Using Machine Learning to Model Early-onset Neonatal Sepsis Risk in Late Preterm and Term Neonates in Uganda and Zimbabwe
Conditions: Neonatal Sepsis
Sponsor: St George's, University of London

 
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FULL/LONG TITLE OF THE STUDY 
 
Using machine learning to model early-
onset neonatal sepsis risk in late preterm 
and term neonates in Uganda 
SHORT STUDY TITLE / ACRONYM 
The short title should be: 
 
Sufficiently detailed to make clear to 
participants what the research is about in 
simple English 
 
If acronyms are used the full title should 
explain them. The proposed acronym should 
not drive the long title 
 
Using machine learning to model early-
onset neonatal sepsis risk in Uganda 
(NeoRisk) 
PROTOCOL VERSION NUMBER AND DATE 
Version control: 
 
All draft versions should be numbered 0.1, 
0.2 etc. 
 
The final version for submission should be 
numbered 1.0 
 
The changes made relative to the previous 
protocol version should be listed after 
submission 
 
1.0 – 30/11/2023 
2.0 -  09/01/2024 
2.1 – 09/02/2024 
 
IRAS Number: 
 
JRES Reference Number 
 
Funder Reference Number: 
 
This protocol has regard for the HRA guidance and order of content 
 
 

 
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SIGNATURE PAGE 
The undersigned confirm that the following protocol has been agreed and accepted and that the Chief 
Investigator agrees to conduct the study in compliance with the approved protocol and will adhere to 
the principles outlined in the Declaration of Helsinki, the Sponsor’s SOPs, and other regulatory 
requirement. 
I agree to ensure that the confidential information contained in this document will not be used for any 
other purpose other than the evaluation or conduct of the investigation without the prior written consent 
of the Sponsor. 
I also confirm that I will make the findings of the study publicly available through publication or other 
dissemination tools without any unnecessary delay and that an honest accurate and transparent 
account of the study will be given; and that any discrepancies from the study as planned in this protocol 
will be explained. 
 
For and on behalf of the Study Sponsor: 
Signature:  
...................................................................................................... 
 
Date: 
....../....../...... 
Name (please print): 
...................................................................................................... 
 
 
Position: 
...................................................................................................... 
 
 
 
Chief Investigator: 
Signature: 
..........................................
................ 
 
Name: (please print): 
.
.............................................. 
 
 
 
 

 
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LIST of CONTENTS 
Page No. 
PROTOCOL COMPLIANCE DECLARATION  
1 
TITLE PAGE  
1 
RESEARCH REFERENCE NUMBERS 
 
1 
SIGNATURE PAGE 
2 
LIST OF CONTENTS  
3 
ABBREVIATIONS 
4 
KEY STUDY CONTACTS 
5 
STUDY SUMMARY 
6 
FUNDING 
 
6 
ROLE OF SPONSOR AND FUNDER 
6 
ROLES & RESPONSIBILITIES OF STUDY STEERING GROUPS AND INDIVIDUALS   
6 
STUDY FLOW CHART 
 
7 
1. BACKGROUND 
 
9 
2. RATIONALE 
 
10 
3. THEORETICAL FRAMEWORK 
10 
4. RESEARCH QUESTION/AIM(S)  
15 
5. STUDY DESIGN/METHODS 
16 
6. STUDY SETTING 
19 
7. SAMPLE AND RECRUITMENT 
20 
8. ETHICAL AND REGULATORY COMPLIANCE 
25 
9. DISSEMINATION  POLICY 
28 
10. REFERENCES 
29 
11. APPENDICES 
34 
 
 
 

 
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ABBREVIATIONS 
CI  
Chief Investigator  
CRF  
Case Report Form  
ICF  
Informed Consent Form  
ISF  
Investigator Site File  
NHS  
National Health Service  
NIHR  
National Institute for Health Research  
PI  
Principal Investigator  
REC 
Research Ethics Committee 
SGUL 
St Georges, University of London 
JRES 
(St Georges) Joint Research and 
Enterprise Services 
WHO 
World Health Organisation  
ROM 
Rupture of membranes 
EOS 
Early onset sepsis 
LOS 
Late onset sepsis 
PCR 
Polymerase chain reaction 
LRS 
Low resource setting 

 
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KEY STUDY CONTACTS 
Chief Investigator 
Principal Investigator (In-Country) 
Study Co-ordinator 
Sponsor 
St George’s, University of London 
Funder(s) 
CREATE (Africa Health Research Training Programme) 
Wellcome Trust  
Key Protocol Contributors 

 
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STUDY SUMMARY 
Study Title 
Using machine learning to model early-onset neonatal 
sepsis risk in Uganda 
Internal ref. no. (or short title) 
NeoRisk 
Study Design 
Observational 
Study Participants 
Neonates born at ≥34 weeks’ gestational age at Kawempe 
Hospital in Kampala, Uganda (Phase 1) or Sally Mugabe 
Central Hospital in Harare, Zimbabwe (Phase 3) 
Planned Size of Sample (if applicable) 1600 (Phases 1+2), 900 (Phase 3) 
Follow up duration (if applicable) 
1 month post-delivery 
Planned Study Period 
September 2023-May 2024 
Research Question/Aim(s) 
 
1. Determine antenatal and perinatal factors associated 
with early-onset neonatal sepsis in low-resource 
settings, as diagnosed by a blood culture or a senior 
clinician 
2. Measure the strength of association between the 
above factors and early-onset neonatal sepsis 
3. Combine the above risk factors into a risk stratification 
model for early-onset neonatal sepsis using machine 
learning techniques  
FUNDING AND SUPPORT  
FUNDER(S) 
 
FINANCIAL AND NON FINANCIALSUPPORT 
GIVEN 
CREATE PhD Programme 
 
 
ROLES AND RESPONSIBILITIES OF STUDY MANAGEMENT COMMITEES/GROUPS & 
INDIVIDUALS 
The study has been discussed with the Community Engagement team at Makerere University-Johns 
Hopkins University Research Collaboration in Kampala, Uganda, which works with clinical studies at 
Kawempe Hospital. 
 
The team have provided feedback on the acceptability of this study, including on the taking of a blood 
culture sample, recruitment strategy, and on development of patient information resources. 
 
The study team will continue to work closely with the Community Engagement team throughout the 
study to ensure its acceptability and to disseminate research findings to the community and relevant 
stakeholders. 
 

 
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PROTOCOL CONTRIBUTORS 
No funders have been involved in study design and no funder will be involved in conduct, data analysis 
and interpretation, manuscript writing, or dissemination of results.  The funder does not control the final 
decision regarding these aspects. 
As above, the protocol has been designed in collaboration with the Community Engagement team who 
represent members of the community, patients, and carers.  
 
STUDY Schematic: Phase 1 (Uganda) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Delivery information: 
Enrollment 

Questionnaire (perinatal, immediate 
postnatal outcomes) 

Blood culture sample from neonate 
Potential participant identified by 
clinical team in antenatal clinic, 
antenatal and labour wards (i.e. 
intending to have antenatal and 
delivery care at a study site)  
Recruitment 
Antenatal information: 
Informed consent (No study procedures 
without IC process)Questionnaire 
(medical and obstetric history, 
demographic factors) 
Exclude if miscarriage, stillbirth or 
delivery before 34 weeks gestation is 
completed. 
1 month follow up: 

Telephone questionnaire (outcome, 
clinical signs of sepsis) 
Review of medical notes or 
admission details via 
questionnaire if admitted to 
NICU/re-admitted. 

 
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Phase 2 comprises analysis and modelling of data collected in Phase 1. 
 
STUDY Schematic: Phase 3 (Zimbabwe) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Refer back if miscarriage, stillbirth or 
delivery before 34 completed weeks 
Delivery: 

Questionnaire (perinatal, immediate 
postnatal outcomes) 
Potential participant identified by 
clinical team in antenatal clinic, 
antenatal and labour wards (i.e. 
intending to have antenatal and 
delivery care at a study site)  
Recruitment 
Antenatal: 
Questionnaire (medical and obstetric 
history, demographic factors) 
1 month follow up: 

Telephone questionnaire (outcome, 
clinical signs of sepsis) 
Review of medical notes or 
admission details via 
questionnaire if admitted to 
NICU/readmitted 

 
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STUDY PROTOCOL 
Using machine learning to model early-onset neonatal sepsis risk in Uganda 
 
1 
BACKGROUND 
Sepsis is a leading cause of neonatal mortality worldwide(1), affecting 39.3 per 1000 live 
births in low- and middle-income countries(2).  Mortality from early-onset sepsis can be 
reduced with prompt, correct treatment with antibiotics(3); therefore, identifying those at risk 
early is crucial. The sepsis rate in Uganda is high, with an incidence of 68 per 1000 live 
births, and is highest in hospital facilities(4).   
Antibiotics are recommended for neonates with specific risk factors for sepsis, and those 
with signs of illness, including poor feeding and drowsiness(5).  However, many low-
resource settings diverge from the World Health Organization (WHO)’s recommended 
regimens, often due to antimicrobial resistance (5–7) or drug availability(8). Ideally, 
antibiotics should be given within an hour of birth in cases of suspected early-onset sepsis, 
which accounts for almost ¾ of sepsis cases in Uganda(4,9). 
Additionally, risks of antimicrobial resistance are increasing.  Neonates with sepsis in low- 
and middle-income countries show significant rates of resistance to the extent that the 
majority of proven infections are resistant to first and second line WHO-recommended 
antibiotics(10,11).  Mortality from sepsis is double with resistant versus sensitive 
organisms(12).  Previous studies in Kampala have shown high rates of resistance to first-line 
antibiotics(13).  Prolonged antibiotic therapy without proven infection is associated with 
morbidity and mortality in preterm infants, and may affect normal flora and immunity(14,15).  
Therefore, antimicrobial stewardship is vital for low-resource settings.  Accurate, early 
identification of at-risk neonates allows the benefits of early treatment whilst minimising the 
harms of overtreatment, and unnecessary admission. 
Risk stratification systems are established tools in medicine, including in sub-Saharan 
Africa(16,17), to predict mortality in paediatric inpatients(18) and neonates(19), but 
previously tested systems for prediction of neonatal sepsis in low-resource settings show 
modest performance(20).  This may be due to limited laboratory testing, so systems are built 
at least partially on clinician defined sepsis rather than the gold-standard of blood culture 
diagnosis(20).  In high resource settings, risk stratification systems have focused on 
reducing overtreatment and antibiotic resistance(14,15,21).  Neonatal sepsis risk calculators 
(a risk stratification system with a digital interface), such as the Kaiser Permanente (KP) 
Early-Onset Sepsis (EOS) calculator, have been adopted in the USA and UK(22).  This 
calculator uses local incidence of EOS, gestational age, time from rupture of membranes 
(ROM) to delivery, highest maternal temperature during labour, maternal group B 
Streptococcus status, and type and timing of antepartum antibiotics(23).    
In low-resource settings, the consequences of missing neonatal sepsis are more severe due 
to limited higher acuity care(24).  Therefore, sepsis risk assessment in a low-resource 
setting should prioritise sensitivity over specificity, although prioritisation of limited resources 
for true cases is also helpful(25).   Existing risk calculators may be inappropriate to use in a 

