Local authority variation in primary school-recorded special educational needs provision among children with major congenital anomalies: A research protocol

Kate M Lewis; Vincent G Nguyen; Ania Zylbersztejn; Ruth Gilbert; Bianca De Stavola; Lorraine Dearden

doi:10.3310/nihropenres.13466.1

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Study Protocol

Local authority variation in primary school-recorded special educational needs provision among children with major congenital anomalies: A research protocol

[version 1; peer review: 2 approved with reservations]

Kate M Lewis ¹, Vincent G Nguyen¹, Ania Zylbersztejn¹, Ruth Gilbert¹, Bianca De Stavola¹^*, Lorraine Dearden²^*

Kate M Lewis ¹, Vincent G Nguyen¹, [...] Ania Zylbersztejn¹, Ruth Gilbert¹, Bianca De Stavola¹^*, Lorraine Dearden²^*

^* Equal contributors

PUBLISHED 05 Oct 2023

Author details Author details

¹ Great Ormand Street Institute of Child Health, University College London, London, England, WC1N 1EH, UK
² Social Research Institute, University College London, London, England, WC1H 0AL, UK

Kate M Lewis
Roles: Conceptualization, Data Curation, Methodology, Software

Vincent G Nguyen
Roles: Conceptualization, Data Curation, Software

Ania Zylbersztejn
Roles: Conceptualization, Data Curation, Software

Ruth Gilbert
Roles: Conceptualization, Funding Acquisition

Bianca De Stavola
Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Supervision

Lorraine Dearden
Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Supervision

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Introduction:

Special educational needs (SEN) provision has been called a “postcode lottery” in England, but the extent to which this represents underlying inequities has not been sufficiently investigated. This study will focus on children with similar underlying health characteristics to explore sources of systematic variation in SEN provision by local authority (LA) in England.

Methods and analysis:

We will use linked individual-level state-funded hospital and school records from the Education and Health Insights from Linked Data (ECHILD) database, alongside open-source school-level data. Our cohort will be defined as singleton children with major congenital anomalies born in England between 1 September 2003 and 31 August 2012. We will identify major congenital anomalies from diagnoses in hospital records in the first year of life using European Surveillance of Congenital Anomalies (EUROCAT) guidelines. LA (152 in total) will be defined by child’s residential address reported in education records at entry into year one of school (aged five years old). SEN provision will be defined by a recording of an educational health and care plan or SEN support in any census in Reception, year one or two of primary school (ages four/five to six/seven). To quantify variation in SEN provision we will fit multilevel logistic regression models to the individual records, with a-priori selected individual-, school- and LA-level characteristics. We will report the estimated intraclass correlation coefficient at each stage of the model, signifying the percentage of remaining variation in the odds of recorded SEN provision that is due to differences between LAs.

Ethics and dissemination:

We have existing research ethics approval for analyses of the ECHILD database described in this protocol. We will disseminate our findings to diverse audiences (academics, relevant government departments, service users and providers) through seminars, peer-reviewed publications, short briefing reports and infographics for non-academics (published on the study website).

Plain Language Summary

Plain English summary

Some children need extra help with learning in school. In England, this help is called special educational needs (SEN) provision. Health and education professionals worry that provision is not fair, and there might be a “postcode lottery”. This means that access to support varies depending on where children live, rather than according to their needs. The aim of this research is to find out whether the chance of a pupil getting special educational needs provision in schools is the same across different parts of England. We will use information collected by hospitals and schools for all children who were born in England between 2003 and 2013. We will focus on children with birth defects, as these children are likely to have complex health problems and require additional support from health and education services. This means we can be more confident that these children should get special educational needs provision. We will explore whether characteristics of local areas and schools contribute to differences in provision across regions in England. The results will help us to understand how equal special educational needs provision is across England and inform policies to best support children.

Keywords

administrative data; children; primary school; special education; birth defects; ECHILD database

Corresponding author: Kate M Lewis

Competing interests: No competing interests were disclosed.

Grant information: This project is funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number NIHR202025). RG is supported by a NIHR Senior Investigator award. ECHILD is supported by ADR UK (Administrative Data Research UK), an Economic and Social Research Council (part of UK Research and Innovation) programme (Grant Reference Numbers ES/V000977/1, ES/X003663/1, ES/X000427/1). Research at UCL Great Ormond Street Institute of Child Health is supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre.
The ECHILD Database uses data from the Department for Education (DfE). The DfE does not accept responsibility for any inferences or conclusions derived by the authors. This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates. This research contributes to but was not commissioned by the NIHR Policy Research Programme. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2023 Lewis KM et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Lewis KM, Nguyen VG, Zylbersztejn A et al. Local authority variation in primary school-recorded special educational needs provision among children with major congenital anomalies: A research protocol [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2023, 3:50 (https://doi.org/10.3310/nihropenres.13466.1) First published: 05 Oct 2023, 3:50 (https://doi.org/10.3310/nihropenres.13466.1) Latest published: 05 Oct 2023, 3:50 (https://doi.org/10.3310/nihropenres.13466.1)

Introduction

Special educational needs (SEN) provision for children and young people across England is described as a “postcode lottery”^1–3. Prominently, in their 2010 review of the special educational needs and disability framework, the Office for Standards in Education, Children’s Services and Skills (Ofsted) levelled criticism at local area variation in the appropriateness and timeliness of SEN provision⁴. In an attempt to address these and other perceived shortcomings of SEN provision, significant reforms to the system were introduced as part of the Children and Families Act 2014⁵. However, statistics published by the Department of Education shows continued national variation in school recorded SEN provision; as of January 2022, the proportion of pupils recorded as receiving SEN support (provision arranged by schools offering additional support to children in the classroom) ranged from 8.2% to 17.6% and Education Health and Care Plans (EHCPs, arranged by local authorities (LAs), for children whose needs cannot be met by SEN support) from 1.7% to 5.9% across LAs⁶.

