Dynamic Clinical Decision Support Algorithms to Manage Sick Children in Primary Health Care Settings in Tanzania
Phase: NA
Conditions: Child Health
Sponsor: Center for Primary Care and Public Health (Unisante), University of Lausanne, Switzerland

DYNAMIC project 
Protocol version 4.9 / 10-Sep-2021 
 
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STUDY PROTOCOL 
Dynamic clinical decision support algorithms to manage sick children 
in primary health care settings in Tanzania (DYNAMIC TZ) 
 
Protocol Number 
NIMR/HQ/R.8a/Vol.IX/3486 
Version Number 
4.9 
Document Date 
10.09.2021 
Sponsor Contact 
Centre for Primary Care and Public Health (Unisanté) 
University of Lausanne 
Bugnon 44 - 1011 Lausanne 
Switzerland 
www.unisante.ch  
Principal Investigator 
Prof. Valérie D’Acremont 
Centre for Primary Care and Public Health (Unisanté) 
University of Lausanne 
Bugnon 44 - 1011 Lausanne – Switzerland 
+41 79 556 25 51 
valerie.dacremont@unisante.ch; valerie.dacremont@swisstph.ch  
National Principal 
Investigator 
Dr. Honorati Masanja 
Ifakara Health Institute (IHI) http://ihi.or.tz 
P.O Box 78373 - Dar es Salaam – Tanzania 
hmasanja@ihi.or.tz  
National co-Principal 
Investigator 
Dr. Nyanda Ntinginya 
National Institute of Medical Research - Mbeya Medical Research 
Centre (NIMR-MMRC) http://www.nimr-mmrc.org 
P.O Box 2410 - Mbeya – Tanzania 
nelias@nimr-mmrc.org  
Funding Agency 
- Fondation Botnar, St. Alban-Vorstadt 56 - 4052 Basel – Switzerland 
- Swiss Development Cooperation, Bern – Switzerland 
 
The information contained in this document is confidential. It is intended solely for the  
Investigators, potential Investigators, consultants, or applicable Independent Ethics Committees and Regulatory 
Authorities. It is understood that this information will not be disclosed to others without prior written authorization 
from the Sponsor, except where required by applicable local laws.  
 

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I. List of Investigators and other persons involved 
 
Names  
Institution  
Position  
Function in study  
Prof. Valérie D’Acremont Unisanté 
Head of Digital and Global Health, Department of 
Training, Research and Innovation 
PI, 
overall project 
coordinator 
Swiss TPH 
Scientific group leader, Medical Department 
Dr. Honorati Masanja 
IHI 
Chief Executive Director  
National PI 
Dr. Nyanda Ntinginya 
NIMR-MMRC Centre Director  
National co-PI 
Dr. Irene Masanja 
IHI 
Scientific project leader, Health Systems Research 
Group 
Co-investigator, 
country coordinator 
Dr. Kristina Keitel 
Swiss TPH 
Scientific project leader, Clinical Research Unit, 
Medical Department 
Co-investigator, 
pediatric consultant 
Dr. Mary-Anne Hartley 
Unisanté and 
EPFL 
Scientific project leader, Digital and Global Health, 
Department of Training, Research and Innovation 
Co-investigator, 
ML coordinator 
Dr. Chacha Mangu 
NIMR-MMRC Project manager, TB and Emerging Disease 
department 
Mbeya site 
investigator  
Dr. Alexandra V. 
Kulinkina 
Swiss TPH 
Project leader, Clinical Research Unit, Medical 
Department and SCIH 
Co-investigator, 
Project coordinator 
Dr. Lameck Luwanda 
IHI 
Study physician, Health Systems Research Group 
Morogoro site 
investigator 
Dr. Godfrey Kavishe 
NIMR 
Study physician, TB and Emerging Disease dep. 
Study physician 
Dr. Rainer Tan 
Unisanté 
Research physician, Digital and Global Health, 
Department of Training, Research and Innovation 
Clinical algorithms 
coordinator 
Charles Festo 
IHI 
Head of the IHI Data Systems Unit 
Country IT and 
data coordinator 
Vincent Faivre 
Unisanté 
Deputy head of IT department 
IT coordinator and 
data manager 
Peter Agrea 
NIMR 
Software developer, IT department 
Software developer 
Ibrahim Evans Mtebene 
IHI 
Software developer, IT department 
Software developer 
Geofrey Isdory Ashery 
IHI 
Research officer, Health Systems Research Group Social scientist, 
Morogoro site 
Happy Raphael Mkali 
IHI 
Research scientist, Bagamoyo research laboratory Laboratory 
coordinator 
Prof. Isabella Eckerle 
University of 
Geneva 
Head of the Geneva Centre for Emerging Viral 
Diseases 
Surveillance 
coordinator 
Alan Vonlanthen 
Unisanté 
Project Manager, IT department 
Project manager 
Dr. Tracy Glass 
Swiss TPH 
Scientific project leader, Clinical Statistics and 
Data Management, Medical Department 
Statistician 
Prof. Martin Jaggi 
EPFL 
Head of Machine learning and optimization lab 
ML consultant 
Dr. Sabine Renggli 
IHI 
Senior Research Scientist 
Site investigator, 
Morogoro site 
Martin Norris 
Swiss TPH 
Project Officer/Manager 
Algorithm testing 
consultant 

DYNAMIC project 
Protocol version 4.9 / 10-Sep-2021 
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Dr. Lisa Crump 
Swiss TPH 
Veterinary Epidemiologist 
Surveillance 
Consultant 
Dr. Gillian Levine 
Swiss TPH 
Neonatal Epidemiologist 
Neonatal algorithm 
consultant 
Humphrey Mhagama 
NIMR 
Study nurse 
Study nurse 
Project Location:        Morogoro and Mbeya Regions, Tanzania 
Projection duration:   Project starts on 15.09.2021; Project ends on 31.03.2024 
II. Signatures
The signatures below confirm agreement by the individuals authorized by the sponsor and principal 
participating institutions at the sites that the study will be conducted in compliance with protocol 
version 4.9 dated 10.09.2021. 
Principal Investigator 
Signature 
10.09.2021 
Name 
Prof. Valérie D’Acremont 
Date of Signature 
Job title 
Head, Digital and Global Health, Department Training, Research and Innovation 
Institution 
Centre for Primary Care and Public Health, University of Lausanne 
Address 
Bugnon 44 - 1011 Lausanne, Switzerland 
Phone 
+41 79 556 25 51
Principal Investigator Tanzania 
Signature 
10.09.2021 
Name 
Dr. Honorati Masanja 
Date of Signature 
Job title 
Chief Executive Director 
Institution 
Ifakara Health Institute 
Address 
P.O Box 78373 - Dar es Salaam, Tanzania
Phone 
+255 626 605 048
Co-principal investigator Tanzania 
Signature 
10.09.2021 
Name 
Dr. Nyanda Ntinginya 
Date of Signature 
Title 
Centre Director 
Institution 
National Institute of Medical Research - Mbeya Medical Research Centre 
Address 
P.O Box 2410 - Mbeya, Tanzania
Phone 
+255 766 243 836

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III. Abbreviations / Glossary of terms 
 
AMR 
Antimicrobial Resistance 
CDSA 
Clinical Decision Support Algorithm 
CHMT 
Council Health Management Team 
CRP 
C-reactive protein 
DHIS2 
District Health Information System 2 
EPFL 
École Polytechnique Fédérale de Lausanne 
ePOCT+ 
Electronic point of care tool plus 
HF 
Health facility 
HCW 
Health care worker 
HMIS 
Health Management Information System 
IDSR 
Integrated Disease Surveillance and Response 
IHI 
Ifakara Health Institute 
IMCI 
Integrated management of childhood illness 
M&E 
Monitoring and evaluation 
ML 
Machine learning 
NIMR 
National Institute of Medical Research 
PHC 
Primary health care 
POC 
Point-of-care 
RDT 
Rapid Diagnostic Test 
Swiss TPH 
Swiss Tropical and Public Health Institute 
TZ 
Tanzania 
Unisanté 
Centre for Primary Care and Public Health (University of Lausanne) 
WHO 
World Health Organization 
 
                                                         
  
 
 

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IV. Synopsis 
 
Study Title 
Dynamic clinical decision support algorithms to manage sick children in primary 
health care settings in Tanzania 
Short Title 
DYNAMIC TZ 
Study Category 
Implementation study to support clinical decision making and disease 
surveillance in sick children 
Study rationale 
Children are a well-recognized vulnerable population that still suffers from a high rate 
of acute infectious diseases and preventable deaths. This is especially true in fragile 
health systems of Sub-Saharan Africa, where under-five mortality is 10 times higher 
than in high-income countries. The management of sick children at the primary care 
level in these environments remains of insufficient quality as front-line clinicians lack 
appropriate diagnostics, supervision to improve their skills, and decision support tools. 
Clinically validated POC diagnostic tests are often not available, and practice 
guidelines are quickly outdated by new evidence and changing epidemiology. When 
an epidemic arises, these static, generic guidelines can even become deleterious if the 
event is not detected on time and integrated into the recommendations.  
In the absence of reliable guidance, HCWs tend to over-prescribe antibiotics. 
Approximately 9 out of 10 children at the primary care level in Tanzania receive an 
antibiotic, while only 1 in 10 needs one. Inappropriate antibiotic use disrupts the gut 
flora, favoring the proliferation of pathogens and weakening a child’s immune 
response. It is also a major driver of antibiotic resistance, which is estimated to be 
responsible for up to 10 million deaths per year by 2050. Equally important to antibiotic 
overuse, is its underuse. Missing a child in need of antibiotic treatment or providing a 
child with an inappropriate type or dosage of antibiotic puts them at risk of preventable 
morbidity and death. The same occurs with antimalarials that are not always prescribed 
to the children in need: those with a positive malaria test result. 
Misdiagnoses have consequences that reach beyond the patient. They increase re-
attendance rates, further congesting PHC clinics and accruing economic losses not 
only for families but for the entire health system. Systematic errors in patient-level data 
accumulate, and as they are aggregated to measure population-level indicators, they 
have the potential to bias the statistics used to prioritize health interventions and, 
importantly, identify epidemics.   
The WHO has identified digital health interventions and predictive tools in primary care 
as key accelerators in achieving the 2030 Sustainable Development Goal 3 of ensuring 
good health and well-being for all. New simple and cheap technologies, such as mobile 
devices, coupled with the advances in computing and data science, could help mitigate 
several of the aforementioned challenges. The proposed digital intervention is a third-
generation CDSA intended to help HCWs at the primary care level manage children 
with acute illnesses. The first two versions of the algorithm have undergone rigorous 
evaluations in controlled research conditions as summarized below:  
• 
The first-generation algorithm called ALMANACH was tested in Tanzania in 2010-
2011, achieving improved clinical cure (from 92% to 97%) and a decrease in 
antibiotic prescription (from 84% to 15%) as compared to routine care. 
ALMANACH also led to more consistent clinical assessments without taking more 
time than a conventional consultation and was perceived by clinicians as “a 
powerful and useful” tool.  
• 
The second-generation algorithm called ePOCT was trialed in Tanzania in 2014-
2016. In addition to symptoms and signs, it made use of several POC tests to help 
detect children with severe infections requiring hospital-based treatment (oximetry 
and hemoglobin level) and/or children with serious bacterial infection (CRP). The 
use of ePOCT resulted in higher clinical cure (98%) as compared to ALMANACH 
(96%) and routine care (95%). The algorithm also further reduced antibiotic 
prescription to 11%, as compared to 30% with the use of ALMANACH and 95% 
in routine care. 

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Electronic algorithms can thus be successfully implemented to improve clinical 
guidance and provide feedback to clinicians, as well as allow near-real-time analyses 
of data for M&E of health interventions, disease surveillance and outbreak detection. 
This project is in line with the Tanzanian Digital Health Investment Roadmap 2017-23, 
proposing provision of innovative software, IT infrastructure and skills to support quality 
health care. It also supports the National Action Plan on Antimicrobial Resistance 2017-
22 that advocates for strengthening stewardship for antimicrobial use in HFs, as well 
as the National Malaria Strategic Plan 2014-20, which calls for introducing evidence-
based, innovative diagnostic tools for malaria detection and differential diagnosis of 
other pathogens causing febrile illnesses. The disease surveillance component also 
supports the National Action Plan for Health Security 2017-2021 that aims at 
strengthening and sustaining the capacity of Tanzania to promptly detect and confirm 
outbreaks, the frequency and intensity of which may increase in the coming years due 
to climate change and population growth. 
Study objectives 
The goal of this study is to improve clinical diagnosis, decrease morbidity and mortality 
of children, and mitigate AMR using novel dynamic POC technologies that help front-
line HCWs manage sick patients, enhanced by smart disease surveillance and 
outbreak detection mechanisms. 
More specifically, this study seeks to: 
Objective 1:   Improve the integrated management of children with an acute illness 
through the provision of an electronic CDSA (ePOCT+) to clinicians 
working at primary care level; 
Objective 2: Improve the accuracy of the clinical algorithm and adapt it to 
spatiotemporal variations in epidemiology and resources, based 
on the data generated through the ePOCT+ tool, analyzed using 
machine learning and checked by clinical experts;  
Objective 3:  Enhance the district (and national) disease surveillance and 
outbreak detection capability using the clinical data generated by the 
ePOCT+ tool complemented by targeted microbiological investigations 
and machine learning pattern detection; 
Objective 4:   Enhance the district (and national) health management information 
system for monitoring and evaluation and conducting supportive 
supervision and mentorship in health facilities using the clinical data 
generated by the ePOCT+ tool enhanced by additional data analysis 
and visualization dashboards; 
Objective 5:  Create a framework for the development and implementation of 
dynamic CDSA and disease surveillance tools, for large-scale, 
sustainable, and clinically responsible use of machine learning and data 
science. 
Study design 
The primary intervention study will be conducted in two phases. Additional cross-
sectional mixed-methods studies will take place throughout the intervention period.  
Phase 1: cluster randomized controlled study in 40 health facilities 
The ePOCT+ tool will be provided to HCWs in 20 of 40 primary HFs in two regions 
(Morogoro and Mbeya), while another 20 will serve as controls. The aim is to compare 
the effect of using ePOCT+ with routine case management of sick children on the 
rational prescription of antibiotics, while ensuring good clinical outcomes. Due to the 
pragmatic nature of the study, the design is adaptive in nature, where changes to the 
implementation may be incorporated throughout the study to account for issues 
encountered in monitoring data and feedback from the field. These changes apply to 
the implementation context and exclude significant adaptations of the algorithm.  
Phase 2: scale-up of the intervention in up to 100 health facilities and 
transformation into a dynamic algorithm 

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The ePOCT+ tool will be extended to the 20 HFs serving as controls in Phase 1, as 
well as up to 60 additional neighboring facilities of our target area, to reach a total of 
around 100 facilities (60 in Morogoro and 40 in Mbeya). In Phase 2, an adaptive study 
design will be used to measure the same outcome indicators as in Phase 1. The 
medical content of the algorithm will not be fixed anymore, but rather modifiable. Each 
potential modification will first be evaluated by the Tanzanian clinical expert group for 
its clinical coherence, safety and potential benefit and then applied to the retrospective 
data. If these analyses confirm both a clinically relevant positive impact and estimate 
that there will be sufficient future cases during the study period to detect this 
improvement, the change in the algorithm will be tested in a randomized sub-study 
using the same study design as in Phase 1, except that randomization will take place 
at patient level rather than HF level. If the positive impact is confirmed in the sub-study, 
the modification will be implemented in all relevant locations/patient sub-groups. If the 
retrospective analyses confirm a positive and potentially clinically relevant impact of a 
modification where resource savings is also expected but there will not be sufficient 
power to formally test the change during the study period, the modification will be 
implemented and monitored based on the related outcome indicators. If the impact of 
the modification achieves the expected results given the accumulated data at the end 
of the study period, it will be retained. 
Examples of algorithm modifications expected to have a positive impact that will be 
explored for implementation are: 1) adapting the frequency of malaria testing in areas 
with very low endemicity; 2) improving the accuracy of pneumonia diagnosis; 3) 
assessing the utility of using dengue rapid tests if an outbreak occurs; 4) or typhoid 
rapid tests in areas of high prevalence; 5) improving the prediction of severe disease; 
6) improving the algorithm for children with chronic conditions (malnutrition, HIV or 
sickle-cell). Other type of changes might also be tested if considered important by the 
clinical experts.   
Cross-sectional quality of care surveys: 
Three cross-sectional surveys in a subset of randomly selected HFs will be performed 
to assess the quality of the consultation done by HCWs and their level of compliance 
with the algorithm. Among the 40 HFs included in Phase 1, 18 (10 in Morogoro and 8 
in Mbeya, half interventions and half controls) will be randomly selected. 25 
consultations per HF will be observed for a total 450 HCW/child pairs. The survey will 
be performed once before the start of the study, once during Phase 1 and again 
approximately 6 months later during Phase 2 in the same facilities, with the aims of 
assessing changes in the use of ePOCT+ over time in the initial intervention facilities 
and a before-and-after ePOCT+ analysis in the initial control facilities. 
Syndromic surveillance enhanced by microbiological testing: 
Syndromic surveillance (i.e. based on the clinical data generated by the use of 
ePOCT+) will be enhanced by microbiological surveillance in 6 to 8 sentinel HFs 
representative of different epidemiological profiles, as well as in any facility with 
abnormal data patterns detected by ML (based on predefined clinically supervised 
thresholds), to identify potential outbreaks.  
Mixed-methods operational research: 
Throughout the pilot and Phases 1 and 2, as well as during the last year of the project, 
mixed-methods studies will be conducted with HCWs, patients and caregivers in HFs, 
in communities, as well as at district and national levels, to understand the contextual 
factors that facilitate or impede the implementation, sustainability and scale-up of digital 
health interventions such as ePOCT+ in PHC settings. Examples of such factors may 
include socioeconomic and ecological profiles of communities, working conditions of 
the clinicians in the HFs, patient satisfaction, among others. Data collection associated 
with these activities will be undertaken with special consideration of the HCWs’ time 
and workload and in a way that does not interfere with patient care.   
Study site 
The study will be conducted in Mlimba, Ifakara and Ulanga districts/councils (Morogoro 
Region) and in Mbeya Rural district and Mbeya city council (Mbeya Region). 

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Study population All children aged 1 day to 14 years with an acute condition who present to the 
participating PHC facilities will be included. This differs from the first two generations 
of the algorithm that only included children 2 months to 5 years. 
HCWs attending to these children, caregivers of these children, communities where 
these children live, and key actors of the health system (CHMTs, computer scientists 
and medical experts at national level) will be included in observational mixed-methods 
studies. 
Intervention 
The intervention will include 5 activities, each supported by a specific electronic tool: 
A) 
Acutely ill children will be assessed, diagnosed and treated by routine clinicians 
guided by the ePOCT+ application loaded on a tablet or PC. 
The intervention will be undertaken in routine PHC facilities in Tanzania and entails the 
implementation of the ePOCT+ tool for patient assessment and management, with 
provision of associated POC tests. Healthcare providers will use ePOCT+ throughout 
their medical consultations for children aged 0-14 years presenting with an acute 
medical or surgical problem. The tool will not be used for managing emergency cases. 
These cases will be identified by the tool in the beginning of the consultation (triage 
stage) and referred immediately to a higher level health facility. The CDSA of ePOCT+ 
is based on applicable Tanzanian national and international guidelines for pediatric 
clinical management (TZ version of IMNCI 2014; WHO Pocket Book of Hospital Care 
for Children 2013; TZ National Guidelines for the Diagnosis and Treatment of Malaria 
2014; TZ Standard Treatment Guidelines and Essential Medicines list for Children and 
Adolescents 2018; Standard Treatment Guidelines & National Essential Medicines List 
Tanzania Mainland 2017 (Adults);TZ National Guideline for Neonatal care and 
Establishment of Neonatal Care Unit 2019; TZ National Guidelines for the Management 
of Tuberculosis in Children 2016; WHO IMAI guidelines 2009). Before implementation, 
the medical algorithm will be reviewed by Tanzanian key experts in child health 
(Pediatric Association of Tanzania; Ministry of Health; Muhimbili National Hospital). 
Most of the algorithm content was previously validated in Tanzania (see background 
section). Specifically, the tool will guide clinicians on:  
i) 
Decision on immediate referral to hospital versus home-based treatment 
ii) 
Medical history (questions to ask caregivers and/or children) 
iii) 
Physical exam (signs to look for) 
iv) POC diagnostic tests (which should be performed in a given clinical context) 
v) 
Diagnoses 
vi) Treatments (including medicines and supportive treatments) 
vii) Counseling (explanation on final diagnoses, treatments, and follow-up) 
 
B) 
The algorithm will be regularly updated according to trends in the collected data. 
The modifications will be designed by clinical experts based on the clinical and 
microbiological surveillance data collected during the study. Approved changes 
will then be integrated into the algorithm using the MedAL-Creator tool that 
allows physicians without any IT/programming skills to modify the decision tree. 
During Phase 2, the modifications designed by the clinical expert group will be tested 
on retrospective data before being introduced in the electronic application. Each 
modification will then be loaded on the tablets or PCs of the relevant HCWs (for 
example of a certain region where an epidemic is occurring) with a message explaining 
the rationale and evidence behind this change in the algorithm. 
C) 
HCWs will be trained through an initial in-person session and an e-learning 
platform available online (Phases 1 and 2) 
Initial centralized training on essential clinical skills, the evidence and guidelines 
behind the content of the algorithm, the rational use of antibiotics and the practicalities 
around the use of the electronic tool will be provided to clinicians working in 
intervention facilities. Re-training of incoming personnel and continuous training 
through an e-learning platform on the tablet or PC will also be implemented. 