 
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low-resource setting; background incidence of EOS is much higher in these settings(1,2).  
Other clinical and non-clinical factors may mediate risk, such as HIV status or birth location.  
Few of the usual risk factor inputs are available in a low-resource setting.  For example, 
without adequate antenatal care and imaging, gestational age may be uncertain, and without 
skilled birth attendants, duration of ROM may be unknown.  Finally, diagnostic facilities for 
sepsis such as biochemical tests for inflammatory markers may be limited or unavailable.  
This makes identification of those at-risk using clinical factors, as opposed to laboratory 
testing, even more important. Additionally, lack of access to blood cultures has limited the 
ability of previous models to include this ‘gold standard’ of diagnosis in their modelling(20). 
 
2 
RATIONALE  
 
Current risk stratification systems are not able to predict neonatal sepsis risk in a low-
resource setting with sufficient accuracy, as outlined in section 1.  Reliable prediction of 
neonatal sepsis risk would enable clinicians to prioritise early broad-spectrum antibiotics and 
close monitoring for the highest-risk neonates, reducing associated morbidity and mortality, 
and allow earlier discharge of low-risk neonates without antibiotic treatment, reducing the 
economic and psychological burden to families of remaining in hospital and antimicrobial 
overuse.  In a wider context, the ability to predict sepsis risk more accurately would also aid 
the development of clinical pathways and service planning within maternity and neonatal 
services.  Finally, the resulting model and code from this study could be used by other 
researchers in the future to model risk of other infective conditions, or neonatal sepsis in 
other settings. 
To build a model for risk stratification, data are required that encompass not only a broad 
range of potential risk factors for sepsis, but also accurate outcome data confirming or 
refuting the diagnosis of sepsis.  Previous attempts to create risk stratification systems have 
lacked blood cultures as the gold standard diagnostic method for sepsis(20), so this study 
will include a blood culture sample from every participant.  This study will also include a 
range of patient-reportable potential risk factors to maximise the clinical utility of the model in 
the future. 
 
3 
THEORETICAL FRAMEWORK 
 
Aetiology of neonatal sepsis 
Neonatal sepsis is commonly defined as a severe systemic infection occurring in the neonatal 
period.  Early-onset neonatal sepsis (EOS) refers to those infections presenting within 72 hours 
of life and is usually the result of vertical acquisition of the pathogen.  This is in contrast to late-
onset neonatal sepsis which presents after 72 hours of life usually results from horizontal 
acquisition of the pathogen (i.e. from the neonate’s environment)(26). 
Many risk factors are common to both early- and late-onset sepsis, but their importance differs 
for each of the conditions.  EOS relates more to maternal risk factors(26–29), and LOS to 
neonatal factors(26,27,29)(.  Environmental factors will also influence the risk of LOS, as they 
will affect the risk of horizontal transmission of pathogens(26,27).  As this study aims to create a 
risk stratification system relevant for low-resource settings, it is important to consider patient-

 
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reportable or easily measurable factors that may modify the risk of neonatal sepsis.  These 
have been considered systematically by examining possible related maternal, neonatal, and 
environmental factors and their associations. 
 
Patient-reportable risk factors 
To assess many of the established risk factors for neonatal sepsis, mothers must receive 
skilled antenatal and delivery care; for example, a diagnosis of chorioamnionitis would require 
assessment by a midwife or doctor and may also require measuring of vital signs and 
laboratory tests to confirm the diagnosis.  As the purpose of this project is to statistically model 
risk factors for early-onset neonatal sepsis with the aim to help inform clinical decision making 
in a low-resource setting, it is necessary to consider the association between neonatal sepsis 
and factors which are either patient-reportable or easily measurable without the need for 
laboratory or radiological services that may be unavailable in these settings. 
Therefore, in designing the data collection instrument for this study, we have considered which 
patient-reportable factors might be strongly associated with the established sepsis risk factors 
above, and determined the following fields for data collection, listed in the table below: 
Category 
Risk factor 
Data collection field 
Established Investigational 
Maternal 
factors 
prior to 
pregnancy 
Age 
(years) 
X 
  
Tribe 
  
 X 
Medical comorbidities 
increasing infection 
risk 
  
X 
  
  
HIV 
X 
  
Chronic infection e.g. hepatitis, 
tuberculosis 
X 
  
Regular immunosuppressive 
medications e.g. steroids 
X 
  
Previous abdominal surgery 
  
X 
Obstetric history 
  
X 
  
  
Number of previous pregnancies 
X 
  
Number of previous live deliveries 
 X 
Number of previous neonatal 
deaths 
X 
  
Low socioeconomic 
status 
  
X 
  
  
Maternal and partner’s 
occupation 
  
X 
Location of dwelling 
  
X 
Construction of dwelling  
  
X 
Occupancy of dwelling, including 
number of adults, children, and 
animals 
  
X 

 
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Source of lighting 
  
X 
Maternal and partner’s 
educational level  
  
X 
Cooking facilities at home e.g. 
open fire indoors, gas stove 
  
X 
Access to refrigeration at home 
  
X 
Food insecurity 
  
X 
Presence or absence of debt 
  
X 
Water, sanitation and hygiene 
X 
 
 
Type of sanitation available at 
home (type of toilet/latrine) 
 
X 
 
Source of drinking water 
 
X 
 
Access to handwashing facility at 
home 
 
X 
Nutritional status 
  
X 
  
  
Mid upper arm circumference 
X 
  
BMI 
X 
  
Dietary quality 
  
X 
Substance use 
E.g. recreational drugs, smoking, 
alcohol 
X 
  
Maternal 
factors 
during 
pregnancy 
Vaginal colonisation 
  
X 
  
 
Abnormal discharge during 
pregnancy 
  
X 
Vulval or vaginal 
discomfort/pruritus 
  
X 
Dyspareunia or postcoital 
bleeding 
  
X 
Regularity of menstrual cycle 
  
X 
Urinary tract symptoms during 
pregnancy 
X 
  
Diarrhoea or vomiting during 
pregnancy 
X 
  
Access to sanitation at home  
  
X 
Antibiotic use during pregnancy 
  
X 
Invasive procedures during or 
prior pregnancy e.g. cervical 
cerclage, surgical termination of 
pregnancy 
X 
  
Insufficient or late 
antenatal care 
  
X 
  
  
Number of antenatal appointments 
attended 
  
X 
Number of antenatal ultrasound 
scans 
  
X 
Screening blood tests taken 
antenatally 
  
X 

 
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Provision of antenatal vitamins  
  
X 
Vaccinations given during 
pregnancy 
X 
  
Chorioamnionitis 
  
X 
  
  
Clinical symptoms e.g. fever, 
rigors, malaise, vomiting, 
abnormal discharge 
X 
  
Abnormal vital signs (maternal) 
X 
  
Foetal tachycardia or abnormal 
CTG 
X 
  
Reduced foetal movements 
X 
  
Duration of rupture of 
membranes 
X 
  
Duration of labour 
  
X 
Foul-smelling, purulent, or 
meconium-stained liquor 
X 
  
Iatrogenic 
introduction of 
pathogen 
  
X 
  
  
Invasive procedures during 
labour e.g. vaginal examination, 
invasive foetal monitoring, 
catheterisation 
X 
  
Location of delivery (e.g. home 
or hospital) and hand hygiene 
policies 
  
X 
Cadre of staff present at delivery 
  
X 
Occupancy of ward (antenatal, 
labour, and neonatal); e.g. 
number of admissions per day, 
number of beds 
  
X 
Number of staff on shift (nursing, 
midwifery, medical) 
 
X 
Duration of hospital admission 
prior to delivery 
  
X 
Number of attempts at 
cannulation/intubation/NG tube 
insertion for neonate  
  
X 
Neonatal 
factors 
Low birthweight 
Birthweight, head circumference  
X 
  
Sex 
  
X 
  
Gestational age at 
birth 
By antenatal ultrasound scan 
where available; if not, use of 
birth weight (see inclusion 
criteria) 
X 
  
Congenital anomalies 
e.g. Down syndrome 
X 
  
Mode of delivery 
e.g. forceps, spontaneous 
vaginal 
X 
  

 
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Low APGAR scores 
  
X 
  
Clinical signs of sepsis 
or meningitis within 
first 72h of life 
  
X 
  
  
Temperature instability 
X 
  
Poor feeding 
X 
  
Lethargy 
X 
  
Jaundice at <24h of life 
X 
  
Respiratory distress 
X 
  
Tachycardia 
X 
  
Tachypnoea 
X 
  
Poor perfusion 
X 
  
Abnormal tone 
X 
  
Umbilical cord care 
e.g. application of herbal remedies 
  
X 
Method of feeding 
Breast versus formula 
X 
  
  
Water used to make up formula 
(e.g. filtered, tap) 
  
