Impact of Long-lasting Insecticidal Nets (LLINs) Treated With Pyrethroid Plus Pyriproxyfen vs LLINs Treated With Pyrethroid Plus Piperonyl Butoxide on Malaria Incidence in Uganda: a Cluster-randomised Trial
Phase: NA
Conditions: Malaria
Sponsor: University of California, San Francisco

 
 
 
 
LLIN Evaluation in Uganda Project (LLINEUP2) 
 
 
Study Title: Impact of long-lasting insecticidal nets  
(LLINs) treated with pyrethroid plus pyriproxyfen vs  
LLINs treated with pyrethroid plus piperonyl butoxide  
on malaria incidence in Uganda: a cluster-randomised trial 
 
 
 
 
 
 
 
Sponsored by: National Institute of Allergy and Infectious Diseases (NIAID) 
 
 
DMID Funding Mechanism: NIH/NIAID U19AI089674 
 
 
Principal Investigators: Prof Sarah Staedke, Prof Moses Kamya  
Co-investigators: Prof Grant Dorsey, Prof Martin Donnelly,  
Dr Joaniter Nankabirwa, Dr Catherine Maiteki-Sebuguzi, Dr Jimmy Opigo,  
Dr Bryan Greenhouse, Dr Jessica Briggs, Dr Isabel Rodriquez-Barraquer, 
Dr Catherine Pitt, Katherine Snyman 
 
 
Protocol Version 7.0, 17 Oct 2023 
 
 
 
 
 
 

LLINEUP2 Study protocol, Version 7.0, 17 Oct 2023 
 
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Table of Contents 
List of associated consent forms ........................................................................................................................5 
List of appendices ...............................................................................................................................................5 
Statement of compliance ...................................................................................................................................6 
Signature page ....................................................................................................................................................7 
Abbreviations & acronyms .................................................................................................................................8 
Study summary ...................................................................................................................................................9 
Study personnel and institutions ..................................................................................................................... 10 
Investigators ................................................................................................................................................ 10 
Collaborating institutions ............................................................................................................................ 12 
Institutional review boards.......................................................................................................................... 13 
1 
Background .............................................................................................................................................. 14 
1.1 
Introduction ................................................................................................................................. 14 
1.2 
Malaria control interventions in Uganda .................................................................................... 14 
1.3 
LLINs in Uganda ........................................................................................................................... 15 
1.4 
IRS in Uganda ............................................................................................................................... 16 
1.5 
Health facility surveillance ........................................................................................................... 17 
1.6 
Insecticide resistance................................................................................................................... 19 
1.7 
LLINs treated with piperonyl butoxide ........................................................................................ 19 
1.8 
LLINEUP trial ................................................................................................................................ 20 
1.9 
Dual active-ingredient LLINs with pyriproxyfen .......................................................................... 21 
2 
Rationale .................................................................................................................................................. 22 
3 
Study objectives ....................................................................................................................................... 23 
4 
Study design & methods .......................................................................................................................... 24 
4.1 
Overview ...................................................................................................................................... 24 
4.2 
Study sites .................................................................................................................................... 24 
4.3 
Randomisation ............................................................................................................................. 25 
4.4 
Sensitisation................................................................................................................................. 25 
4.5 
Enumeration survey..................................................................................................................... 25 
5 
Intervention ............................................................................................................................................. 27 
5.1 
Overview ...................................................................................................................................... 27 
5.2 
Household registration and LLIN distribution ............................................................................. 27 

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5.3 
Social behaviour change communication .................................................................................... 28 
6 
Health facility surveillance ....................................................................................................................... 29 
6.1 
Data collection at MRCs ............................................................................................................... 29 
7 
Cross-sectional surveys............................................................................................................................ 30 
7.1 
Overview ...................................................................................................................................... 30 
7.2 
Definitions.................................................................................................................................... 30 
7.3 
Household survey ........................................................................................................................ 30 
7.4 
Clinical survey .............................................................................................................................. 31 
8 
Entomology surveys ................................................................................................................................ 33 
8.1 
Overview ...................................................................................................................................... 33 
8.2 
Recruitment and screening ......................................................................................................... 33 
8.3 
Informed consent ........................................................................................................................ 33 
8.4 
Entomology survey ...................................................................................................................... 33 
9 
LLIN durability assessment ...................................................................................................................... 34 
9.1 
Overview ...................................................................................................................................... 34 
9.2 
Net integrity ................................................................................................................................. 34 
9.3 
Chemical analysis of LLINs ........................................................................................................... 34 
10 
Economic evaluation ........................................................................................................................... 35 
10.1 
Overview ...................................................................................................................................... 35 
10.2  
Estimation of costs .................................................................................................................. 35 
10.2.1  
Intervention costs .................................................................................................................... 35 
10.2.2  
Malaria costs ............................................................................................................................ 36 
10.3 
Estimation of effects .................................................................................................................... 36 
10.5 
Analysis ........................................................................................................................................ 36 
11 
Laboratory procedures ........................................................................................................................ 38 
11.1 
Microscopy .................................................................................................................................. 38 
11.2 
Haemoglobin measurement ........................................................................................................ 38 
11.3 
Finger prick blood samples .......................................................................................................... 38 
11.4 
Rapid diagnostic tests .................................................................................................................. 39 
11.5 
Molecular analysis of malaria vectors ......................................................................................... 39 
11.6 
COVID-19 testing ......................................................................................................................... 39 
12 
Data management ............................................................................................................................... 41 
12.1 
MRC surveillance ......................................................................................................................... 41 
12.2 
Cross-sectional community surveys and entomology surveys .................................................... 41 
12.3 
Laboratory data ........................................................................................................................... 41 

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12.4 
Quality assurance & quality control ............................................................................................ 41 
12.5 
Records & storage ....................................................................................................................... 42 
13 
Statistical issues ................................................................................................................................... 43 
13.1 
Outcome measures...................................................................................................................... 43 
13.2 
Defining the MRC target areas .................................................................................................... 43 
13.3 
Measuring the incidence of malaria in the MRC target areas ..................................................... 43 
13.4 
Sample size and power calculations ............................................................................................ 43 
13.5 
Analytical plan ............................................................................................................................. 44 
14 
Ethical considerations .......................................................................................................................... 45 
14.1 
Institutional review boards .......................................................................................................... 45 
14.2 
Informed consent process ........................................................................................................... 45 
14.3 
Risks and discomforts .................................................................................................................. 45 
14.4 
Compensation .............................................................................................................................. 46 
14.5 
Capacity development ................................................................................................................. 46 
14.6 
Dissemination and publication of research findings ................................................................... 47 
15 
Proposed timeline ............................................................................................................................... 48 
16 
References ........................................................................................................................................... 49 
 
 
 
 

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List of associated consent forms 
 
1 
Cross-sectional community survey consent to participate in a research study (households) [V5.0] 
2 
Cross-sectional clinical survey consent to participate in a research study (parents/guardians) [V5.0] 
3 
Cross-sectional clinical survey consent for future use of biological specimens (parents/guardians) [V4.0] 
4 
Cross-sectional clinical survey assent form to participate in a research study (children > 8 years) [V5.0] 
5 
Entomology survey informed consent to participate in a research study (households) [V5.0] 
6 
Cross-sectional clinical survey consent to participate in a research study (adults) [V1.0] 
7 
Cross-sectional clinical survey consent for future use of biological specimens (adults) [V1.0] 
8 
Costing data collection consent to participate in a research study (health workers) [V1.0] 
 
 
List of appendices 
 
Appendix A: 
Intervention allocation list 
Appendix B: 
LLINEUP2 information sheet for sensitisation 
Appendix C: 
HMIS Form 002 
Appendix D: 
Malaria Reference Centre (MRC) data dictionary 
Appendix E: 
Cross-sectional community survey screening form 
Appendix F: 
Cross-sectional community household questionnaire data dictionary 
Appendix G: 
Cross-sectional clinical survey screening form  
Appendix H: 
Cross-sectional clinical survey questionnaire data dictionaries 
Appendix I: 
Entomology survey screening form  
Appendix J: 
Costing tools 
Appendix K:  
Risk mitigation plan 
 
 

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Statement of compliance 
 
The study will be carried out in accordance with Good Clinical Practice (GCP) as required by the following: 
 
• 
US Code of Federal Regulations applicable to Clinical Studies (45 CFR 46) 
 
• 
ICH GCP E6 
 
• 
Ugandan National Council for Science and Technology 
 
• 
Completion of Human Subjects Protection Training 
 
• 
NIH/NIAID Clinical Terms of Award 
 
 

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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 study 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:  Professor Moses Kamya, MBChB, MPH, PhD 
 
Signed:  ______________________________________________ 
Name: Moses Kamya 
Title: Professor, Department of Medicine 
 
 

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Abbreviations & acronyms 
 
ACT 
 
artemisinin-based combination therapy 
AL 
 
artemether-lumefantrine 
AMF 
 
The Against Malaria Foundation 
CAB 
 
community advisory board 
DFID 
 
UK’s Department for International Development 
DHS 
 
Demographic and health surveys 
EDMIS  
electronic data management information system 
EIR                       entomologic inoculation rate 
GFATM  
Global Fund to Fight AIDS, Tuberculosis, and Malaria 
GIS 
 
geographical information systems 
GPS 
 
global positioning system 
HMIS 
 
Health Management Information System 
HSD 
 
Health Sub-District 
IDRC 
 
Infectious Disease Research Collaboration 
IRB 
 
institutional review board 
IRS 
 
indoor residual spraying 
ITN 
 
insecticide treated net 
LC1 
 
Local council chairman 
LLIN 
 
long-lasting insecticidal net 
LSHTM  
London School of Hygiene and Tropical Medicine 
MIS 
 
Malaria indicator survey 
MOH 
 
Ministry of Health 
MRC 
 
Malaria reference centre 
NCC 
 
National Coordination Committee (for LLIN distribution) 
NMCP/D 
National malaria control programme/division 
PBO 
 
piperonyl butoxide 
PCR 
 
polymerase chain reaction 
PMI 
 
US President’s Malaria Initiative 
PPF 
 
pyriproxyfen 
PRISM  
Programme for Resistance, Immunology, Surveillance and Modelling of Malaria in Uganda 
RDT 
 
rapid diagnostic test 
SBCC 
 
social and behaviour change communication 
SOMREC 
School of Medicine Research and Ethics Committee, Makerere University 
SOP 
 
standard operating procedure 
UCSF 
 
University of California, San Francisco 
UMSP  
Uganda Malaria Surveillance Project 
UNCST  
Uganda National Council of Science and Technology 
WHO 
 
World Health Organization 

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Study summary 
 
Title 
Impact of long-lasting insecticidal nets (LLINs) treated with pyrethroid plus pyriproxyfen vs 
LLINs treated with pyrethroid plus piperonyl butoxide on malaria incidence in Uganda: a 
cluster-randomised trial 
Primary 
objective 
To evaluate the impact of LLINs treated with a pyrethroid insecticide plus pyriproxyfen (PPF LLINs), as compared to LLINs 
treated with a pyrethroid plus piperonyl butoxide (PBO LLINs), on malaria incidence in Uganda. We will test the hypothesis 
that malaria incidence will be lower in intervention clusters (randomised to receive PPF LLINs) than in control clusters 
(randomised to receive PBO LLINs). 
Secondary 
objectives 
1 
To evaluate the impact of PPF LLINs vs PBO LLINs on parasite prevalence, anaemia and vector density (subject to 
available funding) 
2 
To estimate the cost-effectiveness of delivering PPF LLINs, as compared to PBO LLINs 
3 
To assess net durability, survivorship and use of PPF LLINs vs PBO LLINs (funding permitting) 
4 
To evaluate the impact of the COVID-19 pandemic on malaria burden and care in Uganda 
Study site 
With the Ministry of Health, we have established a high-quality malaria surveillance programme. Health facilities selected 
for our malaria surveillance network are referred to as Malaria Reference Centers (MRCs). For this study, a cluster has 
been defined as the target area of an MRC (the village where the MRC is located, and adjacent villages where care-
seeking at the MRC is expected to be high). The study will be conducted in 64 clusters within 32 districts in Uganda, 
covering a wide range of settings with high malaria burden. 
Cluster 
randomisation 
The MRCs will be the focal point of the clusters (32 districts x 2 MRCs per district = 64 clusters). Clusters have been 
randomised in blocks of 2 to receive PPF LLINs (intervention) or PBO LLINs (control), with two sub-counties in each district 
receiving one of the LLIN types in a 1:1 ratio. 
Intervention 
A universal LLIN distribution campaign will be led by the National Malaria Control Division and partners. LLINs will be 
distributed to the sub-county surrounding each cluster.  
Evaluation 
methods & 
sample size 
1) Health facility surveillance. Our malaria surveillance programme supports training in data management and high-
quality laboratory testing (microscopy or rapid diagnostic tests) on all patients with suspected malaria. Using this 
surveillance system, we have developed a method to estimate the incidence of malaria in target areas around the MRCs, 
providing a direct measure of the burden of malaria. For all patients presenting to the MRCs, information on the location 
of their residence will be collected. Estimates of malaria incidence will be calculated by dividing the number of laboratory-
confirmed malaria cases diagnosed at each MRC (among patients residing in the target area per unit time) by the total 
population of the MRC target area. To evaluate the impact of the intervention, we will estimate malaria incidence at the 
MRCs for the 24 months after LLIN distribution. 
2) Enumeration and census surveys. To estimate the population of the MRC target areas, and to generate a sampling 
frame for the first community survey (to be carried out 12 months after LLIN distribution), we will enumerate and map all 
households within each MRC target area. To derive a more accurate estimate of the population of the MRC target areas, 
we will conduct a census survey of the MRC target areas, concurrently with the first cross-sectional survey. 
3) Cross-sectional community surveys. Shortly after LLIN distribution (in selected clusters) and at 12 and 24 months 
following LLIN distribution, randomly selected households will be surveyed from each of the 64 clusters. The survey will 
include a household questionnaire and clinical & laboratory evaluation of children aged 2-10 years. We will survey 50 
households in each cluster (n=3,200), aiming to recruit all eligible children in each household (approximately 5,760) per 
survey. In 32 clusters (one per district), we will enroll residents of all ages from participating households into the clinical 
survey, to estimate the impact of COVID-19 on malaria during the 12-month survey.   
4) Entomology surveys. Funding permitting, mosquitoes from 10 randomly selected households in all 64 clusters will be 
collected using prokopack aspirators to estimate malaria vector density, and to collect samples for monitoring of 
insecticide resistance. Collections will be carried out alongside the cross-sectional surveys.  
5) Economic evaluation. Cost-effectiveness will be assessed at the end of the study by collecting cost data for materials 
and labour and using effectiveness data to estimate the number of malaria cases averted. Incremental cost-effectiveness 
ratios (USD per disability-adjusted life year adverted and per malaria case adverted) will be the primary outcomes. 
6) LLIN survivorship and durability. At 12 and 24 months after distribution of nets, we will quantify net survivorship in the 
cross-sectional surveys and (if funding is available) will withdraw (and replace) a subset of LLINs to assess durability using 
standard WHO methodology.  
Primary 
outcome 
The primary outcome will be malaria incidence within the total population of the MRC target areas. 
Secondary 
outcomes 
1) Community surveys:  In all clusters, prevalence of parasitaemia (in children aged 2-10 years) and prevalence of 
anaemia (haemoglobin < 11 g/dL in children 2-4 years). In selected clusters (n=32), during the 12-month survey, 
prevalence of parasitaemia (all ages & stratified by age), and prevalence of antibody responses, suggesting prior exposure 
to SARS-CoV-2 antigens (COVID-19) 
2) Entomology surveys: Malaria vector density (if funding is available) 
3) Economic evaluation: Incremental cost-effectiveness ratios (USD per disability-adjusted life year averted and per 
malaria case averted) 
4) LLIN survivorship and durability: Survivorship: prevalence of nets present, and in use; Durability: proportionate hole 
index (subject to available funding) 
 