Understanding the reasons behind local variation in SEN provision is an important prerequisite to ensuring equity in access to support. However, to date, limited evidence has been available at the national level. Notably, the National Audit Office reports that “The Department [of Education] believes that the variation reflects local context and practice, but has not investigated the reasons.”^7,p.11 Utilising educational records from children attending primary school between 2010/11 and 2016/17, the Education Policy Institute investigated the predictors of recorded SEN in England². They found that the proportion of academised primary schools and rates of pupils eligible for free school meals was negatively associated with recorded SEN at the LA level, after adjustment for a selection of school and individual-level factors. However, without external measures of the level of need in the underlying population, previous work has been unable to fully account for pupil-level characteristics when looking at geographical variation in SEN provision.

To build on the understanding of variation in SEN provision across England, we will use linked education and health records to focus on children with major congenital anomalies (MCAs). MCAs encompass an array of structural and functional abnormalities of prenatal origin that are present at birth and are estimated to affect between 2-3% of live births in England^8–10. With improvements to neonatal care and early interventions over the last few decades, children with MCAs are now more likely to survive to school age¹¹. MCAs are associated with long term health problems and, often, learning difficulties or disabilities; hence an increased need for SEN provision¹². Focussing on children with MCAs will enable us to investigate geographical variation in SEN provision amongst a population with similar needs. The aim of this study is to describe LA-level variation in school-recorded SEN provision (a proxy for receiving SEN provision) in Reception, year one and two of school amongst children with major congenital anomalies born between 1 September 2003 and 31 August 2012 in England.

Methods

Public and patient involvement

This study was designed following consultations with stakeholders, including parents/carers of children who applied for SEN provision. Themes from these consultations revealed frustration at the variation in timing and support given by schools and local authorities. The HOPE study steering committee includes parents of children with disabilities who will review and advise on the presentation and dissemination of the study's findings.

Study design and setting

Observational study using linked individual health and educational records. In a manner similar to target trial emulation studies (where study design principles of randomised trials are applied to observational data)¹³, we use a structured procedure to guide eligibility, entry, follow-up times and the analysis plan for this study. To do this, we describe design components of the ideal (“target”) population-based study and then emulate this using our observational data. Although our study is not intended to produce causal effects (as in cases where the target trial emulation framework is typically used)¹³, this “target population-based study protocol” enables us to assess and try to mitigate potential biases at the outset of the study.

Data sources and linkage

The primary data source for this study is the ‘Education and Child Health Insights from Linked Data’ (ECHILD) dataset¹⁴. ECHILD is a whole population-based cohort of children and young people in England comprised from linked administrative hospital and school/social care records, Hospital Episode Statistics (HES) and the National Pupil Database (NPD), respectively. HES contains records of all national health service (NHS)-funded hospital activity in England including inpatient admissions, outpatient activity and accident and emergency attendances. Approximately 97% of births in England are captured in HES admission records (the child's “birth record”)¹⁵, with the majority linkable to their mother’s delivery record using a mixture of deterministic and probabilistic methods applied to non-disclosive variables available in both sets of records¹⁶. The NPD contains enrolment data from termly school censuses, including pupil and school characteristics, attainment at the key stages, absences, exclusions, and information from some children’s social services¹⁷. There is no common pseudonymised identifier across individuals in HES and NPD, therefore records are linked by NHS Digital using deterministic linking algorithms based on name, date of birth, sex and postcode¹⁴. To ECHILD, we will link opensource school characteristic data published by the Department for Education and geographical boundary data published by the Office for National Statistics (ONS)^18–21. Details of each dataset that will be used in this study is provided in Table 1.

Table 1. Datasets used in this study.

Dataset	Common acronym	Data provider	Description	In ECHILD?
HES admitted patient care	HES APC	NHS England	Episode level data on inpatient and day case discharges from English NHS hospitals and English NHS commissioned activity in the independent sector	Yes
HES accident and emergency	HES A&E	NHS England	Attendances at A&E in English NHS hospitals and English NHS commissioned activity in the independent sector	Yes
NPD school censuses pupil-level		Department for Education	Pupil-level information for pupils in state-maintained educational settings in England, including the “school census”, alternative provision census and pupil referral unit census	Yes
NPD early years foundation stage profile	EYFSP	Department for Education	Pupil-level results from the statutory assessment of children in reception year in state schools in England	Yes
Get information about schools database	GIAS	Department for Education	Opensource information about schools and colleges in England, including names, type, establishment group and governance (formerly Edubase)	No – linked via school URN
School and College performance measures		Department for Education	Opensource data on results of exams and other performance measures by school and college in England	No – linked via school URN
Schools, pupils and their characteristics		Department for Education	Opensource school-level data, including pupil numbers and their characteristics	No – linked via school URN
Local authority and school finance		Department for Education	Opensource local authority and school spending on education	No – linked via school URN
National Statistics Postcode Lookup	NSPL	ONS	Opensource geographical information mapping Census Output Areas to a range of higher statistical geographies	No – linked via child’s residential MSOA11
ONS Postcode Directory	ONSPD	ONS	Opensource geographical information mapping all current and terminated UK postcodes to a wide range of administrative, health and other geographic areas	No – linked via child’s residential MSOA11

