School-recorded special educational needs provision in children with major congenital anomalies: a linked administrative records study of births in England, 2003-2013

Abstract

Background
Children with major congenital anomalies (MCAs) disproportionately experience complex health problems requiring additional health and educational support.

Objectives
To describe survival to the start of school and recorded special educational needs (SEN) provision among children with and without administrative record-identified MCAs in England. We present results for 12 system-specific MCA subgroups and 25 conditions. We also describe the change of prevalence in recorded SEN provision before and after SEN reforms in 2014, which were implemented to improve and streamline SEN provision.

Methods
We created a birth cohort of 6,180,400 singleton children born in England between 1 September 2003 and 31 August 2013 using linked administrative records from the ECHILD database. MCAs were identified using hospital admission and mortality records during infancy. SEN provision in primary school was defined by one or more recording of SEN provision in state-school records during years 1 to 6 (ages 5/6 years to 10/11 years).

Results
Children with any MCA had a 5-year survival rate of 95.1% (95% confidence interval (CI) 95.0, 95.2) compared with 99.7% (95% CI 99.7, 99.7) among children without an MCA. 41.6% (75,381/181,324) of children with an MCA had any recorded SEN provision in primary school compared with 25.7% (1,285,572/5,008,598) of unaffected children. Of the 12 system-specific MCA subgroups, children with chromosomal, nervous system and eye anomalies had the highest prevalence of recorded SEN provision. The prevalence of recorded SEN provision decreased by 4.8% (99% CI -5.4, -4.3) for children with any MCA compared with a reduction of 4.2% (99% CI -4.3, -4.2) for unaffected children, when comparing pupils in year 1 before and after 2014.

Conclusion
We observed that approximately two fifths of children with MCAs have some type of SEN provision recorded during primary school, but this proportion varied according to condition and declined following the 2014 SEN reforms, similar to children unaffected by MCAs.

Highlights

• We used linked hospital and school records to identify children with different MCAs and follow them up to the end of primary school, when they are 11 years old
• We found that two in five children with an MCA had any recorded SEN provision in primary school – 1.6 times the proportion in children without an MCA
• Our findings also suggest that SEN provision reduced for all children after government reforms of the SEN system in 2014, including those with MCAs
• Our findings provide information about survival to primary school and the likelihood of specialist support in primary school for children with hospital-identified MCAs in England, which may be useful for families, clinicians and service providers

Introduction

Major congenital anomalies (MCAs) include a variety of structural and functional abnormalities of prenatal origin that are present are birth [1, 2]. Whilst individual anomalies are rare, in England, MCAs are estimated to affect between 2-3% of live births [3, 4]. They are termed "major" because of their significant impact on health, survival, or the individual’s physical or social functioning [1]. MCAs can occur in isolation or as a group of anomalies and, for up to half of MCAs, there is no known aetiology [5]. Known causes of MCAs can be stratified into genetic and chromosomal abnormalities occurring pre-conception and environmental exposures post-conception, including teratogenic agents, mechanical forces and vascular disruptions. It is estimated that up to 30% of MCAs are caused by a combination of genetic and environmental factors. Clinical recognition of MCAs is contingent on access to health care, but typically occurs within the first six months of life, and usually before age two years [1, 2].

MCAs are a leading cause of infant mortality [4]. However, over the last few decades, with advances in neonatal care and surgical interventions, the survival of infants born with MCAs has been improving [6, 7]. Ten-year survival estimates for many MCAs are now over 90%, resulting in growing numbers of children with these conditions completing primary school [8]. Studies of selected MCAs, including orofacial clefts and congenital heart disease, show a higher risk of educational achievement below the national average and a greater need of specialist educational support at school for children with these conditions compared to their unaffected peers [9, 10], known as special educational needs (SEN) provision in England. This will contribute important prognostic information for families/carers, as well as help with planning future provision to support this growing population of children.

To the best of our knowledge, evidence on these longer term outcomes is restricted to selected MCAs mostly captured in registry data and, aside from isolated orofacial clefts [11], is not available at a national level in England. Evidence on temporal trends in SEN provision amongst children with similar health conditions can also build an understanding of how external factors, specifically policy changes, have impacted the likelihood of eligibility for SEN provision in school. For example, in 2014, the SEN system in England underwent reforms following the Children and Families Act 2014 and subsequent code of practice [12, 13]. Key principles for the reformed system included: earlier identification; increased participatory decision making; a delegated SEN budget for schools; and changes to the categories of SEN provision offered to children. These reforms were implemented to “offer simpler, improved and consistent help for children and young people with special educational needs” [14]. Yet, as far as we are aware, the precise impact of these changes on children with specific health conditions remains unknown.

Using a national cohort of births in England between 2003 and 2013, we aimed to describe patterns of survival to the start of compulsory education and frequencies of recorded SEN provision across children with and without MCAs identified in hospital and mortality records. We present results for 12 system-specific MCA subgroups and 25 conditions. Specifically, we estimated: rates of survival up to the age 5 years; the prevalence of recorded SEN provision of those attending a state-funded primary school; and differences in the prevalence of recorded SEN provision in year 1 (age 5/6 years) between children attending state-funded school before and after the 2014 SEN reforms.

