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Antibiotic stewardship using ePOCT+, a digital health clinical decision
support algorithm for paediatric outpatient care: results from the
DYNAMIC Tanzania cluster randomized controlled trial
Rainer Tan ( rainer.tan@unisante.ch )
Centre for Primary Care and Public Health, Unisanté https://orcid.org/0000-0002-9273-9632
Godfrey Kavishe*
Mbeya Medical Research Centre - National Institute for Medical Research
Alexandra Kulinkina*
Swiss Tropical and Public Health Institute
Lameck Luwanda*
Ifakara Health Institute
Sabine Renggli
Ifakara Health Institute
Chacha Mangu
Mbeya Medical Research Centre - National Institute for Medical Research
Geofrey Ashery
Ifakara Health Institute
Margaret Jorram
Ifakara Health Institute
Ibrahim Mtebene
Ifakara Health Institute
Peter Agrea
Mbeya Medical Research Centre - National Institute for Medical Research
Humphrey Mhagama
Mbeya Medical Research Centre - National Institute for Medical Research
Alan Vonlanthen
Centre for Primary Care and Public Health, Unisanté
Vincent Faivre
Centre for Primary Care and Public Health, Unisanté
Julien Thabard
Centre for Primary Care and Public Health, Unisanté
Gillian Levine
Swiss Tropical and Public Health Institute
Marie-Annick Le Pogam
Centre for Primary Care and Public Health, Unisanté
Kristina Keitel
University Hospital Bern
Patrick Taffé
Centre for Primary Care and Public Health, Unisanté
Nyanda Ntinginya**
Mbeya Medical Research Centre, National Institute for Medical Research
Honorati Masanja**
Ifakara Health Institute
Valérie D'Acremont Genton**
University of Lausanne
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Abstract
Excessive antibiotic use and antimicrobial resistance are major global public health threats. We developed ePOCT+, a digital Clinical Decision
Support Algorithm in combination with C-reactive protein test, haemoglobin test, pulse oximeter and mentorship, to guide healthcare providers
in managing acutely sick children under 15 years old. To evaluate the impact of ePOCT + compared to usual care, we conducted a cluster-
randomized controlled trial in Tanzanian primary care facilities (NCT05144763). Over 11 months, 23 593 consultations were included in 20
ePOCT + health facilities, and 20 713 in 20 usual care facilities. Antibiotics were prescribed in 23.2% of consultations in ePOCT + facilities,
and 70.1% in usual care facilities (adjusted difference, -46.4%, 95% condence interval (CI) -57.6 to -35.2). Day 7 clinical failure in ePOCT +
facilities was non-inferior to usual care facilities (adjusted relative risk 0.97, 95% CI 0.85 to 1.10). Using ePOCT + could help address the
urgent problem of antimicrobial resistance by safely reducing antibiotic prescribing.
*Shared second authorship; contributed equally.
**Shared last authorship; contributed equally.
Introduction
Bacterial antimicrobial resistance (AMR) was responsible for 1.27million deaths in 2019, with the highest burden in Sub-Saharan Africa.1
This is as many deaths as malaria and HIV combined. Inappropriate and excessive prescription of antibiotics represents one of the primary
contributors of AMR.2–4 In Tanzania and many resource constrained countries, more than 50% of sick children receive antibiotics when
visiting a health facility,5–8 with 80–90% of such antibiotics prescribed at the outpatient level6,9,10 and most deemed innapropriate.5,9−11
Antibiotic use and AMR are projected to increase over the next years, indicating the urgency to take action.12–14 Accordingly, the World Health
Organization (WHO) has declared AMR as ‘one of the biggest threats to global health, food security and development today’.15
Electronic Clinical Decision Support Algorithms (CDSAs) are digital health or mobile health (mHealth) tools that guide healthcare providers on
what symptoms and signs to assess, advise on what tests to perform, and nally propose the appropriate diagnoses, treatments and
managements.16,17 Previous ecacy studies under controlled research conditions have shown the potential for digital CDSAs to signicantly
reduce antibiotic prescription in children 2 to 59 months old.18,19 However, many close to real-world studies have shown little to no reduction
in antibiotic prescription.20–22 In addition, many of the close to real-world studies have a number of methodological limitations as health
facilities were not randomized, and/or safety was not evaluated,16,22,23 emphasizing the need for more evidence on the impact of CDSAs on
antibiotic prescription. Finally, poor uptake remains a challenge with previous and existing CDSAs.24,25
We developed ePOCT+, a novel CDSA with point-of-care tests, to address these challenges.26 The scope of ePOCT + was expanded from
previous versions of the CDSA to include infants under 2 months and children up to age 14 years old, and to address syndromes and
diagnoses not considered by other CDSAs.18,27 The aim of this study was to evaluate the impact of ePOCT + compared to usual care on
antibiotic prescription and day 7 clinical outcome, in a pragmatic, cluster randomized controlled trial in acutely sick children under 15 years of
age presenting to Tanzanian primary care facilities.