X 
 
  
Diagnosis of neonatal sepsis 
The ‘gold standard’ for diagnosis of neonatal sepsis is widely considered to be a microbiological 
culture of neonatal blood(30), using at least a 1 ml sample(31).  However, the proportion of 
neonatal patients with clinical signs of sepsis whose blood cultures are returned as negative 
can be significant, with studies reporting between 6 and 16 cases of ‘culture-negative sepsis’ 
for every culture-positive case(14).  Therefore, although the use of blood culture is the gold 
standard, other diagnostic methods must be considered in order to capture all cases of 
neonatal sepsis and subsequently develop an accurate statistical model. 
Various proxy biochemical and haematological markers of sepsis are commonly measured and 
used in clinical practice in high-resource settings, including C-reactive protein, procalcitonin, 
and full blood counts.  These markers are all useful in making a diagnosis of sepsis and 
monitoring response to treatment, but none have sufficiently high sensitivity or specificity to be 
used in isolation(30).  Additionally, in low-resource settings (including those settings in which 
this study will take place), access to consumables and laboratory facilities to measure these 
markers is unreliable.  For this study, the use of additional biochemical and haematological 
markers has therefore been deemed to have limited usefulness in contributing to the statistical 
model, and so will not be used. 
Clinical diagnosis of sepsis is challenging due to the nonspecific signs exhibited by 
neonates(32) and the lack of a universal definition of clinical sepsis(33).  Signs of sepsis are 
also present in neonates without infection who are transitioning to extra-uterine life, or suffering 
from other conditions(32,34).  However, given the near-universal availability of clinical diagnosis 
and the limitations of blood cultures to identify all cases of sepsis, we feel it is important to 
include clinical diagnosis of sepsis as a secondary outcome.  Given the subjective nature of an 
individual clinician’s diagnosis, we will also include established criteria for the diagnosis of 
sepsis that also assess the level of confidence in the clinical diagnosis, to reflect the spectrum 
of diagnostic certainty.  We will therefore use the GAIA neonatal infection case definition criteria 
to assess cases, which are intended for international use(35,36). 

 
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Use of machine learning for risk stratification modelling 
Machine learning is a field with increasing prominence, where computer systems are used to 
create algorithms to represent a data set(37). Evaluation of risk factors for neonatal sepsis 
could be achieved using traditional statistical techniques, as has been done for risk stratification 
models such as the Kaiser Permanente Sepsis Calculator(38).  However, machine learning 
techniques allow increased flexibility and are better suited to factors with a non-linear 
relationship to the outcome(39), in addition to their ability to analyse large and complex data 
sets(40).  Additionally, machine learning techniques may be able to discover novel associations 
and relationships that, although still requiring human validation as biologically plausible, may 
not have been discovered by traditional human-led analysis.   
Therefore, a machine learning approach could enable the creation of a more accurate risk 
stratification model, making it the appropriate method to use for statistical analysis in this study.  
The success of machine learning methods is dependent on having sufficient data to train the 
model, and the ability to test it on external datasets.  This has been accounted for in the design 
of this study by optimising the quality and accuracy of the training dataset (using a study site 
where blood culture results are reliably available), before testing the model in a second 
population to provide an external dataset. 
 
4 
RESEARCH QUESTION/AIM(S) 
Can machine learning, using a broad range of variables including parent reported risk 
factors, be used to predict the risk of neonatal early-onset sepsis with greater accuracy than 
current local guidelines in low-resource settings (LRS)? 
 
4.1 
Objectives 
 
1. Determine risk factors for neonatal early-onset sepsis in Kampala, Uganda 
2. Use machine learning techniques to create a risk stratification model for early-onset 
sepsis in Uganda 
3. Explore relationship between culture-positive sepsis and clinical diagnostic features 
in Uganda using machine learning techniques, including association between level of 
training of clinician documented diagnosis and blood culture result 
4. Validate the model from Uganda externally using an independent dataset from a 
neonatal population in Harare, Zimbabwe 
 
4.2 
Outcome 
 
Primary outcome for Phase 1: Positive blood culture amongst neonatal participants 
 
Primary outcome for Phase 3: Clinician (specialist neonatal or paediatric doctor) 
diagnosis of sepsis amongst neonatal participants as documented in clinical notes or 
discharge letter 
 
Clinical features of neonatal sepsis as listed in GAIA neonatal infections case 
definition 
 
Admission to neonatal unit 
 
Readmission to hospital in first 28 days amongst neonatal participants 
 
Death before 28 days of age 
 
Sensitivity, specificity, positive and negative predictive value of risk stratification 
model for participants in Kampala and Harare 

 
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5 
STUDY DESIGN and METHODS of DATA COLLECTION AND DATA ANALYSIS 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
There will be three main phases to this study, which is an observational cohort study. 

 
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Phase 1 will take place at Kawempe Hospital in Kampala, Uganda.  Participants will be 
recruited from labour wards by study staff, and interim analysis will be completed after 
recruitment of 10% of the total sample.  See section 7.3 for further details. 
Data will be collected via an online CRF, including the factors listed in the table in Section 3. 
 
Additionally: 
 
All neonates within the study will have a neonatal venous blood sample taken for 
culture by designated study staff  
o This must be taken before administration of antibiotics or within 48 hours of 
birth, whichever is sooner, and regardless of whether the neonate is being 
investigated and treated for sepsis. 
 
Postnatal information will be collected via telephone at 1 month including maternal 
and infant outcome, hospital readmission or clinical illness, and vaccines given 
All data will be checked for quality control by a member of the study team. 
A blood culture will be taken from all neonatal participants at birth, prior to starting antibiotic 
treatment wherever safe to do so.  The volume of blood taken for culture must be 1-2ml and 
will be verified by weighing blood culture bottles.  Training will be provided to paediatric and 
study teams on blood culture sampling methods, including adequate cleaning of the skin 
prior to sampling.  Feedback on sample volumes and contamination rates (see section 7.3 
for definition) will be fed back to paediatric consultants, and additional staff training offered.  
Where sample is remaining after 3ml is added to the culture bottle, the excess will be 
retained as a blood spot sample for use in future ethically approved research, where the 
participant has consented to this. 
The results of the microbiological tests will be communicated to the participant’s clinical 
team in real time – see section 7.3.4 for further details.  Clinician diagnosis of sepsis will be 
recorded where a clinician of registrar level or above has recorded a diagnosis of sepsis 
within 48 hours of birth.  To reduce the subjectivity of this measure, GAIA level of certainty 
for a diagnosis of sepsis will also be assessed (35) and the level of diagnostic certainty 
recorded (1-5, as per GAIA criteria).  Medical records are recorded on paper at Kawempe 
Hospital, before being coded into an electronic medical record by data clerks.  PDF scans of 
medical records will be available where detailed review is required. 
All participant data will be collected by trained study staff, and recorded into an online CRF 
hosted on REDCap, a secure web application.  No patient identifiable data will be stored on 
the main REDCap database.  See section 8.6 for further details of data management plans. 
The postnatal information will be completed via a telephone call at 28 days post-delivery, 
made by study staff.  Participants’ contact number, a contact number for their partner, and a 
contact number for a second friend or family member will be taken at recruitment to minimise 
loss to follow up due to changes to mobile telephone number.  Participant identity will be 
confirmed using three factors (date of birth, full name, address/village). The following 
information will be collected: 
 
Health status of mother and baby 
 
Any visits to medical care for the neonate 
o If attended or admitted to Kawempe hospitals, these electronic medical 
records will be reviewed by study staff 

 
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o If attended elsewhere, the mother will be asked the reason for presenting, 
whether the neonate was admitted to hospital, the main diagnosis, and any 
treatments given. 
Recruitment of the first 160 participants from labour ward (10% of the overall sample) will be 
treated as a pilot, and both recruitment and data will be reviewed at this point with particular 
attention to the following aspects: 
 
Demographic comparison between recruited women and the population attending 
Kawempe 
 
Data completion 
 
Volume of blood culture samples taken  
 
Loss to follow up rates 
 
Association between potential risk factors and clinical sepsis, or culture-positive 
sepsis 
After this pilot analysis, the data collection instrument may be adjusted to be more concise if 
some risk factors appear to have no relationship with neonatal outcomes.  Additionally, the 
recruitment strategy may be adjusted to include women attending antenatal clinics to 
improve the representativeness of the sample if the pilot sample is significantly different 
demographically to the general population of pregnant women attending the same study site 
antenatally.  Finally, feedback and staff training will be given as needed to ensure optimal 
sample volumes and data completeness. 
During Phase 1, two separate focus group discussions with 10 participants each will be held 
with staff from Kawempe Hospital.  One discussion will be with senor clinicians, and one with 
junior clinicians and midwives.  The focus groups will be held on site at Kawempe in English.  
Focus groups are being held in order to complement the quantitative results of this project 
by highlighting risk factors currently perceived as significant (which should then be a key part 
of analysis and results dissemination to confirm their usefulness), inform of current 
diagnostic difficulties to guide future work with the risk stratification model, and provide 
context for development and communication of the model to maximise its utility.  The focus 
groups will address the following issues: 
 
Ascertain knowledge and awareness of neonatal sepsis 
 
Management of neonatal sepsis 
 
 regarding risks and consequences of neonatal sepsis, and  
 
Current diagnostic strategies and their perceived strengths and difficulties 
 
Perceptions of neonatal sepsis and how this might affect diagnosis and treatment 
 
Recommendations to enhance the diagnosis and treatment of neonatal sepsis  
The focus groups will be conducted by members of MUJHU’s social sciences team and Dr 
Sarah Sturrock.  A tool has been developed by the social sciences team and Dr Sturrock for 
these discussions (see attached).  The discussions will be recorded, and recordings 
destroyed once the discussions have been transcribed.  The social sciences team will then 
complete thematic analysis of the discussions. 
We anticipate Phase 1 will take place between January and July 2024. 
Phase 2 will use the data collected in phase 1 to construct a risk stratification model, in 
collaboration with the Advanced Research Computing Centre at University College London. 
We anticipate Phase 2 will take place between March and September 2024. 