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Study personnel and institutions 
Investigators  
 
Prof Sarah Staedke, MD, PhD, DTM&H 
Role in project: Principal investigator 
Professor of Malaria & Global Health, London School of Hygiene & Tropical Medicine, UK  
Infectious Diseases Research Collaboration, Kampala, Uganda  
Email: sarah.staedke@lshtm.ac.uk 
 
Prof Moses Kamya, MBChB, MPH, PhD 
Role in project: Co-PI, Uganda site investigator 
Professor, Department of Medicine, Makerere University, Kampala, Uganda 
Executive Director, Infectious Diseases Research Collaboration, Kampala, Uganda  
Email: mkamya@infocom.co.ug 
 
Prof Grant Dorsey, MD, PhD 
Role in project: Co-investigator 
Professor, Department of Medicine, University of California, San Francisco, USA 
Infectious Diseases Research Collaboration, Kampala, Uganda  
Email: grant.dorsey@ucsf.edu  
 
Dr Joaniter Nankabirwa, MBChB, MSc, PhD 
Role in the project: Co-investigator 
Lecturer, Department of Medicine, Makerere University, Kampala, Uganda 
Epidemiologist, Infectious Diseases Research Collaboration, Kampala, Uganda 
Email: jnankabirwa@yahoo.co.uk 
 
Dr Catherine Maiteki-Sebuguzi, MBChB, MSc 
Role in the project: Co-investigator 
National Malaria Control Division, Uganda Ministry of Health, Kampala, Uganda  
Infectious Diseases Research Collaboration, Kampala, Uganda  
Email: cmaiteki@yahoo.com 
 
Prof Martin Donnelly, MA, MSc, PhD, FRES 
Role in project: Co-investigator 
Professor of Evolutionary Genetics, Liverpool School of Tropical Medicine, UK 
Email: martin.donnelly@lstmed.ac.uk 
 
Dr Bryan Greenhouse, MD, MA 
Role on project: Co-investigator 
Associate Professor, Department of Medicine, University of California, San Francisco, USA 
Email: bryan.greenhouse@ucsf.edu  
 
Dr Jessica Briggs, MD 
Role on project: Co-investigator 
Research Fellow, Department of Medicine, University of California, San Francisco, USA 

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Email: jessica.briggs@ucsf.edu  
 
Dr Isabel Rodriquez-Barraquer, MD, PhD 
Role on project: Co-investigator 
Assistant Professor, Department of Medicine, University of California, San Francisco, USA 
Email: monedita@gmail.com  
 
Dr Jimmy Opigo, MBChB 
Role in the project: Co-investigator 
Assistant Commissioner, National Malaria Control Division, Uganda Ministry of Health, Kampala, Uganda  
Email: opigojimmy@gmail.com  
 
Dr Catherine Pitt, PhD 
Role in the project: Collaborator, economic evaluation 
London School of Hygiene & Tropical Medicine, UK 
Email: catherine.pitt@lshtm.ac.uk 
 
Katherine Snyman, MS 
Role in the project: Collaborator, economic evaluation 
Infectious Diseases Research Collaboration, Kampala, Uganda 
Email: kmsausen@gmail.com  
 
Dr Jane Namuganga, MD, MPH 
Role in the project: Study coordinator 
Monitoring and Evaluation Specialist, Infectious Diseases Research Collaboration, Kampala, Uganda 
Email: jnamuganga@idrc-uganda.org   
 
Dr Samuel Gonahasa, MBChB 
Role in the project: Study coordinator 
Infectious Diseases Research Collaboration, Kampala, Uganda 
Email: sgonahasa1983@gmail.com  
 
 
 

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Collaborating institutions 
 
Infectious Diseases Research Collaboration (IDRC) 
Address: IDRC, Plot 2C Nakasero Road, P.O. Box 7475, Kampala, Uganda 
Contact Person: Catherine Tugaineyo 
Phone Number: +256 (0) 312 281479 
Fax Number: +256 (0) 414 540524 
Email:  ctugaineyo@idrc-uganda.org  
 
University of California, San Francisco (UCSF) – Sponsor 
Address: San Francisco General Hospital, 1001 Potrero Avenue, Building 30, Room 402/408,  
San Francisco, CA 94110, USA 
Contact Person: Tamara Clark 
Phone Number: +1 (415) 206-8790 
Fax Number: +1 (415) 648-8425 
Email: tclark@medsfgh.ucsf.edu 
 
London School of Hygiene & Tropical Medicine (LSHTM) 
 
Address: Room 304(b), LSHTM, Keppel Street, London, WC1E 7HT, UK 
Contact Person:  Susan Sheedy 
Phone Number:  +44 (0) 20 7927 2256 
Fax Number: +44 (0) 20 7927 2739 
Email:  sophie.hahn@lshtm.ac.uk  
 
Liverpool School of Tropical Medicine  
 
Address: Pembroke Place, Liverpool, L3 5QA, UK 
  
Contact Person: Helen McCormack 
 
Phone Number: +44 (0) 151 705 3293 
 
Email: Helen.McCormack@lstmed.ac.uk 
 
 
 
 
 
 

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Institutional review boards 
 
Makerere University School of Medicine Research and Ethics Committee (SOMREC) 
Address: Makerere University College of Health Sciences, PO Box 7072, Kampala, Uganda 
Contact Person: IRB chair, Ponsiano Ocama, MBChB, MMED, PhD, FCP (ECSA) 
 
 
 
Phone Number:  +256 (0) 772 421190 
Email: rresearch9@gmail.com; ponsiano.ocama@gmail.com 
 
 
 
 
       
 
University of California, San Francisco, Committee for Human Research 
Address:  Office of Research, 3333 California Street Suite 315, San Francisco, CA 94118, USA  
Contact Person: Tamara Clark 
 
 
 
 
 
    
Phone Number:  +1 (415) 476-1814 
 
 
 
 
 
      
Fax Number: +1 (415) 502-1347 
Email: Tamara.Clark@ucsf.edu  
 
London School of Hygiene and Tropical Medicine Ethics Committee 
Address:  Keppel Street, London, WC1E 7HT, UK 
Contact Person: Rebecca Carter  
Phone Number: +44 (0) 20 7927 2802 
Email: Ethics@lshtm.ac.uk 
 
Uganda National Council for Science and Technology (UNCST) 
Address: UNCST, Plot 6 Kimera Road, PO Box 6884, Kampala, Uganda           
Contact Person: Executive Secretary (Dr Peter Ndemere) 
 
 
Phone Number:  +256-41-4705500 
 
 
 
 
 
        
Email: info@uncst.go.ug 
 
 
 
 

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1 
Background 
 
1.1 
Introduction 
 
Over the last 15 years, malaria control interventions have been scaled-up dramatically across Africa, 
resulting in an estimated 40% decrease in the incidence of disease due to P. falciparum between 2000 and 
2015 [1]. However, despite these encouraging trends, decreases in the burden of malaria have not been 
uniform across Africa and have been slowest in countries with the highest burden, such as Uganda. Uganda 
is emblematic of high-burden countries in sub-Saharan Africa; it reported the third highest estimated 
number of malaria cases in 2018, representing 5% of the global malaria case burden [2]. Malaria 
transmission occurs throughout the year in 95% of the country, with P. falciparum responsible for 90-98% 
of infections [3]. The most common malaria vectors are Anopheles gambiae s.s., A. arabiensis, and A. 
funestus; A. gambiae s.s. is the dominant species in most areas [4]. Since 2014, Uganda has embarked on 
the Uganda Malaria Reduction Strategic Plan, an ambitious program to reduce malaria mortality to near 
zero, morbidity to 30 cases per 1000 per year, and malaria parasite prevalence to less than 7%, by 2020 [5]. 
However, a review of Uganda’s malaria programme from 2014 to 2019, conducted by the Ministry of 
Health (MOH), provided a mixed picture on progress [6]. From 2014 to 2019, in-patient malaria deaths 
declined from 17 to 9 deaths per 100,000 population, but did not meet the target of 5 deaths per 100,000. 
During that five-year period, the incidence of total malaria cases fell from 460 to 282 per 1000 population, 
but the incidence of confirmed malaria cases increased from 150 to 192 per 1000 population, likely due in 
part to the expansion of diagnostic testing for malaria. These findings highlight the challenges facing 
Uganda, and the urgent need for improved strategies to control and ultimately eliminate malaria in the 
country.  
 
1.2 
Malaria control interventions in Uganda 
 
Malaria control in Uganda, like elsewhere in sub-Saharan Africa, has focused primarily on three 
interventions: case management with artemisinin-based combination therapies (ACTs), long-lasting 
insecticide treated nets (LLINs), and indoor residual spraying of insecticides (IRS). Over the last decade, 
Uganda has dramatically increased ACT use and LLIN coverage, and to a lesser extent coverage with IRS [7]. 
However, evidence of the impact of these interventions on clinical outcomes remains limited. Artemether-
lumefantrine (AL) was adopted as first-line therapy for uncomplicated malaria in 2006, replacing older 
regimens limited by drug resistance, and multiple studies from investigators in our PRISM group have 
documented that this drug and other ACTs remain highly efficacious [8], with no evidence of artemisinin 
resistance [9]. Although the increased availability of AL for malaria treatment has likely played a role in 
reducing malaria-specific mortality [10], little evidence of the impact of effective case management in 
reducing malaria morbidity in high transmission settings is available.  
 
In addition, the COVID-19 pandemic may also have effects on malaria control through decreased availability 
of ACTs or rapid diagnostic tests through interruption of the supply chain; likewise, hospital beds may 
become scarce if communities become overwhelmed by COVID-19 cases. National lockdowns (one of which 
has already occurred and been lifted in Uganda) or travel restrictions may further affect the public’s ability 
to access health facilities and timely care. For example, the WHO and others predict that if LLIN distribution 
stops and case management is significantly disrupted, malaria deaths in sub-Saharan Africa could double 
compared to 2018 [11, 12].  The overlap in symptoms between malaria & COVID-19, particularly fever, may 
impact on the provision of care for both infections. A thorough understanding of the impact of COVID-19 on 
malaria burden and care in Uganda is needed.   
 

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1.3 
LLINs in Uganda 
 
In Uganda, LLINs are the primary tool for malaria prevention, and considerable efforts have been made to 
achieve universal coverage of LLINs [13]. In 2013-14 Uganda became the first country to deliver LLINs free-
of-charge nation-wide, with over 90% of households reporting ownership of at least one LLIN following the 
mass distribution campaign [14]. Subsequently, Uganda conducted a national Malaria Indicator Survey in 
2014-15 which assessed parasite prevalence in children aged 0-59 months. Nationally, parasite prevalence 
was 19% by microscopy (down from 42% in 2009) and 30% by rapid diagnostic test (down from 55% in 
2009) [14]. As part of our PRISM project, we also evaluated the impact of the 2013-14 LLIN distribution 
campaign using data from intensive cohort studies and entomological surveys at 3 sites with varying 
transmission intensity, including Walukuba sub-county (Jinja district), Kihihi sub-county (Kanungu district) 
and Nagongera sub-county (Tororo district). Surprisingly, although the mass distribution campaign 
substantially increased LLIN coverage levels, and was temporally associated with an overall decrease in 
parasite prevalence, we observed little effect on clinical malaria indicators. Only in Kihihi, where medium-
level malaria transmission was documented, did we observe a significant decrease in the incidence of 
malaria following LLIN distribution [15].  
 
In 2017-18, Uganda conducted a second 
mass LLIN distribution campaign. Prior to 
this campaign, our group conducted a large 
cross-sectional survey of 5,200 households 
to estimate baseline LLIN coverage levels in 
104 health sub-districts located in 48 
districts, covering 5 of the 10 regions in the 
country [16]. Three years after the earlier 
LLIN campaign, we found that LLIN coverage 
levels had dropped substantially, with only 
65% of houses owning at least one LLIN 
(down from 94% in 2014), and only 14% of 
households owning at least one LLIN per two 
residents (down from 65% in 2014, Figure 1). 
 
In 2017, we also conducted a clinical survey of 
parasite prevalence in children aged 2-10 years 
[17]. In total, 5,196 households and 8,834 
children with blood smear results were 
included in the analyses. Overall, parasite 
prevalence was 26.0%, ranging from 8.0% in 
the South West to 53.1% in East Central. 
Limiting the analysis to children 2-4 years of 
age residing in the five regions, parasite 
prevalence was 21.4%. Comparisons made 
between the 2009 and 2014-15 Malaria 
Indicator Surveys and the 2017 survey (Figure 
Figure 1. LLIN ownership and coverage from 2014 to 2017 
Reduced ownership & coverage 3 years after the LLIN distribution campaign. 
Figure 2. Change in parasitaemia over time. Parasitaemia in children 
2-4 years fell from 46% in 2009 to 17% in 2014, but rose to 21% in 2017.  