Results
Baseline characteristics of health facilities and patients
A total of 68 out of 259 health facilities from the participating councils met eligibility criteria (Fig. 2). A stratied random sampling process
identied 40 health facilities for inclusion in the study (24 in Morogoro region, and 16 in Mbeya region), which were randomized 1:1 to
ePOCT+ (intervention) or usual care (control). Between 1 December 2021, and 31 October 2022, 59 875 children were screened for inclusion,
and 44 306 (74%) consultations were enrolled (23 593 in ePOCT + health facilities, and 20 713 consultations in usual care health facilities).
The rst health facilities started enrolling patients on 1 December 2021, and the last health facilities started enrolling patients on 13 April
2022. A total of 28 243 unique patients were enrolled with an average of 1.6 consultations per patient over the duration of the study. Among
those enrolled in the intervention health facilities, 17 985 (76.2%) consultations were managed using ePOCT+, and day 7 outcome was
ascertained in 20 355 consultations (86.3%). In usual care health facilities, 18 937 (91.4%) consultations had nal treatment documented in
the eCRF and 17 292 consultations (83.5%) had day 7 outcome ascertained. IT problems and power outages were reported by research
assistants in respectively 293 (7.3%) and 245 (6.1%) health facility-days in ePOCT + facilities, and 160 (4.1%) and 245 (6.1%) health facility
days in usual care facilities. Both issues contributed to children not being able to be enrolled in the study.
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Intervention health facilities saw similar numbers of consultations, but had a slightly lower Service Availability and Readiness Assessment
Pediatric score (Table 1).28 Patients in both study arms were similar in age, sex, type of consultation and previous hospitalization (Table 1).
Young infants less than 2 months of age in the intervention health facilities presented more frequently for fever, convulsions or lethargy, and
slightly less for respiratory conditions, while patients 2 months and above had a similar distribution in presenting complaints (Table 2). Age,
phone availability, and level of health facility differed between patients with and without day 7 outcome ascertained (Supplementary material
table S1and S2). Patients managed and not managed per-protocol were similar, except for the level of health facility (Supplementary material
table S3).
Table 1
Baseline characteristics of enrolled participants and health facilities
Health facilities ePOCT+ (
n
= 20) Usual Care (
n
= 20)
Level of health facilit,
n
Dispensaries
16 16
Health centres 4 4
Region,
n
Morogoro
12 12
Mbeya 8 8
Average number of enrolled patients per health facility per month, median (IQR) 127 (IQR 101; 199) 136 (IQR 73; 163)
Service Availability and Readiness Assessmenta:
General Service Readiness Score, mean (SD)
60.3% +/-10.8 63.7% +/-9.4
Pediatric Score, mean (SD) 55.9% +/-10.8 64.9% +/-10.6
Participants ePOCT+ (n = 23,593) Usual Care (n = 20,713)
Sex: Female, %
(n)
51.2% (12,085) 51.3% (10,075)
Age in days, median (IQR) 583 (IQR 263; 1,202) 555 (IQR 246; 1,189)
Age group, %
(n)
0 to < 2 months
4.0% (954) 5.0% (1,038)
2 to < 60 months 84.1% (19,845) 82.0% (16,984)
5 to < 15 years 11.8% (2,794) 13.0% (2,691)
Type of consultation, %
(n)
New consultation
91.9% (21,680) 90.7% (18,789)
Re-attendance 7.8% (1,841) 9.2% (1,899)
Referral from another health facility 0.3% (72) 0.1% (25)
Hospitalized in the last 14 days, %
(n)
0.3% (65) 0.4% (73)
Phone number available, %
(n)
84.0% (19,808) 83.0% (17,186)
Table legend: Participant data from all enrolled patients. Values of standard deviations (SD, after mean values) are preceded by the +/- sign.