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D) 
HFs and HCWs will receive supportive supervision and mentorship from the 
CHMT using the medAL-monitor tool (Phases 1 and 2) 
Targeted supervision to HFs by the CHMT will be organized based on the quality of 
care indicators calculated in real time from the uploaded data and made available 
through a web-based dashboard called medAL-monitor. Personalized feedback to 
HCWs and persons in charge of HFs will be provided for self-auditing.  
E) 
The medAL-outbreak disease surveillance and outbreak detection system will 
be made available to the district (and national) health authorities (Phases 1 and 
2) for them to be able to decide if disease control measures are needed 
Indicators for disease surveillance and early epidemic warning and response will be 
decided with and provided to CHMTs through a dedicated web-based dashboard 
(medAL-outbreak). These indicators can then be transferred to the DHIS2 platform and 
made available to national health authorities. 
Outcomes 
Objective 1 (Improve integrated management of children with an acute illness): 
Co-Primary outcome measures (cluster randomized controlled study in Phase 1): 
(i) % of children cured at day 7* in the intervention group (ePOCT+) as compared to 
control group (routine care) 
(ii) % of children prescribed an antibiotic at initial consultation, as reported by HCWs, 
in the intervention group (ePOCT+) as compared to control group (routine care) 
* according to caregivers contacted through phone or home visit (non-referred secondary 
hospitalizations will however be considered as clinical failures even if the child is already cured 
at day 7) 
Primary outcome indicators (monitored over time and across geographical areas in 
all HFs in Phase 2): 
- 
% of children prescribed an antibiotic at initial consultation 
- 
% of children cured at day 7 
Secondary outcome measures (compared between intervention and control HFs in 
Phase 1) or indicators (monitored over time and across geographical areas in all HFs 
in Phase 2): 
Re-attendance visits: 
- 
% of children with ≥1 unscheduled re-attendance visits at any HF by day 7 
Severe clinical outcome by day 7: 
- 
% of children with non-referred secondary hospitalization by day 7 
- 
% of children who have died by day 7 
Primary referrals: 
- 
% of children referred to hospital or admitted to inpatient ward at a health centre at 
initial consultation 
- 
% of children who were hospitalized among those referred (i.e. referral completed)  
Appropriate case management for malaria at initial consultation 
- 
% of febrile children tested for malaria 
- 
% of malaria positive children prescribed an antimalarial 
- 
% of malaria negative children prescribed an antimalarial 
- 
% of untested children prescribed an antimalarial 
Appropriate case management for other conditions at initial consultation 
- 
% of key symptoms and signs checked and diagnostic tests performed by HCWs 
- 
Distribution of final diagnoses (including their severity) made by HCWs 
- 
Concordance between treatment prescribed and final diagnoses made by HCWs 
- 
Concordance between final diagnoses as proposed by ePOCT+ and final 
diagnoses by HCW (intervention HFs only) 
Objective 2 (Improve the algorithm and adapt it to spatiotemporal variations):  

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- 
Number of positive impact modifications in the algorithm identified by ML analyses  
- 
Number of positive impact modifications tested through randomized sub-studies 
- 
% of these positive impact modifications confirmed to be safe and useful by 
randomized sub-studies 
- 
Number of modifications implemented, by type of impact (better clinical cure, less 
severe clinical outcomes, less medicines or diagnostic tests needed, shorter 
consultation time) 
Objective 3 (Enhance disease surveillance and outbreak detection):  
- 
Number of indicators related to disease surveillance and outbreak detection based 
on individual data that can be visualized through the medAL-outbreak dashboard 
- 
Average delay between the date of recording of clinical data and the date they are 
available on medAL-outbreak 
- 
Number and type of epidemic alerts raised by the medAL-outbreak tool  
- 
% of these epidemic alerts registered by the district authorities for epidemic 
response 
Objective 4 (Enhance M&E and supportive supervision and mentorship):  
- 
Number of M&E indicators based on individual data that can be visualized through 
the medAL-monitor dashboard 
- 
Number of supervision and mentorship visits to HFs facilitated by the medAL-
monitor tool 
- 
Number of CHWs reporting having used the medAL-monitor tool for self-auditing 
their clinical practices 
- 
Number of CHWs and members of CHMT reporting a positive experience with the 
medAL-monitor tool 
Objective 5 (Create a supportive environment for the use of clinical algorithms 
and disease surveillance tools):  
- 
Number of sick children attending primary care facilities managed by HCWs using 
the electronic ePOCT+ tool 
- 
% of CHWs able to assess key clinical signs  
- 
Frequency of use of medAL-outbreak by district team members 
- 
Frequency of use of the medAL-monitor tool by district team members 
- 
Perception by HCWs of the ePOCT+ tool and intervention and the number of 
improvements they propose for the tool or intervention 
- 
Perception by caregivers and community members of the intervention and the 
number of improvements they propose 
- 
Perception by district team members of the medAL-outbreak tool and the number 
of improvements they propose for the tool 
- 
Number of medical experts able to modify a clinical algorithm using MedAL-Creator 
- 
Number of computer scientists able to maintain the software and the IT 
infrastructure for clinical algorithms 
- 
Number of scientists able to analyze clinical data using ML 
- 
Number of local students trained in applied ML for health care in resource-limited 
settings 
- 
Perception of the current and future utility of ML to improve health care amongst 
clinical experts (deciding on algorithm changes) and health authorities 
- 
Patient and provider expenditures related to care of acutely ill children included in 
the study 
- 
Carbon footprint of the intervention or carbon-saving due to the intervention 
Procedures and 
measurements 
Objective 1: Improve the integrated management of sick children 

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In Phase 1, in each region, after a pilot period of 6 months to test all the e-tools, eligible 
HFs will be randomized (by Region and HF) to the intervention or control arm. Sick 
children will be enrolled over a period of 6 to 9 months.  
Primary outcome measures: (i) For assessing clinical cure at Day 7, a simple 
questionnaire (asking if the child is still sick, was hospitalized secondarily, or died) will 
be administered to all caregivers by phone; (ii) For measuring antibiotics prescribed at 
initial consultation, the treatment data recorded in the tablets or PCs by HCWs 
throughout the study will be compared between intervention and control arms. 
Secondary outcome measures: To determine the quality of the consultation made by 
CHWs, including the rational use of antimicrobials (antibiotics and antimalarials), two 
sources of data will be used and compared between intervention and control arms: 
- the final decision on referral, tests performed, final diagnoses retained and treatment 
prescribed will be recorded in the tablets or PCs by HCWs at initial consultations and 
re-attendance visits. 
- during a cross-sectional survey, observers in the consultation room will record key 
symptoms and signs assessed by HCWs, diagnostic tests requested, final decision on 
referral, final diagnoses retained and treatment prescribed. 
Control arm: In control PHC facilities, sick children will be assessed, diagnosed and 
treated as per routine protocols by clinicians. A similar but simplified tablet or PC 
application will also be used in control facilities, but only to record outcome measure 
related data using an electronic case report form. HCWs in control facilities will be 
offered the usual training and supervision proposed by the CHMT. 
In Phase 2, all selected HFs in the two regions will receive the intervention. Indicators 
monitored over time and across geographical areas will be measured the same way as 
in Phase 1, except that clinical cure, secondary hospitalizations and deaths will be 
assessed through an automated SMS sent to caregivers (rather than a phone call) 
using the same simple questionnaire. 
Objective 2: Improve the algorithm and adapt it to spatiotemporal variations 
The algorithm will be improved in terms of diagnostic and prognostic accuracy and 
adapted to local fluctuations in epidemiology, demography, geography and resource 
availability. These modifications will be designed and supervised by clinical experts, 
based on accumulated data captured during the study in real-time and analyzed, 
including by ML when necessary. All modifications will first be tested on retrospectively 
collected data. If these analyses confirm an expected positive impact, the change in 
the algorithm will be implemented and its impact will be monitored through the 
continuous analysis of data looking at the related outcome indicators. For major 
modifications in the algorithm, a formal randomized sub-study will be conducted to 
assess precisely the potential benefit (or harm) on clinical outcome or non-inferiority. 
HCWs attending a child who could potentially benefit from this modification will be 
informed about the change in the algorithm. Either the modified branch or the standard 
branch of the algorithm will then appear on the screen to guide the HCW’s decisions. 
Antimicrobial prescriptions, clinical cure and other outcomes related to the expected 
impact will be assessed the same way as in Phase 1 and compared between children 
managed with the new and the standard version of ePOCT+. If the positive impact is 
confirmed by the sub-study, the new algorithm branch will be implemented for all 
relevant children in the relevant HFs. Minor modifications in the algorithm (e.g. adding 
a non-critical symptom such as myalgia, or a slight modification of threshold of a 
laboratory test) will be implemented without sub-study, but outcome indicators will be 
closely monitored. 
Objective 3: Enhance disease surveillance and outbreak detection 
All data generated by ePOCT+ will be sent to a central sever based at IHI. Data will be 
visualized through the medAL-outbreak dashboard made available to district health 
authorities using the key indicators they will have chosen. To enhance disease 
surveillance, targeted microbiological testing will complement the syndromic 
surveillance data generated automatically by ePOCT+. This microbiological testing will 
take place during Phases 1 and 2 in 6 to 8 sentinel HFs (3 to 4 per site) on children 

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presenting with at least one of three syndromes (significant respiratory disease, gastro-
intestinal disease or non-specific fever). Rapid tests will be used on site and 
appropriate microbiological tests (PCR and serologies) will be performed at the 
research laboratories of NIMR-Mbeya and IHI-Ifakara/Bagamoyo once every other 
week. Abnormal patters in the surveillance data identified by ML will also be 
investigated by performing the relevant laboratory tests to identify a potential outbreak. 
Results will be discussed with the Epidemiology section under Directorate of 
Preventive Services (DPS), including the necessity for additional confirmatory tests at 
the Geneva Centre for Emerging Viral Diseases. The clinical management algorithm 
will be modified temporarily, if necessary, according to the clinical experts. Additional 
laboratory analyses are intended to improve surveillance capacity at the district and 
national levels. We do not anticipate that additional laboratory analyses will be useful 
in patient management because of the lag time between sample collection and 
availability of the results. However, if this unlikely scenario presents itself and it is 
necessary to contact the patient again for additional treatment, it will be possible to 
retrieve his/her contact information and do so.  
Objective 4: Enhance M&E and supportive supervision and mentorship 
Patient-level data will also enhance M&E activities and supervision and mentorship of 
the HCWs through the medAL-outbreak and medAL-monitor platforms. Clinicians will 
use data generated from their own consultations to self-audit their clinical practices by 
monitoring quality of care indicators in the Clinivisor dashboards and improve their 
skills in specific areas using the e-learning platform. Similarly, members of the CHMT 
will use medAL-monitor to identify and provide support to HFs in need of additional 
training and mentorship. In time, this enhanced ability to capacitate HCWs and improve 
quality of care should become evident in the trends in the M&E indicators displayed in 
medAL-outbreak and subsequently in DHIS2. 
Objective 5: Create a supportive environment for the use of clinical decision 
support algorithms and disease surveillance tools 
During Phases 1 and 2, implementation mixed-methods research will be undertaken to 
assess facilitating factors to ePOCT+ deployment. HCWs, caregivers, community 
members and district health managers will be interviewed to assess the perception and 
evaluate the acceptability of ePOCT+ and related tools. 
Sustainability of the intervention will be evaluated by assessing the level of local human 
capacities that will have been reached at the end of the study in terms of: 1) clinical 
competencies of HCWs; 2) clinical algorithm creation by medical experts; 3) software 
and IT infrastructure maintenance by computer scientists; 4) disease surveillance and 
outbreak detection by health authorities and; 5) ML analysis by data scientists. These 
capacities will be enhanced by specific professional training programs and exchanges 
organized by the different institutions involved in the DYNAMIC project, as well as the 
University of Dar es Salaam. 
Finally, cost analyses will be performed using ML on the data collected during Phases 
1 and 2, assigning a cost to each procedure performed or medication prescribed during 
consultations, as well as to the transport to come to the HF or to the hospitalization of 
the child if any. An ecological assessment of the intervention will also be performed to 
calculate the carbon footprint or carbon-saving of the intervention. 
Statistical 
considerations 
Throughout the study, non-inferiority in clinical cure will be defined as a risk difference 
of <30% in the proportion of clinical failures while superiority in clinical cure will be 
defined as a decrease of ≥30% in the proportion of clinical failures. Superiority 
regarding resources (whenever medicines prescribed or diagnostic tests ordered by 
HCWs or time spent to perform a consultation) will be defined as a decreased need of 
≥25%.  
For the cluster-randomized study in Phase 1, the primary analysis will evaluate whether 
the use of ePOCT+ for case management of sick children in PHC facilities results in 1) 
decreased antibiotic prescriptions at the initial consultation (superiority analysis) and 
2) similar clinical cure at day 7 (non-inferiority analysis), compared to routine care. The 
sample size was calculated for testing the non-inferiority of the clinical cure outcome, 

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which will require a much larger sample size due to the rareness of the event. In 
addition, the testing of differences in antibiotic prescription is conditional on satisfying 
non-inferiority in the clinical outcome. Assuming a cure rate of 97% in the control arm 
based on previous studies, the acceptable absolute non-inferiority margin will thus be 
one third of 3% (100%-97%), thus 1%. We assumed a cluster size of 900 patients and 
an intraclass correlation coefficient of 0.002 in the clinical outcome based on similar 
studies. To have 80% power to detect this difference of ≥1%, for a one-sided 
hypothesis test at alpha of 0.025, we would require 19 clusters and 17,100 patients per 
arm (total patients n=37,620 assuming 10% loss to follow-up), rounded to a total of 40 
HFs and 40’000 patients. 
For Phase 2, sample sizes for the randomized sub-studies, aimed at evaluating the 
effect of a modification in the algorithm leading to a potential positive impact, will be 
calculated following the same general principles. In Phase 2, we expect that an average 
of 200 children per month per HF will be managed using ePOCT+, which corresponds 
to around 500,000 consultations over 2 years in the 100 HFs selected on predefined 
criteria. 
Study duration 
Phase 1 (including pilot phase): January 2021 – March 2022 
Phase 2: April 2022 – October 2023 
Implementation research: January 2021 – January 2023 
Sustainability assessment: January 2023 – March 2024 
Diseases Surveillance: October 2021 – March 2023 
Study schedule 
Cluster-randomized controlled study data collection (Phase 1): Sep 2021 – Mar 2022 
Data collection for the evaluation of the spatiotemporal/prognostic adaptations of 
ePOCT+ (Phase 2): Apr 2022 – Oct 2023 
Data collection for the evaluation of the smart disease surveillance and outbreak 
detection system: Oct 2021 – Mar 2023 
Data collection for the cross-sectional mixed-methods studies: Jan – Mar 2021; Jan – 
Mar 2022; Jan – Mar 2023 
Final data analysis of phase 2 and of the contextual evidence that drives sustainability 
and future scale up: Jan 2023 – Mar 2024 
Ethical 
statement 
The research project will be carried out in accordance with the research plan outlined 
in this protocol and with principles enunciated in the current version of the Declaration 
of Helsinki, Essentials of Good Epidemiological Practice issued by Public Health 
Switzerland (EGEP) as well as all national legal and regulatory requirements as 
applicable.   
Relevant 
references 
• Cordey S, Laubscher F, Hartley M-A, Junier T, Pérez-Rodriguez FJ, Keitel K, Vieille 
G, Samaka J, Mlaganile T, Kagoro F, Boillat-Blanco N, Mbarack Z, Docquier M, 
Tapparel C, Kaiser K , D’Acremont V. Detection of Dicistroviruses RNA in Blood of 
Febrile Tanzanian Children. Emerg Microbes Infect. 2019, 8(1):613-623. 
• Cordey S, Hartley MA, Keitel K, Laubscher F, Brito F, Junier T, Kagoro F, Samaka 
J, Masimba J, Said Z, Temba H, Mlaganile T, Docquier M, Fellay J, Kaiser L, 
D’Acremont V. Detection of novel astroviruses MLB1 and MLB2 in the sera of febrile 
Tanzanian children. Emerg Microbes Infect. 2018; 7(1):27. 
• D'Acremont V, Kilowoko M, Kyungu E, Philipina S, Sangu W, Kahama-Maro J, 
Lengeler C, Cherpillod P, Kaiser L, and Genton B.'Beyond Malaria — Causes of 
Fever in Outpatient Tanzanian Children', NEJM 2014; 370: 809-17. 
• De Santis O, Kilowoko M, Kyungu E, Sangu W, Cherpillod P, Kaiser L, Genton B, 
D’Acremont V. Predictive value of clinical and laboratory features for the main febrile 
diseases in children living in Tanzania: A prospective observational study. PLoS One 
2017;12(5):e0173314. 
• Erdman LK, D'Acremont V, Hayford K, Rajwans N, Kilowoko M, Kyungu E, Hongoa 
P, Alamo L, Streiner DL, Genton B, Kain KC. Biomarkers of Host Response Predict 
Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical 
Pneumonia: A Prospective Cohort Study. PLoS One 2015; 10(9): e0137592. 

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• Keitel K, Kilowoko M, Kyungu E, Genton B, D’Acremont V. Performance of prediction 
rules and guidelines in detecting serious bacterial infections among Tanzanian febrile 
children. BMC Infect Dis 2019; 19:769. 
• Keitel K, Samaka J, Masimba J, Temba H, Said Z, Kagoro F, Mlaganile T, Sangu W, 
Genton B, D'Acremont V. Safety and efficacy of C-reactive protein-guided antibiotic 
use to treat acute respiratory infections in Tanzanian children: a planned subgroup 
analysis of a randomized, controlled non-inferiority trial evaluating a novel electronic 
clinical decision algorithm (ePOCT). Clin Infect Dis. 2019; 69(11):1926-1934. 
• Keitel K, D’Acremont V. Electronic Clinical Decision Algorithms for the integrated 
primary care management of febrile children in low resource settings - Review of 
existing tools. Clin Microbiol Infect. 2018, 24(8):845-855. 
• Keitel K, Kagoro F, Samaka J, Masimba J, Said Z, Temba H, Mlaganile T, Sangu W, 
Rambaud-Althaus C, Gervaix A, Genton B, D’Acremont V. A novel electronic 
algorithm using host biomarker point-of-care-tests for the management of febrile 
illnesses in Tanzanian children (e-POCT): A randomized, controlled non-inferiority 
trial. Plos Medicine 2017;14(10):e1002411. 
• Rambaud-Althaus C, Shao S, Samaka S, Swai N, Perri S, Kahama-Maro J, Mitchell 
M, D'Acremont V, and Genton B. 'Performance of Health Workers Using an 
Electronic Algorithm for the Management of Childhood Illness in Tanzania: A Pilot 
Implementation Study.' Am J Trop Med Hyg 2017; 96: 249-57. 
• Shao AF, Rambaud-Althaus C, Samaka J, Faustine AF, Perri-Moore S, Swai N, 
Kahama-Maro J, Mitchell M, Genton B, D’Acremont V. New Algorithm for Managing 
Childhood Illness Using Mobile Technology (ALMANACH): A Controlled Non-
Inferiority Study on Clinical Outcome and Antibiotic Use in Tanzania. PLoS One 
2015; 10(7): e0132316. 
• Shao A, Rambaud-Althaus C, Swai N, Kahama-Maro J, Genton B, D'Acremont V, 
and Pfeiffer C. 'Can smartphones and tablets improve the management of childhood 
illness in Tanzania? A qualitative study from a primary health care worker's 
perspective.', BMC Health Services Research 2015; 15: 135. 
 
 
 

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TABLE OF CONTENTS 
1 
Background and rationale ................................................................................................. 18 
1.1 
Managing sick children at the primary care level .......................................................................... 18 
1.2 
Antimicrobial resistance at individual and population levels ......................................................... 18 
1.3 
Training, supervision and self-auditing at health facility level........................................................ 19 
1.4 
Monitoring and evaluation of health interventions ....................................................................... 19 
1.5 
Disease surveillance and epidemic detection ................................................................................ 19 
1.6 
Clinical decision support algorithms and results so far achieved in Tanzania ................................. 20 
1.7 
Health system, socioeconomic and ecological impact ................................................................... 22 
2 
Study objectives and theory of change ............................................................................. 23 
2.1 
Objective 1 ................................................................................................................................... 23 
2.2 
Objective 2 ................................................................................................................................... 24 
2.3 
Objective 3 ................................................................................................................................... 24 
2.4 
Objective 4 ................................................................................................................................... 24 
2.5 
Objective 5 ................................................................................................................................... 24 
2.6 
Theory of change .......................................................................................................................... 24 
3 
Study design ...................................................................................................................... 25 
3.1 
Phase 1: cluster randomized controlled study in 40 health facilities .............................................. 25 
3.2 
Phase 2: scale-up of the intervention to 100 health facilities and transformation into a dynamic 
algorithm ................................................................................................................................................. 26 
3.3 
Syndromic surveillance enhanced by microbiological testing ........................................................ 27 
3.4 
Mixed-methods operational research ........................................................................................... 28 
3.4.1 
Cross-sectional quality of care surveys............................................................................ 28 
3.4.2 
In-depth interviews, focus group discussions and surveys with health care workers .. 28 
3.4.3 
Caregiver and patient exit interviews ............................................................................... 29 
3.4.4 
Focus group discussions with selected community groups............................................ 29 
3.4.5 
In-depth interviews with members of the CHMTs ........................................................... 29 
3.4.6 
Economic and ecological evaluations .............................................................................. 29 
4 
Study area .......................................................................................................................... 30 
4.1 
Study sites .................................................................................................................................... 30 
4.2 
Selection of health facilities .......................................................................................................... 32 
5 
Study population ............................................................................................................... 32 
5.1 
Sick children attending PHC facilities ............................................................................................ 32 
5.2 
Other stakeholders and/or beneficiaries....................................................................................... 33 

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6 
Description of the intervention .......................................................................................... 33 
6.1 
Management of sick children by health care workers using ePOCT+ ............................................. 34 
6.2 
Regular update of ePOCT+ to improve accuracy and adapt to epidemiological changes ................ 36 
6.3 
Initial in-person training and e-leaning modules available on tablets ............................................ 37 
6.4 
Supervision of health facilities by the CHMT using medAL-monitor ............................................... 37 
6.5 
Disease surveillance and outbreak detection by district health authorities using medAL-outbreak 37 
7 
Outcome measures and indicators ................................................................................... 40 
7.1 
Outcome measures and indicators for Objective 1 (Improve the integrated management of sick 
children) .................................................................................................................................................. 40 
7.2 
Indicators for Objective 2: Improve the algorithm and adapt it to spatiotemporal variations ........ 42 
7.3 
Indicators for Objective 3: Enhance disease surveillance and outbreak detection ......................... 42 
7.4 
Indicators for Objective 4: Enhance M&E and supportive supervision and mentorship ................. 42 
7.5 
Indicators for Objective 5: Create a supportive environment for the use of clinical algorithms and 
disease surveillance tools......................................................................................................................... 42 
8 
Study procedures .............................................................................................................. 43 
8.1 
Objective 1: Improve the integrated management of sick children ............................................... 43 
8.1.1 
Recruitment, screening and informed consent procedure .............................................. 43 
8.1.2 
Management of sick children during the initial consultation ........................................... 43 
8.1.3 
Re-attendance visits in case of persisting or worsening of symptoms .......................... 46 
8.1.4 
Training of health providers on how to use ePOCT+ ...................................................... 46 
8.1.5 
Supervision of health providers by the CHMT ................................................................. 46 
8.1.6 
Type of care, training and supervision provided at intervention and control facilities .. 47 
8.1.7 
Measurement of primary and secondary outcomes ........................................................ 47 
8.1.8 
Unique study identification number for each participant ................................................. 48 
8.2 
Objective 2: Adapt the algorithm to epidemiological and demographic conditions ....................... 49 
8.3 
Objective 3: Enhance disease surveillance and outbreak detection ............................................... 51 
8.3.1 
Training and feedback of the CHMT on medAL-outbreak .............................................. 51 
8.4 
Objective 4: Enhance M&E and supportive supervision and mentorship ....................................... 51 
8.5 
Objective 5: Create a supportive environment for the use of clinical decision support algorithms 
and disease surveillance tools .................................................................................................................. 52 
8.5.1 
Implementation research ................................................................................................... 52 
8.5.2 
Stakeholder involvement and community sensitization .................................................. 52 
8.5.3 
Development of frameworks for the future scale-up of the e-POCT+ intervention....... 52 
9 
Laboratory procedures ...................................................................................................... 53 

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9.1 
Specimen collection and management ......................................................................................... 53 
9.2 
Serology to characterize exposure to viral and/or bacterial pathogens ......................................... 54 
9.3 
Viral detection and discovery ....................................................................................................... 54 
9.4 
Molecular detection of bacterial pathogens ................................................................................. 54 
10 Statistical methods ............................................................................................................ 54 
10.1 
Sample size calculations ............................................................................................................... 54 
10.1.1 
Cluster-randomized controlled study (Phase 1) .............................................................. 55 
10.1.2 
Randomized sub-studies to test algorithm modifications (Phase 2) .............................. 55 
10.2 
Estimated number of beneficiaries ............................................................................................... 55 
10.3 
Statistical analyses for the primary outcome measures in the cluster randomized controlled study 
of Phase 1 ................................................................................................................................................ 56 
10.4 
Machine learning analyses............................................................................................................ 56 
10.4.1 
Supervised classification and correlation ......................................................................... 56 
10.4.2 
Anomaly detection.............................................................................................................. 57 
10.5 
Analyses of data from operational research studies ...................................................................... 57 
11 Information technology, data management and MONITORING ......................................... 57 
11.1 
Overall IT solution ........................................................................................................................ 57 
11.2 
Data flow ...................................................................................................................................... 58 
11.3 
Data security and privacy.............................................................................................................. 59 
11.4 
Data quality monitoring ................................................................................................................ 60 
11.5 
Monitoring of study procedures and major health system barriers ............................................... 60 
12 Ethical considerations ....................................................................................................... 61 
12.1 
Participant information and consent ............................................................................................ 61 
12.2 
Risk-benefit assessment ............................................................................................................... 61 
12.3 
Premature termination of study ................................................................................................... 62 
12.4 
Local regulations, declaration of Helsinki and protocol amendments ............................................ 63 
12.5 
Alignment of the project with regional and/or governmental strategies ....................................... 63 
13 Organization and timelines ................................................................................................ 63 
13.1 
Organizational structure ............................................................................................................... 63 
13.2 
Roles of investigators and collaborators ....................................................................................... 64 
13.3 
Timelines ...................................................................................................................................... 66 
14 Capacity building, knowledge transfer and dissemination plans ..................................... 67 
15 Budget ............................................................................................................................... 69 
16 References ......................................................................................................................... 71 

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17 Appendices ........................................................................................................................ 74 
 
1 
BACKGROUND AND RATIONALE 
1.1 
Managing sick children at the primary care level  
Children are a well-recognized vulnerable population that still suffers from a high rate of acute 
infectious diseases and preventable deaths due to inadequate management of children at primary 
health care level. This is especially true in the fragile health systems of Sub-Saharan Africa, where 
under-five mortality is 10 times higher than in high-income countries. In these settings, health 
professionals lack appropriate diagnostic tools and skills. Outpatient providers are supposed to rely 
on disease management guidelines, such as the WHO IMCI booklet (WHO 2014A). The IMCI 
strategy, with its integrative disease management approach, where several health problems are 
considered in the same consultation, remains very relevant today. Yet its implementation has faced 
major challenges, largely related to format and content-related shortcomings (Rambaud-Althaus et 
al. 2017; Lange, Mwisongo, and Mæstad 2014; Baiden et al. 2011). For example, the IMCI algorithm 
is over-reliant on clinical signs and symptoms; these inherently lack diagnostic accuracy in children 
and expend much of the short timeframe allocated for consultations (Keitel et al. 2017; Rambaud-
Althaus et al. 2015). Diagnostic POC tests that can help detect children with severe illness or those 
in need of antibiotic treatment have not been integrated into the IMCI algorithm. The IMCI booklet 
asks for integration of information from about 20 pages during a consultation, which is a great 
challenge for HCWs. Practice guidelines like IMCI also become rapidly outdated as they are static in 
nature. With revision cycles of 5-10 years, guidelines cannot keep up with updated evidence and 
epidemiological changes. Existing guidelines are also too generic in that only a single version exists 
for an entire country, while each region faces different types of diseases and levels of transmission. 
When an epidemic arises, such static and generic guidelines can even become dangerous: existing 
recommendations may become inapplicable with changing disease patterns. As a result, HCWs may 
put standard guidelines aside to focus on the disease outbreak. For example, the risk of malaria 
deaths increased during the West African Ebola epidemic as HCWs focused on Ebola (Walker et al. 
2015).  
Another important component of pediatric acute care is patient triage. This ensures that patients with 
problems that require urgent attention are prioritized in the queue. Triage at peripheral HFs is 
practically nonexistent (Hansoti et al. 2017). Patients are seen in the order of arrival; at risk patients, 
such as newborns, may rapidly deteriorate while waiting to be seen. The installation of simple 
registration and triage tools would also allow improving the overall workflow at facilities.  
1.2 
Antimicrobial resistance at individual and population levels 
In the absence of diagnostic tools, clear guidance, and supervision, HCWs tend to over-prescribe 
antimicrobials (Hopkins et al. 2017, Fink et al. 2019). Out of ten febrile children attending primary care 
facilities, only about one or two children needs antibiotic treatment (D'Acremont et al. 2014), but nine 
receive antibiotics. This is because, without diagnostic tools, it is challenging for HCWs to safely 
identify the minority of children with bacterial infections among the predominant cases of self-limiting 
diseases (Rambaud-Althaus et al. 2017). Thus, HCWs tend to prescribe antibiotics - and sometimes 
also antimalarials - in the vast majority of cases “to be on the safe side”. Despite this high-volume and 
indiscriminate antibiotic treatment, mortality from childhood infections remains high. This is mainly 
because children with severe infections, which can be caused by viral, parasitic, or bacterial 
pathogens, are missed (Keitel et al. 2017). The over-reliance on antibiotics has resulted in the neglect 
of other important supportive treatments for such severe infections (e.g. oxygen therapy for viral 
bronchiolitis). Antibiotic over-treatment also hinders the development of the natural, protective 
bacterial gut flora, which is an important immunological barrier (Benoun et al. 2016). The tremendous 
overuse of antibiotics, especially in children at the primary care level where most patient encounters 