HES = hospital episode statistics; MSOA11 = middle super output area 2011; NHS = National Health Service; ONS = Office for National Statistics; URN = unique reference number

Study population

Our cohort will be defined as singleton children with MCAs born in NHS-funded hospitals between 1 September 2003 and 31 August 2012 and alive at entry into school (Reception – age four). Children will be excluded if: their NHS record does not link to NPD (likely indicating they did not attend state-funded school in England); or they do not appear in any NPD school census in reception, year one and/or year two (corresponding to ages four to six at entry; see Figure 1). See the summary protocol in Table 2 for details on the target population-based study protocol and how it is emulated in this study.

Figure 1. Expected age at entry into reception, year one and year two of primary school, by birth year and follow-up year; Y = year; ^adefined according to the academic calendar (i.e. 2003/04 includes 1 September 2003 to 31 August 2004, inclusive).

Table 2. Summary protocol: population-based study emulation to estimate residual variation in SEN provision at the LA level in England.

Protocol component	Target population- based study specification	Emulation study	Concerns	Mitigations
Eligibility criteria	Clinical diagnosis of a major congenital anomaly Attended 1^st year of state-funded school in England between 2008/09 and 2018/19	Live singleton births in NHS-funded hospitals in England between 2003/04 and 2011/12 with a hospital admission recorded ICD-10 code indicating a major congenital anomaly in infancy NHS record linked to the national pupil database Attended first year of state-funded school in England between 2008/09 and 2018/19	Selection bias due to linkage errors, opt outs, exclusion of multiple births and exclusion of births outside English NHS-funded hospitals Ascertainment/ misclassification bias (some minor congenital anomalies likely included in our definition of MCA); MCA prevalence using hospital episode statistics is higher than reported through other sources	Examine potential variation in bias by LA, by investigating hospital specific coding errors Investigate possibility of controlling for LA-specific emigration rates Analysis by MCA subgroups will help build confidence in the results
Study design	Prospective observational study	Observational study using linked administrative records	Information collected for administrative purposes: influenced by payment for results; administrative changes/errors; we cannot choose what to collect information about	We will use data from nine years of data collection, which has improved recording over time We will use consistent definitions across measures
Data structure	LA (of child’s address), school and individual- level data	LA (of child’s MSOA), school and individual-level data	Misclassification bias: MSOA of child’s residential address used to define LA; LAs merging and changing boundaries; Schools changing name and LA; Child attending school in a different LA to their place of residence Unmeasured confounding: We cannot include school level fixed effects because this three-level model is very unlikely to converge (as groups sizes become too small)	We will use consistent definitions of LAs and schools We will include school- level characteristics at the individual-level As we are focussing on children with MCAs, there are unlikely to be many children in the same school, so clustering at the school-level is unlikely to be necessary
Outcome	SEN provision reported by teaching staff	School recorded SEN provision	Misclassification bias: recording errors; SEN provision to children without a school record of SEN; school recorded SEN but no provision	We will use consistent wording and clearly outline the limitations of our measure of “SEN provision”
Target of estimation	Explaining residual variation in SEN provision at the LA level with and without accounting for child and school level characteristics	Explaining residual variation in SEN provision at the LA level with and without accounting for child and school level characteristics
Analysis plan	Residual variance in LA-level SEN provision estimated using multi- level (mixed effects) logistic and multi- level ordinal logistic regression Final analyses will include factors that may explain differences in the need for SEN provision among children with MCA	Residual variance in LA-level SEN provision estimated using multi- level (mixed-effects) logistic and multi-level ordinal logistic regression All analyses will include factors that may explain differences in the need for SEN provision among children with MCA

ICD-10 = International Classification of Diseases 10th Revision; LA=local authority; MCA=Major congenital anomalies; MSOA=Middle Layer Super Output Area; SEN=special educational needs

Key variables

Major congenital anomalies. MCAs will be defined by the presence of specified International Classification of Diseases 10^th Revision (ICD-10) diagnostic codes as any diagnosis (primary/secondary) during any hospital admission before first birthday. The ICD codes, based on the European Surveillance of Congenital Anomalies (EUROCAT) guidelines²², are shown in Table 3. Our analyses will firstly focus on the whole population of children with MCA, followed by system-specific anomaly subgroups (chromosomal, cardiac, nervous system, digestive system). Infants with more than one MCA can belong in more than one subgroup but are only counted once in the “any MCA” group.

Table 3. Major congenital anomalies, by subgroup, defined using EUROCAT code list.