 
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Phase 3 will involve external validation of the risk stratification tool. This phase will take 
place at Sally Mugabe Central Hospital in Harare, Zimbabwe.  Participants will be recruited 
from antenatal clinics and labour wards by study staff. 
The same data will be collected from participants in Phase 3 as in Phase 1.  Ideally, data 
collection will take place via the Neotree platform(41) – this will be decided once the factors 
included in the model have been determined, and whether they are already in routine 
collection via Neotree.  If the Neotree platform will not capture sufficient data to match the 
inputs of the model, an online CRF will be created as in Phase 1 via REDCap.  This decision 
will be made in collaboration with the local study team prior to the start of recruitment.   
The primary outcome for Phase 3 will be senior clinician diagnosis of sepsis recorded within 
48 hours of delivery, and blood cultures (when available) will only be taken in neonates 
judged to be at risk of, or with clinical signs of sepsis as per standard clinical guidelines.  
Using senior clinician diagnosis of sepsis as the outcome is because supply of blood culture 
bottles and laboratory analysis of cultures is unreliable and is unlikely to be available to all 
study participants as in the Phase 1 setting.  The risk stratification model will be applied to 
these data to determine its accuracy in a second population and explore its generalisability. 
We anticipate Phase 3 will take place between September 2024 and March 2025. 
The TRIPOD (Transparent reporting of a multivariable prediction model for individual prognosis 
or diagnosis) statement have been followed in the design of this study and will be followed in its 
reporting(42).  Adjustments will be made as necessary with the release of upcoming artificial 
intelligence-specific guidelines. 
 
Data analysis 
 
Phase 2 will constitute the main data analysis for data from Uganda. 
 
There will be two main streams to data analysis, which will be completed using R and in 
collaboration with the Advanced Research Computing Centre at UCL. 
1. Machine learning analysis: 
a. Data from Phase 1 will form a ‘training data set’, with 20% reserved for testing 
and validation, and be fed into a pre-programmed machine 
b. Previous methods used for sepsis prediction that will be investigated include 
gradient tree boosting and neural networks 
c. Bootstrapping will be considered 
d. Determination of the exact machine learning model will depend on the data 
obtained, but the following will be considered: 
i. Weighted inputs: Allows scaling of measurements 
ii. Conformal prediction: Allows the machine to determine how uncertain 
its prediction is, based on distribution of data it has, and missing data 
iii. Recurrent neural networks: Allows the machine to recognise time-
dependent patterns  
iv. Gradient descent: A method of ‘training’ a machine to minimise the 
error between prediction and reality by gradually adjusting its weights  
2. Traditional logistic regression will be used to identify risk factors associated with 
clinical and microbiological diagnoses of neonatal sepsis.  Although the principal 
model will be created using machine learning techniques, logistic regression will be 

 
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used as a second check of results and to provide additional context with which to 
interpret the machine learning model 
 
For Phase 3, data will be inputted to the machine learning model from Phase 2, and the 
sensitivity, specificity, positive predictive and negative predictive values calculated, as well 
as the AUC value. 
 
 
6 
STUDY SETTING 
 
Phase 1 
Recruitment and data collection will take place at Kawempe Hospital in Kampala, Uganda.  
These sites are sufficiently busy to recruit the proposed sample size, have the necessary 
infrastructure to process blood cultures and have significant experience of running research 
studies within the labour and post-natal ward.  Kawempe Hospital is a national referral 
hospital for high-risk pregnancies and local deliveries.  Approximately 25,000 births occur at 
Kawempe annually, with approximately 11,000 neonatal admissions annually.  Mulago 
Hospital is a large hospital with children’s services(43).  Electronic health records are 
currently used in both including antenatal and perinatal information, which are generated by 
digital data entry from paper hospital records.  Blood cultures are currently processed at 
Makerere Microbiology Laboratories (College of American Pathologists accredited) using 
BACTEC machines within 12 hours of collection(43).   
Phase 2  
Data analysis and modelling will take place in collaboration with the Advanced Research 
Computing Centre at University College London. 
 
Phase 3 
Recruitment and data collection will take place at Sally Mugabe Central Hospital in Harare, 
Zimbabwe.  Again, this site is large enough to recruit the proposed sample size, with 22,000 
births annually, and has existing research infrastructure to enable quality data collection(41).  
The Neotree platform is in routine use at Sally Mugabe Central Hospital. 
 
7 
SAMPLE AND RECRUITMENT 
7.1  
Eligibility Criteria 
 
7.1.1 Inclusion criteria  
All neonates born at one of the study sites are eligible for recruitment.  Where mothers have 
had an ultrasound scan during pregnancy during routine care, neonates will be eligible for 
inclusion if they are born at ≥34 weeks gestational age.  For mothers who have not had an 
ultrasound scan during pregnancy or have uncertain dates, neonates will be eligible for 

 
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inclusion if their birth weight is ≥1400g which will include the majority of births ≥34 weeks 
according to the results of the INTERGROWTH-21st Project(44). 
7.1.2 Exclusion criteria  
Neonates born at <34 weeks gestational age will be excluded due to their higher background 
risk of sepsis, and automatic admission to the neonatal unit with antibiotic administration.  
Similarly, neonates with congenital abnormalities or syndromes associated with increased 
susceptibility to infection will be excluded (e.g. gastroschisis). Neonates whose mothers are 
unable to provide informed consent will also be excluded.  There are no exclusion criteria on 
the basis of maternal medical or obstetric comorbidities. 
 
7.2  
Sampling 
 
7.2.1  Size of sample 
Phase 1: There is currently no accepted way to power a machine learning study, the 
consensus being that more data will always create a better model.  However, to ensure the 
success of this study, we have investigated approximate power to create a diagnostic test to 
estimate the sample size needed for this study.  We have also reviewed this sample size 
calculation with our collaborators at the UCL Advanced Research Computing centre. 
 
A study of a diagnostic test with 90% sensitivity and a background sepsis rate of 5% (the 
estimated rate of culture-positive sepsis in Kampala based on previous studies is 12.8%(13) 
amongst those with clinical signs of sepsis, and up to 5% amongst all neonates according to 
unpublished data from previous studies at Kawempe) would require 1460 participants (95% 
confidence interval), as described by Buderer and Jones et al(45,46).   
 
This sample size has also been calculated according to three suggested methods for 
calculating the sample size for a clinical prediction model: 
o Calculation of size needing to provide precise estimate of overall outcome 
risk 
o Calculation of size needed to produce predicted values with a small mean 
error across all individuals 
o Size needed considering shrinkage of predictor effects 
 
All of the above methods produced sample sizes from 140-1532, hence the size of 1600 
should be sufficient even with some loss to follow up.  If, at interim analysis, the rate of 
culture-positive sepsis is significantly lower, we will explore methods to increase our sample 
size such as using existing data to create simulated data to train our model. 
 
Phase 3: The sample size will be 900 mothers; this is powered to give 95% confidence for a 
diagnostic test which has 90% sensitivity with a background rate of clinical sepsis of 16%, 
using the same calculation method as for the sample size in Phase I.  Although there are 
limited previous studies of neonatal sepsis prevalence in Zimbabwe using blood cultures, 
particularly amongst all neonates, one study found a culture-positive sepsis rate of 21% 
amongst NICU admissions(47), and a recent study reported a clinical diagnosis rate of 
40.8%(48) .  Therefore, this sample size should give an estimated 367 episodes of clinician-
diagnosed sepsis, deemed by Collins et al. to be sufficient for external validation of a 
prognostic model(49). 
 

 
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7.2.2  Sampling technique 
Convenience sampling will be used for this study.  Potential participants will be identified by 
local clinical staff, and then approached by the study team – see section 7.3 for further details 
of recruitment procedures. 
 
7.3 
 Recruitment 
7.3.1 Participant identification  
Study sensitisation will occur in the antenatal clinic at Kawempe, Mulago and Sally Mugabe 
Central Hospitals.  Posters will be available to assist with this, and local staff will be sensitised 
to the study and able to direct interested potential participants to study staff to answer any 
questions. 
Study staff will approach potential participants (identified by local clinical staff) attending labour 
wards and antenatal clinics at participating sites, a recruitment technique which has been used 
successfully in previous studies at Kawempe Hospital.  Patient information sheets, translated 
into local languages, will be provided, and posters and leaflets with study details will also be 
available on site.  Participants in the second stage of labour (fully dilated) will not be 
approached for recruitment.  
Following pilot analysis, if the sample obtained does not seem to be representative of the 
overall population of pregnant women delivering at the site (for example, if the recruited sample 
includes a higher percentage of women with uncomplicated deliveries), recruitment will be 
adjusted to include women attending antenatal clinics at study sites.  Participants will be 
approached if they are attending an antenatal clinic at >34 weeks gestation and planning to 
attend antenatal and delivery care at a study site.   
Normal antenatal and perinatal care will be provided at the same centre as recruitment, so no 
additional study visits should be required. 
 
7.3.2 Consent 
Informed consent will be obtained at the point of recruitment in Uganda, by trained study 
staff, for data collection and the taking of a sample.  Consent must be given by the mother, 
who will have parental responsibility for the neonate. Wherever possible, consent should be 
taken from the mother and the father; where it is not possible to take consent from the 
father, an explanation for this will be entered into the CRF.  Neonates born to mothers who 
die during or after labor must have informed consent taken from their father or caretaker. 
Potential participants, including mothers/fathers/caretakers for the neonates,  will be 
provided with an information leaflet and consent documents, in English or Luganda, and will 
have the opportunity to ask any questions about the study.  They will also be allowed time to 
discuss the study with family members if they wish and will be given contact details for the 
study team.  The study team are experienced with studies involving pregnant people and 
new parents, including those that involve consenting and data collection during labour. 
If happy to proceed, the participant will sign an informed consent form, either in Luganda or 
English.  A photocopy of the signed consent form will be retained by the local study team. 
In Zimbabwe, consent procedures will be finalized once the risk stratification model has 
been constructed and the risk inputs needed to inform the model are known.  If all risk inputs 
are part of the routine Neotree data set, then specific consent will not be sought.  If the 

 
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model structure is such that additional information needs to be collected from participants, 
then specific consent will be sought to collect these data. 
 