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occur, contributes to the global spread of antibiotic resistance, which is a major public health threat, 
estimated to be responsible for up to 10 million deaths per year by 2050 (Holmes et al. 2016, Fink et 
al. 2019). According to the WHO, antibiotic resistance is rising to dangerously high levels, especially 
in low-resource settings. To meet the urgent need to mitigate AMR, including strategies for 
rationalizing and monitoring antibiotic prescriptions, Tanzania has developed a National Action Plan 
for AMR 2017-2022. In this plan, the need for antimicrobial stewardship programs for prescribers is 
included in the Priority action n°9. 
1.3 
Training, supervision and self-auditing at health facility level  
Current standards of training and supervision for HCWs have much potential for further improvement. 
Continuous education still largely consists of centrally organized, generic refresher trainings, which 
often lack relevance to the individual HCW (Kiplagat et al. 2014) and are generally considered less 
effective than on-the-job training (WHO, 2006). Flexible, mobile solutions for continuous education at 
the HF level have not been implemented. Supervision personnel are often located at district level, far 
away from the HCWs. Due to logistical challenges, supervision visits can only happen infrequently 
and lack efficacy and efficiency. This is largely because the district supervision teams do not possess 
data that could inform them about problems and needs at the point of care. Personalized, data-driven 
feedback for front-line providers does not exist to date.  
1.4 
Monitoring and evaluation of health interventions 
Currently the planning, budget allocation, monitoring, and evaluation of health interventions is largely 
based on reports of data aggregated at the facility level, called MTUHA in Tanzania, and managed in 
the DHIS2 platform used by the majority of African countries. Data aggregation at the facility is done 
manually on paper forms or excel tables using paper records that are supposed to be filled by HF 
personnel on a daily basis. Since the filling of these registries is additional work on top of patient care, 
registries are often incomplete and may be filled in retrospectively (Wilms et al. 2014). Even if HCWs 
are motivated to report correct data, they lack the tools to make correct diagnoses. As a result, 
prevalence of many conditions that require diagnostic tools that are often out of stock or not available 
altogether, is falsely represented in DHIS2. For example, bacterial pneumonia is constantly over-
diagnosed as diagnostic tests to distinguish bacterial from viral respiratory infections are not available. 
Furthermore, the manual data aggregation at the facility level and entry process into DHIS2 is error 
prone. In sum, the data available for health management and reporting is of insufficient quality 
(Mghamba et al. 2004, Farnham et al. 2020).  
Misdiagnoses have consequences that reach beyond the patient. They increase re-attendance rates, 
further congesting PHC clinics and accruing economic losses not only for families but for the entire 
health system. Systematic errors in patient-level data accumulate, and as they are aggregated in 
DHIS2 to measure population-level indicators, they have the potential to bias the statistics used to 
prioritize public health interventions and, importantly, identify epidemics.   
1.5 
Disease surveillance and epidemic detection  
The epidemiology of diseases and the number and intensity of epidemics are expected to change 
even more than before, due to the rapid urbanization and climate change (WHO, 2019). The later will 
in particular increase epidemics caused by arboviruses, as they are transmitted by mosquitoes whose 
optimal reproductive temperature is 30°C, a level that Tanzania (presently experiencing an average 
temperature of 26°C) will become closer to in the coming years. It will also shift transmission of malaria 
from West Africa, where it will become too hot for anopheles to reproduce, to East Africa (Ryan et al. 
2015) The altered rainfall patterns due will also cause more floods, thus increasing the risk of 
waterborne and water-related diseases such as cholera, typhoid and leptospirosis. 
Since the true burden of disease is measured neither precisely nor timely, disease surveillance is 
inaccurate and epidemics are detected too late. This results in a distorted prioritization of health 

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interventions. For example, at the beginning of the Ebola crisis, the increase in diarrheal cases in 
Gueckedou were identified late and attributed incorrectly to a cholera epidemic, which caused the 
Ebola virus to spread further (WHO, 2014B). Outside of outbreak scenarios, viral respiratory infections 
are commonly misclassified as bacterial pneumonia. Health authorities then focus on the adequate 
supply of antibiotics, rather than on strengthening the medical treatment for severe viral respiratory 
infections (bronchiolitis and asthma), such as oxygen or bronchodilators (Nantanda et al. 2013). Due 
to the slow manual health data aggregation process and transfer into DHIS2, epidemic response is 
delayed, and diseases may be left uncontained.  
Disease surveillance should normally include laboratory data (i.e. microbiological tests). In the 
absence of laboratory tests, syndromic surveillance (as opposed to microbiological surveillance), 
where information only on signs and symptoms of patients are collected. Syndromic surveillance can 
be helpful if data quality is high (which is often not the case as described above), but is generally 
insufficient due to inability to confirm the causal pathogen. For example, unspecific fever could be 
caused by parasites (e.g. malaria), viruses (e.g. dengue) or bacteria (e.g. leptospirosis), all requiring 
very different prevention and control measures. For surveillance, a higher number of diagnostic tests 
(for example, detecting arboviruses) is thus required, as compared to the few tests that are useful for 
the daily management of patients at primary care level. On the other hand, untargeted microbiological 
surveillance (testing many samples for many different pathogens) is unspecific and very expensive. 
Therefore, microbiological tests should be applied in a few sentinel sites and sub-groups of patients 
with a specific clinical presentation following a predefined strategy. Unfortunately, in the primary HFs, 
the tools to inform targeting of microbiological testing to cases with the highest potential to contribute 
to outbreak detection are not available. 
1.6 
Clinical decision support algorithms and results so far achieved in Tanzania 
The WHO has identified digital health interventions and predictive tools in primary care as key 
accelerators in achieving the 2030 Sustainable Development Goal 3 of ensuring good health and well-
being for all. New simple and cheap technologies, such as mobile devices, coupled with the advances 
in computing and data science, could help mitigate several of the challenges described in sections 
1.1-1.5.  
Thus, Unisanté, Swiss TPH and IHI have developed and clinically validated smartphone-based 
electronic CDSAs for the management of sick children since 2010 (building on experience in 
developing algorithms for returning sick travelers and migrants in Switzerland since 2005). Two 
versions of the algorithm have been developed and evaluated in controlled research conditions, as 
summarized below. These electronic, interactive tools have the advantage over paper-based 
guidelines in that they can integrate sophisticated algorithms (which are necessary to accurately 
assess a sick child) while maintaining simple, user-friendly interfaces and teaching features. 
The first-generation algorithm for the management of childhood illness (ALMANACH) was trialed in 
Tanzania in 2010-2011, achieving improved clinical cure (from 92% to 97%) and a decrease in 
antibiotic prescription (from 84% to 15%) as compared to routine care (Shao et al. 2015A). 
ALMANACH also led to more consistent clinical assessments without taking more time than a 
conventional consultation and was perceived by clinicians as “a powerful and useful” tool (Shao et al. 
2015B).  
The second-generation algorithm called ePOCT, ‘electronic Point of Care Tests’, was based on a 
thorough review of the evidence and the existing national and international guidelines on the 
diagnosis and management of sick children (Keitel et al. 2017). In addition to symptoms and signs, 
it made use of several POC tests to help detect children with severe infections requiring hospital-
based treatment and/or children with serious bacterial infection. The interactive electronic decision 
tree supported a systematic assessment of the child and the consideration of several health 
problems at once (Figure 1). It increased the ability to detect children with severe disease, which 
was further supported by pulse-oximetry and POC hemoglobin testing (to screen for severe anemia, 
a strong predictor of bacteremia). POC inflammatory host biomarkers, such as CRP, detected 

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children in need for antibiotic treatment. Diagnostic and triage tests were only performed in patient 
groups with sufficient pre-test disease probability (which is indicated by the algorithm). Integrating 
such POCTs is important since clinical signs alone lack accuracy in detecting children with severe 
infection and/or in need for antibiotic treatment.  
 
Figure 1: ePOCT addresses the major challenges in pediatric case management. 
 
 
 
 
 
 
 
 
 
The safety of ePOCT was assessed among over 3,000 febrile children aged 2 months to 5 years 
through a randomized trial in Dar es Salaam, Tanzania in 2014-2016 (Keitel et al. 2017); this was an 
essential first step since ePOCT contained important upgrades compared to IMCI. The use of ePOCT 
resulted in higher clinical cure (98%) as compared to ALMANACH (96%) and routine care (95%). The 
algorithm also further reduced antibiotic prescription to 11%, as compared to 30% with the use of 
ALMANACH and 95% in routine care (Figure 2). 
Figure 2. Percentage of antibiotic prescription for children under-five with fever when managed per 
e-POCT and ALMANACH algorithms, and per routine care in Dar es salaam, Tanzania.  
 
 
 
 
 
 
 
 
 
 
 
The great potential of ePOCT to improve care now calls for further validation and implementation 
studies in different countries and epidemiological settings, and in less controlled conditions, as a basis 
for further scale-up (Figure 3). The content of ePOCT should also be extended from febrile children 
aged 2 months to 5 years to the entire pediatric age range and clinical spectrum so that the clinicians 
can use the tool for all of their consultations with children. The development of a young infant (0 to 2 
months) component will be especially important, as this age-group is at highest risk of mortality.  

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Figure 3: The validation cycle for clinical decision support algorithms (adapted from Keitel et al. 2018) 
 
 
1.7 
Health system, socioeconomic and ecological impact 
The ongoing digitalization of the health sector bears great potential for improving delivery and quality 
of care and for using real-time data in public health decision making. However, it also poses certain 
risks that must be mitigated by putting in place appropriate strategies to ensure patient confidentiality 
and data security, to avoid fragmentation and duplication of efforts, and to provide transparency within 
the health system to streamline this process. Furthermore, systems today need to be integrated, 
sustainable, flexible and scalable, while being mindful of their carbon footprint. These features of 
technological innovations in the health sector have increased the need for national frameworks for 
governance and guidelines on e-health. The DYNAMIC project aims at integrating innovative cutting-
edge technologies and methodologies to improve quality of care in PHC settings in Tanzania and to 
provide the health system with innovative tools to improve disease surveillance and enable early 
detection of epidemics. Furthermore, it aims to contribute to the development of national frameworks 
and guidelines for the scale-up of the intervention, while bearing in mind potential socioeconomic and 
ecological impacts.  
ePOCT+ should better identify children with severe disease who require immediate referral, treatment 
and supportive care. Early and appropriate triage and management of sick children not only leads to 
reduced mortality and morbidity but also decreases their exposure to unnecessary antimicrobials, 
which protects their natural gut flora that has recently shown to be essential for the development of 
their immunity. Rational use of antimalarials and antibiotics would result in a reduction of the drug 
pressure in the community and AMR, and eventually a reduction in morbidity, mortality and health 
care costs due to antimicrobial resistant infections. A cost analysis will be included in the project in 
order to estimate costs to the patient, provider, and health care system.  
ePOCT+ is designed to promote social cohesion between patients and HCWs since it presents 
structured consultation steps which encourage dialogue and physical examination (Bessat et al. 
2019). It saves time on trying to establish the right diagnosis, allowing clinicians to focus on social 
communication and fostering confidence through the perceived efficacy and reduced waiting times. 

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Because ePOCT+ is designed to provide personalized treatment algorithms, the clinician is then 
made more aware of the individual characteristics of the patient in her/his environment. The project 
also aims to improve the working relationships between the HCWs and their supervisors by facilitating 
supportive supervision and mentorship activities. 
More accurate reporting of morbidity episodes through a computerized surveillance system would 
allow to monitor more precisely important child health indicators. This will be especially important in 
the coming years where the risk of malnutrition in children might increase in the context of a decreased 
crop yield due to climate change.  
As the implementation of digital health interventions is on the rise, it is important to be mindful of their 
carbon footprint and potential negative ecological effects. Therefore, it is important to evaluate the 
environmental impact of such projects and think about ways to mitigate it prior to planning for scale-
up. The Life Cycle Initiative, a joint project between UNEP and SETAC, has developed a widely 
accepted framework for environmental assessment methodology. The damage-oriented methods 
consider “the cause-effect chain up to the environmental damages, the damages to human health, to 
the natural environment and to natural resources. These may be expressed for example in additional 
cases of human health impairment or species endangerment.” (Jolliet et al. 2004). Damage categories 
are considered to represent “the different areas of protection, human health, natural environment, 
natural resources and man-made environment. These areas of protection, also called safeguard 
subjects, represent operational groups of subjects (humans, biotic, abiotic and built environment) of 
direct value to human society. The damage categories group damages to these areas of protection”. 
Human health damage category considers in an aggregated and disaggregated manner all kind of 
impacts that could influence human health in general. The damage indicator, the DALY (Disability 
Adjusted Life Years), is the common indicator for all these impact categories. Other damage 
categories are important for the global environment and the current political agenda, like carbon 
footprint.  
More precisely, the impact of the DYNAMIC project will be evaluated using a combination of an 
environmental impact analysis, cost-effectiveness analysis, and health impact analysis. The LCA 
methodology will be used, enabling the evaluation of several impacts, as manufacturing, maintenance 
and end of life of tablets and PCs, and how these impacts can be reduced if these devices are used 
for other purposes, such as the electronic medical record and HF management systems that health 
authorities will deploy at the PHC level. Possible improvement of electricity consumption of the tablets 
and PCs and cooling of the central servers will be assessed.  
 
2 
STUDY OBJECTIVES AND THEORY OF CHANGE 
The goal of this study is to decrease morbidity and mortality of children and mitigate AMR using novel 
dynamic POC technologies that help front-line HCWs manage sick patients, enhanced by a smart 
disease surveillance and outbreak detection mechanisms. More specifically, this study seeks to: 
2.1 
Objective 1 
Improve the integrated management of children with an acute illness through the provision 
of an electronic CDSA (ePOCT+) to clinicians working at primary care level.  
Hypothesis 1.1: In routine conditions, sick children attending PHC facilities in Tanzania can 
be prescribed antimicrobials in a more rational way while having a good clinical outcome, if 
HCWs are provided with an electronic CDSA on tablets or PCs. 
Hypothesis 1.2: The use of ePOCT+ in the outpatient PHC setting in Tanzania will result in 
improved quality of care.  
 

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Hypothesis 1.3: E-learning modules incorporated into the digital tools will improve the HCWs’ 
competence and confidence in managing sick children, as compared to one-off IMCI refresher 
trainings. 
2.2 
Objective 2 
Improve the accuracy of the clinical algorithm and allow it to adapt to spatiotemporal 
variations in epidemiology and resources, based on the data generated through the 
ePOCT+ tool, analyzed using machine learning and checked by clinical experts. 
Hypothesis 2.1: The electronic CDSA can be modified over time using the data gathered 
through its use by HCWs, and aided by ML-based analyses, in order to improve its accuracy 
and adapt it to geographical and temporal changes in the disease epidemiology (e.g. 
outbreaks) or in the availability of resources (e.g. rapid tests). 
2.3 
Objective 3 
Enhance the district (and national) disease surveillance and outbreak detection capability 
using the clinical data generated by the ePOCT+ tool complemented by targeted microbiological 
investigations and machine learning pattern detection. 
Hypothesis 3.1 The generated individual patient level clinical data, complemented by targeted 
microbiological testing in a few sentinel sites, visualized and analyzed in near real-time can 
also allow district health authorities to improve disease surveillance and outbreak detection. 
2.4 
Objective 4 
Enhance the district (and national) health management information system for monitoring 
and evaluation and conducting supportive supervision and mentorship in HFs using the 
clinical data generated by the ePOCT+ tool enhanced by additional data analysis and 
visualization dashboards. 
Hypothesis 4.1: The generated individual patient level clinical data that is visualized and 
analyzed in near real-time will improve the ability of the CHMT to monitor the quality of patient 
care and undertake targeted supportive supervision and mentorship of HFs and HCWs. 
2.5 
Objective 5 
Create a framework for the development and implementation of dynamic clinical decision 
support algorithms and disease surveillance tools, for large-scale, sustainable, and 
clinically responsible use of machine learning and data science. 
Hypothesis 5.1: The interdisciplinary nature of the project, with an emphasis on cross-sectoral 
collaboration and knowledge/skills transfer, will enable the development of an evidence-based 
framework for sustainable scale-up of electronic CDSA interventions. 
Hypothesis 5.2: The patient level clinical data, when combined with other available 
spatiotemporal data sources will contribute to a better understanding of the epidemiological 
and public health profile that could serve as a basis for fostering cross-sectoral collaboration 
on the issues of health in the intervention districts and beyond. 
2.6 
Theory of change 
The goal and the objectives of the project are based on the following theory of change (Figure 4). 
 
 
 
 

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Figure 4: DYNAMIC project theory of change 
 
 
 
3 
STUDY DESIGN 
The primary intervention (i.e. ePOCT+ implementation) study will be conducted in two phases, with 
the study design detailed below and summarized in Figure 5.  
3.1 
Phase 1: cluster randomized controlled study in 40 health facilities 
The ePOCT+ tool, as well as the complementary training and supervision tools, will be provided to 
HCWs of 20 of the 40 selected PHC facilities (intervention arm) in Morogoro and Mbeya regions, while 
the other 20 HFs will serve as controls (control arm), using a cluster randomized controlled study 
design. Randomization (1:1) of HFs will be done by region and HF type. Sick children aged 1 day to 
14 years will be managed in accordance with the study arm to which the HF they attend has been 
assigned. The comparator for phase 1 will be routine care as currently implemented in Tanzania (see 
Section 8.1.5 for more detail). To assess the primary and secondary outcomes, final decision on 
referral, diagnoses and treatments at initial consultation will be recorded by HCWs electronically; 
children will also be followed-up by a phone call to their caregiver at day 7 to determine their final 
health status. Due to the pragmatic nature of the study, the design is adaptive in nature, where 
changes to the implementation may be incorporated throughout the study to account for issues 

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encountered in monitoring data and feedback from the field. These changes apply to the 
implementation context and exclude significant adaptations of the algorithm.   
The purpose of Phase 1 is to compare the effect of using ePOCT+ with routine case management of 
sick children on the rational prescription of antibiotics, while ensuring a favorable clinical outcome. To 
date, a prior version of the ePOCT tool was assessed in conditions closer to that of an explanatory 
trial (Keitel et al., 2017). The present study is designed as a pragmatic randomized controlled study 
to assess the safety and impact on antibiotic prescriptions of implementing ePOCT+ (including its 
associated POC tests), supported by initial and continuous training and targeted supervision of health 
workers, in children attending Tanzanian primary health care facilities. The study will be implemented 
in settings of targeted future use and involving routine health care personnel, and include all sick 
children (except those coming for a follow-up visit in the context of a chronic condition). ePOCT+ 
provides support to HCWs in managing these sick children. Since the intervention takes place at the 
health provider level and that their practices are influenced by the context of the HF they are working 
in, randomization at HF level rather than at HCW level or at individual patient level was chosen. 
3.2 
Phase 2: scale-up of the intervention to 100 health facilities and transformation into a 
dynamic algorithm  
The ePOCT+ tool will be extended to the 20 HFs serving as controls in Phase 1, as well as up to 60 
additional neighboring facilities of our target area, to reach a total of up to 100 facilities (around 60 in 
Morogoro and 40 in Mbeya). In Phase 2, an adaptive study design will be used measuring the same 
outcome indicators as in Phase 1. The medical content of the algorithm will no longer be static, but 
rather modifiable according to the local data captured by ePOCT+ during Phase 1. Each modification 
will be designed and evaluated by the clinical expert group for its coherence, safety and potential 
benefit. Modifications in the algorithms designed to perform as well as the original algorithm but using 
fewer/different resources will be implemented in the facilities they were designed to serve (for 
example, if certain facilities do not have access to a certain resource). Similarly, modifications 
designed to personalize the algorithm according to demographic characteristics, geography or season 
will be implemented on the target population for which they were intended. Modifications of ePOCT+ 
aimed at improving health outcomes of subgroups of patients (for example those with respiratory 
infections) will be implemented in a sub-study where patients are randomized at an individual level. 
Modifications confirmed as safe and useful will then be implemented, while the non-beneficial 
changes will not be retained. 
Examples of algorithm modifications expected to have positive impacts that will be explored for 
implementation are: 1) adapting the frequency of malaria testing in areas with very low endemicity; 2) 
improving the accuracy of pneumonia diagnosis, including , including through the use of novel POC 
diagnostic tools; 3) assessing the utility of using dengue rapid tests if an outbreak occurs; 4) or typhoid 
rapid tests in areas of high prevalence; 5) improving the prediction of severe disease; 6) improving 
the algorithm for malnourished or HIV positive children and; 7) testing sick children for sickle cell 
disease.   
 

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Figure 5: Study design overview. 
 
3.3 
Syndromic surveillance enhanced by microbiological testing   
The syndromic surveillance (i.e. based on the clinical data generated by the use of ePOCT+) will be 
enhanced by microbiological surveillance in a few sentinel HFs, as well as in any facility according to 
abnormal data patterns detected by ML (based on predefined clinically supervised thresholds) to 
identify potential outbreaks or endemic diseases requiring specific attention.  
Three to four HFs in each study district (6 to 8 in total) will be selected to play the role of sentinel sites, 
representative of different epidemiological conditions. In these HFs, blood, stool, urine or 
nasopharyngeal samples will be regularly collected from targeted sub-groups of children at risk of 
diseases of specific interest for surveillance (e.g. watery diarrhea to detect cholera or unspecific fever 
to detect dengue). In these sentinel sites, when attending a child presenting with one of the syndromes 
of interest (based on the data entered in the ePOCT+ tablet), the HCW will automatically see an 
additional screen requesting to collect the necessary additional sample (e.g. stool in case of diarrhea 
or blood in case of unspecific fever), if the patient has provided informed consent for that.  
If a child presents a syndrome of specific interest for surveillance (e.g. fever and rash) due to 
pathogens that cannot be detected based on a day 0 sample only (e.g. Rickettsiosia or Leptospira), 
he/she would be asked to return to the facility at day 7 or later and provide an additional sample. In 
such rare cases, the child would most likely not be cured at day 7 and would need to return to the 
facility for further care anyway. If the result turns out to be positive for one of these pathogens, the 
child will be proposed specific treatment if necessary (see chapter 8.1.3).  
In addition to sentinel sites, machine learning anomaly detection will be developed to visualize 
spatiotemporal clusters of events, which aims to help clinicians detect disease outbreaks (e.g. fever 
combined with abdominal tenderness in a cluster of households may be indicative of an outbreak of 
typhoid or another intestinal pathogen). The use of additional available spatiotemporal data sources 
will be explored in further developing the surveillance platform based on well-known risk factors of 

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diseases in the area that are prone to outbreaks. For example, remotely sensed data from satellite 
imagery may be incorporated to monitor environmental conditions (e.g. land surface temperature, 
rainfall, vegetation cover) known to influence seasonal patterns of some diseases. Likewise, 
information about the locations of water and sanitation features can be incorporated to help identify 
areas that are at increased risk of waterborne or water-related diseases. 
3.4 
Mixed-methods operational research 
Throughout Phases 1 and 2, as well as during the last year of the project, mixed-methods studies will 
be conducted in HFs and communities, as well as at district and national levels, to understand the 
contextual factors that facilitate or impede the implementation, sustainability and scale-up of digital 
health interventions such as ePOCT+ in PHC settings. Examples of such factors may include 
socioeconomic and ecological profiles of communities, working conditions of the clinicians in the HFs, 
among others. 
 
