Child Growth and Development in East London
Conditions: Stunted Growth, Stunting, Growth Disorders, Child Development
Sponsor: Queen Mary University of London

 
 
 
 
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Joint Research Management Office (JRMO) Research Protocol 
for Research Studies 
 
 
 
 
Full Title  
Child growth and development in east London 
 
Short Title 
Child growth in east London   
 
Sponsor 
Queen Mary University of London (Queen Mary) 
 
Contact person: 
 
Dr Mays Jawad 
Joint Research Management Office 
Barts Health NHS Trust and Queen Mary 
University of London 
Research Services  
Dept W 
69-89 Mile End Road 
London 
E1 4UJ 
 
Email: research.governance@qmul.ac.uk  
 
IRAS Number 
IRAS project ID: 275717 
Integrated Research Application System 
 
Version Number 
4 
 
Date 
17th August 2023 
 
 
Sponsor (EDGE) Number 
 141964 
 
REC Reference 
21/WA/0385 
Research Ethics Committee 
 
Chief Investigator (CI) 
Prof Andrew Prendergast 
Wellcome Trust Senior Clinical Fellow and Professor of 
Paediatric Infection and Immunology, 
Blizard Institute, Queen Mary University of London,  
4 Newark Street, London E1 2AT 
020 7882 2269; a.prendergast.qmul.ac.uk 
 

 
 
 
 
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List of sites  
Blizard Institute, Queen Mary University of London, 4 Newark Street London E1 
2AT  
Prof Andrew Prendergast  
Prof Robert Walton  
Dr Joseph Freer 
Dr Joanna Orr, study statistician  
 
Centre for Endocrinology, Queen Mary, University of London, Charterhouse 
Square, London EC1M 6BQ  
Prof Leo Dunkel  
Prof Helen Storr 
 
 
Tower Hamlets GP Care Group 
1st Floor, Beaumont House, Mile End Hospital, Bancroft Road, London, E1 4DG  
Kamaljit Kaur  
 
 
 
 
 
 

 
 
 
 
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1. Contents 
 
1. 
Contents ........................................................................................................ 3 
2. 
Glossary ........................................................................................................ 5 
3. 
Signature page .............................................................................................. 6 
4. 
Summary and synopsis ................................................................................. 7 
5. 
Introduction .................................................................................................. 12 
5.1. 
Background ............................................................................................. 12 
5.2. 
Rationale ................................................................................................. 13 
5.3. 
Risks / benefits ........................................................................................ 14 
6. 
Study objectives .......................................................................................... 14 
6.1. 
Primary objective ..................................................................................... 14 
6.2. 
Secondary objective................................................................................. 15 
6.3. 
Primary endpoint ...................................................................................... 15 
6.4. 
Secondary endpoint ................................................................................. 16 
7. 
Study population .......................................................................................... 16 
7.1. 
Inclusion criteria ....................................................................................... 17 
7.2. 
Exclusion criteria ...................................................................................... 17 
8. 
Study design ................................................................................................ 17 
9. 
Study procedures ........................................................................................ 18 
Children will be identified from the central health visitor lists held by the Health 
Visiting team at the Tower Hamlets GP Care Group (GPCG), which is a federation 
of 36 General Practices in Tower Hamlets and a partner in Tower Hamlets 
Together, a local integrated care partnership. ..................................................... 18 
10. 
Statistical considerations ............................................................................. 31 
10.1. 
Sample size ......................................................................................... 31 
10.2. 
Method of analysis ............................................................................... 31 
11. 
Ethics........................................................................................................... 34 
11.1. 
Annual Safety Reporting ...................................................................... 34 

 
 
 
 
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12. 
Public involvement ....................................................................................... 35 
13. 
Data handling and record keeping ............................................................... 35 
13.1. 
Data management ............................................................................... 35 
13.1. 
Source Data ......................................................................................... 36 
13.2. 
Confidentiality ...................................................................................... 37 
13.3. 
Record retention and archiving ............................................................ 37 
14. 
Safety reporting ........................................................................................... 37 
15. 
Monitoring and auditing ............................................................................... 38 
16. 
Study committees ........................................................................................ 38 
17. 
Finance and funding .................................................................................... 39 
18. 
Insurance and indemnity .............................................................................. 39 
19. 
Dissemination of research findings .............................................................. 39 
20. 
References .................................................................................................. 40 
 
 

 
 
 
 
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2. Glossary 
 
Linear growth faltering / short stature / stunting  
Children are defined as having short stature, otherwise known as linear growth 
faltering or stunting, if their height-for-age is more than two standard deviations below 
the population median height. 
Linear growth faltering also encompasses reduced growth velocity, which is defined 
as a growth velocity of less than the 10th percentile, or the growth curve crossing 2 
SD lines or centiles on a growth chart.  
ASQ-3 
Ages and Stages Questionnaire, third edition. Standard instrument used in the 
Healthy Child Programme to assess early child development in children aged one 
month to 5 and a half years.  
GMDS 
Griffiths Mental Development Scale. Development measure administered by child 
psychologists.   
NCMP 
National Child Measurement Programme.  

 
 
 
 
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3. Signature page 
 
 
 
CI Agreement 
 
The study, as detailed within this Research Protocol, will be conducted in accordance 
with the principles of GCP , the UK Policy Framework for Health and Social Care 
Research, and the Declaration of Helsinki and any other applicable regulations. I 
delegate responsibility for the statistical analysis and oversight to a qualified 
statistician (see declaration below). 
 
 
CI name: 
__Prof Andrew Prendergast_______________ 
 
Signature: 
____
_____________ 
 
Date: 
_____9Th September 2022____________ 
 
Statistician’s Agreement 
 
The study as detailed within this research protocol will be conducted in accordance 
with the current UK Policy Framework for Health and Social Care Research, the World 
Medical Association Declaration of Helsinki (1996), Principles of ICH E6-GCP, ICH E9 
- Statistical principles for Clinical Trials and ICH E10 - Choice of Control Groups. 
 
I take responsibility for the statistical work in this protocol is accurate and take 
responsibility for statistical analysis and oversight in this study.  
 
Statistician’s name: 
___Dr Joanna Orr______________ 
 
Signature: 
_____
____________ 
 
Date: 
_______9th September 2022__________ 
 
 
 

 
 
 
 
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4. Summary and synopsis 
 
Short title 
Child growth in east London 
Methodology 
Pilot growth screening programme of 630 children 
recruited from Tower Hamlets, East London 
Objectives / aims 
The overarching objective of this grant is to detect 
linear growth failure early in childhood by employing an 
automated growth screening algorithm for pre-school 
age children. The screening algorithm will be created 
using national data and piloted for feasibility and 
acceptability in Tower Hamlets, East London.  
We have two interlinked aims, with the following 
hypotheses:  
Aim 1: Pilot an automated growth screening algorithm in 
a cohort of 630 children in east London. 
Hypothesis 1: Health visitor monitoring of child height 
using an automated growth-screening algorithm is 
feasible and is acceptable to health visitors and 
caregivers. 
Hypothesis 2: Referral of children to a paediatric 
growth clinic has high uptake and identifies growth 
problems in pre-school age children.  
 
Hypothesis 3: An additional height measurement by a 
health visitor or at the child’s preschool booster 
immunisation appointment at their GP practice is 
feasible and acceptable.  
 
Hypothesis 4: Linkage of children’s health visitor data 
to their height measurement as part of the NCMP is 
feasible and is acceptable to caregivers.  
 
Hypothesis 5: A smartphone app is feasible and 
acceptable for caregivers to use to measure their 
children’s height at pre-school age, and there is 
acceptable inter-test variability between the smartphone 
measurement and the clinician measurement with a 
stadiometer. 
 

 
 
 
 
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Aim 2: Generate pilot data to investigate the best 
indicators to detect poor school readiness and 
developmental problems in early childhood. 
Hypothesis 1: The addition of growth and development 
data to other environmental and socioeconomic 
variables (including Index of Multiple Deprivation) 
increases the pick-up rate of children at risk of poor 
school 
readiness 
and/or 
children 
with 
delayed 
development.  
Hypothesis 2: There is strong correlation between the 
existing measurement of child development in pre-school 
age children (ASQ-3) and a gold standard developmental 
assessment by a child psychologist.  
Number of 
participants 
We will enrol a cohort of 630 children from Tower 
Hamlets, East London into a pilot screening programme 
at age 2-2.5 years. 
 
A clinical sub-study of 200 children with confirmed 
diagnosis of a growth disorder will also be analysed. 
Inclusion and 
exclusion criteria 
Aims 1 and 2 (630 children recruited). 
Inclusion criteria:  
Children aged 2-2.5 years, who live in Tower Hamlets 
and whose caregiver(s) are willing to provide written 
informed consent. 
.   
Exclusion criteria: Children will be excluded if: 
• 
The caregiver does not provide written informed 
consent. 
• 
The child is not able to stand for an accurate 
height measurement. 
 
