Impact of medAL-mentor, a Real-time Mentoring and Benchmarking Tool, on Antibiotic Prescription Among Children in Primary Health Care Facilities in Tanzania
Phase: NA
Conditions: Primary Health Care for Children
Sponsor: Center for Primary Care and Public Health (Unisante), University of Lausanne, Switzerland

DYNAMIC project
Protocol version 5.0 / 07-Sep-2022
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
5.0
Document Date
07.09.2022
Sponsor Contact
Principal Investigator
National Principal
Investigator
National co-Principal
Investigator
Funding Agency
Centre for Primary Care and Public Health (Unisanté)
University of Lausanne
Bugnon 44 - 1011 Lausanne
Switzerland
www.unisante.ch
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
Dr. Honorati Masanja
Ifakara Health Institute (IHI) http://ihi.or.tz
P.O Box 78373 - Dar es Salaam – Tanzania
hmasanja@ihi.or.tz
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
- 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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DYNAMIC project
Protocol version 5.0 / 07-Sep-2022
I. List of Investigators and other persons involved
Names
Prof. Valérie D’Acremont
Dr. Honorati Masanja
Dr. Nyanda Ntinginya
Dr. Irene Masanja
Dr. Kristina Keitel
Dr. Mary-Anne Hartley
Institution
Unisanté
Swiss TPH
IHI
NIMR-MMRC
IHI
Swiss TPH
Unisanté and
EPFL
Position
Head of Digital and Global Health, Department of
Training, Research and Innovation
Scientific group leader, Medical Department
Chief Executive Director
Centre Director
Scientific project leader, Health Systems Research
Group
Scientific project leader, Clinical Research Unit,
Medical Department
Scientific project leader, Digital and Global Health,
Department of Training, Research and Innovation
Function in study
PI,
overall project
coordinator
National PI
National co-PI
Co-investigator,
country coordinator
Co-investigator,
pediatric consultant
Co-investigator,
ML coordinator
Dr. Chacha Mangu
NIMR-MMRC 
Project manager, TB and Emerging Disease
department
Mbeya site
investigator
Dr. Alexandra V.
Kulinkina
Dr. Lameck Luwanda
Dr. Godfrey Kavishe
Dr. Rainer Tan
Charles Festo
Vincent Faivre
Peter Agrea
Ibrahim Evans Mtebene
Geofrey Isdory Ashery
Happy Raphael Mkali
Swiss TPH
IHI
NIMR
Unisanté
IHI
Unisanté
NIMR
IHI
IHI
IHI
Project leader, Clinical Research Unit, Medical
Department and SCIH
Study physician, Health Systems Research Group
Study physician, TB and Emerging Disease dep.
Research physician, Digital and Global Health,
Department of Training, Research and Innovation
Head of the IHI Data Systems Unit
Deputy head of IT department
Software developer, IT department
Software developer, IT department
Research officer, Health Systems Research Group
Research scientist, Bagamoyo research laboratory
Co-investigator,
Project coordinator
Morogoro site
investigator
Study physician
Clinical algorithms
coordinator
Country IT and
data coordinator
IT coordinator and
data manager
Software developer
Software developer
Social scientist,
Morogoro site
Laboratory
coordinator
Prof. Isabella Eckerle
Alan Vonlanthen
University of
Geneva
Unisanté
Head of the Geneva Centre for Emerging Viral
Diseases
Project Manager, IT department
Surveillance
coordinator
Project manager
Dr. Tracy Glass
Prof. Martin Jaggi
Dr. Sabine Renggli
Martin Norris
Swiss TPH
EPFL
IHI
Swiss TPH
Scientific project leader, Clinical Statistics and
Data Management, Medical Department
Head of Machine learning and optimization lab
Senior Research Scientist
Project Officer/Manager
Statistician
ML consultant
Site investigator,
Morogoro site
Algorithm testing
consultant
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DYNAMIC project
Protocol version 5.0 / 07-Sep-2022
Dr. Lisa Crump
Dr. Gillian Levine
Humphrey Mhagama
Dr. Alix Miauton
Swiss TPH
Swiss TPH
NIMR
Unisanté
Veterinary Epidemiologist
Neonatal Epidemiologist
Study nurse
Research physician
Surveillance
Consultant
Neonatal algorithm
consultant
Study nurse
Clinical coordinator
Project Location:
Projection duration:
Morogoro and Mbeya Regions, Tanzania
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
07.09.2022
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
07.09.2022
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
07.09.2022
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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DYNAMIC project
Protocol version 5.0 / 07-Sep-2022
III. Abbreviations / Glossary of terms
AMR
Antimicrobial Resistance
CDSA
CHMT
CRP
DHIS2
EPFL
ePOCT+
HF
HCW
HMIS
IDSR
IHI
IMCI
M&E
ML
NIMR
PHC
POC
RDT
Swiss TPH
TZ
Unisanté
WHO
Clinical Decision Support Algorithm
Council Health Management Team
C-reactive protein
District Health Information System 2
École Polytechnique Fédérale de Lausanne
Electronic point of care tool plus
Health facility
Health care worker
Health Management Information System
Integrated Disease Surveillance and Response
Ifakara Health Institute
Integrated management of childhood illness
Monitoring and evaluation
Machine learning
National Institute of Medical Research
Primary health care
Point-of-care
Rapid Diagnostic Test
Swiss Tropical and Public Health Institute
Tanzania
Centre for Primary Care and Public Health (University of Lausanne)
World Health Organization
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IV. Synopsis
Study Title
Short Title
Dynamic clinical decision support algorithms to manage sick children in primary
health care settings in Tanzania
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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DYNAMIC project
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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. Depending on the spatiotemporal patterns in
the syndromic surveillance data, samples could be taken in health facilities outside of
the sentinel sites using a mobile surveillance team.
