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Early help and early intervention

What is early help and what is early intervention.

Early help and early intervention are forms of support aimed at improving outcomes for children or preventing escalating need or risk. Because of this they are also sometimes referred to as prevention or preventative services.

These services are part of a “continuum of support” and provide help to families who do not, or no longer, meet the threshold for a statutory intervention.  1

Early help and early intervention services can be provided at any stage in a child or young person's life, from the early years right through to adolescence. Services can be delivered to parents, children, or whole families. 

The importance of helping families early is highlighted in national safeguarding guidance across the UK. However, the form services take varies between local areas, depending on local provision.

Is there a difference between the terms?

The terms early help and early intervention are often used interchangeably by practitioners. However, many policymakers and researchers make a distinction between the two (Frost, Abbott and Race, 2015 2 and Plimmer and Poortvliet, 2012).  3

The term early help, most commonly used in England , often covers universal services aimed at improving outcomes for all children, such as:

  • children’s centres
  • open access youth services
  • health visiting.

Early intervention is often used to talk more specifically about targeted and intensive services addressing individual risks and protective factors, such as:

  • behaviour change programmes
  • relationship support for parents
  • mentoring schemes for young people.

> Read our Why language matters blog on the term ‘early help’

Why are early help and early intervention important?

Providing timely support is vital. Identifying and addressing a child or family's needs early on can increase protective factors that positively influence a child’s wellbeing, and decrease risk factors that may be impacting a child’s life negatively.

Research 4 ,5 suggests that early help and intervention can:

  • protect children from harm
  • reduce the need for a referral to child protection services
  • improve children's long-term outcomes
  • improve children’s home and family life
  • support children to develop strengths and skills to prepare them for adult life.

Identifying a child or young person who may benefit from early help

Some groups of children may be more likely to need early help than their peers. 1 ,7 These include children who:

  • have special educational needs
  • are disabled
  • are young carers
  • are showing signs of being encouraged into anti-social or criminal behaviour
  • experience difficulties at home, such as domestic abuse, parental substance abuse or parental mental health problems
  • are at risk of being affected by organised crime and county lines
  • are in care, leaving care or preparing to leave care
  • have poor attendance at, or are excluded from, school
  • are young parents (or about to become young parents)
  • are experiencing housing issues
  • misuse drugs or alcohol
  • are viewing harmful online content or experiencing inappropriate or unsafe online relationships
  • are being bullied or bullying others
  • have poor general health
  • have mental health issues

> Find out more about children and families at risk

> Find out more about the signs a child may be experiencing abuse and neglect

Research 2 also suggests that some children are less likely than others to receive the early help or early intervention that they need. These include: 

  • Black and mixed heritage boys
  • babies born into care
  • adolescents in care proceedings
  • children with mental health needs.

> Find out more about recognising and responding to child mental health

> Find out more about safeguarding children who come from Black, Asian and minoritised ethnic communities

Providing support to children and families

If you think a child, young person or a family might benefit from extra support, you should record any concerns and speak to your nominated child protection lead.

Your nominated child protection lead will use their knowledge of local services and liaise with professional colleagues to identify potential sources of support. If they think a child may be at risk of abuse or neglect, they should follow your organisation's child protection procedures immediately.

You can also contact the NSPCC Helpline on 0808 800 5000 or by emailing [email protected] . Our child protection specialists will talk through your concerns and give you expert advice. 

Working with the child and their family

If your nominated child protection lead believes that early help or early intervention is the most appropriate form of support, they will discuss options with the child and their family. They may ask you to be involved.

> Find out more about our designated and lead officer training courses

Accessing universal services

Many children and families will benefit most from national or local universal services available without a referral from a professional.

While a child’s health visitor, GP or school nurse is often the first point of contact for early support, there are many other services that a family can access directly themselves.

The local authority and local hubs such as children’s centres, the local family support hub in Northern Ireland and family information service in Wales, can advise families on locally available services. They can also refer families on to services providing more targeted support.

Although universal services don’t require a referral, people may still face barriers accessing them. It’s important for professionals to listen to children and families and support them to access the services they need.

> Find out more about helping families access services

Assessing the need for more targeted support

In some cases, your nominated child protection lead may conclude that children and families would benefit from more co-ordinated or targeted support. This may result in a professional conducting an assessment of needs, which should be undertaken with the consent of, and in collaboration with, the child and their family. 

In England , the local early help assessment process is set out by the local safeguarding partners. The process should include the appointment of a lead practitioner for each case (such as a GP, family support worker, school nurse, teacher, health visitor, and/or special educational needs co-ordinator). 1

In Northern Ireland , any concerned professional can use the Understanding the Needs of Children in Northern Ireland (UNOCINI) framework to help identify the strengths and needs of the children and families they work with. 2  

In Scotland , Getting it Right for Every Child (GIRFEC) guidance suggests that a key professional, sometimes referred to as a ‘named person’, be made responsible for recognising each child in Scotland’s wellbeing needs. 3 This named person service is non-statutory, and it’s up to each local council and health board to decide if they want to offer it, and parents to decide if they want to use it. The named person, or a professional working closely with the child and family, can use the tools within the GIRFEC National Practice Model to assess needs and identify potential sources of support.

In Wales , Families First guidance states that any concerned professional can refer families for an assessment through the Joint Assessment Framework for Families (JAFF). This framework is part of Wales’s Families First programme and is used to help local agencies work together to identify a family’s needs and determine the best way to meet them.  4

Following an assessment, a decision will be made about how best to support the family.

Guidance across the UK highlights the importance of providing help to children and families as soon as it is needed.

Each nation of the UK has safeguarding and child protection guidance which states that organisations should identify and support children and families who would benefit from early help or early intervention.

The actual form services take varies depending on local provision.

Find out more about:​

  • Child protection in England
  • Child protection in Northern Ireland
  • Child protection in Scotland
  • Child protection in Wales

Looking for research and resources?

Find out how our Library and Information Service can help.

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Subscribe to our weekly email keeping you up to date with all the developments in child protection policy, research, practice and guidance.

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Browse our list of online and face-to-face training courses to gain the skills you need to help keep children safe from abuse and neglect.

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Early Help Strategy and Rollout of Family Hub Model

A refreshed approach to early help, recognising the landscape for early intervention has changed significantly both locally and nationally

early help case study

  • Lead Member Tameside Metropolitan Borough Council
  • Categories Young People
  • Assets Download Case Study

Principles met

Co-production.

We will develop systems that enable citizens to be equal partners in designing and commissioning public services and in determining the use of public resources.

Democratic engagement

We will support the active engagement of the full range of residents in decision making and priority setting.

We will embrace innovation in how we work with local communities to drive positive change.

New models of meeting priority needs

In exploring new ways of meeting the priority needs of our communities we will encourage models, such as co-operatives and mutuals, which give greater influence and voice to staff and users. in designing and commissioning public services and in determining the use of public resources.

Social partnership

We will strengthen the co-operative partnership between citizens, communities, enterprises and Councils, based on a shared sense of responsibility for wellbeing and mutual benefit.

The Tameside Early Help Strategy 2023-2026 sets out a refreshed approach to early help, recognising the landscape for early intervention has changed significantly both locally and nationally since 2020. Acknowledging current challenges facing families, exacerbated by the cost of living crisis, this new Strategy emphasises the importance of multi-agency working, incorporating lessons from the COVID-19 response.

Recognising the importance of early intervention locally, Tameside has also developed a new Thresholds Document (Tameside Framework for help and support) which reinforces delivery of the Early Help Strategy by supporting professionals to identify signs that families need help and the appropriate level of support required.

A key component to the strategy and the ethos of it encapsulated in practice is the development of a Family Hub and Spoke model within each of the four neighbourhoods (North, East, South and West) of Tameside. Through these Hubs and their satellite locations, families will be able to access services from a variety of community partners for the right help at the right time.

Family Hubs bring together existing family-help services to improve connectivity between families, professionals and services, placing relationships at the heart of our approach. Key partners include the likes of Integrated Care NHS Foundation Trust, Pennine Care, Schools, Leisure/wellbeing Providers, Libraries, Greater Manchester Police and a variety of specialist VCSE Providers.

Given the range of services required to deliver these projects operationally and strategically, developing the approach to the Strategy, the new Thresholds and the Family Hub simultaneously has elevated the Partnership network around Early Help to new heights.

For further information contact:

Simon Brunet Head of Policy, Performance & Intelligence Tameside Council

Tom Hoghton Policy & Strategy Service Manager Tameside Council

  • [email protected]
  • 0161 342 3542

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early help case study

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Please note you do not have access to teaching notes, early help and children’s services: exploring provision and practice across english local authoritiesintroduction.

Journal of Children's Services

ISSN : 1746-6660

Article publication date: 8 February 2021

Issue publication date: 17 February 2021

The purpose of this study was to explore early help provision to children and families not reaching the Children Act (1989) child in need threshold, across all 152 English local authorities in 2017.

Design/methodology/approach

A freedom of information request was used, in September 2017, to obtain information regarding recorded numbers, attributes and referral reasons for Early Help cases, case categorisation, professional groups involved in this provision and models of practice.

Responses revealed there are no common protocols categorising referrals and identified needs of children and young people. Child behavioural issues were the most frequently occurring category followed by parenting issues and child emotional well-being. The numbers of children engaged by Early Help services varied with a range between Barnsley with 7.8% of children under 18 years old and Richmond on Thames with 0.33% and only exceeded children in need in a 7 out of 71 reporting authorities. Models of practice used were most commonly based on the assessment framework, which operates at all social work thresholds including child protection. The enquiry found a diverse workforce involved in Early Help and sets it within a context of local thresholds for dealing with large increases in referral rates to children’s services departments in recent years.

Originality/value

The study provides a unique insight into the nature and scope of Early Help provision across England. The relationship between existing thresholds of intervention in the child welfare system is underexplored in the social work literature.

  • Social work
  • Early intervention
  • Child welfare
  • Children and families

Lucas, S. and John Archard, P. (2021), "Early help and children’s services: exploring provision and practice across English local authoritiesIntroduction", Journal of Children's Services , Vol. 16 No. 1, pp. 74-86. https://doi.org/10.1108/JCS-03-2020-0006

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Efficacy of early interventions for infants and young children with, and at risk for, autism spectrum disorders

Rebecca j. landa.

a Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, USA

b Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

With advances in the field’s ability to identify autism spectrum disorders (ASD) at younger ages, the need for information about the evidence-base for early intervention continues to rise. This review of the ASD early intervention (EI) literature focuses on efficacy studies published within the past 15 years. The neurodevelopmental context for early intervention, timing of initiating intervention, primary intervention approaches, and predictors of treatment outcomes are discussed. The evidence indicates that young children with ASD benefit from EI, and their parents learn to implement child-responsive engagement strategies when a parent-coaching intervention is provided. Evidence supports combining parent-mediated and direct clinician-implemented intervention to maximize child developmental gains. Clinical practice recommendations are presented, based on the literature reviewed.

Introduction

Autism spectrum disorders (ASD) are neurodevelopmental disorders defined by impairing levels of social and communication impairment, along with repetitive and stereotyped patterns of behaviour and interests ( American Psychiatric Association, 2013 ). The average age of diagnosis in the US is 4 years ( Christensen et al., 2016 ). However, ASD can be detected as early as 14 months of age in some children ( Landa, Gross, Stuart, & Faherty, 2013 ; Landa, Holman, & Garrett-Mayer, 2007 ); stability of diagnosis is high by age 18 months ( Ozonoff et al., 2015 ), although many children with signs of risk for ASD will not be detected or diagnosed by this age ( Ozonoff et al., 2015 ). The ability to detect ASD risk at younger ages is heightening the demand for early intervention (EI) services. To support clinicians’ EI decision-making, the EI literature as reported in the last 15 years is reviewed herein.

Neurodevelopmental context of early intervention

Recent meta-analysis studies and systematic reviews have concluded that EI has positive effects on development in young children with ASD (e.g. Beaudoin, Sébire, & Couture, 2014 ; Eldevik et al., 2010 ; Hampton & Kaiser, 2016 ; Lane, Lieberman-Betz, & Gast, 2016 ; Reichow, 2012 ; Virués-Ortega, 2010 ), although effects are greater for some children than others ( Sallows & Graupner, 2005 ). EI is designed to capitalize on experience-dependent neuroplasticity, a fundamental property of the brain, by which neuronal connections are created and organized, and learning occurs in response to a child’s experiences with the environment ( Kolb & Gibb, 2011 ). Self-generated experience, rather than observation ( Cannon et al., 2014 ) or passive experiences ( Weisen, Watkins, & Needham, 2016 ), are most influential in early learning processes. However, infants later diagnosed with ASD have atypical attention and engagement patterns ( Bhat, Galloway, & Landa, 2010 ; Chawarska, Macari, & Shic, 2013 ) and altered sensory and motor functioning (e.g. Baranek, 1999 ; Flanagan, Landa, Bhat, & Bauman, 2012 ) that likely disrupt the quality and quantity of experiences they cultivate for themselves ( Karmiloff-Smith, 2015 ). These disruptions in developmental processes, detected as early as 3 months of age ( Bhat, Galloway, & Landa, 2012 ), characterize a prodromal phase of ASD that may extend into the second and third years of life.

During the prodromal phase, and as ASD symptoms begin to manifest, infants and toddlers may experience diminished, unelaborated, and truncated social and communication learning opportunities that would otherwise occur within sustained, dynamic dyadic (social partner–child) and triadic (social partner–object–child) interactions. Such altered experiences could hamper cortical specialization for faces and language, and associated processes as well as the functional integration of this circuitry ( Johnson et al., 2005 ). Indeed, prospective, longitudinal neuroimaging research has identified a link between expanded cortical surface area and visual attention atypicalities in 6-month-olds later diagnosed with ASD having an older sibling with ASD ( Elison et al., 2013 ). Developmental processes involving selected neural circuitry are, thus, altered, disrupting the refinement of these circuits. Over time, the formation of distributed networks of brain regions and the interaction between these regions is hampered, affecting cognitive and sensorimotor functioning as the ASD symptom complex emerges. The brain abnormality in children with ASD changes across the first 2 years, likely the result of a dynamic interaction between neurobiological and cascading effects of atypical developmental processes ( Karmiloff-Smith, Casey, Massand, Tomalski, & Thomas, 2014 ), with cumulative effects that further contribute to shifting phenotypic features ( Piven, Elison, & Zylka, 2017 ). This highlights the importance of early access to intervention, the need for intervention to address multiple aspects of development, and for ongoing intervention that addresses developmental delays and atypicalities as these unfold over time ( Karmiloff-Smith et al., 2014 ).