Methods

Data sources

We used the ‘Education and Child Health Insights from Linked Data’ (ECHILD) database which comprises linked de-identified data from Hospital Episode Statistics (HES), Office for National Statistics (ONS) mortality records and the National Pupil Database (NPD) [15–17]. HES and NPD contain information recorded on state-funded hospital attendances and school activity in England. HES Admitted Patient Care (APC) contains clinical information on inpatient stays (such as diagnoses and procedures) and captures 97% of all birth admissions in England, meaning that these data can be used to define population-based birth cohorts [15, 18]. Linkage to subsequent hospital admissions in HES APC allows for longitudinal follow-up. ONS mortality data, which is routinely linked to HES, provides information on the cause and date of deaths registered in England and Wales (including those that occur outside of hospital) [19]. The NPD contains several school censuses (Supplementary Table 1, Supplementary Appendix 1), which provide information on school enrolments, as well as pupil-level data, including recorded SEN provision, attainment, absences, and exclusions [16]. We used the Department for Education’s opensource ‘Get Information about Schools’ (GIAS) register to add school type to ECHILD using each school’s unique reference number [20]. We also downloaded congenital anomaly prevalence data submitted by England-based registries to the ‘European Network of Population-based Registries for Congenital Malformations’ (EUROCAT) for external cohort validation [3].

Birth and school cohorts

Our study population (the “birth cohort”) included all live singleton births recorded in NHS-funded hospitals in England between 1 September 2003 and 31 August 2013. Birth admissions were identified using a combination of diagnostic and procedure codes, healthcare resource group codes and administrative variables (as outlined in Zylbersztejn et al. [18]). We excluded infants from multiple births, to minimise the risk of linkage error, and infants who were not resident in England, to minimise the risk of loss to follow up [18]. To create the sub-cohort of children attending state-school (the “school cohort”), we then excluded children who: did not link to the NPD; were not present in any NPD census in school years 1 to 6 (ages 5/6 to 10/11 years); or were only present in NPD censuses ≥2 years outside the expected age (see Supplementary Figure 1, Supplementary Appendix 1). Pupils not following the national curriculum do not have a school year listed in the NPD, so were instead assigned a year based on their age in each academic year. The maximum date of follow up was 31 August 2019 (the last full academic year before the COVID-19 pandemic).

Major congenital anomalies

We used International Classification of Diseases 10th Revision (ICD-10) codes based on EUROCAT guide 1.5 to define MCAs in this study (Supplementary Table 2, Supplementary Appendix 1) [21]. A child was defined as having “any MCA” if they had one or more relevant ICD-10 code(s) in hospital admission or mortality records during the first year of life (i.e. infancy). The same criteria were applied across the whole cohort, i.e. for all birth years. MCAs were classified by 12 system-specific subgroups. Infants could belong to multiple subgroups (e.g. a child with a nervous system and eye anomaly is counted in both groups), but for each of the 12 subgroups we also identified children who did not have MCAs in any of the other systems (‘isolated’ anomalies). Within the 12 subgroups we also defined 25 conditions (including isolated and non-isolated cases), which were selected based on a group size of ≥200 at the end of follow up to avoid problems with statistical disclosure. Conditions were defined using EUROCAT guidelines, apart from congenital diaphragmatic hernias and anorectal malformations, where we used definitions developed previously [22, 23]. We were unable to apply EUROCAT minor anomaly exclusion rules where 5-digit ICD-10 codes were required (see Supplementary Table 2, Supplementary Appendix 1).

Outcome: Mortality

We reported death from any cause before the age of 5 years to coincide with the expected age at entry into year 1, the first full year of compulsory education and the start of key stage 1 in state-funded schools in England. We also present survival rates to age 7 years, corresponding to the expected age at entry into year 3 (and the start of key stage 2). Occurrence and date of death were derived from HES-ONS linked mortality records. Follow-up began at birth and ended at the earliest of: death, the end of the study period (31 August 2019), or each child’s 5^th birthday (or 7^th birthday for survival rates to age 7 years).

Outcome: recorded SEN provision

In state-funded schools in England, children are entitled to receive SEN provision if they have “a significantly greater difficultly in learning than the majority of others of the same age, or have a disability which prevents them from making use of facilities generally provided by mainstream schools” [24]. There are two broad categories of SEN provision [24]: SEN support (previously, ‘School Action’/‘School Action Plus’), which is arranged by the school and may include different educational materials or small group support; and Education, Health and Care Plans (EHCP; previously, ‘statement of SEN’), which is arranged by local authorities for children whose needs cannot be met by the lower level of provision and may include one-to-one support in the classroom and therapies outside school. To this, we added a third category, ‘specialist provision’, to differentiate between children attending non-mainstream schools where support for all children differs, including smaller classroom sizes and an adapted curriculum (Supplementary Table 1, Supplementary Appendix 1).

We used the NPD alternative provision, pupil referral unit and termly school censuses linked to the GIAS register to define four categories of recorded SEN provision in each school year: (1) none, where there was no recorded SEN nor evidence of attendance at a special school or alternative provision in any NPD census; (2) SEN support in mainstream school, where ‘School Action’, ‘School Action Plus’ or ‘SEN support’ (following the 2014 legislative changes) was recorded in at least one census and there was no record of an EHCP nor a record of attending a special school or alternative provision in any NPD census; (3) EHCP in mainstream school, where a ‘statement of SEN’ or ‘EHCP’ (following the 2014 legislative changes) was recorded in at least one census and there was no record of attending a special school or alternative provision in any NPD census; (4) specialist provision, where there is a record of attendance at a special school or alternative provision in any census.

Covariates

We derived the following variables from each child’s HES APC record at birth to compare socio-demographic characteristics of the birth and school cohorts: year of birth; phenotypic sex (female or male); region of residence; five-group index of multiple deprivation, based on the lower super output area of residential address recorded at birth [25]; and racial-ethnic group (six major groups aggregated from 16 categories). We use the term racial-ethnic group purposefully to emphasise that this covariate includes both race- and ethnicity-based identifiers (e.g. “White” and “British“, respectively). This paper is descriptive and therefore we do not include adjustment for any covariates in the main analyses.