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2) indicated that parasite prevalence fell from 45.6% to 16.9% between 2009 and 2014-15, with significant 
decreases in all five regions of the country (p<0.001 for all comparisons). However, in 2017, prevalence 
increased in all areas, which was statistically significant in the Mid-Eastern (6.0% vs. 19.1%, p<0.001) and 
Mid-Western regions (19.5% vs. 25.7%, p=0.02). 
 
Thus, although the 2013-14 national LLIN distribution campaign successfully reached its target coverage 
level, we observed a limited effect on clinical malaria indicators and transient reduction in parasite 
prevalence. Moreover, LLIN coverage dropped substantially after 3 years. These findings highlight the 
challenges of relying on LLINs as the primary method of vector control in Uganda.  
 
1.4 
IRS in Uganda 
 
IRS was adopted as a key 
component of Uganda’s 
malaria control strategy in 
2006. Initial efforts were 
focused on epidemic-prone 
areas in the southwestern 
part of the country. 
However, in 2009, the IRS 
programme was moved to 
10 high burden districts in 
northern Uganda. Initially organochlorine/pyrethroid insecticides were used for IRS, but the insecticide was 
changed to carbamates in 2010, due to the emergence of resistance to pyrethroids and DDT. The IRS 
programme in northern Uganda consistently achieved high coverage levels (above 95% of targeted 
households) and was associated with marked 
reductions in measures of malaria morbidity 
[18]. In 2014, the IRS program was withdrawn 
from the 10 northern districts and relocated to 
14 new districts in north-eastern Uganda, with 
the hope that gains achieved by IRS in the north 
would be sustained by LLINs distributed after the 
last cycle of IRS. However, data from our health 
facility surveillance system showed that 
measures of malaria morbidity rose sharply 
within 4 months of the discontinuation of IRS, 
reaching pre-IRS levels (Figure 3) [19]. These 
data were instrumental in the decision to 
conduct one additional round of IRS in northern 
Uganda in 2017.  
The relocation of the IRS programme to north-
eastern Uganda in 2014 allowed us to estimate 
impact of IRS in Nagongera sub-county, Tororo 
district. Since 2011, we have been following a 
cohort of households and carrying out 
entomology surveys through our PRISM project 
Figure 3. Impact of vector control on malaria test positivity rate in Apac. Monthly trends 
in malaria test positivity rate (TPR, blue line) in Apac following delivery of multiple rounds of IRS with 
different insecticides (red, green, yellow bars) and universal distribution of LLINs (light blue bar).  
Figure 4. Temporal changes in the entomologic and clinical 
malaria indicators from 2011 to 2017. (4A) Monthly trends in daily 
human biting rates, annual entomologic inoculation rates, and rainfall. (4B) 
Monthly trends in symptomatic malaria, microscopic parasitaemia, and 
combined microscopic and sub-microscopic parasitaemia. Blue bars = LLIN 
distribution, Pink bars = IRS with bendiocarb, Yellow bars = IRS with 
pirimiphos-methyl (Nankabirwa, et al, submitted for publication).  

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(Program for Resistance, Immunology, Surveillance, and Modelling of Malaria) (Figure 4). Our detailed 
longitudinal data have shown that repeated rounds of IRS have been temporally associated with dramatic 
reductions of daily human biting rates (a surrogate for malaria transmission, panel 4a) and malaria 
incidence (panel 4b). However, despite the overall reductions, malaria metrics increased just prior to each 
round of IRS, illustrating the fragile nature of the gains achieved.  
Moreover, the prevalence of  parasitaemia based on a highly sensitive measure (loop-mediated isothermal 
amplification [LAMP]) remained >10% after each round of IRS, demonstrating a persistent reservoir 
available to drive a resurgence in malaria transmission and cases, as occurred after IRS was withdrawn in 
the northern districts [20]. In Nagongera, we also observed a shift from A. gambiae s.s. as the primary 
vector to A. arabiensis after the start of IRS, which was also noted after the implementation of IRS in 
northern Uganda [21]. This shift in mosquito vectors could have important implications on the efficacy of 
other vector control interventions, such as LLINs, as A. arabiensis tends to bite earlier in the evening, and is 
more likely to feed on domestic animals, and to feed outdoors, than A. gambiae s.s. There are plans to 
continue the IRS program in the 14 districts in north-eastern Uganda. Spraying will be carried out annually 
using an appropriate chemical rotate at least every 3 years, in accordance with Uganda’s Insecticide 
Resistance Management (IRM) plan for control of malaria 
vectors  in Uganda [22]. There is clear evidence that IRS 
has had a substantial impact on reducing the burden of 
malaria in Uganda. However, gains have been fragile, as 
evidenced by the resurgence of malaria when IRS was 
withdrawn in northern Uganda, and by the peaks in 
malaria metrics seen just prior to each round of IRS. New 
strategies to reduce transmission and sustainably 
maintain malaria control gains are needed to clear a 
pathway toward malaria elimination in Uganda.   
 
1.5 
Health facility surveillance 
 
In Uganda, the Health Management Information System 
(HMIS) is the primary source for malaria surveillance 
data. Aggregate data from all government-run and some 
private health facilities are assembled and reported at 
regular intervals using standardised registers and 
reporting forms. The introduction of the District Health Information System 2 (DHIS2), an electronic form of 
HMIS, in 2012, improved the collation of data at both the district and national level. DHIS2 is a web-based 
system with data from paper-based reports submitted to a central database. Despite improvements, 
concerns remain about the quality of HMIS data due to incomplete reporting, data entry errors, and 
reporting of malaria cases based on poor quality diagnostics or a lack of laboratory confirmation altogether. 
In addition, because the HMIS system relies solely on aggregate data, it is not amenable to stratification or 
subgroup analyses. Consequently, estimates of malaria morbidity may be biased, making it difficult to 
accurately monitor trends over time and space, or to measure the impact of control interventions. Despite 
these limitations, HMIS remains the only source of routine malaria surveillance data.  
 
To improve the quality of malaria surveillance data, our group created the Uganda Malaria Surveillance 
Project (UMSP), in collaboration with the National Malaria Control Division, in 2006. The project initially 
included 6 health facilities, with individual level data collected electronically from all outpatients. Data are 
Figure 5. Malaria reference centers in Uganda. In 
total, 64 malaria reference centers (MRCs) cover 32 districts.   

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collected on patient demographics, laboratory test results, diagnoses, and treatments prescribed. The 
project has supported training in data management and high-quality laboratory testing (microscopy or 
malaria rapid diagnostic tests) on all patients with suspected malaria. In 2014, the programme expanded 
from 6 to 21 health facilities, and was expanded further to 35 sites in 2018-19. With funding support from 
the NIH, we expanded to 64 sites in 2020 (Figure 5). Health facilities selected for our malaria surveillance 
network are referred to as Malaria Reference Centers (MRCs) and provide a wide geographic scope across 
Uganda. The primary metrics used to monitor trends from our malaria surveillance network have been the 
number of laboratory confirmed cases of malaria and the test positivity rate (number of confirmed cases of 
malaria / number of suspected cases undergoing laboratory testing). However, these metrics do not 
provide a direct measure of the burden of malaria in defined populations at risk. To address this limitation, 
we have developed a method to estimate the incidence of malaria in target areas around the health 
facilities.  
 
To accomplish this, we began collecting data on village 
and parish of residence for all patients presenting to the 
health facilities in 2017. To generate maps of villages 
around the MRCs, a preliminary map was made using a 
list of villages obtained from the government and shape 
files that are publicly available. The GPS coordinates of 
each MRC were then verified and the names and shape 
files for all villages of interest were confirmed. A final 
clean version of the map was printed and shared with 
the MRC to help improve the quality of the data 
collection (Figure 6).  
Using population estimates for MRC target 
areas, and assuming all care for malaria within 
the target areas occurs at the health facility (a 
reasonable assumption in rural Uganda), we are 
able to generate estimates of malaria incidence, 
defined as the number of cases of laboratory 
confirmed malaria diagnosed at the health 
facility among patients residing in the target 
area per unit time / the population of the target 
area. For the numerator, the total number of 
malaria episodes from the MRC target area 
during the time of interest is calculated as the 
sum of the following: laboratory confirmed 
malaria cases, plus the estimated number of 
cases of suspected malaria with no test results, pus the estimated number of cases of laboratory confirmed 
malaria cases with missing information on village of residence.  For the denominator, four population 
databases have been consulted, including High Resolution Settlement Layer (HRSL), Uganda Bureau of 
Statistics (UBOS), the AfriPop project (AFRIPOP), and Oak Ridge National Laboratory’s LandScan 
(LANDSCAN) (Figure 7). In the future, we plan to conduct a census survey of each MRC target area to 
confirm the population. By establishing the capacity to estimate malaria incidence for the target areas of 
the MRCs, we have built a platform which will allow us to assess the impact of LLINs distributed through the 
national LLIN campaign on malaria incidence on a widescale.  
Figure 6. Map of Anyeke MRC target area.    
Figure 7. Estimating malaria incidence at the Anyeke MRC. 
Anyeke MRC test positivity rate and malaria incidence by month from 
April 2019 to March 2020, with incidence estimates calculated using four 
different sources) to estimate the population of the target area. 

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1.6 
Insecticide resistance 
 
Resistance against all classes of insecticides has 
been observed in the primary African malaria 
vectors A. gambiae s.s., A. arabiensis, and A. 
funestus [23]. In African Anopheles mosquitoes, 
pyrethroid resistance is primarily mediated through 
two mechanisms; ‘knock down resistance’ (kdr) 
caused by mutations in the voltage-gated sodium 
channel where pyrethroids bind, and metabolic 
resistance resulting from alterations in enzymes that 
detoxify pyrethroids, notably cytochrome P450s.[24, 
25] Since kdr mutations were first documented in 
Ugandan A. gambiae s.s. and A. arabiensis in 2001, 
pyrethroid resistance has continued to spread [26, 
27]. In our 2017 survey of 5,200 households in 48 
districts of Uganda, entomological sampling was 
carried out in a sub-set of 1029 households [4]. The kdr mutation Vgsc-L1014S was found at very high 
frequency in An. gambiae s.s. with the wild-type allele virtually absent. The alleles Cyp4j5-L43F and 
Coeae1d, associated with metabolic resistance, were found at moderate frequencies which varied across 
the study site (Figure 8).  
 
Evidence suggests that insecticide resistance is contributing to sub-optimal vector control in Uganda [18], 
and across sub-Saharan Africa [28]. Phenotypic data from our PRISM project showed high-level resistance 
to pyrethroids among A. gambiae and A. arabiensis at 3 sites (Figure 9), which was associated with limited 
impact of LLINs [15]. 
 
 
1.7 
LLINs treated with piperonyl butoxide 
 
Currently, all conventional LLINs are impregnated with pyrethroid insecticides, due to their favourable 
safety profile, low cost and rapid insecticidal activity [29]. However, malaria control efforts are threatened 
by pyrethroid resistance [30], and newer LLINs treated with more than one agent are being developed [31]. 
One new LLIN incorporates pyrethroids with a synergist, piperonyl butoxide (PBO), which inhibits P450s 
enzymes, thus blocking one other major resistance mechanism and partially restoring pyrethroid 
Figure 8. Resistance and allele frequencies in An 
gambiae s.s. A) Vgsc-1014S, B) Vgsc-1014F, C) Vgsc-1014L, D) 
Cyp4j5-L43F, E) 2La inversion, F) Coeae1d.  
Figure 9. Insecticide resistance monitoring in Jinja (Walukuba sub-county), Kanungu (Kihihi) and Tororo (Nagongera).  
24-hour mortality levels with 95% standard error bars for Anopheles gambiae s.s. (Kihihi and Nagongera only) and Anopheles arabiensis (Walukuba 
and Nagongera only) exposed for one hour to insecticide-treated papers impregnated with WHO diagnostic concentrations of insecticides. By WHO 
convention mortality of 98–100% indicates susceptibility; <98% is suggestive of resistance, and <90% is strongly suggestive of resistance. 

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susceptibility [32]. This strategy is supported by bioassays conducted by our group showing near complete 
recovery of pyrethroid susceptibility in Ugandan anophelines [33]. LLINs with PBO are anticipated to be 
more effective in areas where pyrethroid resistance is mediated at least partially by P450 enzymes. 
However, the effectiveness of PBO LLINs is expected to vary according to local resistance patterns and 
transmission intensity, as well as net characteristics.  
A systematic review of PBO LLINs found that they were associated with higher mosquito mortality and 
lower blood-feeding rates in areas of high-level insecticide resistance, as compared to non-PBO LLINs [34]. 
A cluster-randomised, clinical trial of the effectiveness of a PBO LLIN (Olyset Plus), conducted in Tanzania, 
found that PBO LLINs were associated with lower parasite prevalence, than conventional LLINs, at 9, 16, 
and 21 months after distribution [35]. Subsequently, the WHO issued an interim endorsement of PBO LLINs, 
recommending them for areas of intermediate-level pyrethroid resistance, due at least partly to metabolic 
mechanisms [36].  However, the Tanzanian study had several limitations: the study was restricted to one 
district; parasite prevalence was measured using rapid diagnostic tests, which may have variable specificity 
[37], insecticide resistance was assessed by kdr mutations, not markers of metabolic resistance; and 21-
month data were potentially compromised by routine distribution of new LLINs within the study area.  
 
1.8 
LLINEUP trial 
 
In 2017-18, the Ugandan Ministry of Health distributed 
LLINs with, and without, PBO through a national mass-
distribution campaign, providing a unique opportunity to 
rigorously evaluate PBO LLINs across different 
epidemiological settings. In close collaboration with the 
Ministry of Health, we embedded a cluster-randomised 
trial to evaluate the impact of the LLINs delivered in the 
2017-18 national campaign at an unprecedented scale in 
Eastern and Western Uganda. Overall, 104 clusters (health 
sub-districts) were included, covering 40% of Uganda 
(Figure 10) [38]. Proportionate randomisation was used to 
assign clusters to one of four arms, including LLINs with 
PBO (32 PermaNet 3·0, 20 Olyset Plus), and conventional 
LLINs (37 PermaNet 2·0, 15 Olyset Net). At baseline, 6, 12, 
18 and 25 months after LLIN distribution, cross-sectional 
surveys were conducted in 50 randomly selected 
households per cluster (5,200 per survey); a sub-set of 10 
households per cluster (1,040 per survey) were randomly selected for entomology surveys. The primary 
outcome was parasite prevalence by microscopy in children aged 2-10 years.  
Baseline surveys were conducted in 2017 [4, 16, 17]. LLINs were delivered from March 2017 to March 2018. 
In the ‘as treated’ analysis, three clusters were excluded because no predominant LLIN was received, and 
four clusters were reassigned, resulting in 49 PBO LLIN (31 PermaNet 3.0, 18 Olyset Plus) and 52 non-PBO 
LLIN clusters (39 PermaNet 2.0, 13 Olyset Net). At six months, parasite prevalence was 10.7% in the PBO 
arm vs 14.5% in the non-PBO arm (prevalence ratio [PR] adjusted for baseline values 0.74, 95% CI: 0.62–
0.87, p<0.001). Results were similar at 12 months (10.6% vs 13.0%, PR 0.73, 95% CI: 0.63–0.85, p<0.001) 
and at 18 months (11.8% vs 14.0%, PR 0.84, 95% CI: 0.72–0.98, p=0.03). In the 90 clusters for which follow-
up data were available at 25 months (42 PBO vs 48 non-PBO), parasite prevalence remained lower in the 
PBO arm than the non-PBO arm (17.1% vs 19.8%, PR 0.80, 95% CI: 0.69–0.93, p=0.005). Although overall 
Figure 10. Map of LLINEUP study sites. In total, 104 
health sub-districts (clusters) from 48 districts were included. 