When to start intervention?

The neurosciences offer a compelling rationale for providing strategically enhanced experiences for children with disrupted development early in life. Yet ASD cannot be diagnosed in infants, and there is no clear predictor of ASD in infants. Given that ~20% ( Ozonoff et al., 2011 ) and 30% ( Charman et al., 2017 ) of younger siblings of children with ASD, respectively, will receive a diagnosis of ASD or meet criteria for other developmental disruptions by 36 months of age, a public health approach to detecting ASD risk and monitoring at-risk children is needed (e.g. pre-term infants; Darcy-Mahoney et al., 2016 ). Given the decline in skills and social engagement seen in most children with ASD in the second year of life ( Landa et al., 2007 ), infants or toddlers showing concerning signs of social and communication delays or qualitatively atypical developmental patterns, despite being sub-threshold for an ASD diagnosis, should have access to EI or developmental enrichment programs (including parent coaching to immerse children in development-enhancing experiences to accelerate learning and generalization of skills [ Reichow, 2012 ; Warren et al., 2011 ]). However, access to such services for undiagnosed infants and toddlers is variable, due to the wide discrepancy in eligibility criteria for accessing EI services ( Twardzik, Cotto-Negrón, & MacDonald, 2017 ). Among the children having the lowest enrollment in EI services are those displaying mild severity and those without a diagnosis ( Twardzik et al., 2017 ).

Once a diagnosis has been made, children with ASD often will qualify for public EI services. Intervention is needed because they are not developing in social, play, and, in most cases, language and cognitive domains at the expected pace or in the expected multi-modal, integrated way. Incidental learning during interaction with others is impeded by ASD-related impairments, especially those affecting attention and social initiation. Indeed, a relation between impaired attention-following and incidental vocabulary learning in young children with ASD has been reported ( Yoder & McDuffie, 2006 ). EI aims to accelerate the rate of child learning, foster new development and generalization of skills, and attenuate effects of ASD on development by maximizing the benefit of experience-dependent neuroplasticity.

What are the main early intervention approaches?

Intervention approaches for young children with ASD are behavioural and educational, as there is no medical cure for core ASD impairments ( Kaplan & McCracken, 2012 ). There are two primary evidenced-based approaches to EI: Naturalistic Developmental Behavioural Interventions (NDBI; Schriebman et al., 2015 ), and those more singularly aligned with principles of operant conditioning, commonly referred to as applied behaviour analysis (ABA) or Early Intensive Behavioural Intervention (EIBI), involving a discrete trial instructional format.

Naturalistic developmental behavioural interventions (NDBI)

NDBI ( Schriebman et al., 2015 ) approaches usually cultivate a continuous back-and-forth flow of social engagement patterns between the child and interventionist. Intervention providers respond intentionally and contingently to a child’s interests, communicative bids, and play. Clear and developmentally appropriate cues (antecedents) are provided to the child to elicit specific behaviours, along with natural consequences (rewards/reinforcement) and systematic prompt hierarchies to promote child engagement and skill development. Carefully-timed and formed models may be given, with expansion of the child’s communication, social signals, and play, to scaffold more consistent, complex, and differentiated child language, social, and play behaviour. These goals are interspersed throughout the interaction rather than being discretely and explicitly taught. Developmental sequences are generally followed when determining the level of skill complexity to be targeted. NDBIs are aligned with EI practice recommendations from the Division of Early Childhood (DEC) ( Division for Early Childhood (DEC), 2014 ), which emphasize the importance of embedding EI in routines and contextually relevant environments.

Applied behaviour analysis (ABA) and early intensive behavioural interventions (EIBI)

In contrast to the NDBIs, delivery of ABA principles in an operant conditioning paradigm employs a more explicit, decontextualized, and highly structured approach. This method is commonly referred to as EIBI. Specific, discrete skills are taught in a prescribed order. EIBI curricula are comprehensive, targeting social, communication, cognitive, pre-academic (e.g. matching; colour, letter, and number recognition), and self-management skills ( Smith, 2011 ). Adult-selected materials and tasks are presented in sets of structured discrete trials, often adult-initiated, characterized by antecedent–behaviour–consequence chains. Well-defined prompt hierarchies and reinforcement schedules are used. Unlike in the NDBIs, reinforcers usually are not related to the social–communication–play context and the child’s behaviour (e.g. giving access to a toy car if the child says ‘car’), but, rather, are selected based on individualized motivators for the child (e.g. favourite toy or food). EIBI is usually conducted in a 1:1 instructional, non-distracting context. Upon acquisition of a skill, generalization training begins, system-atically reinforcing target behaviours and teaching children to distinguish between different cues ( Smith, 2011 ).

Interpreting the results of early intervention studies

The results of the EI studies reviewed below provide general themes to guide clinical care. However, inconsistencies in findings across studies are not uncommon. This may be attributable to the wide variation across studies in participants’ phenotypic profile and ages, intervention delivery setting, details of the intervention approach, intervention duration, frequency of intervention delivery, intervention implementer (e.g. parent, clinician, teacher, researcher), and targeted outcomes and outcome measures. Given that ASD is a disorder of neurodevelopment emerging from infancy, affecting multiple brain regions and functional connectivity, behaviourally-based interventions cannot be expected to ‘cure’ ASD. The gains in language, social, play, cognitive, and adaptive functioning, sometimes substantial, associated with some interventions, is very encouraging, and can have sustained effects (e.g. Green et al., 2017 ; Landa & Kalb, 2012 ). However, identification of positive treatment effects in the literature does not imply that EI should be limited to the early childhood period. Supporting children with ASD in surmounting a set of developmental challenges prepares them for the next set of achievements. As children progress into school-age, intervention targets, intensity, context, and methodologies will change to meet the child’s individual needs. EI is expected to reduce developmental and behavioural barriers to participation in inclusive educational contexts.

Early intervention literature review

This review of the evidence-base presents the most scientifically rigorous EI studies published in the past 15 years that focused mainly on children younger than 5 years of age. This review is organized as follows:

  • NDBI approaches focused on infants and 1-year-olds at heightened risk for ASD, but not yet diagnosed. Note that the research in this section is at an immature stage of investigation, particularly the studies that employed single subject or quasi-experimental designs.
  • NDBI approaches focused on toddlers and pre-schoolers already diagnosed with ASD: exclusively parent-mediated interventions being reviewed first, followed by review of primarily professional- or researcher-implemented interventions.
  • ABA/EIBI approaches.

Naturalistic developmental behavioural intervention (NDBI) and related approaches

Pre-diagnosis interventions: high-risk infants and toddlers, null treatment effects.

Three studies, focused on short-term interventions (≤12 sessions) for high-risk infants and toddlers, failed to identify effects on parents’ implementation of child-responsive strategies to children aged 8–25 months when compared to business-as-usual, or no-treatment groups ( Carter et al., 2011 ; Green et al., 2013 ; Rogers et al., 2012 ). The remaining studies, most of which also were short-term interventions, identified intervention effects on parent responsivity during interactions with their child and/or on child behaviour. These are reviewed below.

Positive treatment effects of pivotal response training (PRT)

Two multiple baseline single-subject design studies, involving three parent–child dyads per study, coached parents to implement a small set of PRT ( R. Koegel & Koegel, 2006 ) strategies, emphasizing those most suited to motivating infant engagement ( Koegel, Singh, Koegel, Hollingsworth, & Bradshaw, 2013 ; Steiner, Gengoux, Klin, & Chawarska, 2013 ). In both studies, parents received 1-h weekly training for 12 weeks and reached fidelity in implementing the PRT strategies. In addition, children (aged 4–9 months in Koegel et al.’s (2013) home-based study; 12 months old in Steiner et al.’s (2013) centre-based study) in both studies showed gains in communication development. In Koegel et al.’s (2013) study, parents were trained to pair themselves with children’s preferred activities to increase children’s social motivation. The infants in that study sustained their gains in communication skills at a 2-month follow-up ( Koegel et al., 2013 ).

In a short-term intervention (12 weekly sessions) study (quasi-experimental), Rogers et al. (2014) compared a PRT-like child-responsive approach (Infant Start) to an archival dataset of younger siblings of children with ASD who later received an ASD diagnosis. Group-level analyses indicated that the seven symptomatic children in the Infant Start group, aged 6–15 months, achieved higher levels of non-verbal cognitive and language functioning at 36 months of age. The Infant Start group also exhibited lower rates of ASD diagnosis at 36 months than four infants who qualified for the intervention study at 9 months of age, but whose parents declined to participate.

Positive treatment effects associated with video feedback during parent coaching

Another 12-session parent coaching intervention used an adaptation of Video Interaction for Promoting Positive Parenting (VIPP; Juffer, Bakermans-Kranenburg, & van IJzendoorn, 2008 ) to provide video feedback as a means of supporting parents’ increased understanding of, and responsive adaptation to, their infant’s communicative behaviour and style, with the aim of promoting child social and communication development ( Green et al., 2015 ). Infants were 9-month-old younger siblings of children with ASD. Compared to a no-treatment group, parents in the VIPP group displayed significantly less directiveness in their interaction with their infants. In addition, a non-significant but moderate-sized effect favouring the VIPP group was identified for reduction of child ASD symptoms and attention disengagement, and parent-reported improvement in child social adaptive scores. Non-significant negative treatment effects were identified for child receptive language and P100 in an evoked response potential task. Infant follow-up at 1- and 2-years ( Green et al., 2017 ) identified beneficial treatment effects on reduced severity of ASD symptoms and increased levels of child social engagement with parents in the VIPP group, from baseline to 39 months of age. The negative treatment effects identified in the VIPP group at the end of the intervention period were not sustained; there were no group differences in language performance at either follow-up visit. Unlike Rogers et al.’s (2012) preliminary finding, the VIPP intervention did not yield ASD protective effects. Furthermore, parents did not retain gains in implementation of child-responsive engagement strategies at the 1-year follow-up (child age 27 months), indicating the need for ongoing coaching to support parents’ implementation of child-responsive engagement strategies.

Positive treatment effects: adapted responsive teaching (ART)

In a slightly older group of at-risk children (13–17 months), targeting children’s sensory, social, and communication functioning, Baranek et al. (2015) randomly assigned parent–child dyads to ART, where parents were coached in use of child-responsive engagement strategies, or a community-intervention and monitoring group. Parents received a mean of 33.5 in-home visits and phone/email coaching sessions over ~8 months, nearly 3-times as many as in the studies described above. Like Green et al. (2015) , Baranek et al. (2015) reported a reduction in parent directiveness, and improvement in child social adaptive behaviour. The ART group also exhibited greater gains in language performance and reduction in hypo-responsiveness on parent-report measures. Although these child treatment effects were not observed in a replication study, treatment effects on parent responsiveness and parent production of positive affect during parent–child interactions were identified ( Watson et al., 2017 ). Watson et al. (2017) reported considerable evidence for mediation of the effect of group assignment on child outcomes via changes in parent responsiveness, despite the general lack of main effects when parent responsiveness was not accounted for in the models. The lack of change in control parents’ responsiveness and a decrease in their affect, compared to the ART parents’ gains in both of these domains, implies a protective effect of training parents in use of responsive strategies ( Strain & Bovey, 2011 ; Watson et al., 2017 ).

The studies reviewed above have shown that early development in infants and toddlers at heightened risk for ASD can be accelerated in a brief period of time as their parents adopt child-responsive strategies ( Mahoney & Solomon, 2016 ). This is encouraging, given that precursors to important social and communication skill development are being acquired during this early stage of development. In addition, the reported gains are occurring during a time in development when rate of development is slowing and ASD symptoms are emerging ( Landa et al., 2007 ).

Post-diagnosis: parent-mediated interventions

Null or positive treatment effects on parents only.

Some randomized controlled trials (RCTs) involving young children diagnosed with ASD have focused on parent implementation of child-responsive engagement strategies. In the RCT reporting null effects, parent coaching occurred only once every 6 weeks for 12 months ( Drew et al., 2002 ). Two RCTs identified effects on parent responsivity in a parent coaching condition, compared to a treatment-as-usual group ( Solomon, Van Egeren, Mahoney, Quon Huber, & Zimmerman, 2014 ) or parent advocacy control group ( Siller, Hutman, & Sigman, 2013 ), even when coaching occurred only once monthly for 12 months (similar to Drew et al., 2002 ) ( Solomon et al., 2014 ) or once weekly for 12 weeks ( Siller et al., 2013 ). In a 1-year follow-up of participants in Solomon et al.’s (2014) sample, the lack of child treatment effects persisted ( Oosterling et al., 2010 ). In Siller et al.’s (2013) study, children in the parent coaching group having baseline expressive language skills below the 12-month level showed greater expressive language gains compared to controls 1 year later, at follow-up.

Positive treatment effects on children

Most studies involving even short-term (≤12 weeks) parent-mediated interventions have reported positive treatment effects on measures of child behaviour. Most of the brief early interventions reviewed here are considered targeted interventions, focusing on one (imitation [ Ingersoll, 2010 ]; communication [ Hardan et al., 2014 ]) or a small set of highly related (i.e. joint engagement, joint attention, play [ Kasari, Freeman, and Paparella, 2006 ; Kasari, Gulsrud, Paparella, Hellemann, & Berry, 2015 ]) skills or behaviours.

The briefest of these was a five-session, home-based, attachment-focused intervention similar to that used by Green et al. (2015) (Video-feedback Intervention to promote Positive Parenting adapted to Autism; VIPP-AUTI). This intervention reduced parent intrusiveness during interaction with their child (aged 16–61 months) with ASD ( Poslawsky et al., 2015 ). Compared with parents in the treatment-as-usual group, greater parent-reported self-efficacy in parenting was identified as a treatment effect in the VIPP-AUTI group. Children in the VIPP-AUTI group exhibited more initiation of joint attention (IJA) behaviours at the 3-month follow-up, not mediated by parenting-related intervention effects ( Poslawsky et al., 2015 ). This study indicates that targeted short-term interventions may play a specific role in the intervention process, such as supporting families when they are first receiving the ASD diagnosis or after years of navigating the intervention system.

Another RCT demonstrated treatment effects on children (utterances produced) despite providing a low intensity intervention (12-week) that involved only four parent coaching sessions ( Hardan et al., 2014 ). In that study, parents were coached in the use of PRT strategies, and provided with a manual and illustrative video examples to promote their child’s expressive communication behaviour. One of the features of PRT that may promote communication gains in children is known as ‘child choice’. This strategy involves frequently offering the child a choice between two objects, one usually being a preferred object for the child, creating a communicative temptation. When the child’s request is made using the targeted form of communication (gesture, word, phrase) or, for children with emerging communicative intent, a behaviour that could be shaped into a communicative signal (e.g. a gaze shift to one of the objects), the child is ‘rewarded’ by giving them the requested object. Effective use of this strategy may quickly empower parents to elicit adaptive (speech, speech approximations, gesture) behaviours to replace maladaptive (tantrums, screaming) behaviour from their child ( Wetherby et al., 2014 ). This may hasten children’s behaviour regulation and language acquisition.