Analysis

We firstly compared the prevalence of MCAs in our birth cohort with the average prevalence reported across England-based registries between 2004 and 2014. To estimate the proportion of children who survived to ages 5 and 7 years, we used the Kaplan-Meier estimator with 95% confidence interval (CI). Next, we identified the children who appeared in any of the NPD censuses between year 1 and year 6, to construct the school cohort. We described socio-demographic characteristics (in numbers and percentages) of children in the birth cohort who were alive at school entry compared with those in the school cohort, to assess the extent to which the final cohort was representative of the birth cohort. We then calculated the prevalence of recorded SEN provision at any census between years 1 and 6, by MCA status. We replicated these analyses restricting the definition of recorded SEN to school years 1 to 2 to assess the stability of the main results, given that children born after 31 August 2008 did not have complete follow up to year 6 (Supplementary Figure 1, Supplementary Appendix 1).

To examine whether the prevalence of SEN provision differed for children with and without MCAs before and after the 2014 reforms, we defined SEN provision using NPD censuses in year 1 only because children born in 2012/13 did not have follow-up beyond year 1. We firstly plotted a time series of the prevalence of SEN provision recorded in year 1 for children with and without an MCA. We then divided the school cohort into two (five-year) periods: births from 1 September 2003 to 31 August 2008 and births from 1 September 2008 to 31 August 2013. Those born in the earlier period finished year 1 before 2014/15, when legislative changes were implemented. We described the number and proportion of children with recorded SEN provision in any NPD census in year 1 over these two periods, by MCA. We calculated the absolute difference (in percentage points) in the prevalence of recorded SEN provision between the two periods, by MCA status. We report 99% confidence intervals to conservatively estimate standard errors, acknowledging the multiplicity of related calculations.

All analyses were carried out in Stata v17 within the ONS secure research service. MP, KML, BDS and RG had access to the raw data in this study, and analyses was carried out between 02/12/2022 and 11/09/2023. None of the authors had access to information that could identify individual participants. The code used to create key study variables and implement analyses is available at https://github.com/UCL-CHIG/HOPE_study_MCA_SEN/. The analyses for this study followed directly from the aims, that is to study survival patterns and prevalence of SEN provision by MCA status (no protocol is available). We added time series figures, characteristics of linked/unlinked children and a comparison to prevalence of MCAs reported in registry data in response to peer review comments. This study is reported as per the REporting of studies Conducted using Observational Routinely-collected Data (RECORD) guideline (S1 Checklist).

Results

Birth cohort description and survival rates

After excluding multiple births and non-England residents, our birth cohort comprised 6,180,400 singleton live births in NHS-funded hospitals in England from 1st September 2003 to 31st August 2013 (Figure 1). Of these, 3.5% (219,249) had evidence of at least one MCA identified in hospital or death records in the first year of life. The most common anomalies were those of the cardiac (53,741; 0.9% of the birth cohort), limb (34,366; 0.6%), and urinary systems (29,158; 0.5%; Table 1). The estimated prevalence of MCAs in this study was about twice the average prevalence reported by regional registries for available MCAs over the same period, with some exceptions (Supplementary Table 3, Supplementary Appendix 1). Similar prevalence rates were reported for the orofacial and abdominal wall MCA subgroups and for severe cardiac anomalies, congenital diaphragmatic hernia, limb reduction defect and Down syndrome. The prevalence of children with an identified MCA increased from 3.3% amongst births in 2003/04 to 3.8% in 2012/13, with the largest system specific sub-group increase among children with any cardiac anomaly (from 0.7% to 1.1%; Supplementary Figure 2, Supplementary Appendix 1).

Figure 1: Derivation of the birth and school cohorts. MCA = major congenital anomaly, NPD = national pupil database.

Table 1: Birth cohort description and survival rates, by MCA, system-specific subgroups and selected conditionsa. ^aany groups contain children with and without additional anomalies in other system-specific subgroups (% survival is group average), isolated groups do not contain children with anomalies in other system-specific subgroups; ^bsmall numbers (<8) suppressed to prevent disclosure of identities (including deductive disclosure when subtracting deaths by age 7 years from deaths by age 5 years). CI = confidence interval, MCA = major congenital anomaly.