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parasite prevalence at 25 months was trending upward, it remained significantly lower than at baseline 
(18.6% vs 27.0%, PR 0.71, 95% CI: 0.67–0.77, p<0.001), which was true for both PBO and non-PBO clusters. 
Thus, in the LLINEUP trial, we found that PBO LLINs provided superior protection against malaria in the 
setting of high-level insecticide resistance in Uganda. This innovative trial, embedded within a national LLIN 
distribution campaign, serves as a paradigm for future assessment of malaria control interventions, 
including the trial proposed here.  
1.9 
Dual active-ingredient LLINs with pyriproxyfen 
 
Other next generation LLINs combine a pyrethroid insecticide with a second active ingredient, such as 
pyriproxyfen [39-41]. Treating LLINs with a combination of insecticides with different modes of action may 
improve efficacy, and help to prevent or delay the spread of insecticide resistance. Pyriproxyfen (PPF) is an 
insect growth regulator, which has traditionally been used as a larvicide [42, 43], but also acts as a 
sterilizing agent, reducing the fecundity (egg laying), fertility (production of viable offspring), and longevity 
of adult mosquitoes [44-48]. PPF has a different mechanism of action than pyrethroids and other 
commonly used insecticides, is effective at very low concentrations, and has been demonstrated to be safe 
to humans [42, 49]. In theory, pyrethroid-resistant mosquitoes that survive initial contact with a PPF-
treated LLIN would be sterilized by the PPF. Thus, a dual active-ingredient LLIN including PPF is an attractive 
option.  
 
In initial experimental hut trials conducted in Benin and Cote d’Ivoire, LLINs treated with the pyrethroid 
permethrin and PPF (Olyset Duo, Sumitomo Chemical) were associated with higher mosquito mortality and 
reduced blood-feeding rates, compared to standard LLINs treated with permethrin only (Olyset Net) [41, 
50]. Moreover, surviving mosquitoes exposed to PPF-treated nets had substantially lower fecundity and 
fertility rates [39, 50]. In Kenya, a field trial comparing permethrin + PPF nets (Olyset Duo) to permethrin-
only LLINs (Olyset Net) and a PPF-only treated net showed similar sterilizing effects against wild pyrethroid-
resistant An gambiae s.s. [40]. In a step-wedge, cluster-randomised trial conducted in Burkina Faso, 
permethrin + PPF LLINs (Olyset Duo) were associated with lower clinical incidence in children aged 6-59 
months than permethrin-only LLINs (Olyset Net) (1.5 vs 2.0 episodes per child-year, incidence rate ratio 
0.88, 95% CI 0.77-0.99, p=0.04) [51]. The entomologic inoculation rate was also lower in the permethrin + 
PPF LLIN arm compared to permethrin-only LLINs (42 vs 85 infective bites per transmission season, rate 
ratio 0.49, 95% CI 0.32-0.66, p<0.0001). The PPF-treated LLINs appeared to work by reducing the vector 
population density and lifespan of adult mosquitoes, thus reducing the number of infective bites [51]. 
Another study from Burkina Faso found that the bio-efficacy and durability of PPF-treated LLINs (Olyset 
Duo) was superior to permethrin-only LLINs (Olyset Net) but that net survivorship for both net types was 
poor at 36 months [52]. The World Health Organization has pre-qualified one dual active-ingredient LLIN, 
which is treated with both a pyrethroid (alpha-cypermethrin) + PPF (Royal Guard LLIN, which produced by 
Disease Control Technologies) [53]. PPF-treated dual active-ingredient LLINs are promising, but additional 
epidemiologic studies in different settings are needed.  
 
 

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2 
Rationale 
 
LLINs provide the foundation for vector control in Uganda, and elsewhere in Africa. However, the 
effectiveness of LLINs is threatened by widespread pyrethroid resistance. In the first LLINEUP trial, we 
found that PBO LLINs were superior to conventional LLINs [54]. However, PBO LLINs have several potential 
limitations. PBO is a synergist, not an insecticide, and can only restore sensitivity of pyrethroid insecticides 
if resistance is due to specific metabolic mechanisms. Moreover, PBO cannot fully restore susceptibility in 
all resistant mosquito populations. Newer dual active-ingredient LLINs treated with a combination of 
insecticides using different modes of action are attractive alternatives; these LLINs may provide greater 
protection and delay the spread of insecticide resistance, but like PBO LLINs, they are more expensive than 
conventional nets. Further evidence of the effectiveness and cost-effectiveness of PPF-treated LLINs is 
urgently needed. Royal Guard LLINs, treated with alphacypermethrin and PPF, are one of only two dual 
active-ingredient LLINs prequalified by the WHO [53], which are available for widescale distribution.  
In Uganda, the National Malaria Control Division (NMCD) and implementing partners are planning to 
deliver LLINs nationwide in 2020, through a mass distribution campaign supported by generous 
contributions from international donors. LLINs will be distributed free-of-charge to all Ugandan households, 
aiming to achieve universal coverage. The Against Malaria Foundation has agreed to provide LLINs treated 
with a pyrethroid insecticide plus PPF (Royal Guard, Disease Control Technology) and LLINs treated with a 
pyrethroid insecticide plus PBO (PermaNet 3.0, Vestergaard), presenting an opportunity to rigorously 
evaluate and compare these two LLINs at scale across Uganda. In collaboration with the MOH, we propose 
to embed a cluster-randomised trial to compare the impact of LLINs with PPF to LLINs with PBO into 
Uganda’s 2020 LLIN distribution campaign, as we did successfully for the last LLIN distribution campaign 
conducted in 2017-18.   
A major strength of the proposed trial is the use of malaria incidence as the primary outcome measure. 
Incidence of malaria, defined as the number of symptomatic cases of malaria occurring in a population at 
risk over time, is the gold standard for assessing malaria burden. However, cluster-randomised trials using 
malaria incidence as the primary outcome are very expensive and logistically challenging. A novel approach 
for measuring malaria incidence, which we have proposed here, is to utilize data collected routinely at 
health facilities. By defining target areas around health facilities and collecting data on the location of 
residence of patients diagnosed with malaria, we will be able to generate longitudinal measures of malaria 
incidence at an unprecedented scale across Uganda.  
Finally, there is an urgent need to better understand the potential impact of the COVID-19 pandemic on 
malaria in Uganda. To our knowledge, no published studies have evaluated how malaria control and care 
have evolved in Uganda since the onset of the COVID-19 pandemic. This is partly because of limited testing 
available for SARS-CoV-2, particularly in rural communities where the burden of malaria is highest. Given 
the platform provided by the MRC surveillance, we are uniquely poised to evaluate the association 
between malaria and evidence of prior exposure to COVID-19 across Uganda. These results will inform 
policies and programmes for both malaria and COVID-19 in Uganda.  
 
 
 

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3 
Study objectives 
 
 
We propose to address the following research question: Are LLINs treated with a pyrethroid insecticide plus 
pyriproxyfen (PPF LLINs) more effective than LLINs treated with a pyrethroid plus piperonyl butoxide (PBO 
LLINs) for malaria control in Uganda, particularly in high-burden areas?  
The primary objective of the study is: To evaluate the impact of LLINs treated with a pyrethroid insecticide 
plus pyriproxyfen (PPF LLINs), as compared to LLINs treated with a pyrethroid plus piperonyl butoxide (PBO 
LLINs), on malaria incidence in Uganda. We will test the hypothesis that malaria incidence will be lower in 
intervention clusters (randomised to receive PPF LLINs) than in control clusters (randomised to receive PBO 
LLINs). 
In addition, the following secondary objectives will be addressed: 
 
1 
To evaluate the impact of PPF LLINs, as compared to PBO LLINs, on parasite prevalence, prevalence of 
anaemia, and (funding permitting) malaria vector density. We will test the hypothesis that parasite 
prevalence, prevalence of anaemia and malaria vector density will be lower in intervention clusters 
(PPF LLINs), than in control clusters (PBO LLINs).   
 
2 
To estimate the cost-effectiveness of delivering PPF LLINs, as compared to PBO LLINs. We will estimate 
incremental cost-effectiveness ratios (USD per disability-adjusted life year averted and per malaria case 
averted). 
 
3 
To assess net durability, bio-efficacy, survivorship and use of PPF LLINs vs PBO LLINs in Uganda. We will 
conduct cross-sectional surveys to determine net survivorship, attrition and use, and if funding is 
available, will supplement these with laboratory assessments of net durability and bio-efficacy.  
 
4 
To assess the impact of COVID-19 on malaria burden and care. Through our cross-sectional surveys, we 
will estimate malaria parasite prevalence and seroprevalence of antibody responses to selected SARS-
CoV-2 antigens, suggestive of prior infection with COVID-19, in individuals of all ages.  
 
 

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4 
Study design & methods 
 
4.1 
Overview 
 
We propose to conduct a rigorous, cluster-randomised trial to evaluate the impact of LLINs distributed in 
Uganda through the 2020 national universal coverage campaign. A cluster has been defined as the target 
area of an MRC. A total of 64 clusters have been included in the study, covering 32 high malaria burden 
districts in Uganda where IRS is not being implemented. Clusters have been randomised in a 1:1 ratio in 
blocks of two by district to receive one of two types of LLINs: (1) PPF LLINs (Royal Guard [n=32] and (2) PBO 
LLINs (PermaNet 3.0) [n=32] (Appendix A).  
The intervention, including delivery of the LLINs and social and behaviour change communication (SBCC), 
will be led by the Ugandan NMCD and other stakeholders. Currently, LLINs are scheduled to be delivered in 
the study areas from November 2020 to March 2021. The evaluation will include health facility surveillance 
at the MRCs to generate continuous estimates of malaria incidence for each MRC target area, cross-
sectional community surveys post-LLIN distribution (in a sub-set of clusters), and at 12- and 24-months 
after LLIN distribution (in all 64 clusters) to gather information on net survivorship and use, and parasite 
prevalence in children 2-10 years of age, entomology surveys, and assessment of net durability and 
efficacy. The primary outcome of the trial will be malaria incidence as estimated using the health facility 
surveillance. In 32 selected clusters (one per district), parasite prevalence and the seroprevalence of 
COVID-19 will be assessed during the 12-month survey in individuals of all ages from participating 
households.  
For each cluster we will use a ‘fried egg’ approach for delivering the intervention (‘egg white’) and 
measuring our outcomes (‘egg yolk’). The ‘white’ of the egg will include one sub-county per cluster, where 
the MRC is located.  PPF LLINs and PBO LLINs will be distributed to the designated sub-county, as allocated 
in the randomisation. The ‘yolk’ of the egg will be the target area directly surrounding each MRC, where 
care-seeking at the MRC is expected to be high (i.e. if someone within the target area develops malaria, 
they are likely to seek care at the MRC). To determine the population of the MRC target areas, and to 
generate a sampling frame for the community surveys, we will do the following: (1) define the target area 
of each MRC before the onset of the trial using data on village of residence from patients attending the 
MRCs, (2) map and enumerate all households within the MRC target areas before the 12-month community 
survey, (3) conduct a census survey within each MRC target area to generate an accurate estimate of the 
study population in which study outcomes will be measured concurrently with the 12-month community 
survey.   
The study will be conducted over approximately 2.5 years (30 months) from October 2020 to April 2023. 
The field work in Uganda will be led by IDRC, with oversight from the University of California, San Francisco 
(UCSF), and support from the London School of Hygiene & Tropical Medicine (LSHTM) and Liverpool School 
of Tropical Medicine (LSTM).  
 
4.2 
Study sites 
 
The NMCD and supporting partners will distribute LLINs nationwide across Uganda, including the 32 
districts included in this study. Districts were selected to participate in the study based on the following 
criteria: (1) Not receiving IRS, (2) Selected by the NMCD to receive PBO LLINs, based on available insecticide 
resistance data and guided by Uganda’s insecticide resistance management plan [22], (3) high malaria 
transmission intensity. Once the districts were identified, we then selected MRCs to bring the total to 64.  

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The selection criteria for the LLINEUP2 MRC sites, included: (1) Level III/IV high-volume, public health 
facility (HC III or HC IV), (2) Total OPD attendance between 1000-2000 patients per month, (3) Evidence of 
weekly and monthly reporting in DHIS2, (4) Presence of a functional laboratory at the facility. In addition, 
we aimed to ensure that MRCs within the same district were comparable in terms of level-of-care, and 
were located in different sub-counties to avoid contamination. 
4.3 
Randomisation 
 
Given the open-label study design and the need to generate estimates of the targeted number of LLINs for 
distribution in advance, randomisation was completed at the time of the protocol development. The 
randomisation was carried out by a member of the study team who is not based in Uganda, and who will 
not be directly involved in the field work. The unit of randomisation (cluster) was at the level of the MRC 
and the surrounding sub-county targeted for LLIN distribution. Randomization was done in blocks of 2, with 
each block representing a district containing 2 clusters with one cluster assigned the letter “A” and one 
cluster assigned the letter “B”. For each block, a random number between 0 and 1 was generated using the 
‘runiform’ command in STATA (StataCorp, Texas, USA). If the random number was <0.5, cluster “A” was 
assigned to PBO LLINs and cluster “B” was assigned PPF LLINs. If the random number was >0.5, cluster “A” 
was assigned to PPF LLINs and cluster “B” was assigned PBO LLINs. The final treatment allocations are 
summarized in Table 1, and the full intervention allocation list is provided in Appendix A.  
Table 1. Allocation of LLINs  
Type of LLIN 
Targeted total number of LLINs for distribution 
Number of clusters allocated 
PPF LLIN 
632,359 
32 
PBO LLIN 
696,914 
32 
Total 
1,329,273 
64 
 
4.4 
Sensitisation 
 
Prior to starting and throughout the study, we plan to build awareness, secure commitment, and encourage 
participation from stakeholders at the national and sub-national levels. Sensitisation will cover the purpose 
of the study, research activities, potential impact of the research, and how study findings can be 
communicated to stakeholders. We will engage with members of the Ugandan Ministry of Health in 
Kampala, and other key stakeholders including representatives from the US President’s Malaria Initiative 
(PMI), the World Health Organisation (WHO), and the UK’s Department for International Development 
(DFID). We will also engage with key stakeholders and opinion leaders at the district and community level in 
participating districts, including the LC V chairpersons, Chief Administrative Officers, District Health Officers 
and Malaria Focal Persons, local council chairmen (LCIs), village health team (VHT) members, health care 
workers and religious leaders. Study personnel will use a standard information sheet (Appendix B) to help 
guide sensitisation discussions. 
 