Clinical sub-study (200 children) 
 
Inclusion criteria:  
 
Children or young people with confirmed diagnosis of a 
growth disorder, who are receiving or received 
treatment at the paediatric endocrinology clinic at the 

 
 
 
 
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Barts NHS Trust, and who’s data was collected as part 
of a clinical effectiveness audit.  
 
Exclusion criteria:  
 
• 
Parent or carer of the child does not consent to 
child or parental data to be shared 
• 
Patient aged 16 years or over does not consent 
to his/her data being shared  
Statistical 
methodology and 
analysis (if 
applicable) 
Statistical methodology will be overseen by the study 
statistician, Dr Joanna Orr. 
Aim 1 
We will enrol 630 children into the pilot screening 
programme and acquire measurements of height at three 
time points. Based on a 95% attendance at the two 
follow-up appointments, we aim to have three height 
measurements available on at least 600 children by age 
4-5 years.  
Each child enrolled at age 2.0-2.5 years will have 
baseline 
anthropometry 
(height 
and 
weight), 
demographic data and a developmental assessment 
conducted by their Health Visitor as part of standard 
care. In addition to standard data collection, measured or 
reported parental height will also be recorded. Follow-up 
anthropometric measurements will be repeated 6-12 
months later by the health visitor or at the child’s pre-
school booster immunisation appointment, which takes 
place at approximately age 3 years 4 months. Parents’ 
height will also be measured or reported at the follow-up 
visit if missing. Children will have their height measured 
by a school nurse at age 4-5 years as part of the National 
Child Measurement Programme (NCMP). We will link the 
primary care data to this measurement. This will enable 
the growth screening algorithm to be applied to these 
three time points. The algorithm will be used to detect 
differences in detection of linear growth failure taking into 
account successive additional datapoints: height at age 
2.0-2.5 years, 2.5-3.5 years and 4.0-5.0 years, delta 
height/growth velocity and distance from parental height.  
At each measurement, we will identify the 2-3% of 
children with the poorest growth, in whom prior studies 
show a high prevalence (30-40%) of medical disorders, 

 
 
 
 
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using the screening algorithm, and refer these children 
for medical assessment by a paediatric endocrinologist 
(Prof Storr).1,2,3,4,5 
We estimate in this pilot study that 12 children will be 
referred for investigations (based on referral of 2% 
children with the poorest growth) and that 30-40% of 
these investigated in clinic will have an underlying 
medical disorder identified (n=4-5) based on the 
published literature. This pilot study is not designed to 
evaluate whether the age at diagnosis is significantly 
reduced using an automated screening approach; rather, 
our pilot data will inform feasibility and acceptability for a 
larger NIHR grant application to test the screening 
programme at scale with a primary outcome of age at 
diagnosis of medical disorders. 
We will also investigate the inter-test agreement 
between the heights measured by caregivers using a 
smartphone app and heights measured by health 
professionals using a stadiometer.  
Aim 2 
Aim 2 of the pilot study will generate pilot data to 
investigate the best indicators to detect poor school 
readiness and/or developmental problems in early 
childhood. 
We will investigate whether the addition of growth and 
development 
data 
to 
other 
environmental 
and 
socioeconomic variables increases the pick-up rate of 
children at risk of poor school readiness and/or children 
with delayed development.  
Growth will be assessed using three height 
measurements as described above and development 
assessed as part of standard care by a Health Visitor 
and practice nurse.  
As part of routine care, each child’s development will be 
assessed using both the caregiver and health visitor 
components of the ASQ-3. Following the 2.0-2.5 year 
contact, a random sample of 150 children will also be 
invited to enrol in a sub-study to generate pilot data to 
investigate the correlation between the ASQ-3 and 
development assessed by a child psychologist using a 

 
 
 
 
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Gold Standard development battery, the Griffiths Mental 
Development Scale (GMDS). The GMDS will be used to 
measure gross motor, fine motor, social, language, 
visual-spatial and practical reasoning skills. These data 
will inform a larger NIHR grant application to test the 
sensitivity of the parental and health visitor components 
of the ASQ-3. 
We will use the data to conduct analyses to test the 
hypothesis that linear growth failure either alone or in 
combination with additional factors identifies children 
with reduced neurodevelopment, who are therefore at 
risk of poor school attainment.  
Study duration 
3 years 
 

 
 
 
 
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5. Introduction 
 
Poor growth in childhood can be due to underlying medical causes or socioeconomic 
disadvantage.6 In low- and middle-income countries, poor linear growth (or stunting) 
affects almost one-quarter of children under 5 years.7 The prevalence of stunting in 
high-income countries is less well described and may be clustered sub-nationally in 
areas of disadvantage. A report in 2017 by the Patients Association suggested there 
may be a hidden burden of stunting in deprived areas of the UK (including Tower 
Hamlets) and called for systematic mapping of childhood stunting and consideration 
of pre-school screening.8 We have analysed national data (unpublished) and 
confirmed clusters of high stunting prevalence in east London, particularly in Tower 
Hamlets, Newham and Hackney, after adjustment for ethnicity. Using longitudinal 
data from the Millennium Cohort Study for over 10,000 children with height 
measurements at ages 3, 5, 7, 11, 14 and 17 years, we have found consistent 
associations between stunting at age 3 and cognitive measures throughout 
childhood. Regression models adjusted for socio-economic variables showed a 
robust significant association between stunting and neurodevelopment (unpublished). 
We therefore believe that linear growth failure may be an important marker of 
disadvantage and reduced long-term potential among UK children, particularly in 
regions with substantial deprivation. 
 
A screening programme may be valuable to refer children with severe growth failure 
to hospital for early investigation, diagnosis and management of serious medical 
disorders. Screening would also identify a larger group of children with poor growth but 
no underlying medical cause. We believe short stature in this group of children is a 
marker of disadvantage and vulnerability that is currently overlooked. We will therefore 
evaluate whether linear growth failure, either alone or in combination with other social 
and environmental factors, is a feasible, acceptable and useful marker to detect 
reduced cognitive development and/or poor school readiness; if so, height screening 
could enable targeted pre-school education and social interventions, which would be 
a novel public health approach to improving educational and economic outcomes 
across the life-course. 
 
 
5.1. Background 
 
Children with linear growth failure (i.e. low height-for-age or inadequate height velocity) 
may have an underlying medical cause. Early management of disorders such as 
growth hormone deficiency (GHD) improves long-term outcomes, but diagnosis is 
often delayed.9 In Finland, an automated growth-screening algorithm has been 
adopted nationwide, leading to a younger age at diagnosis of conditions such as GHD 
and Turner syndrome.10 In the UK, children are measured at ages 4-5 and 10-11 years 
through the National Child Measurement Programme (NCMP) and health visitors 
measure growth parameters – with weight often prioritised over height - as part of the 

 
 
 
 
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Healthy Child Programme. However, there is no systematic national programme to 
measure and monitor the heights of pre-school children, the systems do not effectively 
‘join up’ data and there is no referral system for linear growth failure if it is identified by 
the NCMP. Moreover, the NCMP’s focus is childhood obesity rather than linear growth, 
and the current cross-sectional approach of measuring children once on school entry 
and once on exit has limited utility in the detection of disorders underlying growth failure 
(99.4% specificity but < 30% sensitivity). 
Many UK children with linear growth failure have no identified medical cause after 
investigations and are discharged from clinical follow-up. A child’s dietary intake 
explains only a small proportion of stunting, and genetics do not account for all the 
variation in height in the first 5 years of life.11 Linear growth is a sensitive marker of the 
overall wellbeing of a child, and impaired growth has multiple social and environmental 
causes. In low- and middle-income countries, stunting affects almost one-quarter of 
children, and is associated with higher mortality, impaired neurodevelopment, reduced 
educational attainment and lower adult economic productivity.12 Whether these 
associations are also apparent in children living in high-income countries in the 21st 
century is less well studied.  
We believe there are two situations in which UK children would benefit from linear 
growth screening. First, an algorithm with high specificity to detect children with 
medical disorders would lead to earlier referral, diagnosis and targeted treatment to 
improve clinical outcomes. Second, linear growth failure that is not explained by an 
underlying medical disorder may be an important marker of vulnerability that should 
not be ignored. Early identification of these children would ultimately enable tailored 
pre-school interventions to improve health, education and economic prospects across 
the life-course. This may be particularly important in areas such as east London where 
~50% children are living in poverty, the highest rate in London.13 
 
 
5.2. Rationale 
 
Currently, children with linear growth failure are identified ad hoc, if at all, because 
there is no systematic screening programme in the UK. Several other European 
countries systematically monitor linear growth in pre-school children, resulting in earlier 
diagnosis of medical causes of short stature.14 From parent testimonials, it is apparent 
that there are often delays in identifying serious medical disorders such as growth 
hormone deficiency. Developing a screening algorithm for the UK pre-school 
population would be a major step towards identifying growth failure earlier.  
Early identification leads to prompt and appropriate investigation, identification of 
potential co-morbidities and improves prognosis. Timely initiation of appropriate 
therapy is critical to minimise the impact of the disease process and maximise general 
health as well as height gain. Stunting is a sensitive marker of a child’s wellbeing, 
health and educational potential across the life-course. If we generate pilot data 

 
 
 
 
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suggesting that growth and development measures in early childhood both improves 
the early identification of vulnerable children and increases the sensitivity of detecting 
poor school readiness, it will provide a strong rationale for a new public health 
approach to identifying vulnerable children in need of pre-school support through 
simple measurements. Using our algorithm, the screening process would be 
automated and have potential for adoption at scale. 
 