Consent provisions for collection and use of participant data and biological specimens  
 
No patient identifiable data (name, date of birth, address, national ID, hospital number) will 
be stored on the main REDCap database – only pseudonymised data with study 
ID.  Telephone numbers and addresses, to enable follow up, will be stored with patient ID 
and date of birth only, on a separate REDCap database accessible via an encrypted 
password protected laptop stored in a locked cabinet at the hospital site in 
Kawempe.  These patient identifiable data will be destroyed once follow up is 
complete.  Microbiological samples will be stored and managed according to SGUL and 
MUJHU's regulations.  Consent will be sought from participants for storage of samples for 
future ethically approved research. 
Data from Zimbabwe will be stored securely in-country on a local server, in accordance with 
local regulations.  Pseudonymised data only will be extracted for statistical analysis. 
All data will be archived by SGUL after study completion. Pseudonymised data may be used 
for future research projects by the team at SGUL or collaborators, subject to ethical 
approval.  Data will be stored after the completion of the study in SGUL-managed 
storage.  File format conversion will be done to make the data suitable for long-term 
retention. 
Participants will be able to withdraw from the research at any point – their identifiable data 
(such as contact details) would then be removed from the database on REDCap, but 
existing pseudonymised data retained for analysis. 
Participants will have access to the results of the microbiological specimens, which will be 
communicated via their clinical team. 
 
7.3.3 Data collection tool  
 
Case Report Forms will be designed by the CI.  All CRFs will be electronic.  The CI will 
provide logins to relevant and trained site level members of the research team.  
It is the Investigator’s responsibility to ensure the accuracy of all data entered and recorded 
in the CRFs. The Staff Delegation of Responsibilities Log should identify all trial personnel 
responsible for data collection, entry, handling and managing the database. 
Prospective patient-level data will be collected from electronic health records (and 
participant interviews where needed) into an online data entry system (REDCap).  This is a 
secure web application for building and managing online surveys and databases which 
supports online and offline data capture. This data entry system has been chosen as it will 
improve the consistency of the data, and allows them to be immediately available in an 

 
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analysable format.  The data are also stored securely without being stored on an individual 
hard drive, ensuring their safety and long-term access without need for physical backup. 
These data will be anonymised, and will include the parameters outlined in section 5. 
Microbiological samples will be taken from the baby, which will be processed locally for 
blood culture.  Gram stain, organism and resistance profile will be inputted to REDCap. 
Each data entry form will include the username of the member of study staff who filled out 
the form, and the date and time it was filled out.  Each form will also include a field to input 
the date, time, and name of the person marking the form as 'complete'.   The data entry 
forms will be tested by members of study staff before being put into production.  Data will be 
reviewed monthly to ensure that data quality is sufficiently high. 
Quality control mechanism 
Once the study has begun, trained members of study staff will be given their own logins to 
REDCap to input data.  Study staff will also be able to contact the research team to resolve 
any queries about how or where to input data.  Data will either be collected directly from 
research participants or from their electronic hospital records, but will be inputted directly 
into REDCap.  Data will be reviewed by two study staff members to check for data 
completeness with reference to scanned medical records. 
 
All data described above will be inputted into a password protected database designed using 
REDCap. The database will be stored on a secure server at MUJHU. Access will be strictly 
controlled and for the study investigators only. The data will be kept on the database server 
and a file backup server. There will be encrypted back up tapes. These are stored in a 
secure storage facility with 24-hour security. All computers are connected to a local area 
network that is protected by firewalls operating to accepted standards and are protected by 
antivirus software. No patient identifiable information is stored on desktop or portable media.  
 
 
7.3.4 Biological Sample Handling 
Blood samples will be taken from each neonate for culture (1-2ml) into BACTEC blood 
culture bottles and stored at room temperature until arrival at the laboratory.  Sample volume 
will be verified by weight, and samples will be labelled with the neonate’s ID. 
Samples will be processed at Makerere Microbiology Laboratories (College of American 
Pathologists accredited) within 12 hours of collection(43).  Samples will be incubated for 36 
hours.  Any presumptive positive culture will be plated onto selective agar and identified by 
biochemical tests.  Couriers will collect samples from study sites and transport them to the 
Medical and Molecular Laboratories (MML) at Makerere University; coverage is available at 
weekends and some evenings by request from study staff, so a courier will be arranged if a 
sample is taken more than 12 hours before the next scheduled courier collection. 
Sample results will be classified and handled as follows: 
- 
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o Pathogens included as per GAIA neonatal infection outcome list of 
‘recognised pathogens’(35) 
o Study staff will notify clinical team and participant’s parents by telephone; 
written notification also to be provided from laboratory (either via electronic 
system or hard copy) as per routine care 
- 
Contaminant: growth of species considered ‘non pathogenic’ by GAIA list 
o Review clinical notes to determine presence of risk factors for invasive 
disease as a result of a normally commensal/contaminant organism (e.g. 
indwelling venous catheters causing risk of coagulase negative 
staphylococcal sepsis) 
o If no risk factors, written notification provided to clinical team from laboratory 
as above 
o If presence of risk factors or uncertainty, study team to additionally contact 
clinical team via telephone to notify 
- 
Negative result: no growth 
o Written notification provided to clinical team from laboratory as above 
Specific written consent will be taken from participants to store any leftover samples for use 
in future ethically approved research. 
 
8 
ETHICAL AND REGULATORY CONSIDERATIONS 
 
8.1 
Assessment and management of risk 
Risks to participants 
As this is an observational cohort study, the only risk to participants is the discomfort of 
blood sampling for culture.  Venous blood sampling is a safe procedure but can cause 
temporary discomfort and requires the infant to be held still.  Sampling will be done by 
trained members of study staff to minimise discomfort.  The risk prediction model will not be 
shared with clinicians at study sites until study completion to avoid influencing treatment 
decisions. 
There is also a risk of confidentiality loss in the case of any data breach.  To minimise this 
risk, study data will be kept on a separate database to participant identifiable information and 
all data will be kept on secure, encrypted databases accessible only to study staff. 
COVID-19 
At present, there is not expected to be any increased risk of COVID-19 for participants or 
staff as a result of their involvement in the study.  However, should this change, a COVID-19 
risk assessment and management strategy is given below. 
COVID-19 Risk Assessment and Management Strategy 
All staff employed by SGUL and/or SGH NHS Foundation Trust are required to complete an 
ongoing COVID-19 risk assessment prior to undertaking any work on site, which includes 
research activity. This process is continuously monitored by the responsible line manager.  

 
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Participants (unaffected or affected) will not be recruited if they are deemed high risk or are 
in close contact with someone at risk. The Research Team will contact research participants 
ahead of scheduled study visits on-site to check for COVID-19 symptoms and the symptom 
check will be repeated when patients attend the hospital site for the study visit. 
Participants will receive information regarding the extra precautions that will be taken in light 
of the COVID-19 pandemic in the Patient Information Sheet. This will detail steps that 
patients should take if they have concerns about exposure to COVID-19 through 
participating in the research, or believe that they are symptomatic or have been in close 
contact with another person believed to be symptomatic. The Patient Information Sheet will 
also have contact details for the Research Team for patients to get in touch if they have any 
concerns or queries about this. 
All research personnel are expected to comply with the NHS Trust and University policies on 
COVID-19. 
All patients attending the hospital site for research visits and/or routine clinical follow-up will 
be expected to abide by the NHS Trust and University policies on COVID-19 which include 
wearing suitable PPE (provided by the NHS Trust on arrival), adhering to the visitor policy on 
social distancing and following the one-way routing systems whilst on site. 
The schedule of study assessments has been designed so that they align with the current 
routine clinical pathway for this patient population, and follow up can be done remotely via 
telephone call. 
Therefore, research participants and site staff are not perceived to be at any additional risk 
of exposure to COVID-19 through participation in this research study. 
 
8.2  
 Research Ethics Committee (REC) and other Regulatory review & reports 
Before the start of the study, a favourable opinion will be sought from an appropriate REC 
for the study protocol, informed consent forms and other relevant documents e.g. 
advertisements.  
 
Regulatory Review & Compliance  
Before any site can enrol patients into the study, the Chief Investigator/Principal Investigator 
or designee will ensure that appropriate approvals from participating organisations are in 
place. Specific arrangements on how to gain approval from participating organisations are in 
place and comply with the relevant guidance. 
Amendments  
For any amendment to the study, the Chief Investigator or designee, in agreement with the 
sponsor will submit information to the appropriate body in order for them to issue approval 
for the amendment. The Chief Investigator or designee will work with sites (R&D 
departments at NHS sites as well as the study delivery team) so they can put the necessary 
arrangements in place to implement the amendment to confirm their support for the study as 
amended. 

 
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8.3  
Peer review 
This protocol has been peer reviewed by two independent reviewers with expertise in 
infectious disease and epidemiology. 
 
8.4  
Patient & Public Involvement 
The study has been reviewed with the Community Advisory Board at MUJHU Care Ltd at an 
early stage, as well as with local researchers with experience conducting research in Kampala 
and Harare.  As a result, the protocol involves no study visits in-person outside of routine 
clinical care, as many patients travel long distances in order to attend the study sites for 
antenatal and labour care.   
Further PPI activities will take place in-country, including:  
- 
Paper-based surveys for patients to ascertain knowledge and understanding of 
neonatal sepsis 
- 
Focus groups with local clinicians to assess opinions on the use of risk stratification 
systems for clinical decision making 
- 
Regular meetings with the Community Advisory Board 
 
8.5 
Protocol compliance  
Protocol deviations, non-compliances, or breaches are departures from the approved 
protocol. 
All protocol deviations must be adequately documented on the relevant forms and reported 
to the REC, Chief Investigator and Sponsor immediately.  
Deviations from the protocol which are found to frequently recur are not acceptable, will 
require immediate action and could potentially be classified as a serious breach. 
 
8.6 
Data protection and patient confidentiality  
All data should be handled in accordance with the Data Protection Act 2018 (UK 
implementation of the EU General Data Protection Regulation (GDPR)) and local regulations 
in Uganda and Zimbabwe.  
Any Case Report Forms (CRFs) will not bear the participant’s name or other directly 
identifiable data. The participant’s trial Identification Number (ID) only, will be used for 
identification. The Subject ID log can be used to cross reference participant’s identifiable 
information. 
 