One short-term (8-week) but intensive (24 h), hands-on coaching approach guided parents in the use of child-responsive strategies aimed at promoting child joint attention in play routines, and child engagement with people and toys ( Kasari, Gulsrud, Wong, Kwon, & Locke, 2010 ). The children (aged 21–36 months) of coached parents showed greater gains in joint engagement, response to joint attention (RJA), and functional play acts compared to waitlist controls. While no between-group differences in IJA or symbolic play were detected, treatment effects were maintained 1 year after treatment ended ( Kasari et al., 2010 ). In a similar study, Schertz, Odom, Baggett, and Sideris (2013) identified intervention effects in a 16-session home-based Joint Attention Mediated Learning (JAML) intervention group compared to a business-as-usual group. In the JAML condition, a video review of parent–child interactions was used to guide parents’ reflection on children’s (mean age 24–27 months) targeted behaviours (focusing on faces, turn-taking, joint attention) and their own implementation of the intervention principles (focusing, organizing/ planning, encouraging, giving meaning, and expanding). Treatment effects were identified in examiner-measured RJA, attention to faces, and receptive language, and on a standardized parent-report measure of adaptive communication functioning. The gains children made during the JAML treatment period were sustained in the 4–8-week follow-up period, while controls exhibited no significant gains ( Schertz et al., 2013 ). This indicates that an important advance in child social communication has been activated.

The lack of child gains in initiation of social communication (e.g. IJA) in some joint attention-focused interventions (e.g. Kasari et al., 2006 ; Kasari et al., 2015 ; Schertz et al., 2013 ) may reflect the need for greater intervention intensity, a longer treatment interval, or a different intervention approach to cultivate and consolidate these core deficit area skills. Indeed, treatment effects for emerging or fully self-generated IJA have been identified in more intensive interventions targeting this skill. In an RCT comparing a more intensive intervention (104 h in 12 months) to treatment-as-usual, imitation of joint attention (although not spontaneous IJA), enjoyment and involvement in interaction with people, and attention to the activity were identified as treatment effects ( Casenhiser, Shanker, & Stieben, 2013 ). The intervention was delivered in centre- and home-based settings, with a review of videotapes of parent–child interaction to support parents’ implementation of the intervention strategies.

Treatment effects on other aspects of social and communication functioning have been reported in two additional RCTs examining intensive childresponsive parent-mediated interventions ( Green et al., 2010 ; Wetherby et al., 2014 ). In both studies, parents were guided to support social communication development in their children with ASD. Wetherby et al. (2014) compared a 9-month (mean of 88.56 h) 1:1 clinic-, home-, and community-based caregiver coaching intervention to a centre-based play group- + group parent education condition. Children were 18–20 months of age. Treatment effects were identified for examiner-measured social communication and receptive language, and on standardized caregiver-report measures of social, communication, and adaptive skills. Green et al. (2010) compared the Preschool Autism Communication Trial (PACT) intervention to treatment-as-usual with pre-school-aged children (mean age 45 months). The PACT intervention, delivered in centre- and home-based contexts, provided parents with 96 h of video-supported coaching over 12 months. Treatment effects were identified in child communicative initiations and shared attention, and parent synchrony in parent–child interactions ( Green et al., 2010 ). While no group treatment effect was detected in autism severity in that study ( Green et al., 2010 ), follow-up of the participants nearly 6 years later revealed that the PACT group displayed less severe ASD symptoms ( Pickles et al., 2016 ). Attenuation of ASD symptomatology was also reported in pre-schoolers of parents who were coached for 1 year in the use of strategies to promote child communication development, but not in treatment-as-usual controls ( Aldred, Green, & Adams, 2004 ).

Results of a meta-analysis of parent-mediated intervention studies were suggestive of improvements in child vocabulary comprehension and reduced ASD symptom severity, with the most robust effects (statistically significant with strong effect sizes) being improved patterns of parent engagement with children, such as increased shared attention and parent synchrony ( Oono, Honey, & McConachie, 2013 ). While many parents benefit from coaching in implementation of child-responsive strategies, some are slow adopters ( Schertz & Odom, 2007 ; Shire, Gulsrud, & Kasari, 2016 ). This could be due to a number of factors. One possibility is that parents did not consolidate the responsivity skills on which they were coached. This is not surprising, because effective implementation of child-responsive engagement strategies is a complex process. The implementation of responsivity strategies requires many skills, such as the ability to: read a child’s non-linguistic cues, which often are idiosyncratic in young children with ASD; scaffold developmentally-appropriate engagement activities for the child; provide clear and effective cues to the child about the behaviour being targeted; prompt the child to support emergence of more complex and integrated skills; and systematically expand child utterances and expand and vary engagement routines. Furthermore, these child-responsive intervention strategies must be implemented in different ways, as children’s developmental and behavioural profiles change over time. The coaching provided to parents at one point in their child’s development may not generalize to another point in their child’s development. Additional approaches to ensuring greater adoption of responsivity strategies are needed for families having a child with ASD.

Post-diagnosis: primarily, or exclusively, clinician-/teacher-implemented interventions

In addition to parent-mediated intervention, clinician- or teacher-implemented intervention has been recognized as an important component of the intervention package (e.g. Stahmer et al., 2015 ). In a systematic review and meta-analysis of the ASD EI literature, Hampton and Kaiser (2016) found that the greatest gains in spoken language outcomes occur for young children with ASD who received both clinician- and parent-delivered intervention, as opposed to only one or the other. For example, Roberts et al. (2011) found that children randomized to a centre-based clinician-administered group intervention for children, paired with parent training (combined intervention), was associated with better child social communication outcomes than a home-based parent-mediated intervention or waitlist control group. Likewise, parents of children in the combined condition reported having a greater sense of competence and quality-of-life than did parents in the other two groups. Rogers et al. (2012) concluded that parent-mediated interventions do not yield child gains comparable to those of interventionist-delivered treatment (particularly intensive interventions) (e.g. Dawson et al., 2010 ; Landa, Holman, O’Neill, & Stuart, 2011 ; Rickards, Walstab, Wright-Rossi, Simpson, & Reddihough, 2007 ; Roberts et al., 2011 ).

In an RCT comparing centre-based intervention only or a combined centre- + home-based intervention for a mixed group of pre-schoolers (3–5 years) with developmental delays, including ASD, Rickards et al. (2007) found greater cognitive and behavioural improvements in children receiving the home-based supplemental intervention. The home-based supplemental intervention did not provide added benefit for improving family functioning. Yet children in the centre- + home-based intervention who made the greatest gains were those from more highly stressed families. This study highlights the need to intentionally transfer skills taught at school into the home, where those skills can be reinforced. In contrast to findings reported by Rickards et al. (2007) , Roberts et al. (2011) identified greater social and communication gains in children who received a combination of small-group centre-based intervention + parent training and support program compared to children who received individualized home-based intervention only or in waitlist controls. Improvements in parents’ perception of competence and quality-of-life were greater in the combined intervention condition as well ( Roberts et al., 2011 ).

Effects of an intensive home-based intervention were examined in a comparison of the Early Start Denver Model (ESDM) to a business-as-usual condition ( Dawson et al., 2010 ). Children with ASD (18–30 months) in the ESDM condition received 15 h per week of direct 1:1 clinician-child intervention, plus 5 h per week of parent-mediated ESDM intervention. At the end of 2 years, the ESDM group exhibited significantly greater gains in developmental quotient compared to the BAU group by 17.6 vs 7.0 points, respectively. The greatest impact was on receptive and expressive language outcomes.

Landa et al. (2011) conducted a comparative efficacy trial, wherein all of the 2-year-olds with ASD received a comprehensive centre-based group intervention for 10 h per week for 6 months. Children were randomized to receive a supplemental interpersonal synchrony (IS) curriculum or just the comprehensive intervention (non-IS). Parents in both conditions participated in parent education classes, guided classroom observations, and in-home coaching on NDBI strategies. Moderate-to-large effect sizes indicated that children receiving the IS curriculum made greater social, language, and non-verbal cognitive gains than the non-IS group. A significant effect was observed on generalized interpersonally synchronous imitation, favouring the IS group ( Landa et al., 2011 ). In another RCT examining an inclusive classroom-based intervention (Learning Experiences and Alternative Program for Preschoolers and Their Parents), pre-schoolers with ASD in classrooms of trained and coached teachers exhibited significantly greater gains in cognitive, language, social, and problem behaviour domains, as well as reduction in autism symptoms after 2 years of intervention, compared to students of teachers given the intervention manual but no formal training in implementation ( Strain & Bovey, 2011 ). While neither child behaviour nor family socio-economic status at study entry predicted child outcomes, the level of teacher fidelity of implementation of the intervention did ( Strain & Bovey, 2011 ).

In another RCT, an intensive joint attention-focused intervention (80 20-min sessions) delivered by a trained teacher was compared to a treatment-asusual group ( Kaale, Smith, & Sponheim, 2012 ). Treatment effects included more frequent IJA in children aged 21–60 months with teachers and joint engagement with parents ( Kaale et al., 2012 ). The importance of targeting joint attention in EI is highlighted by Gulsrud, Hellemann, Freeman, and Kasari’s (2014) 6-year follow-up of a sub-sample of preschoolers who had received EIBI only or EIBI supplemented with a brief intervention targeting either joint-attention or play ( Kasari et al., 2006 ). The most rapid growth in frequency of triadic gaze and showing behaviour, and most significant expressive language gains, were observed in children who had received the intervention targeting joint attention. From preschool-age to school-age, a positive growth curve was identified for frequency of triadic gaze (looking from an object to the engagement partner, and back to the object) and showing ( Gulsrud et al., 2014 ). Frequency of IJA production, however, decreased over time in all three pre-school-age intervention groups ( Gulsrud et al., 2014 ).

Combining parent- and clinician/teacher-implemented intervention, implemented with high fidelity, has the benefit of immersing children in a learning environment where increasingly complex skills are consistently enticed and reinforced. Professionals, trained in child development and in how to adapt developmental and applied behaviour analysis instructional strategies to the child’s temperament, learning style, strengths and needs, can deliver effective intervention to the child, provide models for parent implementation, and support parents’ acquisition of responsive engagement strategies. Research is needed to address the gap between treatment effects attained in well-controlled clinical research settings and/or executed by trained research staff, and those reported in community settings and executed by community providers ( Dawson et al., 2010 ; Smith, Klorman, & Mruzek, 2015 ).

Early intensive behavioural intervention (EIBI)/applied behaviour analysis

Two reports focused on evaluating the evidence base for ASD early interventions, which have classified EIBI as a well-established intervention approach ( Rogers & Vismara, 2008 ; Smith & Iadarola, 2015 ). This classification is in agreement with most of the systematic reviews and meta-analyses examining individual, comprehensive ABA (e.g. Eldevik et al., 2009 ; Reichow, 2012 ). Four studies of 1:1 (adult:child) implementation of EIBI, based on the original Lovaas model and using a comprehensive curriculum, met Smith and Iadarola’s (2015) criteria for examination of efficacy based on the Journal of Clinical Child and Adolescent Psychology ’s methods criteria. All four of these studies ( Eikeseth, Klintwall, Jahr, & Karlsson, 2012 ; Eikeseth, Smith, Jahr, & Eldevik, 2007 ; Eldevik, Hastings, Jahr, & Hughes, 2012 ; Peters-Scheffer, Didden, Mulders, & Korzilius, 2010 ) employed a quasi-experimental design. Intervention was delivered 6.5–28 h per week in school settings with children between 2 and 7 years of age by trained interventionists. In all four studies, the EIBI group demonstrated greater gains in IQ and adaptive behaviour than the comparison group. The only one of these studies to assess ASD symptoms and problem behaviour ( Peters-Scheffer et al., 2010 ) failed to detect an effect of EIBI on these aspects of child functioning. Greater improvement in IQ and adaptive behaviour is associated with greater intervention intensity (≥ 36 h per week) ( Eldevik et al., 2009 ). Little is known about the efficacy of EIBI for language and social functioning in young children with ASD ( Reichow, Barton, Boyd, & Hume, 2012 ).

Several studies have revealed that not all children benefit equally from EIBI (see section Predictors of positive child outcomes). About 19–30% of children receiving EIBI (vs 8.7% of controls) exhibit gains in IQ beyond that expected, due to random fluctuations in IQ performance ( Eldevik et al., 2009 ; Eldevik et al., 2012 ). These children, likely to reach age-expected IQ and/or adaptive functioning during the study, met Sallows and Graupner’s (2005) criteria for rapid learning ( Eldevik et al., 2009 ). Sallows and Graupner (2005) cautioned that even rapid learners may show uneven rates of development across developmental domains, such as improving more in the cognitive than social domain. Based on a meta-analysis, ~20% of children receiving EIBI (vs 5% of controls) exhibit reliable gains in adaptive behaviour ( Eldevik et al., 2009 ). Poor response to EIBI is expected in 10–20% of children with ASD ( Lovaas, 1987 ; Smith, Groen, & Wynn, 2000 ). EIBI, as delivered in the community, has limited effects on reducing ASD symptom severity ( Smith et al., 2015 ).

EIBI, usually delivered in a 1:1 instructional format, is an effective intervention approach for many children. The comprehensive skill sets targeted by EIBI may contribute to cognitive gains. Research examining the effects of pairing EIBI/ABA with developmental/NDBI approaches to maximize development in children with ASD is needed.

Predictors of positive child outcomes

ASD encompasses a wide range of symptom expression, with heterogeneity in neurobiological (e.g. Salmond, Vargha-Khadem, Gadian, De Haan, & Baldeweg, 2007 ) and behavioural phenotypes, such as symptom severity, intellectual functioning, spoken language ability, social disability, and adaptive functioning. Layered on this heterogeneity is variability across children in environmental experiences (e.g. caregiver engagement style, parental education, socioeconomic status, age at ASD detection, intervention exposure). Not surprisingly, there is variability in children’s response to EI (e.g. Eldevik et al., 2010 ; Smith et al., 2000 ). In a literature review focused on pre-intervention predictors of outcome, Zachor and Ben-Itzchak (2017) organized results by child outcome. Predictors of reduced severity of autism symptoms included age, cognitive functioning, ASD symptom severity, and treatment approach. Predictors of cognitive outcomes included ASD symptom severity, maternal educational level, and treatment type and intensity. Adaptive behaviour outcomes were predicted by the level of cognitive functioning, ASD symptom severity, maternal age, and treatment type and intensity.