 
5.3. Risks / benefits 
 
Benefits: Children in the pilot growth screening programme will benefit from additional 
pre-school height measurements, parental monitoring of longitudinal growth through 
use of an app, and feedback of their growth trajectory, which would not usually be 
undertaken. A subset of 150 children will benefit from a neurodevelopmental 
assessment prior to school enrolment, which could help inform an Education Health 
and Care Plan for additional educational support if needed. Any children with 
substantial developmental delay will be referred for assessment through existing NHS 
systems. Additionally, 2-3% of children (~12 in total) with the poorest growth will be 
referred for medical review/investigations at the Royal London Hospital and we 
anticipate that 4-5 of these children will have a serious, treatable underlying disorder 
detected, based on previous data.15,16 The diagnosis of these disorders (such as 
growth hormone deficiency) is frequently delayed, traumatic for parents, and 
associated with suboptimal long-term outcomes.  
 
Risks: We will collect sensitive data on household deprivation and social determinants 
of health on case report forms (CRFs). We are not able to completely de-identify CRFs 
because we will link the data to the follow-up assessment and (using NHS number) to 
the NCMP growth data held by Tower Hamlets Local Authority. However, data will be 
managed to ensure confidentiality as described in section 13. Research staff will 
undergo Good Clinical Practice and ethics training and the importance of confidentiality 
when reviewing sensitive clinical documents will be emphasised throughout the study. 
Child development assessments are potentially sensitive, because they measure 
cognitive performance, and can be misinterpreted or could be used to disadvantage a 
child. The results will be discussed in confidence with caregivers; we would seek their 
permission before sharing any of these findings and would only do so if it were in the 
best interests of child, for example to inform an Education Health and Care Plan to 
seek additional educational support for the child.  
 
 
6. Study objectives 
 
6.1. Primary objective 

 
 
 
 
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Pilot an automated growth-screening algorithm in east London. 
Within the primary objective we aim to test four hypotheses: 
1. Health visitor monitoring of child height using an automated growth-screening 
algorithm is feasible and is acceptable to health visitors and caregivers.  
2. Referral of children to a paediatric growth clinic has high uptake and identifies 
growth problems in pre-school age children.  
3. An additional height measurement by the child’s health visitor or at the 
preschool booster immunisation appointment at the child’s GP practice is 
feasible and acceptable.  
4. Linkage of children’s health visitor data to their height measurement as part of 
the NCMP is feasible and is acceptable to caregivers.  
5. A smartphone app is feasible and acceptable for caregivers to use to 
measure their children’s height at pre-school age, and there is acceptable 
inter-test variability between the smartphone measurement and the clinician 
measurement with a stadiometer. 
 
6.2. Secondary objective 
Generate pilot data to investigate the best indicators to detect poor school readiness 
and/or developmental problems in early childhood.  
Within the secondary objective we aim to test the following two hypotheses: 
1. The addition of growth and development data to other environmental and 
socioeconomic variables (including Index of Multiple Deprivation) increases the 
pick-up rate of children at risk of poor school readiness and/or children with 
delayed development.  
2. There is strong correlation between the existing measurement of child 
development in pre-school age children (ASQ-3) and a gold standard 
developmental assessment by a child psychologist. 
 
6.3. Primary endpoint 
• 
Feasibility and acceptability of the screening pilot.  
We will assess feasibility using Bowen et al.’s ‘8 areas of focus’:17 acceptability, 
demand, implementation, practicality, adaptation, integration, expansion and limited-
efficacy testing. To achieve this, we will employ a mixed methods evaluation 
including qualitative data collection using focus groups with caregivers and health 
visitors as well as questionnaires distributed to all caregiver participants in the study 
and all health visitors involved in growth and development assessments. We will also 
collect quantitative data on: uptake of growth measurements; number of successful 

 
 
 
 
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growth and development measurements; number of referrals successfully completed 
(i.e. referred to child growth clinic with participant attending one clinic appointment); 
number of participants consenting to identification of their child’s anthropometric data 
from NCMP; and uptake of the smartphone app.  
 
6.4. Secondary endpoints 
• 
Linear growth trajectory 
This will be assessed by child height measurements at age 2-2.5 years, age 2.5-3.5 
years, and age 4-5 years, in combination with target parental height. Parents who 
make use of the app will provide additional longitudinal growth measurements 
between age 2-5 years. All measurements will be entered into an algorithm, 
developed using data from two longitudinal UK cohorts (Millennium Cohort Study and 
Born in Bradford), which will identify children with poor linear growth based on 
absolute height, growth velocity and distance from target height. 
• 
Child development at age 2-2.5 years. 
This will be assessed using the ASQ-3 questionnaire and the GMDS, as described 
above. 
 
7. Study population 
 
This study will enrol a cohort of 630 children living in Tower Hamlets into a pilot 
screening programme at age 2.0-2.5 years and apply a growth-screening algorithm to 
identify children with poor growth. Children will be selected randomly from health visitor 
records in Tower Hamlets to ensure a representative sample, based on the inclusion 
and exclusion criteria outlined in section 7.1 and 7.2. We will include all children whose 
caregivers are willing to provide written informed consent to i) have a height/weight 
measurement (+/- a developmental assessment) together with a baseline 
questionnaire undertaken at age 2.0-2.5 years (by the health visitor) and a repeat 
weight/height measurement at age 2.5-3.5 years (either by the health visitor as an 
additional visit or by the GP surgery at the time of pre-school vaccinations); ii) undergo 
longitudinal growth screening by the parent using an app which measures a child’s 
height; and iii) have the child’s identifiers sent to Tower Hamlets Local Authority in 
order to access their weight/height measurement undertaken at school at age 4.0-5.0 
years through the NCMP programme, to prove feasibility of data linkage. We will work 
in partnership with Tower Hamlets GP Care Group to identify a representative sample 
of children from the borough based on health visitor lists, to ensure we minimise bias 
and include even the hardest-to-reach participants.  
We will also conduct a secondary analysis of a clinical sample of ~200 children with 
confirmed growth disorders e.g. Growth Hormone Deficiency, growth failure 
associated with small for gestational age and Turner syndrome (clinical sub-study). 

 
 
 
 
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These children are seen and managed in the paediatric endocrinology clinic at Barts 
NHS Trust. We will use this sample to test the growth screening algorithm and to 
identify optimal cut off values to maximize sensitivity and specificity. Retrospective 
consent will be sought from carers/parents to link the child’s diagnosis code, age at 
diagnosis and parental height to their previously collected NCMP height 
measurement. When the patient is over 16 years old, consent will be sought from 
both the parent/carer and the patient, as parental data will also be shared.  
 
 
7.1. Inclusion criteria 
 
• 
Aged 2-2.5 years 
• 
Live in Tower Hamlets 
• 
Caregiver willing to provide written informed consent. 
 
 
7.2. Exclusion criteria 
 
Children will be excluded if: 
• 
The caregiver does not provide written informed consent. 
• 
The child is not able to stand for an accurate height measurement. 
7.3 Clinical sub-study inclusion and exclusion criteria 
 
Clinical sub-study children will be included in this analysis if they were part of the 
clinical sample identified as having a growth disorder at Prof Storr’s clinic, and where 
consent can be obtained from the parent/carer and from the patient if the child patient 
is now over 16 years old.  
 