IQR: Interquartile ranges (after median values).
aScores were calculated based on the proportion of prespecied indicators that were present in each health facility during the assessment of
health facilities before the start of the study.28
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Table 2
Presenting complaints of infants and children under 15 years old
Presenting complaints in < 2 months ePOCT+ (
n
= 717) Usual Care
(n =
929)
Fever, convulsions, lethargy 25.7% 13.1%
Respiratory 43.7% 46.8%
Gastrointestinal 22.2% 19.7%
Skin 14.0% 14.5%
Ear/mouth 2.5% 0.9%
Eye 7.0% 5.4%
Feeding / weight 0.3% 1%
Malformation 0.4% 0.4%
Injuries 0.4% 0.1%
Other 6.4% 7.8%
Presenting complaints in > = 2 months to < 15 years ePOCT+ (n = 17 268) Usual Care (n = 17 089)
Fever 61.6% 56.9%
Respiratory (cough / diculty breathing 47.8% 49.4%
Gastrointestinal (diarrhea/vomiting) 23.4% 22.3%
Skin 12.5% 11.9%
Ear/throat/mouth 2.6% 2.3%
Eye 2.1% 2.1%
Genitourinary 1.4% 3.1%
Neurological (Headache, stiff neck) 3% 1.2%
Accident / musculoskeletal (incl. burns, wounds, poison) 1.5% 2.0%
Other 2.1% 4.2%
Table legend: Data from patients for which clinical information was entered into ePOCT + in the intervention arm, and in the eCRF in
control health facilities (per-protocol population). Patients may have multiple complaints
Primary outcomes: Antibiotic prescription and clinical failure
Overall antibiotic prescription at initial consultations for the per-protocol analysis was 23.2% (3,806/16,381) in ePOCT + health facilities, and
70.1% (12,058/17,205) in routine care health facilities, which corresponds to an adjusted absolute difference of -46.4% (95% CI -57.6; -35.2)
(Table 3, Fig. 3). The adjusted analysis found a 65% reduction in the risk of prescribing an antibiotic at day 0 (aRR 0.35, 95% CI 0.29 to 0.43, p-
value < 0.001). Taking a conservative approach by considering that all patients that were not managed per protocol were prescribed an
antibiotic (intention-to-treat analysis), antibiotic prescription remained lower in ePOCT + health facilities than in usual care, with an adjusted
absolute difference of -34.2%, 95% CI -42.1%, -26.4% (Table S4). When including re-attendance cases, antibiotic prescription reduction was
similar, with an adjusted absolute difference of -45.0%, 95% CI -56.3% to -33.6% (Table S5).
The proportion of patients with clinical failure by day 7 was non-inferior in ePOCT + health facilities (3.7% (532/14,396) compared to usual
care health facilities (3.8% (543/14,363), with an adjusted relative risk of 0.97 (95% CI 0.85 to 1.10) in the per-protocol complete case
population (Table 3, Fig. 3). Clinical failure by day 7 was also non-inferior in the intention-to-treat complete case population (Table S4), when
including re-attendance cases (Table S5) and using unadjusted analyses (Table 3, Fig. 3).
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Table 3
Antibiotic prescription and clinical outcomes among sick children in the DYNAMIC trial
ePOCT+, %
(
n/N
)Usual care, %
(
n/N
)Intracluster
Correlation
Coecient
(95% CI)
Crude
Difference
(95% CI)
Adjusted
difference
(95% CI)
Crude
Relative
Risk
(95%
CI)
p-
value Adjusted
Relative
Risk
(95% CI)
p-
value
Primary outcome
Antibiotic
prescription at
day 0
23.2%
(3,806/16,381) 70.1%
(12,058/17,205) 0.3 (0.2;
0.4) -46.9% (
-47.8%;
-45.9%)
-46.4%
(-57.6%;
-35.2%)
0.33
(0.32;
0.34)
<
0.001 0.35
(0.29;
0.43)
<
0.001
Clinical failure
by day 7 3.7%
(532/14,396) 3.8%
(543/14,363) 0.004
(0.001;
0.006)
-0.1% (
-0.5%;
0.4%)
-0.1%
(-0.6%;
0.3%)
0.98
(0.87;
1.10)
0.70 0.97
(0.85;
1.10)
0.59
Secondary and exploratory outcomes
Death by day 7 0.1%
(9/14,396) 0.1%
(11/14,363) < 0.001 0.0%
(-0.1%;
0.0%)
0.0%
(-0.1%;
0.0%)
0.82
(0.34;
1.97)
0.65 0.66
(0.24,
1.84)
0.43
Subjectively
worse at day 7a
0.3%
(41/14,396) 0.3%
(40/14,363) 0.002
(0.000;
0.004)
0.0%
(-0.1%;
0.1%)
0.0%
(-0.1%;
0.2%)
1.02
(0.66;
1.58)
0.92 1.11
(0.71;
1.73)
0.65
Non-referred
secondary
hospitalizations
by day 7
0.4%
(57/14,396) 0.4%
(50/14,363) 0.001
(0.000;
0.002
0.0%
(-0.1%;
0.2%)
0.0%
(-0.0%;
0.2%)
1.14
(0.78;
1.66)
0.51 1.14
(0.77;
1.69)
0.52
Hospitalizations
by day 7a
1.0%
(145/14,396) 0.9%
(130/14,363) 0.01 (0.01;
0.02) 0.1%
(-0.1%;
0.3%)
0.3%
(-0.0%;
0.7%)
1.11
(0.88;
1.41)
0.38 1.43
(1.00;
2.05)
0.05