	ICD-10 codes*
Any MCA	Include: Q-chapter, D215, D821, D1810, P350, P351, P371 Exclude: Q0461, Q0782, Q101, Q102, Q103, Q105, Q135, Q170, Q171, Q172, Q173, Q179, Q174, Q180, Q181, Q182, Q184, Q185, Q186, Q187, Q1880, Q189, Q2111, Q250 if gestational age <37 weeks, Q2541, Q256 if gestational age <37 weeks, Q261, Q270, Q314, Q320, Q331, Q381, Q382, Q3850, Q400, Q4021, Q430, Q4320, Q4381, Q4382, Q523, Q525, Q527, Q53, Q5520, Q5521, Q610, Q627, Q633, Q653-Q656, Q662-Q669, Q670-Q678, Q680, Q6821, Q683-Q685, Q6810, Q7400, Q752, Q753, Q760, Q7643, Q765, Q7660, Q7662, Q7671, Q825, Q8280, Q833, Q845, Q899
MCA subgroup
Nervous system	Include: Q00, Q01, Q02, Q03, Q04, Q05, Q06, Q07 Exclude: Q0461, Q0782
Cardiac	Include: Q20-Q26 Exclude: Q2111, Q250 if gestational age <37 weeks, Q2541, Q256 if gestational age <37 weeks, Q261
Digestive system	Include: Q38-Q45, Q790 Exclude: Q381, Q382, Q3850, Q400, Q401, Q4021, Q430, Q4320, Q4381, Q4382**
Chromosomal	Include: Q90-Q93, Q96-Q99 Exclude: Q936

*excludes minor congenital anomalies, which are described as anomalies with “lesser medical, functional or cosmetic consequences” (see Section 3.2, EUROCAT);²² **unable to include/exclude in this study because code is too specific for HES coding

Local authority. LA will be defined by the middle layer super output area 2011 (MSOA11) of each child’s residential address reported in the pupil-level school censuses (NPD) at entry into school and mapped to one of the 152 LAs in England as defined in 2021. We will use residential MSOA11 rather than school MSOA11 because it is the LA where the pupil lives that is responsible for securing provision and providing top up funding associated with SEN provision²³. We will use 2021 LA classifications to ensure geographical consistency in cases of LA mergers and boundary redrawing.

Outcome: SEN provision. Our study outcome is recording of SEN provision for each pupil during Reception, year one and year two (i.e. the first three years of primary school, ages four to six at entry), categorised as: (i) a binary variable (any versus no recorded SEN provision); (ii) a three-category ordinal variable (EHCP, SEN support, no recorded SEN provision). We will create these outcomes using the variable “SEN provision” from NPD school censuses. An EHCP is defined as at least one recording of an EHCP or statement of SEN during reception, year one or two. SEN support is defined as at least one record of SEN support, school action or school action plus during Reception, year one and two, but no EHCP at any point during this time. No SEN provision is defined by the value “no SEN” for all available school censuses across Reception, year one and two.

For succinctness, we use the term “SEN provision” to refer to the outcome in this protocol; however, a record of SEN provision in the educational data does not necessarily indicate that SEN provision was received.

Explanatory factors. Table 4 outlines the variables we will consider as explanatory factors for variation in SEN provision selected a priori as likely to be associated with the allocation of SEN provision².

Table 4. Potential explanatory factors for variation in recorded SEN provision.

Variable name	Definition	Dataset(s)
Individual
Year of birth	Year of child’s birth (in academic years – 1 September to 31 August)	HES APC (birth record)
Month of birth	Month of child’s birth	HES APC (birth record)
Maternal age	Maternal age at delivery	HES APC (birth record enhanced from maternal delivery record*)
Child’s gender	Child’s parent/self-reported gender in first appearance in school census (female or male)	NPD school censuses (pupil level)
IDACI	IDACI quintile of child’s residential address in first appearance in school census	NPD school censuses (pupil level)
Free school meals	Entitlement to free school meals (yes or no) in first appearance in school census	NPD school censuses (pupil level)
Ethnic group	Ethnicity of the pupil based on most recent recorded ethnicity in the NPD	NPD school censuses (pupil level)
Child Protection Plan	Child has ever been subject to a child protection plan (yes or no) at 31 August before school entry	NPD children in need census
Hospital presentations	Combined number of hospital admissions and A&E attendances after discharge from birth admission to 31 August before school entry	HES APC, HES A&E
EYFSP	EYFSP overall standardised score, measured ages 3 to 5 years	NPD EYFSP dataset
School
School governance	Type of school governance, including sponsor led academy, converter academy, maintained schools and free schools	GIAS
School type	Type of school attended by child (special school, alternative provision or mainstream school)	GIAS
Local authority
Sponsored academies %	Proportion of the pupil population within the LA that are attending sponsored academies	GIAS
Converter academies %	Proportion of the pupil population the LA that are attending converter academies	GIAS
Special school %	Proportion of the pupil population within the LA that are attending special schools	GIAS
Alternative provision %	Proportion of the pupil population within the LA that are attending alternative provision or pupil referral units	GIAS
Children with protection plans %	Proportion of the pupil population within the LA that are subject to a child protection plan	NPD children in need census
LA size	The size of the pupil population	NPD school censuses (pupil level)
LA IDACI	The modal IDACI quintile of children in the LA in state schools based on their home address	NPD school censuses (pupil level)
Free school meals %	The proportion of within the LA that are eligible for free school meals
LA spending	LA spending on education	LA and school finance dataset

*Linkage between birth and delivery records occurred prior to ECHILD linkage; A&E = accident and emergency; APC = admitted patient care; EYFSP = Early years foundation stage profile; HES = hospital episode statistics; IDACI = Income deprivation affecting children index; LA = local authority; NPD = national pupil database

Statistical methods

The distribution of key individual, school and LA characteristics including missing data by SEN provision will be described in numbers and percentages. The association between covariates and the probability of missingness in at least one study variable will be explored using univariable logistic regression. These findings will be used to decide how to treat missing data in this study.