 
8. Study design 
 
We will enrol a longitudinal cohort of 630 children to an observational study in which 
weight and height will be measured as part of standard care by a Health Visitor in the 
community. Enrolment and baseline research data collection will be at age 2.0-2.5 
years, which coincides with a current contact point with Health Visitors. Parental 
height will also be measured at this visit (or reported height recorded if measurement 
is not possible). Prior to the visit, parents will complete the standard pre-school 
developmental assessment sent to all households by the Health Visitor (Ages and 
Stages Questionnaire; ASQ-3), and the informed consent form will request 

 
 
 
 
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permission to record these data for research purposes. A subgroup of 150 children 
will be offered a developmental assessment by a psychologist as a gold-standard 
comparison with the parent-reported ASQ-3. A follow-up height measurement for all 
children will be conducted by the health visitor approximately 6-12 months later as 
part of an extra preschool visit which is to be piloted in Tower Hamlets. If this is not 
operational by the time the study starts, the follow-up measurements will take place 
at the preschool booster vaccine appointment at the child’s GP surgery, which also 
occurs at age 3.0-3.5 years. If this measurement is missed for any reason, health 
visitors will attempt to measure the child at a community/ children’s centre or at home 
on a separate occasion. Children will also have their height and weight measured as 
part of the existing NCMP programme at school entry (age 4-5 years). Additionally, 
parents will be invited to monitor their child’s height through use of an app, if they 
own a smart phone, which can provide additional data-points between age 2-4 years.  
Using this approach, we are maximising opportunities to measure child growth at pre-
school ages by using existing contact points with health visitors and GP surgeries, 
and providing parents with the opportunity to measure their child’s height using a 
novel smartphone app. Longitudinal height measurements, together with data 
collected on parental height, will be offered up to an automated growth-screening 
algorithm that we have developed using existing national datasets. Children with the 
most severe growth faltering (bottom 2-3% of children) will be identified and their 
caregivers/parents contacted to offer them referral to the paediatric endocrinology 
service at the Royal London Hospital for investigation of medical disorders of growth 
(Prof Storr).  
 
 
9. Study procedures 
 
Children will be identified from the central health visitor lists held by the Health 
Visiting team at the Tower Hamlets GP Care Group (GPCG), which is a federation of 
36 General Practices in Tower Hamlets and a partner in Tower Hamlets Together, a 
local integrated care partnership. From these lists, children will be randomly selected, 
with replacement, in two groups: i) the main growth study (N=480), and ii) the growth 
and development sub-study (N=150), for a total of 630 children. Group 1 will only 
undergo growth screening, and group 2 will additionally have a gold standard child 
development assessment conducted at baseline. 
9.2 Screening 
Prior to the 2.0-2.5 year contact, children’s health visitors will contact the family to 
provide preliminary information about the study and explain that a PIS will be 
included with the ASQ-3 questionnaire that is routinely sent to families at this age. 
The health visitor will offer a follow-up phone call, video call or home visit to check 
the family received the PIS, to address any initial queries and to gauge their interest 
in the study. They will be offered a screening and consent visit to coincide with the 

 
 
 
 
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child’s planned 2-2.5 year assessment (section 9.2). At this visit, the child will be 
screened by a health visitor against the inclusion and exclusion criteria to determine 
eligibility. Caregivers of eligible children who are interested in joining will be asked to 
provide written informed consent as outlined in section 9.3. Using demographic 
information from the primary care platform (EMIS), the information will be 
communicated in the main language spoken by the caregiver. 
9.3 Informed consent 
Informed consent will be taken by the study health visitor in a confidential setting 
(within a children’s centre or the child’s house) using e-consent on the REDCap 
database, which includes the questions on the study informed consent form (Form 4). 
If necessary, consent can be taken on paper using the same form and will still be 
considered valid. This will take place either prior to the first 
measurement/development assessment at age 2-2.5 years or on the day of this 
assessment. An electronic signature will be taken; in addition to a tick box and 
declaration, permitted forms of electronic signatures will include a stylus or finger 
drawn signature or a typed name. The health visitor will explain all study procedures, 
risks and benefits and confidentiality considerations. The study health visitor will 
explain that the project involves two assessments by health professionals, 6-12 
months apart (one at the 2-2.5 year health visitor contact and one undertaken either 
by the GP surgery at the preschool booster immunisation appointment, or by the 
health visitor). In addition, as per standard care all children will have their 
development assessed by the health visitor using the parent-reported ASQ-3 
assessment. Parents will also be invited to download a smartphone app and to 
measure their child’s height at 3 timepoints, each time on the same day as the child’s 
height is measured by a health professional (health visitor, GP practice nurse, school 
nurse). Caregivers will also be asked for consent to health visitors’ collection of data 
on past medical history from children’s primary care records and linkage of any 
relevant diagnoses or medications to the children’s study data. It will also be made 
clear that there will be data linkage with the NCMP programme through Tower 
Hamlets Local Authority, which involves sharing identifiers (NHS number) if they are 
happy to provide consent for this to occur. Caregivers can opt out of this data linkage 
and their child can still be included in the internal study. Caregivers will be made 
aware that they will be provided with the child’s growth measurements and a 
summary of their growth pattern, and that children with severe growth failure will be 
contacted and referred to the paediatric endocrinology service, and children with 
significant developmental concerns will be referred for further assessment through 
existing NHS referral pathways. In addition to data entry onto the NHS system, the 
caregivers will be made aware that their child’s anthropometric and developmental 
data, and the parental heights will be recorded in a research database (REDCap) by 
the health visitor. Caregivers will be asked for consent for routine clinical data 
collected in the clinic (such as any diagnoses made by the endocrinologist) to be 
linked with the child’s data in the study. Prof Storr is a member of the study team and 

 
 
 
 
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leads the paediatric endocrinology clinic to which the children will be referred, and 
will enter any relevant clinical data onto the REDCap database. 
Sub-studies 
Development sub-study 
A randomly selected subset of 150 children will also be invited to an additional 
appointment with a child psychologist for a detailed developmental assessment, 
which is not part of standard care; children in this sub-study will be enrolled using a 
separate consent form (Form 5). The caregiver will be asked to provide written 
informed consent and to keep a copy of the PIS. The primary caregiver must have 
parental responsibility to provide consent. Assent will not be sought since all children 
will be between 3 and 4 years old.  
Where the developmental assessment suggests the child may be experiencing 
developmental delay, the study psychologist may share the Griffith’s report with the 
child’s Health Visitor with the parent’s permission. Reports will not be generated for 
all children and will only be generated when concerns are identified. The Griffith’s 
assessment result is not a formal diagnosis but can provide extra context for a 
clinician.  
Qualitative sub-study 
Following the first contact, we will invite a subgroup of participants in the study 
(caregivers) and health visitors working in Tower Hamlets to take part in focus 
groups. A total of 16 caregivers will be selected from the sample of children 
participating in the study to participate in two focus groups of between 6 and 8 
participants. Caregivers participating in focus groups will be consented using a 
separate PIS (PIS 16) and consent form (Form 14). 
We will also conduct one focus group with health visitors. A total of eight health 
visitors currently working within the Healthy Child Programme in Tower Hamlets will 
be sampled with the help of the health visiting team at the Tower Hamlets GPCG. 
Separate PIS and consent forms will be used for health visitors (PIS 17, Form 14).  
Clinical sub-study secondary analysis 
A separate clinical sample of children with diagnosed growth disorders will be 
analysed to test the algorithm and obtain optimal cut offs for growth screening. In 
order to obtain a height measurement previous to the child’s first attending growth 
clinic, we will seek to link the children’s clinical data to their NCMP height 
measurement (measured at school and recorded by NHS England at 4 or 5 years 
old).  
We will seek retrospective consent from the child’s parent/carer and, in cases where 
the child is over 16 years old, from the child patient themselves. Consent will be 
obtained electronically using a e-consent form through REDcap (Forms 22 and 23). 

 
 
 
 
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Unique secure links will be sent to patients or their families via email, after first 
sharing an electronic copy of the PIS (PIS 24 and 25).   
 