4.5 
Enumeration survey 
 
To estimate the population of the MRC target areas, and to generate a sampling frame for the 12-months 
cross-sectional community survey, we will enumerate and map all households within each target area prior 
to the evaluation (Figure 6). In advance of the survey, investigators will meet with local officials and 
community representatives to discuss the study and plans for the household enumeration. Using a map of 
the boundaries of the MRC target areas, project personnel will systematically cover the entire area within 
the boundaries to identify and enumerate all households.  
 

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A household will be defined as any single permanent or semi-permanent dwelling structure acting as the 
primary residence for a person or group of people that generally cook and eat together. Some households 
may include members who sleep in other dwelling structures within the same compound, if the members 
are still dependent on the head of household in the main household. All households identified will be 
assigned sequential unique IDs. Household locations will be mapped using GPS receivers. Readings will be 
taken from the door of the household, if possible, or from a point that is most representative of the 
household. At each household, a reading will be taken every five seconds for 2 minutes, and the average 
values from these readings will be recorded (Easting, Northing, and Altitude) in UTM units.  Only GPS 
coordinates will be picked from the households. No additional data will be collected during the 
enumeration survey. 
 
 
 
 
 
 

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5 
Intervention  
 
The NMCD and other stakeholders will take the lead on delivering the LLINs in the 32 districts included in 
the study. The research team will only be responsible for carrying out the evaluation. Here, the plans for 
the net distribution campaign are described to provide background information on the intervention. The 
original timelines for the 2020 LLIN distribution campaign have been delayed by COVID-19 pandemic, and 
the government’s response to prevent transmission and spread of COVID-19.  
 
5.1 
Overview 
 
The LLINs will be distributed according to detailed national guidelines, which build on prior experience from 
a similar net distribution campaigns carried out in 2013-2014 and 2017-18, incorporated guidance for LLIN 
distribution in the context of COVID-19, and lessons from food distribution during COVID-19. The overall 
goal of the 2020 LLIN distribution campaign is to reduce malaria morbidity and mortality in Uganda by 
achieving universal coverage with LLINs, aiming to ensure that: (1) 85% of the targeted population has 
access to a LLIN, and (2) 85% of LLINs distributed are utilised. Members of the research team will engage 
with Uganda’s national committees that are coordinating the LLIN universal coverage campaign, including 
the National Coordination Committee (NCC), which will be responsible for overall coordination and 
oversight of campaign planning, implementation, and engagement with political and traditional authorities, 
the operations sub-committee, the logistics sub-committee and the advocacy, communication and social 
mobilisation sub-committee. All LLINs procured for the campaign will be stored centrally in at the National 
Medical Stores warehouses in Entebbe and will be distributed across the country in waves.  The 32 districts 
selected for this study are included in Waves 3-5 and are scheduled to receive nets from October 2020 to 
February 2021, although the timelines are subject to change depending on procurement and importation of 
the LLINs. The research team will work closely with the NCC and other stakeholders to ensure that the nets 
are allocated per the randomisation scheme.  
5.2 
Household registration and LLIN distribution 
 
A key aspect of the distribution will be to ensure that the community members receive the correct number 
of LLINs. The LLINs will be stored centrally at the National Medical Stores warehouses in Entebbe, and then 
transferred from the central location to sub-county stores, and then onward to the household. A ‘door to 
door’ model of LLIN delivery will be applied with registration and LLIN distribution carried out concurrently. 
The processes will be handled by a multidisciplinary team, comprised of 5 individuals including: village 
health team member (VHT)/data clerk, security personnel, Local Council 1 (LC1), and two LLIN carriers. On 
average, there will be two teams per village for rural settings and four teams per village for urban settings, 
including towns and municipalities. 
The Electronic Data Management Information System (EDMIS) will be used for data management including 
the following: (1) collection of household-level registration data; (2) establishment of household LLIN need; 
(3) allocation of number of LLINs for each household; and (4) assignment of household Chalk ID. The 
VHTs/data clerk will interview the head of household if available, or another adult resident, using the 
EDMIS electronic system. Information collected will include: (1) name of the household head; (2) National 
ID number of the head of household; (3) number of residents; (4) number of children under 5 years of age; 
(5) number of pregnant women; (6) sleeping places available; and (7) the telephone number for the 
household (if available). The EDMIS electronic system will then generate the Chalk ID and allocate LLINs 
automatically for each household based on the registration data. The assigned Chalk ID will be written on 
the household and the allocated LLINs issued to the head of household, or another adult resident, 

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immediately. The household LLIN distribution will be based on household population with one LLIN 
distributed for every two people living in the household. The recipient of the LLINs will acknowledge receipt 
using an appropriate method.  
 
5.3 
Social behaviour change communication 
 
The NMCD and other stakeholders will take the lead on Social Behaviour Change Communication (SBCC). 
The UMIS 2018 showed that over 43% of the LLINs in the country were not being used. Thus, a 
comprehensive SBCC campaign is planned to increase LLIN utilization, with the campaign branded ‘under 
the net 2020-2022’. SBCC activities will use digital and other platforms, similar to those used for the COVID-
19 response, including the following: (1) LLIN launch on television and radio; (2) regional advocacy meetings 
on Zoom; (3) mass media platforms (for advertisements, mini skits, DJ mentions, radio spots, interactive 
talks); (4) social media platforms; (5) VHTs; (6) operation hotlines and toll-free call centres; (7) community 
mobilisation (megaphones); and (8) use of appropriate information, education, and  communication 
materials. Communication will include messages about COVID-19, malaria, and use, care, repair and 
repurposing of LLINs.   
 
 
 
 
 
 

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6 
Health facility surveillance 
 
6.1 
Data collection at MRCs 
 
At each MRC, individual-level data from standardised registers for all patients presenting to the outpatient 
departments are entered into an Access database by on-site data entry officers. Primary data captured 
comes from the HMIS 002 standardised form (Appendix C) and includes location of residence (parish and 
village), age, gender, body temperature, history of subjective fever, type of malaria test done (rapid 
diagnostic test or microscopy), malaria diagnostic test results, any diagnoses given, and any treatments 
prescribed. The research team supports the sites with training, site support supervision, and buffer stock of 
laboratory supplies/consumables. We employ full-time regional surveillance coordinators based around the 
country, each supervising 8-10 MRCs. Team members based in Kampala include the programme manager, 
several study epidemiologists, a data manager, a laboratory manager, and administrative staff. Members of 
the core team visit the MRCs on a regular basis to provide refresher training and feedback, and to conduct 
laboratory quality control procedures. Core team members are also responsible for generating periodic 
reports, communicating with MOH officials and other key stakeholders, and conducting data analyses.   
Table 2. Malaria reference centers 
 
District 
MRC 1 
Sub-county 
Start date 
MRC 2 
Sub-county 
Start date 
1 
Agago 
Patongo HCIII 
Patongo TC 
Aug 14 
Lira-Kato HCIII 
Lapono 
Oct 19 
2 
Amuru 
Atiak HCIV 
Attiak 
Sep 14 
Amuru HCIII 
Amuru 
Nov 19 
3 
Gulu 
Awach HCIV 
Awach 
Aug 14 
Pabwo HCIII 
Bungatira 
Oct 19 
4 
Kitgum 
Namokora HCIV 
Namokora 
Sep 14 
Kitgum Matidi HCIII 
Kitgum Matidi 
Nov 19 
5 
Koboko 
Lobule HCIII 
Lobule 
Aug 18 
Ayipe HCIII 
Kuluba 
Dec 19 
6 
Kole 
Aboke HCIV 
Aboke 
Mar 14 
Bala HCIII 
Bala 
Nov 19 
7 
Lamwo 
Padibe HCIII 
Padibe 
Sep 14 
Madi-Opei HCIII 
Madi-Opei 
Nov 19 
8 
Arua 
Opia HCIII 
Vurra 
Sep 14 
Cilio HCIII 
Aii-vu 
Dec 19 
9 
Nwoya 
Koch Goma HCIV 
Koch Goma 
May 19 
Alero HCIII 
Alero 
Jan 20 
10 
Omoro 
Lalogi HCIV 
Lalogi 
Sep 14 
Bobi HCIII 
Bobi 
Jan 20 
11 
Oyam 
Anyeke HCIV 
Oyam TC 
Apr 14 
Otwal HCIII 
Otwal 
Nov 19 
12 
Amuria 
Asamuk HCIII 
Asamuk 
Nov 19 
Morungatuny HCIII 
Morungatuny 
Dec 19 
13 
Bukedea 
Bukedea HCIV 
Bukedea 
Nov 19 
Kolir HCIII 
Kolir 
Nov 19 
14 
Kumi 
Kamaca HCIII 
Kanyum 
Jan 20 
Omatenga HCIII 
Kumi 
Nov 19 
15 
Apac 
Teboke HCIII 
Chegere 
Feb 20 
Akokoro HCIII 
Akokoro 
Feb 20 
16 
Busia 
Lumino HCIII 
Lumino 
Jun 18 
Busitema HCIII 
Busitema 
Mar 20 
17 
Jinja 
Budondo HCIV 
Budondo 
Jan 20 
Butagaya HCIII 
Butagaya 
Jan 20 
18 
Kapelebyong 
Kapelebyong HCIV 
Kapelebyong 
Nov 19 
Obalanga HCIII 
Obalanga 
Jan 20 
19 
Kwania 
Aduku HCIV 
Aduku TC 
Oct 06 
Apwori HCIII 
Chawente 
Nov 19 
20 
Kaliro 
Bumanya HCIV 
Bumanya 
Dec 19 
Nawaikoke HCIII 
Nawaikoke 
Feb 20 
21 
Luuka 
Kiyunga HCIV 
Luuka TC 
Dec 19 
Ikumbya HCIII 
Ikumbya 
Jan 20 
22 
Masindi 
Bwijanga HCIV 
Bwijanga 
Dec 19 
Kyatiri HCIII 
Pakanyi 
Dec 19 
23 
Mayuge 
Buwaiswa HCIV 
Buwaya 
Jan 20 
Kigandalo HCIV 
Kigandalo 
Feb 20 
24 
Kiryandongo 
Diima HCIII 
Mutunda 
Mar 20 
Kigumba HCIII 
Kigumba 
Feb 20 
25 
Buyende 
Kidera HCIV 
Kidera 
Feb 20 
Bugaya HCIII 
Bugaya 
Feb 20 
26 
Kaabong 
Lokolia HCIV 
Kaabong East 
Jul 18 
Kalapata HCIII 
Kalapata 
Feb 20 
27 
Hoima 
Kigorobya HCIV 
Kigorobya TC 
Feb 18 
Butema HCIII 
Buhanika 
Feb 20 
28 
Kibaale 
Kibaale HCIV 
Kibaale TC 
May 18 
Kyebando HCIII 
Kyebando 
Feb 20 
29 
Kyegegwa 
Kakabara HCIII 
Kakabara 
Jan 20 
Kyegegwa HCIV 
Kyegegwa TC 
Mar 20 
30 
Kayunga 
Bbaale HCIV 
Bbaale 
Feb 18 
Kangulumira HCIV 
Kangulumira 
Mar 20 
31 
Mubende 
Kasambya HCIII 
Kasambya 
Dec 06 
Kiyuni HCIII 
Kiyuni 
Feb 20 
32 
Moyo 
Metu HCIII 
Metu 
Sept 20 
Lefori HCIII 
Lefori 
Sept 20 
 
Data will be collected for all patients presenting to the outpatient departments of the MRCs using the HMIS 
002 outpatient register as the primary data source. Data from registers will be entered into customised 
electronic databases on site (Appendix D). Plans for management of the MRC data are described further in 
section 11.1.  

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7 
Cross-sectional surveys 
7.1 
Overview 
 
We propose to conduct cross-sectional community surveys at baseline (if resources are available), and at 
12- and 24-months after LLIN distribution. The sampling frame for the community surveys will be generated 
from the enumeration of the MRC target areas (which will be conducted prior to the onset of the 
evaluation). Households will be randomly selected from each of the 64 clusters and screened until 50 
households with at least one child aged 2-10 years are enrolled (a minimum of 3,1200 households). The 
cross-sectional community surveys will include two components: (1) a household survey targeting heads of 
households, and (2) a clinical survey of children aged 2-10 years. In 32 selected clusters (one per district), 
individuals of all ages from participating households will be enrolled into the clinical survey during the 12-
month survey. The clinical surveys will include a finger-prick blood sample for thick blood smear, 
measurement of haemoglobin (in children < 5 years), and storage of blood on solid phase media such as 
filter paper.  
 
7.2 
Definitions 
 
— Household: A household will be defined as any single permanent or semi-permanent dwelling 
structure acting as the primary residence for a person or group of people that generally cook and 
eat together.  Some households may include members who sleep in other dwelling structures 
within the same compound, if the members are still dependent on the head of household in the 
main household.    
 
— Head of household: The head of household is an adult person or persons who primarily make 
decisions for the general household (e.g. decisions on healthcare, income, etc.), including 
emancipated minors.   
 
— Household resident: A resident within each household will be defined as a person who intends to 
have a sleeping place primarily at that location for a period of the next 6 months. This may include 
people who sleep in a separate house within the same compound, if they are still dependent on the 
head of household for decisions on finances and health care.   
 