In a systematic prospective study of children with ASD aged 20–59 months enrolled in community-based EIBI, Smith et al. (2015) examined numerous commonly reported treatment outcome predictors. Stereotyped motor movements and sensory responses, as opposed to pre-occupations and inflexible routines, were not linked to attenuated treatment response ( Smith et al., 2015 ), in contrast to findings by Klintwall and Eikeseth (2012) . Social functions (actively seeking social engagement, joint attention, and imitation), originally expected to independently predict outcomes, loaded onto a single factor and, thus, were combined to form a social engagement variable. Higher baseline social engagement scores predicted better IQ and adaptive functioning outcomes 1 and 2 years later ( Smith et al., 2015 ). Similarly, Gulsrud et al. (2014) found that the frequency of IJA production at pre-school-age was associated with the degree of expressive language gains in expressive language between ages 8–10 years in children who received EIBI, with and without a supplemental developmental intervention targeting play or joint attention at pre-school-age. In this same sample, frequency of IJA production at pre-school-age was associated with gains in expressive language at ages 8–10 years. Children with the mildest ASD symptoms at follow-up exhibited the steepest growth curves in frequency of triadic gaze and greatest overall gains in expressive language. In another follow-up examination of the children in Kasari et al.’s (2006) study, Kasari, Gulsrud, Freeman, Paparella, and Hellemann, (2012) identified baseline play level and play diversity as predictors of spoken language and cognitive scores, respectively. Production of spoken language at baseline is reportedly associated with greater spoken language improvement in augmentative and alternative communication (AAC)-focused intervention ( Ganz et al., 2014 ).

Age at intervention enrollment has repeatedly been identified as a predictor of social-communication outcomes ( Rogers et al., 2012 ). In their systematic examination of treatment outcome predictors for children receiving community-based EIBI, Smith et al. (2015) also identified age at entry into the intervention as a predictor of functioning 1–2 years after baseline. Younger children made the greatest gains in IQ and adaptive domains, sometimes attaining greater reduction in ASD symptom severity ( Smith et al., 2015 ). The most rapid gains in development and greatest reduction in symptom severity appear to occur in the first 2 years of intervention, most notably in the first year ( Dawson et al., 2010 ; Howlin, Magiati, & Charman, 2009 ; Smith et al., 2015 ). Rate of learning in the early stages of intervention predicts later gains ( Hayward, Eikeseth, Gale, & Morgan, 2009 ; Sallows & Graupner, 2005 ).

Greater intervention intensity (hours and duration in months) is associated with greater child gains (e.g. Eldevik et al., 2010 ; Magiati, Charman, & Howlin, 2007 ; Virués-Ortega, Rodriguez, & Yu, 2013 ). Related to dosage is fidelity of implementation, which is associated with improved child behaviour and reduced parent stress ( Aldred et al., 2004 ; Shire et al., 2016 ; Strauss et al., 2012 ). Some researchers did not identify dosage effects, but instead reported that interventionist characteristics (e.g. expertise) are associated with child outcomes ( Fernell et al., 2011 ; Strauss et al., 2012 ; Vivanti, Dissanayake, Zierhut, Rogers, & Victorian ASELCC Team, 2013 ). For example, intervention delivered by community providers yielded less than half the gains achieved by university-delivered intervention ( Dawson et al., 2010 ; Smith et al., 2015 ). Considering the multi-system nature of ASD impairments and limited generalization ability of children with ASD, providing sufficient training to intervention providers and dosage of intervention to the child in EI process is of great importance.

There are numerous predictors of child outcome, and these are likely to differ depending on the intervention approach, fidelity, and consistency of intervention delivery, parent buy-in, and so forth. As indicated in many studies, early enrollment in intervention is important, and multiple intervention approaches may be needed to maximize child outcomes.

Recommendations for clinical practice

Currently available evidence supports the following recommendations.

  • Initiate intervention early, when signs of ASD risk appear ( Rogers et al., 2012 ).
  • Address all developmental domains in intervention ( Gulsrud et al., 2014 ; Landa et al., 2011 ; Smith & Iadarola, 2015 ).
  • Shift strategies and targets as children show increasing expressive language skills ( Siller et al., 2013 ).
  • Provide coaching to parents for at least 9–12 months at a frequency greater than once per month to promote consolidation of learning (e.g. Carter et al., 2011 ; Drew et al., 2002 ; Rogers et al., 2012 ; Wetherby et al., 2014 ).
  • Provide video feedback to parents to support understanding of intervention strategies and facilitate insights into their child’s social and communication signals, and the contingency between their own and their child’s behaviour.
  • Provide direct hands-on coaching of parents rather than psychoeducation provided without, or with few, hands-on coaching sessions ( Carter et al., 2011 ; Kasari et al., 2015 ).
  • Provide at least part of the training in structured contexts with minimal distractions to achieve more focused training on intervention session (higher dosage) and more opportunities for parents to practice implementation of strategies (e.g. Ingersoll & Gergans, 2007 ; Wallace & Rogers, 2010 ).
  • Coach parents in a few child-responsive engagement strategies at a time to promote learning consolidation ( Ingersoll & Gergans, 2007 ; Koegel et al., 2013 ).
  • Provide parent coaching in multiple settings to promote parent and child generalization ( Wetherby et al., 2014 ).
  • Provide parents with booster sessions to support ongoing use and adaptation of intervention strategies after coaching support ends ( Carter et al., 2011 ; Green et al., 2017 ).
  • Consider aided AAC (speech generating device or Picture Exchange Communication System ( Frost & Bondy, 2002 )), when speech does not emerge early; AAC will not impede spoken language acquisition ( Schlosser & Wendt, 2008 ).
  • Combine professional-delivered intervention with parent-mediated intervention ( Hampton & Kaiser (2016) ; Rickards et al., 2007 ; Roberts et al. (2011) ; Rogers et al. (2012) ; Stahmer et al., 2015 ).
  • Train intervention providers to fidelity in implementation of intervention approaches.

Conclusions

ASD is a complex, multi-system neurobiological disorder with no medical cure or pharmacologic treatment for core social and communication impairments. Meta-analyses and systematic reviews have shown that EI has moderate-to-large effects on child outcomes, with effect size depending on a variety of factors. Equipping parents to implement development-enhancing strategies while engaged with their children is a vital intervention component. However, methods of preparing parents to adopt such strategies, implement them with fidelity, adapt them to the child’s changing skills and behaviours, and sustain use of the strategies have not been adequately defined. Child outcomes are enhanced when both clinician- and parent-implemented intervention components are included.

Considerably more high quality research, particularly with large sample sizes, is needed to understand the impact of prodromal interventions, improve personalization of interventions, determine what is needed to sustain treatment effects, define active ingredients of intervention approaches, examine timing of targeting specific types of skills, and establish adaptive treatment pathways for low responders. In the meantime, NDBI, EIBI/ABA, and aided AAC interventions are efficacious. Such information would support clinical decision-making for the heterogeneous population of young children with ASD.

Acknowledgments

Special appreciation is extended to Dr Julie Feuerstein for her comments on this manuscript during preparation, and to Chelsea Homa and Erin McAuliffe for their help with preparing the reference document.

Funding This work was supported by Maternal and Child Health Bureau [R40 MC26193] and National Institute on Deafness and Other Communication Disorders [R21 DC015846].

Disclosure statement

The author reports no conflicts of interest. The author alone is responsible for the content and writing of the paper.

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Ealing Grid for Learning

Ealing Grid for Learning

early help case study

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  • Early help assessment and plan (EHAP)

The EHAP is a process by which the needs of a child or young person are assessed and an action plan to meet those needs is agreed and progressed.

The aim of the process is the delivery of multi-agency, multi-disciplinary or targeted support as early as possible to tackle an emerging problem/issue before it becomes bigger, harder to address and affects the development or life chances of the child or children in the family.

The EHAP is only used when a multi-agency or targeted approach is necessary. Where one service can address the needs of the child successfully, an EHAP is not required.

For schools and other organisations that employ professionals from different services/sectors e.g. from the NHS, social care, mental health, education, etcetera – engaging the expertise of these professionals to support a child/young person and their family is an example of multi-agency and targeted support even though they may all work for the same school or organisation.

Family information service

The family information service (FIS) is the supporting service for EHAP use.

The FIS give EHAP advice, register EHAPS, and deliver training, supply copies of the EHAP pack which contains all the forms and guidance needed to use the process.

EHAP training

EHAP training aims to support professionals working with families to improve their understanding of the EHAP process in Ealing - offering children, young people and their families a simple route to multi-agency and targeted early help and support.

Online EHAP training dates

Training sessions are free and take place online with MS Teams. Select link for more details and to book a place via Ealing CPD online (Early years section):

  • Wednesday 19 October 2022 at 10am-12:30 event code: EYC 22/177
  • Tuesday 15 November 2022 at 10am-12:30 event code: EYC 22/178
  • Wednesday 18 January 2023 at 10am-12:30 event code: EYC 22/179
  • Thursday 16 February 2023 at 10am-12:30 event code: EYC 22/180
  • Tuesday 14 March 2023 at 10am-12:30 event code: EYC 22/181

EHAP support for schools

SAFE has two EHAP advice and consultancy workers based within the Family Information Service:

Their role supports schools initiating and leading on EHAPs and can also support with the following:

  • Discussing with a school if an EHAP is appropriate
  • Reassurance and support to schools who are unsure about initiating an EHAP or taking on the Lead Professional role
  • Supporting schools with improving relationships with families where there has been a breakdown in relation to early help
  • Supporting schools engaging families in early help
  • Attending team around the family (TAF) meetings
  • Supporting the chair at TAF meetings
  • Attending home visits (agreed at TAF meetings)
  • Supporting schools in accessing services
  • Liaising between ECIRS and schools
  • Case discussion.

Please contact Rachel or Satwant to discuss how they can support your school working with families that may need early help and support.

EHAP support service organisations

Below are a list of internal and external services that may support the family you are currently working with. These services can be integrated within your EHAP, as part of your action plan. If you are unable to find what you are looking for, or are aware of any other services you think should be listed here, please call us on the contact details below.

  • Activities (pdf)
  • Alcohol and substance misuse (pdf)
  • Back to work support (pdf)
  • Benefits and money management (pdf)
  • Bereavement (pdf)
  • Domestic violence (pdf)
  • Ealing parenting service group (pdf)
  • Gender identity (pdf)
  • Grants (pdf)
  • Housing support and advice (pdf)
  • Mental health support (pdf)
  • Relationship counselling and family mediation (pdf)
  • School exclusion support (pdf)
  • Young carers (pdf)

Family information service Tel: 020 8825 5588 Email: [email protected]

EHAP support team contacts (pdf) How to start using EHAP (ealing.gov.uk) EHAP registration process for professionals (pdf) Interactive EHAP form 2018 (pdf) Police officers linked to Ealing high schools (pdf)

  • Family information service (FIS) , Early years, childcare, childrens centres and SAFE: [email protected] 020 8825 5588

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Services for children

  • Raising SEN support expectations
  • Roles and responsibilities
  • Quality first teaching
  • Broad area of needs
  • Interventions and approaches
  • Useful SEND web links
  • Glossary of SEND terms
  • ELP SEND and inclusion committee
  • ELP SEN and inclusion key dates
  • Ealing’s strategy for additional and SEND and inclusion 2023-2027
  • Special school and ARP and SEN unit provision in Ealing
  • SEN وإدراج مقاطع الفيديو باللغة البنجابية
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Casebook: Developmentally Appropriate Practice in Early Childhood Programs Serving Children from Birth Through Age 8

Preservice teachers gathered around a table discussing cases

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About the book.

  • Make connections to the fourth edition of Developmentally Appropriate Practice in Early Childhood Programs 
  • Think critically about the influence of context on educator, child, and family actions 
  • Discuss the effectiveness of the teaching practices and how they might be improved 
  • Support your responses with evidence from the DAP position statement and book 
  • Explore next steps beyond the case details 
  • Apply the learning to your own situation 