	Birth cohort		Up to age 5 years			Up to age 7 years
	N (%)		Deaths	5-year survival % (95% CI)		Deaths	7-year survival % (95% CI)
No MCA	5961151 (96.5)		18565	99.7 (99.7, 99.7)		19265	99.7 (99.7, 99.7)
Any MCA	219249 (3.5)		10691	95.1 (95.0, 95.2)		10953	95.0 (94.9, 95.1)
Nervous system anomalies
Any nervous system anomaly	11971 (0.19)		2094	82.5 (81.8, 83.2)		2205	81.6 (80.8, 82.2)
Isolated nervous system	7322 (0.12)		870	88.1 (87.4, 88.8)		927	87.3 (86.5, 88.1)
Microcephaly	2349 (0.04)		478	79.7 (78.0, 81.2)		525	77.6 (75.8, 79.2)
Hydrocephaly	2574 (0.04)		463	82.0 (80.5, 83.4)		486	81.1 (79.5, 82.6)
Spina Bifida	1343 (0.02)		174	87.0 (85.1, 88.7)		b	b
Eye anomalies
Any eye anomaly	4404 (0.07)		376	91.5 (90.6, 92.3)		402	90.9 (90.0, 91.7)
Isolated eye anomaly	2849 (0.05)		23	99.2 (98.8, 99.5)		b	b
Anophthalmos/ Microphthalmos	672 (0.01)		119	82.3 (79.2, 85.0)		b	b
Congenital cataract	1271 (0.02)		88	93.1 (91.5, 94.3)		b	b
Congenital Glaucoma	448 (0.01)		20	95.5 (93.2, 97.1)		b	b
Ear, face, and neck anomalies
Any ear, face, and neck	1663 (0.03)		91	94.5 (93.3, 95.5)		b	b
Isolated ear, face, and neck	993 (0.02)		b	b		b	b
Cardiac anomalies
Any cardiac anomaly	53741 (0.87)		6050	88.7 (88.5, 89.0)		6161	88.5 (88.3, 88.8)
Isolated cardiac anomaly	37080 (0.60)		3015	91.9 (91.6, 92.1)		3057	91.8 (91.5, 92.0)
Severe cardiac	14260 (0.23)		2584	81.9 (81.2, 82.5)		2626	81.6 (80.9, 82.2)
Respiratory anomalies
Any respiratory anomaly	3941 (0.06)		493	87.5 (86.4, 88.5)		b	b
Isolated respiratory anomaly	2121 (0.03)		83	96.1 (95.2, 96.8)		b	b
Choanal atresia	804 (0.01)		82	89.8 (87.5, 91.7)		b	b
Orofacial anomalies
Any orofacial anomaly	9611 (0.16)		480	95.0 (94.6, 95.4)		492	94.9 (94.4, 95.3)
Isolated orofacial anomaly	6503 (0.11)		29	99.6 (99.4, 99.7)		b	b
Cleft lip	2589 (0.04)		51	98.0 (97.4, 98.5)		b	b
Cleft palate	4891 (0.08)		300	93.9 (93.2, 94.5)		310	93.7 (92.9, 94.3)
Cleft lip and palate	3656 (0.06)		183	95.0 (94.2, 95.7)		b	b
Digestive system anomalies
Any digestive system anomaly	16098 (0.26)		1731	89.2 (88.8, 89.7)		1763	89.0 (88.5, 89.5)
Isolated digestive system anomaly	9343 (0.15)		518	94.5 (94.0, 94.9)		b	b
Oesophageal atresia	1664 (0.03)		200	88.0 (86.3, 89.5)		b	b
Small intestine atresia	1934 (0.03)		167	91.4 (90.0, 92.5)		b	b
Hirschsprung’s disease	1508 (0.02)		60	96.0 (94.9, 96.9)		b	b
Biliary atresia	440 (0.01)		45	89.8 (86.5, 92.3)		b	b
Anorectal malformation	1713 (0.03)		173	89.9 (88.4, 91.2)		b	b
Congenital diaphragmatic hernia	1524 (0.02)		505	66.9 (64.4, 69.2)		b	b
Abdominal wall anomalies
Any abdominal wall anomaly	4110 (0.07)		292	92.9 (92.1, 93.6)		b	b
Isolated abdominal wall anomaly	2854 (0.05)		97	96.6 (95.9, 97.2)		b	b
Omphalocele	1286 (0.02)		155	87.9 (86.0, 89.6)		b	b
Gastroschisis	2539 (0.04)		139	94.5 (93.6, 95.3)		b	b
Urinary system anomalies
Any urinary system anomaly	29158 (0.47)		1200	95.9 (95.7, 96.1)		1228	95.8 (95.5, 96.0)
Isolated urinary system anomaly	24703 (0.40)		469	98.1 (97.9, 98.3)		b	b
Bladder exstrophy	704 (0.01)		b	b		b	b
Genital anomalies
Any genital anomaly	25914 (0.42)		488	98.1 (97.9, 98.3)		b	b
Isolated genital anomaly	22252 (0.36)		100	99.6 (99.5, 99.6)		b	b
Hypospadias	17727 (0.29)		235	98.7 (98.5, 98.8)		b	b
Indeterminate sex	1044 (0.02)		119	88.6 (86.5, 90.4)		b	b
Limb anomalies
Any limb anomaly	34366 (0.56)		985	97.1 (97.0, 97.3)		1010	97.1 (96.9, 97.2)
Isolated limb anomaly	28911 (0.47)		159	99.5 (99.4, 99.5)		b	b
Limb reduction defect	2180 (0.04)		142	93.5 (92.4, 94.4)		b	b
Chromosomal anomalies
Any chromosomal anomaly	9734 (0.16)		1612	83.4 (82.7, 84.2)		1646	83.1 (82.3, 83.8)
Isolated chromosomal anomaly	3330 (0.05)		347	89.6 (88.5, 90.6)		b	b
Down syndrome	6260 (0.10)		492	92.1 (91.4, 92.8)		503	92.0 (91.3, 92.6)
Turner syndrome	381 (0.01)		32	91.6 (88.3, 94.0)		b	b

For children without an MCA, survival to age 5 and 7 years was stable at 99.7% (95% CI, 99.7-99.7 for each age). Children with at least one MCA had survival rates of 95.1% (95% CI, 95.0-95.2) at 5 years and 95.0% (95% CI, 94.9-95.1) at 7 years. Among the 12 system-specific subgroups, average 5-year survival was under 90% for those with any nervous system, cardiac, respiratory, digestive or chromosomal anomaly. Children with isolated anomalies had higher survival rates than on average for children in their anomaly subgroup. Conversely, survival rates were lower for the isolated chromosomal anomaly group, compared with specific conditions (Down syndrome and Turner syndrome). The condition with the lowest survival rate was congenital diaphragmatic hernia (66.9% to age 5 years, 95% CI 64.4-69.2).