Primary
referrals at day
0
1.2%
(194/16,381) 1.0%
(170/17,205) 0.03 ( 0.01;
0.04) 0.1%
(-0.2%;
0.3%)
0.8%
(0.1%;
1.5%)
1.2
(0.98;
1.47)
0.08 2.08
(1.15;
3.74)
0.02
Referral
resulting in
hospitalization
by day 7b
16.8%
(25/149) 20.3% (29/143) 0.05 (0.00;
0.14) -3.5%
(-5.4%;
12.4%)
-2.8%
(-11.8%;
6.2%)
0.83
(0.51;
1.34)
0.44 0.86
(0.53;
1.40)
0.55
Uplanned re-
attendance
visits by day 7c
1.8%
(256/14,603) 2.9%
(425/14,723) 0.03 (0.01;
0.04) -1.1%
(-1.5%;
-0.8%)
-1.0%
(-2.8%;
0.9%)
0.61
(0.52;
0.71)
<
0.001 0.67
(0.32;
1.44)
0.31
Additional
medication
taken after
initial
consultation up
to day 7
7.1%
(1,006/14,244) 7.2%
(1,017/14,229) 0.006 -0.1%
(-0.7%;
0.5%)
-0.9%
(-2.1%;
0.4%)
0.99
(0.91;
1.07)
0.78 0.88
(0.74;
1.05)
0.17
Table legend:All data shown for day 0 outcomes are per-protocol, and all data for day 7 outcomes are per-protocol and complete case (day 7
outcomes assessed). Clinical failure by day 7 dened as “not cured” and “not improved”, or unscheduled hospitalization as reported by
caregivers. Non-referred secondary hospitalizations by day 7 are hospitalizations at least a day after the initial consultation that was not
referred by a healthcare provider. Unplanned re-attendance visits by day 7 are return visits between day 1 to 7 that were not proposed by the
initial healthcare provider. Adjusted relative risks and differences were estimated using a random-effects logistic regression model adjusting
for clustering (health facility and patient), as well as individual (age, sex, complaints, availability of phone) and health facility (council of
health facility, level of health facility, average number of patients seen per month at the health facility) baseline characteristics.
a Post-hoc exploratory outcome not pre-specied.
b Denominator is based on consultations for which a primary referral was proposed and day 7 hospitalization data was ascertained, as such
may be less than the total number of primary referrals at day 0.
c Including unplanned outpatient and hospitalized re-attendance visits
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Secondary and exploratory clinical safety outcomes
There were no signicant differences in the proportion of patients who died, were subjectively worse, were hospitalized after the day of the
initial consultation without a referral (non-referred secondary hospitalization), all hospitalizations by day 7, or unplanned re-attendance visits
(Table 3). There was however a signicant reduction in unplanned re-attendance visits by day 7 in unadjusted analyses. The proportion of
patients who died (0.1%) or were hospitalized (1.0%) were low in both arms. Results in the intention-to-treat population, and when including re-
attendance visits were similar (Table S4 and S5).
Additional medications by day 7 and antibiotic prescription over time
At day 7 (range 6–14), additional medicines were taken after the initial consultation in a similar proportion of patients between study arms
(7.1% vs 7.2% in intervention vs control health facilities, Table 3). When evaluating evolution of mean antibiotic prescription rates over time, it
appears to decrease over time in ePOCT + health facilities, while no change was found in usual care facilities (Figure S1).
Referral and hospitalizations
Healthcare providers identied 3.6% (582/15,799) of cases as having a severe diagnosis in ePOCT + facilities compared to 2.6% (453/17,205)
in usual care facilities (per-protocol in initial cases). The proportion of cases referred for hospitalization was higher in ePOCT + facilities
(1.2%) than in usual care facilities (1.0%) (aRR 2.08 (95% CI, 1.15 to 3.74), Table 3). The proportion of children referred that resulted in
hospitalization was low and similar in both study arms (Table 3). The proportion of cases referred to specialized outpatient clinics
(malnutrition clinic, tuberculosis investigation, HIV clinic) was low and similar between ePOCT + and usual care health facilities (Table S6).
Subgroup analyses (sex, age, complaints)
The effect of the intervention on antibiotic prescription at day 0 was more pronounced in children presenting with respiratory complaints
(absolute difference − 62.1%; 95% CI -63.3%, -60.9%) and the 2–59 month age group (absolute difference − 48.9%; 95% CI -49.9%, -47.9%) (Fig.