7.3 
Household survey 
 
7.3.1 
Selection of households 
 
Households from each of the 64 clusters will be randomly selected for participation in each of the 
community surveys. Within each cluster (MRC target area), households will be randomly sampled from a 
list of households enumerated by the study team, until 50 households with children aged 2-10 years are 
sampled per cluster. 
 
7.3.2 
Screening 
 
When a household is identified, study personnel will briefly describe the purpose of the study to the head 
of the household (or their designate) in the appropriate language, and screen for eligibility (Appendix E).  
 
The inclusion criteria are:  
1 
At least one adult aged 18 years or older present  
2 
Adult is a usual resident who slept in the sampled household on the night before the survey 
3 
Agreement of the adult resident to provide informed consent for the household survey 
 
 
The exclusion criteria are:  
1 
Dwelling destroyed or not found 

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2 
Household vacant 
3 
No adult resident home on more than 3 occasions 
7.3.3 
Informed consent 
 
A detailed description of the informed consent procedures is provided in section 14.2. Briefly, study 
personnel will carry out the informed consent discussion with the head of the household (or their 
designate) in the appropriate language, and a translator will be used if necessary. The information sheets 
and consent forms will be available in English and the appropriate local languages. Following the consent 
discussion, the respondent will be asked by the study personnel to sign a written consent form to 
participate in a research study (consent form #1). If the respondent is unable to read or write, their 
fingerprint will substitute for a signature, and a signature from an impartial witness to the process will be 
obtained.  
7.3.4 
Household survey questionnaire 
 
The household questionnaire (Appendix F) will be administered to the head of the household (or their 
designate), after obtaining their consent using a hand-held tablet computer. Information will be gathered 
on the characteristics of households and residents, proxy indicators of wealth including ownership of 
assets, and ownership and use of LLINs in the households, specifically focusing the nets distributed in the 
2020-2021 LLIN campaign. The household survey questionnaire has been adapted from prior cross-
sectional community surveys conducted in Uganda, including the national Malaria Indicator Surveys and the 
LLINEUP trial [14, 38, 54, 55]. For the 12-month survey, we have added additional questions to assess 
knowledge, attitudes, and beliefs regarding COVID-19 and its effect on malaria care and control, as well as 
costs and treatment seeking behaviours for fever in the past two weeks.  
7.4 
Clinical survey 
 
7.4.1 
Recruitment of participants 
 
Recruitment of households into the community survey will continue until 50 households with children aged 
2-10 years are enrolled. All children aged 2-10 years from enrolled households who are present will be 
eligible for participation in the clinical survey. In 32 selected clusters (one per district), individuals of all ages 
from participating households will be enrolled into the clinical survey. Children will be identified from the 
household survey questionnaires. If an enrolled household has no children of appropriate age, they will be 
included in the household survey only, and will not take part in the clinical survey.  
7.4.2 
Screening of participants 
 
At the end of the household questionnaire, study personnel will discuss the clinical survey with the head of 
the household (or their designate), if the household includes children age 2-10 years. Study personnel will 
briefly describe the purpose of the clinical survey in the appropriate language and will screen for eligibility 
criteria (Appendix G).   
 
The inclusion criteria are:  
1 
Child aged 2-10 years (in all 64 sites) - In 32 selected clusters, individuals of all ages will be eligible 
2 
Usual resident who was present in the sampled household on the night before the survey 
3 
Agreement of adult or parent/guardian (of children) to provide informed consent  
4 
Agreement of child aged 8 years or older to provide assent 
 
The exclusion criterion is:  
1 
Child not home on day of survey 
 

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7.4.3 
Informed consent 
 
A detailed description of the informed consent procedures is provided in section 14.2. Briefly, study 
personnel will carry out the informed consent discussion with the adult or parent(s) or guardian(s) of 
children. Informed consent will be conducted in the appropriate language and a translator will be used if 
necessary. Consent forms will be available in English and the local languages. Following the informed 
consent discussion, adults or parents/guardians will be asked by the study personnel to sign a written 
consent form to participate, or for their child(ren) to participate, in a research study (consent forms #2 & 
#6) and a second approved consent form for the future use of biological specimens obtained during the 
study (consent forms #3 & #7). Written assent to participate in the study will also be obtained from children 
aged 8 years and older at the time of screening (consent form #4). If an adult respondent or 
parent/guardian is unable to read or write, their fingerprint will substitute for a signature, and a signature 
from a witness to the informed consent procedures will be obtained. 
7.4.4 
Clinical survey procedures 
 
The clinical surveys will be carried out by study personnel, including teams consisting of one clinician plus 
one research assistant. The surveys include measurement of temperature, subjective fever and a finger-
prick blood sample for measurement of thick blood smear and haemoglobin (in children < 5 years), and 
storage of blood on solid phase media such as filter paper (Appendix H).  
7.4.5 
Management of ill participants 
 
Participants who have a temperature of > 38.0⁰C, or who report fever in the past 48 hours, will have an RDT 
performed by study personnel.  Febrile participants will be treated with paracetamol as appropriate. 
Participants with a positive RDT and no evidence of severe malaria will be treated with artemether-
lumefantrine (AL), which is the first-line recommended treatment for uncomplicated malaria in Uganda. 
Participants with a positive RDT and evidence of danger signs of severe disease will be referred for further 
evaluation and treatment.  Any participant with other concerning clinical symptoms will also be referred to 
an appropriate health care facility at the discretion of the study personnel. 
7.4.6 
Number and timing of surveys 
 
We anticipate carrying out at least 2 rounds of surveys (the 12- and 24-month post-distribution surveys at a 
minimum). Additional surveys (including a survey shortly after the distribution of LLINs) may be conducted, 
depending on the availability of funding. We currently have funding to conduct post-LLIN distribution 
surveys in 12 MRC target areas. 
 
 

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8 
Entomology surveys 
8.1 
Overview 
 
Funding permitting, entomology surveys will be carried out to collect mosquito specimens for estimating 
vector density and insecticide resistance monitoring. Collections will be carried out concurrently with the 
cross-sectional community surveys at 12- and 24- months. Mosquitoes will be collected from a sub-set of 
10 households per cluster enrolled in the cross-sectional community survey. Female anopheles mosquitoes 
will be identified, and will be stored with silica gel in the field sites, prior to shipment to Kampala and onto 
LSTM for further analysis. 
8.2 
Recruitment and screening 
 
In each cluster, 10 households will be selected randomly for the entomology survey from the list of 
households enrolled into the community household surveys. Study personnel will re-visit households on the 
list of randomly selected households to carry out the recruitment for the entomology survey. When a 
household on the selection list is identified, study personnel will briefly describe the purpose of the study in 
the appropriate language with the head of household (or their designate) and proceed with screening 
(Appendix I).  
 
The inclusion criteria are:  
1 
At least one adult aged 18 years or older present  
2 
Adult is a usual resident who slept in the sampled household on the night before the survey 
3 
Agreement of the adult resident to provide informed consent for the entomology survey 
 
The exclusion criteria are:  
1 
Dwelling destroyed or not found 
2 
Household vacant 
3 
No adult resident home on more than 3 occasions 
8.3 
Informed consent 
 
A detailed description of the informed consent procedures is provided in section 13.2. Briefly, study 
personnel will conduct the informed consent discussion with the head of household (or their designate). 
Informed consent will be conducted in the appropriate language and a translator will be used if necessary. 
Consent forms will be available in English and the local languages (consent form #5). Following the 
informed consent discussion, the head of household (or their designate) will be asked to sign a written 
consent form for mosquitoes to be collected from their household. If the head of household (or their 
designate) is unable to read or write, their fingerprint will substitute for a signature, and a signature from a 
witness to the informed consent procedures will be obtained.  
8.4 
Entomology survey 
 
Mosquitoes resting on interior surfaces will be collected by entomology technicians using Prokopack 
aspirators (John W. Hock Co., USA). Collections will be carried out just after dawn and continue until 10am. 
A standardised collection duration of ten minutes per house will be used, which is sufficient to mechanically 
aspirate mosquitoes from all resting surfaces in a typical house, while minimising disruption. Female 
anopheles mosquitoes will identified phenotypically, enumerated, and stored on silica gel in the field, 
before being transported and refrigerated at a central laboratory in Kampala for potential future molecular 
analysis depending on the availability of resources. Mosquito samples may be shipped to the Liverpool 
School of Tropical Medicine (LSTM) to conduct molecular studies that cannot be conducted in Uganda. 
 

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9 
LLIN durability assessment 
9.1 
Overview 
 
Funding permitting, a sub-set of LLINs distributed during the 2020-2021 universal LLIN campaign will be 
withdrawn (and replaced) from households enrolled into the 12- and 24-month cross-sectional community 
surveys to assess net durability. During each community survey, 248 households (4 per cluster, 124 per 
study arm), will be randomly selected from the 64 clusters to participate in the net durability study and will 
have one net withdrawn for durability assessment.  A sample of 124 nets (one per household) of two 
different types, PPF LLINs (Royal Guard) and PBO LLINs (PermaNet 3.0) will be collected, packaged, labelled 
and stored. Each net collected will be replaced by a new net of the same type set aside by the 
NMCP/Ministry of Health.  
 
9.2 
Net integrity 
 
Net integrity will be assessed using standard WHO guidelines [56]. The nets will be assessed by counting the 
number of holes (including tears in the netting and spilt seams) by their location on the net and their size.  
 
Holes will be classified into the following categories: 
• 
Smaller than a thumb (0.5-2cm) 
• 
Larger than a thumb, but smaller than a fist (2-10cm) 
• 
Larger than a fist, but smaller than a head (10-25cm) 
• 
Larger than a head (>25cm) 
 
Holes less than 0.5cm will be ignored. Evidence of repairs to the net fabric and the type of repair will be 
noted and recorded. Net use, care and repair will be emphasised as part of this process. Novel net 
durability assessment procedures are being developed by a number of transnational research groups and 
may be incorporated into the LLIN integrity assessments. The new assessments will not affect net 
replacement, measurement, or storage protocols.  
 
9.3 
Chemical analysis of LLINs 
 
If resources are available, HPLC will be conducted on samples of nets taken from the top surface of the 
LLINs withdrawn at the 24-month timepoint. The insecticides deltamethrin, alphacypermethrin, and 
pyriproxyfen, and the synergist PBO, will be extracted from the net samples using standard solvent 
extraction protocols, as in prior studies [54]. The chemicals extracted from the net will then be filtered to 
remove impurities before quantitative analysis performed via HPLC using controls of known insecticide 
concentration. This will allow the total concentration of insecticide and or synergist remaining on each net 
to be measured. Comparison to results of HPLC analysis on unused nets from the same distribution batch 
will allow the chemical degradation of the nets to be determined. 
 
 
 

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10 
Economic evaluation 
 
10.1 
Overview 
 
An economic evaluation will be conducted to compare the incremental costs and cost-effectiveness of LLIN 
strategies using either Royal Guard LLINs or PBO LLINs in accordance with the reference case for economic 
evaluations in low- and middle-income countries [57]. The analysis will combine primary data on costs and 
effectiveness from the trial with additional secondary data to inform policy choices regarding the choice of 
LLIN. The analysis will take a disaggregated societal perspective, meaning that it will include costs to 
households, the health service, and donors, separately and together [57]. Research costs will not be 
included in the cost-effectiveness analyses. Efficiency will be measured in incremental cost-effectiveness 
ratios. A decision tree will be used to calculate the incremental cost-effectiveness of the Royal Guard LLIN 
compared to PBO LLINs [58, 59]. Parameter values for the model will include effectiveness inputs, cost 
inputs, probability inputs, and payoffs [60].Implementation will be modelled over the expected 3-year 
lifespan of the LLINs, and costs and outcomes of malaria episodes occurring in these three years will be 
modelled over a lifetime horizon [61, 62]. Additional analyses may be conducted using a shorter lifespan for 
one or both of the nets based on durability data. Where possible, the parameter values will be directly 
measured during the clinical trial. For values that cannot be directly measured, estimates will be derived 
from the literature and other relevant sources. In addition, a Markov model will be explored as a possible 
alternative method of assessing the cost-effectiveness of the LLINs. Markov models are useful for decision 
problems involving risks that change over time and events which may occur more than once [63, 64]. Unlike 
the decision tree model, which is linear and assumes that different outcomes are mutually exclusive, a 
Markov model is well-suited for analysis of LLIN interventions, which can capture repeated malaria 
episodes. The main outcome will be cost in USD per disability-adjusted life-year (DALY) averted and per 
malaria case averted. The incremental cost per DALY averted will be compared to plausible cost-
effectiveness thresholds [65].  
 
