following items:  
  
 
a) 
Electricity? 
1. Yes 
2. No 
99. Don’t know 
 
 
b) 
A television? 
1. Yes 
2. No 
99. Don’t know 
 
c) 
A refrigerator? 
1. Yes 
2. No 
     99. Don’t know 
 
d) 
A radio? 
1. Yes 
2. No 
      99. Don’t know 
 
e) 
A mobile phone (at least one member 
of the household has)? 
1. Yes 
2. No 
      99. Don’t know 
 
 
 

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f) 
A motorcycle (at least one member of 
the household has)? 
1. Yes 
2. No 
      99. Don’t know 
 
 
g) 
A car/truck? 
1. Yes 
2. No 
      99. Don’t know 
 
 
h) 
A bank account (at least one member 
of the household has)? 
1. Yes 
2. No 
      99. Don’t know 
 
 
5.3 
 
How many of the following livestock 
does your household have? 
1. None  
99. Don't know 
 
 
a) 
Cows 
  
 
b) 
Sheep 
  
 
c) 
Goats 
 
 
d) 
Pigs 
 
 
5.4 
What kind of toilet does your 
household have? 
2. Flush or pour flush toilet  
3. VIP / Ventilated improved pit latrine 
4. Pit latrine with slab  
5. Pit latrine without slab  
6. Composting toilet  
7. Bucket toilet 
8. No facility / bush / field  
9. Other (Please specify): 
____________________ 
 
5.5 
What type of fuel does your household 
mainly use for cooking? 
1. Liquefied petroleum gas 
2. Paraffin/Kerosene 
3. Charcoal 
4. Firewood 
5. Dung 
6. Biogas 
7. Crop residue 
8. Other (Specify)_____________ _ 
 
5.6 
Do you/your family own the house you 
live in? 
1. Own the house 
2. Rent the house 
 

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5.7 
What is the main material of the floor in 
your house? 
3. Earthen  
4. Cement 
5. Floor Tiles  
6. Wood planks  
7. Polished wood 
8. Other (please specify) 
______________________ 
 
5.8 
What is the main material of the walls 
in your house? 
1. Stone  
2. Brick  
3. Timber 
4. Iron Sheet 
5. Mud  
6. Wood  
7. Cement 
8. Other (please 
specify)_____________ 
 
5.9 
What is the main material of the roof of 
your house? 
1. Iron sheets  
2. Roof tiles  
3. Grass Thatched 
4. Wood  
5. Other (please 
specify)______________ 
 
5.10 
How many acres/hectares/feet of land 
for farming does your household own?  
 1.None ............................................... 0 
 2. Acres______ 
 3. Square Feet (xx by xx)_______ 
 
 9999. Don't know 
 
5.11 
What is the highest level of schooling 
you completed?  
[In the case the child is ill, please 
collect this information for the 
parent/guardian of the child] 
1. None 
2. Primary 
3. Secondary 
4. College 
5. University 
 
 
 
5.12 
What is your primary occupation? 
In the case the child is ill, please collect 
this information for the parent/guardian 
of the child] 
 
Agriculture 
1. Farming/Livestock keeping 
Paid employee 
2. Government or parastatal 
3. Private(specify: ......................... ) 
4. Self-employed With employees 
5. Without employees (e.g. 
motorcycle taxi, vendor) 
 

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6. Unpaid family helper in a business  
Other (Not in agriculture) 
7. Casual worker/day laborer 
8. House help  
9. Homemaker 
10. Student 
11. Not available to work 
12. Other (Specify) ____________  
 
 
 
 
 

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