4 and Table S7). Antibiotic prescription was reduced by at least 25 percentage points in all subgroups, with the smallest reduction found in
infants under 2 months old (absolute difference − 25.5%; 95% CI -30.3%, -20.6%). Young infants less than 2 months old had the largest
reduction in day 7 clinical failure (aRR 0.61; 95% CI 0.37, 1.00; p-value 0.05) (Fig. 5 and Table S8).
Discussion
In this cluster randomized controlled trial involving 44 306 sick children under 15 years of age in Tanzania, the use of the ePOCT + digital
clinical decision support algorithm (CDSA) package resulted in a 3-fold reduction in the likelihood of a sick child receiving an antibiotic
prescription compared to children in usual care facilities. Despite substantially fewer antibiotic prescriptions, clinical failure did not increase in
intervention facilities.
The reduction of antibiotic prescription associated with the ePOCT + intervention in our study is consistent with our previous research with
CDSAs in Tanzania in more controlled research settings.18,19,29 However, the results differ from other studies evaluating CDSAs implemented
in routine health programs in Nigeria, Afghanistan, and Burkina Faso, which found little to no reduction in antibiotic prescription.20–22, 30
There are a number of differences that may explain the divergent results. First and foremost, the clinical algorithm of ePOCT + differs from
other CDSAs. It notably has a wider scope including additional conditions and point-of-care tests such as CRP, not included in IMCI.26 A
randomized controlled trial comparing two different CDSAs found differences in the impact of antibiotic stewardship due to the addition of
CRP and other algorithm modications, demonstrating that not all CDSAs are equal.18 Other differences that may explain the divergent results
include 1) differences in the supportive training and mentorship provided, 2) disease epidemiology, and 3) healthcare provider skills and
adherence. The extent of the impact on antibiotic stewardship in our study is also greater than those observed in other antibiotic stewardship
studies that included one single intervention rather than an intervention package.31,32 ePOCT + integrates multiple proven antibiotic
stewardship interventions together, including clinical decision support,18,19,29 the use of point-of-care CRP tests,33 pulse oximeter,34 and
continuous quality improvement mentorship support with data feedback to healthcare providers utilizing benchmarking of health
facilities.35,36
Clinical failure was not higher in patients managed in ePOCT + health facilities, despite a signicant reduction in antibiotic prescription in line
with other antibiotic stewardship studies.33 Similarly the proportion of children who died, were hospitalized without referral, or had unplanned
re-attendance visits were not higher. While previous CDSA studies were able to demonstrate signicant reductions in clinical failure, the
current trial was not powered to do so.18,19,21 Nonetheless, the greatest benet on clinical cure compared to usual care was observed in the
subgroup of under 2 month olds, important results given that this population represents more than 50% of mortality in under 5 year olds.37
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Our study possesses several strengths that contribute to its robustness. Firstly, we employed a cluster randomized controlled study design,
which was adequately powered to assess non-inferiority of clinical failure. Secondly, the implementation of our intervention encompassed a
wide range of epidemiological settings, including both rural and urban areas, with varying levels of malaria transmission and facilities such
as dispensaries and health centres. Moreover, our study employed comprehensive patient inclusion criteria that were designed to be inclusive.
By incorporating theses inclusive criteria, randomly sampling health facilities for inclusion, and observing consistent effects across
subgroups at both the health facility and individual levels, our ndings can be generalized to a broader population.
There are several limitations to our study. First, antibiotic prescription data relied on documentation by the healthcare provider, an approach
often used in pragmatic trials.38,39 When using a conservative ITT approach considering that all patients for which treatment was not
documented were considered to have been prescribed an antibiotic, ePOCT + still reduced antibiotic prescription considerably (table S4).
Second, despite multiple phone calls and home visits, 15% of cases were lost to follow up, consistent with that found in similar studies (13–
25%).40–42 To account for potential biases in loss to follow-up, we adjusted the nal model for baseline variables associated with missing
outcome data, analogous to performing multiple imputation in the case of a single endpoint. Third, the fact that a child has not improved
after day 7 sometimes reects the natural course of the disease, rather than the poor quality of care at the initial consultation, and may not
therefore be expected to be inuenced by the intervention for all clinical syndromes. To show an effect on more severe outcomes such as
secondary hospitalization or death would require a very large sample size due to the rarity of the event at the primary care level. Further
complicating assessment of these severe outcomes are challenges linked to referral, and quality of care at admitting hospitals.
In conclusion, the ePOCT + electronic clinical decision support algorithm (CDSA) in association with point-of-care tests (CRP, haemoglobin,
pulse oximeter) and mentorship support informed by clinical-practice data, safely and substantially reduced antibiotic prescribtion in sick
children less than 15 years presenting to primary care facilities in Tanzania. Widespread implementation of ePOCT + could help address the
urgent problem of antimicrobial resistance by reducing excessive antibiotic prescription in sick children while maintaining clinical safety.