Table of Contents

  • Editors, Contributors, and Reviewers
  • Introduction and Book Overview | Jennifer J. Chen and Dana Battaglia
  • 1.1 Missed Opportunities: Relationship Building in Inclusive Classrooms | Julia Torquati
  • 1.2 “My Name Is Not a Shame” | Kevin McGowan
  • 1.3 Fostering Developmentally Appropriate Practice Through Virtual Family Connections | Lea Ann Christenson
  • 1.4 Counting Collections in Community | Amy Schmidtke
  • 1.5 The Joy Jar: Celebrating Kindness | Leah Schoenberg Muccio
  • 1.6 Prioritizing Listening to and Learning from Families | Amy Schmidtke 
  • 2.1 Julio’s Village: Early Childhood Education Supports for Teen Parents | Donna Kirkwood
  • 2.2 Healthy Boundaries: Listening to Children and Learning from Families | Jovanna Archuleta
  • 2.3 Roadmap of Family Engagement to Kindergarten: An Ecological Systems Approach | Marcela Andrés
  • 2.4 Taking Trust for Granted? The Importance of Communication and Outreach in Family Partnerships | Suzanna Ewert
  • 2.5 Book Reading: Learning About Migration and Our Family Stories | Sarah Rendón García 
  • 3.1 Pairing Standardized Scale with Observation | Megan Schumaker-Murphy
  • 3.2 The Power of Observing Jordan | Marsha Shigeyo Hawley and Barbara Abel
  • 3.3 “But What Is My Child Learning?” | Janet Thompson and Jennifer Gonzalez
  • 3.4 Drawing and Dialogue: Using Authentic Assessment to Understand Children’s Sense of Self and Observe Early Literacy Skills | Brandon L. Gilbert
  • 3.5 The ABCs of Kindergarten Registration: Assessment, Background, and Collaboration Between Home and School | Bridget Amory
  • 3.6 Creating Opportunities for Individualized Assessment Activities for Biliteracy Development | Esther Garza
  • 3.7 Observing Second-Graders’ Vocabulary Development | Marie Ann Donovan
  • 3.8 Writing Isn’t the Only Way! Multiple Means of Expressing Learning | Lee Ann Jungiv 
  • 4.1 Engaging with Families to Individualize Teaching | Marie L. Masterson 
  • 4.2 Tumbling Towers with Toddlers: Intention and Decision Making Over Blocks | Ron Grady  
  • 4.3 What My Heart Holds: Exploring Identity with Preschool Learners | Cierra Kaler-Jones 
  • 4.4 “I See a Really Big Gecko!” When Background Knowledge and Teaching Materials Don’t Match | Germaine Kaleilehua Tauati and Colleen E. Whittingham 
  • 4.5 Using a Humanizing and Restorative Approach for Young Children to Develop Responsibility and Self-Regulation | Saili S. Kulkarni, Sunyoung Kim, and Nicola Holdman 
  • 4.6 Joyful, Developmentally Appropriate Learning Environments for African American Youth | Lauren C. Mims, Addison Duane, LaKenya Johnson, and Erika Bocknek 
  • 5.1 Using the Environment and Materials as Curriculum for Promoting Infants’ and Toddlers’ Exploration of Basic Cause-and-Effect Principles | Guadalupe Rivas 
  • 5.2 Social Play Connections Among a Small Group of Preschoolers | Leah Catching 
  • 5.3 Can Preschoolers Code? A Sneak Peek into a Developmentally Appropriate Coding Lesson | Olabisi Adesuyi-Fasuyi 
  • 5.4 Everyday Gifts: Children Show Us the Path—We Observe and Scaffold | Martha Melgoza 
  • 5.5 Learning to Conquer the Slide Through Persistence and Engaging in Social Interaction | Sueli Nunes 
  • 5.6 “Sabes que todos los caracoles pueden tener bebés? Do You Know that All Snails Can Have Babies?” Supporting Children’s Emerging Interests in a Dual Language Preschool Classroom | Isauro M. Escamilla 
  • 5.7 “Can We Read this One?” A Conversation About Book Selection in Kindergarten | Larissa Hsia-Wong  
  • 6.1 Take a Chance on Coaching: It’s Worth It! | Lauren Bond 
  • 6.2 It Started with a Friendship Parade | Angela Vargas 
  • 6.3 The World Outside of the Classroom: Letting Your Voice Be Heard | Meghann Hickey 
  • 7.1 Communication as a Two-Way Street? Creating Opportunities for Engagement During Meaningful Language Routines | Kameron C. Cardenv 
  • 7.2 Eli Goes to Preschool: Inclusion for a Child with Autism Spectrum Disorder | Abby Hodges
  • 7.3 Preschool Classroom Supports and Embedded Interventions with Coteaching | Racheal Kuperus and Desarae Orgo
  • 7.4 Addressing Challenging Behavior Using the Pyramid Model | Ellie Bold
  • 7.5 Dual Language or Disability? How Teachers Can Be the First to Help | Alyssa Brillante
  • 7.6 Adapting and Modifying Instruction Using Reader’s Theater | Michelle Gonzalez
  • 7.7 Supporting Children with Learning Disabilities in Mathematics: The Importance of Observation, Content Knowledge, and Context | Renee B. Whelan 
  • 8.1 Facilitating a Child’s Transition from Home to Group Care Through the Use of Cultural Caring Routines | Josephine Ahmadein
  • 8.2 Engaging Dual Language Learners in Conversation to Support Translanguaging During a Small Group Activity | Valeria Erdosi and Jennifer J. Chen
  • 8.3 Incorporating Children’s Cultures and Languages in Learning Activities | Eleni Zgourou
  • 8.4 Adapting Teaching Materials for Dual Language Learners to Reflect Their Home Languages and Cultures in a Math Lesson | Karen Nemeth
  • 8.5 Studying Celestial Bodies: Science and Cultural Stories | Zeynep Isik-Ercan
  • 8.6 Respecting Diverse Cultures and Languages by Sharing and Learning About Cultural Poems, Songs, and Stories From Others | Janis Strasser

Book Details

Faculty resources.

To access tips and resources for teaching the cases, please complete this brief form.  You’ll be able to download the items after you complete the form. 

Teacher Inquiry Group Resources

To access reflection questions to deepen your learning, please click here.

More DAP Resources

To read the position statement, access related resources, and stay up-to-the-minute on all things DAP, visit  NAEYC.org/resources/developmentally-appropriate-practice .

Pamela Brillante,  EdD, is professor in the Department of Special Education, Professional Counseling and Disability Studies, at William Paterson University. She has worked as an early childhood special educator, administrator, and New Jersey state specialist in early childhood special education. She is the author of the NAEYC book The Essentials: Supporting Young Children with Disabilities in the Classroom. Dr. Brillante continues to work with schools to develop high-quality inclusive early childhood programs. 

Pamela Brillante

Jennifer J. Chen, EdD, is professor of early childhood and family studies at Kean University. She earned her doctorate from Harvard University. She has authored or coauthored more than 60 publications in early childhood education. Dr. Chen has received several awards, including the 2020 NAECTE Foundation Established Career Award for Research on ECTE, the 2021 Kean Presidential Excellence Award for Distinguished Scholarship, and the 2022 NJAECTE’s Distinguished Scholarship in ECTE/ECE Award. 

Stephany Cuevas, EdD, is assistant professor of education in the Attallah College of Educational Studies at Chapman University. Dr. Cuevas is an interdisciplinary education scholar whose research focuses on family engagement, Latinx families, and the postsecondary trajectories of first-generation students. She is the author of Apoyo Sacrifical, Sacrificial Support: How Undocumented Parents Get Their Children to College (Teachers College Press). 

Christyn Dundorf, PhD, has more than 30 years of experience in the early learning field as a teacher, administrator, and adult educator. She serves as codirector of Teaching Preschool Partners, a nonprofit organization working to grow playful learning and inquiry practices in school-based pre-K programs and infuse those practices up into the early grades.

Emily Brown Hoffman, PhD, is assistant professor in early childhood education at National Louis University in Chicago. She received her PhD from the University of Illinois at Chicago in Curriculum & Instruction, Literacy, Language, & Culture. Her focuses include emergent literacy, leadership, play and creativity, and school, family, and community partnerships. 

Daniel R. Meier, PhD, is professor of elementary education at San Francisco State University. His publications include Critical Issues in Infant-Toddler Language Development: Connecting Theory to Practice (editor), Supporting Literacies for Children of Color: A Strength-Based Approach to Preschool Literacy (author), and Learning Stories and Teacher Inquiry Groups: Reimagining Teaching and Assessment in Early Childhood Education (coauthor). 

Gayle Mindes, EdD, is professor emerita, DePaul University. She is the author of Assessing Young Children , fifth edition (with Lee Ann Jung), and Social Studies for Young Children: Preschool and Primary Curriculum Anchor, third edition (with Mark Newman). Dr. Mindes is also the editor of Teaching Young Children with Challenging Behaviors: Practical Strategies for Early Childhood Educators and Contemporary Challenges in Teaching Young Children: Meeting the Needs of All Students . 

Lisa R. Roy, EdD, is executive director for the Colorado Department of Early Childhood. Dr. Roy has supported families with young children for over 30 years, serving as the director of program development for the Buffett Early Childhood Institute, as the executive director of early childhood education for Denver Public Schools, and in various nonprofit and government roles.

Cover of Casebook: Developmentally Appropriate Practice in Early Childhood Programs Serving Children from Birth Through Age 8

Early childhood services local case examples

17 May 2021

A series of case studies based on EIF's work with local areas looking at contemporary practice in delivering maternity and early years services through local centres or hubs.

In 2020, we engaged 14 local areas across England to see and understand different approaches to providing early childhood services through community venues. The experiences and reflections of some of these areas are captured in these case studies.

Please note that these case examples are intended to illustrate what others are doing in this field; we do not endorse any of the specific decisions, plans or actions.  

Find out more

Case examples, share this page, related content.

LifeLine Projects

Case Study: Early Help Collective

  • Case Studies
  • Early Help Collective

The Early Help Collective is an initiative funded by the London Borough of Barking and Dagenham to provide guidance and support to parents. LifeLine Projects is working alongside six other organisations in the Collective to answer parents’ questions, signpost them to helpful services, bring them together to support each other, and support their mental health.

Marisa was referred to us a few months ago, presenting multiple issues. She was receiving support from a PPIMHS practitioner, but said she frequently felt low and rarely left the house, isolating herself in the bedroom. Her partner reports she cries a lot and often shouts at her children when they misbehave.

She looks after her seven children in a two-bedroom house, which she says has lots of mould. She also said that the mould has made her children ill, which has been reported repeatedly to both her GP and health visitors.

Her children frequently get in trouble at school for not doing homework, which they say is due to not having the space at home. She says she struggles to control their behaviour, and they frequently run around the house and fight with each other.

Following the referral, I went to meet Marisa at her home and compete a full assessment of her situation. English was not her first language, so there was some difficulty at first. However, her partner was on-hand and could help us communicate as well as provide emotional support. Once fully appraised of her situation, I went away and developed an action plan for her.

We made arrangements for her to take part in local English classes. She was very happy to join in and found the classes extremely helpful. As part of the on-going support, we decided to meet up weekly—her improving English allowed us to build a rapport and engage in conversation without having to rely on her partner. I also assisted her in purchasing a baby carrier, which meant she could go out—particularly to attend the English classes—with both her youngest children in tow.

Meanwhile, with Marisa’s permission, I got in touch with the housing officer assigned to her case, hoping to learn more about the housing situation and how the council could help. Unfortunately, I’ve not yet been able to meet with him due to his heavy workload. I’ve kept Marisa appraised of the developments, and we hope to meet with the officer soon.

After exploring some further options, I met with Marisa’s sister. She agreed to support Marisa, offering time out of her own day to check in on Marisa and help out where she can.

While my work with Marisa is still ongoing, it’s clear that she has already become more relaxed and more positive compared to when we first met—her mood is definitely brighter now. She continues to attend English classes and enjoys conversing with me.

Moving forward, I’ll be continuing to engage with the council and her housing officer to find a solution to her housing issue. We’ll also be looking at ways for her to better deal with her children’s challenging behaviour at home and how she can improve her mental health and outlook further.

early help case study

Theo , Youth Development Worker

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Helping Marisa was quite challenging at first due to the language barrier, but she’s speaking English quite well thanks to the classes. I’m happy to report we managed to resolve her housing situation now and that she’ll be moving into a new home shortly.

Without the Early Help Collective, Marisa could have remained isolated from the community and stuck in a house not fit for purpose. It goes to show just how vital having proper wrap-around support really is.

early help case study

Stephen Callendar Senior Family Support Worker

These articles may contain testimonials by LifeLine staff members and service users of our programmes and/or services. These testimonials reflect the real-life experiences and opinions of such staff members/ service users. However, the experiences are personal to those staff members/ service users and may not necessarily be representative of all staff members/ service users of our programmes and/or services. We do not claim, and you should not assume, that all staff members/ service users will have the same experiences. Individual results may vary.

Testimonials are submitted in various forms such as text, audio and/or video, and are reviewed by us before being posted. They appear in the newsletter in words as given by the staff members and service users, except for the correction of grammar or typing errors. Some testimonials may have been shortened for the sake of brevity where the full testimonial contained extraneous information not relevant to the general audience.

The views and opinions contained in the testimonials belong solely to the individual user and do not reflect our views and opinions. Staff members/ service users are not paid or otherwise compensated for their testimonials.

early help case study

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early help case study

  • Education, training and skills
  • School curriculum
  • Early years curriculum

Early years foundation stage: exemplification materials

To support teachers in making early years foundation stage profile (EYFSP) judgements.

Applies to England

Using these materials.

These exemplification materials are to support teachers in assessing children’s development at the end of the early years foundation stage ( EYFS ).

They show teachers using their professional judgement and knowledge of the child’s overall development to make EYFSP judgements. These conversations help to support a successful transition to key stage 1.

There’s no requirement for you to use these materials. All statutory requirements regarding the EYFSP are set out in the EYFSP handbook .

Although each example only focuses on certain areas of learning, you must assess all early learning goals for each child in your class.

You should also read the supporting information ( PDF , 158 KB , 3 pages ) for these videos.

Case study materials

Case study 1

This case study focuses on:

  • communication and language

It shows the impact of promoting communication and language in the classroom, and how there’s been positive outcomes from grouping different areas of learning together.

Case study 2

This case study is about child who met the early learning goals in all areas, but focuses on:

  • mathematics

It shows how he has been supported with ambitious vocabulary teaching after starting school with no spoken language.

Case study 3

This case study is about a child with English as an additional language who meets the early learning goals in:

The teacher uses their knowledge of the child to discuss their stage of development, without the need for extensive recorded evidence.

Case study 4

This case study is about a child with English as an additional language who meets the expected standard in all areas. It focuses on:

  • expressive arts and design

It shows how the focus on communication and language has allowed the child’s speaking and vocabulary to develop. It also shows how the teachers use their knowledge and understanding of what the child knows to assess her attainment in other related areas of learning.

Case study 5

This case study is about a child who meets the early learning goals in:

  • personal, social, and emotional development
  • understanding the world

It shows how the teachers work together and use their knowledge of the child to make a plan to help her reach the expected level of development.

Case study 6

It discusses how the child has made progress in his spoken skills and vocabulary by using the Nuffield Early Language Intervention (NELI) programme .

Case study 7

This case study is about:

  • personal, social and emotional development

The child is emerging in self-regulation and has met the expected level of development in the other early learning goals discussed.

It shows the importance of understanding the child’s circumstances at home and at school.

Acknowledgements

We would like to thank the following schools for their help in making these materials:

  • Horn’s Mill Primary School in Cheshire
  • Vicarage Primary in East Ham
  • Claremont Primary School in Tunbridge Wells
  • Mount Stewart Infant School in Harrow
  • John Harrison Church of England Primary in Barrow upon Humber
  • Langford Primary in Fulham
  • Margaret McMillan Primary School in Bradford

Re-added the case study 7 video. This was temporarily removed due to a technical issue.

First published.

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Manor College Library

Early Childhood Education: How to do a Child Case Study-Best Practice

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  • How to do a Child Case Study-Best Practice
  • ED105: From Teacher Interview to Final Project
  • Pennsylvania Initiatives

Description of Assignment

During your time at Manor, you will need to conduct a child case study. To do well, you will need to plan ahead and keep a schedule for observing the child. A case study at Manor typically includes the following components: 

  • Three observations of the child: one qualitative, one quantitative, and one of your choice. 
  • Three artifact collections and review: one qualitative, one quantitative, and one of your choice. 
  • A Narrative

Within this tab, we will discuss how to complete all portions of the case study.  A copy of the rubric for the assignment is attached. 

  • Case Study Rubric (Online)
  • Case Study Rubric (Hybrid/F2F)

Qualitative and Quantitative Observation Tips

Remember your observation notes should provide the following detailed information about the child:

  • child’s age,
  • physical appearance,
  • the setting, and
  • any other important background information.