School cohort description and SEN provision

Of the 6,180,400 children in the birth cohort, 16.0% (990,478) were excluded from the school cohort, including: 29,253 children who died before the expected age of entry into year 1; 734,563 children without a linked NPD record; 225,991 children (with a linked NPD record) who were not present in any NPD census during years 1 to 6 within the study period; and 372 present in NPD after date of death (evidence of possible linkage error; Figure 1). The final school cohort consisted of 5,189,922 children, including 3.5% (181,324) with evidence of at least one MCA. Children were more likely to be included in the school cohort if they were born in later study years, lived outside London and were from a White racial-ethnic group (Supplementary Table 4, Supplementary Appendix 1). The proportion of children in the birth cohort who were included in the school cohort was 84.0% among children without an MCA compared with 82.7% of children with any MCA (rising to 84.3% and 86.9%, respectively, when accounting for deaths before age 5 years; Supplementary Table 5, Supplementary Appendix 1). Of all studied MCA sub-groups and conditions only one (indeterminate sex) had inclusion rates under 84% after accounting for deaths before age 5 years.

1 Of the 5,008,598 children in the school cohort without an MCA, 25.7% (1,285,572) had any recorded SEN provision during years 1 to 6 (22.5% with SEN support in mainstream school, 1.8% with an EHCP in mainstream school and 1.4% in specialist provision; Table 2; Figure 2). In comparison, of the 181,324 children with at least one MCA, 41.6% (75,381) had any recorded SEN (27.6% with SEN support in mainstream school, 6.6% with an EHCP in mainstream school and 7.3% in specialist provision). Within system-specific subgroups, any recorded SEN provision was highest among children with any chromosomal (93.1%; 6,712/7,208), nervous system (73.3%; 6,358/8,676) or eye anomalies (66.0%; 2373/3,599). 8.7% (624/7,208) of children with any chromosomal anomaly had SEN support in mainstream school recorded, compared with 25.8% to 39.1% among other system-specific anomaly subgroups.

Table 2: Number and proportion of children in the school cohort with SEN provision recorded at least once during years 1 to 6, by category of recorded SEN, MCA, system-specific subgroupa and selected conditions. ^aany groups contain children with and without additional anomalies in other system-specific subgroups, isolated groups do not contain children with anomalies in other system-specific subgroups. EHCP = education, health and care plan, MCA = major congenital anomaly, SEN = special educational needs.

	School cohort N	Any recorded SEN provision N (%)	SEN support in mainstream school N (%)	EHCP in mainstream school N (%)	Specialist provision N (%)
No MCA	5,008,598	1285572 (25.7)	1125783 (22.5)	88108 (1.8)	71681 (1.4)
Any MCA	181,324	75381 (41.6)	50133 (27.6)	11954 (6.6)	13294 (7.3)
Nervous system anomalies
Any nervous system anomaly	8,676	6363 (73.3)	2251 (25.9)	1440 (16.6)	2672 (30.8)
Isolated nervous system anomaly	5,656	3868 (68.4)	1560 (27.6)	828 (14.6)	1480 (26.2)
Microcephaly	1,659	1441 (86.9)	274 (16.5)	215 (13.0)	952 (57.4)
Hydrocephaly	1,883	1526 (81.0)	477 (25.3)	392 (20.8)	657 (34.9)
Spina Bifida	1,022	803 (78.6)	345 (33.8)	296 (29.0)	162 (15.9)
Eye anomalies
Any eye anomaly	3,599	2374 (66.0)	1152 (32.0)	555 (15.4)	667 (18.5)
Isolated eye anomaly	2,510	1446 (57.6)	922 (36.7)	316 (12.6)	208 (8.3)
Anophthalmos/Microphthalmos	506	432 (85.4)	158 (31.2)	111 (21.9)	163 (32.2)
Congenital cataract	1,069	745 (69.7)	408 (38.2)	151 (14.1)	186 (17.4)
Congenital Glaucoma	390	289 (74.1)	148 (37.9)	73 (18.7)	68 (17.4)
Ear, face, and neck anomalies
Any ear, face, and neck anomaly	1,336	698 (52.2)	416 (31.1)	115 (8.6)	167 (12.5)
Isolated ear, face, and neck anomaly	829	332 (40.0)	265 (32.0)	36 (4.3)	31 (3.7)
Cardiac anomalies
Any cardiac anomaly	41857	21995 (52.5)	12271 (29.3)	4536 (10.8)	5188 (12.4)
Isolated cardiac anomaly	29745	12853 (43.2)	9290 (31.2)	1878 (6.3)	1685 (5.7)
Severe cardiac	10337	5913 (57.2)	3074 (29.7)	1292 (12.5)	1547 (15.0)
Respiratory anomalies
Any respiratory anomaly	2997	1594 (53.2)	884 (29.5)	341 (11.4)	369 (12.3)
Isolated respiratory anomaly	1755	689 (39.3)	514 (29.3)	94 (5.4)	81 (4.6)
Choanal atresia	647	391 (60.4)	160 (24.7)	98 (15.1)	133 (20.6)
Orofacial anomalies
Any orofacial anomaly	7993	4452 (55.7)	3123 (39.1)	647 (8.1)	682 (8.5)
Isolated orofacial anomaly	5653	2771 (49.0)	2256 (39.9)	290 (5.1)	225 (4.0)
Cleft lip	2242	921 (41.1)	733 (32.7)	90 (4.0)	98 (4.4)
Cleft palate	4030	2474 (61.4)	1576 (39.1)	423 (10.5)	475 (11.8)
Cleft lip and palate	3051	1862 (61.0)	1401 (45.9)	235 (7.7)	226 (7.4)
Digestive anomalies
Any digestive anomaly	12544	5950 (47.4)	3690 (29.4)	1081 (8.6)	1179 (9.4)
Isolated digestive anomaly	7657	2862 (37.4)	2207 (28.8)	349 (4.6)	306 (4.0)
Oesophageal atresia	1297	704 (54.3)	442 (34.1)	158 (12.2)	104 (8.0)
Small intestine atresia	1545	790 (51.1)	382 (24.7)	192 (12.4)	216 (14.0)
Hirschsprung’s disease	1298	659 (50.8)	392 (30.2)	117 (9.0)	150 (11.6)
Biliary atresia	346	178 (51.4)	116 (33.5)	26 (7.5)	36 (10.4)
Anorectal malformation	896	376 (42.0)	258 (28.8)	56 (6.3)	62 (6.9)
Congenital diaphragmatic hernia	1363	783 (57.4)	477 (35.0)	167 (12.3)	139 (10.2)
Abdominal wall anomalies
Any abdominal wall anomaly	3352	1374 (41.0)	1082 (32.3)	158 (4.7)	134 (4.0)
Isolated abdominal wall anomaly	2398	879 (36.7)	752 (31.4)	63 (2.6)	64 (2.7)
Omphalocele	968	434 (44.8)	302 (31.2)	76 (7.9)	56 (5.8)
Gastroschisis	2141	834 (39.0)	713 (33.3)	65 (3.0)	56 (2.6)
Urinary system anomalies
Any urinary system anomaly	24203	8934 (36.9)	6674 (27.6)	1121 (4.6)	1139 (4.7)
Isolated urinary system anomaly	20949	6884 (32.9)	5681 (27.1)	653 (3.1)	550 (2.6)
Bladder exstrophy	596	255 (42.8)	181 (30.4)	40 (6.7)	34 (5.7)
Genital anomalies
Any genital anomaly	21923	8600 (39.2)	6575 (30.0)	1015 (4.6)	1010 (4.6)
Isolated genital anomaly	19093	6910 (36.2)	5703 (29.9)	665 (3.5)	542 (2.8)
Hypospadias	15243	6034 (39.6)	4669 (30.6)	696 (4.6)	669 (4.4)
Indeterminate sex	637	336 (52.7)	201 (31.6)	66 (10.4)	69 (10.8)
Limb anomalies
Any limb anomaly	28951	10291 (35.5)	7468 (25.8)	1368 (4.7)	1455 (5.0)
Isolated limb anomaly	24911	7772 (31.2)	6316 (25.4)	751 (3.0)	705 (2.8)
Limb reduction defect	1783	982 (55.1)	695 (39.0)	159 (8.9)	128 (7.2)
Chromosomal anomalies
Any chromosomal anomaly	7208	6712 (93.1)	624 (8.7)	2548 (35.3)	3540 (49.1)
Isolated chromosomal anomaly	2626	2321 (88.4)	257 (9.8)	936 (35.6)	1128 (43.0)
Down syndrome	5157	5087 (98.6)	147 (2.9)	2214 (42.9)	2726 (52.9)
Turner syndrome	305	198 (64.9)	118 (38.7)	28 (9.2)	52 (17.0)