To explore geographical variation in recorded SEN provision for children with MCA at the LA level we will use multi-level logistic (for the binary outcome) and ordinal logistic (for the three-category outcome) regression models for child-specific SEN provision. The Hausman specification test will be used to determine whether a mixed effects or random effects model is suitable. The model has two levels to reflect the clustering of children within LA. Beginning with an empty model with one random or fixed intercept to capture the similarities in log-odds within each LA, we will add individual-level and LA-level variables as detailed in Table 3. We will then add school characteristics at the individual level of the model. We will report the estimated intraclass correlation coefficient (ICC) at each stage of the modelling, signifying the percentage of variation in (log) odds of SEN provision that is due to unaccounted differences between LAs. By adding first individual- and then LA-specific variables (with and without school variables), this percentage should reduce if these variables can explain part of the variation in addition to those already included. Given the reported differences in the experiences of girls and boys in school and hospital settings²⁴, we will replicate this analysis stratified by child’s gender (as defined in Table 4).

A further extension of the empty model will consider modelling sudden shifts in the propensity for pupils to receive SEN provision, such as the period of SEN reforms after Children and Families Act 2014 or the academisation of schools^25,26. We will firstly examine observed patterns of SEN provision amongst children in our cohort to determine whether to proceed with this step. An example of such model, showing the use of an academic year variable to capture time before and after 2014/15 (the “SEN reform” variable), is specified below. Here, the SEN reform takes the value “0” if year one attendance occurred during the pre-reform period (i.e. children born before 31 August 2009 who entered primary school before 1 September 2014) or “1” if year one attendance occurred during the post-reform period (i.e. children born after 1 September 2009). Adding this variable to the multi-level model that only includes calendar time will identify if there are any step changes associated with the SEN reforms; adding their interaction (parametrised as differential slopes before and after 2014/15) will allow for time trends in provision to change after the reform. To see whether pre- and post-reform trends differ by LA, we can allow the coefficients for calendar time during the pre- and post-reform period to vary by LA (i.e. the model will have both a random intercept for the variation in pre-reform LA provision and random slopes for both pre- and post-reform periods).

Analyses will be conducted in the Office for National Statistics Secure Research Service using Stata 17 (with R a suggested opensource alternative).

Model specification (example). To allow for a piece-wise linear shape (with change of slopes for school year one in 2014/15, starting from school year one in 2008/09) of the average relationship between academic year and the outcome (expressed on the logit scale), we will parametrise the model as follows:

\begin{array}{l} l o g i t [Pr (Y_{ij} = 1)] \\ = (β_{0} + u_{0 j}) + I_{t \leq 2013} {(β_{1} + u_{1 j}) (t - 2008)} \\ + I_{t > 2013} {(β_{1} + u_{1 j}) (2013 - 2008) + (β_{2} + u_{2 j}) (t - 2013)} + \sum_{k=3}^{K} β_{k} X_{kij} \end{array}

Where: i is a pupil clustered within LA j, Y_ij = 1 denotes recorded SEN provision for individual i and Y_i_j= 0 denotes no recorded SEN provision, β₀ = the mean (across LAs) intercept when school year one is equal to 2008/09; t = academic year; β₁ = the average slope from 2008/09 to 2013/14; β₂ = the average slope after 2013/14, u_0j = the random effect component of the intercept for LA j, and (X_1ij,…,X_Kij) represent K covariates for individual i in LA j.

Ethics

Permissions to use linked, de-identified data from Hospital Episode Statistics and the National Public Database were granted by DfE (DR200604.02B) and NHS Digital (DARS-NIC-381972-Q5F0V-v0.5). Ethical approval for the ECHILD project was granted by the National Research Ethics Service (17/LO/1494), NHS Health Research Authority Research Ethics Committee (20/EE/0180) and UCL Great Ormond Street Institute of Child Health’s Joint Research and Development Office (20PE06/20PE16).

Data availability

No data are associated with this article.

Acknowledgments

We gratefully acknowledge all children and families whose de-identified data are used in this research. We would like to acknowledge the contribution of the wider HOPE study team to this work: Sarah Barnes, Kate Boddy, Kristine Black-Hawkins, Lorraine Dearden, Tamsin Ford, Katie Harron, Lucy Karwatowska, Matthew Lilliman, Stuart Logan, Jacob Matthews, Jugnoo Rahi, Jennifer Saxton, Antony Stone and Isaac Winterburn. We thank Maria Peppa for data curation of the major congenital anomaly cohort, and Ruth Blackburn and Matthew Jay for ECHILD Database support.