You should observe the child a minimum of 5 hours. Make sure you DO NOT use the child's real name in your observations. Always use a pseudo name for course assignments. 

You will use your observations to help write your narrative. When submitting your observations for the course please make sure they are typed so that they are legible for your instructor. This will help them provide feedback to you. 

Qualitative Observations

A qualitative observation is one in which you simply write down what you see using the anecdotal note format listed below. 

Quantitative Observations

A quantitative observation is one in which you will use some type of checklist to assess a child's skills. This can be a checklist that you create and/or one that you find on the web. A great choice of a checklist would be an Ounce Assessment and/or work sampling assessment depending on the age of the child. Below you will find some resources on finding checklists for this portion of the case study. If you are interested in using Ounce or Work Sampling, please see your program director for a copy. 

Remaining Objective 

For both qualitative and quantitative observations, you will only write down what your see and hear. Do not interpret your observation notes. Remain objective versus being subjective.

An example of an objective statement would be the following: "Johnny stacked three blocks vertically on top of a classroom table." or "When prompted by his teacher Johnny wrote his name but omitted the two N's in his name." 

An example of a subjective statement would be the following: "Johnny is happy because he was able to play with the block." or "Johnny omitted the two N's in his name on purpose." 

  • Anecdotal Notes Form Form to use to record your observations.
  • Guidelines for Writing Your Observations
  • Tips for Writing Objective Observations
  • Objective vs. Subjective

Qualitative and Quantitative Artifact Collection and Review Tips

For this section, you will collect artifacts from and/or on the child during the time you observe the child. Here is a list of the different types of artifacts you might collect: 

Potential Qualitative Artifacts 

  • Photos of a child completing a task, during free play, and/or outdoors. 
  • Samples of Artwork 
  • Samples of writing 
  • Products of child-led activities 

Potential Quantitative Artifacts 

  • Checklist 
  • Rating Scales
  • Product Teacher-led activities 

Examples of Components of the Case Study

Here you will find a number of examples of components of the Case Study. Please use them as a guide as best practice for completing your Case Study assignment. 

  • Qualitatitive Example 1
  • Qualitatitive Example 2
  • Quantitative Photo 1
  • Qualitatitive Photo 1
  • Quantitative Observation Example 1
  • Artifact Photo1
  • Artifact Photo 2
  • Artifact Photo 3
  • Artifact Photo 4
  • Artifact Sample Write-Up
  • Case Study Narrative Example Although we do not expect you to have this many pages for your case study, pay close attention to how this case study is organized and written. The is an example of best practice.

Narrative Tips

The Narrative portion of your case study assignment should be written in APA style, double-spaced, and follow the format below:

  • Introduction : Background information about the child (if any is known), setting, age, physical appearance, and other relevant details. There should be an overall feel for what this child and his/her family is like. Remember that the child’s neighborhood, school, community, etc all play a role in development, so make sure you accurately and fully describe this setting! --- 1 page
  • Observations of Development :   The main body of your observations coupled with course material supporting whether or not the observed behavior was typical of the child’s age or not. Report behaviors and statements from both the child observation and from the parent/guardian interview— 1.5  pages
  • Comment on Development: This is the portion of the paper where your professional analysis of your observations are shared. Based on your evidence, what can you generally state regarding the cognitive, social and emotional, and physical development of this child? Include both information from your observations and from your interview— 1.5 pages
  • Conclusion: What are the relative strengths and weaknesses of the family, the child? What could this child benefit from? Make any final remarks regarding the child’s overall development in this section.— 1page
  • Your Case Study Narrative should be a minimum of 5 pages.

Make sure to NOT to use the child’s real name in the Narrative Report. You should make reference to course material, information from your textbook, and class supplemental materials throughout the paper . 

Same rules apply in terms of writing in objective language and only using subjective minimally. REMEMBER to CHECK your grammar, spelling, and APA formatting before submitting to your instructor. It is imperative that you review the rubric of this assignment as well before completing it. 

Biggest Mistakes Students Make on this Assignment

Here is a list of the biggest mistakes that students make on this assignment: 

  • Failing to start early . The case study assignment is one that you will submit in parts throughout the semester. It is important that you begin your observations on the case study before the first assignment is due. Waiting to the last minute will lead to a poor grade on this assignment, which historically has been the case for students who have completed this assignment. 
  • Failing to utilize the rubrics. The rubrics provide students with guidelines on what components are necessary for the assignment. Often students will lose points because they simply read the descriptions of the assignment but did not pay attention to rubric portions of the assignment. 
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  • Published: 21 February 2024

A proteomic classifier panel for early screening of colorectal cancer: a case control study

  • Hanju Hua 1   na1 ,
  • Tingting Wang 2   na1 ,
  • Liangxuan Pan 2 ,
  • Xiaoyao Du 2 ,
  • Tianxue Xia 1 ,
  • Zhenzhong Fa 3 ,
  • Fei Gao 2 ,
  • Chaohui Yu 1 ,
  • Feng Gao 3 ,
  • Lujian Liao   ORCID: orcid.org/0000-0001-7021-5582 2 , 5 &
  • Zhe Shen 1  

Journal of Translational Medicine volume  22 , Article number:  188 ( 2024 ) Cite this article

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Diagnosis of colorectal cancer (CRC) during early stages can greatly improve patient outcome. Although technical advances in the field of genomics and proteomics have identified a number of candidate biomarkers for non-invasive screening and diagnosis, developing more sensitive and specific methods with improved cost-effectiveness and patient compliance has tremendous potential to help combat the disease.

We enrolled three cohorts of 479 subjects, including 226 CRC cases, 197 healthy controls, and 56 advanced precancerous lesions (APC). In the discovery cohort, we used quantitative mass spectrometry to measure the expression profile of plasma proteins and applied machine-learning to select candidate proteins. We then developed a targeted mass spectrometry assay to measure plasma concentrations of seven proteins and a logistic regression classifier to distinguish CRC from healthy subjects. The classifier was further validated using two independent cohorts.

The seven-protein panel consisted of leucine rich alpha-2-glycoprotein 1 (LRG1), complement C9 (C9), insulin-like growth factor binding protein 2 (IGFBP2), carnosine dipeptidase 1 (CNDP1), inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3), serpin family A member 1 (SERPINA1), and alpha-1-acid glycoprotein 1 (ORM1). The panel classified CRC and healthy subjects with high accuracy, since the area under curve (AUC) of the training and testing cohort reached 0.954 and 0.958. The AUC of the two independent validation cohorts was 0.905 and 0.909. In one validation cohort, the panel had an overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.9%, 81.8%, 89.2%, and 82.9%, respectively. In another blinded validation cohort, the panel classified CRC from healthy subjects with a sensitivity of 81.5%, specificity of 97.9%, and overall accuracy of 92.0%. Finally, the panel was able to detect APC with a sensitivity of 49%.

Conclusions

This seven-protein classifier is a clear improvement compared to previously published blood-based protein biomarkers for detecting early-stage CRC, and is of translational potential to develop into a clinically useful assay.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. In the United States alone, CRC is the second most common cancer with a mortality rate ranks the second among all cancers [ 1 ]. The outcome of CRC patient is generally poor, to a large extent due to lack of effective testing methods to detect tumors at early stages. If tumor is detected at precancerous or localized stage, 90% of CRC patients can survive more than five years. Whereas if it is diagnosed at stages IIb or later, the survival rate is dramatically lower, primarily due to complications associated with tumor spreading and metastasis [ 2 ]. Currently, the “gold standard” of clinical procedure for diagnosing CRC is the colonoscopy [ 3 ], during which the tumors can be removed and pathologically examined. There are also a number of stool- and blood-based non-invasive screening methods to aid the detection of CRC at early stages [ 4 ]. The FOBT/FIT uses immunoassay to measure the hemoglobin in the stool [ 5 ]. Two DNA tests measure the methylation status at the promoter region of mSEPT9 or SDC2 gene in the blood and the stool respectively [ 6 , 7 ]. Immunoassays to measure the traditional tumor antigens including carbohydrate antigen CA19-9 and carcinoembryonic antigen (CEA) [ 8 , 9 ] are widely used in the clinic. In recent years, the burgeoning field of liquid biopsy has made great strides to non-invasive test of multiple cancer types at early stages, including CRC [ 10 , 11 , 12 ]. The assay detecting septin9 gene methylation in the blood has offered a laboratory developed test (LDT, Epi procolon) [ 13 ]. Another LDT combines FIT and detecting BMP3/NDRG4 DNA methylation as well as KRAS mutation in the fecal sample (Cologuard) [ 14 ]. These exciting developments have opened up new opportunities for early detection of CRC.

However, there are still windows for improvement in terms of sensitivity and specificity as well as other issues such as cost-effectiveness and patient compliance. The FOBT/FIT test yields a sensitivity of 73.8% with a specificity of 95% [ 14 ], whereas the protein biomarkers provide a sensitivity of 80% [ 15 ]. Epi procolon test has a specificity of 88%, yet the sensitivity is 87% for early-stage CRC [ 13 ]. Cologuard has a sensitivity of 92.3%; its specificity falls to 86.6% [ 14 ]. Improving both the sensitivity and specificity simultaneously can screen CRC with much improved accuracy, limiting both the false positive and false negative calls. On the other hand, protein markers in clinical use such as CEA and CA19-9 is far from sensitive. Although colonoscopy is the “gold standard”, the invasive nature raises the issue of patient compliance. Therefore, a non-invasive, sensitive and specific screening method that detects and diagnoses CRC at the earliest stage is needed.

Advanced precancerous lesions (APC), including advanced adenomas and large sessile serrated polyps (greater than 1 cm in size), are considered to have a high probability of developing into fully grown cancers [ 16 ]. Nevertheless, the progression time window of advanced adenomas can be as long as ten years, providing both an opportunity and a challenge to detect these lesions in a timely manner [ 16 , 17 ]. The standard for detecting advanced adenomas is also optical colonoscopy [ 18 ]. Due to patient compliance and rare complications, applying colonoscopy as a general screening method to detect APC remains to be a challenge [ 16 ]. Although liquid biopsy based on gene test to detect adenomas has been developed, it still suffers from low sensitivity [ 14 ]. Therefore, developing much improved assays to detect precancerous lesions is also highly desirable.

Currently, mass spectrometry technologies have achieved high sensitivity and data acquisition speed. Whereas traditional data-dependent acquisition (DDA) provides straightforward protein identification and ease of data interpretation, data-independent acquisition (DIA) dramatically expanded the dynamic range of quantitation [ 19 , 20 ]. Multi-reaction monitoring (MRM), another mass spectrometry technology widely used in pharmaceutical industry, is well suited to accurately quantify hundreds of peptides in one experiment [ 21 , 22 ]. As such, we applied both DDA and DIA to investigate the plasma proteome in patients diagnosed with CRC and APC. Our analysis captured unique molecular features of the plasma proteome in these conditions. We further developed an MRM assay combined with machine learning to discover and validate a panel of protein biomarkers to distinguish CRC from benign polyps and from healthy subjects.

Human samples

Plasma samples of the Chinese population were collected from three hospitals between September 2020 and September 2022. Peripheral venous blood samples were collected before any treatment procedure, and were centrifuged at 500 × g for 10 min to obtain plasma within two hours and stored at − 80 ℃. All plasma samples were transported to the central lab by cold chain system at − 80 ℃ and stored at − 80 ℃ before MS experiments.

In the discovery cohort, plasma samples from 70 patients diagnosed with colorectal cancer (CRC) and 72 healthy subjects were collected from Shanghai Tenth People’s Hospital, Tongji University School of Medicine. In the validation cohorts, the plasma samples were collected from Shanghai Tenth People’s Hospital, the First Affiliated Hospital of Zhejiang University and Changzhou Wujin People’s Hospital, respectively. We obtained written informed consent from each participant.

We excluded patients with any malignant tumors within five years prior to current diagnosis. The inclusion criteria were as follows: the age of all subjects is over 40 years (inclusive), with a balanced gender distribution; in the group of healthy subjects there should have no evidence of malignant tumor nor colorectal neoplasm; in the CRC group all diagnosis was confirmed by pathological evidence; in the APC group all patients went through colonoscopy and the diagnosis of either advanced adenomas or sessile serrated polyps were made by an experienced pathologist.

Processing of plasma samples

For the two discovery cohorts, the plasma samples were processed to deplete the top 14 high-abundant proteins (Cat. # A36370, Thermo Science, USA), and protein concentration was determined using the BCA kit (Cat. # P0012, Beyotime, China). The 14 proteins included Albumin, IgA, IgD, IgE, IgG, IgG (light chains), IgM, Alpha-1-acid glycoprotein, Alpha-1-antitrypsin, Alpha-2-macroglobulin, Apolipoprotein A1, Fibrinogen, Haptoglobin, and Transferrin. From each sample, 25 μg protein was suspended in 50 mM NH 4 HCO 3 solution. The proteins were treated with 10 mM DTT at 95 °C for 10 min and alkylated with 15 mM iodoacetamide (Cat. # I1149, Sigma Aldrich, USA) in the dark for 30 min. Then the protein was digested with sequencing grade trypsin (1:50, Cat. # V5113, Promega) overnight at 37 °C. The resulting peptides were desalted with a 96-well SOLA solid-phase extraction apparatus and vacuum dried. The peptides were stored in a freezer at − 80 ℃. and ready for mass spectrometry analysis.

For the four validation cohorts, the plasma samples were processed for mass spectrometry analysis without depleting the high-abundant proteins and the rest of experiment procedure was identical to the discovery cohorts.

LC–MS/MS analysis of plasma samples using DDA and DIA

The plasma protein digests were analyzed using an EASY-nLC1000 liquid chromatography coupled with an Orbitrap Exploris™ 240 mass spectrometer (Thermo Fisher Scientific, USA). Peptides were resuspended in buffer A (2% ACN, 0.1% formic acid) and spiked with indexed retention time (iRT) peptides (Omicsolution, China). The iRT peptides are a set of standard peptides used in DIA experiments for high-accuracy calibration of chromatographic elution time, so as to improve reproducibility of MS experiments across laboratories. An equivalent to 2 µg of protein digest from each sample was loaded onto a C18 column (Cat. # 164534, Thermo Scientific, USA) linked with a pre-column (Cat. # 164535, Thermo Fisher Scientific, USA) and separated at a flow rate of 250 nL/ min. The mobile phases consisted of buffer A and buffer B (98% ACN, 0.1% formic acid). A 90 min gradient from 1 to 8% buffer B in 1 min, 8% to 28% in 71 min, 28% to 40% in 9 min, 40% to 100% in 2 min, and 100% for 7 min was used. For DIA analysis, peptides were resuspended in buffer A and spiked with iRT peptides. 1.5 µg of protein digest from each sample was loaded onto the C18 column.