3.4.1 Cross-sectional quality of care surveys   
Before the start of phase 1, during the second half of phase 1, and around 6 months after the 
beginning of phase 2, three cross-sectional surveys in a subset of randomly selected HFs will be 
performed to assess the quality of the consultation done by HCWs and their level of compliance to 
the algorithm (and thus to guidelines). Among the 40 HFs included in Phase 1, 18 HFs (10 in Morogoro 
and 8 in Mbeya, half of them being intervention HFs and the other half control HFs) will be randomly 
selected. Observers will be trained to attend and observe (without interfering) 25 consecutive 
consultations with children aged 1 day to 14 years per HF, thus a total 450 HCW/child pairs. Changes 
over time will be assessed during the three cross-sectional surveys. Before-and-after intervention 
analysis will be performed between the baseline analysis and the phase 1 intervention arm HFs, as 
well as between the control arm HFs in phase 1 and those same HFs that will then use the ePOCT+ 
tool in Phase 2. For the9 intervention HFs, this will provide another way (besides data recorded 
through the ePOCT+ tool) to measure potential improvement in the quality of the consultation over 
time. The tools for the quality of care surveys can be found in Appendix 1. 
 
3.4.2 In-depth interviews, focus group discussions and surveys with health care workers 
Interviewees (health providers) will be selected through a mix of purposive and random sampling. 
Based on the data collected through the medAL-monitor tool, we will select health facilities based on 
relevant criteria such as levels of uptake of and compliance with the algorithm. Within the health 
facilities, providers will be randomly selected among those available on that day.  
Interviews will be conducted using a semi-structured interview guide developed for the study. The 
questionnaire will cover several themes, such as 1) experience using the ePOCT+ tool, 2) 
management of tablets in the facility, 3) training on the content and use of the algorithm, 4) experience 
with the e-learning modules and the supervision and mentorship tool (medAL-monitor). 
For each question, probes will be used to generate further explanations from study participants. Each 
interview will be conducted as an informal discussion with open questions, leaving room for the HCW 
to express additional elements. In order to reduce study bias, participants will be strongly encouraged 
to be as open as possible in expressing their opinion and reassured on full confidentiality. The tools 
for the interviews and focus groups can be found in Appendix 2. 
To complement the qualitative data, we will quantitatively evaluate the impact of using the ePOCT+ 
tool and the additional training and mentorship provided through the project on clinicians’ 
competencies and confidence in their skills and decisions in Phase 1. The initial 3-day training will be 
implemented with a pre- and post- test administered to the participants. The e-learning platform will 
also come with short tests at the end of each module for the learners to self-assess their 

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understanding of the content. Lastly, baseline and end-line assessments and surveys will be used to 
compare intervention and control facilities. Similar assessment tools will be used in Phase 2, except 
a control group will not be available for comparing the difference in clinical competencies between 
baseline and end-line. 
 
3.4.3 Caregiver and patient exit interviews 
Structured exit interviews will be conducted with caregivers of sick children (as well as potentially 
older children aged 9-14 years) who attended HFs that are part of the study. A purposive sampling 
method will be used to select a wide range of children treated for different pathologies at participating 
HFs. Face-to-face interviews will be conducted using a semi-structure questionnaire. Themes that will 
be explored include: 1) quality of care received; 2) influence of the tablet on the interaction between 
clinician and patient/caregiver, 3) Confidence in treatment and management using ePOCT+ and 4) 
Impact of the use of ePOCT+ on future health seeking behavior. The questionnaires are adapted from 
the Service Provision Assessment (SPA) sick child client exit interviews (USAID et al. The 
Demographic and Health Surveys Program), a methodology recommended by the WHO. The tools 
for the exit interviews can be found in Appendix 2. 
 
3.4.4 Focus group discussions with selected community groups  
Focus group discussions will be organized with members of the community regardless of their 
experience with the use of ePOCT+ for clinical consultation. This is meant to gather unbiased 
opinions, perceptions, and attitudes of community members toward the use of ePOCT+ and digital 
health tools in general. The tools for the interviews and focus groups can be found in Appendix 2. 
 
3.4.5 In-depth interviews with members of the CHMTs 
Development and implementation of the medAL-monitor and the medAL-outbreak tools will be done in 
close collaboration with the district team and the HMIS department of the MoH from the very 
beginning. Once implemented, members of the CHMT will be interviewed about the impact of medAL-
monitor on their ability to supervise and mentor HF staff. Similar interviews will be conducted with 
members of the HMIS department responsible for surveillance regarding the impact of the medAL-
outbreak tool on their ability to detect and manage outbreaks. For example, they may be asked if the 
respective tool is accessible with internet connectivity available to them, if the indicators displayed in 
the dashboards are relevant and appropriate, and if the information displayed is clear, easily 
interpretable, and actionable. The interviews may be conducted several times for the purpose of 
continuous improvement of the tools and for final evaluation of their effectiveness. The tools for the 
interviews and focus groups can be found in Appendix 2. 
3.4.6 Economic and ecological evaluations 
Ecological and human impact  
An ecological and human impact evaluation of the project will be assessed in collaboration with the 
“Sustainability Center” and the “Institute of Earth Surface Dynamics” of the University of Lausanne, to 
ensure that the intervention has limited impact on (or is even beneficial to) the environment and global 
warming. Indeed, the CO2 emissions and pollution due to the manufacturing and use of tablets might 
be lower than that due to the manufacturing and international and national transport of (unnecessary) 
antibiotics and HF re-attendance visits or hospitalizations. Life Cycle Analysis (LCA) is a systematic 
study of the environmental impacts of product systems, which analyzes energy and material flows 
during the whole life cycle: extraction and acquisition of raw material, use, end-of-life treatment and 
final waste disposal (Frischknecht et al. 2007). Through such a systematic perspective, the 
displacement of potential environmental loads between the different stages of the life cycle or between 

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particular processes can be identified and avoided. The International Organization for Standardization 
published international standards on LCA, which gives a framework for LCA and guarantees the 
transparency of the analysis (see ISO 14040; 2006A and 2006B). This methodology is fully applicable 
to a project development. 
Cost analysis 
Cost analysis will compare the cost of care provision under routine conditions and with the use of 
ePOCT+. Costs will include costs to patients, providers, health system, and project implementation 
(Yukich et al. 2010). Costs of resource inputs for providers will be determined on the basis of the 
Tanzania current pharmaceutical and supply price list and interviews with the appropriate financial 
managers of the Dar es Salaam City Medical Office of Health. Patient costs will be valued according 
to self-reported expenditures and lost income due to the travel to and time spent in the HF. Costs of 
project implementation will include tablets and the associated IT infrastructure, initial and refresher 
trainings and supervisory visits, additional laboratory tests, but will exclude specific research costs.  
4 
STUDY AREA 
4.1 Study sites 
This study will be conducted in the Morogoro region (Mlimba, Ifakara and Ulanga districts) and in the 
Mbeya Region (Mbeya rural district and Mbeya city council) of Tanzania (Figure 6). Key health 
indicators of the selected regions compared to national average are outlined in Table 1. Since the 
previous randomized efficacy study was performed in urban primary health care centers in Dar es 
Salaam, we chose rural and semi-urban areas to extend the generalizability of the evaluation of the 
intervention. To evaluate whether the effect of the intervention varies depending on the level of 
malaria transmission, we chose areas with low (Mbeya rural district, Mbeya city council) and moderate 
(Mlimba, Ifakara and Ulanga districts) malaria endemicity. A direct train connection between Ifakara 
and Mbeya (TAZARA) will facilitate travel of investigators between the two sites. 
 
Both, Ulanga and Mlimba Districts are found in the South-eastern region of Tanzania. Ulanga DC has 
a population of 265’000 inhabitants while Mlimba DC, which was previously called Kilombero DC has 
a population of 410,783 with the headquarters in Mngeta. Ifaraka Town Council is part of Mlimba DC 
with its own administration. Main economic activities in the two districts is subsistence farming mainly 
rice and smaller proportion of pastoralists who migrated from the north and center of the country. Most 
economic activities in Ifakara Town Council include agricultural trades, farming and provision of higher 
education in the biomedical field.  
 
Mbeya DC is among seven districts of Mbeya region in the Southern highlands of Tanzania with a 
population of 366,986, with the headquarters in Inyala and a total of 74 total health facilities. The main 
economic activities are subsistent farming (commercial and food crops) and livestock keeping. Mbeya 
city is estimated to have a population of 471,971 by 2018, and it is the capital, commercial city of 
Mbeya. 
 
 

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Figure 6: Map of Africa and Tanzania (A) with study regions (B) and districts (C and D) highlighted. 
 
 
Table 1: Key indicators of child health in Morogoro and Mbeya regions 
Indicator  
Morogoro region 
Mbeya region 
National average 
Under five mortality rate (per 1000 live births)1 
163 (2002) 
165 (2002) 
153 (2002), 81 (2010) 
Malaria prevalence in children aged 6-59 months 
(2017)2 
9.5% 
4.0% 
7.3% 
HIV prevalence (Age 15+; 2017)3 
4.2% 
9.3% 
7.0% 
Pneumococcal vaccine coverage (2017)4 
81% 
67% 
99% 
DTP - Hib - HepB3 (Diphtheria, tetanus, pertussis, 
Hemophilus influenzae type B, and hepatitis B) 
vaccine coverage (2017)4 
110.4% (2018) 
96.3% (2018) 
99% 
Prevalence of low hemoglobin in children (<8g/dl)2 
4.7% 
1.4% 
3.6% 
Percentage of children with fever within the past 
two weeks2 
18.9% 
16.2% 
20.4% 
 
1National Bureau of Statistics, Tanzania; available at: http://tanzania.opendataforafrica.org/TZSOCECD2016/social-economics-of-tanzania-
2016?region=1000130-mbeya&indicator=1007140-under-5-mortality-rate-deaths-per-1-000-live-births 
2Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDGEC) [Tanzania Mainland], Ministry of Health (MoH) 
[Zanzibar], National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF. 2017. Tanzania Malaria 
Indicator Survey 2017. Dar es Salaam, Tanzania, and Rockville, Maryland, USA: MoHCDGEC, MoH, NBS, OCGS, and ICF. Available at: 
https://dhsprogram.com/pubs/pdf/MIS31/MIS31.pdf 
3Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC). Tanzania HIV Impact Survey (THIS) 2016-2017: Final 
Report. 
Dar 
es 
Salaam, 
Tanzania. 
December 
2018. 
Available 
at: 
THIS 
2007: 
https://phia.icap.columbia.edu/wp-
content/uploads/2017/11/Tanzania_SummarySheet_A4.English.v19.pdf 
4USAID. 
Tanzania 
Immunization 
Fact 
Sheet. 
July 
2018. 
Accessed 
from: 
https://www.usaid.gov/sites/default/files/documents/1860/Immunization_Fact_Sheet_July_2018.pdf 
 

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4.2 Selection of health facilities 
PHC facilities are defined as the first point of contact to seek curative services. Facilities eligible for 
randomization in phase 1 will be determined in collaboration with the Regional Health Authorities prior 
to the start of the study. Permission for facility participation in the study and agreement to implement 
care practices as per randomization assignment at the level of the facility will be ascertained from HF 
administrative leadership prior to randomization. In phase 1, we anticipate enrolling 40 HFs (20 per 
arm), 24 in the Morogoro Region and 16 in the Mbeya Region. In phase 2, we plan to enroll 60 
additional HFs, to reach a total of around 100 HFs. 
 
Inclusion Criteria: 
- 
Government and private government-designated Primary care HFs (health posts, 
dispensaries and health centers). 
- 
Located in the Mlimba, Ifakara and Ulanga districts (Morogoro Region) or in Mbeya 
Rural district and Mbeya city council (Mbeya Region). 
- 
Seeing at least 20 children aged 2 months to 5 years per week on average over the 
past 6 months (to facilitate participant recruitment). 
 
Exclusion Criteria: 
- 
Secondary and Tertiary HFs (district, regional, zonal and specialized hospitals). 
5 
STUDY POPULATION 
5.1 Sick children attending PHC facilities  
All patients aged 1 day to 14 years that present to the participating PHC facilities for an acute medical 
or surgical problem will be included into the study.  
The ePOCT+ medical algorithm comprises evaluation and management content for acute medical 
and surgical conditions for children aged 1 day to 14 years. The lower limit of 1 day has been chosen 
to exclude peripartum conditions and immediate postnatal care; neonates who present to care 
immediately after birth (within the first 24 hours of life) will hence not be managed using ePOCT+ and 
will not be included into the study. The upper limit of 14 years has been chosen to be in line with the 
Tanzanian and WHO upper age range for adolescents.  
The algorithm does not include chronic disease management or content to inform the delivery of 
routine preventive health services independent of evaluation and management of an acute problem. 
The algorithm also does not cover routine neonatal visits.  
No specification of the primary complaint or symptoms at presentation will be required for eligibility 
and inclusion criteria were kept as broad as possible so as to maximize the generalizability of our 
findings. 
Inclusion criteria for Phases 1 and 2: 
- Aged 1 day (24 hours) to 14 years 
- Presenting for an acute medical or surgical condition 
Exclusion Criteria for Phases 1 and 2: 
- Presenting for scheduled consultation for a chronic disease (e.g. HIV, TB, NCD, 
malnutrition) 
- Presenting for routine preventive care (e.g. growth monitoring, vitamin supplementation, 
deworming, vaccination)  

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- Caregiver unavailable, unable or unwilling to provide informed consent (except for older 
children who can provide assent with an adult witness during the consenting process). 
* This does not impede the child to be managed using ePOCT+ but will lead to erasing of the electronic data at 
the end of the consultation and no follow-up via phone call or SMS / home visit will be made at day 7. 
5.2 
Other stakeholders and/or beneficiaries 
Other stakeholders and/or beneficiaries will be included in observational mixed-methods studies. 
HCWs attending to sick children and caregivers of these children will be involved in observational 
studies evaluating quality of care and interviews evaluating their experience with the ePOCT+ 
application. Communities where these children live will be involved in surveys and interviews to 
evaluate their perception of the intervention and any potential changes of the health system overall 
as a result of the intervention. Key actors of the health system (CHMTs of Morogoro and Mbeya 
regions, HMIS department and IDSR staff responsible for surveillance and data reporting, clinical 
directorate staff responsible for clinical services, computer scientists at the University of Dar es 
Salaam, research institutions in Tanzania involved in the ML training program, medical experts 
working at the national level) will also be interviewed about their perception of and experience with 
any components of the project that are relevant to their functions. Only those who have not had any 
interaction with the project (i.e. any of the electronic tools being implemented) or are unwilling to 
participate will be excluded from these additional studies. 
6 
DESCRIPTION OF THE INTERVENTION 
The intervention will include five activities, each supported by a specific electronic tool (Figure 7). 
 
Figure 7: Electronic tools on tablets or PCs used in the DYNAMIC project to improve the clinical 
management of sick children and enhance M&E, disease surveillance and outbreak detection. 
 
 
 

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6.1 
Management of sick children by health care workers using ePOCT+ 
Acutely ill children will be assessed, diagnosed and treated by routine clinicians guided by the 
ePOCT+ application, which will be loaded on a tablet or PC. 
The intervention will be undertaken in routine PHC facilities in Tanzania and entails the 
implementation of the ePOCT+ tool for patient assessment and management, with provision 
of associated POC tests. Healthcare providers will use ePOCT+ throughout their medical 
consultations for children aged 0-14 years presenting with an acute medical or surgical 
problem. In case of a re-attendance visit between day 0 and day 7, ePOCT+ will be used for 
managing the child in the same way as during the initial visit, as the algorithm takes into 
account duration of symptoms and previous treatments provided. Equipment, reagents, 
supplies/consumables for all laboratory and diagnostic tests included in the algorithm that are 
not currently part of the standard of care in this setting, will be made available to providers 
throughout the duration of the study in the intervention arm.  
The CDSA of ePOCT+ is already based on applicable Tanzanian national and international 
guidelines for pediatric clinical management (TZ version of IMNCI 2014; WHO Pocket Book 
of Hospital Care for Children 2013; TZ National Guidelines for the Diagnosis and Treatment 
of Malaria 2014; TZ Standard Treatment Guidelines and Essential Medicines list for Children 
and Adolescents 2018; Standard Treatment Guidelines & National Essential Medicines List 
Tanzania Mainland 2017 (Adults);TZ National Guideline for Neonatal care and Establishment 
of Neonatal Care Unit 2019; TZ National Guidelines for the Management of Tuberculosis in 
Children 2016; WHO IMAI guidelines 2009). Before implementation, the medical algorithm 
will be reviewed by Tanzanian key experts in child health (Pediatric Association of Tanzania; 
Ministry of Health; Muhimbili National Hospital).  
The majority of the algorithm content was previously validated in Tanzania (see background section). 
The exact medical content of the ePOCT+ algorithm will be published online, on the website that will 
be dedicated to the DYNAMIC project. Any subsequent modifications made to the original algorithm 
will be published as soon as they are implemented in HFs.  
Specifically, the tool will guide clinicians on:  
i) 
Decision on immediate referral to hospital versus home-based treatment 
ii) 
Medical history (questions to ask caregivers and/or children) 
iii) 
Physical exam (signs to look for) 
iv) 
POC tests (which should be performed in a given clinical context (see Table 2) 
v) 
Diagnoses 
vi) 
Treatments (including medicines and supportive treatments) 
vii) 
Counseling (explanation on final diagnoses, treatments, and follow-up) 
 
Biosensors (presently pulse oximeter and hemoglobinometer) and rapid tests (presently malaria, HIV 
and semi-quantitative CRP, urine dipstick) will have to meet requirements for implementation at PHC 
level, including adequate measurement accuracy, stability in high-temperature and dusty settings, as 
well as affordability. Once adequate biosensors and POCTs are identified, manufacturer negotiations 
are complete, and the regulatory framework is in place, they will be used by HCWs upon 
recommendation provided by ePOCT+ (Table 2).  
 
 

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Table 2: Point-of-care tests included in the ePOCT+ CDSA 
POCT 
Targeted patient group 
Rationale 
Pulse oximetry (POX)* 
Patients 
with 
respiratory 
complaints and severe disease 
Young infants with suspected 
severe disease  
POX may improve detection of children with 
hypoxemia  
In neonates and young infants, among whom 
accurate respiratory rate can be difficult to 
ascertain and short instances of rapid breathing or 
apnea can be physiologic, POX may improve 
diagnosis and classification of severe illness 
Hemoglobin (Hb)* 
Patients ≤ 5 years with fever 
Patients 
6-14 
years 
with 
malnutrition, palmar pallor or 
conjunctival pallor 
Regular Hb measurements in febrile children will 
improve detection of severe anemia, which is a 
marker of severe disease for a variety of disease 
pathways, including severe malaria, sickle cell 
disease, and severe bacterial infections  
Malaria rapid diagnostic 
test (mRDT) 
Patients with fever 
Malaria is a potentially fatal disease that must be 
excluded in patients presenting with fever, or in a 
sub-group of febrile patients at high risk in very low 
endemic areas, as per current WHO guidelines 
C-reactive 
protein 
(CRP)* 
Febrile patients at risk for 
bacterial pneumonia (based on 
clinical 
and 
demographic 
features) 
Febrile patients without focal 
clinical signs 
Febrile patients with joint or 
extremity pain 
Infants aged 1-2 months with 
suspected 
pneumonia 
or 
bacterial infections  
CRP improves targeting of antibiotic prescription in 
children with respiratory symptoms, with fever 
without focal signs, and with joint/extremity pain at 
risk of septic arthritis or osteomyelitis 
CRP may improve specificity of diagnoses among 
young infants with non-specific signs of possible 
severe illness and inform targeted antibiotic 
treatment and appropriate supportive care 
HIV rapid test 
Patients with possible exposure 
to HIV (mother with HIV or 
mother 
with 
unknown 
HIV 
status 
and 
unavailable 
for 
testing, or the patient is sexually 
active). 
Informed consent from the 
caregiver must be acquired.  
Tanzania national AIDS control program promotes 
provider-initiated HIV testing to all children 
presenting at HFs. Identifying HIV early to start 
antiretrovirals in a patient is essential to improve 
morbidity/mortality 
and 
prevent 
further 
transmission of HIV. 
Urine 
dipstick 
/ 
Urinalysis 
Febrile 
patients 
<3 
years 
without 
focal 
clinical 
signs  
 
 
Febrile patients > 3 years with 
complaints 
suggestive 
of 
urinary tract infection.   
CRP does not reliably rise in children with febrile 
urinary tract infections; febrile children <2 years 
should be assessed for febrile urinary tract 
infections per national guidelines.   
In older patients, for which urine collection is 
easier, a urinary tract infection can be excluded 
using a urine dipstick.  
*POX, CRP and Hb cuvettes will be provided by the DYNAMIC project, all other tests are 
routinely available at PHCThe ePOCT+ CDSA guides the clinician step-by-step through the 
consultation process. The algorithm is able to integrate all the information from the consultation and 

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to provide diagnostic classifications and related treatment recommendations. The application will be 
radically different from the existing medical software as it will allow clinicians to freely navigate up and 
down the algorithm, adding or modifying clinical information of any type (exposures, symptoms, signs, 
lab test results) at any time, in order to follow the natural consultation process (rather than forcing a 
clinician to assess the patient condition in a fixed order).The interface will also be much more user 
friendly, available in all necessary Tanzanian national languages, and allow integration of alerts of 
ongoing epidemics, definitions or translation of medical terms in local languages, explanations and 
illustrations (pictures or videos) of symptoms or signs that are difficult to assess and those that are 
specific to rare diseases, and automated teaching information on why a certain disease is being 
considered or not considered in a particular patient. 
 
In addition to the patient management algorithm, the overall ePOCT+ ecosystem contains a patient 
registration module and a triage module that can be used by different providers within the same HF, 
using tablets that are connected via WIFI. The purpose of the triage module is to prioritize patients 
that most urgently need medical attention and to allow task-shifting and optimization of care 
processes. The ePOCT+ triage module is based on the WHO ETAT guidelines and the IMCI-based 
SCREEN tool and was vetted in terms of reliability and feasibility through a Delphi process involving 
30 providers involved in primary care in Tanzania. The triage module can be switched on and off 
within the application to allow flexibility to the facility staff to decide on how best to organize their 
workflows and task-shift (e.g. by having a lower level staff member recording all vital signs or 
performing the necessary rapid tests) so that the clinician can gain time during the consultation for 
tasks that cannot be delegated. Based on previous experience, the average consultation time while 
using ePOCT+ is expected to be 15 minutes.  
 
ePOCT+ can be used on a tablet or computer, depending on the information technology 
platform already in use in the HF and level of comfort of providers with various electronic 
devices. We will perform pilot testing of the ePOCT+ tool in select HFs with direct supervision 
from qualified medical personnel before the start of study recruitment. The aim of this pilot 
phase is to ensure that ePOCT+ is understandable and acceptable to the users and that its 
flow conforms with the consultation process. The aim is also to optimize the interface and 
user experience of the application.  
 
No major modifications in the algorithm clinical content are foreseen for the duration of phase 1 but 
minor changes may be made in case of malfunctioning of the application. 
 
6.2 
Regular update of ePOCT+ to improve accuracy and adapt to epidemiological changes 
The ePOCT+ algorithm will be programmed into an electronic software support system. An in-depth 
internal IT validation will be performed through the interface of MedAL-Creator that allows direct 
visualization of the programmed algorithm. 
For this, a specific software solution is currently being developed for the Tanzanian setting which has 
three main components (see Figure 8): 
• 
The clinical algorithm management system that will allow to manage, reference, modify and 
deploy the different versions of the algorithm into a mobile device (called MedAL-Creator) 
• 
The application (tablet or PC) that will contain the algorithm and be used for data acquisition 
by the HCW (called ePOCT+), as well as automated capture of results from biosensors and 
rapid tests 
• 
The data management system that will aggregate and manage the data into a central server 
with a database 
 

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Figure 8: General architecture of the MedAL-Creator software solution 
 
During phase 2, the algorithm will be modified according to trends in the collected data detected using 
ML or conventional statistical methods. The modifications will be designed by clinical experts based 
on the clinical and microbiological surveillance data collected during the study. Approved changes will 
then be integrated into the algorithm using MedAL-Creator that allows physicians without any IT 
programming skills to modify the decision tree and a new version of the algorithm released. Each 
version will then be loaded on the tablets or PCs of the relevant HCWs (for example of a certain region 
where an epidemic is occurring) with a message explaining the rationale and evidence behind the 
change in the algorithm. 
6.3 
Initial in-person training and e-leaning modules available on tablets  
Initial centralized training on essential clinical skills, the evidence and guidelines behind the content 
of the algorithm, and the practicalities around the use of the electronic tool and POCTs will be provided 
to clinicians working in intervention facilities. Re-training of incoming personnel will also be 
implemented. 
 