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.
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Protocol version 5.0 / 07-Sep-2022
Study site
Study population
Intervention
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).
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.
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
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DYNAMIC project
Protocol version 5.0 / 07-Sep-2022
intervention facilities. Re-training of incoming personnel and continuous training
through an e-learning platform on the tablet or PC will also be implemented.
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
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-
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):
-
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
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-
Carbon footprint of the intervention or carbon-saving due to the intervention
Procedures and
measurements
Objective 1: Improve the integrated management of sick children
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 may or may
not be assessed through a phone call or an automated SMS or call to caregivers 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 some outcome indicators
may be 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
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Statistical
considerations
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) – and potentially
other HFs with abnormal spatiotemporal patterns in syndromic surveillance data – 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. 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.
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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Study duration
Study schedule
Ethical
statement
Relevant
references
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,
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.
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
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
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.
· 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.
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· 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.
· 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
6      Description of the intervention............................................................................................. 33
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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........................................44
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..................................50
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
9.1      Specimen collection and management .............................................................................53
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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 ...................................................................57
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.........................................................................................63
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..........................................................................................................................67
14 Capacity building, knowledge transfer and dissemination plans ......................................68
15 Budget.................................................................................................................................... 69
16 References ............................................................................................................................. 71
17 Appendices ............................................................................................................................ 74
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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
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
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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
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
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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
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
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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.
Because ePOCT+ is designed to provide personalized treatment algorithms, the clinician is then
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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.
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.
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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. In response to the
spatiotemporal analyses, at a later stage of the surveillance study, we may expand surveillance
sample collection beyond the sentinel sites using a mobile surveillance team.
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
Under five mortality rate (per 1000 live births)1
Malaria prevalence in children aged 6-59 months
(2017)2
HIV prevalence (Age 15+; 2017)3
Pneumococcal vaccine coverage (2017)4
DTP - Hib - HepB3 (Diphtheria, tetanus, pertussis,
Hemophilus influenzae type B, and hepatitis B)
vaccine coverage (2017)4
Prevalence of low hemoglobin in children (<8g/dl)2
Percentage of children with fever within the past
two weeks2
Morogoro region
163 (2002)
9.5%
4.2%
81%
110.4% (2018)
4.7%
18.9%
Mbeya region
165 (2002)
4.0%
9.3%
67%
96.3% (2018)
1.4%
16.2%
National average
153 (2002), 81 (2010)
7.3%
7.0%
99%
99%
3.6%
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)
- 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).
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* 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,
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
POX may improve detection of children with
hypoxemia
Young infants with suspected
severe disease
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)*
Malaria rapid diagnostic
test (mRDT)
Patients ≤ 5 years with fever
Patients
6-14
years
with
malnutrition, palmar pallor or
conjunctival pallor
Patients with fever
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 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
(CRP)*
protein Febrile patients at risk for
bacterial pneumonia (based on
clinical
and
demographic
features)
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
Febrile patients
clinical signs
Febrile patients
extremity pain
without focal
with joint or
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
Infants aged 1-2 months with
suspected
pneumonia
or
bacterial infections
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
without
patients
<3
focal
clinical
years CRP does not reliably rise in children with febrile
signs urinary tract infections; febrile children <2 years
should be assessed for febrile urinary tract
infections per national guidelines.
Febrile patients > 3 years with
complaints
suggestive
of
urinary tract infection.
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 PHC
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The 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 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 (called MedAL-Reader).
·
The data management system that will aggregate and manage the data into a central server
with a database (called MedAL-Data).
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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 15 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
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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)
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.
After the completion of the Phase 1 study, if the benefits of the intervention are confirmed, in the
routine implementation of ePOCT+ the individual consent process will be simplified (consent at district
and health facility level will be maintained). District and health facility level consent (obtained in
sequence) will be documented on the same consent form and copies will be kept at district level,
health facility level, and with the investigators. The registration form will be reduced and integrated
into the routine registration process. Consent will be limited to a verbal agreement that data recorded
in the tablet during the consultation can be used for research. Ticking “yes” in the application means
that consultation data will be sent to the research database. Ticking “no” means that the data will
remain on the tablet/local data server and will not be analysed. Otherwise, all children will be treated
in the same way (using the tablet). In case of any significant modifications in the algorithm content
that require validation using a sub-study, in health facilities participating to the sub-studies, written
consent procedure as outlined in Phase 1 will be maintained. Patients participating in the surveillance
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sub-study will also undergo the full written consent process.