8.7 
Indemnity 
 
St George’s University of London sponsored research: 
As this is a non-interventional, observational study and in accordance with local and SGUL 
guidelines, insurance is not required to cover injury as a result of the study. 

 
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Where the Trial is conducted in a hospital, the hospital has a duty of care to participants. St 
George’s University of London will not accept liability for any breach in the hospital’s duty of 
care, or any negligence on the part of hospital employees.  
 
8.8 
Access to the final study dataset 
Aggregated and coded anonymised data only will be suitable for sharing.    
All data requests will be handled by the SGUL Research Data Management Service, alongside 
project and university representatives, in accordance with university policies.  Data access will 
be granted for research use, dependent on relevant ethical and institutional approval and only 
pseudonymised data.  Data will only be available to the research team until the data are 
published, as soon as possible after the end of the study and within 6 months of close of the 
study. 
To enable the anonymised data to be discovered and responsibly accessed by academic 
parties, it will be publicised through networks in the UK, Uganda and Zimbabwe, presentations, 
and peer reviewed publications.  Metadata will be openly available with instructions on how 
these data can be accessed via the SGUL Research Data Repository.   
 
9 
DISSEMINATION POLICY 
9.1  
Dissemination policy 
Publication: “Any activity that discloses, outside of the circle of trial investigators, any final or 
interim data or results of the Trial, or any details of the Trial methodology that have not been 
made public by the Sponsor including, for example, presentations at symposia, national or 
regional professional meetings, publications in journals, theses or dissertations.” 
All scientific contributors to the Trial have a responsibility to ensure that results of scientific 
interest arising from Trial are appropriately published and disseminated. The Sponsor has a 
firm commitment to publish the results of the Trial in a transparent and unbiased manner 
without consideration for commercial objectives.  
To maximise the impact and scientific validity of the Trial, data shall be consolidated over the 
duration of the trial, reviewed internally among all investigators and not be submitted for 
publication prematurely. Lead in any publications arising from the Trial shall lie with the chief 
investigator in the first instance.  
Before the official completion of the Trial,  
All publications during this period are subject to permission by the Sponsor. If an investigator 
wishes to publish a sub-set of data without permission by the Sponsor during this period, the 
Steering Committee/the Funder shall have the final say.  
Exempt from this requirement are student theses that can be submitted for confidential 
evaluation but are subject to embargo for a period not shorter than the anticipated remaining 
duration of the trial.      
Up to 180 days after the official completion of the Trial  
During this period the Chief Investigator shall liaise with all investigators and strive to 
consolidate data and results and submit a manuscript for peer-review with a view to publication 
in a reputable academic journal or similar outlet as the Main Publication.  

 
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 
The Chief Investigator shall be senior and corresponding author of the Main 
Publication.  
 
Insofar as compatible with the policies of the publication outlet and good academic 
practice, the other Investigators shall be listed in alphabetic order.  
 
Providers of analytical or technical services shall be acknowledged, but will only be 
listed as co-authors if their services were provided in a non-routine manner as part 
of a scientific collaboration.  
 
Members of the Steering Group shall only be acknowledged as co-authors if they 
contributed in other capacities as well.   
 
If there are disagreements about the substance, content, style, conclusions, or 
author list of the Main Publication, the Chief Investigator shall ask the Steering 
Group to arbitrate.     
Beyond 180 days after the official completion of the Trial  
After the Main Publication or after 180 days from Trial end date any Investigator or group of 
investigators may prepare further publications.  In order to ensure that the Sponsor will be able 
to make comments and suggestions where pertinent, material for public dissemination will be 
submitted to the Sponsor for review at least sixty (60) days prior to submission for publication, 
public dissemination, or review by a publication committee. Sponsor’s reasonable comments 
shall be reflected. All publications related to the Trial shall credit the Chief and Co-Investigators 
as co-authors where this would be in accordance with normal academic practice and shall 
acknowledge the Sponsor and the Funders.    
 
9.2          Archiving Arrangements  
Each site will be responsible for their onsite level study archiving. The trial essential TMF along 
with any central trial database will be archived in accordance with the sponsor SOP.  
 
10 
REFERENCES 
1. Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, and national 
causes of under-5 mortality in 2000–15: an updated systematic analysis with 
implications for the Sustainable Development Goals. The Lancet. 2016 
Dec;388(10063):3027–35.  
2. Velaphi SC, Westercamp M, Moleleki M, Pondo T, Dangor Z, Wolter N, et al. 
Surveillance for incidence and etiology of early-onset neonatal sepsis in Soweto, South 
Africa. Larson SD, editor. PLoS ONE. 2019 Apr 10;14(4):e0214077.  
3. Schmatz M, Srinivasan L, Grundmeier RW, Elci OU, Weiss SL, Masino AJ, et al. 
Surviving Sepsis in a Referral Neonatal Intensive Care Unit: Association between Time 
to Antibiotic Administration and In-Hospital Outcomes. The Journal of Pediatrics. 2020 
Feb;217:59-65.e1.  
4. Migamba SM, Kisaakye E, Komakech A, Nakanwagi M, Nakamya P, Mutumba R, et al. 
Trends and spatial distribution of neonatal sepsis, Uganda, 2016–2020. BMC Pregnancy 
Childbirth. 2023 Nov 4;23(1):770.  
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understanding global patterns of antimicrobial use and resistance in neonatal sepsis: 
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availability and use in 20 low- and middle-income countries. Bull World Health Organ. 
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8. Mendelson M, Røttingen JA, Gopinathan U, Hamer DH, Wertheim H, Basnyat B, et al. 
Maximising access to achieve appropriate human antimicrobial use in low-income and 
middle-income countries. The Lancet. 2016 Jan;387(10014):188–98.  
9. National Institute for Health and Care Excellence. Neonatal infection: antibiotics for 
prevention and treatment. 2021.  
10. Sands K, Carvalho MJ, Portal E, Thomson K, Dyer C, Akpulu C, et al. Characterization 
of antimicrobial-resistant Gram-negative bacteria that cause neonatal sepsis in seven 
low- and middle-income countries. Nat Microbiol. 2021 Mar 29;6(4):512–23.  
11. Thomson KM, Dyer C, Liu F, Sands K, Portal E, Carvalho MJ, et al. Effects of antibiotic 
resistance, drug target attainment, bacterial pathogenicity and virulence, and antibiotic 
access and affordability on outcomes in neonatal sepsis: an international microbiology 
and drug evaluation prospective substudy (BARNARDS). The Lancet Infectious 
Diseases. 2021 Dec;21(12):1677–88.  
12. Kayange N, Kamugisha E, Mwizamholya DL, Jeremiah S, Mshana SE. Predictors of 
positive blood culture and deaths among neonates with suspected neonatal sepsis in a 
tertiary hospital, Mwanza- Tanzania. BMC Pediatr. 2010 Dec;10(1):39.  
13. Tumuhamye J, Sommerfelt H, Bwanga F, Ndeezi G, Mukunya D, Napyo A, et al. 
Neonatal sepsis at Mulago national referral hospital in Uganda: Etiology, antimicrobial 
resistance, associated factors and case fatality risk. Butaye P, editor. PLoS ONE. 2020 
Aug 10;15(8):e0237085.  
14. Klingenberg C, Kornelisse RF, Buonocore G, Maier RF, Stocker M. Culture-Negative 
Early-Onset Neonatal Sepsis — At the Crossroad Between Efficient Sepsis Care and 
Antimicrobial Stewardship. Front Pediatr. 2018 Oct 9;6:285.  
15. Popescu CR, Cavanagh MMM, Tembo B, Chiume M, Lufesi N, Goldfarb DM, et al. 
Neonatal sepsis in low-income countries: epidemiology, diagnosis and prevention. 
Expert Review of Anti-infective Therapy. 2020 May 3;18(5):443–52.  
16. Kluyts HL, Le Manach Y, Munlemvo DM, Madzimbamuto F, Basenero A, Coulibaly Y, et 
al. The ASOS Surgical Risk Calculator: development and validation of a tool for 
identifying African surgical patients at risk of severe postoperative complications. British 
Journal of Anaesthesia. 2018 Dec;121(6):1357–63.  
17. Biccard BM, Du Toit L, Lesosky M, Stephens T, Myer L, Prempeh AB, et al. Enhanced 
postoperative surveillance versus standard of care to reduce mortality among adult 
surgical patients in Africa (ASOS-2): a cluster-randomised controlled trial. The Lancet 
Global Health. 2021 Oct;9(10):e1391–401.  