The mass spectrometry was operated in positive mode in all cases. For DDA analysis, the nano-electrospray was operated using the ion transfer tube with a temperature setting of 275 °C. One full scan MS from 400 to 1400 m/z followed by 12 MS 2 scans were cycled throughout the entire MS experiment. MS spectra were acquired with a resolution of 70000 with a maximum injection time (IT) of 60 s and an automatic gain control (AGC) target value of 3e6. MS 2 spectra were obtained in the higher-energy collisional dissociation (HCD) mode with an isolation window of 1.6 m/z, using a normalized collision energy of 27%, resolution at 17500 with a maximum injection time of 50 s and an AGC target of 5e5. Centroid mode was used to collect both the MS and MS 2 spectra.

For DIA analysis, the ion transfer tube was operated with a temperature setting of 320 °C. MS spectra were acquired with a resolution of 60000 with a maximum injection time (IT) of 120 ms and an AGC target value of 3e6. Isolation window for MS 2 was set to 20 Da for the mass range 350–400 m/z, 9 Da window for the mass range 400–800 m/z, 12 Da window for the mass range 800–1000 m/z, and 25 Da window for the mass range 1000–1200 m/z.

Multiple reaction monitoring quantitation of plasma proteins

Concentrations of target proteins in the plasma were measured using MRM on a QTRAP 5500 mass spectrometer (Sciex, USA) equipped with a turbo v ion source (Sciex, USA). The instrument parameters of the MRM assay were optimized for each synthetic peptide by directly infusing the peptides into the mass spectrometer. The top three high-intensity product ions of each peptide precursor ion were selected based on the optimal collision energy (CE) values and collision cell exit potential (CXP). All optimized data were collected and compared to theoretical spectra, and three high-intensity y-ions were used for subsequent MRM assays.

The peptides were separated using an LC-20AD (SHIMADZU, Japan) liquid chromatographic system. Buffer A was 0.1% formic acid in distilled water and buffer B was 0.1% formic acid in 98% acetonitrile. Peptides were reconstituted in buffer A and 15 µL of each sample was loaded into the sample loop. A gradient consisting of 6% buffer B for 2 min, 6–28% buffer B for 16 min, 28–98% buffer B for 0.5 min, 98% buffer B for 3 min, 98– 6% buffer B for 0.5 min, and 6% buffer B for 3 min was used. The MS detection was carried out in positive mode with the following parameters: electrospray voltage of 5500 V, curtain gas at 40 psi, ion source gas 1 (GS1) at 55 psi, ion source gas 2 (GS2) at 55 psi, and temperature at 500 °C. Quantitation were performed using the scheduled MRM mode. The time of MRM detection window was 180 s, and the cycle time was 1.0 s. The mass spectrometer was controlled by the Analyst software (Sciex, USA).

Mass spectrometry data analysis

The DDA spectra were searched using Protein Discoverer 2.4 (Thermo Fisher Scientific, USA) against a UniprotKB human database (UP000005640). The search parameters were set as the following: trypsin was set to the protease type and two missed cleavages were allowed, the precursor mass tolerance was set to 10 ppm, and the fragmentation ion mass tolerance was set to 0.02 Da. The false discovery rate (FDR) was set to 1% at both the peptide and protein level. The maximum number of variable modifications was set to two.

The DIA spectra were searched using the DIA-NN (version 1.7.15) software [ 23 ] with a UniprotKB human database (UP000005640). The precursor mass tolerance was 10 ppm; trypsin was set as the protease and two missed cleavages were allowed. The maximum number of variable modifications was set to three. The precursor mass range was from 350 to 1250 m/z, and the fragmentation ion mass range was from 100 to 2000 m/z. The false discovery rate (FDR) was set at 1% at the peptide level.

GO and KEGG pathway analysis

GO and KEGG enrichment analysis were performed using bioinformatics resources including Metascape ( https://metascape.org/ ) and David v6.8 ( https://david.ncifcrf.gov/ ). R package ClusterProfiler [ 24 ] was applied to generate the graphs.

Feature selection and logistic regression

The mean decrease of Gini index (MDG) was calculated in a random forest feature selection model for both the DDA and DIA data. Gini index is a measurement of variance in random forest algorithm, in which lower variance and thus lower Gini index results in more accurate classification. We selected top 40 proteins based on the MDG values. To further narrow down the list of biomarker candidates from these 40 proteins, we applied the following criteria: 1. These proteins are differentially expressed in both datasets. 2. Literature reported cancer biomarkers were given priority for consideration.

The expression values of the peptide surrogates for the candidate proteins were used to build a logistic regression model to classify subjects as either healthy or colorectal cancer patients. The finalized panel of proteins were validated by new patient cohorts.

Statistical analysis

R (version 4.2.2) was used for all the statistical analyses, including data preprocessing, differential expression analysis, volcano plot, and principal component analysis (PCA). For differential expression analysis, p value < 0.05 were considered statistically significant and fold change of > 1.25 or < 0.80 were considered up- or down-regulated, respectively. We utilized a more subtle fold change criterion in order to obtain more potential protein markers in the discovery phase, and further validated them in target validation phase. Proteins with more than 50% missing values were removed. The distribution of protein expression was tested for normality across all samples; then, t-test was applied for those with normal distribution, while Wilcoxon ranked sum test was performed for those failed to pass the normality test.

The discovery phase of the study was designed to obtain a statistical power of 85%, a one-sided type I error rate of 0.05, and a median effect size. With these parameters, the calculated sample size is 59 healthy subjects and 59 colorectal cancer patients, respectively.

Study design

The design of this study is illustrated in Fig.  1 . In the discovery phase, we enrolled a total of 142 subjects (including 70 CRC patients and 72 healthy controls, cohort 1) from one hospital. The plasma samples from half of the discovery cohort were analyzed using DDA method, whereas the other half were analyzed using DIA method. To monitor the data acquisition process, we interspersed quality control (QC) samples during mass spectrometry data acquisition, which composed of small portion of plasma samples from all patients in the discovery cohort (Fig.  1 A). In the assay development phase, MRM method was applied to part of the samples from cohort 1 to measure the plasma concentration of selected protein biomarkers based on the signature peptides. In the meantime, MRM assay were developed to assess the linear range, limit of quantification, and reproducibility (Fig.  1 B). The validation phase included 129 CRC patients and 77 healthy controls from two different hospitals as well as 47 cases of APC as cohort 2 (Fig.  1 C). We then enrolled another independent, blinded validation cohort of patients as cohort 3 from the third hospital, in which the laboratory did not know the diagnosis of the patients until the blood test results were complete (Fig.  1 D). In all cohorts, cancer patients either went through colonoscopy or surgery and the tumor tissues were pathologically confirmed. The demographic data for the patients enrolled in the discovery cohort is shown in Table  1 . The demographic data for all the patients enrolled in validation cohorts is shown in Additional file 6 : Table S1.

figure 1

Study design. Flow chart showing the discovery and validation phases of this study. A Discovery cohort for quantitative proteomic analysis using DDA and DIA methods. B MRM targeted proteomic assay development. C Applying the MRM assay to an independent validation cohort of patients to classify CRC and APC patients from healthy controls. D Applying the MRM assay to a blinded validation cohort, in which the diagnosis was disclosed only after the test results were given

Quantitative mass spectrometric analysis of plasma proteome using two different data acquisition methods on independent batches of patient samples

The QC samples showed good correlation (and thus reproducibility) in DDA data among themselves in a pair-wise comparison matrix, as shown by the Pearson correlation in Additional file 1 : Fig. S1A. The correlation was even better in DIA data as shown by the correlation matrix (Additional file 1 : Fig. S1B), indicating that DIA provide more overall accuracy and consistency than DDA as have been shown previously [ 25 ]. Both DDA and DIA covered a dynamic range of 6 order of magnitude, with DIA covered a wider dynamic range (Additional file 1 : Fig. S1C–D). Furthermore, DIA quantified more proteins than DDA in all samples, as shown in Additional file 1 : Fig. S1E–F. We quantified 607 proteins in DDA experiments and 714 proteins in DIA experiments (Additional file 7 : Table S2 and Additional file 8 : Table S3). Using a statistical significance cutoff of 0.05 and fold change cutoff of 1.25, we identified 21 up-regulated and 16 down-regulated proteins in DDA experiments, and 50 up-regulated and 106 down-regulated proteins in DIA experiments as shown in the volcano plots (Fig.  2 A, B). Principal component analysis using the entire proteomic data failed to separate the disease, healthy and the QC samples, neither in the DDA data (Fig.  2 C) nor in the DIA data (Fig.  2 D). In up-regulated proteins, gene ontology of the most enriched biological function was primarily acute-phase response and humoral immune response, with the corresponding KEGG pathways involved being complement and coagulation cascades (Fig.  2 E and Additional file 2 : Fig. S2). Whereas in down-regulated proteins, the most enriched biological function was wound healing which involves remodeling of the extracellular matrix, and the corresponding KEGG pathways involved were carbon metabolism and biosynthesis of amino acids (Fig.  2 F and Additional file 2 : Fig. S2). Several important intracellular signaling pathways including Wnt signaling, PI3K-Akt signaling, and p53 signaling are frequently dysregulated in CRC [ 26 , 27 , 28 ]. A recent study demonstrated that CRC cell survival was related to an impaired hypoxia-inducible factor 1-alpha (HlF-1a) signaling in low oxygen condition [ 29 ]. Among these important driving events of tumorigenesis, some were captured in our findings [ 27 , 28 ].

figure 2

Quantitative proteomic analysis of plasma samples from CRC patients and healthy controls. A Volcano plot of DDA data obtained from discovery cohort 1. Differentially expressed proteins are shown in blue (down) or red (up) circles. X-axis shows log2-fold change of plasma proteins between CRC patients and healthy subjects, and y-axis shows log10 of statistical significance values. B Volcano plot of DIA data obtained from discovery cohort 2. The label for the differentially expressed proteins and the two axes are the same as in A. C Principal component analysis of the protein expression data in cohort 1. D Principal component analysis of the protein expression data from cohort 2. E Gene ontology analysis of up-regulated proteins in the DIA data. F Gene ontology analysis of down-regulated proteins in the DIA data

Discovery of a plasma protein biomarker panel to identify CRC at early stages

To identify biomarkers in the plasma that can accurately distinguish early-stage CRC from healthy subjects, we selected the top 40 proteins based on MDG values (Additional file 3 : Fig. S3). From these proteins we identified consistent up-regulation of LRG1, C9, IGFBP2, ITIH3, and SERPINA1 and consistent down-regulation of CNDP1 in CRC plasma as potential biomarker candidates (Fig.  3 A, B). ORM1 was found in DIA data but did not show differential expression; however, it was up-regulated in the DDA data (Fig.  3 A). This discrepancy maybe due to variation in mass spectrometric data acquisition. Because ORM1 has been shown to be a biomarker for CRC [ 30 , 31 ], we added it to the potential list of our biomarker panel. The peptide sequences of the panel of seven proteins were listed in Additional file 4 : Fig. S4. Using the expression data of these seven proteins, principal component analysis (PCA) showed much improved separation between CRC and healthy subjects, with the first component explained 33.9% (DDA data) and 64.8% (DIA data) of the variability (Fig.  3 C, D).

figure 3

Seven feature proteins selected based on DDA and DIA proteome data. A and B Boxplot of protein intensity of feature proteins selected for logistic regression. Differential expression of seven proteins between CRC patients ( C ) and healthy subjects H from the DDA data ( A ) and the DIA data ( B ) are shown. C Principal component analysis using the expression levels of the seven proteins in cohort 1. D Principal component analysis using the expression levels of the six proteins in cohort 2

The plasma protein biomarker panel is capable of detecting colorectal cancer with high accuracy

Because DDA and DIA are relative quantification methods and MRM is able to measure the absolution concentration of an analyte with higher throughput, we developed an MRM assay using signature peptides to measure the concentration of the seven proteins in the plasma, in a subset of 60 healthy subjects and 60 CRC patients from the discovery cohort (Fig.  1 B). The MRM assay combined internal standards using heavy arginine/lysine-labeled peptides with external calibration using unlabeled peptides. The product ion peak of each peptide appeared superior in our MRM assay (Additional file 5 : Fig. S5A–D) with the majority of the covariance (CV) of the measured peptide concentration below 10% (Fig.  5 E). Using concentrations of the seven proteins to build a logistic regression model, we calculated a classification score for each patient. The score was calculated using the following equation:

log(P/(1-P) = -7.8709 + 4.5956 × IGFBP2 + 0.2732 × ITIH3 + 0.0909 × LRG1 + 0.1015 × C9 -3.2205 × CNDP1 + 0.0239 × ORM1 + 0.0811 × SERPINA1, where P is a value between 0 and 1 that represents the probability of the event (colorectal cancer). The optimal cut-off value for the classifier was 0.318.

This logistic regression classifier achieved an average area under the curve (AUC) of the receiver operator characteristic (ROC) curve of 0.954 (95% CI 0.915–0.994) in the training dataset and 0.958 (95% CI 0.883–1.0) in the testing dataset (Fig.  4 A). Furthermore, we applied the MRM assay to quantify the concentration of the seven proteins in plasma samples collected from an independent validation cohort from different hospitals (Additional file 9 : Table S4). Applying the locked logistic regression parameters to calculate the likelihood of CRC and the fixed cutoff probability value of 0.318, we achieved an average AUC of 0.905 (Fig. 4B, 95% CI 0.864–0.946). We enrolled another independent validation cohort in a blinded fashion, in which the diagnosis of the patients was disclosed only after the classification was determined (Additional file 9 : Table S4). We achieved an average AUC of 0.909 using the same assay and the locked logistic regression parameters and probability cutoff value (Fig. 4C, 95% CI 0.827–0.99).

figure 4

MRM quantification and logistic regression classification of CRC and healthy subjects. A ROC curves of a seven-protein logistic regression classifier (LRG1, C9, IGFBP2, CDNP1, ITIH3, SERPINA1, and ORM1) for distinguishing CRC and healthy subjects in the training and testing datasets. B ROC curve showing the performance of the seven-protein classifier in distinguishing CRC and healthy subjects in an independent validation cohort. C ROC curve showing the performance of the seven-protein classifier in distinguishing CRC and healthy subjects in a blinded validation cohort. D – F Confusion matrix showing the classification accuracy in the training ( D ), independent validation E , and blinded validation F cohorts

Based on the confusion matrix, the assay had a sensitivity, specificity, PPV, and NPV of 93.3%, 80.0%, 82.4%, and 92.3% respectively in the training cohort (Fig.  4 D), and 89.9%, 81.8%, 89.2%, and 82.9% respectively in the validation cohort (Fig.  4 E). In the blinded validation cohort, the sensitivity, specificity, PPV, and NPV were 81.5%, 97.9%, 95.6%, and 90.4% respectively (Fig.  4 F); the classification accuracy was 92.0% (Table  2 B). Overall, the sensitivity of detecting CRC achieved 91.0% with a specificity of 81.0% (Table  2 A).