 
9.4 Schedule of study interventions  
The study schedule is shown in Table 1. 
 
Baseline (age 2-2.5 
years) 
 
Qualitative 
research 
sub-study  
Development 
sub-study2 
Follow-up (age 
2.5-3.5 years)3 
 
NCMP 
measurement  
Screening for 
eligibility (Form 3) 
Informed 
consent 
(Form ) 
Child 
development – 
GMDS 
Height 
measurement 
(child) 
Height 
measurement  
Informed consent 
(Form 4) 
Qualitative 
interview/ 
focus 
group  
(Form 12) 
Informed 
consent (Form 
5) 
Height 
measurement 
(parental) 
Weight 
measurement 
CRF_1 baseline 
questionnaire 
 
 
School enrolment 
information / 
SENCO input  
CRF_2 
endline 
questionnaire 
Height 
measurement 
 
 
 
 
Weight 
measurement 
 
 
 
 
Parental height1 
 
 
 
 
Child development 
(ASQ-3)  
 
 
 
 
 
1Attending parent’s height (s) measured (where possible) or estimated height 
documented if not attending 
2Children in the development sub-study will have all baseline procedures conducted 
in addition to the developmental assessment conducted by a psychologist 

 
 
 
 
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3Conducted either by the GP at the time of pre-school vaccinations or the health 
visitor through a home visit 
 
 
The following study forms will be used to capture data or provide information: 
 
Form name 
Details 
Administered by 
Patient Information Sheet 
– growth study (Form 1) 
 
Detailed information 
about what the study is 
about and what 
participation involves   
Health visitor  
Patient Information Sheet 
– growth and 
development study (Form 
2) 
 
Detailed information 
about what the study is 
about and what 
participation involves 
(including additional 
developmental 
assessment)  
Health visitor  
Screening form – 
inclusion and exclusion 
(Form 3) 
 
Initial check that child 
lives in study catchment 
area and there are no 
exclusion criteria 
Health visitor   
Consent form for growth 
study (Form 4) 
 
Signed by caregiver if 
informed consent given 
Health visitor   
Consent form for growth 
and development sub-
study (Form 5) 
 
Signed by caregiver if 
informed consent given 
Health visitor   
Baseline form (Form 6) 
 
Baseline data form 
includes height, weight, 
development and 
demographics. No 
identifiers.  
Health visitor  

 
 
 
 
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Identifiers/contact form 
(Form 7) 
 
Form with patient 
identifiers and contact 
details  
Study team 
Follow-up form (Form 8) 
 
Follow-up data form 
includes height, weight, 
development. No 
identifiers. 
Health visitor/ 
psychologist  
Exit form (Form 9) 
 
Used if caregiver wishes 
to withdraw from the 
study.  
Health visitor  
Referral form (Form 10) 
 
Template to write to 
participant’s GP asking to 
refer to paediatric clinic.  
PI  
NCMP linkage form (Form 
11) 
 
Identifiable information to 
submit to Tower Hamlets 
Local Authority to link to 
NCMP data.  
Study team  
Focus groups – 
qualitative research sub-
study discussion guide – 
caregivers (Form 12) 
Guidance for moderators 
of the focus groups of 
caregivers. 
Study team 
Focus groups – 
qualitative research sub-
study discussion guide – 
health visitors (Form 13) 
Guidance for moderators 
of the focus groups of 
health visitors. 
Study team 
Focus groups – 
caregivers. Consent form 
(Form 14) 
Signed by caregiver if 
informed consent given 
Study team  
Focus groups – health 
visitors. Consent form 
(Form 15) 
Signed by health visitor if 
informed consent given 
Study team  
Focus groups – 
caregivers. Information 
sheet (Form 16) 
Detailed information on 
the purpose of the sub-
study and what 
participation involves   
Study team  

 
 
 
 
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Focus groups – health 
visitors. Information sheet 
(Form 17) 
Detailed information on 
the purpose of the sub-
study and what 
participation involves   
Study team  
Evaluation questionnaire 
(caregivers) – 2.5-3.5 
years  (Form 18) 
Evaluation questionnaire 
for all caregivers to be 
completed after the 2.0-
2.5 year and the 2.5-3.5 
year contacts 
Study team (completed 
by caregiver) 
Evaluation questionnaire 
(caregivers) – end of 
study (Form 19) 
Evaluation questionnaire 
for all caregivers at the 
end of the study (after the 
4-5 year contact) 
Study team (completed 
by caregiver) 
Evaluation questionnaire 
– caregivers – 
smartphone App (Form 
20) 
Evaluation questionnaire 
for all caregivers 
regarding use of the 
Smartphone App  
Study team (completed 
by caregiver) 
Evaluation questionnaire - 
health visitors (Form 21) 
Evaluation questionnaire 
for all health visitors 
participating in the main 
growth study  
Study team (completed 
by health visitor) 
Clinical sample linkage 
consent form for 
carers/parents (Form 22) 
Consent form for clinical 
sample NCMP linkage  
Study team (completed 
by patient or caregiver) 
Clinical sample linkage 
consent form for patient 
aged 16 or over (Form 
23) 
Consent form for clinical 
sample NCMP linkage  
Study team (completed 
by patient or caregiver) 
Clinical sample linkage 
information sheet for 
carers/parents (Form 24) 
Information leaflet for 
clinical sample NCMP 
linkage 
Study team  
Clinical sample linkage 
information sheet for 
patient aged 16 or over 
(Form 25) 
Information leaflet for 
clinical sample NCMP 
linkage 
Study team  
 
Baseline visit (age 2.0-2.5 years): 

 
 
 
 
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At the baseline visit, data will be collected using CRF1 (Baseline visit). 
Demographic data will be collected from the caregiver. Caregivers will be asked to 
have the personal child health record (Red Book) available, from which will be 
transcribed: birth weight and length (if available) and any previous measurements of 
height, weight and head circumference. We will also collect data on gestation and 
growth on antenatal scans, feeding difficulties including any previous interventions 
from dietetics or paediatrics, chronic health conditions and any hospital 
attendances/admissions. The child’s height will be measured to the nearest 1mm 
using a stadiometer according to an SOP. The child’s weight will be measured to the 
nearest 100g using a stand-on scale according to an SOP. The accompanying 
parents will have height measured using the same methods; if either parent is 
absent, an estimate will be made of their height based on parental recall. If both 
parents are not present (i.e. the child is cared for by different family member), we will 
attempt to contact the parents to record their reported heights. If the child is not in 
contact with their biological parents, we will not make further attempts to contact 
family members and will record this as missing data. Health visitors will support 
caregivers to download a smartphone growth application and demonstrate how to 
use it. Caregivers will be invited to take the first smartphone measurement at this 
point. The app converts height data from the camera image to a numerical figure, 
and this will be transferred to a secure central server with additional relevant 
demographic data (e.g. gender, ethnicity, weight, date of birth and parents’ heights) 
and an anonymous identifier in GDPR-approved secure space. Each participant will 
have an anonymised unique identifier. No patient images / names will be 
transferred. The smartphone app has existing ethical approval (IRAS project 
ID: 286683; REC reference: 21/WM/0032).  
 
Follow-up visit 2.5-3.5 years  
Children will have a further height measurement at approximately 3.0 years by the 
health visitor. If the preschool health visiting contact is not operational at the time, the 
child will instead have their height measured by the nurse/healthcare assistant 
administering the immunisations at the preschool booster appointment at their GP 
practice, or at a planned research visit appointment Through the health visitor contact 
at 3.0 years children will have a repeat developmental assessment using the ASQ-3 
questionnaire. Data will be collected using CRF 2 (Follow-up visit). Child height will 
be assessed using the same tool as at baseline. If there are any missing parental 
heights a further attempt will be made to measure or record an estimated height. 
Families will be occasionally contacted by the study team by phone to improve 
retention in the study and to remind caregivers about the smartphone app.  
School entry measurement (age 4-5 years) 
Child height and weight will be recorded as part of the routine NCMP programme at 
school entry (4-5 years of age). We will provide identifiers to Tower Hamlets Local 
Authority to enable them to link the NCMP measurement to enrolled children and 
provide us with their heights and weights recorded at 4-5 years of age. This is a 

 
 
 
 