Figure 2: Percentage of children in the school cohort with different categories of SEN provision recorded at least once during years 1 to 6, by MCA, system-specific subgroup and selected conditions. EF&N = ear, face, and neck anomalies, EHCP = education, health and care plan, MCA = major congenital anomaly, SEN = special educational needs.

EHCPs in mainstream school were most frequently recorded among children with any chromosomal (35.3%; 3,548/7,208), nervous system (16.6%; 1,440/8,676) and eye anomalies (15.4%; 555/3,599). Those with abdominal wall, urinary system, genital or limb anomalies were the least likely to have such provision (<5%). Broadly, the proportion of each MCA group with recorded EHCPs in mainstream school was similar to the proportion attending specialist provision, except for children with nervous system and chromosomal anomalies. Among children with microcephaly, 57.4% (952/1,659) attended specialist provision compared with 13.0% (215/1,659) with a recorded EHCP in a mainstream setting. Within the system-specific subgroups, children with isolated anomalies had lower proportions of recorded SEN than on average for the subgroup. The overall pattern of results is comparable when restricting the results to key stage 1 only (years 1 and/or 2), with a slight decrease in the prevalence of recorded SEN across all MCA subgroups and conditions (Supplementary Table 6 and Supplementary Figure 3, Supplementary Appendix 1).

Differences in recorded SEN provision, by reform period

Overall, the prevalence of children with SEN provision recorded in year 1 decreased from 21.1% in 2009/10 to 15.0% in 2018/19 (Figure 3), with the same decreasing pattern observed for children with and without an MCA. As shown in Supplementary Figure 4 (Supplementary Appendix 1), when split by category of recorded SEN provision, this decrease is only observable for SEN support (the largest group; 88.3% of all recorded SEN provision in year 1).

Figure 3: Prevalence of SEN provision recorded in year 1, by academic year and MCA status.

The proportion of children with any recorded SEN provision in year 1 decreased by 4.2% (99% CI -4.3,- 4.2) after the 2014 SEN reforms for children without an MCA, compared with 4.8% (99% CI -5.4,- 4.3) for those with any MCA (Figure 4; Supplementary Table 7, Supplementary Appendix 1). Of the MCA subgroups, the proportion of children with any recorded SEN provision decreased most for abdominal wall (-8.2%, 99% CI -12.2,- 4.1), respiratory system (-6.3%, 99% CI -11.0,- 1.6) and cardiac (-6.1%, 99% CI -7.3,- 4.8) anomalies. Of the categories of SEN provision, these decreases are only observed in SEN support in mainstream school for children without an MCA (-4.6%, 99% CI -4.6,- 4.5) (Figure 5; Supplementary Table 7, Supplementary Appendix 1). Children with any MCA had a similar decrease in SEN support (-4.4%, 99% CI -4.9,- 3.9), together with slight decrease (-0.4%, 99% CI -0.7,- 0.1) in the prevalence of EHCPs in mainstream school.