Expression patterns of the seven protein biomarkers at different CRC stages

We further analyzed the capability of the seven-protein panel to distinguish CRC at different stages. In general, each individual protein showed significantly different expression levels in every pathological stage compared to healthy subjects; however, there were no obvious statistical difference between stages II ~ IV (Fig.  5 A). Nevertheless, there were significant differences between later stages and stage I in proteins IGFBP2, C9, SERPINA1, and ORM1 (Additional file 10 : Table S5). The sensitivity of our protein panel reached over 90% in detecting CRC at all stages; however, there was a clear pattern of increased sensitivity in later stages (II ~ IV) than stage I (Fig.  5 B). The sensitivity of detecting APC was around 40% for lesions smaller than 1 cm or between 1 and 1.5 cm, but increased to nearly 60% for lesions greater than 1.5 cm (Fig.  5 C). This result showed an increased sensitivity in detecting APC than a published DNA test [ 14 ]. Finally, the sensitivity of detecting colorectal polyps of distinct anatomic morphology was 50% for sessile serrated polyps, 100% for tubulovillous adenoma and high-grade dysplasia (Fig.  5 D), all of which are considered as having high probability of transforming into cancer. Thus, our panel of protein biomarkers can not only detect CRC at early stages, but also detect highly malignant adenomas.

figure 5

Expression patterns of the protein biomarkers in different CRC stages. A Box plot of plasma concentration of the seven proteins in four different CRC stages. B Sensitivity of the seven-protein biomarker in distinguishing CRC at four different stages from healthy subjects. C Sensitivity of the seven-protein biomarker in distinguishing APC with different sizes from healthy subjects. D Sensitivity of the seven-protein biomarker in identifying precancerous lesions with different grades

The biological functions of many of the biomarker proteins identified in this study have been documented. Overall, they play distinct roles in cancer development and progression. For example, LRG1 is a cell adhesion molecule whose up-regulation is involved in tumor metastasis [ 32 ], and has recently been shown to be valuable in diagnosis of colorectal cancer and pancreatic cancer [ 16 , 33 ]. As a complement component that is linked to inflammation and immune response, C9 is unlikely to provide specific information predicting tumor formation. Nevertheless, a recent study found aberrant expression of C9 in the plasma of CRC patients [ 34 ]. IGFBP2 is a node of insulin signaling and has been shown to have prognostic value in multiple cancers in a meta-analysis [ 35 ], including metastatic CRC [ 36 ]. Similarly, ORM1, SERPINA1, and ITIH3 has all been shown to be valuable predicting clinical outcomes of CRC patients [ 31 , 37 , 38 ]. Down-regulation of CNDP1 was associated with cancer cachexia [ 39 ], and it was recently reported that CNDP1 level was significantly reduced in hepatocellular carcinoma tissues [ 40 ]. Although these studies focused on different cancer types, they all point to CNDP1 down-regulation in cancers, which are consistent with our study. By accurately measuring the plasma concentration of these proteins, we could predict CRC at an early stage using logistic regression.

There have been numerous attempts to develop protein-based liquid biopsy assays for detection of CRC. A noticeable study used proximity extension assay (Olink) to measure plasma concentration of 92 proteins from 89 subjects, and identified an eight-protein biomarker with an adjusted AUC of 0.77 and a sensitivity of 0.44 at 90% specificity in the validation set [ 41 ]. Because of the high sensitivity of Olink technology [ 42 ], it is able to quantify plasma concentration of very-low abundant proteins such as growth factors, cytokines, and tumor antigens, none of which overlapped with our panel components. However, the resulting performance of predicting CRC remained unsatisfactory, presumably due to the overfitting issue of measuring large number of proteins. Another study used mass spectrometry to identify a five-protein biomarker signature to effectively distinguish CRC from control in a training cohort of 200 cases and a validation cohort of 269 cases, with an AUC of 0.84 and an overall accuracy of 72% [ 43 ]. Of note is that the five-protein panel includes LRG1, one of the feature protein in our seven-protein panel. Another protein in this five-protein panel is SERPINA3, which is a close relative to SERPINA1 in our seven-protein panel. This indicates that mass spectrometry tends to detect proteins at similar expression levels. Compard to these studies, our assay of seven-protein panel resulted an AUC of 0.905 and 0.909 in two independent validation cohorts, providing a much-improved performance. Even compared to a study that combines DNA test and fecal immune test which provided a sensitivity of detecting CRC at 92.3% and specificity of 86.6% [ 14 ], our assay provides a slightly lower but comparable sensitivity (91.0%) and specificity (81.0%) (Table  2 A). In addition to detecting cancers, there is a growing need to identify colorectal adenomas using protein biomarkers in the blood. A recent study found that serum biomarkers F5, ITIH4, LRG1, and VTN were elevated in colorectal adenoma patients as well as in a mouse model of colorectal adenomas [ 16 ]. From colonoscopy-confirmed patients with advanced precancerous lesions, the average sensitivity of our assay achieved 49% (Table  2 B), better than aforementioned study. Our assay also showed a clear improvement compared to the DNA test which provided a sensitivity of 42.4% to detect APC [ 14 ].

From our quantitative proteomic results, it appears that DDA has more upregulated proteins and DIA has more of downregulated proteins. Because DDA is data dependent, it favors detection and quantification of relatively abundant peptides. DIA is utilizing homogeneous scanning over the entire mass range with a wider mass window on each scan, and detects and quantifies peptides regardless of their abundance. Therefore, DIA is capable of detecting low abundance peptides and quantifying more proteins. It is also possible that the differentially expressed proteins in DDA and DIA experiments might distribute with different patterns. Regardless, six out of the seven significantly changed marker proteins in both DDA and DIA experiments showed consistent direction of change (Fig.  3 A, B). We believe that DDA and DIA are complementary, and there no black-and-white answer as to which one is more trustworthy over the other. Largely due to the stochastic nature of mass spectrometry data acquisition, quantifying large number of peptides may results in certain degree of error rate.

For the profiling experiments in discovery phase, we took the conventional approach to remove the 14 most abundant plasma proteins using the antibody-based depletion kit. Depletion of abundant proteins can reduce the interference from these proteins during LC–MS/MS data acquisition and improve the depth of the coverage of the plasma proteome [ 44 ]. However, because it introduces more sample processing steps, it considerably affects the reproducibility of the experiment. This may be further exaggerated when dealing with large number of clinical samples. Therefore, during target validation phase of the MRM assay development, we innovatively simplified the sample preparation process by omitting the depletion step. Of the proteins we measured, the plasma concentrations ranged between 1 ng/ml for CNDP1 and 120 ng/ml for ORM1, and we obtained decent product ion signals in all seven peptides (Additional file 5 : Fig. S5A–D). The results also point to acceptable precision in clinical settings even though the experiments were performed manually, as demonstrated by lower than 10% CV in QC samples for the majority of the proteins measured (Additional file 4 : Fig. S4E). Only the concentration of IGFBP2 showed a CV of over 10%, presumably due to its extremely-low abundance. On the other hand, ORM1 is one of 14 high-abundance proteins targeted for depletion; removing it runs the risk of removing a potentially meaningful biomarker candidate. With our simplified sample processing method, we envision that by incorporating automation in the future, we will further improve the precision and accuracy of our measurement.

The strength of this study lies in several technical and strategical advantages. First, many previous CRC protein biomarker studies started from comprehensive literature search for candidate proteins, and then developed quantitative assays to build models and further validated the model. This approach may be difficult to discover new biomarkers. In our study, we started from an unbiased quantitative analysis of plasma proteome using the state-of-art mass spectrometry technology to discover candidate proteins; this study design facilitated MRM assay development in the validation phase. Second, after establishing a machine learning model based on the training cohort of 120 subjects, we enrolled two independent patient cohorts of 253 and 84 subjects from three different hospitals to validate the model. In particular, the second validation cohort were performed in a blinded fashion, moving one step closer to a prospective study. Noticeably, in protein-based biomarker studies, the cohort size in our study is reasonably large. Third, when applying the MRM assay and the “locked” logistic regression model parameters on both independent validation cohorts, our assay maintained an accuracy of over 90%. Taking into consideration the CRC prevalence of around 0.25% in Chinese population [ 45 ] with the sensitivity of 81.5% and specificity of 97.9%, the calculated NPV of our assay can reach 99.95%. The sensitivity and specificity of our assay compare favorably to existing clinical diagnosis and screening methods including stool FOBT/FIT [ 5 ], blood and stool DNA methylation test [ 6 , 7 ], and performs similarly to an FDA approved, stool-based DNA test (Cologuard) [ 14 ], well suited for early screen. Fourth, because sampling blood is more convenient than procuring stool samples and its non-invasive nature surpasses colonoscopy, patient compliance is expected to be much better than conventional diagnostic methods. If applied as an early screening method, it can prevent a significant number of medium-to-high risk subjects from invasive procedures. Finally, our plasma sample processing approach omitted the use of expensive depletion kit, which is not only technically advantageous but also has the benefit of significantly cutting the cost of the assay. This advantage could be further augmented if the assay is applied in settings involving population screen.

One limitation of this study is the relatively sub-optimal sample size. Another limitation is that the subjects were recruited from hospital patients; whereas in a real-world screening test, the subjects would come from the population undergone preventive physical examination and thus the number of cancer patients would have been much less. Further refinement and validation of this panel of biomarker proteins in a population-wide scenario for a clinical trial is warranted.

We have developed a non-invasive, targeted mass spectrometry assay to measure plasma concentrations of seven proteins that is capable of distinguishing colorectal cancer from healthy subjects. This seven-protein classifier is of translational value and warrants further development into a clinically useful assay.

Availability of data and materials

The datasets generated (mass spectrometry raw data) during the current study has been deposited to the ProteomeXchange repository with the accession numbers PXD042639 and PXD042652 ( https://proteomecentral.proteomexchange.org/cgi/GetDataset ). The analyzed data is included in the supplementary tables. The clinical samples and analytical methods will not be available to the public.

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Acknowledgements

We cordially thank Antony Y. Liao for carefully proofreading and editing the manuscript.

This work was funded by Durbrain Medical Laboratory, by the National Natural Science Foundation of China (# 82170533), and by Zhejiang Provincial Program for the Cultivation of High-level Innovative Health Talents (# 2021C03115).

Author information

Hanju Hua and Tingting Wang contributed equally to this work.

Authors and Affiliations

Department of Colorectal Surgery (H.H), and Department of Gastroenterology (C.Y. and Z.S.), College of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou, 310006, Zhejiang, China

Hanju Hua, Tianxue Xia, Chaohui Yu & Zhe Shen

Durbrain Medical Laboratory, Hangzhou, 310000, Zhejiang, China

Tingting Wang, Liangxuan Pan, Xiaoyao Du, Fei Gao & Lujian Liao

Changzhou Wujin People’s Hospital, Changzhou, 213000, Jiangsu, China

Zhenzhong Fa & Feng Gao

Department of General Surgery, School of Medicine, Shanghai Tenth People’s Hospital, Tongji University, Shanghai, 200072, China

Shanghai Key Laboratory of Regulatory Biology, School of Life Sciences, East China Normal University, Shanghai, 200241, China

Lujian Liao

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Contributions

LL, TW and FG conceptualized the study; TW, LP, XD performed the experiments; HH, TX, ZF, LG, FG, YC, ZS participated the study; LP and TW analyzed the data; HH, LL, ZS provided part of the funding support; LL, FG supervised the entire study; LL, TW and FG wrote the manuscript.

Corresponding authors

Correspondence to Chaohui Yu , Feng Gao , Lujian Liao or Zhe Shen .

Ethics declarations

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The study conformed to the Declaration of Helsinki and was approved by the Medical Ethics Committee of Shanghai Tenth People's Hospital (Study license number SHDSYY-2020-3645), the Medical Ethics Committee of Changzhou Wujin People’s Hospital (Study license number 2022-SR-091), and the Medical Ethics Committee of the First Affiliated Hospital of Zhejiang University (Study license number IIT20230142B).

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The authors LL, TW, FG declare that they have competing interests, and a patent related to this study has been filed. Other authors declare that there is no conflict of interest.

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Supplementary Information

Additional file 1: figure s1..

Quality of the DDA and DIA data. (A) Correlation analysis of the quality control (QC) samples from DDA data. (B) Correlation analysis of the quality control (QC) samples from DIA data. (C) Distribution of protein abundance of all quantified proteins from DDA data. (D) Distribution of protein abundance of all quantified proteins from DIA data. (E) Number of quantified proteins in all samples from DDA data. (F) Number of quantified proteins in all samples from DIA data.

Additional file 2: Figure S2.

Heatmap showing KEGG pathways of up- and down-regulated proteins in the plasma from CRC patients.

Additional file 3: Figure. S3.

Selection of protein panels to classify CRC from healthy subjects. (A) Mean decrease of Gini index in DDA data. (B) Mean decrease of Gini index in DIA data.

Additional file 4: Figure S4.

Peptide sequence and ion mass information of selected protein markers.

Additional file 5: Figure S5.

Quality of the MRM data. (A–D). Extracted chromatograms of the 7-peptide biomarkers. (E) Variable coefficient values in QC samples for the 7-peptide biomarkers.

Additional file 6: Table S1.

Demographic data of all the patients enrolled in MRM experiments.

Additional file 7: Table S2.

Protein expression data from DDA experiments.

Additional file 8: Table S3.

Protein expression data from DIA experiments.

Additional file 9: Table S4.

Concentration of each protein of the seven-protein panel measured by MRM in all subjects.

Additional file 10: Table S5.

Concentration and statistical analysis of each protein of the seven-protein panel measured by MRM in CRC cases shown in Figure 4, D and E.

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Hua, H., Wang, T., Pan, L. et al. A proteomic classifier panel for early screening of colorectal cancer: a case control study. J Transl Med 22 , 188 (2024). https://doi.org/10.1186/s12967-024-04983-5

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