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feasibility piece to see if data could be used from NCMP in a larger screening 
programme. We will also collect data on school enrolment, and screen for educational 
concerns by health visitors screening school nursing notes to review if there has been 
any input from a special educational needs co-ordinator at school. After the NCMP 
measurement, we will distribute questionnaires for caregivers and health visitors to 
collect data on feasibility and acceptability of the study procedures. We will also 
conduct focus groups with small groups of participants (see below).  
At each of the 3 measurements of height we will identify the 2-3% of children with the 
poorest growth, as categorised by the algorithm (based on parental height, child height, 
and child growth velocity), in whom prior studies show a high prevalence (30-40%) of 
medical disorders. We will contact parents of these children to offer referral to the 
paediatric endocrinology clinic at the Royal London Hospital. A member of the study 
team will call the parents to inform them and will write to the child’s GP to request a 
referral into the Royal London Hospital clinic via the e-referral system. We will evaluate 
acceptability and feasibility of the screening and referral process, using parental and 
primary care staff questionnaires. We will evaluate uptake by calculating the 
proportions screened, referred, investigated in clinic and diagnosed with medical 
disorders. At the clinic, children will be assessed by a paediatric endocrinologist and 
will undergo routine medical evaluation for short stature, with appropriate follow-up.  
Development sub-study (age 2.0-2.5 years): 
A random sample of 150 children will also be invited to enrol in a sub-study to generate 
pilot data on the sensitivity of the parent-reported ASQ-3 to assess child development 
compared to a gold standard development battery, the GMDS, undertaken by a 
psychologist. These children will undergo a neurodevelopmental assessment during 
their routine health visitor visit of their motor, cognitive, language and social skills using 
a standardized test battery (GMDS) by a child psychologist. Using data from the ASQ-
3 developmental assessments conducted at baseline and follow up visits by the health 
visitor, we will investigate relationships between growth and development and assess 
content validity of the ASQ-3 by comparing it with the GMDS.  
Qualitative sub-study (following the 2-2.5 year visit) 
We will also invite a subgroup of participants in the study (caregivers) and health 
visitors working in Tower Hamlets to take part in focus groups. We will convenience 
sample 2 focus groups of 6-8 caregiver participants, with language interpretation if 
appropriate. With support from the Child Growth Foundation and using our own 
experience of qualitative research with families of children experiencing stunting in 
sub-Saharan Africa we will raise and explore generative questions with the focus 
groups. We will convene the focus groups following the first contact to gather 
qualitative data on acceptability of the study procedures (in addition to questionnaires). 
We will also convene a separate group of 6-8 health visitors working in Tower Hamlets 
to discuss child growth and development monitoring and referral pathways. We will 
transcribe the data and generate coding categories to analyse with NVivo software.  

 
 
 
 
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Flowchart of study procedures  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Clinical sample secondary analysis 

 
 
 
 
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A sample of ~200 children with diagnosed growth disorders will be linked to NCMP 
data and used to test the performance, sensitivity and specificity of the growth 
screening algorithm. Retrospective consent will be sought from the patient and their 
parent/carer to link their clinical data to their NCMP height measurement. The patients 
NHS number, diagnosis, age at diagnosis and parental heights will then be shared with 
NHS England (data controllers for NCMP). Here, data will be linked to NCMP 
measurements. We will also seek consent to share the patient’s first name, surname, 
postcode and date of birth with NHS England to link the data in cases where the NHS 
number is missing from the NCMP dataset. Data will be pseudo-anonymised by 
removing NHS numbers or other identifiers and generating random ID numbers before 
being shared with the Office for Health Improvement and Disparities (OHID) at the 
Department of Health and Social Care.  
 
Data will be analysed in-house at OHID according to data sharing agreements between 
OHID and NHS England, and to minimize the sharing of personal data. The data 
analysis plan has been developed by researchers at QMUL and analysis will be 
conducted collaboratively, although data will not be shared outside of OHID at this 
stage. The growth-screening algorithm will be applied to NCMP data to determine the 
children’s standardized height for age and sex as well as the distance from their mid-
parental heights. We will use an age-matched sample of children participating in the 
Born in Bradford study as controls. We will assess the sensitivity and specificity of 
using different cut-offs to find children with an underlying growth disorder. We will also 
estimate the potential improvement in age at diagnosis when using the screening 
algorithm by calculating the mean of the subtracted age at NCMP measurement from 
the true age at diagnosis for the children who were correctly identified as having a 
growth disorder by the algorithm. Analysis will be conducted using Stata 17. 
Aggregated results will then be shared with researchers at QMUL.  
 
This sub-study will run parallel to the main pilot study, and as it uses a separate dataset 
is not included in the flowchart of study procedures. A flowchart of the linkage 
procedure is shown below. 
 
 
 
 
 
 
 
 
 
 
 
 
Flow diagram of data sharing and linkage procedure for clinical sample secondary 
analysis 

 
 
 
 
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9.4 Data collection and storage 
 
Data will be handled in accordance with Sponsor protocols to ensure confidentiality 
and robust data management. Identifiable data will be collected by the research team 
in order to maintain contact with families to schedule follow-up visits, to allow linkage 
with the follow-up assessment data, and in order for Tower Hamlets Local Authority 
to identify the children and link the study data with NCMP data. All identifiable data 
will be stored separately from any clinical information, with a unique study identifier 
enabling the NCMP measurements to be added to the clinical data.  
The identifiers will be retained for the length of the study as the child may need referring 
to a paediatric clinical service but will then be destroyed once Aim 2 of the study has 
been completed.   

 
 
 
 
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9.5 Follow-up procedures  
 
Participants who have short stature for their age and parental heights will be 
identified using the screening algorithm at baseline. These participants will be 
referred for further clinical investigation. At follow-up, growth velocity will also be 
included in the screening algorithm, and any children found to have reduced growth 
velocity will be referred. Children will be followed up in a dedicated research clinic 
within Barts Health NHS Trust. Prof Storr’s clinic is an established paediatric 
endocrine/growth clinic at the Royal London Hospital. Children will be seen and 
assessed as per standard clinical practice. They will have auxological assessment 
(height, weight, head circumference and recording of parental heights), a clinical 
review (history and physical examination). Patients requiring further investigations 
for  short stature will follow a current best practice guideline investigation protocol. 
More detailed genetics (if a specific genetic disorder is suspected), Growth 
Hormone testing (as per standard departmental protocols) or referral to another 
speciality (e.g. gastroenterology) may be requested if clinically indicated.  
 
 
9.6 Laboratory and radiological assessments  
 
Laboratory and radiological investigations will not be performed as part of the study, 
but children who are identified in Aim 1 as having abnormal growth will be 
investigated in the referral clinic at the Royal London Hospital. This is not part of the 
study and represents normal clinical care. 
 
9.7 Participant withdrawal  
 
Participants will be able to withdraw from the study at any time and their data will be 
de-identified. Withdrawals from the study trigger completion of the exit form (Form 9). 
 
 
9.8 End of Study Definition  
 
The study will end for each child after their follow-up assessment is completed (after 
their school NCMP measurement at age 4-5 years). All fieldwork will be completed 
when the target sample of 630 children has been recruited (allowing for 5% loss to 

 
 
 
 
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follow up from the cohort of 630), with full measurements of all children on three 
occasions and developmental assessments on the subset of 150 children. Following 
the end of clinical assessments, the study will continue until the data have been 
analysed, linkage to NCMP measurements have been made, and those individuals 
identified with poor growth have been referred to Prof Storr’s paediatric endocrinology 
clinic.  
 
 
10. Statistical considerations 
 
 
 
10.1. Sample size  
 
The existing literature on growth screening shows that around 30-40% of children 
identified as having short stature will have an underlying medical disorder. During the 
course of this study, we estimate we will refer 12 out of 600 children for clinical 
investigation (based on referral of 2% children with the poorest growth). If the 
algorithm is successful at identifying children with a high likelihood of an underlying 
condition we would expect a total of 4 (35%) of the 12 children referred to be 
diagnosed with a medical condition. The sample size of 12 (2% of 600) gives us 85% 
power at 5% significance to detect a difference if the algorithm identifies less than 5% 
of children rather than the expected 35%. Therefore, if fewer than one (5% of 12) 
child referred to clinic is found to have an underlying medical condition we would be 
able to conclude that the algorithm had not achieved the goal of identifying children 
with a higher risk of underlying medical conditions.   
 
10.2. Method of analysis  
 
The overarching goal of this pilot study is to assess the feasibility of a growth 
screening programme. We are guided by Bowen’s framework for feasibility studies, in 
which three key questions for feasibility are presented.18 These are: Can it work? 
Does it work? and Will it work? In this study we focus largely on the first question, 
can it work, and seek to provide some preliminary data on the second and third 
questions. Bowen’s areas of focus for feasibility studies informs how we will answer 
these questions:  
 
Can it work?  
 
Acceptability: Focus groups with parents and health visitors will provide information 
on the acceptability of growth screening (see Qualitative Analyses, below). We will 
also examine ongoing study participation (retention rate).  

 
 
 
 
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Demand: The uptake of invitations to participate in the study (response rate), as well 
as uptake of the screening mobile phone app will provide data on demand for growth 
screening.  
 
Integration: We will test integration into current health visiting systems by assessing 
the success of data linkage with NCMP, the proportion of parents who consent to 
NCMP data linkage, and through focus group interviews with health visitors.  
 
Does it work? Will it work? 
 
Below we outline how we seek to provide evidence for limited-efficacy testing, to gain 
an initial understanding of whether the growth screening algorithm is able to identify 
children for further clinical investigation. Acceptability, demand and integration are 
also interwoven into the data analysis strategy throughout.  
 