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.
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
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
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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
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
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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, only caregivers of children participating to the sub-studies
will receive a phone call or an automated SMS or automated phone call on day 7 with the same
questions as in the questionnaire used in Phase 1. 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
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
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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
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:
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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.
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 may 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). In response to the spatiotemporal analyses,
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at a later stage of the surveillance study, we may expand surveillance sample collection beyond the
sentinel sites using a mobile surveillance team. 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. All children
participating in the surveillance sample collection will undergo the full written consent process (same
as children participating in other sub-studies).
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. Due to lower enrollment than initially anticipated, an independent
statistician will use the clinical failure outcome data from the control arm, and projected number of
patients enrolled after all health facilities have enrolled patients for a minimum of 6 months to re-
calculate the sample size, If the sample size recalculation does not have sufficient power to meet the
study objectives, the study may be extended, or the number of health facilities increased. To avoid
implementation bias, the clinical failure proportion in the control arm will not be shared to the study
team.
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,
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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
(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.
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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 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:
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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.
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:
-
MedAL-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
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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.
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:
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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:
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
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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 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 (except in the routine implementation of
ePOCT+ in Phase 2 where the consent process will be simplified, see Section 8.1.1).
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.
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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 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
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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
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.
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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
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
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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
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.)
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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.
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.
Dr. Alix Miauton will will be responsible for providing technical support to all activities required to
implement the ePOCT+ tool and associated algorithm adaptations in Phase 2 of the study. She will
contribute to the writing of progress reports, scientific manuscripts and dissemination of results to
partners.
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
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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
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
2020
2021
2022
2023
2024
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Project phase
Tools implementation
Sensitisation meetings
HCWs in person training
Onsite face-to-face
training
Pilot
Phase 1
Phase 2
X
X
X
X
X
X
e-learning platform
ePOCT+ provided to
intervention HFs
ePOCT+ provided to
former control and
additional HFs
Modifications of ePOCT+
medAL-monitor
medAL-outbreak
Data collection
Consultation data
Follow-up data
Cross-sectional surveys
Samples analysed at IHI &
NIMR laboratories
Qualitative studies
Data analysis
Clinical data
Laboratory data
Qualitative data
Dissemination
Publications and reports
Dissemination of findings
Framework for scale-up
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
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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.
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
Clinical knowledge
Information technology
Software development and
maintenance
Database set-up and
management
Health system level
National
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
Set-up and maintenance of
IT infrastructure through
continuous technical
support from study team
Development and
maintenance of ePOCT+
software through
involvement of Tanzanian
software engineers
Set-up and maintenance of
the database
District
Increase in medical
knowledge through support
in supervision visits by
study personnel
Supervision at HF level
through the development
and implementation of
medAL-monitor
Set-up and maintenance of
IT infrastructure through
initial capacity building and
continuous technical
support from study team
Maintenance of software at
the district level through
continuous capacity
building of the district IT
team throughout the project
Health Facility
Improved clinical skills of
HCWs through the use of
ePOCT+ and e-learning
modules
Knowledge in the use of
electronic tools for patient
management
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Data interpretation and use
for decision-making
Machine learning
Definition of relevant
indicators through
ownership of database by
MoH and technical support
Use of machine learning to
analyze health data
through involvement of
Tanzanian partners in
machine learning activities
and a training program
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
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
IT infrastructure and equipment
15,000
ePOCT+ implementation (Phases 1 and 2) 15,000
medAL-monitor/outbreak implementation
-
Disease surveillance
-
Mixed methods operational research
5,000
Stakeholder meetings and dissemination
5,000
Administrative costs
10,000
Overhead (10%)
23,800
TOTAL
25,000
70,000
70,000
10,000
100,000
20,000
10,000
25,000
61,000
25,000
70,000
60,000
20,000
300,000
25,000
10,000
25,000
81,500
15,000
15,000
50,000
30,000
300,000
15,000
5,000
10,000
72,000
15,000
10,000
15,000
10,000
100,000
5,000
10,000
10,000
35,500
88,000
180,000
210,000
70,000
800,000
75,000
40,000
82,000
273,800
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
69

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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
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malaria in low-transmission areas: evidence from Dar es Salaam, Tanzania. Am J Trop Med Hyg. 2010;
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DYNAMIC project
Protocol version 5.0 / 07-Sep-2022
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APPENDICES
Appendix 1: Case Report Forms
Appendix 2: Mixed-methods operational research tools
Appendix 3: Informed Consents
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