Online Methods
Study design and setting
The DYNAMIC Tanzania study was a pragmatic, open-label, parallel-group, cluster randomized trial conducted in 40 primary health facilities in
Tanzania. The health facility was the unit of randomization, since the intervention was targeted at the health facility level.
Study sites were purposefully chosen to represent a variety of health care and epidemiological settings within 5 councils in the Mbeya and
Morogoro region, with a total population in those councils of 1,701,717.43 Two councils were semi-urban (Mbeya city and Ifakara Town
councils), while the three otherswere rural (Mbeya, Ulanga and Mlimba district councils). 42.8% of the Tanzanian population is less than 15
years old.44 The malaria prevalence in febrile children age 6–59 months is 5.8% in the Morogoro region, and 3.4% in the Mbeya region.45 HIV
prevalence among children less than 15 years old is 0.5% in both regions.46 Health care for acute illnesses at government or government
designated primary health facilities are free of charge for children under 5 years, including the cost of medications such as antibiotics. For
patients above 5 years, health care expenses are at the charge of the patient, unless they have a health insurance plan (around 10% of
Tanzanians).47
Inclusion & Ethics
Ethical approval
was obtained in Tanzania from the Ifakara Health Institute (IHI/IRB/No: 11-2020), the Mbeya Medical Research Ethics Committee
(
SZEC-
2439/R.A/V.1/65), the National Institute for Medical Research Ethics Committee (NIMR/HQ/R.8a/Vol. IX/3486 and NIMR/HQ/R.8a/Vol.
IX/3583), and in Switzerland from the cantonal ethics review board of Vaud (CER-VD 2020–02800). The study was registered on
ClinicalTrials.gov number NCT05144763, where the trial protocol and statistical analysis plan can be found.
The study design and implementation was developed collaboratively between the Ifakara Health Institute, Mbeya Medical Research Centre,
Swiss Tropical and Public Health Institute and the Centre for Primary Care and Public Health, University of Lausanne, based on feedback from
stakeholders, patients and healthcare providers involved in our similar trials in Tanzania.18,19,29 Over 100 community engagement meetings
with over 7 000 participants were conducted before and during the study.
Participants
Primary care health facilities (dispensaries or health centres) were eligible for inclusion if they performed on average 20 or more consultations
with children 2 months to 5 years per week, were government or government-designated health facilities, and were located less than 150 km
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from the research institutions. Acute outpatient care is routinely provided by nurses and clinical ocers in primary health facilities, while
medical doctors provide care on certain occasions at health centres. Clinical ocers, the principal health providers at primary health facilities,
are non-physician health professionals with 2–3 years of clinical training following secondary school.48
Infants and children aged between 1 day old and under 15 years of age seeking care for an acute medical or surgical condition at
participating health facilities were eligible. Children presenting solely for scheduled consultations for a chronic disease (e.g. HIV, tuberculosis,
malnutrition), or for routine preventive care (e.g. growth monitoring, vaccination) were not eligible. Written informed consent was obtained
from all parents or guardians of participants when attending the participating health facility during the enrollment period.
Sampling, randomization and masking
The 40 health facilities were randomly selected from all eligible health facilities in the participating councils following a 3:2 ratio between
health facilities from the Morogoro and Mbeya region (in order to include more health facilities in the higher malaria transmission area). In
addition, to include a representative sample of health centres compared to dispensaries, 4 health centres per region were included.
The sampled health facilities were then randomized (1:1), to ePOCT+ (intervention) or usual care (control). Randomization was stratied by
region, council, level of health facility (health center versus dispensary), and attendance rate. An independent statistician in Switzerland was
provided the list of all eligible health facilities, and performed the computer-generated sampling and randomization. Intervention allocation by
the study team was only shared to study investigators in Tanzania once all council leaders conrmed the participation of their selected health
facilities. The nature of the intervention did not allow for masking of the intervention to healthcare providers, patients, or study implementers.
Intervention
The intervention consisted of providing ePOCT + with the supporting IT infrastructure, C-reactive protein (CRP) semi quantitative lateral ow
test, hemoglobin point-of-care tests (and hemoglobinometer if not already available), pulse oximeter, training and supportive mentorship
(Fig.4). The development process and details of the ePOCT + CDSA, and the novel medAL-reader Android-based application used to deploy
ePOCT + were described in detail previously.26 In summary the clinical algorithm of ePOCT + is based on previous generation CDSAs
(ALMANACH and ePOCT),18,27 international and national clinical guidelines, input from national and international expert panels, and was
adapted based on piloting and healthcare provider feedback.26 Mentorship by the implementation team included visits to health facilities
every 2–3 months and communication by phone call or group messages 3–4 times per month, to resolve issues and provide guidance and
feedback on the use of the new tools. Results from quality of care dashboards were shared through group messages to give feedback on the
use of ePOCT+, a strategy often described as ‘benchmarking’, allowing healthcare providers to compare their antibiotic prescription, uptake,
and other quality of care indicators with other health facilities.49 Control health facilities provided care as usual, with no access to clinical
data dashboards.