 
Aim 1 
We will employ an algorithm based on previous methodology, using variables 
individually and in combination, after checking for co-linearity. Different cut-off values 
will be chosen and the proportion of the population judged to have linear growth failure 
estimated for each cut-off. We will then examine the detection rate for each cut-off by 
applying it to data from ~200 local children treated in the specialist paediatric 
endocrinology centre with known growth disorders e.g. Growth Hormone Deficiency 
(GHD), Turner Syndrome (TS) and Small for Gestational Age without catch-up growth 
(SGA). Consent will be obtained retrospectively from these patients and their 
parents/carers to link their routinely obtained clinical data (diagnosis, age at diagnosis, 
height and parental height) to previously height data obtained at school entry via the 
NCMP. To do this linkage, the hospital clinical data (and the associated NHS number 
or other identifiers) will be shared with NHS England. This will allow us to calculate 
sensitivity-and-specificity algorithm cut-offs for the detection of these conditions. This 
will enable a trade-off between detection rate (sensitivity) and the false positive rate 
(1-specificity) to be evaluated. Additionally, the time elapsed from the first abnormal 
screening result until the clinical diagnosis of growth hormone deficiency will be related 
to the cut-off values.  
The outcomes for Aim 1 are age of identification of stunting (which will be reported but 
the study is not powered to detect age of diagnosis); feasibility of measuring children 
in the community; the percentage of children referred; the percentage of children found 
to have underlying medical conditions; and the feasibility, acceptability and inter-test 
variability of a smartphone app and stadiometer height measures. 
The algorithm will be applied to measures collected from each child at each visit, with 
children who are identified by the algorithm as being in the bottom 2% for height or 

 
 
 
 
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growth being referred to clinic for further investigation. The proportion of children with 
complete observations, including height at three time points and parental height, will 
be compared to the proportion of children with fewer observation points or missing 
parental height. Descriptive statistics will be reported: the number and percentage of 
children found to be stunted and their characteristics (height, gender, age, socio-
demographic factors). We will also report the number of children referred to clinic,  
attending clinic and found to have underlying medical conditions. The age at which 
each condition is identified will be reported and compared to the average age at 
diagnosis.  
Height data collected by parents using a smartphone app will be analysed. The number 
of parents consenting to use of the smartphone app and reporting one or more height 
measurements through the app will be reported. The number of time points at which 
parent’s upload smartphone measured heights will also be reported. Inter-rater 
reliability between parent-measured height using the smartphone app and Health 
Visitor measured height using a stadiometer will be assessed using Gwet’s Agreement 
Coefficient.19  
Aim 2 
All children will undergo a developmental assessment in the community, conducted by 
a health visitor with input from the child’s parent, as part of standard care (ASQ-3 
questionnaire). A subset of 150 children will, in addition, have a gold standard 
developmental assessment conducted by a child psychologist using the GMDS, which 
measures gross motor, fine motor, social, language, visual-spatial and practical 
reasoning skills.  
Data on socioeconomic circumstances will be used to model the probability of a child’s 
risk of developmental delay or sub-threshold delay on the ASQ-3 (n=600) and GMDS 
(n=150). Poor growth (assessed by the algorithm) will then be included in these models 
to assess whether model fit is improved, and whether poor growth contributes to the 
predictive power of socioeconomic variables to predict developmental delay.  
Concurrent validity of the ASQ-3 and GMDS will be assessed. Each measure will be 
used to assess whether the participant has any degree of developmental delay, based 
on measure-dependent cut offs. The degree of agreement will be assessed using 
Cohen’s kappa, and interpreted using existing guidelines, where a kappa value of >0.4 
reflects moderate agreement, and a kappa of >0.6 reflects substantial agreement.20 
The GMDS will also be used as a gold standard child development measure to assess 
the performance of the ASQ-3. Sensitivity and specificity will be calculated to assess 
the ability of the ASQ-3 of identifying children identified as having a delay by the 
GMDS.   
The outcomes for Aim 2 are the correlation between the ASQ-3 and the GMDS; and 
the change in sensitivity with the addition of anthropometric and developmental data 
to an existing vulnerability index.   

 
 
 
 
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Qualitative analyses 
To further our understanding of the acceptability of the proposed screening 
programme, we will conduct two focus groups with parents participating in the study, 
as well as one focus group with health visitors. The transcribed data from the focus 
groups with caregivers and health visitors will be used to generate coding categories 
and will be analysed using NVivo, using thematic analysis.  
 
 
11. Ethics 
 
Research Ethics Committee approval will be sought. This is not a trial and there is no 
intervention which could result in direct harm to participants. However, there are 
potential indirect harms related to the study. We will aim to mitigate these as far as 
possible and will include detail of these potential harms in the patient information and 
informed consent procedures.  
 
Firstly, the study will involve collecting sensitive data, which if handled incorrectly could 
lead to identification of individual children or groups of children. The study procedures 
to de-identify or pseudo-anonymise children will reduce the risk of identification of 
individuals. The potential for ‘group harm’, in which a particular subpopulation (e.g. by 
area, ethnicity or gender) are stigmatized because of study findings, such as the finding 
of high prevalence of growth failure, will be minimized in publications and 
dissemination of study findings, by not releasing results relating to small areas.  
 
An identification of short stature, faltering growth or delayed development (and 
subsequent referral for specialist care) could result in distress for children and their 
families and could lead to stigma. We will include information about stigma and discuss 
this with caregivers as part of the informed consent procedures. Further, if we identify 
issues with growth and/or development, children will be referred earlier than would 
have happened without their involvement in the study. Children will be immediately 
referred to a paediatric growth or community development clinic and as such will be 
provided care, support and if appropriate treatment for relevant conditions.  
 
We will be providing vouchers worth £15 for the baseline visit as compensation for time 
spent during participation in the study. These are not intended as rewards for 
participation, but we are aware of the possibility of these payments leading to coercion. 
Research staff will undergo Good Clinical Practice and ethics training and will be 
careful to avoid coercion in recruitment procedures.  
11.1. Annual Safety Reporting  
 
N/A  

 
 
 
 
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12. Public involvement 
 
The screening algorithm will be created using national data and piloted for feasibility 
and acceptability in east London, which we have shown is a national hotspot for 
stunting based on an analysis of NCMP data (currently in review). We have engaged 
with the Patients Association, an independent national charity which campaigns for 
improvements in health and social care for patients, who stated in a letter to us that in 
order “to ensure an effective co-production approach to this research bid we welcome 
the opportunity to work in partnership with Queen Mary, University of London and other 
local stakeholders to undertake research into child malnutrition which will build on the 
work we have previously undertaken in 2016/2017.”  
The Child Growth Foundation (CGF) have identified this area of research as one of 
their key missions / research priorities and provided a letter of support for this work. As 
Medical Advisor for the CGF, Prof Storr will disseminate and obtain feedback from 
CGF members via their website, newsletters and annual congress. The CGF are also 
able to provide individuals for a project advisory board, focus groups and workshops. 
As per Section 9.4, we will also engage groups of caregivers in focus groups to develop 
our understanding of caregivers’ experience and understanding of child growth and 
development, and their thoughts about the current processes for monitoring of these 
metrics in Tower Hamlets.   
 
13. Data handling and record keeping 
 
13.1. Data management 
 
During Aim 1, we will collect sensitive clinical data including height, weight, 
developmental information and carer-recalled past medical history. We will also collect 
sensitive sociodemographic information including data on household deprivation and 
other social determinants of health. Data from anthropometric assessments, 
developmental assessments and questionnaires will be entered onto a secure 
password protected access database (REDCap), and the hard copies of the 
questionnaires will be stored centrally in a locked cupboard. Two health visitors will 
double enter the data and resolve any discrepancies.  
 
All data will be linked to an identifier kept separate from the sensitive data. No copies 
of paper documents will be made. Data will be identifiable only by a unique study ID 
number and will not display participant identifiable information. A linkage form will be 
used to link study ID with patient identifiable information (pseudo-anonymisation), 
which will be stored as a password-protected file on a locked QMUL computer. 
 

 
 
 
 
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The smartphone app we will invite caregivers to use converts height data from the 
camera image to a numerical figure, and this will be transferred to a secure central 
server with additional relevant demographic data (e.g. gender, ethnicity, weight, date 
of birth and parents’ heights) and an anonymous identifier in GDPR-approved secure 
space. Each patient will have an anonymised unique identifier. No patient images / 
names will be transferred.  
 
Research staff will undergo Good Clinical Practice and ethics training and the 
importance of confidentiality when reviewing sensitive clinical documents will be 
emphasised throughout the study. Child development assessments are potentially 
sensitive, because they measure cognitive performance, and can be misinterpreted or 
could be used to disadvantage a child. The results will be discussed in confidence with 
caregivers; we would seek their permission before sharing any of these findings and 
would only do so if it were in the best interests of child, for example to inform an 
Education Health and Care Plan to seek additional educational support for the child.  
 