To continue improving providers’ clinical skills throughout the project, e-learning modules will be 
developed. Such modules will help clinicians assess important clinical signs, especially pediatric 
danger signs (e.g. chest indrawing). First, we will perform a needs assessment for additional training 
among HCWs. Next, we will screen available video tutorials for suitability and adapt and translate 
them into the Tanzanian national languages. We will also develop additional training modules on 
clinical topics for which no training materials are available. The e-learning modules will then be pilot-
tested among HCWs before routine deployment, and then deployed onto the clinicians’ tablets and 
integrated with the ePOCT+ workflows. 
6.4 
Supervision of health facilities by the CHMT using medAL-monitor  
To enable better supervision of HCWs by the district medical team, we will also develop and deploy 
an electronic supervision tool called medAL-monitor. We will define clinical performance indicators, 
based on the data collected through ePOCT+, and integrate them into the e-supervision tool.  The 
latter will contain a dashboard to visualize these indicators per HF and per HCW over time, as well as 
a system to generate forms for targeted feedback to HCWs that will allow them to perform self-audits. 
The tool will also enable and encourage more frequent two-way communication between remote 
HCWs and the district team. The supervision tool will be pilot-tested with the district team and HCWs 
before routine implementation.  
6.5 
Disease surveillance and outbreak detection by district health authorities using medAL-
outbreak 
The medAL-outbreak disease surveillance and outbreak detection system will be made available to 
the district (and national) health authorities (Phases 1 and 2) for them to monitor certain health 

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indicators over time, be alerted if indicator values are higher than expected for a given month in a 
given location, suggesting a potential outbreak, and be able to decide if disease control measures are 
needed. 
Several versions of the dashboard will be developed: for the HFs, the district level and the national 
level. The dashboards will have several tabs for different types of health statistics: general information, 
main symptoms or syndromes, acute diseases, chronic conditions, and clinical management including 
treatment and referral.  
Specific indicators for self-audits (HF level), supervision (district level), monitoring and evaluation 
(district and national level) and disease surveillance (district and national level), will also be available. 
Important M&E indicators related to the impact of the present intervention that can all be stratified by 
geographical area, time period and age group and are similar to the Tanzanian HMIS indicators for 
child health, are given as an example in Table 3.  
In addition to the M&E indications, indicators for early epidemic warning and response will be selected 
with and provided to CHMTs through dedicated web-based dashboards (medAL-monitor/medAL-
outbreak). These indicators can then be transferred to the DHIS2 platform and to national health 
authorities. MoH CDEC will thus be able to compare the data provided by their DHIS2 dashboard with 
that provided by the medAL-outbreak dashboard, for the 3 districts where ePOCT+ will have been 
implemented. Once the MoH will consider that they are ready to do so, interoperability between the 
two systems will be enabled. 
 
 

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Table 3: Examples of M&E indicators that will be possible to display in the medAL-monitor/medAL-
outbreak dashboards based on ePOCT+ data 
Consultations 
 
• Number of outpatient consultations 
Main indicators 
 
• Number and % of deaths 
• Number and % of children referred to hospital 
• Number and % of children with malaria (severe and non-severe) 
• Number and % of children with bacterial pneumonia (severe and non-severe) 
• Number and % of children with diarrhea (severe and non-severe) 
• Number of measles cases 
• Number and % of children prescribed an antibiotic (by level of disease severity) 
• Number and % of children prescribed an antimalarial (by mRDT status) 
• % of final diagnoses made by HCWs for which the appropriate treatment is prescribed 
Danger signs 
 
• % of children with at least one danger sign  
• % of children with each type of danger sign  
• % of children with at least one danger sign prescribed an antibiotic  
• % of children with at least one danger sign prescribed an antimalarial 
Among children with cough/difficulty breathing 
 
• Number and % of children with cough/common cold, viral pneumonia and bacterial pneumonia 
• Median and distribution of respiratory rate measurements 
• % of fast breathing among children with cough/difficult breathing 
• % of chest indrawing among children with cough/difficult breathing 
• Median and distribution of oxygen saturation measurements 
• % of hypoxemia among children with fast breathing or chest indrawing 
• % of children with bacterial pneumonia prescribed an antibiotic 
• % of children with viral pneumonia prescribed an antibiotic 
• % of children with cough/common cold prescribed an antibiotic 
• % of children with bacterial pneumonia prescribed the first line antibiotic 
Among children with diarrhea 
 
• % of children with diarrhea  
• % of dehydration among children with diarrhea 
• % of dysentery/persistent diarrhea among children with diarrhea 
• % of children with diarrhea prescribed ORS/zinc 
• % of children with diarrhea prescribed an antibiotic 
Among children with fever/elevated temperature 
 
• % of children with confirmed versus clinical malaria 
• % of children with positive mRDT result 
• % of mRDT-positive children prescribed an antimalarial  
• % of mRDT-negative malaria children prescribed an antimalarial  
• % of children with malaria prescribed the first line antimalarial 
Screening for chronic conditions 
 
• % of sick children tested for anemia  
• % of sick children assessed for malnutrition  
• % of sick children tested for HIV  
• % of children with anemia (severe and non-severe) 
• % of children with malnutrition (severe and moderate) 
• % of HIV-positive children 
 
 

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7 
OUTCOME MEASURES AND INDICATORS 
7.1 
Outcome measures and indicators for Objective 1 (Improve the integrated management 
of sick children) 
 
The primary aim of the cluster-randomized study is to concurrently evaluate whether the use 
of ePOCT+ for case management of sick children in primary care, compared to routine care: 
 
(i) 
results in less antibiotic prescriptions at initial consultation; 
(ii) 
is non-inferior in clinical outcomes at day 7. 
 
Rationale for choosing the primary aim and related primary analysis: 
The second-generation ePOCT CDSA showed potential as an antimicrobial stewardship intervention for 
managing febrile children aged 2 months to 5 years, in a controlled trial setting in health facilities conducted in 
Dar es Salaam in 2015-16. Management with ePOCT resulted in a drastic reduction in (unnecessary) antibiotic 
prescriptions while maintaining similar, or possibly achieving better clinical outcomes by day 7. The current 
study seeks to generate evidence that would be more generalizable to a wider Tanzanian population. It aims at 
assessing the effect of an intervention package including the i) extended CDSA, called ePOCT+, ii) training and 
iii) supervision, when implemented under conditions closer to those that would be encountered in routine 
practice. ePOCT+ is an expansion of ePOCT that includes content for managing a wider age group (1 day to 
14 years) with more comprehensive medical concerns (any acute medical or surgical problem). To determine 
whether ePOCT+ is a successful antimicrobial stewardship intervention, a significant reduction (by at least 25%) 
in antibiotic use without a negative effect on patient outcomes needs to be demonstrated (Gillespie et al., 2018). 
 
Co-primary outcome measures for the cluster randomized controlled study in Phase 1: 
(i) % of children cured at day 7 (according to caregivers contacted through a phone call or home visit) in the 
intervention group (ePOCT+) as compared to control group (routine care) 
(ii) % of children prescribed an antibiotic at initial consultation, as reported by HCWs, in the intervention group 
(ePOCT+) as compared to control group (routine care) 
 
Main outcome indicators (monitored over time and across geographical areas in all HFs in Phase 2): 
- % of children cured at day 7 
- % of children prescribed an antibiotic at initial consultation 
 
Secondary outcome measures (Phase 1) or indicators (Phase 2): 
These measures or indicators include the evaluation of the effects of the use of ePOCT+ 
(versus routine care in Phase 1; over time and across geographical areas in Phase 2) for 
managing sick children aged under 14 years presenting to PHC facilities on: 
 
Unscheduled secondary consultations: 
- % of children with one or more unscheduled re-attendance visits at any HF by day 7 
Severe outcome by day 7: 
- % of children with non-referred secondary hospitalization by day 7 
- % of children who have died by day 7 
 
Primary referrals: 

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- % of children referred to hospital or admitted to inpatient ward at a health centre at initial 
consultation 
- % of children who were hospitalized among those referred (i.e. referral completed)  
Appropriate case management for malaria at initial consultation 
- % of febrile children tested for malaria 
- % of malaria positive children prescribed an antimalarial 
- % of malaria negative children prescribed an antimalarial 
- % of untested children prescribed an antimalarial 
Appropriate case management for other conditions at initial consultation 
- % of key symptoms and signs checked and diagnostic tests performed by HCWs 
- Distribution of final diagnoses (including their severity) made by HCWs 
- Concordance between treatment prescribed and final diagnoses made by HCWs 
- Concordance between final diagnoses as proposed by ePOCT+ and final diagnoses by HCW 
(intervention HFs only) 
Definitions used for the outcome measures and indicators of objective 1 
- 
Clinical cure is a positive answer to the question “Is the child cured?” OR “Has the child improved?” (if 
not cured) asked to caregiver through a phone call (or home visit) at day 7. Non-referred secondary 
hospitalizations (see definition below) will however be considered as clinical failures even if the child is 
already cured at day 7. 
- 
Initial consultation is the first visit of a sick child for an acute problem at a HF participating in the study 
(and thus registered electronically); timeframe from completion of the initial visit up to midnight of the 
same day. 
- 
Re-attendance visit is a consultation (not necessarily at a HF participating in the study) taking place 
from the day after initial consultation up to day 7 included. A re-attendance visit can be scheduled 
(proposed by the HCW on a certain day) or unscheduled (upon decision by caregivers). 
- 
Primary referral is a decision of referring the child to hospital taken by the HCW at the initial consultation 
- 
Secondary referral is a decision of referring the child to hospital taken by the HCW during a re-
attendance visit taking place from the day after initial consultation up to day 7 included.  
- 
Primary hospitalization is an admission to a hospital ward taking place the same day as the initial 
consultation 
- 
Secondary hospitalization is an admission to a hospital ward taking place from the day after initial 
consultation up to the phone call at day 7 included. A non-referred secondary hospitalization is a direct 
visit to hospital without a re-attendance visit the same day at a HF participating in the study.   
- 
Antibiotic prescription is any oral, intramuscular or intravenous (but not topical) antibiotic prescribed by 
a HCW during the initial consultation or a re-attendance visit  
- 
Antimalarial prescription is any oral, rectal, intramuscular or intravenous antimalarial prescribed by a 
HCW during the initial consultation or a re-attendance visit 
- 
Febrile child is a child with a history of fever and/or a high temperature  
- 
Malaria testing is a malaria RDT or microscopy ordered by a HCW during an outpatient visit  
- 
Day 7 is a range of 6-14 days post initial consultation to allow for completion of home visit for children 
whose caregiver reports having no phone / no phone number. 
 
Additional longitudinal analyses: 
- Change in % of children prescribed an antibiotic over time 
- Change in % of children with basic anthropometrics and clinical signs measured over time (intervention arm 
only) 
- Change in % of consultations where ePOCT+ was used over time (intervention arm only) 
 

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7.2 
Indicators for Objective 2: Improve the algorithm and adapt it to spatiotemporal 
variations 
- Number of positive impact modifications in the algorithm identified by ML-based analyses  
- Number of positive impact modifications tested through randomized sub-studies 
- % of these positive impact modifications confirmed to be safe and useful by randomized sub-studies 
- Number of modifications implemented by type of impact (better clinical cure, less severe clinical 
outcomes, less medicines or diagnostic tests needed, shorter consultation time…) 
Definitions used for the indicators of objective 2 
Positive impact modification is defined as a change in the algorithm expected to increase the clinical cure rate 
and/or decrease resources needed* and/or to have another positive clinical or health system related impact. 
* Resources needed include but are not limited to medicines prescribed, diagnostic tests ordered by HCWs, or time spent 
to perform a consultation. 
7.3 
Indicators for Objective 3: Enhance disease surveillance and outbreak detection 
 
- Number of M&E and HMIS indicators based on individual data that can be visualized through the 
medAL-outbreak dashboard 
- Average delay between the date of recording of clinical data and the date they are available on 
medAL-outbreak  
- Number and type of epidemic alerts raised by medAL-outbreak 
- % of these epidemic alerts registered by the district authorities for epidemic response 
7.4 
Indicators for Objective 4: Enhance M&E and supportive supervision and mentorship 
- Number of M&E indicators based on individual data that can be visualized through the medAL-
monitor 
- Number of supervision and mentorship visits to HFs facilitated by medAL-monitor 
- Number of HCWs reporting having used medAL-monitor for self-auditing their clinical practices 
- Number of HCWs and members of CHMT reporting a positive experience with medAL-monitor 
7.5 
Indicators for Objective 5: Create a supportive environment for the use of clinical 
algorithms and disease surveillance tools 
- Number of sick children attending primary care facilities managed by HCWs using the electronic 
ePOCT+ tool 
- % of HCWs able to assess key clinical signs  
- Frequency of use of medAL-outbreak by district team members 
- Frequency of use of the medAL-monitor tool by district team members 
- Perception by HCWs of the ePOCT+ tool and intervention and the number of improvements they 
propose for the tool or intervention 
- Perception by caregivers and community members of the intervention and the number of 
improvements they propose 
- Perception by district team members of medAL-monitor/medAL-outbreak and the number of 
improvements they propose for the tool 
- Number of medical experts able to modify a clinical algorithm using MedAL-Creator 

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- Number of computer scientists able to maintain the software and the IT infrastructure for clinical 
algorithms 
- Number of scientists able to analyze clinical data using ML 
- Number of local students trained in applied ML for health care in resource-limited settings 
- Perception of the current and future utility of ML to improve health care amongst clinical experts 
(deciding on algorithm changes) and health authorities 
- Patient and provider expenditures related to care of acutely ill children included in the study 
- Carbon footprint of the intervention or carbon-saving due to the intervention 
8 
STUDY PROCEDURES 
8.1 
Objective 1: Improve the integrated management of sick children 
 
8.1.1 Recruitment, screening and informed consent procedure  
As the intervention is implemented at the facility level, approvals from relevant authorities will 
be sought before engaging with persons in charge of HFs and HCWs. Sensitization meeting 
will be organized in the villages or suburban wards within the catchment areas of enrolled 
HFs. District and HF consent will be obtained for the intervention. HFs will be informed if they 
have been allocated to the intervention or control group and that, if allocated to the control 
group, they will benefit from the intervention a maximum of one year after the start of 
implementation, until at least the end of year 2022. Individual consent from caregivers will be 
sought for electronic recording of data and follow-up by phone call or SMS. Upon the start of 
the study, information will be provided in the waiting areas of facilities.  
 
All children aged 1 day to 14 years presenting for an acute problem during or after working 
hours to a HF that is part of the study will be asked if they wish to participate in the study by 
the person usually doing registration of patients (using the registration module in the tablet) or 
another appropriate staff member trained in ascertaining consent. The consent process will be 
integrated into the routine work flow at the health facility. Caregivers will be asked consent for 
electronic data recording and follow-up by phone or SMS for their child. They will be informed 
that not providing consent will not affect the medical management of their child and, in intervention 
facilities, the child can still benefit from being managed by the ePOCT+ tool and that their data will 
not be used for research purposes. All caregivers will be provided a paper-based participant 
information sheet and consent form providing sufficient information to make an informed decision 
about their participation in the study. If the clinical condition of the child requires immediate treatment, 
this will supersede the written consent process, which will be conducted once the child is stabilized. 
The consent form will be retained as part of the study records.  
 
8.1.2 Management of sick children during the initial consultation 
In HFs with dedicated staff for triaging patients, sick children will first be assessed by this person 
using the triage module of ePOCT+ in the tablet. If the child requires urgent treatment, the triaging 
person will immediately inform the attending HCW(s), so that she or he can give priority to that child 
over others waiting to be seen. If not, the child will queue normally and be attended by the next 
available HCW. As part of triage, the vital signs will be measured (temperature, respiratory rate, 
weight and MUAC) as well as, if the child fulfils certain clinical criteria, oxygen saturation and cardiac 
rate using the pulse oximeter. If there are no dedicated triage staff, the triage module will be de-
activated and vital signs will be measured during the consultation.  
 

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The attending HCW will either manage the child using the ePOCT+ algorithm (in intervention facilities) 
or as per routine (in control facilities). If the child needs to go to another room for laboratory 
investigations (rapid tests or other), the HCW will be able to suspend the ePOCT+ algorithm and data 
recording in the tablet and attend another child in the meantime, opening a parallel session in the 
tablet. When the child returns with the test results, the HCW will resume the ePOCT+ algorithm and 
data recording of that child and continue the consultation normally. At the end of the consultation, the 
HCW, in both intervention and control facilities, will be asked to enter the final decision on referral, 
tests performed, final diagnoses and treatments prescribed (see chapter 8.1.6 on outcomes 
measurements). After the consultation, this information will be synced to the server at IHI offices as 
soon as there is internet connectivity. 

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Figure 9:  Flowchart of health facilities and patients included in Phases 1 and 2 
 
 
 

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8.1.3 Re-attendance visits in case of persisting or worsening of symptoms 
At initial consultation, the advice to caregivers on when to bring the child back to the HF is left at the 
discretion of the HCW. In intervention facilities, HCWs will be guided by the ePOCT+ tool, which 
recommends conditional follow-up based on WHO and national guidelines.  
At re-attendance visits, whether scheduled or unscheduled, children will be managed according to 
the study arm to which they were initially assigned (ePOCT+ or routine care) in Phase 1, and with the 
support of ePOCT+ for all children in Phase 2. In case children still present fever at day 7, the 
algorithm will recommend in all cases to give an antibiotic to cover the possibility of an 
undetected bacterial infection. If surveillance laboratory results show that the child was 
suffering from a rare bacterial infection not covered by the antibiotic provided at day 7 (e.g. 
rickettsiosis or leptospirosis), the caretakers will be contacted to ensure that the child is 
indeed cured. If the child is still or again sick, caretakers will be asked to bring the child to the 
health facility to be assessed clinically and receive the necessary specific antibiotic. 
If the child comes back to the same HF as for the initial consultation, the HCW will have access to the 
electronic data of that initial consultation that is saved in the HF tablets. If the child goes to a different 
HF that is participating to the study, the HCW will not have access to that data. Investigators will 
however be able to link data collected at different facilities (as long as they are part of the study) for 
a same child through the unique identification number provided to caregivers. If the child goes to a 
HF not participating in the study, no data will be available for that consultation. The ePOCT+ 
registration module, used at all participating facilities, will inquire about the type of visit (initial / 
scheduled re-attendance / unscheduled re-attendance) and the main reason for that visit. 
 
8.1.4 Training of health providers on how to use ePOCT+ 
In intervention facilities, HCWs will participate in centralized in-person 3-day training course. 
They will receive general training on the epidemiology of diseases in Tanzania, the most 
important pediatric diseases and their clinical predictors, the content of the ePOCT+ algorithm 
and the importance of rational antibiotic and antimalarial use. General, key clinical skills will 
also be reviewed during this training and an introduction to the e-learning tool aimed at 
supporting these skills once they return to their facilities will be given. In addition, HCWs will 
receive training on how to use the ePOCT+ application on the tablet, using clinical case 
studies, as well as job aids for conducting and interpreting the results of POC tests which are 
not currently part of standard care at PHC level but are included in the algorithm (e.g. pulse 
oximetry and CRP rapid test). 
 
During the month following this centralized training, HCWs will benefit from one visit for in-
service face-to-face training during which trainers will accompany them in some of their 
consultations with real patients and help them optimize the way they use ePOCT+. Trainers 
will also support reorganization of workflows in the HF if necessary.  
 
At the end of Phase 1, all HCWs from control HFs as well as those from the new participating 
facilities will receive the same training as described above. During both Phases 1 and 2, new 
personnel at study facilities will be trained through a dedicated module on the tablet and by 
their peers. This module will be available to all HCWs using ePOCT+ so that they can refresh 
their knowledge any time. 
 
8.1.5 Supervision of health providers by the CHMT  
The CHMT will be provided the medAL-monitor tool which is a dashboard available on PCs and tablets 

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allowing them to monitor indicators (see Table 3) related to pediatric consultations taking place in the 
HFs enrolled in the study. Data on the child’s or the HCW’s identification will not be provided, only the 
name of the heath facility will be available. Based on these indicators, the supervision team will be 
able to better organize on-site visits to HFs, prioritizing the lowest performing ones, knowing in 
advance what areas of improvement to concentrate on. As the persons in charge of HFs, as well as 
each HCW, will also have access to these indicators (fully anonymized except for their own data) to 
allow them to do self-auditing at HF level so that they are aware of their own performance in advance 
of receiving a CHMT supervision visit. The aim of the medAL-monitor tool is to stimulate HCWs’ 
motivation and self-leadership to improve their skills and competencies, while enabling the 
supervision team to help them resolve remaining problems (Bessat et al. 2019).    
 
8.1.6 Type of care, training and supervision provided at intervention and control facilities 
In intervention HFs, HCWs will be guided by the ePOCT+ recommendations throughout the 
consultation. However, health care providers are ultimately responsible for the clinical management 
of all patients and may override recommendations of the algorithm and provide additional or withhold 
treatment according to their own clinical judgement. All concomitant diagnostic interventions and 
treatments will thus be left at the discretion of the provider but recorded in the ePOCT+ tool. In control 
HFs, routine care as currently implemented in Tanzanian primary care settings will be provided. 
Before the start of Phase 1, all skilled HCWs who are engaged in triage, assessment, and 
medical management of sick children in both intervention and control facilities will receive 
training in study-related procedures, including consent for electronic recording of data, and 
documentation of the final decision on referral, tests performed, final diagnoses and treatments 
using the tablet. In intervention HFs, training on the ePOCT+ tool will be provided as 
described above while in control facilities, standard paper guidelines (IMCI and Tanzanian 
national treatment guidelines) will be provided and HCWs reminded that these guidelines 
should be followed during patient management. They will also be informed that, after the end 
of phase 1, they will receive the intervention exactly as HCWs of initial intervention HFs did. 
 
In intervention HFs, HCWs will receive feedback on the data registered through the ePOCT+ 
tool, and the CHMT will be using medAL-monitor to better target supervision and support to 
facilities. In control facilities, supervision by the district team will be done using the available 
DHIS2 data as usual. 
 
In both intervention and control facilities, it is assumed that usual diagnostic tests and treatments will 
be provided through the routine health care system. Control HFs will not receive additional POC tests 
that HFs in Tanzania would not normally have available in similar settings (i.e. CRP test). Care for 
children under the age of five years is free of charge while caregivers are expected to pay 
out of pocket for health care expenses of older children (unless expenses are covered under 
an insurance policy). However, when not available at the HF, patients must procure their 
medications from nearby pharmacies or other HFs. To allow testing of the intervention under 
conditions as close as possible to those expected for future implementation, we will not 
provide routine diagnostics or medications in either intervention or control facilities. However, 
we will ensure that immediate life-saving treatments are available and will support the facility 
staff in managing stocks through routine mechanisms.  
  
8.1.7 Measurement of primary and secondary outcomes 
Data captured on the tablets used by health care workers during the consultation 
During the pilot phase before Phase 1 begins, tablets will be provided to HCWs in both intervention 
and control HFs. In intervention HFs the tablets will contain the patient registration module followed 

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by the ePOCT+ algorithm that will guide them throughout the consultation process. In control HFs, 
tablets will only collect the patient demographics (see Appendix 1).  
To determine the percentage of children prescribed an antibiotic or an antimalarial, as well as to know 
the final decision on referral and final diagnoses retained by HCWs, four questions will appear on the 
screen at the end of the consultation process, i.e. once the clinician has gone through the entire 
algorithm in intervention HFs or just after the patient registration module in control HFs. HCWs will be 
asked to document the final decision regarding the need for referral, tests performed and the results, 
decision on the date of follow-up (if needed), final diagnoses and final treatments prescribed. For each 
medicine prescribed, the dosage and duration of the treatment will also be recorded (see Appendix 
1). 
Data captured through phone calls or home visits (Phase 1) or automated phone calls or SMS 
(Phase 2) to caregivers 
For Phase 1, research assistants will be trained to administer a simple questionnaire by phone to 
caregivers of all children at day 7 (range 6-14) after their initial consultation with a HCW of participating 
facilities, regardless of re-attendance visits. If the phone follow-up fails, up to four additional attempts 
will be made on the subsequent days, after which they will be deemed lost to follow-up. These 
research assistants will be placed outside HFs and blinded to which facility the child has attended as 
much as possible. Data recorded during these phone interviews will be collected electronically and 
sent at least once a day to the central server of the study at IHI. For the question related to the clinical 
primary outcome measure (percentage of children cured at day 7), field workers will be particularly 
trained not to influence caregivers when they answer the questions “Is the child cured?” and insist 
that they simply answer by “Yes” or “No”. If the answer is “No”, the caregiver will be strongly advised 
to re-attend a HF, if possible, the one they went to for the initial consultation. Same procedure will be 
implemented if home visits are required using a REDCap mobile application. The questions included 
in the phone questionnaire are listed in Appendix 1. 
During Phase 2, instead of a phone call, caregivers will receive an automated SMS or automated 
phone call on day 7 with the same questions as in the questionnaire applied by phone. The automated 
follow-up system will be tested during Phase 1 in parallel with phone calls in a subset of patients, to 
check for congruency between the answers obtained by a caller and by automated voice message or 
SMS.  
In Phases 1 and 2, when the participant reports that the family has no phone number, or no response 
is received from the phone call or SMS, the field assistant may make a physical follow up to ascertain 
the responses of several unanswered contact attempts with support of the local village leaders as key 
informants. 
Data captured during the three cross-sectional quality of care surveys 
Research clinical observers who have been trained on the study procedures will visit randomly 
selected HFs. They will ask HCWs present on that day for permission to observe a series of 
consecutive consultations. They will then ask the HCW to request oral permission from their patient 
and to sign a consent form indicating their own and the patient’s consent to have an observer in the 
room. Observers will then record, using an electronic questionnaire, key symptoms (danger signs, 
main exposures and main symptoms) and signs (vital signs, main signs according to the child’s main 
symptoms) assessed by HCWs, diagnostic tests requested, final decision on referral, final diagnoses 
retained, treatments prescribed and main information and advice given to caregivers. Observers will 
be trained not to interfere at all with the consultation process; only values of vital signs can be asked 
to the HCWs if necessary, to be recorded precisely by the observer.  
 