All participating health facilities were provided with IT infrastructure to support the tablet based ePOCT + CDSA or in the case of control health
facilities, to support the use of tablet based electronic case report forms (eCRF). The IT infrastructure included a tablet for each outpatient
consultation room, router, local server (Rasberry Pi), internet, and if needed back up power (battery), or solar system. In addition, weighing
scales, mid-upper arm circumference (MUAC) bands, and thermometers were provided to health facilities in both arms if not already available.
Healthcare providers from both intervention and control health facilities received equivalent clinical refresher training based on the Integrated
Management of Childhood Ilness (IMCI) chartbook. In addition specic training was provided on the use of the ePOCT + CDSA in intervention
facilities, and the use of the eCRF in control facilities.
Study procedures
Children seeking care at included health facilities were screened for eligibility by a research assistant between 8:00 to 16:00 on weekdays. If
eligible, demographic information was collected and entered in the eCRF (ePOCT + for intervention health facilities, and eCRF for usual care
facilities within the data collection system medAL-
reader
). Healthcare providers in the control health facilities managed the patients as usual,
but documented the main complaints, anthropometrics and test results (if performed), diagnoses, treatments and referral decision in the
eCRF. To harmonize data collection across the intervention and control facilities, the eCRF for the control facilities was also programmed into
the medAL-reader platform, but no decision support was provided. Research questions were included in the eCRF to capture if an oral or
systemic antibiotic was prescribed, and if the patient was referred for inpatient hospitalization or other outpatient investigations. In
intervention health facilities, in addition to the same information collected in the eCRF, symptoms and signs of the patients were recorded in
the ePOCT + CDSA during the consultation with the patient. The symptoms and signs entered are used by the ePOCT + CDSA to guide the
clinical consultation. Healthcare providers who documented the nal treatment for a consultation in ePOCT + or the eCRF were categorized as
having been managed per-protocol, as recording of the nal treatment is required to complete the ePOCT + CDSA.
Page 10/16
All patients were called or visited at their home by research assistants to assess clinical outcomes and their care and treatment seeking
behaviour at day 7 (range 6–14 days). Research assistants performing the phone calls were blinded to the intervention status, and were not
part of the team enrolling patients at health facilities. Home visits rather than phone calls were conducted if the caregiver of patients did not
have a phone number or did not know somebody with a phone near their home, or if research assistants were not able to reach the provided
phone number after 5 attempts. The home visits were performed by the research assistants enrolling patients from the same health facility, as
such they were not blinded to intervention allocation. Patients who were still sick at follow-up were encouraged to return to a health facility for
follow-up care. Day 7 data was recorded using RED Cap web for phone calls, and RED Cap mobile application for home visits.
Outcomes
The co-primary outcomes measured at the individual patient level included: 1) antibiotic prescription at the time of the initial consultation as
documented by the healthcare provider (superiority analysis); and 2) clinical failure at day 7 dened as “not cured” and “not improved”, or
unscheduled hospitalization as reported by caregivers (non-inferiority analysis). Secondary outcomes include unscheduled re-attedance visits
at any health facility by day 7, non-referred secondary hospitalization by day 7, death by day 7, and referral for inpatient hospitalization at
initial consultation. The intervention was deemed a success if ePOCT + was non-inferior in terms of clinical failure and reduced antibiotic
prescription by at least 25%. Pre-specied additional outcomes are outlined in the statistical analysis plan.
Sample size
The sample size was calculated for testing non-inferiority of the clinical failure outcome given that it would require a higher sample size than
for the antibiotic prescription co-primary outcome. We assumed a cluster size of 900 patients (average of 150 patients per month x 6 months)
based on routine data within the natinoal health management information system, an intraclass correlation coecient of 0.002, and a clinical
failure rate of 3%. To have 80% power to detect an acceptable non-inferiority margin of a relative risk of 1.3, corresponding to 3.9%, we
required 19 clusters and 17,100 patients per arm (total patients n = 37,620 assuming 10% loss to follow-up). Given the uncertainty of some of
the assumptions, the total number of health facilities was rounded up to 20 clusters per arm.
No interim analysis was planned, however due to lower enrollment than expected, after 8 months of recruitment, we planned an ad-hoc
sample size recalculation by an independent statistician to calculate the expected power of the study based on updated parameters. The
study team pre-specicied the specications and approach, documented in an update to the statistical analysis plan.