Linkage of the children’s data we collect with the pseudo-anonymised national data 
will contain information such as postcode. Although not a direct identifier, this could 
lead to identification of groups of children. The potential for ‘group harm’, in which a 
particular subpopulation (e.g. by area, ethnicity or gender) are stigmatized because of 
study findings, such as the finding of high prevalence of growth failure, will be 
minimized in publications and dissemination of study findings, by not releasing results 
relating to small areas.  
 
We will also conduct a linkage exercise of previously collected routine clinical data to 
historic height measurements. Retrospective consent will be sought from patients and 
parents or carers of a sample of patients who received treatment for growth-related 
conditions at Professor Helen Storr’s paediatric endocrinology clinic at Barts Health 
NHS Trust. Data on the child’s diagnosis, age at diagnosis and parental heights, as 
well as their NHS number and other identifiers, will be shared with NHS England. NHS 
England will link these data, using the NHS number, or other identifiers when the NHS 
number is not available in the NCMP dataset, to the child’s first NCMP height 
measurement, as well as the age of the child when the measurement was taken in 
months. These data will then be anonymised by removing the NHS number or other 
identifiers and a new randomly generated identifier assigned to each child. Finally, this 
pseudo-anonymised dataset will be shared with the Office for Health Improvements 
and Disparities (OHID), part of the Department of Health and Social Care. Data will be 
analysed at OHID according to an analysis strategy developed at QMUL, to minimize 
data sharing. OHID has robust data protection protocols and safe and encrypted 
sharing pathways with NHS England.  
 
 
13.1. 
Source Data 
 

 
 
 
 
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Source data include the anthropometric measurements and developmental 
assessments and sociodemographic data collected from children and their carers in 
the community. From experience, it is expected that most children will be brought to 
their appointment by at least one parent. In this case, the child’s parent will also have 
their height measured, and will be asked about the height of the other parent. We will 
not apply for approval to access the medical notes of either the child or of the 
carers/parents, especially since heights entered in medical notes are often recorded 
from a verbal report from the patient given to the clinician coding the measurement, 
rather than a direct measurement.  
 
 
13.2. Confidentiality 
 
Information related to participants will be kept confidential and managed in 
accordance with the Data Protection Act, the General Data Protection Regulation 
(GDPR), NHS Caldecott Principles, the UK Policy Framework for Health and Social 
Care Research, and the conditions of Research Ethics Committee favourable 
opinion.  
 
 
13.3. Record retention and archiving 
 
We will keep research records for 20 years after the project has completed, as per 
the UK Policy Framework for Health and Social Care Research. We will use the Trust 
Corporate Records Centre as the repository for long-term storage. All research 
documentation will be archived in physical form.  
 
 
14. Safety reporting 
 
Given the study design, we do not anticipate adverse events (AE) or adverse 
reactions (AR). Adverse events could plausibly occur during the face-to-face 
components of the study. There are two points at which participants will be seen 
face-to-face. One at age 2-2.5 years, where an anthropometric assessment and 
developmental assessment will take place on the same day, and an almost identical 
assessment between 6-12 months later at the GP surgery. The third measurement 
will take place at the child’s school as part of the NCMP, and any children seen in the 
paediatric endocrinology clinic by Prof Storr will be receiving normal clinical practice. 
The NCMP measurement and the clinic appointment are not strictly part of the study 
procedures. There is a very low likelihood of physical injury during the growth and 
development assessments and/or while the child and caregiver are on the premises. 

 
 
 
 
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The research nurses will make an initial assessment of any injury and will seek 
medical attention as appropriate.   
Other contacts will be with the families of children who warrant referral to a paediatric 
clinical service, which will be in the form of a telephone call and/or letter to the family 
and to the child’s GP. There is potential for distress or psychological harm of the child 
or family member when being informed of abnormal growth or development or when 
being referred to the growth clinic. This will be minimized by the breaking of news or 
discussion of clinical information being undertaken by a senior medically trained 
member of the research team with advanced training in communication skills.  
Any AEs that are plausibly related to the study and which are identified by a member 
of the research team (including research nurses, psychologist, health visitors or any 
of the study co-investigators) will be reported to the study CI. The CI or another 
medically qualified research team member will assess each identified AE for severity 
and relatedness to establish if it should be classified as a serious adverse event 
(SAE) and whether the event was related to the study. The assessment will be 
documented in the participant’s study records. The CI or delegate will report any 
severe AEs or SAEs that are possibly, probably or definitely related to study 
procedures to the JRMO; a safety reporting form will be completed for each event 
that requires reporting. 
Adverse events or SAEs occurring between visits/questionnaires/sample collections 
will not be recorded or reported as they are not the aim or focus of this project. 
 
 
15. Monitoring and auditing 
 
 
The Sponsor or delegate retains the right to audit any study, study site or central 
facility. In addition, any part of the study may be audited by the funders where 
applicable. 
 
Onsite monitoring will be performed as per the study monitoring plan. Monitoring will 
include source data verification. We will check 100% of consent forms and the CI will 
undertake spot checks on the quality of recorded data.   
 
 
16. Study committees 
 
 
We will form a study management group consisting of the CI, PIs, collaborators, 
grant holders, statisticians and study coordination team. Since this is a pilot, minimal 

 
 
 
 
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risk study, we will not have a Data Monitoring Committee or a Trial Steering 
Committee. 
 
 
17. Finance and funding 
 
The project is funded by Barts Charity (12 Cock Ln, Farringdon, London EC1A 9BU); 
grant number MRC0219).  
 
 
18. Insurance and indemnity 
 
The insurance that Queen Mary University of London has in place provides cover for 
the design and management of the study as well as "No Fault Compensation" for 
participants, which provides an indemnity to participants for negligent and non-
negligent harm. 
 
19. Dissemination of research findings 
 
We will disseminate findings through conferences and 2-3 open access peer-reviewed 
manuscripts. We will produce policy briefs and press releases to communicate findings 
to policymakers, including Public Health England, the Patients Association and non-
governmental organisations such as Save The Children, who have a strong focus on 
national health and education policy. If we find a substantial hidden burden of stunting, 
we anticipate drafting a Viewpoint for a journal such as the BMJ or Lancet to highlight 
the issue, as well as dissemination to the press. There is a need for earlier diagnosis 
of serious underlying medical disorders in children with growth failure, as is evident 
from the testimonials we have included with this proposal.  
We will leverage the support of the Child Growth Foundation, for whom Prof Storr is a 
medical advisor, to disseminate the results of the automated growth screening 
algorithm which has enormous national potential. This would require further evaluation 
in a follow-on grant, and we plan to apply for NIHR funding following the results of our 
pilot assessment. We will establish a community advisory board with representation 
from parents, primary care and local education; this will enable us to gain a range of 
viewpoints on the proposed work and its implications. We will engage parents through 
letters prior to the start and end of the project, and through an invited parents’ evening 
to disseminate findings. This work synergizes with ongoing work in sub-Saharan Africa, 
which focuses on stunting as a marker of poverty, and aims to identify solutions to child 
linear growth failure. We have an ongoing collaboration with the Centre of the Cell at 
the Blizard Institute to develop a game and app on undernutrition in Africa; we will use 

 
 
 
 
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this as an opportunity to also highlight issues related to local undernutrition for the 
general public. 
We will produce policy briefs and press releases to communicate findings to 
policymakers, including Public Health England, the Patients Association and non-
governmental organisations such as Save The Children, who have a strong focus on 
national health and education policy. 
 
 
 
 
 
 
 
 
 
 
 
20. References 
 
  
 
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2 Voss LD et al. Poor growth in school entrants as an index of organic disease: the Wessex 
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4  Hauer NN et al. Clinical relevance of systematic phenotyping and exome sequencing in 
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5 Green AA et al. Method for the earlier recognition of abnormal stature. Arch Dis 
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Bank. https://data.worldbank. org/indicator/SH.STA.STNT.ZS 
 

 
 
 
 
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8 The Patients Association. Child Undernutrition Project Report (2017) 
 
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15 Ahmed ML et al. Evaluation of a district growth screening programme: the Oxford Growth 
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16 Hauer NN et al. Clinical relevance of systematic phenotyping and exome sequencing in 
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17 Bowen DJ, Kreuter M, Spring B, et al. How we design feasibility studies. Am J Prev Med. 
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18 Bowen, D. J., Kreuter, M., Spring, B., Cofta-Woerpel, L., Linnan, L., Weiner, D., Bakken, S., 
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19 Gwet. Handbook of Inter-Rater Reliability, 4th Edition: The Definitive Guide to Measuring 
The Extent of Agreement Among Raters. 2014. Advanced Analytics, LLC 
 
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