10.2  
Estimation of costs 
 
The financial and economic costs of LLIN distribution will be estimated using the ingredients approach with 
quantities and values reported separately (Appendix J) [66]. Economic costs capture opportunity costs, 
recognizing that the cost of using resources means that these resources are unavailable for productive use 
elsewhere. Economic costs include direct and indirect costs to the LLIN provider and recipient, and reflect 
the full value of resources used to implement the net campaign including those which do not incur a 
financial cost to the health service such as donated goods or services and time required of household 
members or village health team workers (VHT). There are two main components of cost to be considered in 
malaria economic evaluations, the cost of the intervention and the cost savings from averted cases of 
malaria. The total cost of intervention is represented by the following equation:   
Total cost = cost of intervention – cost saving from cases averted 
 
10.2.1  Intervention costs 
 
Data on incremental costs of net distribution and their sources will be collected alongside the interventions 
and will allow comparisons of 2-yearly vs 3-yearly distribution campaigns. We will collect costs for materials 
including the nets, storage, ‘door-to-door’ delivery, data management using Electronic Data Management 
Information System (EDMIS), social and behaviour change communication (SBCC), local and international 
transportation of the materials and equipment and decision-making at national, regional, district level. 
Results will be reported in US dollars and local currency (Ugandan Shillings). Initial capital costs, including 

LLINEUP2 Study protocol, Version 7.0, 17 Oct 2023 
 
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the nets, will be amortized over the useful life of the asset.  Additionally, we will explore opportunities to 
collect data on costs of net distribution across all of Uganda. Financial costs will be recorded from financial 
reports and account of the implementation partners. We will also attempt to report on any leakage of 
resources, differentiating between measured outputs and outputs that reach the intended target [67]. 
10.2.2  Malaria costs 
 
The cost savings from malaria cases averted for a given population can be represented by the following 
simple equation:  
𝐶𝑜𝑠𝑡 𝑠𝑎𝑣𝑖𝑛𝑔𝑠 𝑓𝑟𝑜𝑚 𝑚𝑎𝑙𝑎𝑟𝑖𝑎 𝑐𝑎𝑠𝑒𝑠 𝑎𝑣𝑒𝑟𝑡𝑒𝑑
= (𝑐𝑎𝑠𝑒𝑠𝑢𝑛𝑐𝑜𝑚𝑝𝑙𝑖𝑐𝑎𝑡𝑒𝑑× 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑐𝑜𝑠𝑡𝑢𝑛𝑐𝑜𝑚𝑝𝑙𝑖𝑐𝑎𝑡𝑒𝑑)
+ (𝑐𝑎𝑠𝑒𝑠𝑠𝑒𝑣𝑒𝑟𝑒× 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑐𝑜𝑠𝑡𝑠𝑒𝑣𝑒𝑟𝑒)  
Therefore, we will be collecting data on the savings from malaria cases averted from the provider and 
societal perspective. Incremental benefits due to malaria cases averted including savings on diagnostics, 
drugs, treatment, health worker time, transport, caregiver time, and productivity gains will be estimated 
using the cross-sectional survey and malaria treatment cost data collected from selected MRCs. The 
provider costs will be appraised at a subset of ~7-10 MRCs using a combination of step-down & micro-
costing methods. Demographic and malaria transmission levels will be considered when selecting the MRCs 
to ensure a representative sample. Step-down methods focus on capturing all resources available to a 
health facility and allocates appropriate shares of costs to final services like outpatient visits and 
vaccinations. Micro-costing methods give detailed information on disease specific costs for a sample of 
patients. Data will be collected through (1) a review of MRC expenditure records and clinic registries, (2) 
health worker interviews, and (3) a time-in-motion study. The cross-sectional survey will include questions 
on malaria care access, utilization, quality of care and direct (diagnosis, treatment, transport, special food) 
and indirect costs (lost wages by caregivers, opportunity cost of time, productivity losses due to 
neurological sequelae and productivity losses due to premature death). Productivity losses will be 
estimated using the human capital approach.  
10.3 
Estimation of effects 
 
The measure of effectiveness will be number of malaria cases averted derived from incidence data, which 
will be calculated by dividing the number of laboratory-confirmed malaria cases diagnosed at each MRC 
(among patients residing in the target area per unit time) by the total population of the MRC target area 
[68-70]. In addition, the health outcomes of each intervention are evaluated in Disability Adjusted Life 
Years (DALYs) averted, to allow for comparison with other malaria control interventions and interventions 
aimed at other diseases. The DALY, the measure favoured by the World Health organization in a LMIC 
context [57], is a composite of the years of life lost (YLL) and the years of life lived with disability (YLD) [71].  
YLDs will be calculated based on the duration of disability and morbidity, and disability weights (ranging 
from 0 to 1) will be given to each condition using data from the Global Burden of Disease study [72]. YLLs 
will be calculated based on the average age at death and remaining life expectancy at death from standard 
life tables. The YLLs and YLDs averted by an intervention will be summed to give the DALYs averted. DALYs 
will be discounted at 3% with no age weighting [73].   
10.5 
Analysis 
 
Cost per net delivered and cost per person sleeping under a net will also be presented as this may prove 
useful for policy decisions [67].Univariate analysis will be conducted to assess the impact of uncertainty and 
heterogeneity in pre-selected parameters on study results.  A probabilistic sensitivity analysis will be 

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performed to allow for multivariate uncertainty by estimating distributions instead of point estimates for 
model parameters [74]. Data for input variables will be derived from the clinical trial, and are expected to 
have beta, gamma, and lognormal distributional forms. Where information is unattainable or is not 
testable, the uncertain parameters will be disaggregated at incremental levels and the cost-effectiveness 
will be plotted according to cost effectiveness acceptability curves and surfaces [59]. Best estimate 
incremental cost-effectiveness ratios (ICERs) will be calculated for clear presentation of results to 
policymakers. To estimate uncertainty around ICERs, Monte Carlo simulations will be performed, allowing 
input variables for cost and outcome to vary within given distributions [74]. At each iteration, input 
parameter values will be chosen at random from the probability distributions, and overall costs and DALYs 
averted will be recorded. Due to on-going debate about the use of cost effectiveness thresholds, results will 
also be presented in terms of a cost-effectiveness efficiency frontier, where net costs and net benefits of 
different interventions are compared [75]. Differences in costs, outcome and cost-effectiveness that can be 
explained by variations between subgroups of patients will be reported. Sub-group analyses will be 
conducted to look at any differences with respect to sex, age or socio-economic status in terms of both 
baseline characteristics and relative treatment effects.  
 
 
 
 

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11 
Laboratory procedures 
11.1 
Microscopy 
 
Thick blood smears will be prepared in the field for microscopy using blood samples obtained from a finger-
prick. New glass slides, frosted at one end, will be used to make the thick blood smears. Before making the 
smears, a barcode label will be placed on the under-side of the glass slides at the frosted end linked to the 
appropriate cross-sectional community survey. Thick blood smears will be made by placing a drop of blood 
in middle of the slide.  An applicator stick will be used to spread the blood into a spot of approximately 1 
cm in diameter. The blood smear will be dried on a slide tray, in an ideally dust-free environment. Slides will 
be kept at the field site protected from excessive heat and light for no longer than 1 week to avoid auto-
fixation. The slides will be kept in a slide box and stored in the coolest place possible. The blood slides for 
malaria will be periodically transported to the IDRC Molecular Research Laboratory (MOLAB) in Kampala for 
reading. At the MOLAB, thick blood smears will be stained with 2% Giemsa for 30 minutes, and will be 
evaluated for the presence of parasitaemia (asexual forms only). Parasite densities will be calculated from 
thick blood smears by counting the number of asexual parasites, respectively, per 200 leukocytes (or per 
500, if the count is less than 10 parasites per 200 leukocytes), assuming a leukocyte count of 8,000/l.  A 
thick blood smear will be considered negative when the examination of 100 high power fields does not 
reveal asexual parasites. For quality control, all slides will be read by a second microscopist and a third 
reviewer will settle any discrepant readings. 
11.2 
Haemoglobin measurement 
 
Haemoglobin analysis will be carried out on site using a drop of blood collected from a finger-prick. The test 
will be conducted using a battery-operated portable HemoCue analyzer (HemoCue, Anglom, Sweden) 
which provides a result within one minute. The haemoglobin results will be provided to the caregiver of the 
participant verbally, and will be recorded on the appropriate case record form.  Any participant who is 
found to have severe anaemia requiring treatment will be referred to an appropriate health care facility for 
further management. 
11.3 
Finger prick blood samples  
 
11.3.1 Collection 
 
Finger prick blood will be collected onto filter paper or similar solid phase media to store for future 
laboratory studies, which may include serologic response to malaria, quantitative PCR, speciation of malaria 
parasites based on nested PCR of cytochrome b [77], analyses of polymorphisms in parasite and/or human 
genes for mutations that may impact on clinical malaria or other diseases, detection of HRP-2 deletions, 
and genotyping and/or whole genome sequencing of malaria parasites. Filter paper (Whatman no 1, 
Whatman 3MM; Whatman, Maidstone, UK) will be pre-cut into individual squares and stapled to a thick 
card which will serve as its cover. Blood spots will be collected onto the filter paper in volumes of 
approximately 25 µl aliquots per blood spot (4 blood spots per sample). Filter paper samples labelled with 
the sample’s bar codes on the covering cardboard, and will be allowed to dry at ambient temperature and 
relative humidity before closing the card over the filter paper (like closing a matchbook). Solid-phase blood 
collection devices, e.g. lateral flow devices similar to RDTs, may be used in addition to or as an alternative 
to filter paper for collection and storage of finger prick blood. 
11.3.2 Storage 
 
Filter paper samples will be transported from the field in a zip lock bag and will be placed into a stock card 
filter paper box for final storage with a dessicant. Filter paper samples will be stored initially in Kampala, at 

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IDRC’s Molecular Laboratory (MOLAB), in -20⁰C freezers.  Filter paper samples may be stored for up to 10 
years, and ultimately will be destroyed using incineration. Future laboratory studies would be performed 
only for research purposes and will have no impact on the clinical management of study participants.  
11.4 
Rapid diagnostic tests 
 
Rapid diagnostic tests for malaria (RDTs) will be performed in the field on cross-sectional survey 
participants who are found to have a temperature of > 38.0⁰C, or who report fever in the past 48 hours. 
RDTs will be performed according to the directions provided for the specific tests, using the blood transfer 
device and reagent provided by the manufacturer. Tests will be performed by study personnel, and results 
will be available within 15 minutes. The results of the RDT will be provided to the participant’s caregiver 
verbally, and will be recorded on the appropriate case record form.  Participants who test positive for 
malaria, and who are deemed to have uncomplicated disease, will be provided a full course of antimalarial 
treatment, and will also be counselled to go to the nearest health facility immediately if their condition 
worsens. Any participant with evidence of danger signs of severe malaria, or other concerning clinical 
symptoms, will also be referred to an appropriate health care facility at the discretion of the study 
personnel. 
 
11.5 
Molecular analysis of malaria vectors 
 
If funding is secured for the entomologic surveillance, molecular analysis of malaria vectors will be 
conducted. Female anopheles mosquitoes will be identified, and will be stored and refrigerated in the 
regional field sites, prior to shipment to Kampala for further analysis. Where possible analyses will be 
conducted in the MOLAB in Kampala, but for some procedures and for whole genome sequencing, it will be 
necessary to ship specimens to the Liverpool School of Tropical Medicine and/or the Wellcome Sanger 
Institute in the UK. We propose to use a combination of pyrethroid resistance associated molecular 
markers which together explain a substantial fraction of the variation in resistance phenotype. Mosquitoes 
will be identified to species and screened of malaria infection using standard PCR-based assays [78, 79]. An. 
gambiae and An. arabiensis will be screened for key insecticide target sites (e.g. Vgsc) together with 
variants in metabolic resistance genes known to be resistance associated in east Africa (e.g. Gste4,  
Cyp6aa1-Dup1, Cyp6p4-236M, Cyp4j5 and Coeae1d) [80, 81]. 
 
11.6 
COVID-19 testing 
 
To evaluate for prior exposure to COVID-19, we will measure antibody responses to three SARS-CoV-2 
antigens: spike (S), receptor binding domain of the spike protein (RBD), and nucleocapsid (N), using a 
multiplex Luminex assay. This assay was developed at UCSF and has been validated in a longitudinal cohort 
of COVID-19 positive patients. Concentration values will be calculated from the Luminex median 
fluorescent intensity using a plate-specific standard curve consisting of serial dilutions of a pool of positive 
control samples from Uganda. A cut-off for positivity will be established for each antigen above the 
maximum concentration value observed across pre-pandemic SARS-CoV-2 negative control samples from 
Uganda tested on the platform. A logistic regression model including the concentration values of the three 
antigens for each sample will be used to establish a cut-off for positivity; this method had the highest cross-
validation accuracy for classification in a prior population-based study [82]. 
 
11.7. Genotyping parasite DNA from parasite-positive dried blood spots 
For participants who consented to future use of biological specimens at the time of sample collection, 
parasite DNA from parasite-positive dried blood spots (as determined by quantitative varATS PCR) will be 

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genotyped in order to: (1) survey for drug and diagnostic resistance and (2) generate parasite genetic 
diversity data that can be used to better understand variation in transmission intensity in Uganda. Parasite 
genomic DNA will be extracted from dried blood spots using Chelex or purified using another standard 
technique such as a spin column. To genotype the samples a modular multiplex amplicon panel 
(MadHatter) will be used, which has 107 targets for diagnostic and drug resistance and 178 targets for 
genetic diversity and geographic assignment [83]. These high throughput amplicon-based approaches may 
also be complemented with other genotyping techniques such as Sanger sequencing of specific genes of 
interest, molecular inversion probes, qPCR evaluation of gene copy number, Oxford Nanopore long 
amplicon sequencing, and/or whole genome sequencing to validate data. When possible, these genotyping 
assays will be performed at the Central Public Health Laboratory in Butabika, Uganda, but for some 
procedures it will be necessary to ship extracted DNA or DBS to the University of California, San Francisco, 
and/or the Wellcome Sanger Institute in the UK. 
 
 
 
 
 

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12 
Data management 
12.1 
MRC surveillance 
 
Data will be collected for all patients presenting to the outpatient departments of the MRCs using the HMIS 
031 outpatient register as the primary data source. Data from registers will be entered into customised 
electronic databases on site (Appendix D). Data from each MRC will be submitted to the team in Kampala 
on a monthly basis using a secure on-line system. Standardised data checks will be applied to assess for 
missing data and data errors. Data queries will be submitted back to the sites and corrected whenever 
possible. Interval data submitted from the MRCs will be merged into an existing master database. Each 
time the master database is updated, existing programmes will be run to perform variable transformations 
and generate standardised indicator variables. Standardised reports summarizing key indicators of disease 
burden and case management practices will be generated on a quarterly basis and shared with the MRCs 
and other partners. These reports will provide additional details beyond routine HMIS data, including 
stratification of data across demographic variables (e.g. age) and geospatial representation of disease 
burden.  
12.2 
Cross-sectional community surveys and entomology surveys 
 
All data will be collected by survey teams using hand-held tablet computers. Prior to conducting the surveys 
information from the questionnaires and fields for entering results of biomarker testing will be 
programmed into the tablet computers. Programming will include range checks, structure checks and 
internal consistency checks. Before leaving the household, an inventory will be made of the completed 
questionnaires and blood samples collected; both will be checked to make sure they are labelled correctly. 
The completed questionnaires will be checked for mistakes and completeness. Data from these devices will 
be transferred at the end of every day to our data core facilities in Kampala and stored on a secure server. 
The data file will be kept on a separate network so that only authorized survey staff will have access to the 
data during collection and processing phase. The file with data from the questionnaires will be merged with 
results from reading the malaria slides at the laboratory, using the unique bar codes. All filter paper 
samples and blood slides will be returned to the IDRC offices in Kampala.  
12.3 
Laboratory data 
 
Laboratory data, including results of microscopy, will be recorded by study personnel on standardised data 
forms. Data entered onto paper record forms will be entered into a computerised database (Microsoft 
Access) by a data entry clerk and will be double entered to verify accuracy.  An audit trail of the date and 
time of data entry, and a record of any changes made, will be kept in compliance with Good Clinical 
Practice (GCP).  
12.4 
Quality assurance & quality control 
 
All members of the study personnel will be trained in the project objectives, methods of effective 
communication with study participants, collection of high-quality data and principles of ethical research 
practice. Study personnel members will receive additional training specific to the tasks they will perform 
within the project including interviewing techniques, administration of surveys, completing questionnaires, 
and use of tablet devices. Standard Operating Procedures (SOPs) will be written for all project activities and 
booklets of all relevant documents provided to each member of the project team. Frequent study group 
meetings will be conducted by the investigators and study coordinators to assess progress of the study, 
address any difficulties, and provide performance feedback to the members of the study group.  
 