8.1.8 Unique study identification number for each participant 
Patients will be given a unique study identification number through a barcode or a QR code 
that will be placed into the patient paper medical record booklet. The ID identification labels 

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will be pre-printed in bulk (specific to the district and HF) and delivered to the facilities. The 
patient will also be given a card with information on the study and additional barcode stickers. 
The sticker will be scanned using a barcode/QR code reader in the tablet each time the child 
presents at a HF. In the registration module on the tablet, an advanced search system will be 
created that uses the study identification number, the name of the child, the name of the 
mother or father or caregiver and the date of birth, so that the child can be identified with 
certainty and former consultations, if any, can be retrieved, and linked to the new consultation. 
The same code will also be used in order to link the laboratory results from surveillance 
activities back to the child’s consultation information. 
8.2 
Objective 2: Adapt the algorithm to epidemiological and demographic conditions 
The algorithm will be improved in terms of diagnostic and prognostic accuracy and adapted to local 
fluctuations in epidemiology, demography, geography and resource availability. Each potential 
modification will first be evaluated by the Tanzanian clinical expert group for its clinical coherence, 
safety and potential benefit and then applied to the retrospective data. If these analyses confirm both 
a clinically relevant positive impact and estimate that there will be sufficient future cases during the 
study period to detect this improvement, the change in the algorithm will tested in a randomized sub-
study using the same study design as in Phase 1, except that randomization will take place at patient 
level rather than HF level. If the positive impact is confirmed in the sub-study, the modification will be 
implemented in all relevant locations/patient sub-groups. If the retrospective analyses confirm a 
positive and potentially clinically relevant impact of a modification where resource savings is also 
expected but there will not be sufficient power to formally test the change during the study period, the 
modification will be implemented and monitored based on the related outcome indicators. If the impact 
of the modification achieves the expected results given the accumulated data at the end of the study 
period, it will be retained. 
During the sub-studies, HCWs attending a child who could potentially benefit from this modification in 
the algorithm will be informed about the potential change in the algorithm. Either the modified branch 
or the original branch of the algorithm will then randomly appear on the screen to guide the HCW’s 
decisions. Antimicrobial prescriptions and clinical cure, as well as all the other available and outcome 
measures relevant for the algorithm change, will be assessed the same way as in Phase 1 and 
compared between children managed with the modified and the original version of ePOCT+. If the 
benefit is confirmed by the sub-study, the new algorithm branch will be implemented for all relevant 
children in the relevant HFs. If the benefit is not confirmed, the original version of the algorithm will be 
used in all involved children. 
If shown to be promising according to the analyses of the retrospective data and considered feasible 
to implement, we will be testing modifications in the algorithm with a focus on the aims described 
below. Other types of modifications might also be tested if considered important by the clinical experts. 
Adapting the frequency of mRDT testing to malaria prevalence: 
As recommended by WHO (WHO 2011), in geographical areas where the positivity rate of malaria 
RDT is normally or seasonally falls below 1% (based on the data gathered through the use of 
ePOCT+), we will modify the “fever” branch of the algorithm to target malaria testing on the patient 
population at risk of having malaria. To do so, we will use the combination of demographic (e.g. village 
where the child lives), exposure (e.g. recent travel to an endemic area) and clinical (e.g. no other 
documented infection) criteria shown in the data analyses to best predict the presence of malaria. In 
this specific geographical area, we will thus randomize (at patient level) febrile children to be managed 
either using the original algorithm “fever” branch (that recommends all children to be tested for 
malaria), or using the modified version of the “fever” branch (that targets testing to the children at risk 
for malaria). If we measure that the clinical cure rate is equivalent to that of children managed with 
the original “fever” branch, the later will be implemented for all children in that geographical area. In 

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case the positivity rate would later increase in that area due to resurgence of malaria, the application 
would revert to the original “fever” branch until malaria prevalence goes down again. 
This dynamic malaria testing strategy would reduce the number of malaria tests used (that would 
likely have been negative) and also increase the percentage of mRDTs that are positive, which will 
improve the motivation and trust of HCWs in malaria testing. This strategy has already been 
implemented in some areas or countries in Africa using malaria predictors described in the literature, 
but not yet based on individual patient local data as it will be done in the present study.  
Improving the accuracy of pneumonia diagnosis: 
The same study design as for malaria testing will be used to improve pneumonia diagnosis accuracy 
using a combination of clinical predictors or new diagnostic tools, and/or to save CRP rapid tests in 
children with cough or difficult breathing. The new “cough” branch of the algorithm will be implemented 
in all children either if the clinical cure rate with the new branch is expected to be better than that of 
children managed with the original “cough” branch OR if the number of CRP tests used is expected 
to decrease. 
Assessing the utility of dengue rapid tests in outbreak conditions: 
If the microbiological surveillance in a few sentinel sites (see chapter 8.3) identifies a dengue 
outbreak, a dengue rapid test will be added to the “unspecific fever” branch of the algorithm and tested 
using the same study design as above. The modified “unspecific fever” branch including dengue 
testing will be implemented in all children if the clinical cure rate with this new branch is better (i.e. 
>30% decrease in the clinical failure rate) than that of children managed with the original “unspecific 
fever” branch. As soon as the dengue outbreak is over, the “unspecific fever” branch will revert to the 
original version and the use of dengue rapid tests will be stopped. 
Assessing the utility of typhoid rapid tests in high prevalence conditions: 
If the prevalence of typhoid in febrile children is shown to be >10% in a certain area or time period 
(based on the microbiological surveillance data), using rapid tests, even with sub-optimal sensitivity 
and specificity as it is the case for now, could be beneficial from a clinical point of view. Indeed, with 
a pre-test probability of 10% and a positive likelihood ratio of 4 for the typhoid RDT, the post-test 
probability is 30%. The performance of the test can be enhanced by adding clinical predictors 
identified in previous studies in Tanzania (De Santis et al. 2017). 
In this situation, a typhoid rapid test will be added to the “unspecific fever” branch of the algorithm and 
its clinical utility assessed using the same design as above. The new “unspecific fever” branch 
including the typhoid rapid test would be implemented in all children of the high prevalence area, if 
the clinical cure rate with the new branch is better (i.e. >30% decrease in the clinical failure rate) than 
that of the original branch. When typhoid prevalence decreases, the “unspecific fever” branch will 
revert to its original version. 
Improving prediction of a severe clinical outcome: 
If a combination of demographic, clinical and/or laboratory predictors, or a new POC test, seems to 
allow for better identification of children who will develop severe disease and thus be hospitalized 
secondarily or die, the benefit of adding an alert to HCWs in the algorithm, recommending them to 
refer the child to hospital, will be evaluated. If adding this alert allows to decrease mortality by at least 
10%, it will be implemented for all children.    
Adapting the algorithm to improve cure rates of children with chronic disease risk factors: 
If specific predictors of clinical failure can be identified in children with chronic disease risk factors 
(e.g. malnourished, HIV positive children, children with sickle cell disease), the relevant branches of 
the algorithm will be modified for these children (e.g. an antibiotic treatment may be recommended in 
a child with a certain clinical constellation). This personalized algorithm will be implemented in all 
children with the chronic disease risk factor if there is a clinical benefit as described above. 

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8.3 
Objective 3: Enhance disease surveillance and outbreak detection 
All data generated by ePOCT+ will be sent to a central sever based at IHI. Data will be visualized 
through the medAL-outbreak dashboard made available to district health authorities using the key 
M&E and HMIS indicators they will have chosen. To enhance disease surveillance, targeted 
microbiological testing will complement the syndromic surveillance data generated automatically by 
ePOCT+ in HFs. This microbiological testing will take place during Phases 1 and 2 in 6 to 8 sentinel 
HFs (3 to 4 per site) on children presenting with at least one of three syndromes (significant respiratory 
disease, gastro-intestinal disease or non-specific fever). Rapid tests will be used on site and 
appropriate microbiological tests (PCR and serologies) will be performed at the research laboratories 
of NIMR-Mbeya and IHI-Ifakara/Bagamoyo once every other week.  
To be able to perform these specific investigations for surveillance purposes, from which sick children 
will not benefit directly for the present acute episode (only indirectly when they attend for another 
episode and the algorithm has been improved in the meantime), caregivers of targeted children will 
be asked by the HCW to sign a separate written consent form allowing biological specimens to be 
collected. Caregivers and children will be asked if they agree to provide an additional blood, stool, 
urine or nasopharyngeal sample for research and surveillance purposes, and be provided a paper-
based participant information and sample collection form providing sufficient information for 
caregivers to make an informed decision about their participation in this sub-study. 
Thresholds for the detection of abnormalities in the data and for alerts for potential epidemics will be 
integrated in the medAL-outbreak system. Results will be discussed with the Epidemiology section of 
the MoH under the Directorate of Preventive Services (DPS), including the necessity for additional 
confirmatory tests at the Geneva Centre for Emerging Viral Diseases. This will allow health authorities 
to investigate if the cause of abnormal trends in the data is due to a real local epidemic or another 
reason. For example, a sudden increase in malaria cases could be due to a problem with the quality 
of the batch of malaria RDTs used. If the cause of the abnormality is not obvious, more in-depth 
investigations will be undertaken, such as performing clinical evaluations by an experienced physician 
or using pathogen rapid test (such as a Dengue or typhoid RDT) on site or other targeted lab tests at 
the research laboratory of IHI in Bagamoyo or NIMR in Mbeya (such as multiplex PCR with a tropical 
fever panel). These more in-depth data will also feed into the ML analyses and improve prediction 
capacity of children’ clinical outcome. The clinical management algorithm will be modified temporarily, 
if necessary, according to the clinical experts.  
8.3.1 Training and feedback of the CHMT on medAL-outbreak 
After finalizing the list of health indicators with the person in charge of M&E and HMIS at the district 
health office, the medAL-outbreak dashboard will be installed on her or his PC. She or he will be 
trained on how to use the tool and interpret trends in the indicators for decision-making. Her or his 
feedback on the interface will be solicited and the dashboard will be modified, if necessary, to ensure 
optimal user experience. Regular discussion between the site investigator and the district team will 
take place throughout the study to modify or further improve the medAL-outbreak tool.  
8.4 
Objective 4: Enhance M&E and supportive supervision and mentorship 
A similar process will be undertaken to ensure optimal use of the medAL-monitor tool. Individuals who 
normally conduct supervision visits to HFs will be trained on how to use and interpret the information 
displayed on the dashboards. HCWs and facility in-charges will also receive training on how to monitor 
their clinical practices at the individual and facility levels, perform self-audits, and build internal 
capacity and culture of quality improvement. User feedback will be solicited throughout the project 
and the interface improved.  

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8.5 
Objective 5: Create a supportive environment for the use of clinical decision support 
algorithms and disease surveillance tools 
8.5.1 Implementation research 
During Phases 1 and 2, qualitative studies will be undertaken to assess facilitating factors and barriers 
to ePOCT+ deployment. HCWs, caregivers, community members and district health managers will 
be interviewed to assess the perception and acceptability of ePOCT+ and related tools. 
Sustainability of the intervention will be evaluated by assessing the level of local human capacities 
that will have been reached at the end of the study in terms of: 1) clinical competencies by HCWs; 2) 
clinical algorithm creation by medical experts; 3) software and IT infrastructure maintenance by 
computer scientists; 4) disease surveillance and outbreak detection by health authorities and; 5) ML 
analysis by data scientists. These capacities will be enhanced by specific professional training 
programs and exchanges organized by the different institutions involved in the DYNAMIC project, as 
well as the University of Dar es Salaam for ML. 
Finally, costing analyses will be performed using ML on the data collected throughout the study, 
assigning a cost to each procedure performed during consultations, as well as to transportation to the 
HF or to the hospitalization of the child. An ecological assessment of the intervention will also be 
performed to calculate the carbon footprint or carbon-saving of the intervention. 
 
8.5.2 Stakeholder involvement and community sensitization 
The objectives of the study and the way in which it will be conducted have already been discussed 
with the ICT and M&E Technical Working Group of the Ministry of Health in Tanzania and Child Health 
unit in the Reproductive and Child Health section of the Department of Preventive Services at the 
Ministry of Health. The ICT and M&E section of the President’s Office Regional Administration and 
Local Government (PO-RALG) have been informed of the study and their support for the study 
obtained in principle.  
In addition, the District Medical Officers and the CHMT members of the study districts in Ifakara TC, 
Kilombero DC and Mbeya DC have been contacted, informed of the study and provided support in 
the initial selection of study facilities by providing some information relevant for the study inclusion 
and exclusion process. Both have indicated willingness to support the study and work together once 
all approvals are obtained from respective authorities.  
A Tanzanian stakeholder group will be established. It will contribute to the content of key documents 
and give feedback on the planned activities. If needed, international experts can be invited at certain 
time points. The stakeholders group might evolve along time but the initial composition will include 
Child health experts, Member/ leaders of the Pediatric association in Tanzania, Influential people 
(elders or religious), ICT/ ML experts in Tanzania, political leader at local communities, and CHMT or 
RHMT representative. 
Members of the community will also be involved throughout the study. Meetings with community 
leaders will be held in convenient locations to encourage participation. In initial study introduction 
meetings, community leaders will be informed of the study and asked to disseminate the information 
to their communities. Subsequently, wider community meetings will be held to solicit feedback about 
the intervention throughout the study period. Lastly, focus group discussions will allow community 
members to formally participate in the study.  
 
8.5.3 Development of frameworks for the future scale-up of the e-POCT+ intervention 
During the last year of the DYNAMIC project, we will develop for the Tanzanian national and district 
health authorities, as the ultimate owners and users of ePOCT+ and its data, the necessary tools for 
further national scale-up. These tools will include but are not limited to: 

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• 
An optimized version of the software allowing health authorities to modify the e-algorithms 
without the help of software programmers whenever they need (MedAL-Creator). This 
software application will allow clinical experts to program, maintain and update any type of 
algorithm aimed at guiding HCWs during a patient consultation, whatever the age or medical 
condition. It will also include a feature to deploy or export a new version of the algorithm into 
all tablets located at the HFs. Through an intuitive interface, the user will be able to draw the 
medical algorithm by dragging and dropping some predefined elements such as exposures, 
signs, symptoms and diagnostic tests. 
• 
A manual for local experts on the evidence-based path that needs to be followed to adapt and 
update the algorithm’s medical content.  
• 
A document on the best practices for the application of ML to clinical big data. 
• 
To be able to ensure maintenance of the MedAL-Creator open-source software in the long 
term, Unisanté is presently considering creating a non-profit foundation linked to Unisanté that 
would provide services at a very low price for low-income countries or public entities, with the 
aim of reaching the largest possible number of patients with this public good. 
• 
To the extent possible, interoperability guidelines will be developed for the prevailing eHealth 
tools with which ePOCT+ could be integrated. Examples include, but are not limited to, 
aggregation of patient level data from ePOCT+ for automated reporting into the DHIS2 
platform, or linkage of ePOCT+ to the registration module of an electronic medical records 
system so that information from patient encounters managed using ePOCT+ can be stored in 
the patients’ medical record. 
• 
Support to health authorities willing to develop implementation guidelines that take into 
account the facilitators and barriers discovered during the project will be provided upon 
request. 
9 
LABORATORY PROCEDURES 
9.1 Specimen collection and management 
Trained HF routine staff will collect venous blood samples by standard venipuncture from the right or 
left antebrachium into pre-labeled vacutainer tubes containing EDTA and serum separator tubes. The 
total volume (for clinical management and surveillance) will not exceed 2.4 ml/kg per patient weight 
(maximum 15 ml), as recommended by KEMRI and WHO guidelines, of which a maximum of 1 ml/kg 
(maximum 6 ml) will be used for surveillance purposes (WHO 2013). Two nasopharyngeal samples 
will be collected by gently rubbing a sterile, synthetic swab on the patient’s nasal mucosa for 2-5 
seconds. For stool samples, the subject will be provided a pre-labeled container in which to collect 
the sample and instructions on how to collect uncontaminated samples. 
All collected samples will be stored temporarily at the HF in a fridge and then transferred in batches 
in a cooler box with ice packs to the reference research laboratories for storage in an ultralow  
(-20⁰C or -80⁰C) freezer until analysis. In the two reference research laboratories in Mbeya and 
Bagamoyo, a testing strategy combining several levels of basic and advanced diagnostic tests, 
specific to the syndrome presented by the children, will be applied on the samples (that will be pooled 
whenever possible to save testing resources).  
All Tanzanian and Swiss-based implementing partners/personnel handling specimens and involved 
in their transport will be trained prior to conducting the procedures. Training will be provided at each 
site by the laboratory coordinator and supported by site investigators under the supervision of the 
country coordinator. Only trained HF staff and project personnel will have access to the specimens. 
To address participant privacy and confidentiality, all biological samples will be labeled with a unique 
identification code assigned to each enrolled sampled child, and then logged in a register (sample 
log/map or database). Results of the surveillance laboratory investigations, which will be available 2 

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to 3 weeks after the initial consultation, will be communicated back to health workers by the research 
team from IHI or NIMR. Health workers will forward the results to the caretakers. 
 
The majority of the laboratory analyses will be conducted at research laboratories in Tanzania at 
reference research laboratories in Mbeya or Ifakara in accordance with the procedures described 
below. In rare cases, Material Transfer Agreements (MTA) will be filled by all partners prior to any 
sample transfer from Tanzania to the Centre for Viral Emerging Diseases in Geneva, Switzerland. 
When shipping samples between Tanzania and the Centre for Emerging Viral Diseases in Geneva, 
the shipper will obtain all necessary documentation and permits from the appropriate regulatory 
authorities and follow all safety and International Air Transport Association (IATA) guidelines. The 
shipper will notify the approved couriers for transport in approved secured shippers and provide the 
recipient with the air bill/tracking number(s) and itemized shipping log.  
Biobanks: All specimens will be securely stored in locked and temperature monitored ultralow 
freezers in Mbeya and Ifakara research laboratories for up to 15 years after project completion. The 
specimen database will be password-protected, and personal identifying information kept in a secure 
manner and maintained by the surveillance and information management team, and not share to a 
third part. Access will be restricted to project personnel only for surveillance data analysis.  
9.2 Serology to characterize exposure to viral and/or bacterial pathogens 
Commercial and established standardized serological assays for known agents, both bacterial and 
viral will be used to screen children with syndromes of interest. When appropriate, serological assays 
will be performed on-site, and for advanced characterization the samples will be analyzed at the 
reference research laboratories in Mbeya or Ifakara and at Geneva Centre for Emerging Viral 
Diseases. 
9.3 Viral detection and discovery 
Virus detection in collected samples will be performed using a combination of multiplex PCR such as 
BioFilm Array and advanced analyses such as high throughput sequencing (HTS) and full genome 
sequencing to detect known and discover novel viruses from different sample types. All personnel 
handling these samples will follow biosafety guidelines and perform viral nucleic acid extraction and 
molecular detection protocols in a required biosafety level facility at Mbeya or Ifakara. Virus targeted 
are of potential pandemic or epidemic significance and those for which public and preventive 
measures (i.e. vaccination, education) exist.  
9.4 Molecular detection of bacterial pathogens 
Appropriate microbiological tests and multiplex PCR targeting conserved genes will be used to identify 
the full range of bacterial pathogens and to identify new bacterial agents in samples from children 
with diseases of unknown origin where the viral infections have been ruled out. 
10 
STATISTICAL METHODS 
10.1 Sample size calculations 
Throughout the study, non-inferiority in clinical cure will be defined as a risk difference of <30% in the 
proportion of clinical failures while superiority in clinical cure will be defined as a decrease of ≥30% in 
the proportion of clinical failures. Superiority regarding resources (whenever medicines prescribed or 
diagnostic tests ordered by HCWs or time spent to perform a consultation) will be defined as a 
decreased need of ≥25%.  

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10.1.1 Cluster-randomized controlled study (Phase 1) 
For the cluster-randomized study in Phase 1, the primary analysis will evaluate whether the use of 
ePOCT+ for case management of sick children in PHC facilities 1) results in similar clinical cure at 
day 7 (non-inferiority analysis), as compared to routine care and 2) decreased antibiotic prescriptions 
(superiority analysis) at the initial consultation. The sample size was calculated for testing the non-
inferiority of the clinical cure outcome, which will require a much larger sample size due to the rareness 
of the event. In addition, the testing of differences in antibiotic prescription is conditional on satisfying 
non-inferiority in the clinical outcome. Assuming a failure rate of 3% in the control arm (corresponding 
to a 97% clinical success rate), based on previous studies, the acceptable non-inferiority margin will 
be a risk ratio of 1.3. This corresponds to the upper bound of the difference in failure rate not 
exceeding 0.9% if the control group failure rate is 3%. We utilized a fixed risk ratio rather than an 
absolute risk difference to protect against potential unexpected event rates (Head et al. 2012).  
We assumed a cluster size of 900 patients and an intraclass correlation coefficient of 0.002 in the 
clinical outcome based on similar studies. To have 80% power to detect a risk ratio 1.3, for a one-
sided hypothesis test at alpha of 0.025, we would require 19 clusters and 17,100 patients per arm 
(total patients n=37,620 assuming 10% loss to follow-up), rounded to a total of 40 HFs and 40’000 
patients. However, due to uncertainty regarding the assumptions of the sample size calculation and 
to capture a wider more representative range of conditions, we will continue recruitment for Phase 1 
for a minimum of 6 months.  
 
10.1.2 Randomized sub-studies to test algorithm modifications (Phase 2) 
Sample sizes for the randomized sub-studies aimed at evaluating the benefit of a modification in an 
algorithm branch will be calculated following the same general principles as for Phase 1. 
Consideration of the desired or acceptable difference in clinical cure rate will be determined according 
to the type of intervention applied. This acceptable difference cannot be decided prior to knowing 
what intervention will be implemented, and on which population or subpopulation this intervention will 
be applied.  For example, a small improvement in the cure rate from 90% to 93% in a certain sub-
population of patients will be considered beneficial if not too demanding in terms of resources to be 
added. The same considerations will be applied for interventions aiming at improving the rational use 
of medicines (e.g. antibiotics or antimalarials). The highest sample size between that required for 
assessing the clinical outcome and that for the resource saving outcome will be used.  
10.2 Estimated number of beneficiaries  
In Phase 1, the number of direct beneficiaries include children in the intervention arm of the study 
(n=17,100) and HCWs using ePOCT+ (n=75) for a total of 17,175 people. Indirect beneficiaries may 
include caregivers and immediate family members who are less exposed to infectious diseases as a 
result of better cure rates of their child/sibling. Using an average rural household size of 5 people 
according to the 2004-2005 Demographic and Health Survey, the number of expected indirect 
beneficiaries is approximately 68,400 (i.e. 4*17,100, excluding the sick child who is a direct 
beneficiary).  
In Phase 2, we expect that an average of 200 children per month per HF will be managed using 
ePOCT+, which corresponds to around 500,000 consultations over 2 years in the 100 HFs. The 
number of direct beneficiaries will be less than this, because children may come to the facility more 
than once, either for the same illness or a different illness over the 2-year study period. In Phase 2, 
approximately 300 HCWs are expected to directly benefit from using ePOCT+ and their clinical 
decision support and e-learning functions. Additionally, approximately 20 users of medAL-monitor and 
30 users of medAL-outbreak at the district and national levels will also benefit directly. Lastly, all 
individuals who are capacitated IT systems management, ML, adaptation of the algorithm through 
MedAL-Creator, etc. will be included as direct beneficiaries (n=50). Because of high coverage of the 
intervention in phase 2, indirect beneficiaries will be the entire population of the intervention districts 

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(n=1,053,000) as a result of 1) less exposure to infectious diseases due to better cure rates of sick 
children; 2) reduced drug pressure and likelihood of antibiotic resistance due to reduced antibiotic 
prescriptions; and 3) reduced risk of illness due to more effective outbreak detection and response. 
10.3 
Statistical analyses for the primary outcome measures in the cluster randomized 
controlled study of Phase 1 
Analyses will follow CONSORT guidelines (Schulz et al. 2010, Piaggio et al. 2012) and intention-to-
treat (ITT) principles, that is including all participants as randomized. A flowchart will describe the 
inclusion and follow-up of participants by study arm. Baseline characteristics will be described by 
study arm with summary statistics such as median and interquartile range or number and percentage; 
no formal testing between arms will be performed (Pocock et al. 2002). Outcomes will be described 
by arm using summary statistics. For the non-inferiority comparison between ePOCT+ and the control 
arm for clinical cure, a CI approach will be used. A figure illustrating the CIs and the non-inferiority 
margin will be presented. Primary analyses for the non-inferiority comparison will be performed on 
both the per protocol and ITT sets (Committee for Proprietary Medicinal P 2001). If ePOCT+ is found 
to be non-inferior to the control in terms of clinical cure rate, then we will assess for superiority using 
the ITT set. If non-inferiority is established, we will test for significant differences in the co-primary 
outcome of antibiotic prescription rate. For this outcome, the percentage of patients receiving 
antibiotics at day 0 will be assessed using multi-level logistic regression model regression with the 
cluster included as a random effect. Binary secondary outcomes will be evaluated in the same way. 
All estimates will be reported with 95% confidence intervals (CI).  
 