Faculty Opinions recommended

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14. Mc Grath-Lone L, Libuy N, Harron K, et al.: Data Resource Profile: The Education and Child Health Insights from Linked Data (ECHILD) Database. Int J Epidemiol. 2022; 51(1): 17–17f. PubMed Abstract | Publisher Full Text | Free Full Text
15. Herbert A, Wijlaars L, Zylbersztejn A, et al.: Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). Int J Epidemiol. 2017; 46(4): 1093–1093i. PubMed Abstract | Publisher Full Text | Free Full Text
16. Harron K, Gilbert R, Cromwell D, et al.: Linking Data for Mothers and Babies in De-Identified Electronic Health Data. PLoS One. 2016; 11(10): e0164667. PubMed Abstract | Publisher Full Text | Free Full Text
17. Jay MA, Mc Grath-Lone L, Gilbert R: Data Resource: the National Pupil Database (NPD). Int J Popul Data Sci. 2019; 4(1): 1101. PubMed Abstract | Publisher Full Text | Free Full Text
18. GOV.UK: Get Information about Schools. 2022; (accessed July 11, 2022). Reference Source
19. GOV.UK: School and college performance measures. Department for Education, (accessed Feb 1, 2023). Reference Source
20. GOV.UK: Schools, pupils and their characteristics. Department for Education. Reference Source
21. ONS: Postcode products. 2016. Reference Source
22. EUROCAT: EUROCAT Guide 1.5: Chapter 3.3 EUROCAT Subgroups of Congenital Anomalies. 2022; (accessed July 18, 2022). Reference Source
23. Education & Skills Funding Agency: High needs funding: 2021 to 2022 Operational guide. Reference Source
24. Kollmayer M, Schober B, Spiel C: Gender stereotypes in education: Development, consequences, and interventions. Eur J Dev Psychol. 2018; 15(4): 361–77. Publisher Full Text
25. Black A, Bessudnov A, Liu Y, et al.: Academisation of Schools in England and Placements of Pupils With Special Educational Needs: An Analysis of Trends, 2011-2017. Front Educ. 2019; 4. Publisher Full Text
26. Liu Y, Bessudnov A, Black A, et al.: School autonomy and educational inclusion of children with special needs: Evidence from England. Br Educ Res J. 2020; 46(3): 532–52. Publisher Full Text

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Author details Author details

¹ Great Ormand Street Institute of Child Health, University College London, London, England, WC1N 1EH, UK
² Social Research Institute, University College London, London, England, WC1H 0AL, UK

Kate M Lewis
Roles: Conceptualization, Data Curation, Methodology, Software

Vincent G Nguyen
Roles: Conceptualization, Data Curation, Software

Ania Zylbersztejn
Roles: Conceptualization, Data Curation, Software

Ruth Gilbert
Roles: Conceptualization, Funding Acquisition

Bianca De Stavola
Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Supervision

Lorraine Dearden
Roles: Conceptualization, Data Curation, Funding Acquisition, Methodology, Supervision

Competing interests

No competing interests were disclosed.

Grant information

This project is funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number NIHR202025). RG is supported by a NIHR Senior Investigator award. ECHILD is supported by ADR UK (Administrative Data Research UK), an Economic and Social Research Council (part of UK Research and Innovation) programme (Grant Reference Numbers ES/V000977/1, ES/X003663/1, ES/X000427/1). Research at UCL Great Ormond Street Institute of Child Health is supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre.
The ECHILD Database uses data from the Department for Education (DfE). The DfE does not accept responsibility for any inferences or conclusions derived by the authors. This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets which may not exactly reproduce National Statistics aggregates. This research contributes to but was not commissioned by the NIHR Policy Research Programme. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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https://doi.org/10.3310/nihropenres.13466.1

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Lewis KM, Nguyen VG, Zylbersztejn A et al. Local authority variation in primary school-recorded special educational needs provision among children with major congenital anomalies: A research protocol [version 1; peer review: 2 approved with reservations]. NIHR Open Res 2023, 3:50 (https://doi.org/10.3310/nihropenres.13466.1)

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VERSION 1

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Reviewer Report 11 May 2024

Loretta Mason-Williams, Binghamton University, Binghamton, New York, USA

Approved with Reservations

https://doi.org/10.3310/nihropenres.14609.r31026

Lewis and colleagues employ several administrative datasets to examine geographical relationship with special education needs provision among children with major congenital anomalies. I find the rationale compelling and the methodology intriguing and inventive. Further, the explanation regarding data sources, linkages, ... Continue reading

Lewis and colleagues employ several administrative datasets to examine geographical relationship with special education needs provision among children with major congenital anomalies. I find the rationale compelling and the methodology intriguing and inventive. Further, the explanation regarding data sources, linkages, and decisions provides clear guidelines for replication and extension. However, I am concerned by the narrow focus of only children with major congenital anomalies (MCAs). I recognize that coding for this with hospital data may be an efficient mechanism to initiate the analysis, but I am not convinced there is a relationship between all MCAs with later special education needs potentially leading to erroneous rejection of the null hypothesis (i.e., a significant number of MCAs may not be related to later SEN, regardless of postcode). This may be remedied by providing additional data within the introduction of the relationship between MCAs and SENs to allow the reader to feel more confident in the narrow participant focus. Alternatively, there may be other variables within the natal records that may provide for similar and comparative analyses, such as low birthweight or prematurity.

Is the rationale for, and objectives of, the study clearly described?

Yes
Is the study design appropriate for the research question?

Yes
Are sufficient details of the methods provided to allow replication by others?