 
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Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda. 
Brock G, editor. PLoS ONE. 2015 Jul 28;10(7):e0133950.  
19. Medvedev MM, Brotherton H, Gai A, Tann C, Gale C, Waiswa P, et al. Development and 
validation of a simplified score to predict neonatal mortality risk among neonates 
weighing 2000 g or less (NMR-2000): an analysis using data from the UK and The 
Gambia. Lancet Child Adolesc Health. 2020 Apr;4(4):299–311.  
20. Neal SR, Fitzgerald F, Chimhuya S, Heys M, Cortina-Borja M, Chimhini G. Diagnosing 
early-onset neonatal sepsis in low-resource settings: development of a multivariable 
prediction model. Arch Dis Child. 2023 Aug;108(8):608–15.  
21. Folgori L, Ellis SJ, Bielicki JA, Heath PT, Sharland M, Balasegaram M. Tackling 
antimicrobial resistance in neonatal sepsis. The Lancet Global Health. 2017 
Nov;5(11):e1066–8.  
22. Goel N, Shrestha S, Smith R, Mehta A, Ketty M, Muxworthy H, et al. Screening for early 
onset neonatal sepsis: NICE guidance-based practice versus projected application of the 
Kaiser Permanente sepsis risk calculator in the UK population. Arch Dis Child Fetal 
Neonatal Ed. 2020 Mar;105(2):118–22.  
23. Kaiser Permanente. Probability of Neonatal Early-Onset Sepsis Based on Maternal Risk 
Factors and the Infant’s Clinical Presentation. [Internet]. Available from: 
https://neonatalsepsiscalculator.kaiserpermanente.org 
24. World Health Organization. WHO launches new roadmap on human resource strategies 
to ensure that all newborns survive and thrive [Internet]. 2020. Available from: 
https://www.who.int/news/item/17-11-2020-who-launches-new-roadmap-on-human-
resource-strategies-to-ensure-that-all-newborns-survive-and-thrive 
25. Bonniface M, Nambatya W, Rajab K. An Evaluation of Antibiotic Prescribing Practices in 
a Rural Refugee Settlement District in Uganda. Antibiotics. 2021 Feb 9;10(2):172.  
26. Glaser MA, Hughes LM, Jnah A, Newberry D. Neonatal Sepsis: A Review of 
Pathophysiology and Current Management Strategies. Advances in Neonatal Care. 
2021 Feb;21(1):49–60.  
27. Simonsen KA, Anderson-Berry AL, Delair SF, Davies HD. Early-Onset Neonatal Sepsis. 
Clin Microbiol Rev. 2014 Jan;27(1):21–47.  
28. Patras KA, Nizet V. Group B Streptococcal Maternal Colonization and Neonatal Disease: 
Molecular Mechanisms and Preventative Approaches. Front Pediatr. 2018 Feb 22;6:27.  
29. Ocviyanti D, Wahono WT. Risk Factors for Neonatal Sepsis in Pregnant Women with 
Premature Rupture of the Membrane. J Pregnancy. 2018;2018:4823404.  
30. Celik IH, Hanna M, Canpolat FE, Mohan Pammi. Diagnosis of neonatal sepsis: the past, 
present and future. Pediatr Res. 2022 Jan;91(2):337–50.  
31. Huber S, Hetzer B, Crazzolara R, Orth-Höller D. The correct blood volume for paediatric 
blood cultures: a conundrum? Clinical Microbiology and Infection. 2020 Feb;26(2):168–
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32. Wynn JL. Defining neonatal sepsis. Current Opinion in Pediatrics. 2016 Apr;28(2):135–
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33. on behalf of the Infection, Inflammation, Immunology and Immunisation (I4) section of 
the ESPR, McGovern M, Giannoni E, Kuester H, Turner MA, Van Den Hoogen A, et al. 
Challenges in developing a consensus definition of neonatal sepsis. Pediatr Res. 2020 
Jul;88(1):14–26.  
34. Sands K, Spiller OB, Thomson K, Portal EA, Iregbu KC, Walsh TR. Early-Onset 
Neonatal Sepsis in Low- and Middle-Income Countries: Current Challenges and Future 
Opportunities. IDR. 2022 Mar;Volume 15:933–46.  
35. Vergnano S, Buttery J, Cailes B, Chandrasekaran R, Chiappini E, Clark E, et al. 
Neonatal infections: Case definition and guidelines for data collection, analysis, and 
presentation of immunisation safety data. Vaccine. 2016 Dec;34(49):6038–46.  
36. Munoz FM, Eckert LO, Katz MA, Lambach P, Ortiz JR, Bauwens J, et al. Key terms for 
the assessment of the safety of vaccines in pregnancy: Results of a global consultative 
process to initiate harmonization of adverse event definitions. Vaccine. 2015 
Nov;33(47):6441–52.  
37. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to 
Machine Learning, Neural Networks, and Deep Learning. Transl Vis Sci Technol. 2020 
Feb 27;9(2):14.  
38. Kuzniewicz MW, Walsh EM, Li S, Fischer A, Escobar GJ. Development and 
Implementation of an Early-Onset Sepsis Calculator to Guide Antibiotic Management in 
Late Preterm and Term Neonates. The Joint Commission Journal on Quality and Patient 
Safety. 2016 May;42(5):232–9.  
39. Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP. Multicenter 
Comparison of Machine Learning Methods and Conventional Regression for Predicting 
Clinical Deterioration on the Wards: Critical Care Medicine. 2016 Feb;44(2):368–74.  
40. Wiens J, Shenoy ES. Machine Learning for Healthcare: On the Verge of a Major Shift in 
Healthcare Epidemiology. Clinical Infectious Diseases. 2018 Jan 6;66(1):149–53.  
41. Gannon H, Chimhuya S, Chimhini G, Neal SR, Shaw LP, Crehan C, et al. Electronic 
application to improve management of infections in low-income neonatal units: pilot 
implementation of the NeoTree beta app in a public sector hospital in Zimbabwe. BMJ 
Open Qual. 2021 Jan;10(1):e001043.  
42. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a 
multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the 
TRIPOD statement. BMJ. 2015 Jan 7;350(jan07 4):g7594–g7594.  
43. Kyohere M, Davies HG, Musoke P, Nakimuli A, Tusubira V, Tasimwa HB, et al. 
Seroepidemiology of maternally-derived antibody against Group B Streptococcus (GBS) 
in Mulago/Kawempe Hospitals Uganda - PROGRESS GBS. Gates Open Res. 2020 Nov 
13;4:155.  
44. Villar J, Giuliani F, Bhutta ZA, Bertino E, Ohuma EO, Ismail LC, et al. Postnatal growth 
standards for preterm infants: the Preterm Postnatal Follow-up Study of the 
INTERGROWTH-21 st Project. The Lancet Global Health. 2015 Nov;3(11):e681–91.  

 
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45. Buderer NMF. Statistical Methodology: I. Incorporating the Prevalence of Disease into 
the Sample Size Calculation for Sensitivity and Specificity. Academic Emergency 
Medicine. 1996 Sep;3(9):895–900.  
46. Jones SR, Carley S, Harrison M. An introduction to power and sample size estimation. 
Emerg Med J. 2003 Sep;20(5):453–8.  
47. Nathoo KJ, Mason PR, Chimbira TH. Neonatal septicaemia in Harare Hospital: aetiology 
and risk factors. The Puerperal Sepsis Study Group. Cent Afr J Med. 1990 
Jun;36(6):150–6.  
48. Mathela SK, Mapfumo C, Mzingwane ML. Risk factors and practices contributing to 
sepsis in neonates admitted at a Children’s Hospital in Zimbabwe [Internet]. In Review; 
2022 May [cited 2023 Dec 6]. Available from: https://www.researchsquare.com/article/rs-
1641840/v1 
49. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external 
validation of a multivariable prognostic model: a resampling study. Statistics in Medicine. 
2016 Jan 30;35(2):214–26.  
 
 
 

 
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11.  
APPENDICIES 
 
 
11.1  Appendix 1 
 
Schedule of Procedures 
Procedures 
Visits (insert visit numbers as appropriate) 
Screening 
Antenatal 
Delivery 
1 Month 
Informed consent 
x 
 
 
 
Completion of relevant 
CRF 
 
x 
x 
x 
Blood culture (neonatal) 
 
 
x 
 
Blood spot for PCR 
(neonatal) 
 
 
x 
 
Follow up (telephone)  
 
 
 
x 
 
 
11.2 
Appendix 2 
Amendment Log 
Amendment 
No. 
Protocol 
version no. 
Date issued 
Author(s) of 
changes 
Details of changes made 
1 
2.1 
09.02.2024 
Clarification on focus group rationale; 
see section 5 
 
 
11.3   Appendix 3 
Complete the form below. It will require review and sign-off by the Institute Director (SGUL) 
or the Care Group Lead (SGHFT). 
 
 
 
 
Research Data Protection Impact Assessment (DPIA) 
 

 
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Data Protection Impact Assessments (DPIAs) are a tool which can help organisations identify the 
most effective way to comply with their data protection obligations under the Data Protection Act 
2018 (DPA 18) and meet individuals’ expectations of privacy.  
 
A DPIA helps identify data privacy risks when planning new, or revising existing, projects and to 
identify actions to mitigate these risks. In the rare cases where risks cannot be mitigated at all it 
may be necessary to consult with the Information Commissioner's Office (ICO). Under data 
protection legislation it is a legal requirement to complete a DPIA in the following circumstances: 
 
• where data processing is likely to result in a high risk of harm to individuals, e.g. new, 
invasive technology is proposed 
 
• when large volumes of personal data are processed, e.g. use of behavioural profiles 
based on website usage 
 
• when processing special category personal data on a large scale, e.g. healthcare data, 
genetic tests to assess and predict the disease/health risks 
• where publicly accessible areas are monitored, e.g. CCTV or when filming public areas 
 
Therefore a DPIA will be carried out for both internal and partnership projects which require the 
collection/processing of personal data in any format for the purpose of research.   
 
The DPIA should be carried out towards the start of the project, in order to identify any associated 
information risks and mitigate in the early stages, before you start processing. 
 
 
Study Title/Acronym: 
 
Using machine learning to model early-onset neonatal sepsis risk 
in Uganda 
 
JRES Reference Number: 
TBD 
 
Chief Investigator Name: 
Chief Investigator Email 
Address: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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PROJECT DETAILS 
Project / process description: 
- include / attach processing operations (include a flow diagram or another way of explaining data flows), 
the purpose and, where applicable, what St George’s lawful basis is for the processing of the information. 
 
Phase 1 & 2 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Phase 3 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
REDCap @ MUJHU 
Study 
participant 
(mother) 
eCRF 
Laboratory 
@ MUJHU 
Sample collection with 
patient identifier 
Sample result 
(study ID only) 
Pseudonymised 
data collection 
SGUL Research 
Team 
Advanced Research 
Computing Team @ UCL 
Statistical analysis and modelling 
Local server 
Study participant 
NeoTree 
app 
Anonymised 
data extraction 
SGUL Research 
Team 
Clinical 
team 
Sample result 
Separate 
eCRF  
Separate 
REDCap 
database  
Study 
participant 
(baby) 
Routine data collection 

 
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All research data will be anonymised at source, upon entrance to REDCap where a study ID will be 
used and no patient-identifiable information.  This anonymised data will be transferred from MUJHU 
and BRTI respectively to SGUL for statistical analysis.  Anonymised data only will be transferred 
between SGUL and UCL (Advanced Research Computing team) for the purposes of statistical 
modelling. 
 