Effect modification of the primary outcome of antibiotic prescription of the primary endpoints will be 
performed in groups of patients where the highest reduction in antibiotic prescription is expected, and 
areas of extension of the content of the algorithm compared to the version tested in the previous 
ePOCT study. Effect modification by sex, age groups (young infants ages 1 to 59 days, children aged 
2 months to 5 years, and children aged 5 to 14 years), respiratory symptoms, fever without clinical 
source, gastrointestinal complaints (vomiting and diarrhea), skin problems, ear and throat problems, 
anemia, and malnutrition will be assessed by incorporating an interaction between arm and the 
respective variable, acknowledging that power will be low.   All models will be adjusted for clustering 
and the stratification factors (Kahan et al. 2012). Primary analyses will be complete case; we may 
adjust for further baseline variables which are associated with missing outcome data (which is 
analogous to performing multiple imputation in the case of a single endpoint). Further details will be 
provided in the statistical analysis plan. 
10.4 
Machine learning analyses 
Machine learning (ML) is a specialized branch of statistics that creates updatable algorithms, 
which learn from evolving data in real time. Phase 2 of this project will make use of a specially 
designed algorithm-building platform, that will help clinicians explore and visualize patterns 
in the data collected by ePOCT+ during phase 1. Two main types of ML methods will be used: 
1) Supervised classification and correlation and 2) anomaly detection. The former aims to 
create algorithms that predict the value of a labeled feature (e.g. the result of a malaria rapid 
test) using alternative data.  Anomaly detection, on the other hand aims to find unusual 
clusters of values for a certain place, time or person, and are the basis of outbreak detection 
and discovering erroneous data inputs that could be used to train field workers. 
 
10.4.1 Supervised classification and correlation 
Using the insights gleaned from visualizing the data, the expert clinician group will lead the 
generation of clinically supervised modifications to the static algorithms in ePOCT+ that aim 
to 1) reduce resource consumption (e.g. predicting the results of expensive or unavailable 

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diagnostic tests), 2) improve or personalize diagnostic accuracy (e.g. creating algorithms 
adapted to local epidemiology, or better identifying patients who had severe outcomes in 
phase 1) or 3) improve the completeness of data collected by asking questions that would 
better predict the values that are missing.  For this, the algorithm building platform will allow 
clinicians to explore various predictive methods, ranging from logistic regression, neural 
networks and random forests. They will control the inputs required to make the prediction and 
decide at what threshold the predicted result will impact care decisions. The algorithm will 
then be updated with accumulating data to best reflect its dynamic environment. 
 
10.4.2 Anomaly detection 
Various methods allow us to identify and visualize clusters of abnormal data, by highlighting 
these instances, clinicians and epidemiologists can train the algorithms by labeling which are 
of interest and what, if any, actions should be taken when they are detected. Various methods 
will be explored for this aim and adapted to the type of anomaly under investigation (for 
example unsupervised density-based approaches such as isolation forests, and k-nearest 
neighbors [KNN] or multidimensional non-linear kernel-based techniques such as self-
organizing maps [SOM] and support vector clustering [SVC]). The clusters deemed 
interesting will then be investigated for gold-standard labeling by clinicians and the 
surveillance team and visualizations may be exported to users of the tool for training purposes 
(medAL-monitor). 
10.5 
Analyses of data from operational research studies 
Quantitative data from cross-sectional quality of care surveys, provider clinical competency or 
knowledge assessments, and caregiver and patient interviews will be analyzed using conventional 
statistical methods to compare means (t-tests or paired t-tests) or frequencies (chi-square tests) 
across intervention groups or time periods. Regression analysis may also be used to explore 
associations of multiple predictors with outcomes of interest.  
All interviews and focus-group discussions will be audio-recorded with the participant’s informed 
consent. Detailed notes will be taken during the interviews. Records will be manually transcribed in 
its original language (Kiswahili) and translated into English. A qualitative data analysis software (e.g. 
Nvivo) will be used to code the English interview transcripts in order to identify emerging themes. A 
predefined list of codes based on the interview guide and the recurrent themes highlighted in the 
detailed notes will be used. New codes will be created when new themes are identified during more 
detailed analysis of the transcripts. Deductive coding will be used to extract the themes based on the 
interview guide, but inductive coding will also be used to extract any unexpected themes that arise 
during the interviews. 
11 
INFORMATION TECHNOLOGY, DATA MANAGEMENT AND MONITORING 
11.1 Overall IT solution 
The overall IT solution is provided by several components; some of them have been already 
discussed or presented previously, the diagram (Figure 10) below is the complete overview of the 
solution. From left to right we have the following components: 
MedAL-Creator: The clinical algorithm management system that will allow to manage, reference, 
modify and deploy the different versions of the algorithm into a mobile device. MedAL-Creator is 
hosted in Switzerland and managed by the IT Unisanté team. 

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ePOCT+: The application (tablet or PC) that will contain the algorithm and be used for data acquisition 
by the HCW. The ePOCT+ could be deployed in a standalone or multi-device mode. In a standalone 
mode the application will receive updates of the algorithm directly from MedAL-Creator. 
Local Data: The application that enables ePOCT+ to work in a multi-device environment. It plays a 
role of central database at the HF level. This software will receive the updates of the algorithm from 
MedAL-Creator.    
Data Management System: this component will be considered as the data hub of our solution. Hosted 
on a server at the Dar es Salaam head office of IHI:  
- Main Data: This is the data collector for the country, it plays several roles: 
o Data collector 
o Data manager (Clean, Quality Check, Audit trail, Anonymization) 
o Data publisher 
o Data monitoring 
- medAL-outbreak: Indicators for disease surveillance and early epidemic warning and 
response will be decided with and provided to CHMTs through a dedicated web-based 
application 
- medAL-monitor: Targeted supervision to HFs by the CHMT will be organized based on the 
quality of care indicators calculated in real time from the uploaded data and made available 
through a web-based dashboard 
- Research Database: This is a database only with anonymized data where the machine 
learning team will do their analyses.   
 
Figure 10: IT architecture.  
11.2 
Data flow 
The data flow is shown in Figure 11. Data collection starts at the HF where the study clinicians 
capture the data using ePOCT+. When an Internet connection is available ePOCT+ (or Local 
Data in multi-devices set-up) will sync the data with the data management system hosted at 
IHI. The data management team at IHI will monitor, clean and anonymize the data in this 
system and make the anonymized data available for the Unisanté IT team. The Unisanté IT 
team will monitor the data received, communicate the possible errors or anomalies before 
sending them to the EPFL ML team for ML-based statistics.    

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Figure 11: Data flow. 
 
 
 
11.3 
Data security and privacy 
IHI (where all the data will be stored) working with Unisanté, in collaboration with the local health 
management teams, will be responsible for ensuring the privacy and security of all data collected 
during the project, especially with respect to individual patient data. Our methods are designed to 
comply with international and Tanzanian human rights standards that apply to regulating patient data 
protection, ownership, storage and processing. 
Primary data collection devices:  
Tablets used for primary data collection will be password-protected and assigned to individual HFs 
and/or HCWs. A secure PIN will be necessary on each request to view an existing patient file. When 
not in use, the tablets will be locked and stored in physically secure location.   
Transfer of data to secure local servers:  
All data uploaded from the primary data collection devices to the local server at IHI in Tanzania will 
be encrypted using an SSL connection. All raw data will remain on the secure local servers in 
Tanzania and will not be exported as such.  
Anonymization and machine learning IPD privacy:  
Before being transmitted to Switzerland for machine learning analysis or the creation of reporting 
metrics, automated anonymization algorithms will remove identifying features of the raw data. 
Features removed will include names, dates of birth, addresses and identification numbers. At no 
point will these confidential data attributes be exported outside of the secure local server.  
Transfer of data from local to Swiss servers:  

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When transferred from Tanzania to Unisanté in Switzerland, the data will travel through a secured 
VPN connection. Data Transfer Agreements (DTA) will be filled by all partners prior to any data 
transfer. 
Additionally, machine learning analyses will explore new techniques of identifying additional feature 
combinations that put patient privacy at risk. We will also investigate means to export elements of the 
data in a manner that renders it impervious to the latest re-identification attacks, for instance, by 
adding carefully calibrated noise (differential privacy) or by advanced encryption. Finally, we will also 
explore the possibility of decentralized learning that will allow analyses to take place without data 
transmission.  
11.4 
Data quality monitoring 
In order to minimize data recording errors by HCWs on the tablet, ePOCT+ data fields will include 
validations that limit the values to plausible ranges. Cleaning and quality assessment of the 
anonymized raw data arriving on the server at IHI will first be performed by the data management 
team using predefined algorithms developed in collaboration with the Swiss data management team. 
Queries related to data captured through day 7 phone calls will be brought to and solved directly with 
the research assistants performing the calls. Systematic missing or inconsistent data coming from 
HFs will be discussed first with the CHMT of the corresponding district and then with the person in-
charge of the HF, if necessary. A quarterly data review meeting will be organized by the study team 
along with a representative of CHMTs of each district to discuss any outstanding data quality issues. 
Regular quality checks will also be performed on the cleaned anonymized data sent to the Unisanté 
server by the Swiss team and queries sent to and solved with the IHI data management team if 
necessary. The database will be locked after all data has been verified and all raised queries have 
been resolved. Conventional and ML analyses will then start to be performed on this locked database. 
11.5 
Monitoring of study procedures and major health system barriers 
All data related to child health or quality of the consultation, such as compliance with the 
algorithm by HCWs, are outcomes and indicators that will be monitored by HF teams and the 
CHMT using the medAL-monitor tool, and thus not directly by the study team. Indeed, it is 
essential that the later does not interfere with decisions taken by HCWs, persons in charge 
of HFs or district teams.  
 
Regarding study procedures, monitoring will be delegated to the monitoring units of IHI and 
NIMR, which will function as independent monitors. In addition, the study team will perform 
routine on-site and off-site internal data quality and monitoring activities. The main goal of 
monitoring will be to assure data quality, and the wellbeing of study participants. The study 
monitoring team will visit research assistants performing the day 7 phone calls once a month 
during Phase 1. The aim is to ensure that they attempt to reach caregivers who did not 
respond to the first call at least twice more, and that they do not influence caregivers in their 
responses regarding the child clinical outcome. The study monitoring team will visit each HF 
once shortly after the on-site face-to-face training to check if the intervention tools are in place 
and used for the purpose for which they have been designed (e.g. that informed consent is 
obtained correctly, or that ePOCT+ is used during the consultation and that data are not 
entered retrospectively after the consultation is finished) and that there are no major issues 
impeding basic clinical procedures (e.g. stock out of antimalarials or antibiotics for weeks). 
The study team will also have regular meetings with the district teams to discuss major 
problems related to the study, the intervention, or the tools (that may jeopardize the study) 
and propose solutions if any. The study team will accompany the CHMT during some of their 

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supervision visits to monitor how they use the medAL-monitor tool and how HCW are able to 
perform self-auditing. A detailed monitoring plan will be developed before the start of the 
study. 
 
12 
ETHICAL CONSIDERATIONS 
12.1 Participant information and consent 
Inclusion of patients in the intervention study, as well as caregivers, HCWs, community members or 
CHMT members in the mixed-methods studies, will occur only if the participant has provided written 
informed consent. Written informed consent will be obtained prior to data collection, all non-routine 
clinical assessments, and any other study-related activities.  
District and HF consent will also be obtained for the intervention. Information and consenting tasks 
for sick children and their caregivers may be performed by any HF staff (for more details on the 
informed consent procedure, see Section 8.1.1). Information and consenting tasks related to mixed-
methods studies will be performed by the research scientists and their study team members. All these 
individuals will undergo adequate training with a special emphasis on the process of unbiased 
information of study participants ensuring that non-participation in the study does not result in a child 
not being adequately managed. During this training, theoretical and practical sessions will be devoted 
to research ethics and informed consent procedures. 
The participant information sheet/informed consent documents will be available in Kiswahili. These 
documents, as well as their translated versions in English, are attached to the present protocol (see 
Appendix 3). 
12.2 
Risk-benefit assessment  
Patients and caregivers:  
The proposed intervention is based on international and national treatment guidelines, 
supplemented by POC tests that are routinely used for managing sick children at hospitals. 
As such, we do not anticipate that the implementation of this intervention will pose a risk to 
patients. We rather anticipate that the implementation of ePOCT+ will provide a direct benefit 
to patients by improving case management of sick children at PHC level. With reduced 
antibiotic prescriptions, we anticipate children to develop healthier immune systems, making 
them less likely to be sick in the future. Additional benefit to patients and their caregivers is 
the reduced cost of healthcare due to fewer reattendance visits and secondary admissions.  
 
Community:  
Communities in the intervention area are also expected to benefit from the project in several 
ways. Reduced antibiotic prescriptions will reduce antimicrobial drug pressure in the 
community, and hence the risk of drug resistance. Better cure rates are expected to lessen 
the exposure to infectious diseases in the community. Enhanced disease surveillance and 
outbreak detection is also expected to decrease infectious disease transmission.  
 
Health care workers: 
HCWs are expected to develop better clinical skills and practices through the use of ePOCT+ 
and the associated training and mentorship provided through the project. The data generated 
with ePOCT+ will produce an opportunity for their work to also be audited by their supervisors 
and the CHMT. In some circumstances, this could pose some perceived or actual risk to their 

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job security. All data available to higher managers or authorities will be de-identified to 
mitigate these risks as much as possible.  
 
District health authorities:  
The CHMT will be able to monitor the quality of the consultations performed by HCWs and plan better 
for their supervision and mentorship visits to HFs. To ensure that this insight into their daily work is 
not perceived by HCWs as external control but rather a support mechanism for them to improve their 
competencies and skills, they will have access to their own data and will be encouraged to perform 
self-auditing before the supervision visit by the CHMT, so that a fruitful and respectful discussion can 
take place during the visit. This strategy has already been shown to be very well accepted by HCWs 
in Burkina Faso during the implementation of a CDSA in routine conditions at primary care level 
(Bessat et al. 2019).  
 
Central health system: 
The cluster randomized controlled study will generate evidence about the impact of ePOCT+ 
under routine (versus experimental) conditions. This will support the development of 
guidelines for potential national scale-up of the intervention, and similar digital health tools, 
in the future. The economic evaluation will provide additional information on the overall costs 
of healthcare provision with and without the use of ePOCT+. Lower costs of medicines are 
anticipated, which equates to lower costs of healthcare provision at the central level because 
under-5 children are provided health services free of charge in Tanzania. 
 
The project overall:  
The project provides many benefits, as described above, but also comes with some risks. 
For example, clinicians may be hesitant to use the tool, because it is difficult to use or they 
feel it is too prescriptive, etc. As CDSAs cannot cover all possible clinical situations, even in 
a well-defined patient target group, HCWs are ultimately responsible for the clinical management 
of all patients and may override the recommendations of the algorithm and provide additional or 
withhold treatment according to their own clinical judgement. Additionally, qualitative research will be 
conducted to solicit user feedback and facilitate improvements to ensure that the ePOCT+ application 
meets user needs. Furthermore, the application may be perceived by some stakeholders as a “black 
box” and be wary of the use of machine learning for algorithm adaptation, which may reduce their 
trust in the system. To ensure full transparency, as requested more and more by stakeholders 
(Ansermino et al. 2019), the medical content of the algorithm used during Phase 1, as well as any 
modification introduced during Phase 2, even if applied only in some geographical areas or for a short 
period of time, will be published in real-time on the website dedicated to the DYNAMIC study. Health 
authorities and HCWs will thus always have access to this content and know the exact combinations 
of clinical predictors used to suspect or diagnose each disease. No change in the algorithm will take 
place automatically and all modifications will be introduced manually. ML will be used only in the 
background, to complement conventional statistical methods to analyze data and guide decisions of 
the clinical expert group. For safety reasons, ML will never be used within the algorithm. Lastly, in 
Phase 2 of the study, there will be no control group. There is a possibility that due to circumstances 
that are external to the project (e.g. severe weather events, food insecurity, disease outbreaks) 
childhood morbidity and mortality could increase. In order not to incorrectly attribute poor outcomes 
to the study, we will need to monitor these events as closely as possible. 
12.3  Premature termination of study 
The Principal Investigators may decide to terminate the study prematurely according to certain 
circumstances, such as ethical concerns that cannot be resolved, in case of safety of the participants 
being compromized (e.g. if the benefits no longer outweigh the risks), alterations in accepted clinical 

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practice that make the continuation of the study unwise, or evidence of harm of the intervention. In 
case of premature study termination, the National Health Research Ethics Committee at NIMR will be 
notified as per local guidelines.  
12.4 
Local regulations, declaration of Helsinki and protocol amendments 
The research project will be carried out in accordance with the research plan outlined in this protocol, 
principles enunciated in the current version of the Declaration of Helsinki, Essentials of Good 
Epidemiological Practice issued by Public Health Switzerland (EGEP), and all national, legal, and 
regulatory requirements as applicable. The DYNAMIC study will be registered on ClinicalTrials.gov.   
 
All protocol modifications will be documented. A protocol amendment can be initiated by either the 
Sponsor or any Investigator. The Investigator will provide the reasons for the proposed amendment 
in writing and will discuss with the Sponsor and the Swiss and Tanzanian Principal Investigators. Any 
protocol amendment must be approved and signed by the Sponsor and the Principal Investigator and 
will be submitted to the National Health Research Ethics Committee at NIMR for information and 
approval. Approval will be obtained before any changes can be implemented, except for changes 
necessary to eliminate an immediate hazard to study participants, or when the change involves only 
logistical or administrative aspects of the study. An annual report by the National investigators will be 
submitted to the National Health Research Ethics Committee at NIMR per national guidelines. 
12.5 
Alignment of the project with regional and/or governmental strategies  
Our project is perfectly aligned with the Tanzania Digital Health Investment Roadmap 2017-23, 
proposing provision of innovative software, IT infrastructure and skills to enable client tracking and 
monitoring to support quality health care. Investments are required in data systems to strengthen the 
ability of health authorities to make evidence-based decisions. The project is also in line with the 
National Action Plan on Antimicrobial Resistance 2017-22 that advocates for strengthening 
stewardship for antimicrobial use in HFs by providing evidence-based prescribing and dispensing 
guidelines, as well as monitoring and evaluating the use and consumption of antibiotics at all levels. 
Our project also supports the National Malaria Strategic Plan 2014-20, which calls for introducing 
evidence-based, innovative diagnostic tools for malaria detection and differential diagnosis of other 
pathogens causing febrile illnesses. The disease surveillance component also supports the  National 
Action Plan for Health Security 2017-2021 that aims at strengthening  and  sustaining  the  capacity  
of Tanzania  to  promptly  detect  and  confirm outbreaks, the frequency and intensity of which may 
increase in the coming years due to climate change. 
13 
ORGANIZATION AND TIMELINES 
 
13.1 Organizational structure 
Unisanté is the leading institution in close collaboration with the IHI, the Mbeya branch of NIMR-
MMRC, the Swiss TPH, Basel Switzerland and the Machine learning and optimization laboratory of 
EPFL, Lausanne Switzerland.  
The Project Technical Committee (PTC) will ensure an appropriate framework to achieve project 
objectives, take necessary decisions to effectively implement the workplan and achieve its 
deliverables, by allocating funds and resources according to activities and milestones. The 
implementation team, lead by two site coordinators, will ensure implementation of all activities in the 
field in the Mbeya and Morogoro regions, with the support of the different technical teams lead by one 
team member from and based in Tanzania, seconded by one team member from Switzerland. 
The Project Management team will coordinate and monitor activities against the implementation 
plan; negotiate and administer funds; organize meetings and teleconferences; coordinate report 

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writing; ensure effective communications between partners; monitor milestones and deliverables; 
and coordinate the dissemination of results obtained by the project to the scientific community (see  
Figure 12). 
The National Stakeholders Group will review and discuss progress regarding of ePOCT+ 
implementation, discuss, review and give feedback on the annual project reports, provide guidance 
on aligning activities with national priorities, call upon harmonized specialised technical assistance in 
key areas when necessary, identify, discuss and propose solutions for possible problems related to 
the intervention and/or the health system. 
 
 
Figure 12: Organizational structure for the project 
 
 
 
13.2 Roles of investigators and collaborators 
Unisanté will act as the main sponsor for the study. The principal investigators and co-investigators 
at IHI, NIMR, Unisanté, Swiss TPH and EPFL, are responsible for the overall design and conduct of 
the study. The Tanzanian principal and co-principal investigators at IHI and NIMR will oversee the 
study conduct in Tanzania. This includes assuring that the study is conducted in accordance with the 
protocol, the rights, safety and welfare of the study participants are protected, local requirements of 
the ethical committee are adhered to, adequate training of site personnel and integrity of study data 
are maintained. The team from Switzerland (Unisanté and Swiss TPH) will provide epidemiological, 
statistical, IT, administrative and financial management support. The Geneva Centre for Emerging 
Viral Diseases in Switzerland will provide technical support to the research laboratories in Mbeya and 
Ifakara for the microbiological surveillance. 
Prof. Valérie D’Acremont will serve as the overall scientific lead and the key contact for study design 
and writing of the protocol.  She will lead the process of documenting findings and lessons learnt 

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through the project. She will oversee the development and writing of scientific manuscripts, as well 
as technical and financial reports. She will lead writing of progress reports and dissemination of results 
to partners and stakeholders. 
Dr. Honorati Masanja and Dr. Nyanda Ntinginya will be responsible for conduct of research activities 
according to ethical guidelines. They will contribute to the development of the research protocol and 
serve as main contacts for ethics committees and the MoHCDEC. They will contribute to the 
dissemination of results to partners and stakeholders. 
Dr. Kristina Keitel will provide key technical input on the study design of the ePOCT+ cluster-
randomized study. She will contribute to the analysis of the results of the study, technical and scientific 
manuscripts, and dissemination to partners and stakeholders. Dr. Keitel will provide pediatric 
expertise to the clinical team responsible for approving the initial content of the clinical algorithm, as 
well as the major modifications taking place during the study. She will also support knowledge transfer 
of the development and adaptation of clinical decision support algorithms to Tanzanian stakeholders. 
Dr. Mary-Anne Hartley will lead the machine learning analyses, with the support of Prof. Martin Jaggi, 
as well as the related preparation of progress reports, technical and scientific manuscripts, and 
dissemination to partners and stakeholders. They will also organize knowledge transfer to 
professionals in Tanzania willing and able to learn the use of applied machine learning for clinical 
interventions in resource-limited settings. 
Dr. Chacha Mangu will oversee all the research field activities in Mbeya. He will also participate in the 
coordination of activities related to disease surveillance and outbreak detection. He will contribute to 
the writing of progress reports, scientific manuscripts and dissemination of results to partners. 
Dr. Alexandra Kulinkina will be the overall project coordinator who will coordinate activities between 
the different groups involved in the study in Switzerland, as well as with the Tanzania research team. 
She will support the PI in all her activities and coordinate the development and writing of scientific and 
technical manuscripts, as well as the writing of progress and financial reports and dissemination of 
results to partners and stakeholders. 
Dr. Lameck Luwanda will lead the implementation of the ePOCT+ tool at HF level and the medAL-
outbreak tool at district level in the Morogoro Region. He will contribute to the development and expert 
approval of the ePOCT+ algorithms, writing of progress reports, scientific manuscripts and 
dissemination of results to partners. 
Dr. Godfrey Kavishe will lead the implementation of the ePOCT+ tool at HF level and the medAL-
outbreak tool at district level in the Mbeya Region. He will contribute to the writing of progress reports, 
scientific manuscripts and dissemination of results to partners. 
Dr. Rainer Tan will be responsible for providing technical support to all activities required to implement 
the ePOCT+ tool at HF level and the medAL-outbreak tool at district level in the Mbeya and Morogoro 
Regions. He will lead the development and approval of the ePOCT+ algorithms by the medical experts 
and coordinate the monitoring the implementation of ePOCT+ in HFs. He will contribute to the writing 
of progress reports, scientific manuscripts and dissemination of results to partners. 
Charles Festo will coordinate the National IT teams. He will make sure that the infrastructure for the 
data collection is operational, secure, maintained and backed up (at 2 sites). He will also coordinate 
the daily operations (data management incl. data cleaning, back-up, etc.) 
Vincent Faivre will lead the development and maintenance of the software and assess the IT 
infrastructure needed with the local IT team. His team will also develop the necessary tools for the 
data collection and management. During the data collection, he will also coordinate data verification, 
in close collaboration with the IT and data management teams of IHI and NIMR. The IHI team will in 
particular be responsible for initial data cleaning and anonymization of the raw data stored on the 
server based at IHI. 