Statistical analysis
All outcomes were evaluated using random effects logistic regression models using the cluster (health facility) and patient as random effects,
with further adjustment using xed effect terms for randomization stratication factors,50 and baseline characteristics hypothecized to be
associated with the outcome, imbalances between arms, and imbalances between characteristics among patients for whom day 7 data was
available and not available (lost to follow-up). These included the patient characteristics of age, sex, presenting complaints (fever, respiratory,
gastrointestinal, skin) and phone availability, and the health facility characteristics of care provision level (dispensary versus health center),
attendance rate per month and council. A partitioning method was used to separate within- and beween- cluster effects to account for
confounding by cluster.51,52 In the case of too few events, and small variance among health facilities which did not allow the model to
converge, the health facility was incorporated in the model as a xed effect. Adjusted relative risk (aRR) and absolute differences was
estimated based on the computed marginal probabilities of the conditional probabilities.53,54 Formal adjustments were not performed for
multiple testing, as adjustments would likely be overly conservative given that the outcomes are not all independent,55 and variable selection
was not based on statistical tests of signicance.56
Non-inferiority was determined if the upper-limit of the 95% condence interval (CI) of the adjusted relative risk (aRR) was below 1.3. All
analyses based on outcomes from day 0 were performed in the per-protocol population, and outcomes determined at day 7 were performed in
the per-protocol and complete case population (only in those for which day 7 outcomes were ascertained) and displayed accordingly unless
stated otherwise. The primary analyses were performed on the rst visit for an illness, with re-attendance visits (a second visit to a health
facility for the same illness) included in exploratory analyses. Prespecied analyses to assess the effect of the intervention in different
population groups were performed by sex, age group, and consultation complaint categories (respiratory symptoms, fever, gastrointestinal,
skin problem, ear, nose and throat problem). All analyses were performed using Stata v16 and v17.57
Declarations
Data availability
De-identied data can be found onhttps://doi.org/10.5281/zenodo.8043523
Page 11/16
Role of the funding source
This study was supported by funding from the Fondation Botnar, Switzerland (grant number 6278) and the Swiss Development Cooperation
(project number 7F-10361.01.01). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or
writing of the report.
Acknowledgements
We would like to acknowledge the contributions of the research assistants at the Ifakara Health Institute and Mbeya Medical Research Centre
– National Institute of Medical Research, who assisted in the data collection, the Information Technology staff at Unisanté (Sylvain
Schaufelberger, Greg Martin), and staff at Wavemind (Emmanuel Barchichat, Alain Fresco, and Quentin Girard) for their work on the medAL-
suite during the study, healthcare providers in the participating health facilities, and Community Health Management Team members in the 5
participating councils in Tanzania for their collaboration in implementing the study. We acknowledge researchers of the Tools of Integrated
Management of Childhood Ilness and DYNAMIC Rwanda project for their contributions to ePOCT+ and the many common research activities
(Dr Fenella Beynon, Dr Lena Matata, Dr Helene Langet, Dr Leah Boole, Dr Ludovico Cobuccio, Dr Victor Rwandacu, Dr Robert Moshiro, Martin
Norris, Lisa Cleveley). We thank again the Tanzania expert panel for their valuable work helping develop the ePOCT+ Tanzania algorithm (Prof
Karim Manji, Dr Nahya Salim, Dr Helga Naburi, Dr Joyce Massaro, Dr Delila Moshi, Dr Winnie Ndembeka, Dr Lulu Chirande, Dr Ezekiel Mbao).
Thank you to Dr Mary-Anne Hartley for her work on the initial project proposal and guidance on various aspects of the DYNAMIC TZ project,
and Dr Arjun Chandna for his valuable comments on the manuscript. A special thank you to all the participating patients and caregivers of the
study. Finally we would like to acknowledge the work of Dr Irene Masanja on the initial development of the study, who regretably passed away
before the start of the study.
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Figures
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Figure 1
Health facility and patient ow diagram
Legend: Boxes highlighted in grey correspond to the co-primary outcome populations
Figure 2
Page 15/16
Co-primary outcomes
Legend: A) Proportion of antibiotic prescription in ePOCT+ and usual care health facilities; B) Relative risk of day 7 clinical failure between
ePOCT+ and usual care health facilities; with non-inferiority pre-specied as an adjusted relative risk of <1.3. ITT: Intention-to-treat; PP: Per-
protocol; RR: Adjusted relative risk
Figure 3
Antibiotic prescription and clinical failure by sex, age groups, and main complaints
A) Adjusted differences of day 0 antibiotic prescription between ePOCT+ health facilities and usual care health facilities. B) Aadjusted relative
risk with 95% CI of clinical failure in ePOCT+ compared to usual care health facilities