Yes
Are the datasets clearly presented in a useable and accessible format?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Special education, teacher workforce, secondary data analysis

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 15 Dec 2023

Silvia Dell'Anna, Free University of Bozen-Bolzano, Brixen, Italy

Approved with Reservations

https://doi.org/10.3310/nihropenres.14609.r30709

The study proposed is highly relevant, not only for England but also for the European and international landscape. The method is explained clearly and seems to be based on a rigorous protocol. However, I suggest further reflection on the relationship ... Continue reading

The study proposed is highly relevant, not only for England but also for the European and international landscape. The method is explained clearly and seems to be based on a rigorous protocol. However, I suggest further reflection on the relationship between pathology and the degree of disability. From the perspective of ICF (International Classification of Functioning, Disability and Health, WHO, 2001, 2007), an interpretative model that highlights the complex relationship between health and contextual factors, functioning and, consequently, disability do not necessarily and/or solely depend on diagnosis. In the study, instead, they risk to take for granted that children with major congenital anomalies represent an homogeneous group in terms of school needs.

Is the rationale for, and objectives of, the study clearly described?

Yes
Is the study design appropriate for the research question?

Yes
Are sufficient details of the methods provided to allow replication by others?

Yes
Are the datasets clearly presented in a useable and accessible format?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Special and Inclusive Education

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Silvia Dell'Anna, Free University of Bozen-Bolzano, Brixen, Italy
Loretta Mason-Williams, Binghamton University, Binghamton, USA

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Reviewer Report

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11 May 2024 | for Version 1

Loretta Mason-Williams, Binghamton University, Binghamton, New York, USA

5 Views Cite this report Responses(0)

Approved With Reservations

Lewis and colleagues employ several administrative datasets to examine geographical relationship with special education needs provision among children with major congenital anomalies. I find the rationale compelling and the methodology intriguing and inventive. Further, the explanation regarding data sources, linkages, and decisions provides clear guidelines for replication and extension. However, I am concerned by the narrow focus of only children with major congenital anomalies (MCAs). I recognize that coding for this with hospital data may be an efficient mechanism to initiate the analysis, but I am not convinced there is a relationship between all MCAs with later special education needs potentially leading to erroneous rejection of the null hypothesis (i.e., a significant number of MCAs may not be related to later SEN, regardless of postcode). This may be remedied by providing additional data within the introduction of the relationship between MCAs and SENs to allow the reader to feel more confident in the narrow participant focus. Alternatively, there may be other variables within the natal records that may provide for similar and comparative analyses, such as low birthweight or prematurity.

Is the rationale for, and objectives of, the study clearly described?

Yes
Is the study design appropriate for the research question?

Yes
Are sufficient details of the methods provided to allow replication by others?

Yes
Are the datasets clearly presented in a useable and accessible format?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Special education, teacher workforce, secondary data analysis

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

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Reviewer Report

12 Views

15 Dec 2023 | for Version 1

Silvia Dell'Anna, Free University of Bozen-Bolzano, Brixen, Italy

12 Views Cite this report Responses(0)

Approved With Reservations

The study proposed is highly relevant, not only for England but also for the European and international landscape. The method is explained clearly and seems to be based on a rigorous protocol. However, I suggest further reflection on the relationship between pathology and the degree of disability. From the perspective of ICF (International Classification of Functioning, Disability and Health, WHO, 2001, 2007), an interpretative model that highlights the complex relationship between health and contextual factors, functioning and, consequently, disability do not necessarily and/or solely depend on diagnosis. In the study, instead, they risk to take for granted that children with major congenital anomalies represent an homogeneous group in terms of school needs.

Is the rationale for, and objectives of, the study clearly described?

Yes
Is the study design appropriate for the research question?

Yes
Are sufficient details of the methods provided to allow replication by others?

Yes
Are the datasets clearly presented in a useable and accessible format?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Special and Inclusive Education

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

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[1] 1. Department for Education and Skills: Removing barriers to achievement: the government’s strategy for SEN. 2004. Reference Source

[2] 2. Hutchinson J: Identifying pupils with special educational needs and disabilities. Education Policy Institute, 2021. Reference Source

[3] 3. Hodkinson A: Pre-service teacher training and special educational needs in England 1970-2008: is government learning the lessons of the past or is it experiencing a groundhog day? Eur J Spec Needs Educ. 2009; 24(3): 277–89. Publisher Full Text

[4] 4. Ofsted: The special educational needs and disability review: A statement is not enough. 2010. Reference Source

[5] 5. Children and Families Act 2014. 2014. Reference Source

[6] 6. Department for Education: Special educational needs in England: 01 - Pupils in all schools, by type of SEN provision - including independent schools and general hospital schools - 2016 to 2022. 2022. Reference Source

[7] 7. National Audit Office: Support for pupils with special educational needs and disabilities in England. 2019; (accessed March 20, 2023). Reference Source

[8] 8. DeSilva M, Munoz FM, McMillan M, et al.: Congenital anomalies: Case definition and guidelines for data collection, analysis, and presentation of immunization safety data. Vaccine. 2016; 34(49): 6015–26. PubMed Abstract | Publisher Full Text | Free Full Text

[9] 9. Zylbersztejn A, Verfürden M, Hardelid P, et al.: Phenotyping congenital anomalies in administrative hospital records. Paediatr Perinat Epidemiol. 2020; 34(1): 21–8. PubMed Abstract | Publisher Full Text | Free Full Text

[10] 10. EUROCAT: EUROCAT Guide 1.4: Instruction for the registration of congenital anomalies. University of Ulster: EUROCAT Central Registry, 2013. Reference Source