Figure 4: Absolute % difference (99% CI) between the prevalence of any recorded SEN provision amongst children in year 1 between 2014/15 and 2018/19 (after the 2014 SEN reforms) compared with children in year 1 between 2009/10 and 2013/14 (before the 2014 SEN reforms), by MCA, system-specific subgroup and selected conditions. EF&N = ear, face and neck anomalies, MCA = major congenital anomaly, SEN = special educational needs.

Figure 5: Absolute % difference (99% CI) between the prevalence of recorded SEN amongst children in year 1 between 2014/15 and 2018/19 (after the 2014 SEN reforms) compared with children in year 1 between 2009/10 and 2013/14 (before the 2014 SEN reforms), by category of recorded SEN provision, MCA, system-specific subgroup and selected conditions. EF&N = ear, face and neck anomalies, EHCP = education, health and care plan, MCA = major congenital anomaly, SEN = special educational needs.

Discussion

We found 5-year survival rates of 95.1% for children with MCAs born between 1 September 2003 and 31 August 2013, compared with 99.7% of children without these conditions. 41.5% of children with any MCA attending state school had any recorded SEN provision compared with 25.6% of children without an MCA. Differences in any recorded SEN provision between those with and without MCAs were greater for EHCPs and specialist school provision. Among children with MCAs, there was substantial variation in recorded SEN provision, with those with chromosomal, nervous system and eye anomalies having the highest prevalence of any recorded SEN provision. Nearly 1 in 20 fewer children had recorded SEN provision in year 1 after the 2014 reforms, with the decline mainly observed in support at the lower level in mainstream schools.

Interpretation and comparison with other studies

Patterns of survival for congenital anomalies in our study are similar to pooled estimates presented in a 2020 meta-analysis, highlighting increasing survival for children with many of these conditions over time [6]. Estimates of SEN provision for children with MCAs are similar to those provided from registry data linked to national administrative educational records in four areas of England (41.6% in our study compared with 44.0% from the EUROlinkCAT report) [10]. The decline in the proportion of children with SEN support (formerly ‘School Action’/‘School Action Plus’) following 2014 government reforms fits with patterns reported elsewhere [26]. We add to this evidence by quantifying this decrease (approximately 1 in 20 fewer children receiving SEN provision) and showing that changes affected children with and without MCAs who had additional learning needs at the lower level of provision.

As illustrated in our time series plot, the decrease in SEN support had already begun several years before formal introduction of reforms. Sweeping public sector austerity policies in the early 2010’s, including cuts to school budgets, alongside criticism of the current system of SEN identification likely contributed to this decrease [27]. Prior to 2010, SEN provision had been rising [20], culminating in a report by the Office for Standards in Education, Children’s Services and Skills, which stated that there was “over-identification” of children at the lower level of SEN provision in lieu of “better teaching” [21]. Decreases in recorded SEN provision over this period may therefore reflect changing criteria of the children who should receive SEN provision and at what age. This may explain the trend differences across MCA groupings, although one should not over interpret these differences given the relatively small numbers involved. The intended impact of the 2014 SEN reform on better teaching contrasts with evidence on the experiences of children, their families and teachers, which indicate that changes in SEN provision reflects rising unmet need [28–30]. The possible detrimental impact of these changes is further supported by reports published in 2019 by the House of Commons Education Committee and the National Audit Office that present a picture of a fragmented and increasingly unsustainable SEN system [28, 29].

Strengths and limitations

A strength of our study is the cohort size and comprehensive national coverage. We used a cohort of over six million children (5,189,922 in the SEN provision analysis) to describe less commonly investigated MCA subgroups and conditions. Longitudinal records over a 10-year study period meant that we could examine SEN provision before and after a major policy change. A major limitation relates to the recording of SEN provision not being based on clear criteria for service delivery. Firstly, a record of SEN provision in the educational data does not necessarily indicate that SEN provision was received, and there is no information about the precise elements of support received (hence our label of ‘recorded’ SEN provision). Secondly, in practice, SEN provision varies by factors beyond a child’s learning needs, as highlighted by the time-varying patterns presenting in our study. Other research has shown SEN provision to vary by school, geographical location, child’s ethnicity and previous contact with social services, amongst other factors [31]. A further limitation is that diagnostic codes of MCAs are not necessarily indicate additional learning needs, as they do not capture severity or functional impairment. For example, the diagnostic codes used to define conditions in the eye anomaly group (anophthalmos/microphthalmos, congenital cataract and congenital glaucoma) do not indicate whether a child has unilateral or bilateral eye disease, with the former unlikely to require SEN provision in isolation [32]. We are undertaking further work, with comprehensive clinical input, to define groups of children who are most likely to require SEN provision.

Using hospital admission and mortality records during the first year of life, we identify MCAs in 354.7 per 10,000 live singleton births, compared with 185.5 per 10,000 live and still births in registry data over the same period [3]. Differences in completeness of reporting, coverage (England-wide compared with one third of the population) and ages at data collection (infancy compared with predominately birth or newborn examinations) over the time period studied may partially explain our higher rates [4]. However, our study also misclassifies some children with minor anomalies due to the granularity of ICD-10 codes required to exclude some conditions. We also find an increase in the prevalence of MCAs in our study over time, which may indicate both increased detection and depth of coding in hospital records over time, particularly for milder congenital anomalies [7, 22]. Arguably, changes to the population of children with MCAs captured over time (potentially with less need for education support, on average), may have led to an overestimation in the difference of SEN provision after SEN reforms. However, the same pattern of results are observed for children with orofacial and abdominal anomalies—MCA subgroups with comparable rates to registry data and stable prevalence rates over the study period—, which give us confidence in the interpretation of our results. That is, that children with lower level SEN needs (with and without MCAs) were less likely to have SEN provision recorded in the latter half of the 2010’s. Further studies, including validation via linkage to the national congenital anomaly and rare disease registration service (national coverage available since 2018 [33]) and primary care records, are required to improve case ascertainment and confidence in, or adjustment for, the precision of results for a wider group of congenital anomalies [34].