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Peter Agrea (NIMR) and Ibrahim Evans Mtebene (IHI) will help develop and test the data collectors 
(“Local Data” at the facility level, “Main Data” at the National level), in close collaboration with the 
Swiss and Mbeya IT teams. He will also provide support and training to the users and to daily 
operations (data management incl. data cleaning, backup, etc.) He will also be involved in the medAL-
outbreak and medAL-monitor dashboards and assess the feasibility of the integration with the 
Government of Tanzania’s Hospital Management Information System (GoT-Homis). 
Geofrey Isdory Ashery will participate to the qualitative studies and contribute to the writing of 
progress reports, scientific manuscripts and dissemination of results to partners. 
Happy Mkali will lead the biological and laboratory aspects of disease surveillance and outbreak 
detection in the field and contribute to the writing of progress reports, scientific manuscripts and 
dissemination of results to partners. 
Prof. Isabella Eckerle will coordinate the disease surveillance and outbreak detection component, in 
collaboration with the Tanzanian partners, as well as the related preparation of progress reports, 
technical and scientific manuscripts, and dissemination to partners and stakeholders. She will in 
particular be responsible for the laboratory analyses and interpretation of results on emerging viral 
diseases at the Geneva Centre for Viral Emerging Diseases. 
Dr. Lisa Crump will give technical inputs and field support to the disease surveillance and outbreak 
detection component, in particular related to zoonotic and bacterial diseases, in collaboration with the 
Tanzanian partners. She will participate to the related preparation of progress reports, technical and 
scientific manuscripts, and dissemination to partners and stakeholders. 
Alan Vonlanthen will ensure project coordination and communication between the different teams 
(clinical, data & IT, ML and project management) involved in the project. 
Dr. Tracy Glass will serve as the lead statistician of the ePOCT+ cluster-randomized controlled study 
of Phase 1. 
Dr. Gillian Levine will support the clinical team with developing and validating the clinical content of 
the young infant algorithms. She will assist with analyzing the data and contribute to scientific 
manuscripts. 
Dr. Sabine Renggli will support the Morogoro site team with the implementation of the project 
activities. She will assist with writing of progress reports, scientific manuscripts and dissemination of 
results to partners and stakeholders. 
Martin Norris will support the project management and IT teams with coordination of the technical 
activities related to the electronic platform. He will also assist the clinical team with trouble-shooting 
and verifying changes in clinical content and its translation in the electronic tool. 
Humphrey Mhagama will support the study implementation in the Mbeya site as a study nurse.  
 
The Sponsors, Investigators, and key study members as outlined above have all contributed to the 
writing of the present protocol. They may delegate tasks and responsibilities as appropriate but will 
provide sufficient oversight over all tasks delegated and will ensure that each individual, to whom a 
task is delegated, is qualified by virtue of education, training, and experience to perform each of their 
delegated tasks. 
 
13.3 Timelines 
The DYNAMIC project started on the 1st April 2019 and will cover a five-year period up to 31st March 
2024. Phase 1 (including the pilot phase) will last from January 2021 to March 2021, phase 2 from 
April 2022 to October 2023, implementation research from January 2021 to March 2023, assessment 

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of sustainability from January 2023 to March 2024 and diseases surveillance from October 2021 to 
March 2023. A project timeline is shown in Figure 13. 
 
Figure 13: Project timeline 
 
14 
CAPACITY BUILDING, KNOWLEDGE TRANSFER AND DISSEMINATION PLANS 
The proposed project offers many opportunities for capacity building and transfer of technology, which 
are summarized in Table 4. 
A website dedicated to the DYNAMIC project will be set up to publish the medical algorithm and its 
modifications in real time. Regular news on the status of the project, teaching material and tutorials 
developed for the project and preliminary results of studies will also be made available on this website.  
 
2020 
2021 
2022 
2023 
2024 
 
Q2 
Q3 
Q4 
Q1 
Q2 
Q3 
Q4 
Q1 
Q2 
Q3 
Q4 
Q1 
Q2 
Q3 
Q4 
Q1 
Project phase 
 
 
 
Pilot 
Phase 1 
Phase 2 
 
 
Tools implementation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Sensitisation meetings 
 
 
X 
 
 
X 
 
 
 
 
 
 
 
 
 
 
HCWs in person training 
 
 
X 
 
 
X 
 
 
 
 
 
 
 
 
 
 
Onsite face-to-face 
training 
 
 
X 
 
 
X 
 
 
 
 
 
 
 
 
 
 
e-learning platform 
 
 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
X 
 
 
ePOCT+ provided to 
intervention HFs 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
ePOCT+ provided to 
former control and 
additional HFs 
 
 
 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
Modifications of ePOCT+ 
 
 
 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
medAL-monitor 
 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
medAL-outbreak 
 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
Data collection 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Consultation data 
 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
Follow-up data 
 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
Cross-sectional surveys 
 
 
 
X 
 
 
 
X 
 
 
 
X 
 
 
 
 
Samples analysed at IHI & 
NIMR laboratories 
 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
Qualitative studies 
 
 
 
 
 
 
X 
X 
X 
X 
X 
 
 
 
 
 
Data analysis 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Clinical data 
 
 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
 
Laboratory data 
 
 
 
 
 
 
X 
X 
X 
X 
X 
X 
 
 
 
 
Qualitative data 
 
 
 
 
 
 
X 
X 
X 
X 
X 
 
 
 
 
 
Dissemination 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Publications and reports 
 
 
 
 
 
X 
 
X 
X 
 
 
 
 
X 
X 
X 
Dissemination of findings 
 
 
 
 
 
 
 
X 
X 
 
 
X 
X 
X 
X 
X 
Framework for scale-up 
 
 
 
 
 
 
 
 
 
 
 
X 
X 
X 
X 
X 

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As soon as they are available, the final results of the cluster randomized controlled study of Phase 1 
will be presented to and discussed with the CHMT as well as with the staff of all HFs participating in 
the study. They will also be presented and discussed with the communities of the catchment areas of 
these HFs. The same will be done again with results of studies in Phase 2. National dissemination 
meetings will be held to share the gained knowledge among all stakeholders in Tanzania. Reports for 
stakeholders and funders will be submitted annually and/or upon reaching key milestones in the 
project.  
Results from this project will be published in open-source peer reviewed scientific journals and 
presented at relevant conferences and during symposia at national and international level, thus 
contributing to the dissemination of knowledge gained for the benefit of stakeholders worldwide 
involved in child health, digital health, disease surveillance and the use of ML in the health sector. 
 
Table 4: Areas of anticipated capacity building and technology transfer by health system level. 
Area of capacity 
building/knowledge 
transfer  
Health system level 
National 
District 
Health Facility 
Clinical knowledge  
Evidence-based clinical 
guideline development and 
continuous update of 
clinical content through the 
involvement of key 
pediatric stakeholders in 
the development of the 
ePOCT+ content 
Increased capacity of 
Tanzanian MDs in clinical 
algorithm validation and 
translation into health 
policy through PhD 
program 
Increase in medical 
knowledge through support 
in supervision visits by 
study personnel  
Supervision at HF level 
through the development 
and implementation of 
medAL-monitor 
Improved clinical skills of 
HCWs through the use of 
ePOCT+ and e-learning 
modules 
 
Information technology 
Set-up and maintenance of 
IT infrastructure through 
continuous technical 
support from study team 
Set-up and maintenance of 
IT infrastructure through 
initial capacity building and 
continuous technical 
support from study team 
 
Software development and 
maintenance 
Development and 
maintenance of ePOCT+ 
software through 
involvement of Tanzanian 
software engineers 
Maintenance of software at 
the district level through 
continuous capacity 
building of the district IT 
team throughout the project 
Knowledge in the use of 
electronic tools for patient 
management 
Database set-up and 
management 
 
Set-up and maintenance of 
the database 
 
 
 
 

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Data interpretation and use 
for decision-making 
Definition of relevant 
indicators through 
ownership of database by 
MoH and technical support 
Management and 
interpretation of health 
surveillance data, 
interpretation of 
dashboards through 
technical support by study 
team 
Improved ability of HCWs 
to interpret data due to 
information provided 
through medAL-monitor 
Machine learning  
Use of machine learning to 
analyze health data 
through involvement of 
Tanzanian partners in 
machine learning activities 
and a training program 
 
 
 
15 
BUDGET 
The budget for the DYNAMIC project administered from Tanzania is 3,011,800 Swiss francs (CHF), 
which is equivalent to approximately 7,150,000 Tanzanian Shillings. Table  breaks down the budget 
by project year (1st April 2019 through 31st March 2024) and project activity. The collaborating 
institutions each have their own budget, with a total of 1,600,000 CHF allocated to IHI and 1,400,000 
CHF allocated to NIMR, inclusive of 10% institutional overhead charges. The budget includes the 
planning year (Year 1), which ends on 31-Mar-2020. It is displayed in gray color because no study 
activities were undertaken. 
 
Table 5: Total budget in Swiss francs. 
 In CHF (Swiss francs) 
Year 1 
Year 2 
Year 3 
Year 4 
Year 5 
Total 
Staffing 
180,000  280,000  
280,000  
280,000  
180,000  
1,200,000  
Training and workshops 
8,000  
25,000  
25,000  
15,000  
15,000  
88,000  
IT infrastructure and equipment 
15,000  70,000  
70,000  
15,000  
10,000  
180,000  
ePOCT+ implementation (Phases 1 and 2) 15,000  70,000  
60,000  
50,000  
15,000  
210,000  
medAL-monitor/outbreak implementation 
-    
10,000  
20,000  
30,000  
10,000  
70,000  
Disease surveillance 
-    
100,000  
300,000  
300,000  
100,000  
800,000  
Mixed methods operational research 
5,000  
20,000  
25,000  
15,000  
5,000  
75,000  
Stakeholder meetings and dissemination 
5,000  
10,000  
10,000  
5,000  
10,000  
40,000  
Administrative costs 
10,000  25,000  
25,000  
10,000  
10,000  
82,000  
Overhead (10%) 
23,800  61,000  
81,500  
72,000  
35,500  
273,800  
TOTAL 
 
 
 
 
 
3,011,800  
 
 
Budget justification 
Staffing costs include salaries of all permanent IHI and NIMR employees who are fully or partially 
allocated to the DYNAMIC project, as well as any temporary staff hired specifically for the project.  
Training and workshop costs include rental fees for meeting venues, development and printing of all 
training materials, and per diems/allowances for meeting participants. 
IT infrastructure and equipment costs include the procurement of all tablets, computers, servers, 
routers, and other items required to establish connectivity between tablets in study facilities and with 

DYNAMIC project 
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the main server located at IHI. This category also incudes the set up and maintenance of the call 
center in Phase 1 and SMS system in Phase 2. 
Transportation costs include hiring or purchase of vehicles, motorbikes, and other means of 
transportation between the study sites and/or to all the HFs for data collection or monitoring 
associated with all study activities. Driver salaries are also included in this category.  
ePOCT+ implementation costs include the procurement of POC lab tests and medicines (if needed), 
printing of labels for identifying patients, salary top-ups for HF staff that perform additional duties for 
the study, internet bundles (if needed), and other miscellaneous expenses. 
medAL-monitor/outbreak implementation costs include meetings and consultations with the users and 
relevant stakeholders regarding the content and visualization of the data, regular visits to district 
health officials to build capacity in interpreting the dashboards, and facilitating regular district visits to 
HFs for mentorship and supervision of the HCWs. 
Disease surveillance costs include all specimen collection, storage, transport, and laboratory analysis 
costs. Also included in this category is potential hiring of laboratory staff and/or training existing HCWs 
on the procedures specific to the surveillance activities.  
Mixed methods operational research costs include the development of finalization of study tools, the 
costs of field data collection, software for data analysis, and the hiring of temporary field enumerators.  
Stakeholder meetings and dissemination costs include rental fees for meeting venues, refreshments, 
development and printing of materials, reimbursement of transportation costs or per 
diems/allowances for meeting participants (if needed). Also included in this category are publication 
fees and development of dissemination materials at the end of the study (e.g. pamphlets, booklets, 
etc.). 
Administrative costs include office space rental fees, office supplies, fees for various approvals, and 
other miscellaneous expenses. 
 
 
 

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16 
REFERENCES 
• Asermino JM Wiens M, Kissoon N. Evidence and Transparency are Needed to Develop a Frontline Health 
Worker mHealth Assessment Platform. Am. J. Trop. Med. Hyg., 101(4), 2019, p. 948. 
• Benoun, J. M., J. C. Labuda, and S. J. McSorley. 2016. 'Collateral Damage: Detrimental Effect of 
Antibiotics on the Development of Protective Immune Memory', MBio, 2016: 7. 
• Bessat C, Zonon NA, D'Acremont V. Large-scale implementation of electronic Integrated Management 
of Childhood Illness (eIMCI) at the primary care level in Burkina Faso: a qualitative study on health worker 
perception of its medical content, usability and impact on antibiotic prescription and resistance. BMC 
Public Health. 2019 Apr 29;19(1):449. 
• Committee for Proprietary Medicinal P. Points to consider on switching between superiority and 
noninferiority. Br J Clin Pharmacol. 2001;52(3):223-8. 
• Cordey S, Laubscher F, Hartley M-A, Junier T, Pérez-Rodriguez FJ, Keitel K, Vieille G, Samaka J, 
Mlaganile T, Kagoro F, Boillat-Blanco N, Mbarack Z, Docquier M, Tapparel C, Kaiser K , D’Acremont V. 
Detection of Dicistroviruses RNA in Blood of Febrile Tanzanian Children. Emerg Microbes Infect. 2019, 
8(1):613-623. 
• Cordey S, Hartley MA, Keitel K, Laubscher F, Brito F, Junier T, Kagoro F, Samaka J, Masimba J, Said Z, 
Temba H, Mlaganile T, Docquier M, Fellay J, Kaiser L, D’Acremont V. Detection of novel astroviruses 
MLB1 and MLB2 in the sera of febrile Tanzanian children. Emerg Microbes Infect. 2018; 7(1):27. 
• D'Acremont V, Kilowoko M, Kyungu E, Philipina S, Sangu W, Kahama-Maro J, Lengeler C, Cherpillod P, 
Kaiser L, and Genton B.'Beyond Malaria — Causes of Fever in Outpatient Tanzanian Children', NEJM 
2014; 370: 809-17. 
• De Santis O, Kilowoko M, Kyungu E, Sangu W, Cherpillod P, Kaiser L, Genton B, D’Acremont V. 
Predictive value of clinical and laboratory features for the main febrile diseases in children living in 
Tanzania: A prospective observational study. PLoS One 2017;12(5):e0173314. 
• Erdman LK, D'Acremont V, Hayford K, Rajwans N, Kilowoko M, Kyungu E, Hongoa P, Alamo L, Streiner 
DL, Genton B, Kain KC. Biomarkers of Host Response Predict Primary End-Point Radiological 
Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study. PLoS One 2015; 
10(9): e0137592. 
• Farnham A, Utzinger J, Kulinkina AV, Winkler MS. Using district health information to monitor sustainable 
development. 
Bull 
World 
Health 
Organ. 
2020 
Jan 
1;98(1):69-71. 
https://www.who.int/bulletin/volumes/98/1/19-239970/en/ 
• Fink G, D'Acremont V, Leslie HH, Cohen J. Antibiotic exposure among children younger than 5 years in 
low-income and middle-income countries: a cross-sectional study of nationally representative facility-
based and household-based surveys. Lancet Infect Dis. 2019 Dec 13. 
• Frischknecht R., Jungbluth N., Althaus H.-J., Doka G., Heck T., Hellweg S., Hischier R., Nemecek T., 
Rebitzer G., Spielmann M., Wernet G. Overview and Methodology. ecoinvent report No. 1. Swiss Centre 
for Life Cycle Inventories, Dübendorf, 2007. 
• Hansoti B, Jenson A, Keefe D, De Ramirez SS, Anest T, Twomey M, Lobner K, Kelen G, Wallis L. 
Reliability and validity of pediatric triage tools evaluated in Low resource settings: a systematic review. 
BMC Pediatr. 2017 Jan 26;17(1):37.  
• Hansoti B, Jenson A, Kironji AG, Katz J, Levin S, Rothman R, Kelen GD, Wallis LA. SCREEN: A simple 
layperson administered screening algorithm in low resource international settings significantly reduces 
waiting time for critically ill children in primary healthcare clinics. PLoS One. 2017 Aug 
29;12(8):e0183520. 
• Head SJ, Kaul S, Bogers AJ, Kappetein AP. Non-inferiority study design: lessons to be learned from 
cardiovascular trials. Eur Heart J. 2012 Jun;33(11):1318-24. 
• Holmes AH, Moore LS, Sundsfjord A, Steinbakk M, Regmi S, Karkey A, Guerin PJ, Piddock LJ. 
Understanding the mechanisms and drivers of antimicrobial resistance. Lancet. 2016 Jan 
9;387(10014):176-87. 
• Hopkins H, Cairns ME, Chandler CIR, Leurent B, Ansah EK, Baiden F, Baltzell KA, Björkman A, Burchett 
HED, Clarke SE, DiLiberto D, Elfving K, Goodman C, Hansen KS, Kachur SP, Lalloo DG, Leslie T, 

DYNAMIC project 
Protocol version 4.9 / 10-Sep-2021 
 
72 
 
Magnussen P, Jefferies M, Mårtensson A, Mayan I, Mbonye AK, Msellem MI, OE, Owusu-Agyei S, 
Reyburn H, Rowland MW, Shakely D, Vestergaard LS, Webster J, Wiseman VL, Yeung S, Schellenberg 
D, Staedke SG, Whitty CJM. Impact of introduction of rapid diagnostic tests for malaria on antibiotic 
prescribing: analysis of observational and randomised studies in public and private healthcare settings. 
BMJ 2017;356:j1054. 
• International Organization for Standardization (ISO). Environmental management - Life cycle assessment 
– Principles and framework. ISO 14040: 2006A; Second Edition 2006-06, Geneva. 
• International Organization for Standardization (ISO). Environmental management - Life cycle assessment 
– Requirements and guidelines. ISO 14044: 2006B; First edition 2006-07-01, Geneva. 
• Jolliet O, Müller-Wenk R, Bare J, Brent A, Goedkoop M, Heijungs R, Itsubo N, Peña C, Pennington D, 
Potting J, Rebitzer G, Stewart M, Udo de Haes H and Weidema B. The LCIA Midpoint-damage 
Framework of the UNEP/SETAC Life Cycle Initiative, Int J LCA 2004: 9 (6) 394 – 404. 
• Kahan BC, Morris TP. Reporting and analysis of trials using stratified randomisation in leading medical 
journals: review and reanalysis. BMJ. 2012;345:e5840. 
• Keitel K, Kilowoko M, Kyungu E, Genton B, D’Acremont V. Performance of prediction rules and guidelines 
in detecting serious bacterial infections among Tanzanian febrile children. BMC Infect Dis 2019; 19:769. 
• Keitel K, Samaka J, Masimba J, Temba H, Said Z, Kagoro F, Mlaganile T, Sangu W, Genton B, 
D'Acremont V. Safety and efficacy of C-reactive protein-guided antibiotic use to treat acute respiratory 
infections in Tanzanian children: a planned subgroup analysis of a randomized, controlled non-inferiority 
trial evaluating a novel electronic clinical decision algorithm (ePOCT). Clin Infect Dis. 2019; 69(11):1926-
1934. 
• Keitel K, D’Acremont V. Electronic Clinical Decision Algorithms for the integrated primary care 
management of febrile children in low resource settings - Review of existing tools. Clin Microbiol Infect. 
2018, 24(8):845-855. 
• Keitel K, Kagoro F, Samaka J, Masimba J, Said Z, Temba H, Mlaganile T, Sangu W, Rambaud-Althaus 
C, Gervaix A, Genton B, D’Acremont V. A novel electronic algorithm using host biomarker point-of-care-
tests for the management of febrile illnesses in Tanzanian children (e-POCT): A randomized, controlled 
non-inferiority trial. Plos Medicine 2017;14(10):e1002411. 
• Lange S, Mwisongo A, Mæstad O. Why don't clinicians adhere more consistently to guidelines for the 
Integrated Management of Childhood Illness (IMCI)? Soc Sci Med. 2014;104:56-63. 
• Mghamba, J., Mboera, L., Krekamoo, W., Senkoro, K., Rumisha, S., Shayo, E., & Mmbuji,. Challenges 
of implementing an integrated disease surveillance and response strategy using the current health 
management information system in Tanzania. P. J. T. J. o. H. R. 2004; 6(2), 57-63 
• Nantanda, Rebecca, James K. Tumwine, Grace Ndeezi, and Marianne S. Ostergaard. 'Asthma and 
pneumonia among children less than five years with acute respiratory symptoms in Mulago Hospital, 
Uganda: Evidence of under-diagnosis of asthma', PLoS ONE 2013, 8: 1-9. 
• Piaggio G, Elbourne DR, Pocock SJ, Evans SJ, Altman DG, Group C. Reporting of noninferiority and 
equivalence randomized trials: extension of the CONSORT 2010 statement. JAMA. 2012;308(24):2594-
604. 
• Pocock SJ, Assmann SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline 
comparisons in clinical trial reporting: current practice and problems. Stat Med. 2002;21(19):2917-30. 
• Rambaud-Althaus, Clotilde, Fabrice Althaus, Blaise Genton, and Valérie D'Acremont. 'Clinical features 
for diagnosis of pneumonia in children younger than 5 years: a systematic review and meta-analysis.', 
The Lancet. Infectious diseases, 2015; 15: 439-50. 
• Rambaud-Althaus C, Shao S, Samaka S, Swai N, Perri S, Kahama-Maro J, Mitchell M, D'Acremont V, 
and Genton B. 'Performance of Health Workers Using an Electronic Algorithm for the Management of 
Childhood Illness in Tanzania: A Pilot Implementation Study.' Am J Trop Med Hyg 2017; 96: 249-57. 
• Ryan, S. J., McNally, A., Johnson, L. R., Mordecai, E. A., Ben-Horin, T., Paaijmans, K., Lafferty, K. 
Mapping Physiological Suitability Limits for Malaria in Africa Under Climate Change. Vector-Borne And 
Zoonotic Diseases. 2015; 15 (12): 718-725 
• Schulz KF, Altman DG, Moher D, Group C. CONSORT. Statement: updated guidelines for reporting 
parallel group randomised trials. Trials. 2010;11:32. 

DYNAMIC project 
Protocol version 4.9 / 10-Sep-2021 
 
73 
 
• Shao AF, Rambaud-Althaus C, Samaka J, Faustine AF, Perri-Moore S, Swai N, Kahama-Maro J, Mitchell 
M, Genton B, D’Acremont V. New Algorithm for Managing Childhood Illness Using Mobile Technology 
(ALMANACH): A Controlled Non-Inferiority Study on Clinical Outcome and Antibiotic Use in Tanzania. 
PLoS One 2015A; 10(7): e0132316. 
• Shao A, Rambaud-Althaus C, Swai N, Kahama-Maro J, Genton B, D'Acremont V, and Pfeiffer C. 'Can 
smartphones and tablets improve the management of childhood illness in Tanzania? A qualitative study 
from a primary health care worker's perspective.', BMC Health Services Research 2015B; 15: 135. 
• USAID. The Demographic and Health Surveys Program. https://dhsprogram.com/What-We-Do/Survey-
Types/SPA-Questionnaires.cfm 
• Walker, P. G., M. T. White, J. T. Griffin, A. Reynolds, N. M. Ferguson, and A. C. Ghani. 'Malaria morbidity 
and mortality in Ebola-affected countries caused by decreased health-care capacity, and the potential 
effect of mitigation strategies: a modelling analysis', Lancet Infect Dis 2015; 15: 825-32. 
• World Health Organization 2006. ‘Improving health worker performance:in search of promising practice’. 
https://www.who.int/hrh/resources/improving_hw_performance.pdf 
• World Health Organization 2011. Interagency ‘Universal access to malaria diagnostic testing: an 
operational 
manual’. 
WHO, 
Geneva, 
page 
24. 
http://www.who.int/malaria/publications/atoz/9789241502092/en/index.html 
• World Health Organization 2013. Howie S. Blood sample volumes in child health research: review of safe 
limits. http://www.who.int/bulletin/volumes/89/1/10-080010/en/# 
• World 
Health 
Organization 
2014A. 
'IMCI 
Chart 
Booklet'. 
https://www.who.int/maternal_child_adolescent/documents/IMCI_chartbooklet/en/ 
• World Health Organization 2014B. “Ground zero in Guinea: the Ebola outbreak smoulders – undetected 
– for more than 3 months”. http://www.who.int/csr/disease/ebola/ebola-6-months/guinea/en/ 
• World Health Organization 2018. Climate change and health: Key facts. Geneva, Feb 1. 
https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health 
• Yukich J, D’Acremont V, Kahama J, Swai N, Lengeler C. Cost savings with rapid diagnostic tests for 
malaria in low-transmission areas: evidence from Dar es Salaam, Tanzania. Am J Trop Med Hyg. 2010; 
83(1): 61-8. 
 
 
 
 
 

DYNAMIC project 
Protocol version 4.9 / 10-Sep-2021 
 
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17 
APPENDICES 
 
Appendix 1: Case Report Forms 
 
Appendix 2: Mixed-methods operational research tools 
 
Appendix 3: Informed Consents