 

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12.5 
Records & storage 
 
Records for this study will be maintained and stored in compliance with the principles of GCP and 
regulatory and institutional requirements, and in compliance of the requirements for the protection of 
confidentiality of participants. Only study personnel members will have access to these records.  All forms 
with participant names will be kept in a locked cabinet, when not in use. Participants will be identified by 
their study ID number, and participant names will not be included in databases used for analysis. 
Authorised representatives of the sponsor, the ethics committee(s) or regulatory bodies may inspect all 
documents and records required to be maintained by the investigators. The investigators will allow all 
requested monitoring visits, audits or reviews. Data will be stored for at least 10 years. Anonymized data 
collected in this study may also be shared with other investigators and/or placed into the public domain via 
a data repository.  
  
  
 
 

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13 
Statistical issues 
 
13.1 
Outcome measures  
 
The primary outcome of the study will be malaria incidence (defined as the number of cases of laboratory-
confirmed malaria diagnosed at the MRC among patients residing in the target area, per unit time over the 
24-month follow-up period, divided by the total population of the target area) in patients of all ages.  
 
Secondary outcomes will include: 
1 
Cross-sectional surveys:  Prevalence of parasitaemia (in children aged 2-10 years; in selected 
clusters, in individuals of all ages), prevalence of anaemia (haemoglobin < 11 g/dL in children 2-4 
years), LLIN ownership (the proportion of households that owned at least one LLIN), adequate LLIN 
coverage (the proportion of households that owned at least one LLIN for every two occupants), 
LLIN access (the proportion of residents who could sleep under a LLIN if each LLIN in the household 
were used by up to two residents) and LLIN use (the proportion of household residents who slept 
under an LLIN the previous night); in selected clusters, seroprevalence of COVID-19 
2 
Entomology surveys (funding permitting): Vector density, and insecticide resistance marker 
frequency variation  
3 
LLIN integrity and chemical analysis of LLINs (funding permitting): LLIN integrity: number and 
estimated area of holes in the net fabric; Chemical analysis of LLINs: total concentration of 
insecticide and or synergist remaining on each net to be measured 
4 
Economic evaluation: Incremental cost-effectiveness ratios (USD per disability-adjusted life year 
adverted and per malaria case adverted) 
 
13.2 
Defining the MRC target areas 
 
Target areas around the MRCs will include the village where the MRC is located and adjacent villages that 
meet all of the following criteria: 1) do not contain another health facility, 2) are within the same sub-
county as the village where the MRC is located, 3) have a similar incidence of malaria as the village where 
the MRC is located, and 4) provide an estimated total target area population of at least 1500 persons.  
13.3 
Measuring the incidence of malaria in the MRC target areas 
 
Incidence of malaria for each MRC target area will be calculated by dividing the number of laboratory 
confirmed cases of malaria from patients who report living within the target area, per unit time over the 
24-month follow-up period, by the total population of target area, with two correction factors: 1) for 
patients who reside within the target area with suspected malaria who do not undergo laboratory testing, 
and 2) for patients with laboratory confirmed malaria whose village of residence is missing.  
 
13.4 
Sample size and power calculations 
 
13.4.1 Primary outcome 
 
Our sample size of 32 clusters per arm was calculated to detect a 26% decrease (incidence rate ratio (IRR) = 
0.74) in the incidence of malaria over the 24-month period following the intervention (the primary 
endpoint of the study) between the two study arms, given a power of 80% and a two-sided significance 
level of 0.05. This sample size calculation assumes an incidence of 332 malaria cases per 1000 person-years 
in the control arm and a coefficient of variation (CV) of 0.42 calculated from the 14 MRCs where estimates 

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of malaria incidence are available over the last 6 month at the time of protocol development. Our effect 
size came from the difference observed in the primary outcome after 12 months of follow up in the original 
LLINEUP study (reference).  
13.4.2 Secondary outcomes 
 
We will sample all eligible children aged 2-10 years from 50 households in the 64 clusters in each round of 
surveys, aiming to maximise the potential prevalence ratio detectable in the intervention arm, as well as 
the cost/value of the trial. Assuming an average of 1.8 children aged 2-10 years per households, we 
estimate that we will survey 5,760 children from 3,200 households. Assuming a coefficient of variation of 
0.4, across a wide range of prevalence measures in the control arm (10-70%) our sample size will allow us 
to detect a 25-28% decrease in the prevalence measure of interest (prevalence ratio (PR) = 0.72-0.75), 
given a power of 80% and a two-sided significance level of 0.05. 
Funding permitting, mosquitoes will be collected from 10 randomly selected households in each of the 64 
clusters. This sampling approach was sufficient to detect an approximate 80% difference in anopheles 
density ratios in the original LLINEUP study [38, 54]  
13.5 
Analytical plan  
 
All analyses will be conducted using an intention-to-treat approach. For our primary outcome, we will 
compare cluster level estimates of the incidence of malaria between intervention and control arms using a 
mixed effects Poisson regression model since randomization will occur at the district level (each district will 
include two clusters, randomized to the two study arms). We will also adjust our model for baseline 
estimates of malaria incidence (3 months prior to the intervention) and additional cluster-level covariates 
from the cross-sectional surveys including treatment seeking behaviour (to account for cases of malaria not 
captured at the MRCs) and the diagnostic accuracy of RDTs vs microscopy (to account for differences in the 
use of these diagnostic tests between clusters). The effect of the intervention will be expressed as an 
incidence rate ratio (incidence in the intervention arm/incidence in the control arm). Our primary analysis 
will evaluate malaria incidence over 24-months following the intervention. We will also perform secondary 
analyses of malaria incidence stratified by time following the intervention (year 1 vs year 2).  
For our secondary outcomes, comparisons between intervention and control arms will be made using an 
individual-level approach to the analysis due to the large number of clusters per arm. Prevalence measures 
(parasitaemia, anaemia, LLIN ownership, LLIN coverage, LLIN access and LLIN use) will be compared using 
mixed effects logistic regression models with random effects at the level of the cluster and household. The 
effect of the intervention will be expressed as the prevalence ratio (prevalence in the intervention 
arm/prevalence in the control arm). For comparison of vector density, LLIN integrity, and bio-efficacy 
(funding permitting) between treatment arms, regression models will be used with generalized estimating 
equations to allow for within-cluster correlations. The effect of the intervention will be expressed as the 
density or rate ratio (density or rate in the intervention arm / density or rate in the control arm). Chemical 
analysis of withdrawn LLINs and their unused controls will be compared using the Wilcoxon rank sum test. 
 
 

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14 
Ethical considerations 
14.1 
Institutional review boards 
 
This protocol and the informed consent documents and any subsequent amendments or modifications will 
be reviewed and approved by all institutional review boards (IRBs) before the study begins, including: (1) 
Makerere University School of Medicine Sciences Research and Ethical Committee; (2) Uganda National 
Council of Science and Technology; (3) UCSF Committee for Human Research; (4) London School of Hygiene 
& Tropical Medicine Ethics Committee; and (5) Liverpool School of Tropical Medicine. 
14.2 
Informed consent process 
 
An introductory letter to the districts will be obtained from the Ministry of Health leadership, and approval 
from local leaders will be sought before beginning activities in the project area.  All informed consent 
discussion will be conducted in the appropriate language and a translator will be used if necessary. 
Information sheets and consent forms will be available in English and appropriate local languages, 
describing the purpose of the project and the procedures to be followed, and the risks and benefits of 
participation.  During the consent discussions, each section of the consent form will be read exactly as it is 
written either by study personnel or by the translator, and then further explained to the respondent 
(participant or parent/guardian) if necessary. The translator will also assist with the discussion and 
assessment of comprehension. All participants and parents/guardians will be informed that participation in 
the study is completely voluntary and that they may withdraw from the study at any time. 
Written consent to participate in the research study will be documented on the appropriate form for 
participation in the community surveys and entomology surveys. In all cases involving participation of 
children aged 8 years or older, written assent will also be obtained from the child.  Written consent for 
future use of biological specimens will also be obtained for the community surveys. If the person asked to 
provide consent is unable to read or write, their fingerprint will substitute for a signature, and a signature 
from a witness to the informed consent procedures will be obtained.  
14.3 
Risks and discomforts 
 
14.3.1 Randomisation 
 
In this cluster-randomised trial, clusters will be randomly assigned to receive two different types of LLINs. 
The PPF LLINs may prove to be more, or less efficacious, and/or more, or less well-tolerated than the PBO 
LLINs, or conventional LLINs. Thus, there is the risk that clusters will be randomised to receive a less 
efficacious and/or less well-tolerated LLIN. However, the risks associated with randomisation in this study 
are likely to be low.   
14.3.2 Blood draws 
 
The potential risks of drawing blood from a finger-prick include temporary discomfort, pain, transient 
bleeding, bruising, skin infection, and fainting.  The volumes of blood taken will be too small to produce any 
adverse effects from the blood drawing, and overall the risks associated with blood draws are likely to be 
low. To reduce the potential risks, study staff will be trained in the proper conduct of a finger-prick 
according to standard operating procedures to minimize the risk of discomfort and infection.  
14.3.3 Positive malaria tests 
 
Results of blood slides collected in the community surveys will not be returned to participants. RDTs will be 
performed on participants with temperature of > 38.0⁰C, or who report fever in the past 48 hours in the 
community surveys.  RDT results will be provided to participants and treatment will be offered if RDT 

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results are positive, and the participant has no evidence of severe disease.  Participants with positive tests 
will be told to seek care at the health facility if their illness worsens, or if they develop signs or symptoms of 
severe malaria or other illnesses. It is possible that for some cases in the community surveys, the RDT will 
be negative, but microscopy will be positive. This most commonly occurs when parasitaemia is very low, 
below the level of detection for RDTs. The risk of developing symptomatic or severe illness is very low from 
a presumably low parasitaemia in an RDT-negative asymptomatic individual. Of note, RDTs are used 
nationally at the point of care in health facilities, with treatment based on the result of the RDT, without 
confirming the RDT result by microscopy.  
14.3.4 Entomology surveys 
 
Potential risks and discomforts to participating households include loss of privacy, but this will likely be 
minimal. Care will be taken to protect the privacy of participating households, as described in this protocol. 
However, there is a risk that others may inadvertently see participants’ information, and thus their privacy 
compromised. Intrusion by the study staff into the household, and discomforts related to the study 
procedures, are other concerns.  Study personnel will be instructed to interact with the households in a 
courteous and respectful manner in order to limit this possible discomfort. All field workers will obtain 
training in confidentiality and gender sensitivity before working in the households. 
14.3.5 Confidentiality 
 
Participation in a research study may involve a loss of privacy, but successful implementation of the study 
will require that the confidentiality of all study participants be strictly maintained. The risks associated with 
loss of privacy in this study are likely to be low. To ensure confidentiality is maintained, all information 
gathered will be treated as private by the study personnel, and records will be kept securely in locked filing 
cabinets and offices. For all data collected as part of the study, participants will be assigned a unique 
identification number. No personal identification information such as names will be used in any reports 
arising out of this research. All project staff will be trained on procedures for maintaining confidentiality.  
14.3.6 COVID-19 
 
COVID-19 is caused by a novel coronavirus (SARS-CoV-2), and was declared a pandemic by the WHO in 
March 2020. IDRC has developed standard operating procedures (SOP) to give guidance on how to prevent 
and minimize the risk COVID -19 infection during study activities (Appendix K).  
14.4 
Compensation 
 
Participants will not be paid for taking part in this study. If survey participants are referred by study 
personnel to a health facility for further assessment, transportation may be facilitated by the project on a 
case to case basis. Survey participants diagnosed with uncomplicated malaria during our study by RDT will 
be treated as per the national treatment guidelines; participants in the community surveys with 
uncomplicated malaria will be treated with AL, and participants with severe malaria and other illnesses will 
be referred to the appropriate health facility for further management. 
14.5 
Capacity development 
 
Building capacity of young researchers both in Ugandan and internationally is a major aim of IDRC and the 
PRISM project. Capacity development activities including internship placements, supporting master’s and 
PhD projects, sharing samples, and providing hands-on support with data analysis for different career 
development projects. A summary of trainees attached to the PRISM project is presented below (Table 3).  
 
 

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14.6 
Dissemination and publication of research findings 
 
The results from this research will be communicated to stakeholders through dissemination meetings and 
to participants using language-appropriate information sheets. Investigators will present results at relevant 
conferences and submit manuscript(s) to peer-reviewed journals in accordance with guidelines from the 
funder (the US National Institutes of Health), sponsor (University of California, San Francisco), and IRBs at 
Makerere University and the London School of Hygiene & Tropical Medicine, as well as the Ugandan 
National Council of Science and Technology.  
 
Table 3: Trainees attached to the PRISM project 
Name 
Nationality 
Training 
level 
University 
Status 
Joaniter Nankabirwa 
Ugandan 
K43 grant  
Makerere University 
On-going 
Melissa Conrad 
American 
K01 grant  
University of California San Francisco 
On-going 
Jaffer Okiring 
Ugandan 
PhD 
Makerere University 
On-going 
Henry Mawejje 
Ugandan 
PhD 
London School of Hygiene & Tropical Medicine 
On-going 
Isaac Ssewanyana 
Ugandan 
PhD 
London School of Hygiene & Tropical Medicine 
Completed 
Simon Peter Kigozi 
Ugandan 
PhD 
London School of Hygiene & Tropical Medicine 
Completed 
Steven Tukwasibwe 
Ugandan 
PhD 
Cambridge University 
Completed 
Chiara Andolina 
Italian 
PhD 
Radboud University 
On-going 
Emmanuel Arinaitwe 
Ugandan 
PhD 
London School of Hygiene & Tropical Medicine 
Completed 
Alex Musiime 
Ugandan 
MSc 
Makerere University  
Completed 
Rek John 
Ugandan 
MSc 
London School of Hygiene & Tropical Medicine 
Completed 
Kayongo Edward 
Ugandan 
MSc 
Makerere University 
Completed 
 
 
 
 

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48 
 
15 
Proposed timeline 
 
 

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