Overall, 84% of children in the birth cohort and alive at age 5 years were included in the school cohort; meaning the results may not be representative of all children born in England [17]. Approximately half of the children excluded would be expected to: attend non-state funded, or private, school (7% of the population) [17]; be home schooled (estimated to be 0.6% of the population) [17]; or have emigrated out of England before starting school (no reliable estimates are available but, based on emigration statistics for all ages in England, rates are unlikely to exceed 0.5%) [35]. The remaining non-links (~8%) represent missed links or potential duplicates between the health and educational datasets (leading to an inflated denominator). Nonetheless, rates of linkage across MCA groups and children without an MCA are similar, giving us confidence in our comparative results. The rate of inclusion in the school cohort was below 70% for children with indeterminate sex, suggesting that findings for this condition should be interpreted with some caution. We theorise that lower rates of follow-up in this group are the result of linkage error, since the algorithm for linkage between datasets in ECHILD uses child sex and children with indeterminate sex are more likely to have changes to sex/gender assignment over time. Continued improvements to linkage in the ECHILD database over time, will further strengthen the national application of results to children attending state-funded schools in England [17].

Implications

Clinicians and families want precise information about the prognosis of children with MCAs beyond infancy. The work presented here shows how large administrative datasets can be harnessed to provide such information, albeit through a rationed service that is subject to policy change. As the ECHILD database is accessible to government and UK researchers [36], the approach in this study could be developed to use de-identified data routinely for planning and monitoring of SEN provision. For example, together with our ongoing work to phenotype children in need of SEN provision, the need for early specialised support could be identified in HES by the end of infancy to inform service planning by local authorities. Specialist paediatric services could use such information to prepare parents for the type of support likely to be received. Administrative data offers an important resource for understanding variation and inequities in the provision of SEN services and, if combined with qualitative evidence of areas of good practice, and randomised or quasi randomised methods, to assess SEN practices that have a beneficial effect and for whom. The application of these methods could extend to examining the impact of changes in clinical practices or support (for example, a new drug or therapy) on educational outcomes, such as attainment and school absence.

Future research comparing MCA cohorts across UK countries and internationally will provide evidence on how education and health outcomes in children with MCAs, compared with peers, vary in different jurisdictions with contrasting timing, intensity and pedagogical approaches to SEN provision. Continued work on validating coding for specific MCA groups is important to support this comparative work. SEN provision has been called ‘a postcode lottery’ in England [31], but the extent to which this represents underlying inequities has been difficult to capture to date. Our approach of defining similar groups of children using health records could be used to explore whether children with the same underlying needs are systematically managed differently in educational settings, depending on the school or local authority or both. Extension of this work to children without MCAs who are in need of SEN provision is also important, given that we find that children with MCAs only account for a minority (5.5%) of all children with any recorded SEN provision and less than two in ten children with EHCPs.

Conclusion

In this study, we described the proportions of children born in England who have MCAs, survive to primary school, and have recorded SEN provision before and after the 2014 SEN system reforms. We found that recorded SEN provision among children with major congenital anomalies was markedly higher than for those without these conditions, but over 50% had no recorded SEN provision. Our findings also suggest that following the government reforms to the SEN system in 2014/15, there was a continued reduction in recorded SEN provision for children with and without MCAs. The proportion of recorded SEN provision varied depending on the type of MCA and whether the MCA was isolated or linked to congenital anomalies affecting multiple body systems. Further validation studies, including linkage to high-quality congenital anomaly registry data and primary care records to account for misclassification, will improve the validity of results obtained from administrative records.

Acknowledgements

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, Jugnoo Rahi, Vincent Nguyen, Jennifer Saxton, Antony Stone, Isaac Winterburn and Ania Zylbersztejn. We further thank Ruth Blackburn, Matthew Jay (ECHILD Database support) and Jill Ellis (clinical input). We thank Paolo De Coppi, Kathryn Ford and Stavros Loukogeorgakis for their contributions in defining congenital diaphragmatic hernia and anorectal malformations.

NIHR GOSH-BRC had no role in the design, conduct, analysis or interpretation of the study and no role in drafting the manuscript. 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.

Statement of Conflicts of Interest

None declared.

Ethics statement

Existing research ethics approval has been granted for analyses of the ECHILD database for the purposes set out in the HOPE study (20/EE/0180). Permissions to use linked, de-identified data from HES and the NPD were granted by NHS Digital (DARS-NIC-381972-Q5F0V-v0.5) and DfE (DR200604.02B). Patient consent was not required to use the deidentified data in this study.

Data availability statement

The ECHILD database is made available for free for approved research based in the UK, via the ONS Secure Research Service. Enquiries to access the ECHILD database can be made by emailing ich.echild@ucl.ac.uk. Researchers will need to be approved and submit a successful application to the ECHILD Data Access Committee and ONS Research Accreditation Panel to access the data, with strict statistical disclosure controls of all outputs of analyses.

Funding statement

MP was funded by the National Institute for Health Research Great Ormond Street Hospital Biomedical Research Centre (NIHR GOSH-BRC). KML was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (NIHR202025, The HOPE Study). 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 (ES/V000977/1, ES/X003663/1, ES/X000427/1).

Abbreviations

APC	Admitted Patient Care
ECHILD	Education and Child Health Insights from Linked Data
EHCP	Education, Health and Care Plans
EUROCAT	European Network of Population-based Registries for Congenital Malformations
GIAS	Get Information about Schools
HES	Hospital Episode Statistics
MCA	major congenital anomaly
NPD	National Pupil Database
ONS	Office for National Statistics
SEN	special educational needs

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