[11] 11. Glinianaia SV, Morris JK, Best KE, et al.: Long-term survival of children born with congenital anomalies: A systematic review and meta-analysis of population-based studies. PLoS Med. 2020; 17(9): e1003356. PubMed Abstract | Publisher Full Text | Free Full Text

[12] 12. Glinianaia SV, McLean A, Moffat M, et al.: Academic achievement and needs of school-aged children born with selected congenital anomalies: A systematic review and meta-analysis. Birth Defects Res. 2021; 113(20): 1431–62. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Matthews AA, Danaei G, Islam N, et al.: Target trial emulation: applying principles of randomised trials to observational studies. BMJ. 2022; 378: e071108. PubMed Abstract | Publisher Full Text

[14] 14. Mc Grath-Lone L, Libuy N, Harron K, et al.: Data Resource Profile: The Education and Child Health Insights from Linked Data (ECHILD) Database. Int J Epidemiol. 2022; 51(1): 17–17f. PubMed Abstract | Publisher Full Text | Free Full Text

[15] 15. Herbert A, Wijlaars L, Zylbersztejn A, et al.: Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). Int J Epidemiol. 2017; 46(4): 1093–1093i. PubMed Abstract | Publisher Full Text | Free Full Text

[16] 16. Harron K, Gilbert R, Cromwell D, et al.: Linking Data for Mothers and Babies in De-Identified Electronic Health Data. PLoS One. 2016; 11(10): e0164667. PubMed Abstract | Publisher Full Text | Free Full Text

[17] 17. Jay MA, Mc Grath-Lone L, Gilbert R: Data Resource: the National Pupil Database (NPD). Int J Popul Data Sci. 2019; 4(1): 1101. PubMed Abstract | Publisher Full Text | Free Full Text

[18] 18. GOV.UK: Get Information about Schools. 2022; (accessed July 11, 2022). Reference Source

[19] 19. GOV.UK: School and college performance measures. Department for Education, (accessed Feb 1, 2023). Reference Source

[20] 20. GOV.UK: Schools, pupils and their characteristics. Department for Education. Reference Source

[21] 21. ONS: Postcode products. 2016. Reference Source

[22] 22. EUROCAT: EUROCAT Guide 1.5: Chapter 3.3 EUROCAT Subgroups of Congenital Anomalies. 2022; (accessed July 18, 2022). Reference Source

[23] 23. Education & Skills Funding Agency: High needs funding: 2021 to 2022 Operational guide. Reference Source

[24] 24. Kollmayer M, Schober B, Spiel C: Gender stereotypes in education: Development, consequences, and interventions. Eur J Dev Psychol. 2018; 15(4): 361–77. Publisher Full Text

[25] 25. Black A, Bessudnov A, Liu Y, et al.: Academisation of Schools in England and Placements of Pupils With Special Educational Needs: An Analysis of Trends, 2011-2017. Front Educ. 2019; 4. Publisher Full Text

[26] 26. Liu Y, Bessudnov A, Black A, et al.: School autonomy and educational inclusion of children with special needs: Evidence from England. Br Educ Res J. 2020; 46(3): 532–52. Publisher Full Text

Local authority variation in primary school-recorded special educational needs provision among children with major congenital anomalies: A research protocol

Abstract

Introduction:

Methods and analysis:

Ethics and dissemination:

Plain Language Summary

Plain English summary

Keywords

Introduction

Methods

Public and patient involvement

Study design and setting

Data sources and linkage

Table 1. Datasets used in this study.

Study population

Figure 1. Expected age at entry into reception, year one and year two of primary school, by birth year and follow-up year; Y = year; ^adefined according to the academic calendar (i.e. 2003/04 includes 1 September 2003 to 31 August 2004, inclusive).

Table 2. Summary protocol: population-based study emulation to estimate residual variation in SEN provision at the LA level in England.

Key variables

Table 3. Major congenital anomalies, by subgroup, defined using EUROCAT code list.

Table 4. Potential explanatory factors for variation in recorded SEN provision.

Statistical methods

Ethics

Data availability

Acknowledgments

References

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Local authority variation in primary school-recorded special educational needs provision among children with major congenital anomalies: A research protocol

Abstract

Introduction:

Methods and analysis:

Ethics and dissemination:

Plain Language Summary

Plain English summary

Keywords

Introduction

Methods

Public and patient involvement

Study design and setting

Data sources and linkage

Table 1. Datasets used in this study.

Study population

Figure 1. Expected age at entry into reception, year one and year two of primary school, by birth year and follow-up year; Y = year; adefined according to the academic calendar (i.e. 2003/04 includes 1 September 2003 to 31 August 2004, inclusive).

Table 2. Summary protocol: population-based study emulation to estimate residual variation in SEN provision at the LA level in England.

Key variables

Table 3. Major congenital anomalies, by subgroup, defined using EUROCAT code list.

Table 4. Potential explanatory factors for variation in recorded SEN provision.

Statistical methods

Ethics

Data availability

Acknowledgments

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Competing Interests Policy

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Are you an NIHR-funded researcher?

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Figure 1. Expected age at entry into reception, year one and year two of primary school, by birth year and follow-up year; Y = year; ^adefined according to the academic calendar (i.e. 2003/04 includes 1 September 2003 to 31 August 2004, inclusive).