Data will be held locally on secure servers at MUJHU and BRTI, and centrally at SGUL. 
 
What personal data do you intend to use, and why? (List all categories)  
 
The data listed in the table in Section 3 will be collected via an online CRF. 
 
These data will be used because all are potentially related to an infant’s risk of early-onset neonatal 
sepsis, and therefore are relevant and required to answer the study’s research question. 
 
Will the personal data be identifiable, pseudonymised or anonymised (if a mix tick accordingly)  
Identifiable 
 
 
*Pseudonymised 
Yes 
 
Anonymised 
 
 
*Confirm that the key to this data is kept securely away from the used data with strict controlled access 
  
 
 
List all organisations / agencies which will have access to the personal data collection used for this project 
/ process 
St George’s, University of London 
Makerere University Johns Hopkins University collaboration 
Biomedical Research and Training Institute, Zimbabwe 
University College London 
Primary institutional review boards and monitors 
 
Length of the study – include an assessment of the necessity and proportionality of the processing in 
relation to the purpose.  Also include who, internally & externally, has been consulted in the preparation of 
this DPIA. 
 
Recruitment will take place over an 18 month period from September 2023-March 2025.  The overall 
project is scheduled to be completed by March 2026. 
 
The followin  eo le have been consulted in preparation of this DPIA: 

 
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If external organisations / agencies are involved, is there a contract or information sharing agreement in 
place with suitable clauses for data protection and data incident reporting,? If not why not? 
A data sharing agreement will be in place between SGUL and UCL, SGUL and MUJHU, and SGUL 
and BRTI prior to the transfer or sharing of any data in this study, with suitable clauses for data 
protection and data incident reporting.  Only pseudonymised data will be shared between SGUL and 
UCL, for purposes of statistical analysis and modelling only. 
 
RISK 
Can you achieve your objectives using anonymised data? – see ICO Code of Practice on Anonymisation  
Yes 
X 
 
No 
 
Why 
not? 
 
What are the benefits to the individual of their personal data being used for this purpose?  
 
There are no benefits to specific individual, but this study will hopefully help to develop a tool that can be 
applied in future to aid in timely diagnosis and management of neonatal sepsis in their community.  
 
What are the organisational benefits of the individual’s personal data being used for this purpose? 
 
Use of personal data for the purposes of this study will be used to generate a risk stratification model for 
neonatal sepsis.  Any resulting manuscripts or conference presentations will recognise the contribution of 
the organisation in creating this research, and will assist in building the organisation’s profile within 
neonatal and paediatric infectious diseases research.  Additionally, although the code used to create the 
model will be available via Github for use by others, this model may be used to develop an app or web 
interface to aid in neonatal sepsis risk stratification, which would also raise the organisation’s profile within 
health technology. 
 
What are potential negative impacts to the individual of their personal data being used for this purpose in 
the event of a Data Breach occurring? 
 
All sensitive information will be fully anonymised at the point of data entry into REDCap, so in the event of a 
Data Breach an individual will not be identifiable.  Similarly, a data breach during data sharing between 
MUJHU or BRTI and SGUL should not result in the release of any patient identifiable information or data.  
 
How will you avoid causing unwarranted or substantial damage/distress to the individual when using their 
personal data for this purpose?  
All sensitive information will be anonymised at the point of entry into REDCap using a study ID.   
All data will be collected and stored in accordance with SGUL and MUJHU's information governance 
and data management policies, which are compliant with the NHS Data Security and Protection Toolkit 
and are Cyber Essentials Plus certified.  SGUL and MUJHU are REDCap consortium partners, and 
each have a dedicated server which uses Secure Sockets Layer (SSL) to protect data in transit.  Data 

 
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sharing agreements will be in place between SGUL and UCL, and SGUL and BRTI to enable secure 
data sharing for statistical analysis. 
Is the data already held by St George’s? 
Yes 
 
 
No 
X 
Prospective study 
Is it held by one of the partner organisations / agencies involved in this process/project? 
Yes 
 
 
No 
X 
Which agency will be 
collecting the data 
MUJHU, BRTI 
Have you told the individuals whose personal data you want to use for this purpose, how and why you 
intend to use their data? 
Yes 
 
 
No 
X 
Prospective collection 
If not, are you intending to tell them? 
Yes 
X 
Will be included in Informed Consent form and Patient Information 
Sheet 
No 
 
 
 
 
 
Why 
not? 
 
 
 
 
 
 
 
 
Do you already have the individual’s consent to use their data for this purpose?  
Yes 
 
 
No 
X 
Why 
not? 
Prospective study – participants will provide consent at 
recruitment 
If not, are you going to ask for their permission? 
Yes 
X 
 
No 
 
Why 
not? 
 
Have individuals been given the opportunity to refuse us permission to use their data for this purpose?  
Yes 
X 
Declining to consent to participation in the study will have no impact on 
their clinical care 
No 
 
 
How will you make sure that the personal data you are using is kept accurate and up to date?  
 
Information will be verified at each study visit, with particular attention paid to contact information (e.g. 
telephone number) to enable follow up.  This patient identifiable information will be stored with patient ID 
and date of birth only, on a separate REDCap database accessible via an encrypted password protected 
laptop stored in a locked cabinet at the hospital site in Kawempe.  These data will be destroyed once follow 
up is complete.   
 
What steps or controls are you taking to minimise risks to privacy? 
Please tick those which apply and provide details of how each is ensured 
 
Risks to individual privacy are minimal 
 
Personal data is pseudonymised 
 
Encryption of data at rest, i.e. when stored 
 
Encryption used in transfers 
 
Limited identifiable data collected 
 
Pseudonymisation at point of online CRF 

 
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 
Information compliance training for staff has 
been completed - data protection, 
information security, FOI 
 
Special category personal data is not used 
 
Participant opt out at any stage of the research 
 
Research is not used to make decisions 
directly affecting individuals 
 
Short retention limits 
 
Restricted access controls 
 
 
Encryption used in storage and transfer by 
using REDCap and data sharing 
agreements 
 
Information compliance training will be 
completed by all study staff 
 
No special category personal data will be 
stored in REDCap 
 
Participants can withdraw at any point 
 
Personal data will be kept in country 
(Uganda or Zimbabwe) 
 
Data will only be stored for as long as is 
necessary 
 
Access restricted to trained study staff 
How long will you need to hold the personal data for after the study has completed? 
Personal data to enable contact, and identifiable personal data, such as telephone numbers and address to 
enable follow up will be destroyed by the local site once follow up is complete. 
Study data will be archived by SGUL after study completion in accordance with standard SGUL procedures. 
How will you make sure that you are holding data for the appropriate length of time and no longer? 
The study protocol and data management plan will be peer reviewed, and reviewed by the relevant research 
ethics authorities. 
 
How will the data be held /stored?  
 
No patient identifiable data (name, date of birth, address, national ID or hospital number) will be stored on 
the main REDCap database.  Telephone numbers and addresses, to enable follow up, will be stored with 
patient ID and date of birth only, on a separate REDCap database accessible via an encrypted password 
protected laptop stored in a locked cabinet at the hospital site in Kawempe.  These data will be destroyed 
once follow up is complete.  Microbiological samples will be stored and managed according to SGUL and 
MUJHU's regulations.  Consent will be sought from participants for storage of samples for future ethically 
approved research. 
 
Data will be archived after the completion of the study in SGUL-managed storage.  File format conversion 
will be done to make the data suitable for long-term retention. 
 
Will you be using any electronic and/or paper Case Report Forms (CRFs) to collect data? If so what are 
these and how will they be held securely and managed at the end of the project? 
Prospective patient-level data will be collected from electronic health records (and participant interviews 
where needed) into an online data entry system (REDCap).  This is a secure web application for building and 
managing online surveys and databases which supports online and offline data capture. This data entry 
system has been chosen as it will improve the consistency of the data, and allows them to be immediately 
available in an analysable format.  The data are also stored securely without being stored on an individual 
hard drive, ensuring their safety and long-term access without need for physical backup. 
Informed Consent Forms will be stored securely by the local research team according to MUJHU policies, 
and electronic medical records data will be held on a secure local areas network at Kawempe National 
Referral Hospital and extracted periodically to a secure server at SGUL. 
 

 
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Will personal data be transferred/shared between the organisations involved in this project? If so how? 
No identifiable data will be shared between organisations.  Data sharing agreements will be in place for 
sharing of anonymised data 
 
Will you be transferring personal data to a country or territory outside of the UK? If yes, name countries and 
receiving parties. 
Yes – within EEA 
 
 
Yes – outside of EEA 
 
 
No 
X 
Anonymised data will be transferred from countries outside the UK to 
the UK for analysis 
How will you ensure that third parties will comply with data protection obligations? 
Study staff members will be required to complete study-specific training before being given access to 
REDCap.  Study staff members will also need to show evidence of locally relevant research training, such as 
Good Clinical Practice certification, GDPR training and international equivalent. 
 
What measures are in place to ensure only appropriate and authorised access to and use of, personal 
data? 
 
All study staff will have an individual log in to REDCap to input data with specific permissions granted for 
data entry and/or editing as required.  All will have an end date added to their access to REDCap, and this 
end date will be amended as necessary in the case of staff leaving the study team. 
 
The access list on REDCap will be reviewed by the study management team on a monthly basis to 
ensure it is current and accurate.   
 
How will technical and organisational security be monitored/audited? 
The PI will have primary responsibility for data management and storage.  The project's dedicated data 
manager, project manager, and study coordinator will be responsible for daily administration of the data, 
data quality and data curation.  Data security responsibility will be shared between project members and 
SGUL IT staff. 
 
 
Declaration 
 
I confirm that the information recorded on this form is, to the best of my knowledge, an accurate 
and complete assessment of the potential privacy impacts of this study. 
 
Name: 
 
Signature: 
 
 
 
Date
 
 
Institute Director (SGUL) or Care Group Lead (SGHFT) 
 

 
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Name: 
 
Signature: 
 
Date: 
 
 
JRES Reviewer 
 
Name: 
 
Signature: 
 
Date: